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index df1c9057..4886e895 100644
--- a/assistants/explorer-assistant/uv.lock
+++ b/assistants/explorer-assistant/uv.lock
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+ { name = "pydantic-settings" },
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{ name = "azure-ai-contentsafety" },
{ name = "azure-core", extra = ["aio"] },
{ name = "azure-identity" },
+ { name = "function-registry" },
{ name = "openai" },
{ name = "pillow" },
+ { name = "python-liquid" },
{ name = "semantic-workbench-assistant" },
{ name = "tiktoken" },
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{ name = "azure-core", extras = ["aio"], specifier = ">=1.30.0" },
{ name = "azure-identity", specifier = ">=1.17.1" },
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{ name = "openai", specifier = ">=1.3.9" },
{ name = "pillow", specifier = ">=11.0.0" },
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{ name = "semantic-workbench-assistant", editable = "../../libraries/python/semantic-workbench-assistant" },
{ name = "tiktoken", specifier = ">=0.7.0" },
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diff --git a/assistants/guided-conversation-assistant/uv.lock b/assistants/guided-conversation-assistant/uv.lock
index 10bee024..160db72f 100644
--- a/assistants/guided-conversation-assistant/uv.lock
+++ b/assistants/guided-conversation-assistant/uv.lock
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+ { name = "pytest-repeat", specifier = ">=0.9.3" },
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+ { name = "openai" },
+ { name = "pydantic" },
+ { name = "pydantic-settings" },
+ { name = "python-dotenv" },
+ { name = "requests" },
+ { name = "tiktoken" },
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+ { name = "pytest-repeat", specifier = ">=0.9.3" },
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{ name = "azure-core", extra = ["aio"] },
{ name = "azure-identity" },
+ { name = "function-registry" },
{ name = "openai" },
{ name = "pillow" },
+ { name = "python-liquid" },
{ name = "semantic-workbench-assistant" },
{ name = "tiktoken" },
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{ name = "azure-core", extras = ["aio"], specifier = ">=1.30.0" },
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{ name = "openai", specifier = ">=1.3.9" },
{ name = "pillow", specifier = ">=11.0.0" },
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{ name = "semantic-workbench-assistant", editable = "../../libraries/python/semantic-workbench-assistant" },
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diff --git a/assistants/prospector-assistant/uv.lock b/assistants/prospector-assistant/uv.lock
index 50943faf..7bf660ca 100644
--- a/assistants/prospector-assistant/uv.lock
+++ b/assistants/prospector-assistant/uv.lock
@@ -393,6 +393,7 @@ dependencies = [
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{ name = "function-registry" },
{ name = "openai" },
+ { name = "openai-client" },
{ name = "pydantic" },
{ name = "pydantic-settings" },
{ name = "python-dotenv" },
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{ name = "function-registry", editable = "../../libraries/python/function-registry" },
{ name = "openai", specifier = ">=1.16.1" },
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{ name = "pydantic-settings", specifier = ">=2.3.4" },
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{ name = "azure-core", extra = ["aio"] },
{ name = "azure-identity" },
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{ name = "openai" },
{ name = "pillow" },
+ { name = "python-liquid" },
{ name = "semantic-workbench-assistant" },
{ name = "tiktoken" },
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+sdist = { url = "https://files.pythonhosted.org/packages/59/b4/c4e39470edf507056333cd688a36bb7fbdb17090105d9f454ecf6ff09845/python_liquid-1.12.1.tar.gz", hash = "sha256:7367e979125859fb4116f360f224a89a52ecb455fb26843c43e4d800b389d325", size = 124636 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/2c/6e/bfd01926e28f6cf355a7a8460271ba135013870f5857b06f35dbf65ab237/python_liquid-1.12.1-py3-none-any.whl", hash = "sha256:2224312944be16c1a44406398eb8a07c7e57398d5c0ef15ff950946dbefe7c33", size = 206592 },
+]
+
[[package]]
name = "python-multipart"
version = "0.0.12"
@@ -2023,6 +2064,7 @@ name = "skill-library"
version = "0.1.0"
source = { editable = "../../libraries/python/skills/skill-library" }
dependencies = [
+ { name = "assistant-drive" },
{ name = "chat-driver" },
{ name = "context" },
{ name = "events" },
@@ -2037,6 +2079,7 @@ dependencies = [
[package.metadata]
requires-dist = [
+ { name = "assistant-drive", editable = "../../libraries/python/assistant-drive" },
{ name = "chat-driver", editable = "../../libraries/python/chat-driver" },
{ name = "context", editable = "../../libraries/python/context" },
{ name = "events", editable = "../../libraries/python/events" },
@@ -2049,6 +2092,13 @@ requires-dist = [
{ name = "tiktoken", specifier = ">=0.7.0" },
]
+[package.metadata.requires-dev]
+dev = [
+ { name = "pytest", specifier = ">=8.3.1" },
+ { name = "pytest-asyncio", specifier = ">=0.23.8" },
+ { name = "pytest-repeat", specifier = ">=0.9.3" },
+]
+
[[package]]
name = "sniffio"
version = "1.3.1"
diff --git a/assistants/skill-assistant/assistant/assistant_registry.py b/assistants/skill-assistant/assistant/assistant_registry.py
new file mode 100644
index 00000000..5285da1f
--- /dev/null
+++ b/assistants/skill-assistant/assistant/assistant_registry.py
@@ -0,0 +1,94 @@
+import asyncio
+import logging
+from pathlib import Path
+from typing import List
+
+from chat_driver import ChatDriverConfig
+from skill_library import Assistant, Skill
+
+from assistant.skill_event_mapper import SkillEventMapperProtocol
+
+logger = logging.getLogger(__name__)
+
+
+# TODO: Put this registry in the skill library.
+class AssistantRegistry:
+ """
+ This class handles the creation and management of skill assistant instances
+ for this service. Each conversation has its own assistant and we start each
+ assistant in it's own thread so that all events are able to be
+ asynchronously passed on to the Semantic Workbench.
+ """
+
+ def __init__(self) -> None:
+ self.assistants: dict[str, Assistant] = {}
+ self.tasks: set[asyncio.Task] = set()
+
+ async def get_or_create_assistant(
+ self,
+ session_id: str,
+ event_mapper: SkillEventMapperProtocol,
+ chat_driver_config: ChatDriverConfig,
+ skills: List[Skill] = [],
+ ) -> Assistant:
+ """
+ Get or create an assistant for the given conversation context.
+ """
+ assistant = self.get_assistant(session_id)
+ if not assistant:
+ assistant = await self.register_assistant(
+ session_id, event_mapper, chat_driver_config, skills
+ )
+ return assistant
+
+ def get_assistant(
+ self,
+ session_id: str,
+ ) -> Assistant | None:
+ if session_id in self.assistants:
+ return self.assistants[session_id]
+ return None
+
+ async def register_assistant(
+ self,
+ session_id: str,
+ event_mapper: SkillEventMapperProtocol,
+ chat_driver_config: ChatDriverConfig,
+ skills: List[Skill] = [],
+ ) -> Assistant:
+ """
+ Define the skill assistant that you want to have backing this assistant
+ service. You can configure the assistant instructions and which skills
+ to include here.
+ """
+
+ # Create the assistant.
+ assistant = Assistant(
+ name="Assistant",
+ chat_driver_config=chat_driver_config,
+ session_id=session_id,
+ metadrive_root=Path(".data") / session_id / ".assistant",
+ )
+ assistant.register_skills(skills)
+
+ # Assistant event consumer.
+ async def subscribe() -> None:
+ """Event consumer for the assistant."""
+ async for skill_event in assistant.events:
+ try:
+ await event_mapper.map(skill_event)
+ except Exception:
+ logging.exception("Exception in skill assistant event handling")
+ await assistant.wait()
+
+ # Register the assistant
+ self.assistants[session_id] = assistant
+
+ # Start an event consumer task and save a reference.
+ task = asyncio.create_task(subscribe())
+ self.tasks.add(task)
+
+ # Remove the task from the set when it completes.
+ task.add_done_callback(self.tasks.remove)
+
+ return assistant
diff --git a/assistants/skill-assistant/assistant/skill_assistant.py b/assistants/skill-assistant/assistant/skill_assistant.py
index aa08e2a0..777a6998 100644
--- a/assistants/skill-assistant/assistant/skill_assistant.py
+++ b/assistants/skill-assistant/assistant/skill_assistant.py
@@ -8,8 +8,13 @@
# the skills library to create a skill-based assistant.
import logging
+from pathlib import Path
+import openai_client
+from chat_driver import ChatDriverConfig
from content_safety.evaluators import CombinedContentSafetyEvaluator
+from form_filler_skill import FormFillerSkill
+from posix_skill import PosixSkill
from semantic_workbench_api_model.workbench_model import (
ConversationEvent,
ConversationMessage,
@@ -25,8 +30,10 @@
ConversationContext,
)
+from assistant.skill_event_mapper import SkillEventMapper
+
+from .assistant_registry import AssistantRegistry
from .config import AssistantConfigModel
-from .skill_controller import AssistantRegistry
logger = logging.getLogger(__name__)
@@ -139,36 +146,69 @@ async def on_conversation_created(context: ConversationContext) -> None:
# Core response logic for handling messages (chat or command) in the conversation.
async def respond_to_conversation(
- context: ConversationContext, event: ConversationEvent, message: ConversationMessage
+ conversation_context: ConversationContext,
+ event: ConversationEvent,
+ message: ConversationMessage,
) -> None:
"""
Respond to a conversation message.
"""
- # TODO: pass metadata to the assistant for at least adding the content safety metadata to debug
+ # Get the assistant configuration.
+ config = await assistant_config.get(conversation_context.assistant)
- # get the assistant configuration
- config = await assistant_config.get(context.assistant)
-
- # TODO: pass metadata to the assistant for at least adding the content safety metadata to debug
+ # TODO: pass metadata to the assistant for at least adding the content safety metadata to debug.
# metadata = {"debug": {"content_safety": event.data.get(content_safety.metadata_key, {})}}
- # update the participant status to indicate the assistant is thinking
- await context.update_participant_me(UpdateParticipant(status="thinking..."))
+ # Update the participant status to indicate the assistant is thinking.
+ await conversation_context.update_participant_me(UpdateParticipant(status="thinking..."))
+
+ # Get an assistant from the skill library.
+ assistant = assistant_registry.get_assistant(conversation_context.id)
+
+ # Create and register an assistant if necessary.
+ if not assistant:
+ try:
+ async_client = openai_client.create_client(config.service_config)
+ chat_driver_config = ChatDriverConfig(
+ openai_client=async_client,
+ model=config.chat_driver_config.openai_model,
+ instructions=config.chat_driver_config.instructions,
+ # context will be overwritten by the assistant when initialized.
+ )
+ assistant = await assistant_registry.register_assistant(
+ conversation_context.id,
+ SkillEventMapper(conversation_context),
+ chat_driver_config,
+ [
+ PosixSkill(
+ sandbox_dir=Path(".data") / conversation_context.id,
+ mount_dir="/mnt/data",
+ chat_driver_config=chat_driver_config,
+ ),
+ FormFillerSkill(
+ chat_driver_config=chat_driver_config,
+ ),
+ ],
+ )
+
+ except Exception as e:
+ logging.exception("exception in on_message_created")
+ await conversation_context.send_messages(
+ NewConversationMessage(
+ message_type=MessageType.note,
+ content=f"Unhandled error: {e}",
+ )
+ )
+ return
+ finally:
+ await conversation_context.update_participant_me(UpdateParticipant(status=None))
+
try:
- # replace the following with your own logic for processing a message created event
- assistant = await assistant_registry.get_assistant(
- context,
- config.chat_driver_config,
- config.service_config,
- )
- if assistant:
- await assistant.put_message(message.content)
- else:
- logging.error("Assistant not created")
+ await assistant.put_message(message.content)
except Exception as e:
logging.exception("exception in on_message_created")
- await context.send_messages(
+ await conversation_context.send_messages(
NewConversationMessage(
message_type=MessageType.note,
content=f"Unhandled error: {e}",
@@ -176,4 +216,4 @@ async def respond_to_conversation(
)
finally:
# update the participant status to indicate the assistant is done thinking
- await context.update_participant_me(UpdateParticipant(status=None))
+ await conversation_context.update_participant_me(UpdateParticipant(status=None))
diff --git a/assistants/skill-assistant/assistant/skill_controller.py b/assistants/skill-assistant/assistant/skill_controller.py
deleted file mode 100644
index 274af7f1..00000000
--- a/assistants/skill-assistant/assistant/skill_controller.py
+++ /dev/null
@@ -1,170 +0,0 @@
-import asyncio
-import logging
-from pathlib import Path
-
-import openai_client
-from chat_driver import ChatDriverConfig
-from document_skill import DocumentSkill
-from events import events as skill_events
-from posix_skill import PosixSkill
-from semantic_workbench_api_model.workbench_model import (
- MessageType,
- NewConversationMessage,
- UpdateParticipant,
-)
-from semantic_workbench_assistant.assistant_app import (
- ConversationContext,
-)
-from skill_library import Assistant
-
-from . import config
-
-logger = logging.getLogger(__name__)
-
-
-async def _event_mapper(
- conversation_context: ConversationContext,
- skill_event: skill_events.EventProtocol,
-) -> None:
- """
- Maps events emitted by the skill assistant (from running actions or
- routines) to message types understood by the Semantic Workbench.
- """
- metadata = {"debug": skill_event.metadata} if skill_event.metadata else None
-
- match skill_event:
- case skill_events.MessageEvent():
- await conversation_context.send_messages(
- NewConversationMessage(
- content=skill_event.message or "",
- metadata=metadata,
- )
- )
-
- case skill_events.InformationEvent():
- if skill_event.message:
- await conversation_context.send_messages(
- NewConversationMessage(
- content=f"Information event: {skill_event.message}",
- message_type=MessageType.note,
- metadata=metadata,
- ),
- )
-
- case skill_events.ErrorEvent():
- await conversation_context.send_messages(
- NewConversationMessage(
- content=skill_event.message or "",
- metadata=metadata,
- )
- )
-
- case skill_events.StatusUpdatedEvent():
- await conversation_context.update_participant_me(UpdateParticipant(status=skill_event.message))
-
- case _:
- logger.warning("Unhandled event: %s", skill_event)
-
-
-class AssistantRegistry:
- """
- This class handles the creation and management of skill assistant instances
- for this service. Each conversation has its own assistant and we start each
- assistant in it's own thread so that all events are able to be
- asynchronously passed on to the Semantic Workbench.
-
- """
-
- def __init__(self) -> None:
- self.assistants: dict[str, Assistant] = {}
- self.tasks: set[asyncio.Task] = set()
-
- async def get_assistant(
- self,
- conversation_context: ConversationContext,
- chat_driver_config: config.ChatDriverConfig,
- service_config: openai_client.ServiceConfig,
- ) -> Assistant | None:
- # If the assistant already exists, return it.
- if conversation_context.id in self.assistants:
- return self.assistants[conversation_context.id]
-
- # Create a new assistant.
- assistant = await self.create_assistant(conversation_context, chat_driver_config, service_config)
- if not assistant:
- return
-
- async def subscribe() -> None:
- """Event consumer for the assistant."""
- async for skill_event in assistant.events:
- try:
- await _event_mapper(conversation_context, skill_event)
- except Exception:
- logging.exception("Exception in skill assistant event handling")
- await assistant.wait()
-
- # Register the assistant
- self.assistants[conversation_context.id] = assistant
- # Start an event consumer task and save a reference
- task = asyncio.create_task(subscribe())
- self.tasks.add(task)
- # Remove the task from the set when it completes
- task.add_done_callback(self.tasks.remove)
-
- return assistant
-
- async def create_assistant(
- self,
- conversation_context: ConversationContext,
- chat_driver_config: config.ChatDriverConfig,
- service_config: openai_client.ServiceConfig,
- ) -> Assistant | None:
- """
- Define the skill assistant that you want to have backing this assistant
- service. You can configure the assistant instructions and which skills
- to include here.
- """
- # Get an OpenAI client.
- try:
- async_client = openai_client.create_client(service_config)
- except Exception as e:
- logging.exception("Failed to create OpenAI client")
- await conversation_context.send_messages(
- NewConversationMessage(message_type=MessageType.note, content=f"Could not create an OpenAI client: {e}")
- )
- return
-
- # Create the assistant.
- assistant = Assistant(
- name="Assistant",
- chat_driver_config=ChatDriverConfig(
- openai_client=async_client,
- model=chat_driver_config.openai_model,
- instructions=chat_driver_config.instructions,
- ),
- session_id=str(conversation_context.id),
- )
-
- # Define the skills this assistant should have.
- posix_skill = PosixSkill(
- context=assistant.context,
- sandbox_dir=Path(".data"),
- mount_dir="/mnt/data",
- chat_driver_config=ChatDriverConfig(
- openai_client=async_client,
- model=chat_driver_config.openai_model,
- ),
- )
-
- document_skill = DocumentSkill(
- context=assistant.context,
- chat_driver_config=ChatDriverConfig(
- openai_client=async_client,
- model=chat_driver_config.openai_model,
- ),
- )
-
- # Register the skills with the assistant.
- assistant.register_skills([posix_skill, document_skill])
-
- return assistant
diff --git a/assistants/skill-assistant/assistant/skill_event_mapper.py b/assistants/skill-assistant/assistant/skill_event_mapper.py
new file mode 100644
index 00000000..bf273da7
--- /dev/null
+++ b/assistants/skill-assistant/assistant/skill_event_mapper.py
@@ -0,0 +1,70 @@
+import logging
+from typing import Protocol
+
+from events import events as skill_events
+from semantic_workbench_api_model.workbench_model import (
+ MessageType,
+ NewConversationMessage,
+ UpdateParticipant,
+)
+from semantic_workbench_assistant.assistant_app import (
+ ConversationContext,
+)
+
+logger = logging.getLogger(__name__)
+
+
+# TODO: Put this protocol in the skill library.
+class SkillEventMapperProtocol(Protocol):
+ @staticmethod
+ async def map(
+ skill_event: skill_events.EventProtocol,
+ ) -> None: ...
+
+
+class SkillEventMapper(SkillEventMapperProtocol):
+ def __init__(self, conversation_context: ConversationContext) -> None:
+ self.conversation_context = conversation_context
+
+ async def map(
+ self,
+ skill_event: skill_events.EventProtocol,
+ ) -> None:
+ """
+ Maps events emitted by the skill assistant (from running actions or
+ routines) to message types understood by the Semantic Workbench.
+ """
+ metadata = {"debug": skill_event.metadata} if skill_event.metadata else None
+
+ match skill_event:
+ case skill_events.MessageEvent():
+ await self.conversation_context.send_messages(
+ NewConversationMessage(
+ content=skill_event.message or "",
+ metadata=metadata,
+ )
+ )
+
+ case skill_events.InformationEvent():
+ if skill_event.message:
+ await self.conversation_context.send_messages(
+ NewConversationMessage(
+ content=f"Information event: {skill_event.message}",
+ message_type=MessageType.note,
+ metadata=metadata,
+ ),
+ )
+
+ case skill_events.ErrorEvent():
+ await self.conversation_context.send_messages(
+ NewConversationMessage(
+ content=skill_event.message or "",
+ metadata=metadata,
+ )
+ )
+
+ case skill_events.StatusUpdatedEvent():
+ await self.conversation_context.update_participant_me(UpdateParticipant(status=skill_event.message))
+
+ case _:
+ logger.warning("Unhandled event: %s", skill_event)
diff --git a/assistants/skill-assistant/pyproject.toml b/assistants/skill-assistant/pyproject.toml
index e9328917..7742b8ba 100644
--- a/assistants/skill-assistant/pyproject.toml
+++ b/assistants/skill-assistant/pyproject.toml
@@ -7,26 +7,29 @@ readme = "README.md"
requires-python = ">=3.11"
dependencies = [
"azure-ai-contentsafety>=1.0.0",
- "azure-identity>=1.16.0",
"azure-core[aio]>=1.30.0",
- "openai>=1.3.9",
- "semantic-workbench-assistant>=0.1.0",
+ "azure-identity>=1.16.0",
"content-safety>=0.1.0",
- "posix-skill>=0.1.0",
- "document-skill>=0.1.0",
+ "context>=0.1.0",
+ # "document-skill>=0.1.0",
+ "form-filler-skill>=0.1.0",
"openai-client>=0.1.0",
+ "openai>=1.3.9",
+ "posix-skill>=0.1.0",
+ "semantic-workbench-assistant>=0.1.0",
]
[tool.uv]
package = true
[tool.uv.sources]
-# If you copy this file to your project, you should verify the relative path to the following:
-semantic-workbench-assistant = { path = "../../libraries/python/semantic-workbench-assistant", editable = true }
content-safety = { path = "../../libraries/python/content-safety", editable = true }
-posix-skill = { path = "../../libraries/python/skills/skills/posix-skill", editable = true }
-document-skill = { path = "../../libraries/python/skills/skills/document-skill", editable = true }
+context = { path = "../../libraries/python/context", editable = true }
+# document-skill = { path = "../../libraries/python/skills/skills/document-skill", editable = true }
+form-filler-skill = { path = "../../libraries/python/skills/skills/form-filler-skill", editable = true }
openai-client = { path = "../../libraries/python/openai-client", editable = true }
+posix-skill = { path = "../../libraries/python/skills/skills/posix-skill", editable = true }
+semantic-workbench-assistant = { path = "../../libraries/python/semantic-workbench-assistant", editable = true }
[build-system]
requires = ["hatchling"]
diff --git a/assistants/skill-assistant/uv.lock b/assistants/skill-assistant/uv.lock
index 2994d747..e165030c 100644
--- a/assistants/skill-assistant/uv.lock
+++ b/assistants/skill-assistant/uv.lock
@@ -130,7 +130,8 @@ dependencies = [
{ name = "azure-core", extra = ["aio"] },
{ name = "azure-identity" },
{ name = "content-safety" },
- { name = "document-skill" },
+ { name = "context" },
+ { name = "form-filler-skill" },
{ name = "openai" },
{ name = "openai-client" },
{ name = "posix-skill" },
@@ -143,13 +144,39 @@ requires-dist = [
{ name = "azure-core", extras = ["aio"], specifier = ">=1.30.0" },
{ name = "azure-identity", specifier = ">=1.16.0" },
{ name = "content-safety", editable = "../../libraries/python/content-safety" },
- { name = "document-skill", editable = "../../libraries/python/skills/skills/document-skill" },
+ { name = "context", editable = "../../libraries/python/context" },
+ { name = "form-filler-skill", editable = "../../libraries/python/skills/skills/form-filler-skill" },
{ name = "openai", specifier = ">=1.3.9" },
{ name = "openai-client", editable = "../../libraries/python/openai-client" },
{ name = "posix-skill", editable = "../../libraries/python/skills/skills/posix-skill" },
{ name = "semantic-workbench-assistant", editable = "../../libraries/python/semantic-workbench-assistant" },
]
+[[package]]
+name = "assistant-drive"
+version = "0.1.0"
+source = { editable = "../../libraries/python/assistant-drive" }
+dependencies = [
+ { name = "context" },
+ { name = "pydantic" },
+ { name = "pydantic-settings" },
+]
+
+[package.metadata]
+requires-dist = [
+ { name = "context", editable = "../../libraries/python/context" },
+ { name = "pydantic", specifier = ">=2.6.1" },
+ { name = "pydantic-settings", specifier = ">=2.5.2" },
+]
+
+[package.metadata.requires-dev]
+dev = [
+ { name = "ipykernel", specifier = ">=6.29.5" },
+ { name = "pytest", specifier = ">=8.3.1" },
+ { name = "pytest-asyncio", specifier = ">=0.23.8" },
+ { name = "pytest-repeat", specifier = ">=0.9.3" },
+]
+
[[package]]
name = "attrs"
version = "24.2.0"
@@ -319,6 +346,7 @@ dependencies = [
{ name = "events" },
{ name = "function-registry" },
{ name = "openai" },
+ { name = "openai-client" },
{ name = "pydantic" },
{ name = "pydantic-settings" },
{ name = "python-dotenv" },
@@ -333,6 +361,7 @@ requires-dist = [
{ name = "events", editable = "../../libraries/python/events" },
{ name = "function-registry", editable = "../../libraries/python/function-registry" },
{ name = "openai", specifier = ">=1.16.1" },
+ { name = "openai-client", editable = "../../libraries/python/openai-client" },
{ name = "pydantic", specifier = ">=2.6.1" },
{ name = "pydantic-settings", specifier = ">=2.3.4" },
{ name = "python-dotenv", specifier = ">=1.0.1" },
@@ -456,25 +485,6 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/87/a1/8c5287991ddb8d3e4662f71356d9656d91ab3a36618c3dd11b280df0d255/dnspython-2.6.1-py3-none-any.whl", hash = "sha256:5ef3b9680161f6fa89daf8ad451b5f1a33b18ae8a1c6778cdf4b43f08c0a6e50", size = 307696 },
]
-[[package]]
-name = "document-skill"
-version = "0.1.0"
-source = { editable = "../../libraries/python/skills/skills/document-skill" }
-dependencies = [
- { name = "chat-driver" },
- { name = "context" },
- { name = "events" },
- { name = "skill-library" },
-]
-
-[package.metadata]
-requires-dist = [
- { name = "chat-driver", editable = "../../libraries/python/chat-driver" },
- { name = "context", editable = "../../libraries/python/context" },
- { name = "events", editable = "../../libraries/python/events" },
- { name = "skill-library", editable = "../../libraries/python/skills/skill-library" },
-]
-
[[package]]
name = "email-validator"
version = "2.2.0"
@@ -541,6 +551,27 @@ standard = [
{ name = "uvicorn", extra = ["standard"] },
]
+[[package]]
+name = "form-filler-skill"
+version = "0.1.0"
+source = { editable = "../../libraries/python/skills/skills/form-filler-skill" }
+dependencies = [
+ { name = "chat-driver" },
+ { name = "context" },
+ { name = "events" },
+ { name = "openai-client" },
+ { name = "skill-library" },
+]
+
+[package.metadata]
+requires-dist = [
+ { name = "chat-driver", editable = "../../libraries/python/chat-driver" },
+ { name = "context", editable = "../../libraries/python/context" },
+ { name = "events", editable = "../../libraries/python/events" },
+ { name = "openai-client", editable = "../../libraries/python/openai-client" },
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{ name = "azure-core", extra = ["aio"] },
{ name = "azure-identity" },
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{ name = "openai" },
{ name = "pillow" },
+ { name = "python-liquid" },
{ name = "semantic-workbench-assistant" },
{ name = "tiktoken" },
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{ name = "azure-core", extras = ["aio"], specifier = ">=1.30.0" },
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{ name = "openai", specifier = ">=1.3.9" },
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{ name = "tiktoken", specifier = ">=0.7.0" },
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source = { editable = "../../libraries/python/skills/skill-library" }
dependencies = [
+ { name = "assistant-drive" },
{ name = "chat-driver" },
{ name = "context" },
{ name = "events" },
@@ -1345,6 +1416,7 @@ dependencies = [
[package.metadata]
requires-dist = [
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{ name = "chat-driver", editable = "../../libraries/python/chat-driver" },
{ name = "context", editable = "../../libraries/python/context" },
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{ name = "azure-core", extra = ["aio"] },
{ name = "azure-identity" },
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{ name = "openai" },
{ name = "pillow" },
+ { name = "python-liquid" },
{ name = "semantic-workbench-assistant" },
{ name = "tiktoken" },
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{ name = "azure-core", extras = ["aio"], specifier = ">=1.30.0" },
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{ name = "openai", specifier = ">=1.3.9" },
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{ name = "tiktoken", specifier = ">=0.7.0" },
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[[package]]
name = "python-dotenv"
version = "1.0.1"
@@ -1284,6 +1372,20 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/35/a6/145655273568ee78a581e734cf35beb9e33a370b29c5d3c8fee3744de29f/python_json_logger-2.0.7-py3-none-any.whl", hash = "sha256:f380b826a991ebbe3de4d897aeec42760035ac760345e57b812938dc8b35e2bd", size = 8067 },
]
+[[package]]
+name = "python-liquid"
+version = "1.12.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "importlib-resources" },
+ { name = "python-dateutil" },
+ { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/59/b4/c4e39470edf507056333cd688a36bb7fbdb17090105d9f454ecf6ff09845/python_liquid-1.12.1.tar.gz", hash = "sha256:7367e979125859fb4116f360f224a89a52ecb455fb26843c43e4d800b389d325", size = 124636 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/2c/6e/bfd01926e28f6cf355a7a8460271ba135013870f5857b06f35dbf65ab237/python_liquid-1.12.1-py3-none-any.whl", hash = "sha256:2224312944be16c1a44406398eb8a07c7e57398d5c0ef15ff950946dbefe7c33", size = 206592 },
+]
+
[[package]]
name = "python-multipart"
version = "0.0.12"
diff --git a/libraries/python/assistant-drive/assistant_drive/drive.py b/libraries/python/assistant-drive/assistant_drive/drive.py
index fe5d1b22..407393fd 100644
--- a/libraries/python/assistant-drive/assistant_drive/drive.py
+++ b/libraries/python/assistant-drive/assistant_drive/drive.py
@@ -58,7 +58,9 @@ def __str__(self) -> str:
path = self.filename
else:
path = f"{self.dir}/{self.filename}"
- return f"{path}\t{self.content_type}\t{self.size} bytes\t{self.created_at}\t{self.updated_at}"
+ return (
+ f"{path}\t{self.content_type}\t{self.size} bytes\t{self.created_at}\t{self.updated_at}"
+ )
@staticmethod
def from_bytes(content: BinaryIO, filename: str, dir: str | None = None) -> "FileMetadata":
@@ -99,7 +101,9 @@ def _path_for(self, filename: str | None = None, dir: str | None = None) -> path
return namespace_path
return namespace_path / filename
- def _metadata_path_for(self, filename: str | None = None, dir: str | None = None) -> pathlib.Path:
+ def _metadata_path_for(
+ self, filename: str | None = None, dir: str | None = None
+ ) -> pathlib.Path:
"""Return the actual path for a dir/file combo, creating the dir as needed."""
namespace_path = self.metadata_path / (dir or "")
namespace_path.mkdir(parents=True, exist_ok=True)
@@ -117,6 +121,12 @@ def _unique_filename(self, filename: str, dir: str | None) -> str:
return filename
+ def subdrive(self, dir: PathLike | str) -> "Drive":
+ config = DriveConfig(
+ root=self.root_path / dir, default_if_exists_behavior=self.default_if_exists_behavior
+ )
+ return Drive(config)
+
def write(
self,
content: BinaryIO,
@@ -216,11 +226,12 @@ def write_model(
filename: str,
dir: str | None = None,
serialization_context: dict[str, Any] | None = None,
- if_exists: IfDriveFileExistsBehavior = IfDriveFileExistsBehavior.AUTO_RENAME,
+ if_exists: IfDriveFileExistsBehavior | None = None,
) -> None:
"""Write a pydantic model to a file."""
data_json = value.model_dump_json(context=serialization_context)
data_bytes = data_json.encode("utf-8")
+ if_exists = if_exists or self.default_if_exists_behavior
self.write(io.BytesIO(data_bytes), filename, dir, if_exists)
ModelT = TypeVar("ModelT", bound=BaseModel)
diff --git a/libraries/python/chat-driver/chat_driver/__init__.py b/libraries/python/chat-driver/chat_driver/__init__.py
index eeb0d77b..6ff55fe5 100644
--- a/libraries/python/chat-driver/chat_driver/__init__.py
+++ b/libraries/python/chat-driver/chat_driver/__init__.py
@@ -1,19 +1,27 @@
# As a convenience, allow users to import the Context and ContextProtocol from
# the chat_driver package.
+import logging
+
from context import Context, ContextProtocol
+from openai_client.completion import JSON_OBJECT_RESPONSE_FORMAT, TEXT_RESPONSE_FORMAT
+from .chat_driver import (
+ ChatDriver,
+ ChatDriverConfig,
+)
from .local_message_history_provider import LocalMessageHistoryProvider, LocalMessageHistoryProviderConfig
from .message_history_provider import MessageHistoryProviderProtocol
-from .openai_chat_completion_driver import TEXT_RESPONSE_FORMAT, ChatDriver, ChatDriverConfig, ResponseFormat
+
+logger = logging.getLogger(__name__)
__all__ = [
"ChatDriver",
"ChatDriverConfig",
"Context",
"ContextProtocol",
+ "JSON_OBJECT_RESPONSE_FORMAT",
"LocalMessageHistoryProvider",
"LocalMessageHistoryProviderConfig",
"MessageHistoryProviderProtocol",
- "ResponseFormat",
"TEXT_RESPONSE_FORMAT",
]
diff --git a/libraries/python/chat-driver/chat_driver/chat_driver.py b/libraries/python/chat-driver/chat_driver/chat_driver.py
new file mode 100644
index 00000000..6f21f155
--- /dev/null
+++ b/libraries/python/chat-driver/chat_driver/chat_driver.py
@@ -0,0 +1,227 @@
+from dataclasses import dataclass, field
+from typing import Any, Callable, Union
+
+from context import Context, ContextProtocol
+from events import BaseEvent, ErrorEvent, MessageEvent
+from function_registry.function_registry import FunctionRegistry
+from openai import AsyncOpenAI
+from openai.types.chat import (
+ ChatCompletionMessageParam,
+ ChatCompletionSystemMessageParam,
+ ChatCompletionUserMessageParam,
+)
+from openai.types.chat.completion_create_params import ResponseFormat
+from openai_client.completion import TEXT_RESPONSE_FORMAT, completion_message_string
+from openai_client.errors import CompletionError
+from openai_client.messages import MessageFormatter, format_message
+from openai_client.tools import complete_with_tool_calls, function_registry_to_tools, tool_choice
+from pydantic import BaseModel
+
+from .local_message_history_provider import (
+ LocalMessageHistoryProvider,
+ LocalMessageHistoryProviderConfig,
+)
+from .message_history_provider import MessageHistoryProviderProtocol
+
+
+@dataclass
+class ChatDriverConfig:
+ openai_client: AsyncOpenAI
+ model: str
+ instructions: str | list[str] = "You are a helpful assistant."
+ instruction_formatter: MessageFormatter | None = None
+ context: ContextProtocol | None = None
+ message_provider: MessageHistoryProviderProtocol | None = None
+ commands: list[Callable] = field(default_factory=list)
+ functions: list[Callable] = field(default_factory=list)
+
+
+class ChatDriver:
+ """
+ A ChatDriver is a class that manages a conversation with a user. It provides
+ methods to add messages to the conversation, and to generate responses to
+ user messages. The ChatDriver uses the OpenAI API to generate responses, and
+ can call functions registered with the ChatDriver to generate parts of the
+ response. The ChatDriver also provides a way to register commands that can
+ be called by the user to execute functions directly.
+
+ Instructions are messages that are sent to the OpenAI model before any other
+ messages. These instructions are used to guide the model in generating a
+ response. The ChatDriver allows you to set instructions that can be
+ formatted with variables.
+
+ If you want to just generate responses using the OpenAI API, you should use
+ the client directly (but we do have some helpers in the openai_helpers
+ module) to make this simpler.
+ """
+
+ def __init__(self, config: ChatDriverConfig) -> None:
+ # A context object holds information about the current session, such as
+ # the session ID, the user ID, and the conversation ID. It also provides
+ # a method to emit events. If you do not supply one, one will be created
+ # for you with a random session ID.
+ self.context: ContextProtocol = config.context or Context()
+
+ # Set up a default message provider. This provider stores messages in a
+ # local file. You can replace this with your own message provider that
+ # implements the MessageHistoryProviderProtocol.
+ self.message_provider: MessageHistoryProviderProtocol = config.message_provider or LocalMessageHistoryProvider(
+ LocalMessageHistoryProviderConfig(context=self.context)
+ )
+
+ self.instructions: list[str] = (
+ config.instructions if isinstance(config.instructions, list) else [config.instructions]
+ )
+ self.instruction_formatter = config.instruction_formatter or format_message
+
+ # Now set up the OpenAI client and model.
+ self.client = config.openai_client
+ self.model = config.model
+
+ # Functions that you register with the chat driver will be available to
+ # for GPT to call while generating a response. If the model generates a
+ # call to a function, the function will be executed, the result passed
+ # back to the model, and the model will continue generating the
+ # response.
+ self.function_registry = FunctionRegistry(self.context, config.functions)
+ self.functions = self.function_registry.functions
+
+ # Commands are functions that can be called by the user by typing a
+ # command in the chat. When a command is received, the chat driver will
+ # execute the corresponding function and return the result to the user
+ # directly.
+ self.command_registry = FunctionRegistry(self.context, config.commands)
+ self.commands = self.command_registry.functions
+
+ def _formatted_instructions(self, vars: dict[str, Any] | None) -> list[ChatCompletionSystemMessageParam]:
+ return ChatDriver.format_instructions(self.instructions, vars, self.instruction_formatter)
+
+ async def add_message(self, message: ChatCompletionMessageParam) -> None:
+ await self.message_provider.append(message)
+
+ # Commands are available to be run by the user message.
+ def register_command(self, function: Callable) -> None:
+ self.command_registry.register_function(function)
+
+ def register_commands(self, functions: list[Callable]) -> None:
+ self.command_registry.register_functions(functions)
+
+ # Functions are available to be called by the model during response
+ # generation.
+ def register_function(self, function: Callable) -> None:
+ self.function_registry.register_function(function)
+
+ def register_functions(self, functions: list[Callable]) -> None:
+ self.function_registry.register_functions(functions)
+
+ # Sometimes we want to register a function to be used by both the user and
+ # the model.
+ def register_function_and_command(self, function: Callable) -> None:
+ self.register_command(function)
+ self.register_function(function)
+
+ def register_functions_and_commands(self, functions: list[Callable]) -> None:
+ self.register_commands(functions)
+ self.register_functions(functions)
+
+ def get_functions(self) -> list[Callable]:
+ return [function.fn for function in self.function_registry.get_functions()]
+
+ def get_commands(self) -> list[Callable]:
+ commands = [function.fn for function in self.command_registry.get_functions()]
+ return commands
+
+ async def respond(
+ self,
+ message: str | None = None,
+ response_format: Union[ResponseFormat, type[BaseModel]] = TEXT_RESPONSE_FORMAT,
+ function_choice: list[str] | None = None,
+ instruction_parameters: dict[str, Any] | None = None,
+ ) -> BaseEvent:
+ """
+ Respond to a user message.
+
+ If the user message is a command, the command will be executed and the
+ result returned.
+
+ All generated messages are added to the chat driver's message history.
+
+ Otherwise, the message will be passed to the chat completion API and the
+ response returned in the specified request_format.
+
+ The API response might be a request to call functions registered with
+ the chat driver. If so, we execute the functions and give the results
+ back to the model for the final response generation.
+ """
+
+ # If the message contains a command, execute it.
+ if message and message.startswith("/"):
+ command_string = message[1:]
+ try:
+ results = await self.command_registry.execute_function_string_with_string_response(command_string)
+ return MessageEvent(message=results)
+ except Exception as e:
+ return ErrorEvent(message=f"Error! {e}", metadata={"error": str(e)})
+
+ # If not a command, add the message to the history.
+ if message is not None:
+ user_message: ChatCompletionUserMessageParam = {
+ "role": "user",
+ "content": message,
+ }
+ await self.add_message(user_message)
+
+ # Generate a response.
+ metadata = {}
+
+ completion_args = {
+ "model": self.model,
+ "messages": [*self._formatted_instructions(instruction_parameters), *(await self.message_provider.get())],
+ "tools": function_registry_to_tools(self.function_registry),
+ "tool_choice": tool_choice(function_choice),
+ "response_format": response_format,
+ }
+ try:
+ completion, new_messages = await complete_with_tool_calls(
+ self.client,
+ self.function_registry,
+ completion_args,
+ metadata=metadata,
+ )
+ except CompletionError as e:
+ return ErrorEvent(message=f"Error: {e.message}", metadata=metadata)
+
+ # Add the new messages to the history.
+ for new_message in new_messages:
+ await self.add_message(new_message)
+
+ # Return the response.
+
+ return MessageEvent(
+ message=completion_message_string(completion) or None,
+ metadata=metadata,
+ )
+
+ @staticmethod
+ def format_instructions(
+ instructions: list[str],
+ vars: dict[str, Any] | None = None,
+ formatter: MessageFormatter | None = None,
+ ) -> list[ChatCompletionSystemMessageParam]:
+ """
+ Chat Driver instructions are system messages given to the OpenAI model
+ before any other messages. These instructions are used to guide the model in
+ generating a response. We oftentimes need inject variables into the
+ instructions, so we provide a formatter function to format the instructions
+ with the variables. This method returns a list of system messages formatted
+ with the variables.
+ """
+ formatter = formatter or format_message
+ instruction_messages: list[ChatCompletionSystemMessageParam] = []
+ for instruction in instructions:
+ if vars:
+ formatted_instruction = formatter(instruction, vars)
+ else:
+ formatted_instruction = instruction
+ instruction_messages.append({"role": "system", "content": formatted_instruction})
+ return instruction_messages
diff --git a/libraries/python/chat-driver/chat_driver/in_memory_message_history_provider.py b/libraries/python/chat-driver/chat_driver/in_memory_message_history_provider.py
new file mode 100644
index 00000000..9314a383
--- /dev/null
+++ b/libraries/python/chat-driver/chat_driver/in_memory_message_history_provider.py
@@ -0,0 +1,55 @@
+import asyncio
+from typing import Any, Iterable
+
+from openai.types.chat import ChatCompletionMessageParam, ChatCompletionMessageToolCallParam
+from openai_client.messages import (
+ MessageFormatter,
+ assistant_message,
+ format_message,
+ system_message,
+ user_message,
+)
+
+
+class InMemoryMessageHistoryProvider:
+ def __init__(
+ self,
+ messages: list[ChatCompletionMessageParam] | None = None,
+ formatter: MessageFormatter | None = None,
+ ) -> None:
+ self.formatter: MessageFormatter = formatter or format_message
+ self.messages = messages or []
+
+ async def get(self) -> list[ChatCompletionMessageParam]:
+ """Get all messages. This method is required for conforming to the
+ MessageFormatter protocol."""
+ return self.messages
+
+ async def append(self, message: ChatCompletionMessageParam) -> None:
+ """Append a message to the history. This method is required for
+ conforming to the MessageFormatter protocol."""
+ self.messages.append(message)
+
+ def extend(self, messages: list[ChatCompletionMessageParam]) -> None:
+ self.messages.extend(messages)
+
+ def set(self, messages: list[ChatCompletionMessageParam], vars: dict[str, Any]) -> None:
+ self.messages = messages
+
+ def delete_all(self) -> None:
+ self.messages = []
+
+ def append_system_message(self, content: str, var: dict[str, Any] | None = None) -> None:
+ asyncio.run(self.append(system_message(content, var, self.formatter)))
+
+ def append_user_message(self, content: str, var: dict[str, Any] | None = None) -> None:
+ asyncio.run(self.append(user_message(content, var, self.formatter)))
+
+ def append_assistant_message(
+ self,
+ content: str,
+ refusal: str | None = None,
+ tool_calls: Iterable[ChatCompletionMessageToolCallParam] | None = None,
+ var: dict[str, Any] | None = None,
+ ) -> None:
+ asyncio.run(self.append(assistant_message(content, refusal, tool_calls, var, self.formatter)))
diff --git a/libraries/python/chat-driver/chat_driver/local_message_history_provider.py b/libraries/python/chat-driver/chat_driver/local_message_history_provider.py
index fecf2ae9..53cd6ec5 100644
--- a/libraries/python/chat-driver/chat_driver/local_message_history_provider.py
+++ b/libraries/python/chat-driver/chat_driver/local_message_history_provider.py
@@ -2,14 +2,22 @@
from dataclasses import dataclass, field
from os import PathLike
from pathlib import Path
-from typing import Any
+from typing import Any, Iterable
from context.context import ContextProtocol
from openai.types.chat import (
ChatCompletionMessageParam,
+ ChatCompletionMessageToolCallParam,
+)
+from openai_client.messages import (
+ MessageFormatter,
+ assistant_message,
+ format_message,
+ system_message,
+ user_message,
)
-from .message_formatter import MessageFormatter, format_message
+from .message_history_provider import MessageHistoryProviderProtocol
DEFAULT_DATA_DIR = Path(".data")
@@ -22,13 +30,13 @@ class LocalMessageHistoryProviderConfig:
formatter: MessageFormatter | None = None
-class LocalMessageHistoryProvider:
+class LocalMessageHistoryProvider(MessageHistoryProviderProtocol):
def __init__(self, config: LocalMessageHistoryProviderConfig) -> None:
if not config.data_dir:
self.data_dir = DEFAULT_DATA_DIR / "chat_driver" / config.context.session_id
else:
self.data_dir = Path(config.data_dir)
- self.formatter = config.formatter or format_message
+ self.formatter: MessageFormatter = config.formatter or format_message
# Create the messages file if it doesn't exist.
if not self.data_dir.exists():
@@ -38,22 +46,49 @@ def __init__(self, config: LocalMessageHistoryProviderConfig) -> None:
self.messages_file.write_text("[]")
async def get(self) -> list[ChatCompletionMessageParam]:
+ """
+ Get all messages. This method is required for conforming to the
+ MessageFormatter protocol.
+ """
return json.loads(self.messages_file.read_text())
async def append(self, message: ChatCompletionMessageParam) -> None:
+ """
+ Append a message to the history. This method is required for conforming
+ to the MessageFormatter protocol.
+ """
messages = await self.get()
messages.append(message)
self.messages_file.write_text(json.dumps(messages, indent=2))
- # Additional methods.
-
async def extend(self, messages: list[ChatCompletionMessageParam]) -> None:
+ """
+ Append a list of messages to the history.
+ """
existing_messages = await self.get()
existing_messages.extend(messages)
self.messages_file.write_text(json.dumps(existing_messages, indent=2))
async def set(self, messages: list[ChatCompletionMessageParam], vars: dict[str, Any]) -> None:
+ """
+ Completely replace the messages with the new messages.
+ """
self.messages_file.write_text(json.dumps(messages, indent=2))
def delete_all(self) -> None:
self.messages_file.write_text("[]")
+
+ async def append_system_message(self, content: str, var: dict[str, Any] | None = None) -> None:
+ await self.append(system_message(content, var, self.formatter))
+
+ async def append_user_message(self, content: str, var: dict[str, Any] | None = None) -> None:
+ await self.append(user_message(content, var, self.formatter))
+
+ async def append_assistant_message(
+ self,
+ content: str,
+ refusal: str,
+ tool_calls: Iterable[ChatCompletionMessageToolCallParam] | None = None,
+ var: dict[str, Any] | None = None,
+ ) -> None:
+ await self.append(assistant_message(content, refusal, tool_calls, var, self.formatter))
diff --git a/libraries/python/chat-driver/chat_driver/logger.py b/libraries/python/chat-driver/chat_driver/logger.py
deleted file mode 100644
index 0f1503f6..00000000
--- a/libraries/python/chat-driver/chat_driver/logger.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import json
-import logging
-from typing import Any
-
-from context import ContextProtocol
-
-
-class Logger:
- def __init__(self, name: str, context: ContextProtocol) -> None:
- self.logger = logging.getLogger(name)
- self.context = context
-
- def _extra(self, data: Any) -> dict[str, Any]:
- extra = {"session_id": self.context.session_id, "run_id": self.context.run_id}
-
- # Ensure data is a JSON-serializable object.
- try:
- data = json.loads(json.dumps(data))
- except Exception:
- data = None
-
- if data:
- extra["data"] = data
-
- return extra
-
- def info(self, message: str, data: Any = None) -> None:
- self.logger.info(message, extra=self._extra(data))
-
- def warning(self, message: str, data: Any = None) -> None:
- self.logger.warning(message, extra=self._extra(data))
-
- def error(self, message: str, data: Any = None) -> None:
- self.logger.error(message, extra=self._extra(data))
-
- def debug(self, message: str, data: Any = None) -> None:
- self.logger.debug(message, extra=self._extra(data))
-
- def critical(self, message: str, data: Any = None) -> None:
- self.logger.critical(message, extra=self._extra(data))
-
- def exception(self, message: str, data: Any = None) -> None:
- self.logger.exception(message, extra=self._extra(data))
diff --git a/libraries/python/chat-driver/chat_driver/message_formatter.py b/libraries/python/chat-driver/chat_driver/message_formatter.py
deleted file mode 100644
index 69dccf5a..00000000
--- a/libraries/python/chat-driver/chat_driver/message_formatter.py
+++ /dev/null
@@ -1,16 +0,0 @@
-from typing import Any, Callable
-
-MessageFormatter = Callable[[str, dict[str, Any]], str]
-
-
-def format_message(message: str, vars: dict[str, Any]) -> str:
- """
- Format a message with the given variables.
- """
- if message and vars:
- for key, value in vars.items():
- try:
- message = message.format(**{key: value})
- except KeyError:
- pass
- return message
diff --git a/libraries/python/chat-driver/chat_driver/openai_chat_completion_driver.py b/libraries/python/chat-driver/chat_driver/openai_chat_completion_driver.py
deleted file mode 100644
index 61ce85b3..00000000
--- a/libraries/python/chat-driver/chat_driver/openai_chat_completion_driver.py
+++ /dev/null
@@ -1,289 +0,0 @@
-import json
-from dataclasses import dataclass, field
-from typing import Any, Callable, Iterable, List
-
-from context.context import Context, ContextProtocol
-from events import BaseEvent, MessageEvent
-from function_registry.function_registry import FunctionRegistry
-from openai import APIConnectionError, APIStatusError, AsyncOpenAI, NotGiven, RateLimitError
-from openai.types.chat import (
- ChatCompletionAssistantMessageParam,
- ChatCompletionMessageParam,
- ChatCompletionSystemMessageParam,
- ChatCompletionToolMessageParam,
- ChatCompletionToolParam,
- ChatCompletionUserMessageParam,
-)
-from openai.types.chat.completion_create_params import ResponseFormat
-
-from .local_message_history_provider import LocalMessageHistoryProvider, LocalMessageHistoryProviderConfig
-from .logger import Logger
-from .message_formatter import MessageFormatter, format_message
-from .message_history_provider import MessageHistoryProviderProtocol
-
-DEFAULT_MAX_RETRIES = 3
-TEXT_RESPONSE_FORMAT: ResponseFormat = {"type": "text"}
-
-
-# TODO: Make the chat driver handle json and json schema responses.
-JSON_OBJECT_RESPONSE_FORMAT: ResponseFormat = {"type": "json_object"}
-# {"type": "json_schema", "json_schema": {"name": "test", "schema": {}}}
-
-
-@dataclass
-class ChatDriverConfig:
- openai_client: AsyncOpenAI
- model: str
- instructions: str = "You are a helpful assistant."
- instruction_formatter: MessageFormatter | None = None
- context: ContextProtocol | None = None
- message_provider: MessageHistoryProviderProtocol | None = None
- commands: list[Callable] = field(default_factory=list)
- functions: list[Callable] = field(default_factory=list)
-
-
-class ChatDriver:
- def __init__(self, config: ChatDriverConfig) -> None:
- # A context object holds information about the current session, such as
- # the session ID, the user ID, and the conversation ID. It also provides
- # a method to emit events. If you do not supply one, one will be created
- # for you with a random session ID.
- self.context = config.context or Context()
-
- # Set up logging.
- self.logger = Logger(__name__, self.context)
-
- # Set up a default message provider. This provider stores messages in a
- # local file. You can replace this with your own message provider that
- # implements the MessageHistoryProviderProtocol.
- self.message_provider = config.message_provider or LocalMessageHistoryProvider(
- LocalMessageHistoryProviderConfig(context=self.context)
- )
-
- self.instructions = config.instructions
- self.instruction_formatter = config.instruction_formatter or format_message
-
- # Now set up the OpenAI client and model.
- self.client = config.openai_client
- self.model = config.model
-
- # Functions that you register with the chat driver will be available to
- # for GPT to call while generating a response. If the model generates a
- # call to a function, the function will be executed, the result passed
- # back to the model, and the model will continue generating the
- # response.
- self.function_registry = FunctionRegistry(self.context, config.functions)
- self.functions = self.function_registry.functions
-
- # Commands are functions that can be called by the user by typing a
- # command in the chat. When a command is received, the chat driver will
- # execute the corresponding function and return the result to the user
- # directly.
- self.command_registry = FunctionRegistry(self.context, config.commands)
- self.commands = self.command_registry.functions
-
- def _formatted_instruction(self, vars: dict[str, Any] | None) -> ChatCompletionSystemMessageParam | None:
- if not self.instructions:
- return
- if vars:
- formatted_instruction = self.instruction_formatter(self.instructions, vars)
- else:
- formatted_instruction = self.instructions
- return {"role": "system", "content": formatted_instruction}
-
- async def add_message(self, message: ChatCompletionMessageParam) -> None:
- await self.message_provider.append(message)
-
- async def add_error_message(self, message: str) -> None:
- error_message: ChatCompletionSystemMessageParam = {
- "role": "system",
- "content": f"Error: {message}",
- }
- await self.add_message(error_message)
-
- # Commands are available to be run by the user message.
- def register_command(self, function: Callable) -> None:
- self.command_registry.register_function(function)
-
- def register_commands(self, functions: list[Callable]) -> None:
- self.command_registry.register_functions(functions)
-
- # Functions are available to be called by the model during response
- # generation.
- def register_function(self, function: Callable) -> None:
- self.function_registry.register_function(function)
-
- def register_functions(self, functions: list[Callable]) -> None:
- self.function_registry.register_functions(functions)
-
- # Sometimes we want to register a function to be used by both the user and
- # the model.
- def register_function_and_command(self, function: Callable) -> None:
- self.register_command(function)
- self.register_function(function)
-
- def register_functions_and_commands(self, functions: list[Callable]) -> None:
- self.register_commands(functions)
- self.register_functions(functions)
-
- def get_functions(self) -> list[Callable]:
- return [function.fn for function in self.function_registry.get_functions()]
-
- def get_commands(self) -> list[Callable]:
- commands = [function.fn for function in self.command_registry.get_functions()]
- return commands
-
- def _get_tools(self) -> Iterable[ChatCompletionToolParam]:
- # Only the "functions" tool is available in the Chat API.
- # https://platform.openai.com/docs/guides/function-calling
-
- functions: List[ChatCompletionToolParam] = []
- for function in [func.schema for func in self.function_registry.get_functions()]:
- functions.append(
- ChatCompletionToolParam(**{
- "type": "function",
- "function": function,
- })
- )
-
- return functions
-
- async def respond(
- self,
- message: str,
- response_format: ResponseFormat = TEXT_RESPONSE_FORMAT,
- instruction_parameters: dict[str, Any] | None = None,
- ) -> BaseEvent:
- """Respond to a user message.
-
- If the user message is a command, the command will be executed and the
- result returned.
-
- Otherwise, the message will be passed to the chat completion API and the
- response returned.
-
- The api response might be a request to call functions registered with
- the chat driver. If so, we execute the functions and give the results
- back to the model for the final response generation."""
-
- if instruction_parameters is None:
- instruction_parameters = {}
-
- # If the message contains a command, execute it.
- if message.startswith("/"):
- command_string = message[1:]
- try:
- results = await self.command_registry.execute_function_string_with_string_response(command_string)
- return MessageEvent(message=results)
- except Exception as e:
- await self.add_error_message(f"Error: {e}")
- return MessageEvent(message="Error!", metadata={"error": str(e)})
-
- # Otherwise, generate a response.
- user_message: ChatCompletionUserMessageParam = {
- "role": "user",
- "content": message,
- }
- await self.add_message(user_message)
-
- completion_args = {
- "messages": [self._formatted_instruction(instruction_parameters), *(await self.message_provider.get())],
- "model": "gpt-4o",
- "tools": self._get_tools(),
- "response_format": response_format,
- }
- metadata: dict[str, Any] = {"completion_args": completion_args}
- self.logger.debug("Chat completion args.", data=completion_args)
- if not completion_args["tools"]:
- completion_args["tools"] = NotGiven()
-
- try:
- completion = await self.client.chat.completions.create(**completion_args)
- log_data = {
- "messages": completion_args["messages"],
- "model": completion_args["model"],
- "tools": completion_args["tools"],
- "response_format": completion_args["response_format"],
- }
- metadata["completion_response"] = completion.model_dump()
- self.logger.debug("Chat Completion response.", data=log_data)
- except APIConnectionError as e:
- msg = f"The server could not be reached: {e.__cause__}"
- self.logger.error(msg)
- await self.add_error_message(msg)
- metadata["error"] = str(e)
- return MessageEvent(message="The server could not be reached.", metadata=metadata)
- except RateLimitError:
- msg = "A 429 status code was received; we should back off a bit."
- self.logger.error(msg)
- await self.add_error_message(msg)
- return MessageEvent(message="Rate limit error.", metadata=metadata)
- except APIStatusError as e:
- msg = f"Another non-200-range status code was received. {e.status_code}: {e.response}"
- self.logger.error(msg)
- await self.add_error_message(msg)
- metadata["error"] = str(e)
- return MessageEvent(message="API status error.", metadata=metadata)
-
- # Get response and add to messages.
- response_message = completion.choices[0].message
- tool_calls = response_message.tool_calls
- assistant_response = ChatCompletionAssistantMessageParam(**response_message.model_dump())
- await self.add_message(assistant_response)
- message_for_return = assistant_response
- metadata["assistant_response"] = assistant_response
-
- # If the response includes function tool calls, execute them and pass
- # the result back to the API.
- if tool_calls:
- for tool_call in tool_calls:
- function = tool_call.function
- if self.function_registry.has_function(function.name):
- # Call function.
- self.logger.debug("Function call.", data={"name": function.name, "arguments": function.arguments})
- try:
- kwargs: dict[str, Any] = json.loads(function.arguments)
- value = await self.function_registry.execute_function(function.name, (), kwargs)
- except Exception as e:
- self.logger.error("Error.", data={"error": e})
- value = f"Error: {e}"
-
- # Create function message.
- content = ""
- if response_format["type"] == "text":
- content = str(value)
- elif response_format["type"] == "json_object":
- content = json.dumps(value)
- function_call_result_message: ChatCompletionToolMessageParam = {
- "role": "tool",
- "content": content,
- "tool_call_id": tool_call.id,
- }
- await self.add_message(function_call_result_message)
- self.logger.debug("Function response.", data={"content": content})
- metadata["function_call_result_message"] = function_call_result_message
-
- # Call assistant for final response.
- assistant_tool_completion_args = {
- "messages": [self._formatted_instruction(instruction_parameters), *(await self.message_provider.get())],
- "model": "gpt-4o",
- }
- self.logger.debug("Calling Completions API with tool response.", data=assistant_tool_completion_args)
- metadata["assistant_tool_completion_args"] = assistant_tool_completion_args
- completion = await self.client.chat.completions.create(**assistant_tool_completion_args)
-
- # Add assistant response to messages.
- assistant_tool_response = ChatCompletionAssistantMessageParam(**completion.choices[0].message.model_dump())
- await self.add_message(assistant_tool_response)
- message_for_return = assistant_tool_response
- self.logger.debug("Assistant tool response.", data=assistant_tool_response)
- metadata["assistant_tool_response"] = assistant_tool_response
-
- # TODO: Check if it's another tool call?
-
- # Return the last message.
- if message_for_return.get("role") == "assistant":
- content = message_for_return.get("content")
- return MessageEvent(message=str(content), metadata=metadata)
-
- return MessageEvent(message="No response.", metadata=metadata)
diff --git a/libraries/python/chat-driver/chat_driver/tests/formatted_instructions_test.py b/libraries/python/chat-driver/chat_driver/tests/formatted_instructions_test.py
new file mode 100644
index 00000000..a8331315
--- /dev/null
+++ b/libraries/python/chat-driver/chat_driver/tests/formatted_instructions_test.py
@@ -0,0 +1,62 @@
+from chat_driver.chat_driver import ChatDriver
+from openai_client.messages import format_message
+
+
+def test_formatted_instructions() -> None:
+ # Set instructions.
+ instructions = [
+ (
+ "Generate an outline for the document, including title. The outline should include the key points that will"
+ " be covered in the document. Consider the attachments, the rationale for why they were uploaded, and the"
+ " conversation that has taken place. The outline should be a hierarchical structure with multiple levels of"
+ " detail, and it should be clear and easy to understand. The outline should be generated in a way that is"
+ " consistent with the document that will be generated from it."
+ ),
+ "{{chat_history}}",
+ "{% for attachment in attachments %}{{attachment.filename}}{{attachment.content}}{% endfor %}",
+ "{{outline_versions.last}}",
+ "{{user_feedback}}",
+ ]
+
+ # Set vars.
+ attachments = [
+ {"filename": "filename1", "content": "content1"},
+ {"filename": "filename2", "content": "content2"},
+ ]
+ outline_versions = ["outline1", "outline2"]
+ user_feedback = "feedback"
+ chat_history = "history"
+ formatted_instructions = ChatDriver.format_instructions(
+ instructions=instructions,
+ vars={
+ "attachments": attachments,
+ "outline_versions": outline_versions,
+ "user_feedback": user_feedback,
+ "chat_history": chat_history,
+ },
+ formatter=format_message,
+ )
+
+ expected = [
+ {
+ "role": "system",
+ "content": "Generate an outline for the document, including title. The outline should include the key points that will be covered in the document. Consider the attachments, the rationale for why they were uploaded, and the conversation that has taken place. The outline should be a hierarchical structure with multiple levels of detail, and it should be clear and easy to understand. The outline should be generated in a way that is consistent with the document that will be generated from it.",
+ },
+ {"role": "system", "content": "history"},
+ # {
+ # "role": "system",
+ # "content": "filename1content1",
+ # },
+ # {
+ # "role": "system",
+ # "content": "filename2content2",
+ # },
+ {
+ "role": "system",
+ "content": "filename1content1filename2content2",
+ },
+ {"role": "system", "content": "outline2"},
+ {"role": "system", "content": "feedback"},
+ ]
+
+ assert formatted_instructions == expected
diff --git a/libraries/python/chat-driver/pyproject.toml b/libraries/python/chat-driver/pyproject.toml
index 8d181953..4a8832b1 100644
--- a/libraries/python/chat-driver/pyproject.toml
+++ b/libraries/python/chat-driver/pyproject.toml
@@ -6,16 +6,17 @@ authors = [{name="MADE:Explorers"}]
readme = "README.md"
requires-python = ">=3.11"
dependencies = [
+ "azure-identity>=1.17.1",
+ "context>=0.1.0",
+ "events>=0.1.0",
+ "function-registry>=0.1.0",
"openai>=1.16.1",
- "pydantic>=2.6.1",
+ "openai-client>=0.1.0",
"pydantic-settings>=2.3.4",
+ "pydantic>=2.6.1",
"python-dotenv>=1.0.1",
"requests>=2.32.0",
"tiktoken>=0.7.0",
- "azure-identity>=1.17.1",
- "context>=0.1.0",
- "events>=0.1.0",
- "function-registry>=0.1.0",
]
[tool.uv]
@@ -25,6 +26,7 @@ package = true
context = { path = "../context", editable = true }
events = { path = "../events", editable = true }
function-registry = { path = "../function-registry", editable = true }
+openai-client = { path = "../openai-client", editable = true }
[build-system]
requires = ["hatchling"]
diff --git a/libraries/python/chat-driver/uv.lock b/libraries/python/chat-driver/uv.lock
index e847a66c..06fcbc92 100644
--- a/libraries/python/chat-driver/uv.lock
+++ b/libraries/python/chat-driver/uv.lock
@@ -5,6 +5,88 @@ resolution-markers = [
"python_full_version >= '3.13'",
]
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+]
+
+[[package]]
+name = "aiohttp"
+version = "3.10.10"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "aiohappyeyeballs" },
+ { name = "aiosignal" },
+ { name = "attrs" },
+ { name = "frozenlist" },
+ { name = "multidict" },
+ { name = "yarl" },
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diff --git a/libraries/python/context/context/context.py b/libraries/python/context/context/context.py
index 256199b7..3bde2d04 100644
--- a/libraries/python/context/context/context.py
+++ b/libraries/python/context/context/context.py
@@ -27,7 +27,7 @@ class ContextProtocol(Protocol):
# The emit function is used to send events to the event bus. The component
# that creates this context object will be responsible for instantiating an
# event bus and handling the events sent to it with this function.
- emit: Optional[Callable[[EventProtocol], None]]
+ emit: Callable[[EventProtocol], None]
class LogEmitter:
@@ -38,7 +38,7 @@ def emit(self, event: EventProtocol) -> None:
logging.info(event.to_json())
-class Context:
+class Context(ContextProtocol):
"""A default context object that implements the ContextProtocol. The context
object that is passed to all components (functions, action, routines, etc.).
The context object is used to pass information between these components,
@@ -56,7 +56,11 @@ def __init__(
self.emit = emit or LogEmitter().emit
def to_dict(self) -> dict[str, Any]:
- return {"session_id": self.session_id, "run_id": self.run_id, "emit": self.emit.__class__.__name__}
+ return {
+ "session_id": self.session_id,
+ "run_id": self.run_id,
+ "emit": self.emit.__class__.__name__,
+ }
def __repr__(self) -> str:
return f"Context({self.session_id})"
diff --git a/libraries/python/events/events/events.py b/libraries/python/events/events/events.py
index dbe84b46..80ba9075 100644
--- a/libraries/python/events/events/events.py
+++ b/libraries/python/events/events/events.py
@@ -38,7 +38,7 @@ class BaseEvent(BaseModel):
session_id: str | None = Field(default=None)
timestamp: datetime = Field(default_factory=datetime.now)
message: str | None = Field(default=None)
- metadata: dict[str, Any] = {}
+ metadata: dict[str, Any] = Field(default_factory=dict)
def __str__(self) -> str:
return f"{self.__class__.__name__}: {self.message}"
diff --git a/libraries/python/function-registry/function_registry/function.py b/libraries/python/function-registry/function_registry/function.py
index 8cc0865e..39bdbbb7 100644
--- a/libraries/python/function-registry/function_registry/function.py
+++ b/libraries/python/function-registry/function_registry/function.py
@@ -1,9 +1,10 @@
+import inspect
from dataclasses import dataclass
from typing import Any, Callable
-import inspect
+
+from context.context import ContextProtocol
from pydantic import create_model
from pydantic.fields import FieldInfo
-from context.context import ContextProtocol
@dataclass
@@ -15,13 +16,15 @@ class Parameter:
class Function:
- def __init__(self, name: str, description: str | None, parameters: list[Parameter], fn: Callable) -> None:
+ def __init__(
+ self, name: str, description: str | None, parameters: list[Parameter], fn: Callable, strict_schema: bool = False
+ ) -> None:
self.name = name
self.description = description or name.replace("_", " ").title()
self.parameters = parameters
self.fn = fn
- self.schema = self._generate_schema()
- self.usage = self._generate_usage()
+ self.schema: dict[str, Any] = self._generate_schema(strict=strict_schema)
+ self.usage: str = self._generate_usage()
async def execute(self, context: ContextProtocol, *args, **kwargs) -> Any:
result = self.fn(context, *args, **kwargs)
@@ -50,7 +53,7 @@ def _generate_usage(self) -> str:
description = self.description
return f"{name}({', '.join(param_usages)}): {description}"
- def _generate_schema(self) -> dict[str, Any]:
+ def _generate_schema(self, strict: bool = True) -> dict[str, Any]:
"""
Generate a JSON schema for a function based on its signature.
"""
@@ -68,19 +71,20 @@ def _generate_schema(self) -> dict[str, Any]:
field_info,
)
pydantic_model = create_model(model_name, **fields)
+ # pydantic_model.model_config = {"field_title_generator": lambda _, _: None}
# Generate the JSON schema from the Pydantic model.
- basic_schema = pydantic_model.model_json_schema()
+ parameters_schema = pydantic_model.model_json_schema(mode="serialization")
# Remove title attribute from all properties.
- properties = basic_schema["properties"]
- for property in properties:
- if "title" in properties[property]:
- del properties[property]["title"]
+ properties = parameters_schema["properties"]
+ for property_key in properties.keys():
+ if "title" in properties[property_key]:
+ del properties[property_key]["title"]
# And from the top-level object.
- if "title" in basic_schema:
- del basic_schema["title"]
+ if "title" in parameters_schema:
+ del parameters_schema["title"]
name = self.fn.__name__
description = inspect.getdoc(self.fn) or name.replace("_", " ").title()
@@ -93,15 +97,26 @@ def _generate_schema(self) -> dict[str, Any]:
# "$id": f"urn:jsonschema:{name}",
"name": name,
"description": description,
+ "strict": strict,
"parameters": {
"type": "object",
- "properties": basic_schema["properties"],
+ "properties": parameters_schema["properties"],
},
}
- if "required" in basic_schema:
- schema["required"] = basic_schema["required"]
+ # If this is a strict schema requirement, OpenAI requires
+ # additionalProperties to be False.
+ # if strict:
+ schema["parameters"]["additionalProperties"] = False
+
+ # Add required fields.
+ if "required" in parameters_schema:
+ schema["parameters"]["required"] = parameters_schema["required"]
+
+ # Add definitions.
+ if "$defs" in parameters_schema:
+ schema["parameters"]["$defs"] = parameters_schema["$defs"]
+ for key in schema["parameters"]["$defs"]:
+ schema["parameters"]["$defs"][key]["additionalProperties"] = False
- if "$defs" in basic_schema:
- schema["$defs"] = basic_schema["$defs"]
return schema
diff --git a/libraries/python/function-registry/function_registry/function_registry.py b/libraries/python/function-registry/function_registry/function_registry.py
index e27ee3db..91459938 100644
--- a/libraries/python/function-registry/function_registry/function_registry.py
+++ b/libraries/python/function-registry/function_registry/function_registry.py
@@ -1,10 +1,11 @@
-from typing import Any, Callable
+import ast
import inspect
-from pydantic import BaseModel
import json
import logging
+from typing import Any, Callable
+
from context.context import ContextProtocol
-import ast
+from pydantic import BaseModel
from .function import Function, Parameter
@@ -32,13 +33,14 @@ class FunctionRegistry:
def __init__(self, context: ContextProtocol, functions: list[Callable] = []) -> None:
self.context = context
+
self.function_map: dict[str, Function] = {}
# By default, every registry has a help function.
- self.register_function(self.help)
+ self.register_function(self.help, strict_schema=True)
# But the help function can be overridden if you give it another one.
- self.register_functions(functions)
+ self.register_functions(functions, strict_schema=True)
# This allows actions to be called as attributes.
self.functions = FunctionHandler(self)
@@ -61,7 +63,7 @@ def has_function(self, name: str) -> bool:
return True
return name in self.function_map
- def register_function(self, function: Callable) -> None:
+ def register_function(self, function: Callable, strict_schema: bool = True) -> None:
# Ensure the first argument of the function is the context.
if not inspect.signature(function).parameters:
raise ValueError(f"Function {function.__name__} must have at least one parameter (context: Context).")
@@ -87,16 +89,17 @@ def register_function(self, function: Callable) -> None:
description=inspect.getdoc(function) or function.__name__.replace("_", " ").title(),
fn=function,
parameters=params,
+ strict_schema=strict_schema,
)
- def register_functions(self, functions: list[Callable]) -> None:
+ def register_functions(self, functions: list[Callable], strict_schema: bool = True) -> None:
for function in functions:
if function.__name__ in self.function_map:
logging.warning(f"Function {function.__name__} already registered.")
continue
if not callable(function):
raise ValueError(f"Function {function} is not callable.")
- self.register_function(function)
+ self.register_function(function, strict_schema=strict_schema)
async def execute_function(self, name: str, args: tuple, kwargs: dict[str, Any]) -> Any:
"""Run a registered function by name. Passes the context as the first argument."""
@@ -105,7 +108,7 @@ async def execute_function(self, name: str, args: tuple, kwargs: dict[str, Any])
raise ValueError(f"Function {name} not found in registry.")
return await function.execute(self.context, *args, **kwargs)
- async def execute_function_with_string_response(self, name: str, *args, **kwargs) -> str:
+ async def execute_function_with_string_response(self, name: str, args: tuple, kwargs: dict[str, Any]) -> str:
"""Run a registered function by name and return a string response."""
try:
result = await self.execute_function(name, args, kwargs)
diff --git a/libraries/python/function-registry/function_registry/tests/test_command_parsing.py b/libraries/python/function-registry/function_registry/tests/test_command_parsing.py
index 73da19dd..ded4b454 100644
--- a/libraries/python/function-registry/function_registry/tests/test_command_parsing.py
+++ b/libraries/python/function-registry/function_registry/tests/test_command_parsing.py
@@ -1,7 +1,8 @@
+from typing import Any, Callable
+
import pytest
-from function_registry.function_registry import FunctionRegistry
from context.context import Context
-from typing import Callable, Any
+from function_registry.function_registry import FunctionRegistry
def no_op(context: Context) -> None:
diff --git a/libraries/python/openai-client/openai_client/__init__.py b/libraries/python/openai-client/openai_client/__init__.py
index a3bffd5b..6543f748 100644
--- a/libraries/python/openai-client/openai_client/__init__.py
+++ b/libraries/python/openai-client/openai_client/__init__.py
@@ -1,3 +1,5 @@
+import logging
+
from .client import (
create_client,
)
@@ -17,6 +19,9 @@
num_tokens_from_tools_and_messages,
)
+logger = logging.getLogger(__name__)
+
+
__all__ = [
"create_client",
"truncate_messages_for_logging",
@@ -28,4 +33,5 @@
"AzureOpenAIServiceConfig",
"OpenAIServiceConfig",
"ServiceConfig",
+ "logger",
]
diff --git a/libraries/python/openai-client/openai_client/completion.py b/libraries/python/openai-client/openai_client/completion.py
new file mode 100644
index 00000000..b42051e4
--- /dev/null
+++ b/libraries/python/openai-client/openai_client/completion.py
@@ -0,0 +1,59 @@
+import json
+from typing import Any
+
+from openai.types.chat import (
+ ChatCompletionAssistantMessageParam,
+ ParsedChatCompletion,
+ ParsedChatCompletionMessage,
+)
+from openai.types.chat.completion_create_params import (
+ ResponseFormat,
+)
+from pydantic import BaseModel
+
+DEFAULT_MAX_RETRIES = 3
+
+# The response method can return a text response, a JSON object response, or as
+# a Pydantic BaseModel. See the `response` method for more details.
+TEXT_RESPONSE_FORMAT: ResponseFormat = {"type": "text"}
+JSON_OBJECT_RESPONSE_FORMAT: ResponseFormat = {"type": "json_object"}
+
+
+def assistant_message_from_completion(completion: ParsedChatCompletion[None]) -> ChatCompletionAssistantMessageParam:
+ completion_message: ParsedChatCompletionMessage = completion.choices[0].message
+ assistant_message = ChatCompletionAssistantMessageParam(role="assistant")
+ if completion_message.tool_calls:
+ response_dict = completion_message.model_dump()
+ assistant_message["tool_calls"] = response_dict["tool_calls"]
+ if completion_message.content:
+ assistant_message["content"] = completion_message.content
+ return assistant_message
+
+
+def completion_message(completion: ParsedChatCompletion) -> ParsedChatCompletionMessage | None:
+ return completion.choices[0].message if completion and completion.choices else None
+
+
+def completion_message_string(completion: ParsedChatCompletion | None) -> str:
+ if not completion or not completion.choices or not completion.choices[0].message:
+ return ""
+ return completion.choices[0].message.content or ""
+
+
+def completion_message_dict(completion: ParsedChatCompletion) -> dict[str, Any] | None:
+ message = completion_message(completion)
+ if message:
+ if message.parsed:
+ if isinstance(message.parsed, BaseModel):
+ return message.parsed.model_dump()
+ elif isinstance(message.parsed, dict):
+ return message.parsed
+ else:
+ try: # Try to parse the JSON string.
+ return json.loads(message.parsed)
+ except json.JSONDecodeError:
+ return None
+ try:
+ return json.loads(message.content or "")
+ except json.JSONDecodeError:
+ return None
diff --git a/libraries/python/openai-client/openai_client/errors.py b/libraries/python/openai-client/openai_client/errors.py
new file mode 100644
index 00000000..d5070768
--- /dev/null
+++ b/libraries/python/openai-client/openai_client/errors.py
@@ -0,0 +1,74 @@
+from typing import Any
+
+from openai import (
+ APIConnectionError,
+ APIStatusError,
+ BadRequestError,
+ RateLimitError,
+)
+from openai.types.chat import (
+ ParsedChatCompletion,
+)
+
+
+class InValidCompletionError(Exception):
+ def __init__(self, message: str, body: dict[str, Any] | None = None) -> None:
+ self.message = message
+ self.body = body
+ super().__init__(self.message)
+
+
+class CompletionIsNoneError(InValidCompletionError):
+ def __init__(self) -> None:
+ super().__init__("The completion response is None.")
+
+
+class CompletionRefusedError(InValidCompletionError):
+ def __init__(self, refusal: str) -> None:
+ super().__init__(f"The model refused to complete the response: {refusal}", {"refusal": refusal})
+
+
+class CompletionWithoutStopError(InValidCompletionError):
+ def __init__(self, finish_reason: str) -> None:
+ super().__init__(f"The model did not complete the response: {finish_reason}", {"finish_reason": finish_reason})
+
+
+class CompletionError(Exception):
+ def __init__(self, error: Exception) -> None:
+ if isinstance(error, APIConnectionError):
+ message = f"The server could not be reached: {error.__cause__}"
+ body = error.body
+ elif isinstance(error, RateLimitError):
+ message = "A 429 status code was received (rate limited); we should back off a bit."
+ body = error.body
+ elif isinstance(error, BadRequestError):
+ message = f"A 400 status code was received. {error.message}"
+ body = error.body
+ elif isinstance(error, APIStatusError):
+ message = f"Another non-200-range status code was received. {error.status_code}: {error.message}"
+ body = error.body
+ elif isinstance(error, InValidCompletionError):
+ message = error.message
+ body = error.body
+ else:
+ message = "An unknown error occurred."
+ body = str(error)
+
+ self.message = message
+ self.body = body
+ super().__init__(message)
+
+
+def validate_completion(completion: ParsedChatCompletion[None]) -> None:
+ if completion is None:
+ raise CompletionIsNoneError()
+
+ # Check for refusal.
+ refusal = completion.choices[0].message.refusal
+ if refusal:
+ raise CompletionRefusedError(refusal)
+
+ # Check "finish_reason" for "max_tokens" or "stop" to see if the response is incomplete.
+ finish_reason = completion.choices[0].finish_reason
+ if finish_reason not in ["stop", "tool_calls", None]:
+ raise CompletionWithoutStopError(finish_reason)
diff --git a/libraries/python/openai-client/openai_client/logging.py b/libraries/python/openai-client/openai_client/logging.py
new file mode 100644
index 00000000..fac8705b
--- /dev/null
+++ b/libraries/python/openai-client/openai_client/logging.py
@@ -0,0 +1,48 @@
+import inspect
+import json
+from typing import Any
+
+from openai import (
+ NotGiven,
+)
+from pydantic import BaseModel
+
+
+def serializable_completion_args(completion_args: dict[str, Any]) -> dict[str, Any]:
+ """
+ We put the completion args into logs and messages, so it's important that
+ they are serializable. This function returns a copy of the completion args
+ that can be serialized.
+ """
+ sanitized = completion_args.copy()
+
+ # NotGiven type (used by OpenAI) is not serializable.
+ if isinstance(completion_args.get("tools"), NotGiven):
+ del sanitized["tools"]
+
+ # A pydantic BaseModel class is not serializable, and we don't want the
+ # whole class anyway, so we just store the name.
+ if completion_args.get("response_format"):
+ response_format = completion_args["response_format"]
+ if inspect.isclass(response_format) and issubclass(response_format, BaseModel):
+ sanitized["response_format"] = response_format.__name__
+
+ return sanitized
+
+
+def extra_data(data: Any) -> dict[str, Any]:
+ """
+ Helper function to add extra data to log messages.
+ """
+ extra = {}
+
+ # Ensure data is a JSON-serializable object.
+ try:
+ data = json.loads(json.dumps(data))
+ except Exception as e:
+ data = str(e)
+
+ if data:
+ extra["data"] = data
+
+ return extra
diff --git a/libraries/python/openai-client/openai_client/messages.py b/libraries/python/openai-client/openai_client/messages.py
index ec4c069b..2f3e58ff 100644
--- a/libraries/python/openai-client/openai_client/messages.py
+++ b/libraries/python/openai-client/openai_client/messages.py
@@ -1,6 +1,13 @@
-from typing import Literal
+from typing import Any, Callable, Iterable, Literal, Optional
-from openai.types.chat import ChatCompletionMessageParam
+from liquid import Template
+from openai.types.chat import (
+ ChatCompletionAssistantMessageParam,
+ ChatCompletionMessageParam,
+ ChatCompletionMessageToolCallParam,
+ ChatCompletionSystemMessageParam,
+ ChatCompletionUserMessageParam,
+)
def truncate_messages_for_logging(
@@ -71,3 +78,62 @@ def process_dict(dict_: dict, maximum_length: int, filler_text: str) -> dict:
case dict():
dict_[key] = process_dict(value, maximum_length, filler_text)
return dict_
+
+
+MessageFormatter = Callable[[str, dict[str, Any]], str]
+
+
+def format_message(message: str, vars: dict[str, Any]) -> str:
+ """
+ Format a message with the given variables using the Python format method.
+ """
+ if message and vars:
+ for key, value in vars.items():
+ try:
+ message = message.format(**{key: value})
+ except KeyError:
+ pass
+ return message
+
+
+def liquid_format(message: str, vars: dict[str, Any]) -> str:
+ """
+ Format a message with the given variables using the Liquid template engine.
+ """
+ out = message
+ if not message:
+ return message
+ template = Template(message)
+ out = template.render(**vars)
+ return out
+
+
+def system_message(
+ content: str, var: dict[str, Any] | None = None, formatter: MessageFormatter = format_message
+) -> ChatCompletionSystemMessageParam:
+ if var:
+ content = formatter(content, var)
+ return {"role": "system", "content": content}
+
+
+def user_message(
+ content: str, var: dict[str, Any] | None = None, formatter: MessageFormatter = format_message
+) -> ChatCompletionUserMessageParam:
+ if var:
+ content = formatter(content, var)
+ return {"role": "user", "content": content}
+
+
+def assistant_message(
+ content: str,
+ refusal: Optional[str] = None,
+ tool_calls: Iterable[ChatCompletionMessageToolCallParam] | None = None,
+ var: dict[str, Any] | None = None,
+ formatter: MessageFormatter = format_message,
+) -> ChatCompletionAssistantMessageParam:
+ if var:
+ content = formatter(content, var)
+ message = ChatCompletionAssistantMessageParam(role="assistant", content=content, refusal=refusal)
+ if tool_calls is not None:
+ message["tool_calls"] = tool_calls
+ return message
diff --git a/libraries/python/openai-client/openai_client/tools.py b/libraries/python/openai-client/openai_client/tools.py
new file mode 100644
index 00000000..8d12f771
--- /dev/null
+++ b/libraries/python/openai-client/openai_client/tools.py
@@ -0,0 +1,161 @@
+import json
+from typing import Any, Iterable
+
+from function_registry import FunctionRegistry
+from openai import (
+ AsyncAzureOpenAI,
+ AsyncOpenAI,
+ NotGiven,
+)
+from openai.types.chat import (
+ ChatCompletionMessageParam,
+ ChatCompletionToolParam,
+ ParsedChatCompletion,
+ ParsedFunctionToolCall,
+)
+
+from . import logger
+from .completion import assistant_message_from_completion
+from .errors import CompletionError, validate_completion
+from .logging import extra_data, serializable_completion_args
+
+
+async def execute_tool_call(
+ tool_call: ParsedFunctionToolCall,
+ function_registry: FunctionRegistry,
+) -> ChatCompletionMessageParam | None:
+ """
+ Execute a tool call function using a Function Registry and return the response as a message.
+ """
+ function = tool_call.function
+ if function_registry.has_function(function.name):
+ logger.debug(
+ "Function call.",
+ extra=extra_data({"name": function.name, "arguments": function.arguments}),
+ )
+ try:
+ kwargs: dict[str, Any] = json.loads(function.arguments)
+ value = await function_registry.execute_function_with_string_response(function.name, (), kwargs)
+ except Exception as e:
+ logger.error("Error.", extra=extra_data({"error": e}))
+ value = f"Error: {e}"
+ finally:
+ logger.debug("Function response.", extra=extra_data({"tool_call_id": tool_call.id, "content": value}))
+ return {
+ "role": "tool",
+ "content": value,
+ "tool_call_id": tool_call.id,
+ }
+ else:
+ logger.error(f"Function not found: {function.name}")
+
+
+def function_registry_to_tools(function_registry: FunctionRegistry) -> Iterable[ChatCompletionToolParam] | NotGiven:
+ # Only the "functions" tool is available in the Chat API.
+ # https://platform.openai.com/docs/guides/function-calling
+
+ # If the only tool is the default function registry help function, then
+ # we don't want to tell the chat response that we have any tools.
+ if function_registry.list_functions() == ["help"]:
+ return NotGiven()
+
+ return [
+ ChatCompletionToolParam(**{
+ "type": "function",
+ "function": func.schema,
+ })
+ for func in function_registry.get_functions()
+ ]
+
+
+def tool_choice(functions: list[str] | None) -> Iterable[ChatCompletionToolParam] | None:
+ if not functions:
+ return None
+ return [
+ ChatCompletionToolParam(**{
+ "type": "function",
+ "function": {"name": name},
+ })
+ for name in functions
+ ] or None
+
+
+async def complete_with_tool_calls(
+ async_client: AsyncOpenAI | AsyncAzureOpenAI,
+ function_registry: FunctionRegistry,
+ completion_args: dict[str, Any],
+ metadata: dict[str, Any] = {},
+) -> tuple[ParsedChatCompletion | None, list[ChatCompletionMessageParam]]:
+ """
+ Complete a chat response with tool calls handled by Function Registry-registered functions.
+
+ Parameters:
+ - async_client: The OpenAI client.
+ - function_registry: The function registry.
+ - completion_args: The completion arguments wassed onto the OpenAI `parse` call. See the OpenAI API docs for more information.
+ - metadata: Metadata to be added to the completion response.
+ """
+ # Pull out a reference to the completion args messages.
+ messages: list[ChatCompletionMessageParam] = completion_args.get("messages", [])
+ new_messages: list[ChatCompletionMessageParam] = []
+
+ # Completion call.
+ logger.debug("Completion call.", extra=extra_data(serializable_completion_args(completion_args)))
+ metadata["completion_args"] = serializable_completion_args(completion_args)
+ try:
+ completion = await async_client.beta.chat.completions.parse(
+ **completion_args,
+ )
+ validate_completion(completion)
+ logger.debug("Completion response.", extra=extra_data({"completion": completion.model_dump()}))
+ metadata["completion"] = completion.model_dump()
+ except CompletionError as e:
+ completion_error = CompletionError(e)
+ metadata["completion_error"] = completion_error.message
+ logger.error(e.message, extra=extra_data({"completion_error": completion_error.body, "metadata": metadata}))
+ raise completion_error from e
+
+ # Extract response and add to messages.
+ assistant_message = assistant_message_from_completion(completion)
+ if assistant_message:
+ new_messages.append(assistant_message)
+
+ # If no tool calls, we're done.
+ completion_message = completion.choices[0].message
+ if not completion_message.tool_calls:
+ return completion, new_messages
+
+ # Call all tool functions and generate return messages.
+ for tool_call in completion_message.tool_calls:
+ function_call_result_message = await execute_tool_call(tool_call, function_registry)
+ if function_call_result_message:
+ new_messages.append(function_call_result_message)
+
+ # Completion call for final response.
+ final_args = {**completion_args, "messages": [*messages, *new_messages]}
+ del final_args["tools"]
+ del final_args["tool_choice"]
+ logger.debug("Tool completion call.", extra=extra_data(serializable_completion_args(final_args)))
+ metadata["tool_completion_args"] = serializable_completion_args(final_args)
+ try:
+ tool_completion = await async_client.beta.chat.completions.parse(
+ **final_args,
+ )
+ validate_completion(tool_completion)
+ logger.debug("Tool completion response.", extra=extra_data({"completion": completion.model_dump()}))
+ metadata["completion"] = completion.model_dump()
+ except Exception as e:
+ tool_completion_error = CompletionError(e)
+ metadata["tool_completion_error"] = tool_completion_error.message
+ logger.error(
+ tool_completion_error.message,
+ extra=extra_data({"completion_error": tool_completion_error.body, "metadata": metadata}),
+ )
+ raise tool_completion_error from e
+
+ # Add assistant response to messages.
+ tool_completion_assistant_message = assistant_message_from_completion(tool_completion)
+ if tool_completion_assistant_message:
+ new_messages.append(tool_completion_assistant_message)
+
+ return tool_completion, new_messages
diff --git a/libraries/python/openai-client/pyproject.toml b/libraries/python/openai-client/pyproject.toml
index f48f7e2e..9ad6c499 100644
--- a/libraries/python/openai-client/pyproject.toml
+++ b/libraries/python/openai-client/pyproject.toml
@@ -9,10 +9,12 @@ dependencies = [
"azure-ai-contentsafety>=1.0.0",
"azure-core[aio]>=1.30.0",
"azure-identity>=1.17.1",
+ "function-registry>=0.1.0",
"openai>=1.3.9",
"pillow>=11.0.0",
- "tiktoken>=0.7.0",
+ "python-liquid>=1.12.1",
"semantic-workbench-assistant>=0.1.0",
+ "tiktoken>=0.7.0",
]
[tool.uv]
@@ -21,6 +23,7 @@ dev-dependencies = ["pytest>=8.3.3"]
[tool.uv.sources]
semantic-workbench-assistant = { path = "../semantic-workbench-assistant", editable = true }
+function-registry = { path = "../function-registry", editable = true }
[build-system]
requires = ["hatchling"]
diff --git a/libraries/python/openai-client/uv.lock b/libraries/python/openai-client/uv.lock
index 9d382c85..17124e1a 100644
--- a/libraries/python/openai-client/uv.lock
+++ b/libraries/python/openai-client/uv.lock
@@ -301,6 +301,24 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335 },
]
+[[package]]
+name = "context"
+version = "0.1.0"
+source = { editable = "../context" }
+dependencies = [
+ { name = "events" },
+]
+
+[package.metadata]
+requires-dist = [{ name = "events", editable = "../events" }]
+
+[package.metadata.requires-dev]
+dev = [
+ { name = "pytest", specifier = ">=8.3.1" },
+ { name = "pytest-asyncio", specifier = ">=0.23.8" },
+ { name = "pytest-repeat", specifier = ">=0.9.3" },
+]
+
[[package]]
name = "cryptography"
version = "43.0.1"
@@ -370,6 +388,17 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/d7/ee/bf0adb559ad3c786f12bcbc9296b3f5675f529199bef03e2df281fa1fadb/email_validator-2.2.0-py3-none-any.whl", hash = "sha256:561977c2d73ce3611850a06fa56b414621e0c8faa9d66f2611407d87465da631", size = 33521 },
]
+[[package]]
+name = "events"
+version = "0.1.0"
+source = { editable = "../events" }
+dependencies = [
+ { name = "pydantic" },
+]
+
+[package.metadata]
+requires-dist = [{ name = "pydantic", specifier = ">=2.6.1" }]
+
[[package]]
name = "fastapi"
version = "0.115.0"
@@ -451,6 +480,40 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/83/10/466fe96dae1bff622021ee687f68e5524d6392b0a2f80d05001cd3a451ba/frozenlist-1.4.1-py3-none-any.whl", hash = "sha256:04ced3e6a46b4cfffe20f9ae482818e34eba9b5fb0ce4056e4cc9b6e212d09b7", size = 11552 },
]
+[[package]]
+name = "function-registry"
+version = "0.1.0"
+source = { editable = "../function-registry" }
+dependencies = [
+ { name = "azure-identity" },
+ { name = "context" },
+ { name = "openai" },
+ { name = "pydantic" },
+ { name = "pydantic-settings" },
+ { name = "python-dotenv" },
+ { name = "requests" },
+ { name = "tiktoken" },
+]
+
+[package.metadata]
+requires-dist = [
+ { name = "azure-identity", specifier = ">=1.17.1" },
+ { name = "context", editable = "../context" },
+ { name = "openai", specifier = ">=1.16.1" },
+ { name = "pydantic", specifier = ">=2.6.1" },
+ { name = "pydantic-settings", specifier = ">=2.3.4" },
+ { name = "python-dotenv", specifier = ">=1.0.1" },
+ { name = "requests", specifier = ">=2.32.0" },
+ { name = "tiktoken", specifier = ">=0.7.0" },
+]
+
+[package.metadata.requires-dev]
+dev = [
+ { name = "pytest", specifier = ">=8.3.1" },
+ { name = "pytest-asyncio", specifier = ">=0.23.8" },
+ { name = "pytest-repeat", specifier = ">=0.9.3" },
+]
+
[[package]]
name = "h11"
version = "0.14.0"
@@ -520,6 +583,15 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/76/c6/c88e154df9c4e1a2a66ccf0005a88dfb2650c1dffb6f5ce603dfbd452ce3/idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3", size = 70442 },
]
+[[package]]
+name = "importlib-resources"
+version = "6.4.5"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/98/be/f3e8c6081b684f176b761e6a2fef02a0be939740ed6f54109a2951d806f3/importlib_resources-6.4.5.tar.gz", hash = "sha256:980862a1d16c9e147a59603677fa2aa5fd82b87f223b6cb870695bcfce830065", size = 43372 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/e1/6a/4604f9ae2fa62ef47b9de2fa5ad599589d28c9fd1d335f32759813dfa91e/importlib_resources-6.4.5-py3-none-any.whl", hash = "sha256:ac29d5f956f01d5e4bb63102a5a19957f1b9175e45649977264a1416783bb717", size = 36115 },
+]
+
[[package]]
name = "iniconfig"
version = "2.0.0"
@@ -742,8 +814,10 @@ dependencies = [
{ name = "azure-ai-contentsafety" },
{ name = "azure-core", extra = ["aio"] },
{ name = "azure-identity" },
+ { name = "function-registry" },
{ name = "openai" },
{ name = "pillow" },
+ { name = "python-liquid" },
{ name = "semantic-workbench-assistant" },
{ name = "tiktoken" },
]
@@ -758,8 +832,10 @@ requires-dist = [
{ name = "azure-ai-contentsafety", specifier = ">=1.0.0" },
{ name = "azure-core", extras = ["aio"], specifier = ">=1.30.0" },
{ name = "azure-identity", specifier = ">=1.17.1" },
+ { name = "function-registry", editable = "../function-registry" },
{ name = "openai", specifier = ">=1.3.9" },
{ name = "pillow", specifier = ">=11.0.0" },
+ { name = "python-liquid", specifier = ">=1.12.1" },
{ name = "semantic-workbench-assistant", editable = "../semantic-workbench-assistant" },
{ name = "tiktoken", specifier = ">=0.7.0" },
]
@@ -967,6 +1043,18 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/6b/77/7440a06a8ead44c7757a64362dd22df5760f9b12dc5f11b6188cd2fc27a0/pytest-8.3.3-py3-none-any.whl", hash = "sha256:a6853c7375b2663155079443d2e45de913a911a11d669df02a50814944db57b2", size = 342341 },
]
+[[package]]
+name = "python-dateutil"
+version = "2.9.0.post0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "six" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/66/c0/0c8b6ad9f17a802ee498c46e004a0eb49bc148f2fd230864601a86dcf6db/python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3", size = 342432 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427", size = 229892 },
+]
+
[[package]]
name = "python-dotenv"
version = "1.0.1"
@@ -985,6 +1073,20 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/35/a6/145655273568ee78a581e734cf35beb9e33a370b29c5d3c8fee3744de29f/python_json_logger-2.0.7-py3-none-any.whl", hash = "sha256:f380b826a991ebbe3de4d897aeec42760035ac760345e57b812938dc8b35e2bd", size = 8067 },
]
+[[package]]
+name = "python-liquid"
+version = "1.12.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "importlib-resources" },
+ { name = "python-dateutil" },
+ { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/59/b4/c4e39470edf507056333cd688a36bb7fbdb17090105d9f454ecf6ff09845/python_liquid-1.12.1.tar.gz", hash = "sha256:7367e979125859fb4116f360f224a89a52ecb455fb26843c43e4d800b389d325", size = 124636 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/2c/6e/bfd01926e28f6cf355a7a8460271ba135013870f5857b06f35dbf65ab237/python_liquid-1.12.1-py3-none-any.whl", hash = "sha256:2224312944be16c1a44406398eb8a07c7e57398d5c0ef15ff950946dbefe7c33", size = 206592 },
+]
+
[[package]]
name = "python-multipart"
version = "0.0.12"
diff --git a/libraries/python/skills/notebooks/notebooks/chat_driver.ipynb b/libraries/python/skills/notebooks/notebooks/chat_driver.ipynb
index 9ee658ee..fb38ce0e 100644
--- a/libraries/python/skills/notebooks/notebooks/chat_driver.ipynb
+++ b/libraries/python/skills/notebooks/notebooks/chat_driver.ipynb
@@ -20,7 +20,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
@@ -37,7 +37,21 @@
"import json\n",
"from pathlib import Path\n",
"\n",
- "# Set up structured logging to a file.\n",
+ "LOGGING = {\n",
+ " \"version\": 1,\n",
+ " \"disable_existing_loggers\": False,\n",
+ " \"formatters\": {\n",
+ " \"json\": {\n",
+ " \"()\": \"pythonjsonlogger.jsonlogger.JsonFormatter\",\n",
+ " \"fmt\": \"%(asctime)s %(levelname)s %(name)s %(message)s\",\n",
+ "\n",
+ " }\n",
+ " },\n",
+ "}\n",
+ "\n",
+ "\n",
+ "# Set up structured logging to a file. All of the cells in this notebook use\n",
+ "# this logger. Find them at .data/logs.jsonl.\n",
"class JsonFormatter(logging.Formatter):\n",
" def format(self, record) -> str:\n",
" record_dict = record.__dict__\n",
@@ -84,8 +98,7 @@
" \"api_version\": os.environ.get(\"AZURE_OPENAI_API_VERSION\", \"\"),\n",
" \"max_retries\": 2,\n",
"}\n",
- "\n",
- "model = azure_openai_config.get(\"azure_deployment\", \"gpt-4o\")\n",
+ "logger.info(\"Azure OpenAI configuration\", extra=azure_openai_config)\n",
"\n",
"async_client = AsyncAzureOpenAI(\n",
" **azure_openai_config,\n",
@@ -101,7 +114,9 @@
" AzureCliCredential(),\n",
" \"https://cognitiveservices.azure.com/.default\",\n",
" ),\n",
- ")"
+ ")\n",
+ "\n",
+ "model: str = azure_openai_config.get(\"azure_deployment\", \"gpt-4o\")\n"
]
},
{
@@ -139,7 +154,7 @@
" ],\n",
" model=model,\n",
")\n",
- "print(completion.model_dump())"
+ "print(completion.model_dump_json(indent=2))"
]
},
{
@@ -155,7 +170,7 @@
"metadata": {},
"outputs": [],
"source": [
- "response = await async_client.chat.completions.create(\n",
+ "message_event = await async_client.chat.completions.create(\n",
" messages=[\n",
" {\n",
" \"role\": \"user\",\n",
@@ -164,7 +179,7 @@
" ],\n",
" model=model,\n",
")\n",
- "print(response)"
+ "print(message_event)"
]
},
{
@@ -194,6 +209,240 @@
" print(chunk.model_dump())"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## OpenAI Helpers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Standardized response handling"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The future of AI is both exciting and complex, with its trajectory shaped by advances in technology, ethical considerations, and societal needs. As we look ahead, several key themes emerge:\n",
+ "\n",
+ "1. **Integration and Personalization**: AI will become increasingly integrated into our daily lives, driving personalized experiences. From healthcare to education, AI systems will tailor recommendations and interventions to individual needs, optimizing outcomes across various sectors.\n",
+ "\n",
+ "2. **AI in Healthcare**: We can expect AI to revolutionize the healthcare industry by enhancing diagnostic accuracy, streamlining administrative processes, and developing personalized medicine. AI-driven tools could predict diseases, recommend treatments, and even aid in surgical procedures, ultimately improving patient care and reducing costs.\n",
+ "\n",
+ "3. **Autonomous Systems**: Autonomous vehicles, drones, and robotic systems will become more prevalent, transforming industries like transportation, logistics, and manufacturing. These systems will improve efficiency and safety while also creating new business models and opportunities.\n",
+ "\n",
+ "4. **Ethical and Responsible AI**: As AI systems become more autonomous, the demand for ethical AI frameworks will increase. Ensuring transparency, fairness, accountability, and privacy will be crucial to maintaining public trust. Developing AI systems that align with human values and societal norms will be an ongoing challenge.\n",
+ "\n",
+ "5. **AI and the Workforce**: While AI will automate certain tasks, it will also create new job opportunities and demand an upskilled workforce. It is important to focus on reskilling and education initiatives to prepare the workforce for an AI-augmented future.\n",
+ "\n",
+ "6. **AI in Climate and Sustainability**: AI will play a significant role in addressing climate change and promoting sustainability. From optimizing energy usage and improving agricultural practices to predicting environmental changes, AI could be a critical tool in creating a more sustainable future.\n",
+ "\n",
+ "7. **AI in Creativity and Arts**: AI is increasingly being used in creative fields, such as music, art, and literature. While it can augment human creativity by offering new tools and perspectives, there will be ongoing discussions about the nature of creativity and authorship.\n",
+ "\n",
+ "8. **AI Governance and Policy**: With AI's growing influence, policy and regulation will need to keep pace to address issues such as data ownership, algorithmic bias, and national security. International collaboration may be necessary to create coherent frameworks addressing these challenges.\n",
+ "\n",
+ "In summary, AI has the potential to greatly benefit society, but its development requires thoughtful consideration of ethical, social, and economic impacts. As we move forward, collaboration among technologists, policymakers, and the public will be essential to harness AI for the greater good.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from context import Context\n",
+ "from openai_client.errors import CompletionError, validate_completion\n",
+ "from openai_client.logging import extra_data, serializable_completion_args\n",
+ "from openai_client.completion import completion_message_string\n",
+ "\n",
+ "context = Context(\"conversation-id-1005\")\n",
+ "\n",
+ "# We use a metadata dictionary in our helpers to store information about the\n",
+ "# completion request.\n",
+ "metadata = {}\n",
+ "\n",
+ "# This is just standard OpenAI completion request arguments.\n",
+ "completion_args = {\n",
+ " \"model\": model,\n",
+ " \"messages\": [\n",
+ " {\n",
+ " \"role\": \"system\",\n",
+ " \"content\": \"You are a famous computer scientist. You are giving a talk at a conference. You are talking about the future of AI and how it will change the world. You are asked a questions by audience members and answer thoughtfully.\",\n",
+ " },\n",
+ " {\n",
+ " \"role\": \"user\",\n",
+ " \"content\": \"What is the future of AI?\",\n",
+ " }\n",
+ " ],\n",
+ "}\n",
+ "\n",
+ "# If we these completion args to logs and metadata, though, they need to be\n",
+ "# serializable. We have a helper for that.\n",
+ "metadata[\"completion_args\"] = serializable_completion_args(completion_args)\n",
+ "\n",
+ "# We have helpers for validating the response and handling exceptions in a\n",
+ "# standardized way. These ensure that logging happens and metadata is loaded up\n",
+ "# properly.\n",
+ "try:\n",
+ " completion = await async_client.beta.chat.completions.parse(**completion_args)\n",
+ "\n",
+ " # This helper looks for any error-like situations (the model refuses to\n",
+ " # answer, content filters, incomplete responses) and throws exceptions that\n",
+ " # are handled by the next helper. The first argument is an identifier that\n",
+ " # will be used for logs and metadata namespacing.\n",
+ " validate_completion(completion)\n",
+ " logger.debug(\"completion response.\", extra=extra_data(completion))\n",
+ " metadata[\"completion\"] = completion.model_dump()\n",
+ "\n",
+ "except Exception as e:\n",
+ " # This helper processes all the types of error conditions you might get from\n",
+ " # the OpenAI API in a standardized way.\n",
+ " completion_error = CompletionError(e)\n",
+ " print(completion_error)\n",
+ " print(completion_error.body)\n",
+ "\n",
+ "else:\n",
+ " # The message_string helper is used to extract the response from the completion\n",
+ " # (which can get tedious).\n",
+ " print(completion_message_string(completion))\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### JSON"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{\n",
+ " \"thoughts\": \"AI is rapidly evolving and has the potential to transform virtually every industry. With advancements in machine learning, natural language processing, and robotics, AI will continue to play a crucial role in automating routine tasks and providing intelligent insights.\",\n",
+ " \"answer\": \"The future of AI involves deeper integration into everyday life and industry. We will likely see AI systems becoming more autonomous, sophisticated, and seamlessly integrated into systems like healthcare, transportation, and personalized services. AI's future encompasses not only technological advancements but also ethical considerations and ensuring beneficial societal impacts.\"\n",
+ "}\n"
+ ]
+ }
+ ],
+ "source": [
+ "from context import Context\n",
+ "from openai_client.errors import CompletionError, validate_completion\n",
+ "from openai_client.logging import extra_data, serializable_completion_args\n",
+ "from openai_client.completion import completion_message_dict, JSON_OBJECT_RESPONSE_FORMAT\n",
+ "\n",
+ "context = Context(\"conversation-id-1002\")\n",
+ "metadata = {}\n",
+ "completion_args = {\n",
+ " \"model\": model,\n",
+ " \"messages\": [\n",
+ " {\n",
+ " \"role\": \"system\",\n",
+ " \"content\": \"You are a famous computer scientist. You are giving a talk at a conference. You are talking about the future of AI and how it will change the world. You are asked a questions by audience members and return your answer as valid JSON like { \\\"thoughts\\\": , \\\"answer\\\": }.\",\n",
+ " },\n",
+ " {\n",
+ " \"role\": \"user\",\n",
+ " \"content\": \"What is the future of AI?\",\n",
+ " }\n",
+ " ],\n",
+ " \"response_format\": JSON_OBJECT_RESPONSE_FORMAT,\n",
+ "}\n",
+ "metadata[\"completion_args\"] = serializable_completion_args(completion_args)\n",
+ "try:\n",
+ " completion = await async_client.beta.chat.completions.parse(**completion_args)\n",
+ " validate_completion(completion)\n",
+ " metadata[\"completion\"] = completion.model_dump()\n",
+ "except Exception as e:\n",
+ " completion_error = CompletionError(e)\n",
+ " metadata[\"completion_error\"] = completion_error.body\n",
+ " logger.error(completion_error.message, extra=extra_data({\"error\": completion_error.body, \"metadata\": metadata}))\n",
+ "else:\n",
+ " message = completion_message_dict(completion)\n",
+ " print(json.dumps(message, indent=2))\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Structured Output\n",
+ "\n",
+ "Any Pydantic BaseModel can be used as the \"response_format\" and OpenAI will try to load it up for you."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{\n",
+ " \"thoughts\": \"AI has shown immense potential in various fields, including healthcare, transportation, and climate science. The trajectory of its development suggests even greater integration into daily life and business operations.\",\n",
+ " \"answer\": \"The future of AI lies in its ability to become more autonomous, adaptive, and integrated into various facets of life and industry. We will likely see AI taking on increasingly complex tasks with minimal human intervention, improving efficiency and productivity. It will transform sectors like healthcare through predictive diagnostics, personalize education, and enhance decision-making in businesses with deeper insights from data analytics. However, it will also be crucial to address ethical concerns, such as privacy, bias, and the impact on jobs, to ensure AI serves the broader good.\"\n",
+ "}\n"
+ ]
+ }
+ ],
+ "source": [
+ "from context import Context\n",
+ "from pydantic import BaseModel\n",
+ "from typing import cast\n",
+ "from openai_client.errors import CompletionError, validate_completion\n",
+ "from openai_client.logging import extra_data, serializable_completion_args\n",
+ "from openai_client.completion import completion_message_dict, JSON_OBJECT_RESPONSE_FORMAT\n",
+ "\n",
+ "class Output(BaseModel):\n",
+ " thoughts: str\n",
+ " answer: str\n",
+ "\n",
+ "context = Context(\"conversation-id-1002\")\n",
+ "metadata = {}\n",
+ "completion_args = {\n",
+ " \"model\": model,\n",
+ " \"messages\": [\n",
+ " {\n",
+ " \"role\": \"system\",\n",
+ " \"content\": \"You are a famous computer scientist. You are giving a talk at a conference. You are talking about the future of AI and how it will change the world. You are asked a questions by audience members and return your answer as valid JSON like { \\\"thoughts\\\": , \\\"answer\\\": }.\",\n",
+ " },\n",
+ " {\n",
+ " \"role\": \"user\",\n",
+ " \"content\": \"What is the future of AI?\",\n",
+ " }\n",
+ " ],\n",
+ " \"response_format\": Output,\n",
+ "}\n",
+ "\n",
+ "metadata[\"completion_args\"] = serializable_completion_args(completion_args)\n",
+ "try:\n",
+ " completion = await async_client.beta.chat.completions.parse(**completion_args)\n",
+ " validate_completion(completion)\n",
+ " metadata[\"completion\"] = completion.model_dump()\n",
+ "except Exception as e:\n",
+ " completion_error = CompletionError(e)\n",
+ " metadata[\"completion_error\"] = completion_error.body\n",
+ " logger.error(completion_error.message, extra=extra_data({\"error\": completion_error.body, \"metadata\": metadata}))\n",
+ "else:\n",
+ " # The parsed message is in the `parsed` attribute.\n",
+ " output = cast(Output, completion.choices[0].message.parsed)\n",
+ " print(output.model_dump_json(indent=2))\n",
+ "\n",
+ " # Or you can just get the text of the message like usual.\n",
+ " # print(completion.choices[0].message.content)\n"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -225,239 +474,184 @@
"- Other utilities... this is just meant to be an interface you can use to forget about all the api complexities."
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Here is the simplest usage of a chat driver\n",
+ "\n",
+ "Notice that a .data directory is created by default. This is where the conversation history is stored."
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "conversation-id-1000\n",
- "\n",
- "Hello, Paul! How can I assist you today?\n",
- "\n",
- "Commands:\n",
- "help(): Return this help message.\n",
- "erase(name: str): Erases a stored value.\n",
- "echo(text: str): Return the text.\n",
- "get_file_contents(file_path: str): Return the contents of a file.\n",
- "\n",
- "conversation-id-1000: Hi, my name is Paul.\n",
- "\n",
- "The contents of the file 123.txt are: \"The purpose of life is to be happy.\" If you need any further assistance or information, feel free to ask!\n",
- "\n",
"{\n",
- " \"id\": \"0222a092-6bfd-4523-9cf8-d895070c554b\",\n",
+ " \"id\": \"70e59248-9ca3-4284-a58b-a843409a6abc\",\n",
" \"session_id\": null,\n",
- " \"timestamp\": \"2024-10-10T17:20:00.290438\",\n",
- " \"message\": \"The contents of the file 123.txt are: \\\"The purpose of life is to be happy.\\\" If you need any further assistance or information, feel free to ask!\",\n",
+ " \"timestamp\": \"2024-11-05T21:22:19.260954\",\n",
+ " \"message\": \"The future of AI is set to be transformative, with significant advancements and implications across many areas of life and industry. Here are some key trends and developments expected in the future of AI:\\n\\n1. **Widespread Integration**: AI will become deeply embedded into everyday life, enhancing technologies in sectors such as healthcare, finance, education, and entertainment. This integration will facilitate more efficient, personalized, and automated experiences.\\n\\n2. **Advanced Machine Learning**: Continuous improvements in machine learning algorithms, including deep learning, will enable AI systems to process and interpret complex data more effectively. This advancement will help AI systems become more autonomous and capable.\\n\\n3. **Ethical and Fair AI**: As AI's role grows, developing ethical guidelines and ensuring fairness will become increasingly important. Efforts will focus on minimizing bias and increasing transparency, fostering trust and acceptability.\\n\\n4. **Human-AI Collaboration**: AI will increasingly act as a collaborative partner, augmenting human abilities and enhancing creativity, decision-making, and productivity across various fields. This symbiosis will redefine how we work and interact with technology.\\n\\n5. **Healthcare Innovations**: AI is expected to revolutionize healthcare through improved diagnostics, personalized treatments, predictive analytics, and efficient management of healthcare operations. This could lead to better patient outcomes and more accessible healthcare services.\\n\\n6. **Growth of Autonomous Systems**: The development of autonomous vehicles, drones, and robotics will continue to advance, transforming industries like transportation, logistics, and agriculture by improving safety, efficiency, and sustainability.\\n\\n7. **AI for Sustainability**: AI will play a crucial role in tackling environmental challenges, optimizing resource usage, improving climate models, and promoting sustainable practices, thereby contributing to environmental conservation efforts.\\n\\n8. **Quantum Computing and AI**: The synergy between AI and quantum computing could result in unprecedented computational capabilities, leading to significant breakthroughs in fields such as cryptography, complex simulations, and material science.\\n\\n9. **Creative Applications**: AI will increasingly contribute to creative industries, aiding in the production of art, music, and literature, and providing innovative tools and mediums for artistic exploration.\\n\\n10. **Focus on Explainability**: As AI systems become more integral to decision-making, the demand for explainable AI will grow. Developing AI that can clearly articulate its decision-making processes will be crucial for building trust and ensuring accountability.\\n\\nWhile the future of AI offers immense potential, it also poses challenges—including ethical considerations, privacy issues, and potential job displacement. Navigating these challenges will require careful thought, regulation, and collaboration across various stakeholders to ensure that AI benefits society as a whole.\",\n",
" \"metadata\": {\n",
" \"completion_args\": {\n",
+ " \"model\": \"gpt-4o\",\n",
" \"messages\": [\n",
" {\n",
- " \"role\": \"system\",\n",
- " \"content\": \"You are a helpful assistant.\"\n",
+ " \"role\": \"user\",\n",
+ " \"content\": \"What is the future of AI?\"\n",
+ " },\n",
+ " {\n",
+ " \"role\": \"assistant\",\n",
+ " \"content\": \"The future of AI is incredibly promising and will likely be revolutionary in many aspects of our lives. In the coming years and decades, we can expect several major trends and developments to shape the landscape of artificial intelligence:\\n\\n1. **Ubiquitous Integration**: AI will continue to permeate all aspects of our daily lives, from personal assistants and smart home devices to complex systems in healthcare, finance, and transportation. This integration will enable more efficient, personalized, and automated experiences.\\n\\n2. **Advanced Machine Learning**: We will see significant advancements in machine learning techniques, particularly in areas such as reinforcement learning, unsupervised learning, and transfer learning. These advancements will improve AI's ability to learn from fewer examples and adapt more flexibly to new and changing environments.\\n\\n3. **Ethical and Explainable AI**: As AI becomes more entrenched in critical decision-making processes, there will be a stronger emphasis on developing ethical guidelines and explainable AI systems. This will involve creating transparent models that can be understood and trusted by humans, ensuring fairness and reducing bias.\\n\\n4. **AI and Human Collaboration**: The future will see AI as a collaborator rather than a mere tool. AI systems will augment human abilities, providing insights and recommendations so that people can make better decisions and enhance creativity and productivity across various fields.\\n\\n5. **AI in Healthcare**: AI will play a transformative role in healthcare, enabling early diagnosis, personalized treatment plans, drug discovery, and efficient management of healthcare systems. This can lead to significant improvements in patient outcomes and accessibility to medical services.\\n\\n6. **Autonomous Systems**: The development of autonomous vehicles, drones, and robots will advance, impacting industries such as logistics, agriculture, and urban planning. These systems will greatly improve efficiency and safety in operations.\\n\\n7. **AI Governance and Regulation**: As AI systems grow more powerful, there will be an increasing need for regulation to ensure they are used responsibly and do not harm society. This includes addressing issues such as privacy, security, and the impact on labor markets.\\n\\n8. **AI for Sustainability**: AI will be crucial in addressing global challenges like climate change, resource management, and conservation efforts, optimizing energy use, predicting environmental changes, and developing sustainable practices.\\n\\n9. **Quantum Computing and AI**: The integration of AI with quantum computing holds the potential to solve problems previously considered intractable, speeding up computation and enhancing AI's capabilities.\\n\\n10. **AI and Creativity**: AI will increasingly be involved in creative domains, assisting with tasks such as art, music, and writing, leading to new forms of expression and collaboration between humans and machines.\\n\\nIn summary, while AI presents numerous opportunities, it also comes with challenges that society must address. Ensuring that its development is aligned with societal values will be crucial for leveraging AI's potential in a way that benefits everyone.\"\n",
+ " },\n",
+ " {\n",
+ " \"role\": \"user\",\n",
+ " \"content\": \"What is the future of AI?\"\n",
+ " },\n",
+ " {\n",
+ " \"role\": \"user\",\n",
+ " \"content\": \"What is the future of AI?\"\n",
" },\n",
" {\n",
" \"role\": \"user\",\n",
- " \"content\": \"Hi, my name is Paul.\"\n",
+ " \"content\": \"What is the future of AI?\"\n",
+ " },\n",
+ " {\n",
+ " \"role\": \"user\",\n",
+ " \"content\": \"What is the future of AI?\"\n",
+ " },\n",
+ " {\n",
+ " \"role\": \"user\",\n",
+ " \"content\": \"What is the future of AI?\"\n",
+ " },\n",
+ " {\n",
+ " \"role\": \"user\",\n",
+ " \"content\": \"What is the future of AI?\"\n",
+ " },\n",
+ " {\n",
+ " \"role\": \"user\",\n",
+ " \"content\": \"What is the future of AI?\"\n",
+ " },\n",
+ " {\n",
+ " \"role\": \"user\",\n",
+ " \"content\": \"What is the future of AI?\"\n",
+ " },\n",
+ " {\n",
+ " \"role\": \"user\",\n",
+ " \"content\": \"What is the future of AI?\"\n",
+ " },\n",
+ " {\n",
+ " \"role\": \"user\",\n",
+ " \"content\": \"What is the future of AI?\"\n",
+ " },\n",
+ " {\n",
+ " \"role\": \"user\",\n",
+ " \"content\": \"What is the future of AI?\"\n",
+ " },\n",
+ " {\n",
+ " \"role\": \"user\",\n",
+ " \"content\": \"What is the future of AI?\"\n",
+ " },\n",
+ " {\n",
+ " \"role\": \"user\",\n",
+ " \"content\": \"What is the future of AI?\"\n",
" },\n",
" {\n",
- " \"content\": \"Hello, Paul! How can I assist you today?\",\n",
- " \"refusal\": null,\n",
" \"role\": \"assistant\",\n",
- " \"function_call\": null,\n",
- " \"tool_calls\": null\n",
+ " \"content\": \"The future of AI is poised to be transformative across multiple dimensions of society, technology, and industry. Here are several key trends and developments expected to shape the trajectory of artificial intelligence in the coming years:\\n\\n1. **Pervasive Integration**: AI will become more deeply integrated into everyday life, enhancing the functionality of devices and services across sectors such as healthcare, education, transportation, and entertainment.\\n\\n2. **Enhanced Machine Learning**: Advances in machine learning, particularly in areas like deep learning, reinforcement learning, and neural networks, will lead to more sophisticated AI models capable of processing vast amounts of data more efficiently and effectively.\\n\\n3. **Ethical AI and Regulation**: As AI systems become more integral to decision-making, there will be increased focus on ethical considerations, fairness, and transparency. Governments and organizations will develop regulations to ensure responsible AI usage.\\n\\n4. **Human-AI Collaboration**: AI will act as an augmentative tool, enhancing human capabilities in various professions. This collaboration will drive innovation and productivity, allowing humans to focus on creativity and strategic decision-making.\\n\\n5. **AI in Healthcare**: The healthcare field will continue to benefit from AI, with improved diagnostics, personalized medicine, robotic surgeries, and efficient healthcare management, ultimately leading to better patient outcomes.\\n\\n6. **Autonomous Systems**: Progress in autonomous systems, such as self-driving cars, drones, and robotics, will revolutionize industries like transportation, logistics, and agriculture, enhancing operational efficiency and safety.\\n\\n7. **AI for Sustainability**: AI will play a vital role in addressing climate change and resource management by optimizing energy consumption, improving climate modeling, and promoting sustainable practices.\\n\\n8. **Quantum Computing and AI**: The fusion of AI and quantum computing could unlock unprecedented computational power, leading to breakthroughs in fields such as cryptography, complex problem-solving, and big data analysis.\\n\\n9. **Cognitive and Emotional AI**: Future AI systems may develop enhanced cognitive abilities, enabling more human-like understanding and emotional interactions, improving customer service, therapy, and education.\\n\\n10. **Creative AI**: AI will contribute significantly to creative industries, assisting in art, music, film, and literature production, leading to innovative and novel forms of artistic expression.\\n\\nOverall, while AI presents exciting opportunities for advancing society, it also raises important challenges, particularly in terms of ethics, governance, and societal impact. Addressing these challenges will be crucial to ensuring that the potential of AI is realized in a way that benefits all of humanity.\"\n",
" },\n",
" {\n",
" \"role\": \"user\",\n",
- " \"content\": \"Please tell me what's in file 123.txt.\"\n",
- " }\n",
- " ],\n",
- " \"model\": \"gpt-4o\",\n",
- " \"tools\": [\n",
+ " \"content\": \"What is the future of AI?\"\n",
+ " },\n",
" {\n",
- " \"type\": \"function\",\n",
- " \"function\": {\n",
- " \"name\": \"help\",\n",
- " \"description\": \"Return this help message.\",\n",
- " \"parameters\": {\n",
- " \"type\": \"object\",\n",
- " \"properties\": {}\n",
- " }\n",
- " }\n",
+ " \"role\": \"assistant\",\n",
+ " \"content\": \"The future of AI is expected to be transformative, impacting a wide range of sectors and fundamentally changing how we interact with technology. Here are some anticipated trends and possibilities:\\n\\n1. **Deep Integration and Ubiquity**: AI will become increasingly embedded in everyday devices and systems, from smartphones and home assistants to large-scale industrial operations, making technology more intuitive and responsive.\\n\\n2. **Advancements in Machine Learning**: Continued progress in machine learning, particularly deep learning and neural networks, will enhance AI's ability to learn from small data sets and improve its understanding of complex, unstructured data.\\n\\n3. **Ethical and Responsible AI**: As AI's role expands, ensuring that it is developed and deployed ethically will become crucial. This includes creating transparent, fair, and unbiased AI systems and establishing robust regulatory frameworks to govern their use.\\n\\n4. **Human-AI Collaboration**: AI will act more as an enabler and collaborator, augmenting human capabilities rather than replacing them. This will lead to new ways of working and creative problem-solving across various industries.\\n\\n5. **Innovations in Healthcare**: AI will drive significant advancements in healthcare, including personalized medicine, early disease detection, robotic surgeries, and streamlined administrative processes, potentially leading to more effective and efficient healthcare systems.\\n\\n6. **Growth of Autonomous Systems**: Autonomous vehicles, drones, and robots will become more prevalent, transforming sectors like transportation, delivery services, and manufacturing, and leading to improved safety and productivity.\\n\\n7. **AI for Environmental Sustainability**: AI will play a critical role in addressing environmental challenges, optimizing resource use, improving energy efficiency, and assisting in climate modeling and conservation efforts.\\n\\n8. **AI and Quantum Computing**: The convergence of AI and quantum computing could revolutionize computation, solving problems beyond the reach of classical computers and enhancing AI's capabilities even further.\\n\\n9. **AI in Creative Fields**: AI will increasingly influence creative industries, helping to generate art, music, and literature, and offering new tools for artists to explore and innovate.\\n\\n10. **Focus on Explainability**: As AI makes more autonomous decisions, the demand for explainable AI will grow, necessitating systems that can clearly articulate their decision-making processes to build trust and understanding.\\n\\nOverall, AI has the potential to significantly enhance human life, but realizing its benefits while minimizing risks will require careful management, ethical considerations, and ongoing dialogue about its impact on society.\"\n",
" },\n",
" {\n",
- " \"type\": \"function\",\n",
- " \"function\": {\n",
- " \"name\": \"erase\",\n",
- " \"description\": \"Erases a stored value.\",\n",
- " \"parameters\": {\n",
- " \"type\": \"object\",\n",
- " \"properties\": {\n",
- " \"name\": {\n",
- " \"type\": \"string\"\n",
- " }\n",
- " }\n",
- " },\n",
- " \"required\": [\n",
- " \"name\"\n",
- " ]\n",
- " }\n",
+ " \"role\": \"user\",\n",
+ " \"content\": \"What is the future of AI?\"\n",
" },\n",
" {\n",
- " \"type\": \"function\",\n",
- " \"function\": {\n",
- " \"name\": \"echo\",\n",
- " \"description\": \"Return the text.\",\n",
- " \"parameters\": {\n",
- " \"type\": \"object\",\n",
- " \"properties\": {\n",
- " \"text\": {\n",
- " \"type\": \"string\"\n",
- " }\n",
- " }\n",
- " },\n",
- " \"required\": [\n",
- " \"text\"\n",
- " ]\n",
- " }\n",
+ " \"role\": \"assistant\",\n",
+ " \"content\": \"The future of AI is poised to be transformative, with significant advancements and widespread impacts across various domains. Here are some key aspects expected to define the future of AI:\\n\\n1. **Widespread Adoption**: AI will become increasingly integrated into everyday life and business, enhancing efficiency and automating routine tasks across industries such as healthcare, finance, retail, and manufacturing.\\n\\n2. **Improved Machine Learning**: Advances in machine learning, including deep learning and neural networks, will enable AI systems to better understand and process complex and unstructured data, leading to more accurate and reliable outputs.\\n\\n3. **Ethical and Transparent AI**: As AI becomes more influential, the emphasis on ethical standards and transparency will grow. Developing AI systems that are free from bias, respect privacy, and are explainable will be crucial.\\n\\n4. **Human-AI Collaboration**: AI will increasingly act as a partner to humans, augmenting their capabilities in various fields. This collaboration can lead to more innovative solutions and improved decision-making.\\n\\n5. **AI in Healthcare**: AI's role in healthcare will expand, from personalized medicine and predictive analytics to automated diagnostics and treatment recommendations, ultimately improving patient outcomes and healthcare efficiency.\\n\\n6. **Autonomous Systems**: The development and deployment of autonomous vehicles, drones, and robotics will continue, enhancing transportation systems, logistics, and agricultural operations in terms of safety and efficiency.\\n\\n7. **AI for Sustainability**: AI will play a crucial role in addressing environmental challenges, optimizing resource management, reducing waste, and aiding in climate modeling and conservation efforts.\\n\\n8. **Quantum Computing Synergy**: The intersection of AI and quantum computing holds potential for breakthroughs in solving complex problems, accelerating innovation in areas such as drug discovery, cryptography, and data analysis.\\n\\n9. **Cognitive and Emotional AI**: Future AI systems may possess improved cognitive abilities and emotional intelligence, enhancing user interaction and providing more personalized and empathetic experiences.\\n\\n10. **Innovation in Creativity**: AI will contribute to creative industries by assisting in generating art, music, literature, and other forms of expression, offering new tools and mediums for creativity.\\n\\nWhile the future of AI presents many exciting opportunities, it also poses challenges that must be addressed, such as ethical considerations, job displacement, and the need for regulation. Balancing innovation with responsibility will be key to ensuring AI's benefits are realized across society.\"\n",
" },\n",
" {\n",
- " \"type\": \"function\",\n",
- " \"function\": {\n",
- " \"name\": \"get_file_contents\",\n",
- " \"description\": \"Return the contents of a file.\",\n",
- " \"parameters\": {\n",
- " \"type\": \"object\",\n",
- " \"properties\": {\n",
- " \"file_path\": {\n",
- " \"type\": \"string\"\n",
- " }\n",
- " }\n",
- " },\n",
- " \"required\": [\n",
- " \"file_path\"\n",
- " ]\n",
- " }\n",
+ " \"role\": \"user\",\n",
+ " \"content\": \"What is the future of AI?\"\n",
+ " },\n",
+ " {\n",
+ " \"role\": \"assistant\",\n",
+ " \"content\": \"The future of AI is expected to be dynamic and deeply transformative, impacting various facets of life and industry. Here's a look at some significant trends and areas where AI is likely to evolve:\\n\\n1. **Greater Integration and Accessibility**: AI will become more embedded in daily life, with increased accessibility through user-friendly interfaces and applications. This integration will make AI tools a regular part of work and home environments.\\n\\n2. **Advanced Machine Learning Techniques**: Innovations in machine learning, including more sophisticated neural networks and reinforcement learning models, will enhance AI's capability to learn and make decisions based on smaller and more complex datasets.\\n\\n3. **Ethical AI and Governance**: With AI's growing influence, there will be an increasing focus on ethical development and use. Efforts will be made to create AI systems that are transparent, fair, and unbiased, accompanied by regulations to ensure responsible deployment.\\n\\n4. **Collaboration Between Humans and AI**: AI will increasingly serve as a partner in various tasks, enhancing human capabilities in sectors such as education, engineering, and creative industries, leading to new ways of problem-solving and innovation.\\n\\n5. **Healthcare Innovations**: AI will play a pivotal role in advancing healthcare, from improving diagnostic accuracy and personalizing treatment plans to streamlining hospital operations and drug discovery processes.\\n\\n6. **Rise of Autonomous Systems**: Autonomous technologies, such as self-driving cars, drones, and intelligent robots, will advance and become more widespread, revolutionizing industries like transportation, logistics, and agriculture.\\n\\n7. **AI for Environmental Sustainability**: AI will be a key tool in tackling environmental issues, optimizing energy consumption, predicting climate patterns, and enhancing conservation efforts to promote sustainability.\\n\\n8. **Intersection with Quantum Computing**: As quantum computing technology matures, its combination with AI could lead to breakthroughs in processing power, enabling the solution of complex problems in areas like cryptography, materials science, and large-scale simulations.\\n\\n9. **AI in Creative Processes**: AI will continue to expand its role in creative domains, assisting with generating art, music, literature, and more, while offering new mediums and tools for creative professionals to explore.\\n\\n10. **Explainable and Accountable AI**: As AI systems take on more critical roles, the demand for explainability will grow. Developing systems that can clearly articulate how decisions are made will be crucial for trust and accountability.\\n\\nIn summary, while AI promises to drive innovation and efficiency, it also presents challenges that need to be addressed, including ethical, legal, and socio-economic considerations. Ensuring that AI evolves in a way that benefits humanity will require careful planning and collaboration across disciplines.\"\n",
+ " },\n",
+ " {\n",
+ " \"role\": \"user\",\n",
+ " \"content\": \"What is the future of AI?\"\n",
+ " },\n",
+ " {\n",
+ " \"role\": \"assistant\",\n",
+ " \"content\": \"The future of AI is anticipated to be transformative, influencing various aspects of society, technology, and industry. Here are some key predictions and trends for the future of AI:\\n\\n1. **Pervasive Integration**: AI is expected to become even more integrated into daily life, enhancing everything from personal assistant devices to complex industrial systems, creating more efficient, personalized, and automated experiences.\\n\\n2. **Advanced Learning Models**: We will likely see significant advancements in AI’s learning capabilities, including improvements in deep learning, reinforcement learning, and unsupervised learning, which will enable AI systems to process complex data more effectively.\\n\\n3. **Ethical and Responsible AI**: As AI systems become more influential, there will be a heightened focus on developing ethical guidelines and frameworks to ensure AI is used responsibly and without bias, with an emphasis on transparency and accountability.\\n\\n4. **Human-AI Collaboration**: AI is expected to function increasingly as a collaborative tool, augmenting human abilities and working alongside people to drive innovation, enhance creativity, and improve decision-making processes.\\n\\n5. **Healthcare Revolution**: AI will continue to revolutionize healthcare by enabling more accurate diagnostics, personalized treatment plans, predictive health analytics, and efficient management of medical records and workflows.\\n\\n6. **Expansion of Autonomous Systems**: The development of autonomous vehicles, drones, and robotic systems will accelerate, transforming industries such as logistics, transportation, and agriculture by improving safety and efficiency.\\n\\n7. **AI for Environmental Sustainability**: AI is anticipated to play a crucial role in addressing environmental challenges, optimizing resource management, improving climate modeling, and promoting sustainable practices.\\n\\n8. **Synergy with Quantum Computing**: The intersection of AI with quantum computing could lead to breakthroughs in computational power, allowing for solving complex problems that are currently intractable with classical computers.\\n\\n9. **Enhanced Creativity and Media**: AI will become a powerful tool in creative industries, helping to generate new forms of art, music, and literature, while offering novel mediums and tools for artists and creators.\\n\\n10. **Focus on Explainability**: As AI becomes more involved in critical decisions, the demand for explainable AI will grow, necessitating systems that can provide insights into their decision-making processes to build trust with users.\\n\\nOverall, while AI offers numerous opportunities, it also presents challenges such as ethical dilemmas, privacy concerns, and potential job displacement. Navigating these challenges will be key to ensuring that AI technology advances in a way that is beneficial and equitable for all.\"\n",
+ " },\n",
+ " {\n",
+ " \"role\": \"user\",\n",
+ " \"content\": \"What is the future of AI?\"\n",
+ " },\n",
+ " {\n",
+ " \"role\": \"assistant\",\n",
+ " \"content\": \"The future of AI holds tremendous potential and is expected to bring about profound changes across various sectors. Here are some key aspects that are likely to define the AI landscape in the coming years:\\n\\n1. **Ubiquitous Integration**: AI will become increasingly integrated into all facets of daily life, from smart homes and personal devices to complex corporate systems, enhancing convenience, personalization, and automation.\\n\\n2. **Evolution of Machine Learning**: Continued advancements in machine learning techniques, such as deep learning and reinforcement learning, will enable AI systems to learn more efficiently, handle complex tasks, and better mimic human cognition.\\n\\n3. **Ethical AI Development**: As AI's influence grows, there will be a heightened emphasis on ethical considerations, including fairness, transparency, and bias reduction, to ensure AI systems are deployed responsibly and equitably.\\n\\n4. **Collaboration with Humans**: AI will augment human capabilities, acting as both a partner and tool that enhances human creativity, problem-solving, and productivity across various disciplines.\\n\\n5. **Transformation in Healthcare**: AI is set to revolutionize healthcare by improving diagnostic accuracy, enabling personalized medicine, optimizing treatment plans, and increasing the efficiency of healthcare delivery.\\n\\n6. **Rise of Autonomous Systems**: The development of autonomous vehicles, drones, and robotic systems will accelerate, transforming transportation, logistics, and manufacturing industries by increasing safety and efficiency.\\n\\n7. **Sustainability and the Environment**: AI will play a crucial role in addressing environmental challenges, from optimizing energy use and reducing waste to enhancing models for climate change prediction and natural resource management.\\n\\n8. **Quantum Computing and AI Synergy**: The fusion of AI with quantum computing could unlock new computational capabilities, solving complex problems in fields such as cryptography, material science, and large-scale simulations.\\n\\n9. **Impact on Creativity**: AI will continue to influence creative industries, helping to generate new forms of art, music, and literature, while providing innovative tools for artists and creators to explore new horizons.\\n\\n10. **Emphasis on Explainability**: As AI systems are increasingly used in critical decision-making processes, there will be a focus on developing explainable AI that provides insights into how decisions are made, fostering trust and accountability.\\n\\nWhile AI presents incredible opportunities, it also poses challenges such as ethical implications, job displacement, and privacy concerns. Addressing these challenges will be essential to ensuring that AI evolves in a way that benefits society as a whole and aligns with human values.\"\n",
+ " },\n",
+ " {\n",
+ " \"role\": \"user\",\n",
+ " \"content\": \"What is the future of AI?\"\n",
+ " },\n",
+ " {\n",
+ " \"role\": \"user\",\n",
+ " \"content\": \"What is the future of AI?\"\n",
+ " },\n",
+ " {\n",
+ " \"role\": \"user\",\n",
+ " \"content\": \"What is the future of AI?\"\n",
" }\n",
" ],\n",
+ " \"tool_choice\": null,\n",
" \"response_format\": {\n",
" \"type\": \"text\"\n",
" }\n",
" },\n",
- " \"completion_response\": {\n",
- " \"id\": \"chatcmpl-AGr7Pmberc78CWRv24nypSR0ALAJv\",\n",
+ " \"completion\": {\n",
+ " \"id\": \"chatcmpl-AQLI04zuaA92sMCEmr0wy8vDYch4F\",\n",
" \"choices\": [\n",
" {\n",
- " \"finish_reason\": \"tool_calls\",\n",
+ " \"finish_reason\": \"stop\",\n",
" \"index\": 0,\n",
" \"logprobs\": null,\n",
" \"message\": {\n",
- " \"content\": null,\n",
+ " \"content\": \"The future of AI is set to be transformative, with significant advancements and implications across many areas of life and industry. Here are some key trends and developments expected in the future of AI:\\n\\n1. **Widespread Integration**: AI will become deeply embedded into everyday life, enhancing technologies in sectors such as healthcare, finance, education, and entertainment. This integration will facilitate more efficient, personalized, and automated experiences.\\n\\n2. **Advanced Machine Learning**: Continuous improvements in machine learning algorithms, including deep learning, will enable AI systems to process and interpret complex data more effectively. This advancement will help AI systems become more autonomous and capable.\\n\\n3. **Ethical and Fair AI**: As AI's role grows, developing ethical guidelines and ensuring fairness will become increasingly important. Efforts will focus on minimizing bias and increasing transparency, fostering trust and acceptability.\\n\\n4. **Human-AI Collaboration**: AI will increasingly act as a collaborative partner, augmenting human abilities and enhancing creativity, decision-making, and productivity across various fields. This symbiosis will redefine how we work and interact with technology.\\n\\n5. **Healthcare Innovations**: AI is expected to revolutionize healthcare through improved diagnostics, personalized treatments, predictive analytics, and efficient management of healthcare operations. This could lead to better patient outcomes and more accessible healthcare services.\\n\\n6. **Growth of Autonomous Systems**: The development of autonomous vehicles, drones, and robotics will continue to advance, transforming industries like transportation, logistics, and agriculture by improving safety, efficiency, and sustainability.\\n\\n7. **AI for Sustainability**: AI will play a crucial role in tackling environmental challenges, optimizing resource usage, improving climate models, and promoting sustainable practices, thereby contributing to environmental conservation efforts.\\n\\n8. **Quantum Computing and AI**: The synergy between AI and quantum computing could result in unprecedented computational capabilities, leading to significant breakthroughs in fields such as cryptography, complex simulations, and material science.\\n\\n9. **Creative Applications**: AI will increasingly contribute to creative industries, aiding in the production of art, music, and literature, and providing innovative tools and mediums for artistic exploration.\\n\\n10. **Focus on Explainability**: As AI systems become more integral to decision-making, the demand for explainable AI will grow. Developing AI that can clearly articulate its decision-making processes will be crucial for building trust and ensuring accountability.\\n\\nWhile the future of AI offers immense potential, it also poses challenges—including ethical considerations, privacy issues, and potential job displacement. Navigating these challenges will require careful thought, regulation, and collaboration across various stakeholders to ensure that AI benefits society as a whole.\",\n",
" \"refusal\": null,\n",
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- " \"tool_calls\": [\n",
- " {\n",
- " \"id\": \"call_Nbica1hw3v0FSis7aUPQ5Zvy\",\n",
- " \"function\": {\n",
- " \"arguments\": \"{\\\"file_path\\\":\\\"123.txt\\\"}\",\n",
- " \"name\": \"get_file_contents\"\n",
- " },\n",
- " \"type\": \"function\"\n",
- " }\n",
- " ]\n",
+ " \"tool_calls\": [],\n",
+ " \"parsed\": null\n",
" }\n",
" }\n",
" ],\n",
- " \"created\": 1728580799,\n",
- " \"model\": \"gpt-4o-mini\",\n",
+ " \"created\": 1730841728,\n",
+ " \"model\": \"gpt-4o-2024-08-06\",\n",
" \"object\": \"chat.completion\",\n",
" \"service_tier\": null,\n",
- " \"system_fingerprint\": \"fp_878413d04d\",\n",
+ " \"system_fingerprint\": \"fp_d54531d9eb\",\n",
" \"usage\": {\n",
- " \"completion_tokens\": 17,\n",
- " \"prompt_tokens\": 136,\n",
- " \"total_tokens\": 153,\n",
+ " \"completion_tokens\": 522,\n",
+ " \"prompt_tokens\": 3836,\n",
+ " \"total_tokens\": 4358,\n",
" \"completion_tokens_details\": null,\n",
" \"prompt_tokens_details\": null\n",
" }\n",
- " },\n",
- " \"assistant_response\": {\n",
- " \"content\": null,\n",
- " \"refusal\": null,\n",
- " \"role\": \"assistant\",\n",
- " \"function_call\": null,\n",
- " \"tool_calls\": [\n",
- " {\n",
- " \"id\": \"call_Nbica1hw3v0FSis7aUPQ5Zvy\",\n",
- " \"function\": {\n",
- " \"arguments\": \"{\\\"file_path\\\":\\\"123.txt\\\"}\",\n",
- " \"name\": \"get_file_contents\"\n",
- " },\n",
- " \"type\": \"function\"\n",
- " }\n",
- " ]\n",
- " },\n",
- " \"function_call_result_message\": {\n",
- " \"role\": \"tool\",\n",
- " \"content\": \"The purpose of life is to be happy.\",\n",
- " \"tool_call_id\": \"call_Nbica1hw3v0FSis7aUPQ5Zvy\"\n",
- " },\n",
- " \"assistant_tool_completion_args\": {\n",
- " \"messages\": [\n",
- " {\n",
- " \"role\": \"system\",\n",
- " \"content\": \"You are a helpful assistant.\"\n",
- " },\n",
- " {\n",
- " \"role\": \"user\",\n",
- " \"content\": \"Hi, my name is Paul.\"\n",
- " },\n",
- " {\n",
- " \"content\": \"Hello, Paul! How can I assist you today?\",\n",
- " \"refusal\": null,\n",
- " \"role\": \"assistant\",\n",
- " \"function_call\": null,\n",
- " \"tool_calls\": null\n",
- " },\n",
- " {\n",
- " \"role\": \"user\",\n",
- " \"content\": \"Please tell me what's in file 123.txt.\"\n",
- " },\n",
- " {\n",
- " \"content\": null,\n",
- " \"refusal\": null,\n",
- " \"role\": \"assistant\",\n",
- " \"function_call\": null,\n",
- " \"tool_calls\": [\n",
- " {\n",
- " \"id\": \"call_Nbica1hw3v0FSis7aUPQ5Zvy\",\n",
- " \"function\": {\n",
- " \"arguments\": \"{\\\"file_path\\\":\\\"123.txt\\\"}\",\n",
- " \"name\": \"get_file_contents\"\n",
- " },\n",
- " \"type\": \"function\"\n",
- " }\n",
- " ]\n",
- " },\n",
- " {\n",
- " \"role\": \"tool\",\n",
- " \"content\": \"The purpose of life is to be happy.\",\n",
- " \"tool_call_id\": \"call_Nbica1hw3v0FSis7aUPQ5Zvy\"\n",
- " }\n",
- " ],\n",
- " \"model\": \"gpt-4o\"\n",
- " },\n",
- " \"assistant_tool_response\": {\n",
- " \"content\": \"The contents of the file 123.txt are: \\\"The purpose of life is to be happy.\\\" If you need any further assistance or information, feel free to ask!\",\n",
- " \"refusal\": null,\n",
- " \"role\": \"assistant\",\n",
- " \"function_call\": null,\n",
- " \"tool_calls\": null\n",
" }\n",
" }\n",
"}\n"
@@ -465,24 +659,212 @@
}
],
"source": [
+ "from chat_driver import ChatDriver, ChatDriverConfig\n",
+ "from context import Context\n",
+ "\n",
+ "# When an chat driver is created, it will automatically create a context with a\n",
+ "# session_id. Or, if you want to use a specific session_id, you can pass it as\n",
+ "# an argument. This is useful for scoping this chat driver instance to an\n",
+ "# external identifier.\n",
+ "context = Context(\"simple-id-1000\")\n",
+ "\n",
+ "instructions = \"You are a famous computer scientist. You are giving a talk at a conference. You are talking about the future of AI and how it will change the world. You are asked a questions by audience members.\"\n",
+ "\n",
+ "chat_driver = ChatDriver(\n",
+ " ChatDriverConfig(\n",
+ " openai_client=async_client,\n",
+ " model=model,\n",
+ " instructions=instructions,\n",
+ " context=context,\n",
+ " ),\n",
+ ")\n",
+ "\n",
+ "message_event = await chat_driver.respond(\"What is the future of AI?\")\n",
+ "print(message_event.model_dump_json(indent=2))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### You can tell the chat driver to always return JSON\n",
+ "\n",
+ "Note: You MUST include the word \"JSON\" somewhere in your instructions. This is an OpenAI requirement for JSON return. However, note that a ChatDriver response method only returns string responses in its returned `MessageEvent` since these events are intended to be used in chat scenarios. If you want to transform it into an actual JSON object, you'll need to `json.loads(response.message)` it yourself."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{ \"thoughts\": \"The future of AI is a topic filled with both excitement and caution, as it holds the potential for transformative impacts across various facets of society.\", \"answer\": \"The future of AI is likely to involve increased integration into everyday life, with advancements in areas such as natural language processing, computer vision, and data analysis. AI is set to enhance fields like healthcare through predictive diagnostics, personalized medicine, and robotic surgery. In transportation, autonomous vehicles will likely become more common. AI will also improve efficiencies in industries like manufacturing and logistics. However, this future will also require addressing ethical considerations, data privacy concerns, and ensuring that AI technologies are accessible and beneficial for all members of society. A key focus will be on developing fair and unbiased AI systems, and creating governance frameworks that promote responsible AI development and usage.\" }\n",
+ "{\n",
+ " \"thoughts\": \"The future of AI is a topic filled with both excitement and caution, as it holds the potential for transformative impacts across various facets of society.\",\n",
+ " \"answer\": \"The future of AI is likely to involve increased integration into everyday life, with advancements in areas such as natural language processing, computer vision, and data analysis. AI is set to enhance fields like healthcare through predictive diagnostics, personalized medicine, and robotic surgery. In transportation, autonomous vehicles will likely become more common. AI will also improve efficiencies in industries like manufacturing and logistics. However, this future will also require addressing ethical considerations, data privacy concerns, and ensuring that AI technologies are accessible and beneficial for all members of society. A key focus will be on developing fair and unbiased AI systems, and creating governance frameworks that promote responsible AI development and usage.\"\n",
+ "}\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "from chat_driver import ChatDriver, JSON_OBJECT_RESPONSE_FORMAT, ChatDriverConfig\n",
+ "from context import Context\n",
+ "\n",
+ "context = Context(\"conversation-id-1002\")\n",
+ "instructions = 'You are a famous computer scientist. You are giving a talk at a conference. You are talking about the future of AI and how it will change the world. You are asked a questions by audience members and return your answer as valid json, like this: { \"thoughts\": , \"answer\": }.'\n",
+ "\n",
+ "chat_driver = ChatDriver(\n",
+ " ChatDriverConfig(\n",
+ " openai_client=async_client,\n",
+ " model=model,\n",
+ " instructions=instructions,\n",
+ " context=context,\n",
+ " ),\n",
+ ")\n",
+ "\n",
+ "message_event = await chat_driver.respond(\"What is the future of AI?\", response_format=JSON_OBJECT_RESPONSE_FORMAT)\n",
+ "print(message_event.message)\n",
+ "\n",
+ "print(json.dumps(json.loads(message_event.message or \"\"), indent=2))\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Chat drivers can return structured responses\n",
+ "\n",
+ "Just give it a class that inherits from Pydantic BaseModel and it will return your response in that structure. Most the time, if you want this kind of thing, you probably don't want a Chat Driver. Just use the OpenAI client with the helpers above. However, in the rare case that you want to actually use the structured output to guarantee the response from the model, this is here for you. You'll need to marshal the object yourself."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{\"thoughts\":\"AI is rapidly advancing and its future holds great potential and considerable responsibility. As AI technologies become more integrated into society, we will see transformations across various sectors, including healthcare, transportation, education, and manufacturing. However, these advancements also come with ethical considerations and the need for responsible development.\",\"answer\":\"The future of AI is both exciting and complex. We will likely see AI becoming more autonomous, improving its ability to understand and interpret nuanced human interactions, and solving complex problems in ways that were previously unimaginable. In healthcare, AI could lead to personalized medicine and better diagnostic tools. In transportation, we'll see more autonomous vehicles that could reshape our cities and daily commutes. However, with these advancements comes the responsibility to address ethical concerns such as privacy, security, and fairness. It's crucial that as AI capabilities grow, we also develop frameworks to guide its ethical use and ensure that its benefits are distributed equitably across society.\"}\n",
+ "{\n",
+ " \"thoughts\": \"AI is rapidly advancing and its future holds great potential and considerable responsibility. As AI technologies become more integrated into society, we will see transformations across various sectors, including healthcare, transportation, education, and manufacturing. However, these advancements also come with ethical considerations and the need for responsible development.\",\n",
+ " \"answer\": \"The future of AI is both exciting and complex. We will likely see AI becoming more autonomous, improving its ability to understand and interpret nuanced human interactions, and solving complex problems in ways that were previously unimaginable. In healthcare, AI could lead to personalized medicine and better diagnostic tools. In transportation, we'll see more autonomous vehicles that could reshape our cities and daily commutes. However, with these advancements comes the responsibility to address ethical concerns such as privacy, security, and fairness. It's crucial that as AI capabilities grow, we also develop frameworks to guide its ethical use and ensure that its benefits are distributed equitably across society.\"\n",
+ "}\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Simple chat driver w/ structured response\n",
+ "\n",
"from typing import cast\n",
- "from chat_driver import ChatDriver, ChatDriverConfig, Context\n",
- "from chat_driver import LocalMessageHistoryProvider #, LocalMessageHistoryProviderConfig\n",
- "# from typing import List\n",
- "# from openai.types.chat import ChatCompletionMessageParam\n",
+ "from chat_driver import ChatDriver, ChatDriverConfig, LocalMessageHistoryProvider\n",
+ "from context import Context\n",
+ "from pydantic import BaseModel\n",
+ "\n",
+ "context = Context(\"structured-1000\")\n",
+ "\n",
+ "instructions = 'You are a famous computer scientist. You are giving a talk at a conference. You are talking about the future of AI and how it will change the world. You are asked a questions by audience members and return a thoughtful response.'\n",
+ "\n",
+ "class ThoughtfulResponse(BaseModel):\n",
+ " \"\"\"A thoughtful response to a question.\"\"\"\n",
+ " thoughts: str\n",
+ " answer: str\n",
+ "\n",
+ "chat_driver = ChatDriver(\n",
+ " ChatDriverConfig(\n",
+ " openai_client=async_client,\n",
+ " model=model,\n",
+ " instructions=instructions,\n",
+ " context=context,\n",
+ " ),\n",
+ ")\n",
+ "\n",
+ "# Let's clear the data from previous runs.\n",
+ "message_provider = cast(LocalMessageHistoryProvider, chat_driver.message_provider)\n",
+ "message_provider.delete_all()\n",
+ "\n",
+ "message_event = await chat_driver.respond(\"What is the future of AI?\", response_format=ThoughtfulResponse)\n",
+ "\n",
+ "# As always, the event will come back with a string message.\n",
+ "print(message_event.message)\n",
+ "\n",
+ "# If you really want to, you can turn the JSON string back into a Pydantic model.\n",
+ "thoughtful_response = ThoughtfulResponse(**json.loads(message_event.message or \"\"))\n",
+ "print(thoughtful_response.model_dump_json(indent=2))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### You can register functions to chat drivers\n",
+ "\n",
+ "Chat drivers will use any functions you give it as both OpenAI tool calls, and as commands.\n",
+ "\n",
+ "With each response call, you can specify what type of response you want to have... string, dictionary, or Pydantic model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "conversation-id-1002\n",
+ "\n",
+ "Hello Paul! How can I assist you today?\n",
+ "\n",
+ "Commands:\n",
+ "help(): Return this help message.\n",
+ "erase(name: str): Erases a stored value.\n",
+ "json_thing(): Return json.\n",
+ "get_weather(input: Input): Return the weather.\n",
+ "echo(text: str): Return the text.\n",
+ "get_file_contents(file_path: str): Return the contents of a file.\n",
+ "\n",
+ "Args:\n",
+ "- file_path: The path to the file.\n",
+ "\n",
+ "conversation-id-1002: Echo this.\n",
+ "\n",
+ "The content of \"123.txt\" is: \"The purpose of life is to be happy.\"\n",
+ "\n",
+ "{\"description\":\"Sunny\",\"cloud_cover\":0.2,\"temp_c\":25.0,\"temp_f\":77.0}\n"
+ ]
+ }
+ ],
+ "source": [
+ "from typing import Any, cast\n",
+ "from chat_driver import ChatDriver, ChatDriverConfig\n",
+ "from context import Context, ContextProtocol\n",
+ "from chat_driver import LocalMessageHistoryProvider\n",
+ "from pydantic import BaseModel, Field\n",
"\n",
"\n",
"# When an chat driver is created, it will automatically create a context with a\n",
"# session_id. Or, if you want to use a specific session_id, you can pass it as\n",
"# an argument. This is useful for scoping this chat driver instance to an\n",
"# external identifier.\n",
- "context = Context(\"conversation-id-1000\")\n",
+ "context = Context(\"conversation-id-1002\")\n",
"\n",
"\n",
"# Define tool functions for the chat driver. All functions used by the chat driver\n",
"# require a session_id as the first argument.\n",
"def get_file_contents(context: Context, file_path: str) -> str:\n",
- " \"\"\"Return the contents of a file.\"\"\"\n",
+ " \"\"\"\n",
+ " Return the contents of a file.\n",
+ "\n",
+ " Args:\n",
+ " - file_path: The path to the file.\n",
+ " \"\"\"\n",
" return \"The purpose of life is to be happy.\"\n",
"\n",
"\n",
@@ -490,6 +872,22 @@
" \"\"\"Erases a stored value.\"\"\"\n",
" return f\"{context.session_id}: {name} erased\"\n",
"\n",
+ "def json_thing(context: Context) -> dict[str, Any]:\n",
+ " \"\"\"Return json.\"\"\"\n",
+ " return {\"key\": \"value\"}\n",
+ "\n",
+ "class Input(BaseModel):\n",
+ " zipcode: str\n",
+ "\n",
+ "class Weather(BaseModel):\n",
+ " description: str = Field(description=\"The weather description.\")\n",
+ " cloud_cover: float\n",
+ " temp_c: float\n",
+ " temp_f: float\n",
+ "\n",
+ "def get_weather(context: Context, input: Input) -> Weather:\n",
+ " \"\"\"Return the weather.\"\"\"\n",
+ " return Weather(description=\"Sunny\", cloud_cover=0.2, temp_c=25.0, temp_f=77.0)\n",
"\n",
"# Define the chat driver.\n",
"instructions = \"You are a helpful assistant.\"\n",
@@ -506,8 +904,8 @@
" instructions=instructions,\n",
" context=context,\n",
" # message_provider=message_provider,\n",
- " commands=[erase], # Commands can be registered when instantiating the chat driver.\n",
- " functions=[erase], # Functions can be registered when instantiating the chat driver.\n",
+ " commands=[erase, json_thing, get_weather], # Commands can be registered when instantiating the chat driver.\n",
+ " functions=[erase, json_thing, get_weather], # Functions can be registered when instantiating the chat driver.\n",
" ),\n",
")\n",
"\n",
@@ -520,7 +918,7 @@
"# Remember, all functions used by the chat driver require a session_id as the\n",
"# first argument.\n",
"@chat_driver.register_function_and_command\n",
- "def echo(context: Context, text: str) -> str:\n",
+ "def echo(context: ContextProtocol, text: str) -> str:\n",
" \"\"\"Return the text.\"\"\"\n",
" return f\"{context.session_id}: {text}\"\n",
"\n",
@@ -532,28 +930,33 @@
"print(chat_driver.context.session_id)\n",
"\n",
"# Let's see if the agent can respond.\n",
- "response = await chat_driver.respond(\"Hi, my name is Paul.\")\n",
+ "message_event = await chat_driver.respond(\"Hi, my name is Paul.\")\n",
"print()\n",
- "print(response.message)\n",
+ "print(message_event.message)\n",
"\n",
"# Help command (shows command available).\n",
- "response = await chat_driver.respond(\"/help\")\n",
+ "message_event = await chat_driver.respond(\"/help\")\n",
"print()\n",
- "print(response.message)\n",
+ "print(message_event.message)\n",
"\n",
"# We can run any function or command directly.\n",
- "response = await chat_driver.functions.echo(\"Hi, my name is Paul.\")\n",
+ "message_event = await chat_driver.functions.echo(\"Echo this.\")\n",
"print()\n",
- "print(response)\n",
+ "print(message_event)\n",
"\n",
"# Let's see if the chat driver has the ability to run it's own registered function.\n",
- "response = await chat_driver.respond(\"Please tell me what's in file 123.txt.\")\n",
+ "message_event = await chat_driver.respond(\"Please tell me what's in file 123.txt.\")\n",
"print()\n",
- "print(response.message)\n",
+ "print(message_event.message)\n",
"\n",
- "# Let's see the full response event.\n",
+ "# Stuctured output.\n",
+ "message_event = await chat_driver.respond(\"What is the weather in 90210?\", response_format=Weather)\n",
"print()\n",
- "print(response.to_json())"
+ "print(message_event.message)\n",
+ "\n",
+ "# Let's see the full response event.\n",
+ "# print()\n",
+ "# print(response.to_json())"
]
},
{
@@ -565,15 +968,33 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "User: /help\n",
+ "Assistant: Commands:\n",
+ "help(): Return this help message.\n",
+ "get_file_contents(file_path: str): Returns the contents of a file.\n",
+ "erase(name: str): Erases a stored value.\n",
+ "echo(value: str | list[str] | dict | int | bool | float): Echos a value as a string.\n",
+ "User: erase(\"something\")\n",
+ "Assistant: It looks like you're trying to erase a stored value identified by the name \"something\". However, to proceed with this action, I need you to confirm the name of the stored value you want to erase, as there's no context to identify what \"something\" refers to. Please provide the exact name if \"something\" is correct, or clarify if it's a placeholder.\n",
+ "User: /erase(\"something\")\n",
+ "Assistant: conversation-id-1006: something erased\n",
+ "User: something is the exact name of the key.\n",
+ "Assistant: The key named \"something\" has been erased successfully. If you need further assistance, feel free to ask!\n"
+ ]
+ }
+ ],
"source": [
- "from typing import Any\n",
"from chat_driver import ChatDriverConfig, ChatDriver\n",
"from context import Context\n",
"\n",
- "context = Context(\"conversation-id-1001\")\n",
+ "context = Context(\"conversation-id-1006\")\n",
"\n",
"\n",
"def get_file_contents(context: Context, file_path: str) -> str:\n",
@@ -586,7 +1007,7 @@
" return f\"{context.session_id}: {name} erased\"\n",
"\n",
"\n",
- "def echo(context: Context, value: Any) -> str:\n",
+ "def echo(context: Context, value: str | list[str] | dict | int | bool | float) -> str:\n",
" \"\"\"Echos a value as a string.\"\"\"\n",
" match value:\n",
" case str():\n",
@@ -623,10 +1044,14 @@
" if message == \"\":\n",
" break\n",
" print(f\"User: {message}\", flush=True)\n",
- " response = await chat_driver.respond(message)\n",
- " # You can print the entire response event! \n",
+ " message_event = await chat_driver.respond(message)\n",
+ " if message_event.metadata.get(\"error\"):\n",
+ " print(f\"Error: {message_event.metadata.get('error')}\")\n",
+ " print(message_event.to_json())\n",
+ " continue\n",
+ " # You can print the entire message event! \n",
" # print(response.to_json())\n",
- " print(f\"Assistant: {response.message}\", flush=True)"
+ " print(f\"Assistant: {message_event.message}\", flush=True)"
]
},
{
@@ -690,10 +1115,10 @@
" if message == \"\":\n",
" break\n",
" print(f\"User: {message}\", flush=True)\n",
- " response = await chat_driver.respond(message)\n",
+ " message_event = await chat_driver.respond(message)\n",
" # You can print the entire response event! \n",
" # print(response.to_json())\n",
- " print(f\"Assistant: {response.message}\", flush=True)"
+ " print(f\"Assistant: {message_event.message}\", flush=True)"
]
}
],
diff --git a/libraries/python/skills/notebooks/uv.lock b/libraries/python/skills/notebooks/uv.lock
index 51e9cdd2..2d936f1f 100644
--- a/libraries/python/skills/notebooks/uv.lock
+++ b/libraries/python/skills/notebooks/uv.lock
@@ -336,6 +336,7 @@ dependencies = [
{ name = "events" },
{ name = "function-registry" },
{ name = "openai" },
+ { name = "openai-client" },
{ name = "pydantic" },
{ name = "pydantic-settings" },
{ name = "python-dotenv" },
@@ -350,6 +351,7 @@ requires-dist = [
{ name = "events", editable = "../../events" },
{ name = "function-registry", editable = "../../function-registry" },
{ name = "openai", specifier = ">=1.16.1" },
+ { name = "openai-client", editable = "../../openai-client" },
{ name = "pydantic", specifier = ">=2.6.1" },
{ name = "pydantic-settings", specifier = ">=2.3.4" },
{ name = "python-dotenv", specifier = ">=1.0.1" },
@@ -707,6 +709,15 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/76/c6/c88e154df9c4e1a2a66ccf0005a88dfb2650c1dffb6f5ce603dfbd452ce3/idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3", size = 70442 },
]
+[[package]]
+name = "importlib-resources"
+version = "6.4.5"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/98/be/f3e8c6081b684f176b761e6a2fef02a0be939740ed6f54109a2951d806f3/importlib_resources-6.4.5.tar.gz", hash = "sha256:980862a1d16c9e147a59603677fa2aa5fd82b87f223b6cb870695bcfce830065", size = 43372 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/e1/6a/4604f9ae2fa62ef47b9de2fa5ad599589d28c9fd1d335f32759813dfa91e/importlib_resources-6.4.5-py3-none-any.whl", hash = "sha256:ac29d5f956f01d5e4bb63102a5a19957f1b9175e45649977264a1416783bb717", size = 36115 },
+]
+
[[package]]
name = "ipykernel"
version = "6.29.5"
@@ -1045,8 +1056,10 @@ dependencies = [
{ name = "azure-ai-contentsafety" },
{ name = "azure-core", extra = ["aio"] },
{ name = "azure-identity" },
+ { name = "function-registry" },
{ name = "openai" },
{ name = "pillow" },
+ { name = "python-liquid" },
{ name = "semantic-workbench-assistant" },
{ name = "tiktoken" },
]
@@ -1056,8 +1069,10 @@ requires-dist = [
{ name = "azure-ai-contentsafety", specifier = ">=1.0.0" },
{ name = "azure-core", extras = ["aio"], specifier = ">=1.30.0" },
{ name = "azure-identity", specifier = ">=1.17.1" },
+ { name = "function-registry", editable = "../../function-registry" },
{ name = "openai", specifier = ">=1.3.9" },
{ name = "pillow", specifier = ">=11.0.0" },
+ { name = "python-liquid", specifier = ">=1.12.1" },
{ name = "semantic-workbench-assistant", editable = "../../semantic-workbench-assistant" },
{ name = "tiktoken", specifier = ">=0.7.0" },
]
@@ -1422,6 +1437,20 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/35/a6/145655273568ee78a581e734cf35beb9e33a370b29c5d3c8fee3744de29f/python_json_logger-2.0.7-py3-none-any.whl", hash = "sha256:f380b826a991ebbe3de4d897aeec42760035ac760345e57b812938dc8b35e2bd", size = 8067 },
]
+[[package]]
+name = "python-liquid"
+version = "1.12.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "importlib-resources" },
+ { name = "python-dateutil" },
+ { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/59/b4/c4e39470edf507056333cd688a36bb7fbdb17090105d9f454ecf6ff09845/python_liquid-1.12.1.tar.gz", hash = "sha256:7367e979125859fb4116f360f224a89a52ecb455fb26843c43e4d800b389d325", size = 124636 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/2c/6e/bfd01926e28f6cf355a7a8460271ba135013870f5857b06f35dbf65ab237/python_liquid-1.12.1-py3-none-any.whl", hash = "sha256:2224312944be16c1a44406398eb8a07c7e57398d5c0ef15ff950946dbefe7c33", size = 206592 },
+]
+
[[package]]
name = "python-multipart"
version = "0.0.12"
@@ -1689,6 +1718,7 @@ name = "skill-library"
version = "0.1.0"
source = { editable = "../skill-library" }
dependencies = [
+ { name = "assistant-drive" },
{ name = "chat-driver" },
{ name = "context" },
{ name = "events" },
@@ -1703,6 +1733,7 @@ dependencies = [
[package.metadata]
requires-dist = [
+ { name = "assistant-drive", editable = "../../assistant-drive" },
{ name = "chat-driver", editable = "../../chat-driver" },
{ name = "context", editable = "../../context" },
{ name = "events", editable = "../../events" },
@@ -1715,6 +1746,13 @@ requires-dist = [
{ name = "tiktoken", specifier = ">=0.7.0" },
]
+[package.metadata.requires-dev]
+dev = [
+ { name = "pytest", specifier = ">=8.3.1" },
+ { name = "pytest-asyncio", specifier = ">=0.23.8" },
+ { name = "pytest-repeat", specifier = ">=0.9.3" },
+]
+
[[package]]
name = "skill-notebooks"
version = "0.1.0"
diff --git a/libraries/python/skills/skill-library/.vscode/settings.json b/libraries/python/skills/skill-library/.vscode/settings.json
index 29b600d7..1d907497 100644
--- a/libraries/python/skills/skill-library/.vscode/settings.json
+++ b/libraries/python/skills/skill-library/.vscode/settings.json
@@ -20,7 +20,7 @@
"python.analysis.inlayHints.functionReturnTypes": true,
"python.analysis.typeCheckingMode": "basic",
"python.defaultInterpreterPath": "${workspaceFolder}/.venv/bin/python",
- "python.testing.pytestEnabled": false,
+ "python.testing.pytestEnabled": true,
"search.exclude": {
"**/.venv": true,
"**/data": true
@@ -52,5 +52,7 @@
"pypdf",
"runtimes",
"tiktoken"
- ]
+ ],
+ "python.testing.pytestArgs": ["skill_library"],
+ "python.testing.unittestEnabled": false
}
diff --git a/libraries/python/skills/skill-library/pyproject.toml b/libraries/python/skills/skill-library/pyproject.toml
index 5765b75a..d3db0f91 100644
--- a/libraries/python/skills/skill-library/pyproject.toml
+++ b/libraries/python/skills/skill-library/pyproject.toml
@@ -6,22 +6,29 @@ authors = [{name="MADE:Explorers"}]
readme = "README.md"
requires-python = ">=3.11"
dependencies = [
+ "assistant-drive>=0.1.0",
+ "chat-driver>=0.1.0",
+ "context>=0.1.0",
+ "events>=0.1.0",
+ "function-registry>=0.1.0",
"openai>=1.16.1",
- "pydantic>=2.6.1",
"pydantic-settings>=2.3.4",
+ "pydantic>=2.6.1",
"python-dotenv>=1.0.1",
"requests>=2.32.0",
"tiktoken>=0.7.0",
- "chat-driver>=0.1.0",
- "context>=0.1.0",
- "events>=0.1.0",
- "function-registry>=0.1.0",
]
[tool.uv]
package = true
+dev-dependencies = [
+ "pytest>=8.3.1",
+ "pytest-asyncio>=0.23.8",
+ "pytest-repeat>=0.9.3",
+]
[tool.uv.sources]
+assistant-drive = { path = "../../assistant-drive", editable = true }
chat-driver = { path = "../../chat-driver", editable = true }
context = { path = "../../context", editable = true }
events = { path = "../../events", editable = true }
diff --git a/libraries/python/skills/skill-library/pytest.ini b/libraries/python/skills/skill-library/pytest.ini
new file mode 100644
index 00000000..02bae23f
--- /dev/null
+++ b/libraries/python/skills/skill-library/pytest.ini
@@ -0,0 +1,13 @@
+# pytest.ini
+[pytest]
+minversion = 6.0
+addopts = -vv -rP
+pythonpath = .
+testpaths = **/tests
+filterwarnings =
+ ignore::DeprecationWarning
+ ignore::PendingDeprecationWarning
+asyncio_mode = auto
+log_cli = true
+log_cli_level = INFO
+log_cli_format = %(asctime)s | %(levelname)-7s | %(name)s | %(message)s
diff --git a/libraries/python/skills/skill-library/skill_library/__init__.py b/libraries/python/skills/skill-library/skill_library/__init__.py
index 3a5fe8f8..c2538675 100644
--- a/libraries/python/skills/skill-library/skill_library/__init__.py
+++ b/libraries/python/skills/skill-library/skill_library/__init__.py
@@ -1,12 +1,15 @@
-from .assistant import Assistant
from context import Context
-from .routine import InstructionRoutine, RoutineTypes
+
+from .assistant import Assistant
+from .routine import FunctionRoutine, InstructionRoutine, ProgramRoutine, RoutineTypes
from .skill import Skill
__all__ = [
"Assistant",
"Context",
+ "FunctionRoutine",
"InstructionRoutine",
+ "ProgramRoutine",
"RoutineTypes",
"Skill",
]
diff --git a/libraries/python/skills/skill-library/skill_library/assistant.py b/libraries/python/skills/skill-library/skill_library/assistant.py
index dae7fb53..a6b5cb1b 100644
--- a/libraries/python/skills/skill-library/skill_library/assistant.py
+++ b/libraries/python/skills/skill-library/skill_library/assistant.py
@@ -1,12 +1,20 @@
import asyncio
+from os import PathLike
from typing import Any, AsyncIterator
-
-from chat_driver import TEXT_RESPONSE_FORMAT, ChatDriver, ChatDriverConfig, ResponseFormat
-from context import Context
+from uuid import uuid4
+
+from assistant_drive import Drive, DriveConfig, IfDriveFileExistsBehavior
+from chat_driver import (
+ TEXT_RESPONSE_FORMAT,
+ ChatDriver,
+ ChatDriverConfig,
+)
from events import BaseEvent, EventProtocol
+from openai.types.chat.completion_create_params import (
+ ResponseFormat,
+)
-from .routine import InstructionRoutine, ProgramRoutine
-from .routine_runners import InstructionRoutineRunner, ProgramRoutineRunner
+from .run_context import RunContext
from .skill import Skill
from .skill_registry import SkillRegistry
@@ -17,20 +25,48 @@ def __init__(
name,
chat_driver_config: ChatDriverConfig,
session_id: str | None = None,
+ skills: list[Skill] = [],
+ drive_root: PathLike | None = None,
+ metadrive_root: PathLike | None = None,
) -> None:
+ self.skill_registry: SkillRegistry = SkillRegistry()
+
self.name = name
- self.skill_registry = SkillRegistry()
self.chat_driver = self._register_chat_driver(chat_driver_config)
+
self._event_queue = asyncio.Queue() # Async queue for events
self._stopped = asyncio.Event() # Event to signal when the assistant has stopped
- self._running_routines = {}
- self.context = Context(session_id=session_id, emit=self._emit)
+ if skills:
+ self.register_skills(skills)
+
+ # The assistant drive can be used to read and write files to a
+ # particular location. The assistant drive should be used for
+ # assistant-specific data and not for general data storage.
+ self.drive: Drive = Drive(
+ DriveConfig(
+ root=drive_root or f".data/{session_id}/assistant",
+ default_if_exists_behavior=IfDriveFileExistsBehavior.OVERWRITE,
+ )
+ )
+
+ # The assistant run context identifies the assistant session (session)
+ # and provides necessary utilities to be used for this particular
+ # assistant session. The run context is passed to the assistant's chat
+ # driver commands and functions and all skill actions and routines that
+ # are run by the assistant.
+ self.run_context = RunContext(
+ session_id=session_id or str(uuid4()),
+ assistant_drive=self.drive,
+ emit=self._emit,
+ run_routine=self.skill_registry.run_routine_by_name,
+ drive_root=metadrive_root,
+ )
######################################
# Lifecycle and event handling
######################################
- async def wait(self) -> Context:
+ async def wait(self) -> RunContext:
"""
After initializing an assistant, call this method to wait for assistant
events. While running, any events produced by the assistant can be
@@ -39,7 +75,7 @@ async def wait(self) -> Context:
"""
await self._stopped.wait()
- return self.context
+ return self.run_context
def stop(self) -> None:
self._stopped.set() # Signal that we are stopping
@@ -59,13 +95,35 @@ async def events(self) -> AsyncIterator[EventProtocol]:
await asyncio.sleep(0.005)
def _emit(self, event: EventProtocol) -> None:
- event.session_id = self.context.session_id
+ event.session_id = self.run_context.session_id
self._event_queue.put_nowait(event)
async def put_message(self, message: str) -> None:
- """Exposed externally for sending messages to the assistant."""
- response = await self.chat_driver.respond(message)
- self._emit(response)
+ """
+ Exposed externally for sending messages to the assistant.
+
+ If a routine is currently running, send the message to the routine.
+
+ This is a simple way to handle conversation flows. In the future, we
+ would like to keep track of multiple topics and allow the assistant to
+ switch between them. The current idea is that we might treat a topic
+ like multi-tasking on a Linux system with one topic at a time being
+ "foreground" and the others being "background". The assistant would
+ decide when to switch topics based on the content of the messages. A
+ topic might include a subset of the conversation history, only specific
+ skills, etc.
+
+ For now, though, we track only a single topic at a time. If a routine is
+ currently running, we send the message to the routine. Otherwise, we
+ send the message to the chat driver.
+ """
+ # If a routine is running, send the message to the routine.
+ if self.run_context.routine_stack.peek():
+ await self.step_active_routine(message)
+ else:
+ # Otherwise, send the message to the chat driver.
+ response = await self.chat_driver.respond(message)
+ self._emit(response)
######################################
# Chat interface
@@ -82,6 +140,11 @@ def _register_chat_driver(self, chat_driver_config: ChatDriverConfig) -> ChatDri
"These vars are like the routines' input.\n\n"
"Available routines and their available vars: {routines}. "
)
+
+ # Overwrite whatever run context was passed in with this assistant's
+ # actual context.
+ config.context = self.run_context
+
chat_functions = ChatFunctions(self)
functions = [chat_functions.list_routines, chat_functions.run_routine]
config.commands = functions
@@ -136,27 +199,13 @@ def list_routines(self) -> list[str]:
async def run_routine(self, name: str, vars: dict[str, Any] | None = None) -> Any:
"""
- Run an assistant routine. This is going to be much of the
- magic of the assistant. Currently, is just runs through the
- steps of a routine, but this will get much more sophisticated.
- It will need to handle configuration, managing results of steps,
- handling errors and retries, etc. ALso, this is where we will put
- meta-cognitive functions such as having the assistant create a plan
- from the routine and executing it dynamically while monitoring progress.
+ Run an assistant routine by name (e.g. .).
"""
- skill, routine = self.skill_registry.get_routine(name)
- if not skill:
- raise ValueError(f"Skill {name} not found.")
- if not routine:
- raise ValueError(f"Routine {name} not found.")
+ await self.skill_registry.run_routine_by_name(self.run_context, name, vars)
- match routine:
- case InstructionRoutine():
- runner = InstructionRoutineRunner(self)
- return await runner.run(skill, routine, vars)
- case ProgramRoutine():
- runner = ProgramRoutineRunner(self)
- return await runner.run(skill, routine, vars)
+ async def step_active_routine(self, message: str) -> None:
+ """Run another step in the current routine."""
+ await self.skill_registry.step_active_routine(self.run_context, message)
class ChatFunctions:
@@ -168,11 +217,11 @@ class ChatFunctions:
def __init__(self, assistant: Assistant) -> None:
self.assistant = assistant
- def list_routines(self, context: Context) -> list[str]:
+ def list_routines(self, context: RunContext) -> list[str]:
"""Lists all the routines available in the assistant."""
return self.assistant.list_routines()
- async def run_routine(self, context: Context, name: str, vars: dict[str, Any] | None = None) -> Any:
+ async def run_routine(self, context: RunContext, name: str, vars: dict[str, Any] | None = None) -> Any:
"""
Run an assistant routine.
"""
diff --git a/libraries/python/skills/skill-library/skill_library/routine.py b/libraries/python/skills/skill-library/skill_library/routine.py
index 541fa249..2c5ad675 100644
--- a/libraries/python/skills/skill-library/skill_library/routine.py
+++ b/libraries/python/skills/skill-library/skill_library/routine.py
@@ -1,5 +1,10 @@
-from typing import Union
import re
+from typing import TYPE_CHECKING, Any, Awaitable, Callable, Dict, Optional, Union
+
+from skill_library.run_context import RunContext
+
+if TYPE_CHECKING:
+ from skill_library.skill import Skill
def find_template_vars(text: str) -> list[str]:
@@ -15,9 +20,17 @@ def __init__(
self,
name: str,
description: str,
+ skill: "Skill",
) -> None:
self.name = name
self.description = description
+ self.skill = skill
+
+ def fullname(self) -> str:
+ return f"{self.skill.name}.{self.name}"
+
+ def __str__(self) -> str:
+ return self.fullname()
class InstructionRoutine(Routine):
@@ -26,10 +39,12 @@ def __init__(
name: str,
description: str,
routine: str,
+ skill: "Skill",
) -> None:
super().__init__(
name=name,
description=description,
+ skill=skill,
)
self.routine = routine
@@ -44,10 +59,12 @@ def __init__(
name: str,
description: str,
program: str,
+ skill: "Skill",
) -> None:
super().__init__(
name=name,
description=description,
+ skill=skill,
)
self.program = program
@@ -56,4 +73,25 @@ def __str__(self) -> str:
return f"{self.name}(vars: {template_vars}): {self.description}"
-RoutineTypes = Union[InstructionRoutine, ProgramRoutine]
+class FunctionRoutine(Routine):
+ def __init__(
+ self,
+ name: str,
+ description: str,
+ init_function: Callable[[RunContext, Optional[Dict[str, Any]]], Awaitable[None]],
+ step_function: Callable[[RunContext, Optional[str]], Awaitable[Optional[str]]],
+ skill: "Skill",
+ ) -> None:
+ super().__init__(
+ name=name,
+ description=description,
+ skill=skill,
+ )
+ self.init_function = init_function
+ self.step_function = step_function
+
+ def __str__(self) -> str:
+ return f"{self.name}: {self.description}"
+
+
+RoutineTypes = Union[InstructionRoutine, ProgramRoutine, FunctionRoutine]
diff --git a/libraries/python/skills/skill-library/skill_library/routine_runners/__init__.py b/libraries/python/skills/skill-library/skill_library/routine_runners/__init__.py
index 927ef360..72a64325 100644
--- a/libraries/python/skills/skill-library/skill_library/routine_runners/__init__.py
+++ b/libraries/python/skills/skill-library/skill_library/routine_runners/__init__.py
@@ -1,8 +1,9 @@
from typing import Union
+from .function_routine_runner import FunctionRoutineRunner
from .instruction_routine_runner import InstructionRoutineRunner
from .program_routine_runner import ProgramRoutineRunner
-RunnerTypes = Union[InstructionRoutineRunner, ProgramRoutineRunner]
+RunnerTypes = Union[InstructionRoutineRunner, ProgramRoutineRunner, FunctionRoutineRunner]
__all__ = ["InstructionRoutineRunner", "RunnerTypes"]
diff --git a/libraries/python/skills/skill-library/skill_library/routine_runners/function_routine_runner.py b/libraries/python/skills/skill-library/skill_library/routine_runners/function_routine_runner.py
new file mode 100644
index 00000000..f57e09a1
--- /dev/null
+++ b/libraries/python/skills/skill-library/skill_library/routine_runners/function_routine_runner.py
@@ -0,0 +1,20 @@
+from typing import Any
+
+from ..routine import FunctionRoutine
+from ..run_context import RunContext
+
+
+class FunctionRoutineRunner:
+ def __init__(self) -> None:
+ pass
+
+ async def run(
+ self, context: RunContext, routine: FunctionRoutine, vars: dict[str, Any] | None = None
+ ) -> Any:
+ routine.init_function(context, vars)
+
+ async def next(self, context: RunContext, routine: FunctionRoutine, message: str) -> Any:
+ """
+ Run the next step in the current routine.
+ """
+ routine.step_function(context, message)
diff --git a/libraries/python/skills/skill-library/skill_library/routine_runners/instruction_routine_runner.py b/libraries/python/skills/skill-library/skill_library/routine_runners/instruction_routine_runner.py
index 995f2b59..49aa53dd 100644
--- a/libraries/python/skills/skill-library/skill_library/routine_runners/instruction_routine_runner.py
+++ b/libraries/python/skills/skill-library/skill_library/routine_runners/instruction_routine_runner.py
@@ -1,20 +1,18 @@
-from typing import TYPE_CHECKING, Any
+from typing import Any
from events import BaseEvent, InformationEvent
from ..routine import InstructionRoutine
-from ..skill import Skill
-
-if TYPE_CHECKING:
- from ..assistant import Assistant
+from ..run_context import RunContext
class InstructionRoutineRunner:
- def __init__(self, assistant: "Assistant") -> None:
- self.skill_registry = assistant.skill_registry
- self.context = assistant.context
+ def __init__(self) -> None:
+ pass
- async def run(self, skill: Skill, routine: InstructionRoutine, vars: dict[str, Any] | None = None) -> Any:
+ async def run(
+ self, context: RunContext, routine: InstructionRoutine, vars: dict[str, Any] | None = None
+ ) -> Any:
"""
Run an Instruction routine. This just runs through the steps of a
routine, sending each one to a skill's response endpoint. Note, this
@@ -34,9 +32,15 @@ async def run(self, skill: Skill, routine: InstructionRoutine, vars: dict[str, A
steps = routine.routine.split("\n")
for step in steps:
- self.context.emit(InformationEvent(message=f"Running step: {step}"))
- response: BaseEvent = await skill.respond(message=step)
+ context.emit(InformationEvent(message=f"Running step: {step}"))
+ response: BaseEvent = await routine.skill.respond(message=step)
informationEvent = InformationEvent(**response.model_dump())
- self.context.emit(informationEvent)
+ context.emit(informationEvent)
return
+
+ async def next(self, context: RunContext, routine: InstructionRoutine, message: str) -> Any:
+ """
+ Run the next step in the current routine.
+ """
+ pass
diff --git a/libraries/python/skills/skill-library/skill_library/routine_runners/program_routine_runner.py b/libraries/python/skills/skill-library/skill_library/routine_runners/program_routine_runner.py
index 6941daf3..4a842828 100644
--- a/libraries/python/skills/skill-library/skill_library/routine_runners/program_routine_runner.py
+++ b/libraries/python/skills/skill-library/skill_library/routine_runners/program_routine_runner.py
@@ -1,18 +1,16 @@
-from typing import TYPE_CHECKING, Any
+from typing import Any
from ..routine import ProgramRoutine
-from ..skill import Skill
-
-if TYPE_CHECKING:
- from ..assistant import Assistant
+from ..run_context import RunContext
class ProgramRoutineRunner:
- def __init__(self, assistant: "Assistant") -> None:
- self.skill_registry = assistant.skill_registry
- self.context = assistant.context
+ def __init__(self) -> None:
+ pass
- async def run(self, skill: Skill, routine: ProgramRoutine, vars: dict[str, Any] | None = None) -> Any:
+ async def run(
+ self, context: RunContext, routine: ProgramRoutine, vars: dict[str, Any] | None = None
+ ) -> Any:
"""
This implementation is not yet working. It is a placeholder for the
future implementation of running a program routine. A program routine
@@ -28,3 +26,9 @@ async def run(self, skill: Skill, routine: ProgramRoutine, vars: dict[str, Any]
# TODO: execute the program.
return
+
+ async def next(self, context: RunContext, routine: ProgramRoutine, message: str) -> Any:
+ """
+ Run the next step in the current routine.
+ """
+ pass
diff --git a/libraries/python/skills/skill-library/skill_library/routine_stack.py b/libraries/python/skills/skill-library/skill_library/routine_stack.py
new file mode 100644
index 00000000..d3de4002
--- /dev/null
+++ b/libraries/python/skills/skill-library/skill_library/routine_stack.py
@@ -0,0 +1,101 @@
+from typing import Any, List
+from uuid import uuid4
+
+from assistant_drive import Drive, IfDriveFileExistsBehavior
+from pydantic import BaseModel, Field
+
+
+class RoutineFrame(BaseModel):
+ name: str
+ id: str = Field(default_factory=lambda: str(uuid4()))
+ state: dict[str, Any] = Field(default_factory=dict)
+
+
+class RoutineStackData(BaseModel):
+ frames: List[RoutineFrame]
+ model_config = {
+ "arbitrary_types_allowed": True,
+ }
+
+
+STACK_FILENAME = "routine_stack.json"
+
+
+class RoutineStack:
+ def __init__(self, drive: Drive):
+ self.drive = drive
+
+ async def get(self) -> List[RoutineFrame]:
+ try:
+ stack = self.drive.read_model(RoutineStackData, STACK_FILENAME)
+ except FileNotFoundError:
+ stack = RoutineStackData(frames=[])
+ await self.set(stack.frames)
+ return stack.frames
+
+ async def push(self, name: str) -> str:
+ stack = await self.get()
+ frame = RoutineFrame(name=name)
+ stack.append(frame)
+ await self.set(stack)
+ return frame.id
+
+ async def pop(self) -> RoutineFrame | None:
+ stack = await self.get()
+ if not stack:
+ return None
+ frame = stack.pop()
+ await self.set(stack)
+ return frame
+
+ async def peek(self) -> RoutineFrame | None:
+ stack = await self.get()
+ if not stack:
+ return None
+ return stack[-1]
+
+ async def update(self, frame: RoutineFrame) -> None:
+ """Updates the top frame in the stack."""
+ stack = await self.get()
+ stack[-1] = frame
+ await self.set(stack)
+
+ async def set(self, frames: List[RoutineFrame]) -> None:
+ """Replaces the stack with the given list of frames."""
+ self.drive.write_model(
+ RoutineStackData(frames=frames),
+ STACK_FILENAME,
+ if_exists=IfDriveFileExistsBehavior.OVERWRITE,
+ )
+
+ async def get_current_state(self) -> dict[str, Any]:
+ """Returns the state of the current routine."""
+ frame = await self.peek()
+ if frame:
+ return frame.state
+ return {}
+
+ async def set_current_state(self, state: dict[str, Any]) -> None:
+ """Updates the state of the current routine."""
+ frame = await self.peek()
+ if frame:
+ frame.state = state
+ await self.update(frame)
+
+ async def get_current_state_key(self, key: str) -> Any:
+ state = await self.get_current_state()
+ return state.get(key)
+
+ async def set_current_state_key(self, key: str, value: Any) -> None:
+ state = await self.get_current_state()
+ state[key] = value
+ await self.set_current_state(state)
+
+ async def clear(self) -> None:
+ await self.set([])
+
+ async def length(self) -> int:
+ return len(await self.get())
+
+ async def string(self) -> str:
+ return str([f"{frame.name}.{frame.id}" for frame in await self.get()])
diff --git a/libraries/python/skills/skill-library/skill_library/run_context.py b/libraries/python/skills/skill-library/skill_library/run_context.py
new file mode 100644
index 00000000..f407ff10
--- /dev/null
+++ b/libraries/python/skills/skill-library/skill_library/run_context.py
@@ -0,0 +1,74 @@
+import logging
+from os import PathLike
+from typing import Any, Callable, Coroutine, Optional
+from uuid import uuid4
+
+from assistant_drive import Drive, DriveConfig, IfDriveFileExistsBehavior
+from context import ContextProtocol
+from events.events import EventProtocol
+
+from .routine_stack import RoutineStack
+
+
+class LogEmitter:
+ """A simple event emitter that logs the event to the console. This will be
+ what is used unless a different emitter is provided."""
+
+ def emit(self, event: EventProtocol) -> None:
+ logging.info(event.to_json())
+
+
+class RunContext(ContextProtocol):
+ def __init__(
+ self,
+ session_id: str,
+ assistant_drive: Drive,
+ emit: Callable[[EventProtocol], None],
+ run_routine: Callable[
+ ["RunContext", str, Optional[dict[str, Any]]], Coroutine[Any, Any, Any]
+ ],
+ drive_root: PathLike | None = None,
+ ) -> None:
+ # A session id is useful for maintaining consistent session state across all
+ # consumers of this context. For example, a session id can be set in an
+ # assistant and all functions called by that assistant can should receive
+ # this same context object to know which session is being used.
+ self.session_id: str = session_id or str(uuid4())
+
+ # The assistant drive is a drive object that can be used to read and
+ # write files to a particular location. The assistant drive should be
+ # used for assistant-specific data and not for general data storage.
+ self.assistant_drive: Drive = assistant_drive
+
+ # A "run" is a particular series of calls within a session. The initial call will
+ # set the run id and all subsequent calls will use the same run id. This is useful
+ # for logging, metrics, and debugging.
+ self.run_id: str = str(uuid4())
+
+ # The emit function is used to send events to the event bus. The component
+ # that creates this context object will be responsible for instantiating an
+ # event bus and handling the events sent to it with this function.
+ self.emit = emit
+
+ # A metadrive to be used for managing assistant metadata. This can be
+ # useful for storing session data, logs, and other information that
+ # needs to be persisted across different calls to the assistant.
+ self.metadrive: Drive = Drive(
+ DriveConfig(
+ root=drive_root or f".data/{session_id}/.assistant",
+ default_if_exists_behavior=IfDriveFileExistsBehavior.OVERWRITE,
+ )
+ )
+
+ # Functions for running routines.
+ self.run_routine = run_routine
+
+ # The routine stack is used to keep track of the current routine being
+ # run by the assistant.
+ self.routine_stack: RoutineStack = RoutineStack(self.metadrive)
+
+ # Helper functions for managing state of the current routine being run.
+ self.state = self.routine_stack.get_current_state
+ self.state_key = self.routine_stack.get_current_state_key
+ self.update_state = self.routine_stack.set_current_state
+ self.update_state_key = self.routine_stack.set_current_state_key
diff --git a/libraries/python/skills/skill-library/skill_library/skill.py b/libraries/python/skills/skill-library/skill_library/skill.py
index b6de3da1..ac6283cc 100644
--- a/libraries/python/skills/skill-library/skill_library/skill.py
+++ b/libraries/python/skills/skill-library/skill_library/skill.py
@@ -1,7 +1,6 @@
from typing import Any, Callable
from chat_driver import TEXT_RESPONSE_FORMAT, ChatDriver, ChatDriverConfig
-from context import Context
from events import BaseEvent
from function_registry import FunctionRegistry
from openai.types.chat.completion_create_params import ResponseFormat
@@ -19,7 +18,6 @@ def __init__(
self,
name: str,
description: str,
- context: Context,
chat_driver_config: ChatDriverConfig | None = None,
skill_actions: list[Callable] = [], # Functions to be registered as skill actions.
routines: list[RoutineTypes] = [],
@@ -28,7 +26,6 @@ def __init__(
# to them efficiently.
self.name = name
self.description = description
- self.context = context
self.routines: dict[str, RoutineTypes] = {routine.name: routine for routine in routines}
# The routines in this skill might use actions from other skills. The dependency on
@@ -44,8 +41,11 @@ def __init__(
# skill subclass).
self.chat_driver = ChatDriver(chat_driver_config) if chat_driver_config else None
+ # TODO: Configure up one of these separate from the chat driver.
+ self.openai_client = chat_driver_config.openai_client if chat_driver_config else None
+
# Register all provided actions with the action registry.
- self.action_registry = FunctionRegistry(context, skill_actions)
+ self.action_registry = FunctionRegistry(skill_actions)
# Also, register any commands provided by the chat driver. All
# commands will be available to the skill.
diff --git a/libraries/python/skills/skill-library/skill_library/skill_registry.py b/libraries/python/skills/skill-library/skill_library/skill_registry.py
index 04d9d948..01caeffd 100644
--- a/libraries/python/skills/skill-library/skill_library/skill_registry.py
+++ b/libraries/python/skills/skill-library/skill_library/skill_registry.py
@@ -1,9 +1,12 @@
-from .routine import RoutineTypes
-from .skill import Skill
-import os
import importlib
+import os
from typing import Any
+from .routine import FunctionRoutine, InstructionRoutine, ProgramRoutine, RoutineTypes
+from .routine_runners import FunctionRoutineRunner, InstructionRoutineRunner, ProgramRoutineRunner
+from .run_context import RunContext
+from .skill import Skill
+
class SkillRegistry:
"""
@@ -82,15 +85,79 @@ def has_routine(self, routine_name: str) -> bool:
return False
return skill.has_routine(routine)
- def get_routine(self, routine_name: str) -> tuple[Skill | None, RoutineTypes | None]:
+ def get_routine(self, routine_name: str) -> RoutineTypes | None:
"""
Get a routine by name.
"""
skill_name, routine = routine_name.split(".")
skill = self.get_skill(skill_name)
if not skill:
- return None, None
- return skill, skill.get_routine(routine)
+ return None
+ return skill.get_routine(routine)
+
+ async def run_routine_by_name(
+ self, context: RunContext, name: str, vars: dict[str, Any] | None = None
+ ) -> Any:
+ """
+ Run an assistant routine by name (.).
+ """
+ routine = self.get_routine(name)
+ if not routine:
+ raise ValueError(f"Routine {name} not found.")
+ response = await self.run_routine(context, routine, vars)
+ return response
+
+ async def run_routine(
+ self, context: RunContext, routine: RoutineTypes, vars: dict[str, Any] | None = None
+ ) -> Any:
+ """
+ Run an assistant routine. This is going to be much of the
+ magic of the assistant. Currently, is just runs through the
+ steps of a routine, but this will get much more sophisticated.
+ It will need to handle configuration, managing results of steps,
+ handling errors and retries, etc. ALso, this is where we will put
+ meta-cognitive functions such as having the assistant create a plan
+ from the routine and executing it dynamically while monitoring progress.
+ name = .
+ """
+ await context.routine_stack.push(routine.fullname())
+ match routine:
+ case InstructionRoutine():
+ runner = InstructionRoutineRunner()
+ done = await runner.run(context, routine, vars)
+ case ProgramRoutine():
+ runner = ProgramRoutineRunner()
+ done = await runner.run(context, routine, vars)
+ case FunctionRoutine():
+ runner = FunctionRoutineRunner()
+ done = await runner.run(context, routine, vars)
+ if done:
+ _ = await context.routine_stack.pop()
+
+ async def step_active_routine(self, context: RunContext, message: str) -> None:
+ """Run another step in the current routine."""
+ routine_frame = await context.routine_stack.peek()
+ if not routine_frame:
+ raise ValueError("No routine to run.")
+
+ routine = self.get_routine(routine_frame.name)
+ if not routine:
+ raise ValueError(f"Routine {routine_frame.name} not found.")
+
+ match routine:
+ case InstructionRoutine():
+ runner = InstructionRoutineRunner()
+ done = await runner.next(context, routine, message)
+ case ProgramRoutine():
+ runner = ProgramRoutineRunner()
+ done = await runner.next(context, routine, message)
+ case FunctionRoutine():
+ runner = FunctionRoutineRunner()
+ done = await runner.next(context, routine, message)
+
+ if done:
+ await context.routine_stack.pop()
+ # TODO: Manage return state for composition in parent steps.
SCRIPT_DIR = os.path.dirname(os.path.realpath(__file__))
diff --git a/libraries/python/skills/skill-library/skill_library/tests/test_routine_stack.py b/libraries/python/skills/skill-library/skill_library/tests/test_routine_stack.py
new file mode 100644
index 00000000..9dee2302
--- /dev/null
+++ b/libraries/python/skills/skill-library/skill_library/tests/test_routine_stack.py
@@ -0,0 +1,56 @@
+from assistant_drive import Drive, DriveConfig, IfDriveFileExistsBehavior
+from skill_library.routine_stack import RoutineStack
+
+
+async def test_routine_stack():
+ drive = Drive(
+ DriveConfig(
+ root=".data/test", default_if_exists_behavior=IfDriveFileExistsBehavior.OVERWRITE
+ )
+ )
+ stack = RoutineStack(drive)
+
+ await stack.clear()
+
+ # Test push
+ frame_id = await stack.push("test")
+ assert frame_id
+
+ # Get
+ frames = await stack.get()
+ assert frames
+ assert len(frames) == 1
+ assert frames[0].id == frame_id
+
+ # Test peek
+ frame = await stack.peek()
+ assert frame
+ assert frame.id == frame_id
+ assert frame.state == {}
+
+ # Test set_current_state_key
+ await stack.set_current_state_key("test", "value")
+ value = await stack.get_current_state_key("test")
+ assert value == "value"
+
+ # Test get_current_state
+ state = await stack.get_current_state()
+ assert state
+ assert state["test"] == "value"
+
+ # Test set_current_state
+ new_state = {"new": "state"}
+ await stack.set_current_state(new_state)
+ state = await stack.get_current_state()
+ assert state
+ assert state == new_state
+
+ # Test pop
+ frame = await stack.pop()
+ assert frame
+ assert frame.id == frame_id
+ assert await stack.length() == 0
+
+ # Test pop empty
+ frame = await stack.pop()
+ assert not frame
diff --git a/libraries/python/skills/skill-library/uv.lock b/libraries/python/skills/skill-library/uv.lock
index 0f99e553..176e66e7 100644
--- a/libraries/python/skills/skill-library/uv.lock
+++ b/libraries/python/skills/skill-library/uv.lock
@@ -5,6 +5,88 @@ resolution-markers = [
"python_full_version >= '3.13'",
]
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.4.3"
+source = { registry = "https://pypi.org/simple" }
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diff --git a/libraries/python/skills/skills/document-skill/document_skill/chat_drivers/__init__.py b/libraries/python/skills/skills/document-skill/document_skill/chat_drivers/__init__.py
new file mode 100644
index 00000000..58a4b776
--- /dev/null
+++ b/libraries/python/skills/skills/document-skill/document_skill/chat_drivers/__init__.py
@@ -0,0 +1,11 @@
+from .draft_content import draft_content
+from .draft_outline import draft_outline
+from .get_user_feedback_for_outline_decision import get_user_feedback_for_outline_decision
+from .get_user_feedback_for_page_decision import get_user_feedback_for_page_decision
+
+__all__ = [
+ "draft_content",
+ "draft_outline",
+ "get_user_feedback_for_outline_decision",
+ "get_user_feedback_for_page_decision",
+]
diff --git a/libraries/python/skills/skills/document-skill/document_skill/chat_drivers/draft_content.py b/libraries/python/skills/skills/document-skill/document_skill/chat_drivers/draft_content.py
new file mode 100644
index 00000000..5c6b39ad
--- /dev/null
+++ b/libraries/python/skills/skills/document-skill/document_skill/chat_drivers/draft_content.py
@@ -0,0 +1,78 @@
+from chat_driver import ChatDriver, ChatDriverConfig, ContextProtocol
+from chat_driver.in_memory_message_history_provider import InMemoryMessageHistoryProvider
+from chat_driver.message_formatter import liquid_format
+from openai import AsyncAzureOpenAI, AsyncOpenAI
+
+from ..document_skill import Outline, Paper
+
+
+async def draft_content(
+ context: ContextProtocol,
+ open_ai_client: AsyncOpenAI | AsyncAzureOpenAI,
+ chat_history: str,
+ attachments: list,
+ outline_versions: list[Outline] = [],
+ paper_versions: list[Paper] = [],
+ user_feedback: str | None = None,
+ decision: str | None = None,
+):
+ history = InMemoryMessageHistoryProvider(formatter=liquid_format)
+
+ if decision == "[ITERATE]":
+ history.append_system_message(
+ (
+ "Following the structure of the outline, iterate on the currently drafted page of the"
+ " document. It's more important to maintain an appropriately useful level of detail. "
+ " After this page is iterated upon, the system will follow up"
+ " and ask for the next page."
+ )
+ )
+
+ else:
+ history.append_system_message(
+ (
+ "Following the structure of the outline, create the content for the next (or first) page of the"
+ " document - don't try to create the entire document in one pass nor wrap it up too quickly, it will be a"
+ " multi-page document so just create the next page. It's more important to maintain"
+ " an appropriately useful level of detail. After this page is generated, the system will follow up"
+ " and ask for the next page. If you have already generated all the pages for the"
+ " document as defined in the outline, return empty content."
+ )
+ )
+
+ history.append_system_message("{{chat_history}}", {"chat_history": chat_history})
+
+ for item in attachments:
+ history.append_system_message(
+ "{{item.filename}}{{item.content}}",
+ {"item": item},
+ )
+
+ if outline_versions:
+ last_outline = outline_versions[-1]
+ history.append_system_message(
+ "{{last_outline}}", {"last_outline": last_outline}
+ )
+
+ if paper_versions:
+ if decision == "[ITERATE]" and user_feedback:
+ content = paper_versions[-1].contents[-1].content
+ history.append_system_message("{{content}}", {"content": content})
+ history.append_system_message(
+ "{{user_feedback}}", {"user_feedback": user_feedback}
+ )
+ else:
+ full_content = ""
+ for content in paper_versions[-1].contents:
+ full_content += content.content
+ history.append_system_message("{{content}}", {"content": content})
+
+ config = ChatDriverConfig(
+ context=context,
+ openai_client=open_ai_client,
+ model="gpt-3.5-turbo",
+ message_provider=history,
+ )
+
+ chat_driver = ChatDriver(config)
+ return await chat_driver.respond()
diff --git a/libraries/python/skills/skills/document-skill/document_skill/chat_drivers/draft_outline.py b/libraries/python/skills/skills/document-skill/document_skill/chat_drivers/draft_outline.py
new file mode 100644
index 00000000..96d7a7c5
--- /dev/null
+++ b/libraries/python/skills/skills/document-skill/document_skill/chat_drivers/draft_outline.py
@@ -0,0 +1,56 @@
+from chat_driver import ChatDriver, ChatDriverConfig, ContextProtocol
+from chat_driver.in_memory_message_history_provider import InMemoryMessageHistoryProvider
+from chat_driver.message_formatter import liquid_format
+from openai import AsyncAzureOpenAI, AsyncOpenAI
+
+from ..document_skill import Outline
+
+
+async def draft_outline(
+ context: ContextProtocol,
+ open_ai_client: AsyncOpenAI | AsyncAzureOpenAI,
+ chat_history: str,
+ attachments: list,
+ outline_versions: list[Outline] = [],
+ user_feedback: str | None = None,
+):
+ history = InMemoryMessageHistoryProvider(formatter=liquid_format)
+
+ history.append_system_message(
+ (
+ "Generate an outline for the document, including title. The outline should include the key points that will"
+ " be covered in the document. Consider the attachments, the rationale for why they were uploaded, and the"
+ " conversation that has taken place. The outline should be a hierarchical structure with multiple levels of"
+ " detail, and it should be clear and easy to understand. The outline should be generated in a way that is"
+ " consistent with the document that will be generated from it."
+ )
+ )
+
+ history.append_system_message("{{chat_history}}", {"chat_history": chat_history})
+
+ for item in attachments:
+ history.append_system_message(
+ "{{item.filename}}{{item.content}}",
+ {"item": item},
+ )
+
+ if len(outline_versions):
+ last_outline = outline_versions[-1]
+ history.append_system_message(
+ "{{last_outline}}", {"last_outline": last_outline}
+ )
+
+ if user_feedback is not None:
+ history.append_system_message(
+ "{{user_feedback}}", {"user_feedback": user_feedback}
+ )
+
+ config = ChatDriverConfig(
+ context=context,
+ openai_client=open_ai_client,
+ model="gpt-3.5-turbo",
+ message_provider=history,
+ )
+
+ chat_driver = ChatDriver(config)
+ return await chat_driver.respond()
diff --git a/libraries/python/skills/skills/document-skill/document_skill/chat_drivers/get_user_feedback_for_outline_decision.py b/libraries/python/skills/skills/document-skill/document_skill/chat_drivers/get_user_feedback_for_outline_decision.py
new file mode 100644
index 00000000..a8ef2fa6
--- /dev/null
+++ b/libraries/python/skills/skills/document-skill/document_skill/chat_drivers/get_user_feedback_for_outline_decision.py
@@ -0,0 +1,75 @@
+from chat_driver import ChatDriver, ChatDriverConfig, ContextProtocol
+from chat_driver.in_memory_message_history_provider import InMemoryMessageHistoryProvider
+from chat_driver.message_formatter import liquid_format
+from openai import AsyncAzureOpenAI, AsyncOpenAI
+
+from ..document_skill import Outline, Paper
+
+
+async def get_user_feedback_for_outline_decision(
+ context: ContextProtocol,
+ open_ai_client: AsyncOpenAI | AsyncAzureOpenAI,
+ chat_history: str,
+ outline_versions: list[Outline] = [],
+ paper_versions: list[Paper] = [],
+ user_feedback: str | None = None,
+ outline: bool = False,
+):
+ history = InMemoryMessageHistoryProvider(formatter=liquid_format)
+
+ if outline:
+ history.append_system_message(
+ (
+ "Use the user's most recent feedback to determine if the user wants to iterate further on the"
+ " provided outline [ITERATE], or if the user is ready to move on to drafting a paper from the"
+ " provided outline [CONTINUE]. Based on the user's feedback on the provided outline, determine if"
+ " the user wants to [ITERATE], [CONTINUE], or [QUIT]. Reply with ONLY [ITERATE], [CONTINUE], or [QUIT]."
+ )
+ )
+ else:
+ history.append_system_message(
+ (
+ "You are an AI assistant that helps draft outlines for a future flushed-out document."
+ " You use the user's most recent feedback to determine if the user wants to iterate further on the"
+ " provided draft content [ITERATE], or if the user is ready to move on to drafting new additional content"
+ " [CONTINUE]. Based on the user's feedback on the provided drafted content, determine if"
+ " the user wants to [ITERATE], [CONTINUE], or [QUIT]. Reply with ONLY [ITERATE], [CONTINUE], or [QUIT]."
+ )
+ )
+
+ history.append_system_message("{{chat_history}}", {"chat_history": chat_history})
+
+ if len(outline_versions):
+ last_outline_content = outline_versions[-1].content
+ if outline:
+ history.append_system_message(
+ "{{outline}}", {"outline": last_outline_content}
+ )
+ else:
+ history.append_system_message(
+ "{{outline}}", {"outline": last_outline_content}
+ )
+
+ if not outline:
+ if paper_versions:
+ full_paper_content = ""
+ for content in paper_versions[-1].contents:
+ full_paper_content += content.content
+ history.append_system_message(
+ "{{content}}", {"content": full_paper_content}
+ )
+
+ if user_feedback is not None:
+ history.append_system_message(
+ "{{user_feedback}}", {"user_feedback": user_feedback}
+ )
+
+ config = ChatDriverConfig(
+ context=context,
+ openai_client=open_ai_client,
+ model="gpt-3.5-turbo",
+ message_provider=history,
+ )
+
+ chat_driver = ChatDriver(config)
+ return await chat_driver.respond()
diff --git a/libraries/python/skills/skills/document-skill/document_skill/chat_drivers/get_user_feedback_for_page_decision.py b/libraries/python/skills/skills/document-skill/document_skill/chat_drivers/get_user_feedback_for_page_decision.py
new file mode 100644
index 00000000..7a10e00d
--- /dev/null
+++ b/libraries/python/skills/skills/document-skill/document_skill/chat_drivers/get_user_feedback_for_page_decision.py
@@ -0,0 +1,75 @@
+from chat_driver import ChatDriver, ChatDriverConfig, ContextProtocol
+from chat_driver.in_memory_message_history_provider import InMemoryMessageHistoryProvider
+from chat_driver.message_formatter import liquid_format
+from openai import AsyncAzureOpenAI, AsyncOpenAI
+
+from ..document_skill import Outline, Paper
+
+
+async def get_user_feedback_for_page_decision(
+ context: ContextProtocol,
+ open_ai_client: AsyncOpenAI | AsyncAzureOpenAI,
+ chat_history: str,
+ outline_versions: list[Outline] = [],
+ paper_versions: list[Paper] = [],
+ user_feedback: str | None = None,
+ outline: bool = False,
+):
+ history = InMemoryMessageHistoryProvider(formatter=liquid_format)
+
+ if outline:
+ history.append_system_message(
+ (
+ "Use the user's most recent feedback to determine if the user wants to iterate further on the"
+ " provided outline [ITERATE], or if the user is ready to move on to drafting a paper from the"
+ " provided outline [CONTINUE]. Based on the user's feedback on the provided outline, determine if"
+ " the user wants to [ITERATE], [CONTINUE], or [QUIT]. Reply with ONLY [ITERATE], [CONTINUE], or [QUIT]."
+ )
+ )
+ else:
+ history.append_system_message(
+ (
+ "You are an AI assistant that helps draft outlines for a future flushed-out document."
+ " You use the user's most recent feedback to determine if the user wants to iterate further on the"
+ " provided draft content [ITERATE], or if the user is ready to move on to drafting new additional content"
+ " [CONTINUE]. Based on the user's feedback on the provided drafted content, determine if"
+ " the user wants to [ITERATE], [CONTINUE], or [QUIT]. Reply with ONLY [ITERATE], [CONTINUE], or [QUIT]."
+ )
+ )
+
+ history.append_system_message("{{chat_history}}", {"chat_history": chat_history})
+
+ if len(outline_versions):
+ last_outline_content = outline_versions[-1].content
+ if outline:
+ history.append_system_message(
+ "{{outline}}", {"outline": last_outline_content}
+ )
+ else:
+ history.append_system_message(
+ "{{outline}}", {"outline": last_outline_content}
+ )
+
+ if not outline:
+ if paper_versions:
+ full_paper_content = ""
+ for content in paper_versions[-1].contents:
+ full_paper_content += content.content
+ history.append_system_message(
+ "{{content}}", {"content": full_paper_content}
+ )
+
+ if user_feedback is not None:
+ history.append_system_message(
+ "{{user_feedback}}", {"user_feedback": user_feedback}
+ )
+
+ config = ChatDriverConfig(
+ context=context,
+ openai_client=open_ai_client,
+ model="gpt-3.5-turbo",
+ message_provider=history,
+ )
+
+ chat_driver = ChatDriver(config)
+ return await chat_driver.respond()
diff --git a/libraries/python/skills/skills/document-skill/document_skill/document_skill.py b/libraries/python/skills/skills/document-skill/document_skill/document_skill.py
index b57d8f21..7e17b9e4 100644
--- a/libraries/python/skills/skills/document-skill/document_skill/document_skill.py
+++ b/libraries/python/skills/skills/document-skill/document_skill/document_skill.py
@@ -1,9 +1,11 @@
-from chat_driver import ChatDriverConfig
+from chat_driver import ChatDriverConfig, ContextProtocol
from context import Context
from pydantic import BaseModel # temp to have something to experiment with
from skill_library import RoutineTypes, Skill
from skill_library.routine import InstructionRoutine, ProgramRoutine
+from .chat_drivers import draft_content, draft_outline, get_user_feedback_decision
+
NAME = "document_skill"
DESCRIPTION = "Anything related to documents - creation, edit, translation"
@@ -28,15 +30,14 @@ class Paper(BaseModel):
contents: list[Content] = []
-class DocumentSkillContext(Context):
- def __init__(self) -> None:
+class DocumentSkillContext:
+ def __init__(self, context: ContextProtocol) -> None:
+ # TODO: Pull in all this state from a Drive.
self.chat_history: str = ""
self.attachments_list: list[Attachment] = []
self.outline_versions: list[Outline] = []
self.paper_versions: list[Paper] = []
- super().__init__() # currently all params of Context can be None.
-
class DocumentSkill(Skill):
def __init__(
@@ -44,35 +45,20 @@ def __init__(
context: Context,
chat_driver_config: ChatDriverConfig,
) -> None:
- self.document_skill_context: DocumentSkillContext = DocumentSkillContext()
+ self.document_skill_context: DocumentSkillContext = DocumentSkillContext(context)
actions = [
- self.test_action,
+ self.ask_user,
+ self.draft_content,
+ self.draft_outline,
+ self.get_user_feedback_decision,
]
- # however, with current skill chat driver config (no chat connection setup),
- # i see an issue when running the instruction routine as a command. It correctly detects the
- # test_action step to run... but the I get the response: "Error running function: 'NoneType' object has no attribute 'chat'"
- # it looks like information events after actions run in posix command are passed through a chat driver with model
- # connectivity... as it explains the result in a natural language response wrapper (not the simple result of directly calling each command).
- # i'm wondering if this is where the skill chat driver gets used as a chat connection?
- # YES. look at instruction_routine_runner. This is how it is designed currently.
- # i.r.r. will use the configured chat driver of the routine's skill to call "respond" with message being the "step" (the function in the routine)...
- # but this means it is passed to the language model with the tool choices (assuming the action/function has also been registered as a function not just
- # as a command... so that it can be included in the tool selection for the skill's chat driver to pass to the language model). Our code will execute (code in the
- # chat driver... i think... i need to look into chat driver code more...) but the wrapped response out of the respond function is returned and messaged
- # out as an information event.
- # so big Q here... should a program routine or instruction routine be run by a language model? I'm not sure I want this... can we have an option?
routines: list[RoutineTypes] = [
self.test_instruction_routine(),
]
# Configure the skill's chat driver.
- #####
- # EVEN IF, the skill will not use the chat driver for any llm call, right now
- # the skill's chat driver is used to forward available skill commands and functions to
- # any higher level assistant that has those skills registered.
- #####
chat_driver_config.commands = actions
chat_driver_config.functions = actions
@@ -97,6 +83,7 @@ def test_instruction_routine(self) -> InstructionRoutine:
name="test_instruction_routine",
description="Description of what the routine does.",
routine=("test_action"),
+ skill=self,
)
def template_example_routine(self) -> ProgramRoutine:
@@ -107,308 +94,97 @@ def template_example_routine(self) -> ProgramRoutine:
name="template_example_routine",
description="Description of what the routine does.",
program=("TBD"),
+ skill=self,
)
##################################
# Actions
##################################
- def test_action(
+ async def draft_content(
self,
- context: Context,
+ context: ContextProtocol,
+ decision: str | None = None,
+ user_feedback: str | None = None,
) -> str:
- return "hello world" #
-
- # This may not be needed as an action... not sure yet how context will be set.
+ response = await draft_content(
+ context=context,
+ open_ai_client=self.open_ai_client,
+ chat_history=self.document_skill_context.chat_history,
+ attachments=self.document_skill_context.attachments_list,
+ outline_versions=self.document_skill_context.outline_versions,
+ paper_versions=self.document_skill_context.paper_versions,
+ user_feedback=user_feedback,
+ decision=decision,
+ )
+ return response.message or ""
+ async def draft_outline(
+ self, context: ContextProtocol, decision: str | None = None, user_feedback: str | None = None
+ ) -> str:
+ response = await draft_outline(
+ context=context,
+ open_ai_client=self.open_ai_client,
+ chat_history=self.document_skill_context.chat_history,
+ attachments=self.document_skill_context.attachments_list,
+ outline_versions=self.document_skill_context.outline_versions,
+ user_feedback=user_feedback,
+ )
+ return response.message or ""
-# def set_context(
-# self,
-# context: Context,
-# chat_history: str,
-# attachments_list: list[Attachment]
-# ) -> None:
-# self.context.chat_history = chat_history
-# self.context.attachments_list = attachments_list
-#
-# async def draft_outline(
-# self,
-# context: Context,
-# openai_client: AsyncOpenAI,
-# model: str,
-# user_feedback: str | None = None
-# ) -> None:
-#
-# # construct completion request - draft outline from existing info
-# messages: list[ChatCompletionMessageParam] = []
-# _add_main_system_message(messages, draft_outline_main_system_message)
-# _add_chat_history_system_message(messages, self.context.chat_history)
-# _add_attachments_system_message(messages, self.context.attachments_list)
-#
-# if len(self.context.outline_versions) != 0:
-# _add_existing_outline_system_message(messages, self.context.outline_versions[-1].content)
-#
-# if user_feedback is not None:
-# _add_user_feedback_system_message(messages, user_feedback)
-#
-# completion_args = {
-# "messages": messages,
-# "model": model,
-# "response_format": {
-# "type": "text"
-# }
-# }
-# completion = await openai_client.chat.completions.create(**completion_args)
-# outline = completion.choices[0].message.content
-#
-# debug_log = json.dumps(completion_args, indent=2) + "\n\n"
-# debug_log += f"Response:\n{completion.model_dump_json(indent=2)}\n\n"
-# #print(debug_log)
-#
-# # message event should be sent from within this action. For now using stdout.
-# chat_output = f"\nAssistant: Outline drafted\n\n{outline}\n"
-# sys.stdout.write(chat_output)
-# sys.stdout.flush()
-#
-# # store generated outline
-# version_no = len(self.context.outline_versions) + 1
-# self.context.outline_versions.append(Outline(version=version_no, content=outline))
-#
-# # update chat_history.
-# self.context.chat_history = self.context.chat_history + chat_output
-# return
-#
-# async def draft_content(
-# self,
-# context: Context,
-# openai_client: AsyncOpenAI,
-# model: str,
-# user_feedback: str | None = None,
-# decision: str | None = None
-# ) -> None:
-# # construct completion request - draft content/page/fragment from existing info
-# messages: list[ChatCompletionMessageParam] = []
-#
-# if decision == "[ITERATE]":
-# _add_main_system_message(messages, draft_page_iterate_main_system_message)
-# else:
-# _add_main_system_message(messages, draft_page_continue_main_system_message)
-#
-# _add_chat_history_system_message(messages, self.context.chat_history)
-# _add_attachments_system_message(messages, self.context.attachments_list)
-# _add_approved_outline_system_message(messages, self.context.outline_versions[-1].content)
-#
-# if len(self.context.paper_versions) != 0:
-# if decision == "[ITERATE]" and user_feedback is not None:
-# _add_existing_content_system_message(messages, self.context.paper_versions[-1].contents[-1].content)
-# _add_user_feedback_system_message(messages, user_feedback)
-# else:
-# full_content = ""
-# for content in self.context.paper_versions[-1].contents:
-# full_content += content.content
-# _add_existing_content_system_message(messages, full_content)
-#
-# completion_args = {
-# "messages": messages,
-# "model": model,
-# "response_format": {
-# "type": "text"
-# }
-# }
-# completion = await openai_client.chat.completions.create(**completion_args)
-# draft = completion.choices[0].message.content
-#
-# debug_log = json.dumps(completion_args, indent=2) + "\n\n"
-# debug_log += f"Response:\n{completion.model_dump_json(indent=2)}\n\n"
-# #print(debug_log)
-#
-# # message event should be sent from within this action. For now using stdout.
-# if decision == "[ITERATE]":
-# chat_output = f"\nAssistant: Updated page based on your feedback:\n\n{draft}\n"
-# else:
-# chat_output = f"\nAssistant: Page drafted:\n\n{draft}\n"
-# sys.stdout.write(chat_output)
-# sys.stdout.flush()
-#
-# # store generated content/page/fragment
-# version_no = len(self.context.paper_versions) + 1
-# if len(self.context.paper_versions) == 0:
-# self.context.paper_versions.append(Paper(version=version_no, contents=[Content(content=draft)]))
-# else:
-# existing_contents = self.context.paper_versions[-1].contents
-# if decision == "[ITERATE]":
-# existing_contents[-1].content = draft
-# else:
-# existing_contents.append(Content(content=draft))
-# self.context.paper_versions.append(Paper(version=version_no, contents=existing_contents))
-#
-# # update chat_history.
-# self.context.chat_history = self.context.chat_history + chat_output
-# return
-#
-# async def get_user_feedback(
-# self,
-# context: Context,
-# openai_client: AsyncOpenAI,
-# model: str,
-# outline: bool
-# ) -> tuple[str, str]:
-#
-# # message event should be sent from within this action. For now using stdout.
-# event_message = ("Please review the above draft and provide me any feedback. You can also "
-# "let me know if you are ready to continue drafting the paper or would like"
-# " to iterate on the current content.")
-# # msg_event = MessageEvent(message=event_message)
-# chat_output = f"\nAssistant: {event_message}\n"
-# sys.stdout.write(chat_output)
-# sys.stdout.flush()
-# self.context.chat_history = self.context.chat_history + chat_output
-#
-# #user response input should be awaited for here from interface. For now using stdout.
-# sys.stdout.write("\nUser: ")
-# user_feedback = input()
-# sys.stdout.write(user_feedback)
-# sys.stdout.flush()
-# chat_output = f"\nUser: {user_feedback}"
-# self.context.chat_history = self.context.chat_history + chat_output
-#
-# # construct completion request - determine user intent from feedback
-# messages: list[ChatCompletionMessageParam] = []
-# if outline is True:
-# _add_main_system_message(messages, outline_feedback_main_system_message)
-# else:
-# _add_main_system_message(messages, draft_page_feedback_main_system_message)
-#
-# _add_chat_history_system_message(messages, self.context.chat_history)
-#
-# if len(self.context.outline_versions) != 0:
-# if outline is True:
-# _add_existing_outline_system_message(messages, self.context.outline_versions[-1].content)
-# else:
-# _add_approved_outline_system_message(messages, self.context.outline_versions[-1].content)
-#
-# if outline is False:
-# if len(self.context.paper_versions) != 0:
-# full_content = ""
-# for content in self.context.paper_versions[-1].contents:
-# full_content += content.content
-# _add_existing_content_system_message(messages, full_content)
-#
-# _add_user_feedback_system_message(messages, user_feedback)
-#
-# completion_args = {
-# "messages": messages,
-# "model": model,
-# "response_format": {
-# "type": "text"
-# }
-# }
-# completion = await openai_client.chat.completions.create(**completion_args)
-# decision = completion.choices[0].message.content # this should NOT be done as a string.
-#
-# debug_log = json.dumps(completion_args, indent=2) + "\n\n"
-# debug_log += f"Response:\n{completion.model_dump_json(indent=2)}\n\n"
-# #print(debug_log)
-#
-# return decision, user_feedback
-#
-# # A little helper
-# def print_final_doc(self, context: Context) -> None:
-# print("Assistant: Looks like we have a completed doc. Here it is!\n")
-# # compose full content for existing paper (the latest version)
-# full_content = ""
-# for content in self.context.paper_versions[-1].contents:
-# full_content += content.content
-# print(full_content)
-#
-#
-## Current approach uses system messages for everything.
-# draft_outline_main_system_message = ("Generate an outline for the document, including title. The outline should include the key points that will"
-# " be covered in the document. Consider the attachments, the rationale for why they were uploaded, and the"
-# " conversation that has taken place. The outline should be a hierarchical structure with multiple levels of"
-# " detail, and it should be clear and easy to understand. The outline should be generated in a way that is"
-# " consistent with the document that will be generated from it.")
-# #("You are an AI assistant that helps draft outlines for a future flushed-out document."
-# # " You use information from a chat history between a user and an assistant, a prior version of a draft"
-# # " outline if it exists, as well as any other attachments provided by the user to inform a newly revised "
-# # "outline draft. Provide ONLY any outline. Provide no further instructions to the user.")
-#
-# outline_feedback_main_system_message = ("Use the user's most recent feedback to determine if the user wants to iterate further on the"
-# " provided outline [ITERATE], or if the user is ready to move on to drafting a paper from the"
-# " provided outline [CONTINUE]. Based on the user's feedback on the provided outline, determine if"
-# " the user wants to [ITERATE], [CONTINUE], or [QUIT]. Reply with ONLY [ITERATE], [CONTINUE], or [QUIT].")
-#
-# draft_page_continue_main_system_message = ("Following the structure of the outline, create the content for the next (or first) page of the"
-# " document - don't try to create the entire document in one pass nor wrap it up too quickly, it will be a"
-# " multi-page document so just create the next page. It's more important to maintain"
-# " an appropriately useful level of detail. After this page is generated, the system will follow up"
-# " and ask for the next page. If you have already generated all the pages for the"
-# " document as defined in the outline, return empty content.")
-# #("You are an AI assistant that helps draft new content of a document based on an outline."
-# # " You use information from a chat history between a user and an assistant, the approved outline from the user,"
-# # "and an existing version of drafted content if it exists, as well as any other attachments provided by the user to inform newly revised "
-# # "content. Newly drafted content does not need to cover the entire outline. Instead it should be limited to a reasonable 100 lines of natural language"
-# # " or subsection of the outline (which ever is shorter). The newly drafted content should be written as to append to any existing drafted content."
-# # " This way the user can review newly drafted content as a subset of the future full document and not be overwhelmed."
-# # "Only provide the newly drafted content. Provide no further instructions to the user.")
-#
-# draft_page_iterate_main_system_message = ("Following the structure of the outline, iterate on the currently drafted page of the"
-# " document. It's more important to maintain"
-# " an appropriately useful level of detail. After this page is iterated upon, the system will follow up"
-# " and ask for the next page.")
-#
-# draft_page_feedback_main_system_message = ("You are an AI assistant that helps draft outlines for a future flushed-out document."
-# " You use the user's most recent feedback to determine if the user wants to iterate further on the"
-# " provided draft content [ITERATE], or if the user is ready to move on to drafting new additional content"
-# " [CONTINUE]. Based on the user's feedback on the provided drafted content, determine if"
-# " the user wants to [ITERATE], [CONTINUE], or [QUIT]. Reply with ONLY [ITERATE], [CONTINUE], or [QUIT].")
-#
-# def _add_main_system_message(messages: list[ChatCompletionMessageParam], prompt: str) -> None:
-# message: ChatCompletionSystemMessageParam = {
-# "role": "system",
-# "content": prompt
-# }
-# messages.append(message)
-#
-# def _add_chat_history_system_message(messages: list[ChatCompletionMessageParam], chat_history: str) -> None:
-# message: ChatCompletionSystemMessageParam = {
-# "role": "system",
-# "content": f"{chat_history}"
-# }
-# messages.append(message)
-#
-# def _add_attachments_system_message(messages: list[ChatCompletionMessageParam], attachments: list[Attachment]) -> None:
-# for item in attachments:
-# message: ChatCompletionSystemMessageParam = {
-# "role": "system",
-# "content": (f"{item.filename}{item.content}")
-# }
-# messages.append(message)
-#
-# def _add_existing_outline_system_message(messages: list[ChatCompletionMessageParam], outline: str) -> None:
-# message: ChatCompletionSystemMessageParam = {
-# "role": "system",
-# "content": (f"{outline}")
-# }
-# messages.append(message)
-#
-# def _add_approved_outline_system_message(messages: list[ChatCompletionMessageParam], outline: str) -> None:
-# message: ChatCompletionSystemMessageParam = {
-# "role": "system",
-# "content": (f"{outline}")
-# }
-# messages.append(message)
-#
-# def _add_existing_content_system_message(messages: list[ChatCompletionMessageParam], content: str) -> None:
-# message: ChatCompletionSystemMessageParam = {
-# "role": "system",
-# "content": (f"{content}")
-# }
-# messages.append(message)
-#
-# def _add_user_feedback_system_message(messages: list[ChatCompletionMessageParam], user_feedback: str) -> None:
-# message: ChatCompletionSystemMessageParam = {
-# "role": "system",
-# "content": (f"{user_feedback}")
-# }
-# messages.append(message)
-#
+ async def get_user_feedback_decision(self, context: ContextProtocol, user_feedback: str, outline: bool) -> str:
+ response = await get_user_feedback_decision(
+ context=context,
+ open_ai_client=self.open_ai_client,
+ chat_history=self.document_skill_context.chat_history,
+ attachments=self.document_skill_context.attachments_list,
+ outline_versions=self.document_skill_context.outline_versions,
+ paper_versions=self.document_skill_context.paper_versions,
+ user_feedback=user_feedback,
+ outline=outline,
+ )
+ return response.message or ""
+
+ async def routine(self, context: ContextProtocol):
+ # Define these vars here to make the following routine look more like a PROGRAM routine.
+ document_skill = self
+
+ async def ask_user(question: str) -> str:
+ return "hello world"
+
+ # Routine.
+ decision = "[ITERATE]"
+ while decision == "[ITERATE]":
+ await document_skill.draft_outline(context, user_feedback=user_feedback)
+ user_feedback = await ask_user("This look good?")
+ decision = await document_skill.get_user_feedback_decision(context, user_feedback, outline=True)
+ if decision == "[QUIT]":
+ exit()
+ await document_skill.draft_content(context)
+ user_feedback = await ask_user("This look good?")
+ decision = await document_skill.get_user_feedback_decision(context, user_feedback, outline=False)
+ while decision != "[QUIT]":
+ content = await document_skill.draft_content(
+ context, user_feedback=user_feedback, chat_history=[], decision=decision
+ )
+ decision, user_feedback = await document_skill.get_user_feedback_decision(
+ context, user_feedback, outline=False
+ )
+ return content
+
+ # decision = "[ITERATE]"
+ # while decision == "[ITERATE]":
+ # document_skill.draft_outline(user_feedback=user_feedback, history=assistant.chat_history)
+ # guided_conversation.run()
+ # user_feedback = await ask_user("This look good?")
+ # decision = document_skill.get_user_feedback_decision(user_feedback, outline=True)
+ # if decision == "[QUIT]":
+ # exit()
+ # document_skill.draft_content()
+ # user_feedback = ask_user("This look good?")
+ # decision = document_skill.get_user_feedback_decision(user_feedback, outline=False)
+ # while decision != "[QUIT]":
+ # content = document_skill.draft_content(user_feedback=user_feedback, decision=decision)
+ # decision, user_feedback = await document_skill.get_user_feedback_decision(
+ # context, user_feedback, outline=False
+ # )
+ # return content
diff --git a/libraries/python/skills/skills/document-skill/uv.lock b/libraries/python/skills/skills/document-skill/uv.lock
index d35ba0be..2a8ddd7d 100644
--- a/libraries/python/skills/skills/document-skill/uv.lock
+++ b/libraries/python/skills/skills/document-skill/uv.lock
@@ -5,6 +5,88 @@ resolution-markers = [
"python_full_version >= '3.13'",
]
+[[package]]
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+version = "2.4.3"
+source = { registry = "https://pypi.org/simple" }
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+wheels = [
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+]
+
+[[package]]
+name = "aiohttp"
+version = "3.10.10"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "aiohappyeyeballs" },
+ { name = "aiosignal" },
+ { name = "attrs" },
+ { name = "frozenlist" },
+ { name = "multidict" },
+ { name = "yarl" },
+]
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diff --git a/libraries/python/skills/skills/form-filler-skill/.vscode/settings.json b/libraries/python/skills/skills/form-filler-skill/.vscode/settings.json
new file mode 100644
index 00000000..7177a8e5
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/.vscode/settings.json
@@ -0,0 +1,57 @@
+{
+ "editor.bracketPairColorization.enabled": true,
+ "editor.codeActionsOnSave": {
+ "source.fixAll": "always",
+ "source.organizeImports": "always"
+ },
+ "editor.defaultFormatter": "esbenp.prettier-vscode",
+ "editor.formatOnPaste": true,
+ "editor.formatOnSave": true,
+ "editor.formatOnType": true,
+ "editor.guides.bracketPairs": "active",
+ "files.eol": "\n",
+ "files.trimTrailingWhitespace": true,
+ "flake8.ignorePatterns": ["**/*.py"], // disable flake8 in favor of ruff
+ "jupyter.debugJustMyCode": false,
+ "python.analysis.autoFormatStrings": true,
+ "python.analysis.autoImportCompletions": true,
+ "python.analysis.diagnosticMode": "workspace",
+ "python.analysis.fixAll": ["source.unusedImports"],
+ "python.analysis.inlayHints.functionReturnTypes": true,
+ "python.analysis.typeCheckingMode": "basic",
+ "python.defaultInterpreterPath": "${workspaceFolder}/.venv/bin/python",
+ "python.testing.cwd": "${workspaceFolder}",
+ "search.exclude": {
+ "**/.venv": true,
+ "**/data": true
+ },
+ "[json]": {
+ "editor.defaultFormatter": "esbenp.prettier-vscode",
+ "editor.formatOnSave": true
+ },
+ "[jsonc]": {
+ "editor.defaultFormatter": "esbenp.prettier-vscode",
+ "editor.formatOnSave": true
+ },
+ "[python]": {
+ "editor.defaultFormatter": "charliermarsh.ruff",
+ "editor.formatOnSave": true,
+ "editor.codeActionsOnSave": {
+ "source.fixAll": "explicit",
+ "source.unusedImports": "explicit",
+ "source.organizeImports": "explicit",
+ "source.formatDocument": "explicit"
+ }
+ },
+ "ruff.nativeServer": "on",
+ "cSpell.words": [
+ "dotenv",
+ "httpx",
+ "openai",
+ "posix",
+ "pydantic",
+ "pypdf",
+ "runtimes",
+ "tiktoken"
+ ]
+}
diff --git a/libraries/python/skills/skills/form-filler-skill/Makefile b/libraries/python/skills/skills/form-filler-skill/Makefile
new file mode 100644
index 00000000..1ad4520a
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/Makefile
@@ -0,0 +1,2 @@
+repo_root = $(shell git rev-parse --show-toplevel)
+include $(repo_root)/tools/makefiles/python.mk
diff --git a/libraries/python/skills/skills/form-filler-skill/README.md b/libraries/python/skills/skills/form-filler-skill/README.md
new file mode 100644
index 00000000..5e19c5bd
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/README.md
@@ -0,0 +1,7 @@
+# Posix Skill
+
+This is a skill that can be registered to an assistant in the [Skill Library](../../skill-library/README.md).
+
+This skill has been primarily created for demonstration purposes and you can see it in action in [this notebook](../../notebooks/notebooks/skills.ipynb).
+
+This skill gives your assistant the capability for running posix-style actions and routines to manage a filesystem.
diff --git a/libraries/python/skills/skills/form-filler-skill/form_filler_skill/__init__.py b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/__init__.py
new file mode 100644
index 00000000..451e61dd
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/__init__.py
@@ -0,0 +1,3 @@
+from form_filler_skill.form_filler_skill import FormFillerSkill
+
+export = FormFillerSkill
diff --git a/libraries/python/skills/skills/form-filler-skill/form_filler_skill/agenda.py b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/agenda.py
new file mode 100644
index 00000000..9d5623e9
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/agenda.py
@@ -0,0 +1,24 @@
+import logging
+
+from pydantic import Field
+
+from form_filler_skill.base_model_llm import BaseModelLLM
+from form_filler_skill.resources import (
+ ResourceConstraintMode,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class AgendaItem(BaseModelLLM):
+ title: str = Field(description="Brief description of the item")
+ resource: int = Field(description="Number of turns required for the item")
+
+
+class Agenda(BaseModelLLM):
+ resource_constraint_mode: ResourceConstraintMode | None = Field(default=None)
+ max_agenda_retries: int = Field(default=2)
+ items: list[AgendaItem] = Field(
+ description="Ordered list of items to be completed in the remainder of the conversation",
+ default_factory=list,
+ )
diff --git a/libraries/python/skills/skills/form-filler-skill/form_filler_skill/artifact.py b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/artifact.py
new file mode 100644
index 00000000..44ef224f
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/artifact.py
@@ -0,0 +1,463 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+
+import json
+import logging
+from typing import Any, Literal, TypeVar, Union, get_args, get_origin, get_type_hints
+
+from pydantic import BaseModel, ValidationError, create_model
+
+from form_filler_skill.base_model_llm import BaseModelLLM
+
+
+from .chat_drivers.fix_artifact_error import fix_artifact_error
+from .message import Conversation, ConversationMessageType, Message
+
+logger = logging.getLogger(__name__)
+
+
+class Artifact:
+ """The Artifact plugin takes in a Pydantic base model, and robustly handles updating the fields of the model
+ A typical use case is as a form an agent must complete throughout a conversation.
+ Another use case is as a working memory for the agent.
+
+ The primary interface is update_artifact, which takes in the field_name to update and its new value.
+ Additionally, the chat_history is passed in to help the agent make informed decisions in case an error occurs.
+
+ The Artifact also exposes several functions to access internal state:
+ get_artifact_for_prompt, get_schema_for_prompt, and get_failed_fields.
+ """
+
+ def __init__(self, input_artifact: BaseModel, max_artifact_field_retries: int = 2) -> None:
+ """
+ Initialize the Artifact plugin with the given Pydantic base model.
+
+ Args:
+ input_artifact (BaseModel): The Pydantic base model to use as the artifact
+ max_artifact_field_retries (int): The maximum number of times to retry updating a field in the artifact
+ """
+
+ self.max_artifact_field_retries = max_artifact_field_retries
+
+ self.original_schema = input_artifact.model_json_schema()
+
+ # Create a new artifact model based on the one provided by the user with
+ # "Unanswered" set for all fields.
+ modified_classes = self._modify_classes(input_artifact)
+ self.artifact = self._modify_base_artifact(input_artifact, modified_classes)()
+
+ # failed_artifact_fields maps a field name to a list of the history of
+ # the failed attempts to update it.
+ # dict: key = field, value = list of tuple[attempt, error message]
+ self.failed_artifact_fields: dict[str, list[tuple[str, str]]] = {}
+
+ async def update_artifact(self, field_name: str, field_value: Any) -> tuple[bool, Conversation]:
+ """
+ The core interface for the Artifact plugin. This function will attempt
+ to update the given field_name to the given field_value. If the
+ field_value fails Pydantic validation, an LLM will determine one of two
+ actions to take.
+
+ Given the conversation as additional context the two actions are:
+ - Retry the update the artifact by fixing the formatting using the
+ previous failed attempts as guidance
+ - Take no action or in other words, resume the conversation to ask
+ the user for more information because the user gave incomplete or
+ incorrect information
+
+ Args:
+ field_name (str): The name of the field to update in the artifact
+ field_value (Any): The value to set the field to conversation
+ (Conversation): The conversation object that contains the history of
+ the conversation
+
+ Returns:
+ A tuple with two fields: a boolean indicating
+ success and a list of conversation messages that may have been
+ generated.
+
+ Several outcomes can happen:
+
+ - The update may have failed due to:
+ - A field_name that is not valid in the artifact.
+ - The field_value failing Pydantic validation and all retries
+ failed.
+ - The model failed to correctly call a tool.
+ In this case, the boolean will be False and the list may contain
+ a message indicating the failure.
+
+ - The agent may have successfully updated the artifact or fixed it.
+ In this case, the boolean will be True and the list will contain
+ a message indicating the update and possibly intermediate
+ messages.
+
+ - The agent may have decided to resume the conversation.
+ In this case, the boolean will be True and the messages may only
+ contain messages indicated previous errors.
+ """
+
+ conversation = Conversation()
+
+ # Check if the field name is valid, and return with a failure message if
+ # not.
+ is_valid_field, msg = self._is_valid_field(field_name)
+ if not is_valid_field:
+ if msg is not None:
+ conversation.messages.append(msg)
+ return False, conversation
+
+ # Try to update the field, and handle any errors that occur until the
+ # field is successfully updated or skipped according to
+ # max_artifact_field_retries.
+ while True:
+ try:
+ # Check if there have been too many previous failed attempts to
+ # update the field.
+ if (
+ len(self.failed_artifact_fields.get(field_name, []))
+ >= self.max_artifact_field_retries
+ ):
+ logger.warning(
+ f"Updating field {field_name} has failed too many times. Skipping."
+ )
+ return False, conversation
+
+ # Attempt to update the artifact.
+ self.artifact.__setattr__(field_name, field_value)
+
+ # This will only be reached if there were no exceptions setting
+ # the artifact field.
+ msg = Message(
+ {
+ "role": "assistant",
+ "content": f"Assistant updated {field_name} to {field_value}",
+ },
+ type=ConversationMessageType.ARTIFACT_UPDATE,
+ turn=None,
+ )
+ conversation.messages.append(msg)
+ return True, conversation
+
+ except Exception as e:
+ logger.warning(f"Error updating field {field_name}: {e}. Retrying...")
+ # Handle update error will increment failed_artifact_fields, once it has failed
+ # greater than self.max_artifact_field_retries the field will be skipped and the loop will break
+ success, new_field_value = await self._handle_update_error(
+ field_name, field_value, conversation, e
+ )
+
+ # The agent has successfully fixed the field.
+ if success:
+ if new_field_value is not None:
+ logger.info(
+ f"Agent successfully fixed field {field_name}. New value: {new_field_value}"
+ )
+ field_value = new_field_value
+ else:
+ # This is the case where the agent has decided to resume the conversation.
+ logger.info(
+ f"Agent could not fix the field itself & decided to resume conversation to fix field {field_name}"
+ )
+ return True, conversation
+
+ logger.warning(f"Agent failed to fix field {field_name}. Retrying...")
+ # Otherwise, the agent has failed and we will go through the loop again
+
+ def get_artifact_for_prompt(self) -> str:
+ """
+ Returns a formatted JSON-like representation of the current state of the
+ fields artifact. Any fields that were failed are completely omitted.
+ """
+ failed_fields = self.get_failed_fields()
+ return json.dumps({
+ k: v for k, v in self.artifact.model_dump().items() if k not in failed_fields
+ })
+
+ def get_schema_for_prompt(self, filter_one_field: str | None = None) -> str:
+ """Gets a clean version of the original artifact schema, optimized for use in an LLM prompt.
+
+ Args:
+ filter_one_field (str | None): If this is provided, only the schema for this one field will be returned.
+
+ Returns:
+ str: The cleaned schema
+ """
+
+ def _clean_properties(schema: dict, failed_fields: list[str]) -> str:
+ properties = schema.get("properties", {})
+ clean_properties = {}
+ for name, property_dict in properties.items():
+ if name not in failed_fields:
+ cleaned_property = {}
+ for k, v in property_dict.items():
+ if k in ["title", "default"]:
+ continue
+ cleaned_property[k] = v
+ clean_properties[name] = cleaned_property
+
+ clean_properties_str = str(clean_properties)
+ clean_properties_str = clean_properties_str.replace("$ref", "type")
+ clean_properties_str = clean_properties_str.replace("#/$defs/", "")
+ return clean_properties_str
+
+ # If filter_one_field is provided, only get the schema for that one field
+ if filter_one_field:
+ if not self._is_valid_field(filter_one_field):
+ logger.error(f'Field "{filter_one_field}" is not a valid field in the artifact.')
+ raise ValueError(
+ f'Field "{filter_one_field}" is not a valid field in the artifact.'
+ )
+ filtered_schema = {
+ "properties": {
+ filter_one_field: self.original_schema["properties"][filter_one_field]
+ }
+ }
+ filtered_schema.update(
+ (k, v) for k, v in self.original_schema.items() if k != "properties"
+ )
+ schema = filtered_schema
+ else:
+ schema = self.original_schema
+
+ failed_fields = self.get_failed_fields()
+ properties = _clean_properties(schema, failed_fields)
+ if not properties:
+ logger.error("No properties found in the schema.")
+ raise ValueError("No properties found in the schema.")
+
+ types_schema = schema.get("$defs", {})
+ custom_types = []
+ for type_name, type_info in types_schema.items():
+ if f"'type': '{type_name}'" in properties:
+ clean_schema = _clean_properties(type_info, [])
+ if clean_schema != "{}":
+ custom_types.append(f"{type_name} = {clean_schema}")
+
+ if custom_types:
+ explanation = (
+ f"If you wanted to create a {type_name} object, for example, you "
+ "would make a JSON object with the following keys: "
+ "{', '.join(types_schema[type_name]['properties'].keys())}."
+ )
+ custom_types_str = "\n".join(custom_types)
+ return (
+ f"{properties}\n\n"
+ "Here are the definitions for the custom types referenced in the artifact schema:\n"
+ f"{custom_types_str}\n\n"
+ f"{explanation}\n"
+ "Remember that when updating the artifact, the field will be the original "
+ "field name in the artifact and the JSON object(s) will be the value."
+ )
+ else:
+ return properties
+
+ def get_failed_fields(self) -> list[str]:
+ """Get a list of fields that have failed all attempts to update.
+
+ Returns:
+ list[str]: A list of field names that have failed all attempts to update.
+ """
+ fields = []
+ for field, attempts in self.failed_artifact_fields.items():
+ if len(attempts) >= self.max_artifact_field_retries:
+ fields.append(field)
+ return fields
+
+
+ T = TypeVar("T")
+
+ def _get_type_if_subtype(self, target_type: type[T], base_type: type[Any]) -> type[T] | None:
+ """
+ Recursively checks the target_type to see if it is a subclass of
+ base_type or a generic including base_type.
+
+ Args:
+ target_type: The type to check.
+ base_type: The type to check against.
+
+ Returns:
+ The class type if target_type is base_type, a subclass of base_type,
+ or a generic including base_type; otherwise, None.
+ """
+ origin = get_origin(target_type)
+ if origin is None:
+ if issubclass(target_type, base_type):
+ return target_type
+ else:
+ # Recursively check if any of the arguments are the target type.
+ for arg in get_args(target_type):
+ result = self._get_type_if_subtype(arg, base_type)
+ if result is not None:
+ return result
+ return None
+
+ def _modify_classes(self, artifact_class: BaseModel) -> dict[str, type[BaseModelLLM]]:
+ """Find all classes used as type hints in the artifact, and modify them to set 'Unanswered' as a default and valid value for all fields."""
+ modified_classes = {}
+ # Find any instances of BaseModel in the artifact class in the first "level" of type hints
+ for field_name, field_type in get_type_hints(artifact_class).items():
+ is_base_model = self._get_type_if_subtype(field_type, BaseModel)
+ if is_base_model is not None:
+ modified_classes[field_name] = self._modify_base_artifact(is_base_model)
+
+ return modified_classes
+
+ def _replace_type_annotations(
+ self, field_annotation: type[Any] | None, modified_classes: dict[str, type[BaseModelLLM]]
+ ) -> type:
+ """
+ Recursively replace type annotations with modified classes where
+ applicable.
+ """
+ # Get the origin of the field annotation, which is the base type for
+ # generic types (e.g., List[str] -> list, Dict[str, int] -> dict)
+ origin = get_origin(field_annotation)
+
+ # Get the type arguments of the generic type (e.g., List[str] -> str,
+ # Dict[str, int] -> str, int)
+ args = get_args(field_annotation)
+
+ if origin is None:
+ # The type is not generic; check if it's a subclass that needs to be replaced
+ if isinstance(field_annotation, type) and issubclass(field_annotation, BaseModelLLM):
+ return modified_classes.get(field_annotation.__name__, field_annotation)
+ return field_annotation if field_annotation is not None else object
+ else:
+ # The type is generic; recursively replace the type annotations of the arguments
+ new_args = tuple(self._replace_type_annotations(arg, modified_classes) for arg in args)
+ return origin[new_args]
+
+ def _modify_base_artifact(
+ self,
+ artifact_model: BaseModel,
+ modified_classes: dict[str, type[BaseModelLLM]] | None = None,
+ ) -> type[BaseModelLLM]:
+ """
+ Create a new artifact model with 'Unanswered' as a default and valid
+ value for all fields.
+ """
+ field_definitions = {}
+ for name, field_info in artifact_model.model_fields.items():
+ # Replace original classes with modified version.
+ if modified_classes is not None:
+ field_info.annotation = self._replace_type_annotations(
+ field_info.annotation, modified_classes
+ )
+
+ # This makes it possible to always set a field to "Unanswered".
+ annotation = Union[field_info.annotation, Literal["Unanswered"]]
+
+ # This sets the default value to "Unanswered".
+ default = "Unanswered"
+
+ # This adds "Unanswered" as a possible value to any regex patterns.
+ metadata = field_info.metadata
+ for m in metadata:
+ if hasattr(m, "pattern"):
+ m.pattern += "|Unanswered"
+
+ field_definitions[name] = (annotation, default, *metadata)
+
+ return create_model("Artifact", __base__=BaseModelLLM, **field_definitions)
+
+ def _is_valid_field(self, field_name: str) -> tuple[bool, Message | None]:
+ """
+ Check if the field_name is a valid field in the artifact. Returns True
+ if it is, False and an error message otherwise.
+ """
+ if field_name not in self.artifact.model_fields:
+ error_message = f'Field "{field_name}" is not a valid field in the artifact.'
+ msg = Message(
+ {"role": "assistant", "content": error_message},
+ type=ConversationMessageType.ARTIFACT_UPDATE,
+ turn=None,
+ )
+ return False, msg
+ return True, None
+
+ async def _handle_update_error(
+ self, field_name: str, field_value: Any, conversation: Conversation, error: Exception
+ ) -> tuple[bool, Any]:
+ """
+ Handles the logic for when an error occurs while updating a field.
+ Creates the appropriate context for the model and calls the LLM to fix
+ the error.
+
+ Args:
+ field_name (str): The name of the field to update in the artifact
+ field_value (Any): The value to set the field to conversation
+ (Conversation): The conversation object that contains the history of
+ the conversation error (Exception): The error that occurred while
+ updating the field
+
+ Returns:
+ tuple[bool, Any]: A tuple containing a boolean indicating success
+ and the new field value if successful (if not, then None)
+ """
+
+ # Keep track of history of failed attempts for each field.
+ previous_attempts = self.failed_artifact_fields.get(field_name, [])
+ error_str = (
+ str(error)
+ if not isinstance(error, ValidationError)
+ else "; ".join([e.get("msg") for e in error.errors()]).replace(
+ "; Input should be 'Unanswered'", " or input should be 'Unanswered'"
+ )
+ )
+ attempt = (str(field_value), error_str)
+ self.failed_artifact_fields[field_name] = previous_attempts + [attempt]
+
+ result = await fix_artifact_error(
+ self.context,
+ self.openai_client,
+ previous_attempts="\n".join([
+ f"Attempt: {attempt}\nError: {error}" for attempt, error in previous_attempts
+ ]),
+ artifact_schema=self.get_schema_for_prompt(filter_one_field=field_name),
+ conversation=conversation,
+ field_name=field_name,
+ )
+
+ # Handling the result of the LLM call
+ if result.message not in ["UPDATE_ARTIFACT", "RESUME_CONVERSATION"]:
+ logger.warning(
+ f"Failed to fix the artifact error due to an invalid response from the LLM: {result.message}"
+ )
+ return False, None
+
+ if result.message == "RESUME_CONVERSATION":
+ return True, None
+
+ if result.message.startswith("UPDATE_ARTIFACT("):
+ field_value = result.message.split("(")[1].split(")")[0]
+ return True, field_value
+
+ logger.warning(
+ f"Failed to fix the artifact error due to an invalid response from the LLM: {result.message}"
+ )
+ return False, None
+
+ def to_json(self) -> dict:
+ artifact_fields = self.artifact.model_dump()
+ return {
+ "artifact": artifact_fields,
+ "failed_fields": self.failed_artifact_fields,
+ }
+
+ @classmethod
+ def from_json(
+ cls,
+ json_data: dict,
+ input_artifact: BaseModel,
+ max_artifact_field_retries: int = 2,
+ ) -> "Artifact":
+ artifact = cls(input_artifact, max_artifact_field_retries)
+
+ artifact.failed_artifact_fields = json_data["failed_fields"]
+
+ # Iterate over artifact fields and set them to the values in the json data
+ # Skip any fields that are set as "Unanswered"
+ for field_name, field_value in json_data["artifact"].items():
+ if field_value != "Unanswered":
+ setattr(artifact.artifact, field_name, field_value)
+ return artifact
diff --git a/libraries/python/skills/skills/form-filler-skill/form_filler_skill/base_model_llm.py b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/base_model_llm.py
new file mode 100644
index 00000000..06407695
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/base_model_llm.py
@@ -0,0 +1,53 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import ast
+from types import NoneType
+from typing import Any, get_args
+
+from pydantic import BaseModel, ValidationInfo, field_validator
+
+
+class BaseModelLLM(BaseModel):
+ """
+ A Pydantic base class for use when an LLM is completing fields. Provides a
+ custom field validator and Pydantic Config.
+ """
+
+ @field_validator("*", mode="before")
+ def parse_literal_eval(cls, value: str, info: ValidationInfo) -> NoneType | str | Any:
+ """
+ An LLM will always result in a string (e.g. '["x", "y"]'), so we need to
+ parse it to the correct type.
+ """
+
+ # Get the type hints for the field. We know the field is present because
+ # pydantic gave it to us, so we can ignore this type error.
+ annotation = cls.model_fields[info.field_name].annotation # type: ignore
+ typehints = get_args(annotation)
+ if len(typehints) == 0:
+ typehints = [annotation]
+
+ # Usually fields that are NoneType have another type hint as well, e.g.
+ # str | None. If the LLM returns "None" and the field allows NoneType,
+ # we should return None without this code, the next if-block would leave
+ # the string "None" as the value
+ if (NoneType in typehints) and (value == "None"):
+ return None
+
+ # If the field allows strings, we don't parse it - otherwise a
+ # validation error might be raised e.g. phone_number = "1234567890"
+ # should not be converted to an int if the type hint is str
+ if str in typehints:
+ return value
+ try:
+ evaluated_value = ast.literal_eval(value)
+ return evaluated_value
+ except Exception:
+ return value
+
+ class Config:
+ # Ensure that validation happens every time a field is updated, not just
+ # when the artifact is created.
+ validate_assignment = True
+ # Do not allow extra fields to be added to the artifact.
+ extra = "forbid"
diff --git a/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/extract_form_fields.py b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/extract_form_fields.py
new file mode 100644
index 00000000..26839318
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/extract_form_fields.py
@@ -0,0 +1,74 @@
+from typing import Any, Literal
+
+import openai_client
+from openai import AsyncOpenAI
+from openai.types.chat import ChatCompletionMessageParam
+from pydantic import BaseModel, Field
+
+
+class FormField(BaseModel):
+ id: str = Field(description="The unique identifier of the field.")
+ name: str = Field(description="The name of the field.")
+ description: str = Field(description="The description of the field.")
+ type: Literal["string", "bool", "multiple_choice"] = Field(description="The type of the field.")
+ options: list[str] = Field(description="The options for multiple choice fields.")
+ required: bool = Field(description="Whether the field is required or not.")
+
+
+class FormFields(BaseModel):
+ error_message: str = Field(
+ description="The error message in the case that the form fields could not be extracted."
+ )
+ fields: list[FormField] = Field(description="The fields in the form.")
+
+
+class NoResponseChoicesError(Exception):
+ pass
+
+
+class NoParsedMessageError(Exception):
+ pass
+
+
+async def extract(
+ async_openai_client: AsyncOpenAI, openai_model: str, max_response_tokens: int, form_content: str
+) -> tuple[FormFields, dict[str, Any]]:
+ messages: list[ChatCompletionMessageParam] = [
+ {
+ "role": "system",
+ "content": (
+ "Extract the form fields from the provided form attachment. Any type of form is allowed, including for example"
+ " tax forms, address forms, surveys, and other official or unofficial form-types. If the content is not a form,"
+ " or the fields cannot be determined, then set the error_message."
+ ),
+ },
+ {
+ "role": "user",
+ "content": form_content,
+ },
+ ]
+
+ async with async_openai_client as client:
+ response = await client.beta.chat.completions.parse(
+ messages=messages,
+ model=openai_model,
+ response_format=FormFields,
+ )
+
+ if not response.choices:
+ raise NoResponseChoicesError()
+
+ if not response.choices[0].message.parsed:
+ raise NoParsedMessageError()
+
+ metadata = {
+ "request": {
+ "model": openai_model,
+ "messages": openai_client.truncate_messages_for_logging(messages),
+ "max_tokens": max_response_tokens,
+ "response_format": FormFields.model_json_schema(),
+ },
+ "response": response.model_dump(),
+ }
+
+ return response.choices[0].message.parsed, metadata
diff --git a/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/final_update.py b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/final_update.py
new file mode 100644
index 00000000..e92814bc
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/final_update.py
@@ -0,0 +1,91 @@
+import logging
+
+from chat_driver import ChatDriver, ChatDriverConfig, ContextProtocol
+from chat_driver.in_memory_message_history_provider import InMemoryMessageHistoryProvider
+from chat_driver.message_formatter import liquid_format
+from form_filler_skill.artifact import Artifact
+from form_filler_skill.definition import GCDefinition
+from form_filler_skill.message import Conversation
+from openai import AsyncAzureOpenAI, AsyncOpenAI
+
+logger = logging.getLogger(__name__)
+
+
+final_update_template = """You are a helpful, thoughtful, and meticulous assistant.
+You just finished a conversation with a user.{% if context %} Here is some additional context about the conversation:
+{{ context }}{% endif %}
+
+Your goal is to complete an artifact as thoroughly and accurately as possible based on the conversation.
+
+This is the schema of the artifact:
+{{ artifact_schema }}
+
+You will be given the current state of the artifact as well as the conversation history.
+Note that if the value for a field in the artifact is 'Unanswered', it means that the field was not completed. \
+Some fields may have already been completed during the conversation.
+
+Your need to determine whether there are any fields that need to be updated, and if so, update them.
+- You should only update a field if both of the following conditions are met: (a) the current state does NOT adequately reflect the conversation \
+and (b) you are able to submit a valid value for a field. \
+You are allowed to update completed fields, but you should only do so if the current state is inadequate, \
+e.g. the user corrected a mistake in their date of birth, but the artifact does not show the corrected version. \
+Remember that it's always an option to reset a field to "Unanswered" - this is often the best choice if the artifact contains incorrect information that cannot be corrected. \
+Do not submit a value that is identical to the current state of the field (e.g. if the field is already "Unanswered" and the user didn't provide any new information about it, you should not submit "Unanswered"). \
+- Make sure the value adheres to the constraints of the field as specified in the artifact schema. \
+If it's not possible to update a field with a valid value (e.g., the user provided an invalid date of birth), you should not update the field.
+- If the artifact schema is open-ended (e.g. it asks you to rate how pressing the user's issue is, without specifying rules for doing so), \
+use your best judgment to determine whether you have enough information to complete the field based on the conversation.
+- Prioritize accuracy over completion. You should never make up information or make assumptions in order to complete a field. \
+For example, if the field asks for a 10-digit phone number, and the user provided a 9-digit phone number, you should not add a digit to the phone number in order to complete the field.
+- If the user wasn't able to provide all of the information needed to complete a field, \
+use your best judgment to determine if a partial answer is appropriate (assuming it adheres to the formatting requirements of the field). \
+For example, if the field asks for a description of symptoms along with details about when the symptoms started, but the user wasn't sure when their symptoms started, \
+it's better to record the information they do have rather than to leave the field unanswered (and to indicate that the user was unsure about the start date).
+- It's possible to update multiple fields at once (assuming you're adhering to the above rules in all cases). It's also possible that no fields need to be updated.
+
+Your task is to state your step-by-step reasoning about what to update, followed by a final recommendation.
+Someone else will be responsible for executing the updates and they will only have access to your output \
+(not any of the conversation history, artifact schema, or other context) so make sure to specify exactly which \
+fields to update and the values to update them with, or to state that no fields need to be updated.
+"""
+
+USER_MESSAGE_TEMPLATE = """Conversation history:
+{{ conversation_history }}
+
+Current state of the artifact:
+{{ artifact_state }}"""
+
+
+async def final_update(
+ context: ContextProtocol,
+ open_ai_client: AsyncOpenAI | AsyncAzureOpenAI,
+ definition: GCDefinition,
+ chat_history: Conversation,
+ artifact: Artifact,
+):
+ history = InMemoryMessageHistoryProvider(formatter=liquid_format)
+
+ history.append_system_message(
+ final_update_template,
+ {
+ "context": definition.conversation_context,
+ "artifact_schema": artifact.get_schema_for_prompt(),
+ },
+ )
+ history.append_user_message(
+ USER_MESSAGE_TEMPLATE,
+ {
+ "conversation_history": str(chat_history),
+ "artifact_state": artifact.get_artifact_for_prompt(),
+ },
+ )
+
+ config = ChatDriverConfig(
+ context=context,
+ openai_client=open_ai_client,
+ model="gpt-3.5-turbo",
+ message_provider=history,
+ )
+
+ chat_driver = ChatDriver(config)
+ return await chat_driver.respond()
diff --git a/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/fix_agenda_error.py b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/fix_agenda_error.py
new file mode 100644
index 00000000..f577b301
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/fix_agenda_error.py
@@ -0,0 +1,57 @@
+import logging
+
+from chat_driver import ChatDriver, ChatDriverConfig, ContextProtocol
+from chat_driver.in_memory_message_history_provider import InMemoryMessageHistoryProvider
+from chat_driver.message_formatter import liquid_format
+from form_filler_skill.message import Conversation, ConversationMessageType
+from openai import AsyncAzureOpenAI, AsyncOpenAI
+
+logger = logging.getLogger(__name__)
+
+AGENDA_ERROR_CORRECTION_SYSTEM_TEMPLATE = """You are a helpful, thoughtful, and meticulous assistant.
+You are conducting a conversation with a user. You tried to update the agenda, but the update was invalid.
+
+You will be provided the history of your conversation with the user, your previous attempt(s) at updating the agenda, and the error message(s) that resulted from your attempt(s).
+Your task is to correct the update so that it is valid.
+
+Your changes should be as minimal as possible - you are focused on fixing the error(s) that caused the update to be invalid.
+
+Note that if the resource allocation is invalid, you must follow these rules:
+
+1. You should not change the description of the first item (since it has already been executed), but you can change its resource allocation.
+2. For all other items, you can combine or split them, or assign them fewer or more resources, but the content they cover collectively should not change (i.e. don't eliminate or add new topics).
+For example, the invalid attempt was "item 1 = ask for date of birth (1 turn), item 2 = ask for phone number (1 turn), item 3 = ask for phone type (1 turn), item 4 = explore treatment history (6 turns)", and the error says you need to correct the total resource allocation to 7 turns. A bad solution is "item 1 = ask for date of birth (1 turn), item 2 = explore treatment history (6 turns)" because it eliminates the phone number and phone type topics. A good solution is "item 1 = ask for date of birth (2 turns), item 2 = ask for phone number, phone type, and treatment history (2 turns), item 3 = explore treatment history (3 turns)."
+"""
+
+
+async def fix_agenda_error(
+ context: ContextProtocol,
+ openai_client: AsyncOpenAI | AsyncAzureOpenAI,
+ previous_attempts: str,
+ conversation: Conversation,
+):
+ history = InMemoryMessageHistoryProvider(formatter=liquid_format)
+
+ history.append_system_message(AGENDA_ERROR_CORRECTION_SYSTEM_TEMPLATE)
+ history.append_user_message(
+ (
+ "Conversation history:\n"
+ "{{ conversation_history }}\n\n"
+ "Previous attempts to update the agenda:\n"
+ "{{ previous_attempts }}"
+ ),
+ {
+ "conversation_history": str(conversation.exclude([ConversationMessageType.REASONING])),
+ "previous_attempts": previous_attempts,
+ },
+ )
+
+ config = ChatDriverConfig(
+ context=context,
+ openai_client=openai_client,
+ model="gpt-3.5-turbo",
+ message_provider=history,
+ )
+
+ chat_driver = ChatDriver(config)
+ return await chat_driver.respond()
diff --git a/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/fix_artifact_error.py b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/fix_artifact_error.py
new file mode 100644
index 00000000..cfd3b842
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/fix_artifact_error.py
@@ -0,0 +1,67 @@
+import logging
+
+from chat_driver import ChatDriver, ChatDriverConfig, ContextProtocol
+from chat_driver.in_memory_message_history_provider import InMemoryMessageHistoryProvider
+from chat_driver.message_formatter import liquid_format
+from events import BaseEvent
+from form_filler_skill.message import Conversation, ConversationMessageType
+from openai import AsyncAzureOpenAI, AsyncOpenAI
+
+logger = logging.getLogger(__name__)
+
+ARTIFACT_ERROR_CORRECTION_SYSTEM_TEMPLATE = """You are a helpful, thoughtful, and meticulous assistant.
+
+You are conducting a conversation with a user. Your goal is to complete an artifact as thoroughly as possible by the end of the conversation.
+
+You have tried to update a field in the artifact, but the value you provided did not adhere to the constraints of the field as specified in the artifact schema.
+
+You will be provided the history of your conversation with the user, the schema for the field, your previous attempt(s) at updating the field, and the error message(s) that resulted from your attempt(s).
+
+Your task is to return the best possible action to take next:
+
+1. UPDATE_FIELD(value)
+- You should pick this action if you have a valid value to submit for the field in question. Replace "value" with the correct value.
+
+2. RESUME_CONVERSATION
+- You should pick this action if: (a) you do NOT have a valid value to submit for the field in question, and (b) you need to ask the user for more information in order to obtain a valid value. For example, if the user stated that their date of birth is June 2000, but the artifact field asks for the date of birth in the format "YYYY-MM-DD", you should resume the conversation and ask the user for the day.
+
+Return only the action, either UPDATE_ARTIFACT(value) or RESUME_CONVERSATION, as your response. If you selected, UPDATE_ARTIFACT, make sure to replace "value" with the correct value.
+"""
+
+
+async def fix_artifact_error(
+ context: ContextProtocol,
+ openai_client: AsyncOpenAI | AsyncAzureOpenAI,
+ previous_attempts: str,
+ artifact_schema: str,
+ conversation: Conversation,
+ field_name: str,
+) -> BaseEvent:
+ history = InMemoryMessageHistoryProvider(formatter=liquid_format)
+ history.append_system_message(ARTIFACT_ERROR_CORRECTION_SYSTEM_TEMPLATE)
+ history.append_user_message(
+ (
+ "Conversation history:\n"
+ "{{ conversation_history }}\n\n"
+ "Schema:\n"
+ "{{ artifact_schema }}\n\n"
+ 'Previous attempts to update the field "{{ field_name }}" in the artifact:\n'
+ "{{ previous_attempts }}"
+ ),
+ {
+ "conversation_history": str(conversation.exclude([ConversationMessageType.REASONING])),
+ "artifact_schema": artifact_schema,
+ "field_name": field_name,
+ "previous_attempts": previous_attempts,
+ },
+ )
+
+ config = ChatDriverConfig(
+ context=context,
+ openai_client=openai_client,
+ model="gpt-3.5-turbo",
+ message_provider=history,
+ )
+
+ chat_driver = ChatDriver(config)
+ return await chat_driver.respond()
diff --git a/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/generate_filled_form.py b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/generate_filled_form.py
new file mode 100644
index 00000000..e69de29b
diff --git a/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/unneeded/choose_action.py b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/unneeded/choose_action.py
new file mode 100644
index 00000000..24a2fee3
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/unneeded/choose_action.py
@@ -0,0 +1,248 @@
+import logging
+
+from chat_driver import ChatDriver, ChatDriverConfig
+from chat_driver.in_memory_message_history_provider import InMemoryMessageHistoryProvider
+from chat_driver.message_formatter import liquid_format
+from form_filler_skill.agenda import Agenda, AgendaItem
+from form_filler_skill.definition import GCDefinition
+from form_filler_skill.message import Conversation
+from form_filler_skill.resources import (
+ GCResource,
+ ResourceConstraintMode,
+ ResourceConstraintUnit,
+ format_resource,
+)
+from openai import AsyncAzureOpenAI, AsyncOpenAI
+from pydantic import ValidationError
+from skill_library.run_context import RunContext
+
+from ...artifact import Artifact
+from ..fix_agenda_error import fix_agenda_error
+from ..update_agenda_template import update_agenda_template
+
+logger = logging.getLogger(__name__)
+
+
+def _get_termination_instructions(resource: GCResource):
+ """
+ Get the termination instructions for the conversation. This is contingent on
+ the resources mode, if any, that is available. Assumes we're always using
+ turns as the resource unit.
+ """
+ # Termination condition under no resource constraints
+ if resource.resource_constraint is None:
+ return (
+ "- You should pick this action as soon as you have completed the artifact to the best of your ability, the"
+ " conversation has come to a natural conclusion, or the user is not cooperating so you cannot continue the"
+ " conversation."
+ )
+
+ # Termination condition under exact resource constraints
+ if resource.resource_constraint.mode == ResourceConstraintMode.EXACT:
+ return "- You should only pick this action if the user is not cooperating so you cannot continue the conversation."
+
+ # Termination condition under maximum resource constraints
+ elif resource.resource_constraint.mode == ResourceConstraintMode.MAXIMUM:
+ return (
+ "- You should pick this action as soon as you have completed the artifact to the best of your ability, the"
+ " conversation has come to a natural conclusion, or the user is not cooperating so you cannot continue the"
+ " conversation."
+ )
+
+ else:
+ logger.error("Invalid resource mode provided.")
+ return ""
+
+
+async def update_agenda(
+ context: RunContext,
+ openai_client: AsyncOpenAI | AsyncAzureOpenAI,
+ definition: GCDefinition,
+ chat_history: Conversation,
+ agenda: Agenda,
+ artifact: Artifact,
+ resource: GCResource,
+) -> bool:
+ # STEP 1: Generate an updated agenda.
+
+ # If there is a resource constraint and there's more than one turn left,
+ # include additional constraint instructions.
+ remaining_resource = resource.remaining_units if resource.remaining_units else 0
+ resource_instructions = resource.get_resource_instructions()
+ if (resource_instructions != "") and (remaining_resource > 1):
+ match resource.get_resource_mode():
+ case ResourceConstraintMode.MAXIMUM:
+ total_resource_str = f"does not exceed the remaining turns ({remaining_resource})."
+ ample_time_str = ""
+ case ResourceConstraintMode.EXACT:
+ total_resource_str = f"is equal to the remaining turns ({remaining_resource}). Do not leave any turns unallocated."
+ ample_time_str = (
+ "If you have many turns remaining, instead of including wrap-up items or repeating "
+ "topics, you should include items that increase the breadth and/or depth of the conversation "
+ 'in a way that\'s directly relevant to the artifact (e.g. "collect additional details about X", '
+ '"ask for clarification about Y", "explore related topic Z", etc.).'
+ )
+ case _:
+ logger.error("Invalid resource mode.")
+ else:
+ total_resource_str = ""
+ ample_time_str = ""
+
+ history = InMemoryMessageHistoryProvider(formatter=liquid_format)
+ history.append_system_message(
+ update_agenda_template,
+ {
+ "context": definition.conversation_context,
+ "artifact_schema": definition.artifact_schema,
+ "rules": definition.rules,
+ "current_state_description": definition.conversation_flow,
+ "show_agenda": True,
+ "remaining_resource": remaining_resource,
+ "total_resource_str": total_resource_str,
+ "ample_time_str": ample_time_str,
+ "termination_instructions": _get_termination_instructions(resource),
+ "resource_instructions": resource_instructions,
+ },
+ )
+ history.append_user_message(
+ (
+ "Conversation history:\n"
+ "{{ chat_history }}\n\n"
+ "Latest agenda:\n"
+ "{{ agenda_state }}\n\n"
+ "Current state of the artifact:\n"
+ "{{ artifact_state }}"
+ ),
+ {
+ "chat_history": str(chat_history),
+ "agenda_state": get_agenda_for_prompt(agenda),
+ "artifact_state": artifact.get_artifact_for_prompt(),
+ },
+ )
+
+ config = ChatDriverConfig(
+ context=context,
+ openai_client=openai_client,
+ model="gpt-4o",
+ message_provider=history,
+ )
+
+ chat_driver = ChatDriver(config)
+ response = await chat_driver.respond()
+ items = response.message
+
+ # STEP 2: Validate/fix the updated agenda.
+
+ previous_attempts = []
+ while True:
+ try:
+ # Pydantic type validation.
+ agenda.items = items # type: ignore
+
+ # Check resource constraints.
+ if agenda.resource_constraint_mode is not None:
+ check_item_constraints(
+ agenda.resource_constraint_mode,
+ agenda.items,
+ resource.estimate_remaining_turns(),
+ )
+
+ logger.info(f"Agenda updated successfully: {get_agenda_for_prompt(agenda)}")
+ return True
+
+ except (ValidationError, ValueError) as e:
+ # If we have reached the maximum number of retries return a failure.
+ if len(previous_attempts) >= agenda.max_agenda_retries:
+ logger.warning(
+ f"Failed to update agenda after {agenda.max_agenda_retries} attempts."
+ )
+ return False
+
+ # Otherwise, get an error string.
+ if isinstance(e, ValidationError):
+ error_str = "; ".join([e.get("msg") for e in e.errors()])
+ error_str = error_str.replace(
+ "; Input should be 'Unanswered'", " or input should be 'Unanswered'"
+ )
+ else:
+ error_str = str(e)
+
+ # Add it to our list of previous attempts.
+ previous_attempts.append((str(items), error_str))
+
+ # And try again.
+ logger.info(f"Attempting to fix the agenda error. Attempt {len(previous_attempts)}.")
+ llm_formatted_attempts = "\n".join([
+ f"Attempt: {attempt}\nError: {error}" for attempt, error in previous_attempts
+ ])
+ response = await fix_agenda_error(
+ context, openai_client, llm_formatted_attempts, chat_history
+ )
+
+ # Now, update the items with the corrected agenda and try to
+ # validate again.
+ items = response.message
+
+
+def check_item_constraints(
+ resource_constraint_mode: ResourceConstraintMode,
+ items: list[AgendaItem],
+ remaining_turns: int,
+) -> None:
+ """
+ Validates if any constraints were violated while performing the agenda
+ update.
+ """
+ # The total, proposed allocation of resources.
+ total_resources = sum([item.resource for item in items])
+
+ violations = []
+ # In maximum mode, the total resources should not exceed the remaining
+ # turns.
+ if (resource_constraint_mode == ResourceConstraintMode.MAXIMUM) and (
+ total_resources > remaining_turns
+ ):
+ violations.append(
+ "The total turns allocated in the agenda "
+ f"must not exceed the remaining amount ({remaining_turns}); "
+ f"but the current total is {total_resources}."
+ )
+
+ # In exact mode if the total resources were not exactly equal to the
+ # remaining turns.
+ if (resource_constraint_mode == ResourceConstraintMode.EXACT) and (
+ total_resources != remaining_turns
+ ):
+ violations.append(
+ "The total turns allocated in the agenda "
+ f"must equal the remaining amount ({remaining_turns}); "
+ f"but the current total is {total_resources}."
+ )
+
+ # Check if any item has a resource value of 0.
+ if any(item.resource <= 0 for item in items):
+ violations.append("All items must have a resource value greater than 0.")
+
+ # Raise an error if any violations were found.
+ if len(violations) > 0:
+ logger.debug(f"Agenda update failed due to the following violations: {violations}.")
+ raise ValueError(" ".join(violations))
+
+
+def get_agenda_for_prompt(agenda: Agenda) -> str:
+ """
+ Gets a string representation of the agenda for use in an LLM prompt.
+ """
+ agenda_json = agenda.model_dump()
+ agenda_items = agenda_json.get("items", [])
+ if len(agenda_items) == 0:
+ return "None"
+ agenda_str = "\n".join([
+ f"{i + 1}. [{format_resource(item['resource'], ResourceConstraintUnit.TURNS)}] {item['title']}"
+ for i, item in enumerate(agenda_items)
+ ])
+ total_resource = format_resource(
+ sum([item["resource"] for item in agenda_items]), ResourceConstraintUnit.TURNS
+ )
+ agenda_str += f"\nTotal = {total_resource}"
+ return agenda_str
diff --git a/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/unneeded/choose_action_template.py b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/unneeded/choose_action_template.py
new file mode 100644
index 00000000..3e533f63
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/unneeded/choose_action_template.py
@@ -0,0 +1,52 @@
+update_agenda_template = """You are a helpful, thoughtful, and meticulous assistant. You are conducting a conversation with a user. Your goal is to complete an artifact as thoroughly as possible by the end of the conversation, and to ensure a smooth experience for the user.
+
+This is the schema of the artifact you are completing:
+{{ artifact_schema }}{% if context %}
+
+Here is some additional context about the conversation:
+{{ context }}{% endif %}
+
+Throughout the conversation, you must abide by these rules:
+{{ rules }}{% if current_state_description %}
+
+Here's a description of the conversation flow:
+{{ current_state_description }}
+Follow this description, and exercise good judgment about when it is appropriate to deviate.{% endif %}
+
+You will be provided the history of your conversation with the user up until now and the current state of the artifact.
+Note that if the value for a field in the artifact is 'Unanswered', it means that the field has not been completed.
+You need to select the best possible action(s), given the state of the conversation and the artifact.
+
+These are the possible actions you can take:
+
+{% if show_agenda %}Update agenda (required parameters: items)
+- If the latest agenda is set to "None", you should always pick this action.
+- You should pick this action if you need to change your plan for the conversation to make the best use of the remaining turns available to you. Consider how long it usually takes to get the information you need (which is a function of the quality and pace of the user's responses), the number, complexity, and importance of the remaining fields in the artifact, and the number of turns remaining ({{ remaining_resource }}). Based on these factors, you might need to accelerate (e.g. combine several topics) or slow down the conversation (e.g. spread out a topic), in which case you should update the agenda accordingly. Note that skipping an artifact field is NOT a valid way to accelerate the conversation.
+- You must provide an ordered list of items to be completed sequentially, where the first item contains everything you will do in the current turn of the conversation (in addition to updating the agenda). For example, if you choose to send a message to the user asking for their name and medical history, then you would write "ask for name and medical history" as the first item. If you think medical history will take longer than asking for the name, then you would write "complete medical history" as the second item, with an estimate of how many turns you think it will take. Do NOT include items that have already been completed. Items must always represent a conversation topic (corresponding to the "Send message to user" action). Updating the artifact (e.g. "update field X based on the discussion") or terminating the conversation is NOT a valid item.
+- The latest agenda was created in the previous turn of the conversation. Even if the total turns in the latest agenda equals the remaining turns, you should still update the agenda if you
+think the current plan is suboptimal (e.g. the first item was completed, the order of items is not ideal, an item is too broad or not a conversation topic, etc.).
+- Each item must have a description and and your best guess for the number of turns required to complete it. Do not provide a range of turns. It is EXTREMELY important that the total turns allocated across all items in the updated agenda (including the first item for the current turn) {{ total_resource_str }} Everything in the agenda should be something you expect to complete in the remaining turns - there shouldn't be any optional "buffer" items. It can be helpful to include the cumulative turns allocated for each item in the agenda to ensure you adhere to this rule, e.g. item 1 = 2 turns (cumulative total = 2), item 2 = 4 turns (cumulative total = 6), etc.
+- Avoid high-level items like "ask follow-up questions" - be specific about what you need to do.
+- Do NOT include wrap-up items such as "review and confirm all information with the user" (you should be doing this throughout the conversation) or "thank the user for their time". Do NOT repeat topics that have already been sufficiently addressed. {{ ample_time_str }}{% endif %}
+
+Send message to user (required parameters: message)
+- If there is no conversation history, you should always pick this action.
+- You should pick this action if (a) the user asked a question or made a statement that you need to respond to, or (b) you need to follow-up with the user because the information they provided is incomplete, invalid, ambiguous, or in some way insufficient to complete the artifact. For example, if the artifact schema indicates that the "date of birth" field must be in the format "YYYY-MM-DD", but the user has only provided the month and year, you should send a message to the user asking for the day. Likewise, if the user claims that their date of birth is February 30, you should send a message to the user asking for a valid date. If the artifact schema is open-ended (e.g. it asks you to rate how pressing the user's issue is, without specifying rules for doing so), use your best judgment to determine whether you have enough information or you need to continue probing the user. It's important to be thorough, but also to avoid asking the user for unnecessary information.
+
+Update artifact fields (required parameters: field, value)
+- You should pick this action as soon as (a) the user provides new information that is not already reflected in the current state of the artifact and (b) you are able to submit a valid value for a field in the artifact using this new information. If you have already updated a field in the artifact and there is no new information to update the field with, you should not pick this action.
+- Make sure the value adheres to the constraints of the field as specified in the artifact schema.
+- If the user has provided all required information to complete a field (i.e. the criteria for "Send message to user" are not satisfied) but the information is in the wrong format, you should not ask the user to reformat their response. Instead, you should simply update the field with the correctly formatted value. For example, if the artifact asks for the date of birth in the format "YYYY-MM-DD", and the user provides their date of birth as "June 15, 2000", you should update the field with the value "2000-06-15".
+- Prioritize accuracy over completion. You should never make up information or make assumptions in order to complete a field. For example, if the field asks for a 10-digit phone number, and the user provided a 9-digit phone number, you should not add a digit to the phone number in order to complete the field. Instead, you should follow-up with the user to ask for the correct phone number. If they still aren't able to provide one, you should leave the field unanswered.
+- If the user isn't able to provide all of the information needed to complete a field, use your best judgment to determine if a partial answer is appropriate (assuming it adheres to the formatting requirements of the field). For example, if the field asks for a description of symptoms along with details about when the symptoms started, but the user isn't sure when their symptoms started, it's better to record the information they do have rather than to leave the field unanswered (and to indicate that the user was unsure about the start date).
+- If it's possible to update multiple fields at once (assuming you're adhering to the above rules in all cases), you should do so. For example, if the user provides their full name and date of birth in the same message, you should select the "update artifact fields" action twice, once for each field.
+
+End conversation (required parameters: None)
+{{ termination_instructions }}
+{{ resource_instructions }}
+
+If you select the "Update artifact field" action or the "Update agenda" action, you should also select one of the "Send message to user" or "End conversation" actions. Note that artifact and updates updates will always be executed before a message is sent to the user or the
+conversation is terminated. Also note that only one message can be sent to the user at a time.
+
+Your task is to state your step-by-step reasoning for the best possible action(s), followed by a final recommendation of which action(s) to take, including all required parameters. Someone else will be responsible for executing the action(s) you select and they will only have access to your output (not any of the conversation history, artifact schema, or other context) so it is EXTREMELY important that you clearly specify the value of all required parameters for each action you select.
+"""
diff --git a/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/unneeded/execute_reasoning.py b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/unneeded/execute_reasoning.py
new file mode 100644
index 00000000..48d48750
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/unneeded/execute_reasoning.py
@@ -0,0 +1,61 @@
+import logging
+
+from chat_driver import ChatDriver, ChatDriverConfig, ContextProtocol
+from chat_driver.in_memory_message_history_provider import InMemoryMessageHistoryProvider
+from chat_driver.message_formatter import liquid_format
+from openai import AsyncAzureOpenAI, AsyncOpenAI
+
+logger = logging.getLogger(__name__)
+
+SYSTEM_TEMPLATE = """You are a helpful, thoughtful, and meticulous assistant. You are conducting a conversation with a user. Your goal is to complete an artifact as thoroughly as possible by the end of the conversation.
+
+You will be given some reasoning about the best possible action(s) to take next given the state of the conversation as well as the artifact schema.
+
+The reasoning is supposed to state the recommended action(s) to take next, along with all required parameters for each action.
+
+Your task is to execute ALL actions recommended in the reasoning in the order they are listed.
+If the reasoning's specification of an action is incomplete (e.g. it doesn't include all required parameters for the action, or some parameters are specified implicitly, such as "send a message that contains a greeting" instead of explicitly providing the value of the "message" parameter), do not execute the action. You should never fill in missing or imprecise
+parameters yourself.
+
+If the reasoning is not clear about which actions to take, or all actions are specified in an incomplete way, return 'None' without selecting any action."""
+
+USER_TEMPLATE = """Artifact schema:
+{{ artifact_schema }}
+
+If the type in the schema is str, the "field_value" parameter in the action should be also be a string.
+These are example parameters for the update_artifact action: {"field_name": "company_name", "field_value": "Contoso"}
+DO NOT write JSON in the "field_value" parameter in this case.
+{"field_name": "company_name", "field_value": "{"value": "Contoso"}"} is INCORRECT.
+
+Reasoning:
+{{ reasoning }}"""
+
+
+async def execute_reasoning(
+ context: ContextProtocol,
+ open_ai_client: AsyncOpenAI | AsyncAzureOpenAI,
+ reasoning: str,
+ artifact_schema: str,
+):
+ history = InMemoryMessageHistoryProvider(formatter=liquid_format)
+
+ history.append_system_message(
+ SYSTEM_TEMPLATE,
+ )
+ history.append_user_message(
+ USER_TEMPLATE,
+ {
+ "artifact_schema": artifact_schema,
+ "reasoning": reasoning,
+ },
+ )
+
+ config = ChatDriverConfig(
+ context=context,
+ openai_client=open_ai_client,
+ model="gpt-3.5-turbo",
+ message_provider=history,
+ )
+
+ chat_driver = ChatDriver(config)
+ return await chat_driver.respond()
diff --git a/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/update_agenda.py b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/update_agenda.py
new file mode 100644
index 00000000..2f99a17b
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/update_agenda.py
@@ -0,0 +1,140 @@
+import logging
+
+from form_filler_skill.agenda import Agenda, AgendaItem
+from form_filler_skill.message import Conversation
+from form_filler_skill.resources import (
+ GCResource,
+ ResourceConstraintMode,
+ ResourceConstraintUnit,
+ format_resource,
+)
+from openai import AsyncAzureOpenAI, AsyncOpenAI
+from pydantic import ValidationError
+from skill_library.run_context import RunContext
+
+from .fix_agenda_error import fix_agenda_error
+
+logger = logging.getLogger(__name__)
+
+
+async def update_agenda(
+ context: RunContext,
+ openai_client: AsyncOpenAI | AsyncAzureOpenAI,
+ items: str,
+ chat_history: Conversation,
+ agenda: Agenda,
+ resource: GCResource,
+) -> bool:
+ previous_attempts = []
+ while True:
+ try:
+ # Pydantic type validation.
+ agenda.items = items # type: ignore
+
+ # Check resource constraints.
+ if agenda.resource_constraint_mode is not None:
+ check_item_constraints(
+ agenda.resource_constraint_mode,
+ agenda.items,
+ resource.estimate_remaining_turns(),
+ )
+
+ logger.info(f"Agenda updated successfully: {get_agenda_for_prompt(agenda)}")
+ return True
+
+ except (ValidationError, ValueError) as e:
+ # If we have reached the maximum number of retries return a failure.
+ if len(previous_attempts) >= agenda.max_agenda_retries:
+ logger.warning(
+ f"Failed to update agenda after {agenda.max_agenda_retries} attempts."
+ )
+ return False
+
+ # Otherwise, get an error string.
+ if isinstance(e, ValidationError):
+ error_str = "; ".join([e.get("msg") for e in e.errors()])
+ error_str = error_str.replace(
+ "; Input should be 'Unanswered'", " or input should be 'Unanswered'"
+ )
+ else:
+ error_str = str(e)
+
+ # Add it to our list of previous attempts.
+ previous_attempts.append((str(items), error_str))
+
+ # And try again.
+ logger.info(f"Attempting to fix the agenda error. Attempt {len(previous_attempts)}.")
+ llm_formatted_attempts = "\n".join([
+ f"Attempt: {attempt}\nError: {error}" for attempt, error in previous_attempts
+ ])
+ response = await fix_agenda_error(
+ context, openai_client, llm_formatted_attempts, chat_history
+ )
+
+ # Now, update the items with the corrected agenda and try to
+ # validate again.
+ items = response.message or ""
+
+
+def check_item_constraints(
+ resource_constraint_mode: ResourceConstraintMode,
+ items: list[AgendaItem],
+ remaining_turns: int,
+) -> None:
+ """
+ Validates if any constraints were violated while performing the agenda
+ update.
+ """
+ # The total, proposed allocation of resources.
+ total_resources = sum([item.resource for item in items])
+
+ violations = []
+ # In maximum mode, the total resources should not exceed the remaining
+ # turns.
+ if (resource_constraint_mode == ResourceConstraintMode.MAXIMUM) and (
+ total_resources > remaining_turns
+ ):
+ violations.append(
+ "The total turns allocated in the agenda "
+ f"must not exceed the remaining amount ({remaining_turns}); "
+ f"but the current total is {total_resources}."
+ )
+
+ # In exact mode if the total resources were not exactly equal to the
+ # remaining turns.
+ if (resource_constraint_mode == ResourceConstraintMode.EXACT) and (
+ total_resources != remaining_turns
+ ):
+ violations.append(
+ "The total turns allocated in the agenda "
+ f"must equal the remaining amount ({remaining_turns}); "
+ f"but the current total is {total_resources}."
+ )
+
+ # Check if any item has a resource value of 0.
+ if any(item.resource <= 0 for item in items):
+ violations.append("All items must have a resource value greater than 0.")
+
+ # Raise an error if any violations were found.
+ if len(violations) > 0:
+ logger.debug(f"Agenda update failed due to the following violations: {violations}.")
+ raise ValueError(" ".join(violations))
+
+
+def get_agenda_for_prompt(agenda: Agenda) -> str:
+ """
+ Gets a string representation of the agenda for use in an LLM prompt.
+ """
+ agenda_json = agenda.model_dump()
+ agenda_items = agenda_json.get("items", [])
+ if len(agenda_items) == 0:
+ return "None"
+ agenda_str = "\n".join([
+ f"{i + 1}. [{format_resource(item['resource'], ResourceConstraintUnit.TURNS)}] {item['title']}"
+ for i, item in enumerate(agenda_items)
+ ])
+ total_resource = format_resource(
+ sum([item["resource"] for item in agenda_items]), ResourceConstraintUnit.TURNS
+ )
+ agenda_str += f"\nTotal = {total_resource}"
+ return agenda_str
diff --git a/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/update_agenda_template.py b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/update_agenda_template.py
new file mode 100644
index 00000000..5bf468ce
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/update_agenda_template.py
@@ -0,0 +1,31 @@
+update_agenda_template = """You are a helpful, thoughtful, and meticulous assistant. You are conducting a conversation with a user. Your goal is to complete an artifact as thoroughly as possible by the end of the conversation, and to ensure a smooth experience for the user.
+
+This is the schema of the artifact you are completing:
+{{ artifact_schema }}{% if context %}
+
+Here is some additional context about the conversation:
+{{ context }}{% endif %}
+
+Throughout the conversation, you must abide by these rules:
+{{ rules }}{% if current_state_description %}
+
+Here's a description of the conversation flow:
+{{ current_state_description }}
+Follow this description, and exercise good judgment about when it is appropriate to deviate.{% endif %}
+
+You will be provided the history of your conversation with the user up until now and the current state of the artifact.
+Note that if the value for a field in the artifact is 'Unanswered', it means that the field has not been completed.
+You need to select the best possible action(s), given the state of the conversation and the artifact.
+
+Here is the action to take:
+
+- If the latest agenda is set to "None", you should always pick this action.
+- You should pick this action if you need to change your plan for the conversation to make the best use of the remaining turns available to you. Consider how long it usually takes to get the information you need (which is a function of the quality and pace of the user's responses), the number, complexity, and importance of the remaining fields in the artifact, and the number of turns remaining ({{ remaining_resource }}). Based on these factors, you might need to accelerate (e.g. combine several topics) or slow down the conversation (e.g. spread out a topic), in which case you should update the agenda accordingly. Note that skipping an artifact field is NOT a valid way to accelerate the conversation.
+- You must provide an ordered list of items to be completed sequentially, where the first item contains everything you will do in the current turn of the conversation (in addition to updating the agenda). For example, if you choose to send a message to the user asking for their name and medical history, then you would write "ask for name and medical history" as the first item. If you think medical history will take longer than asking for the name, then you would write "complete medical history" as the second item, with an estimate of how many turns you think it will take. Do NOT include items that have already been completed. Items must always represent a conversation topic (corresponding to the "Send message to user" action). Updating the artifact (e.g. "update field X based on the discussion") or terminating the conversation is NOT a valid item.
+- The latest agenda was created in the previous turn of the conversation. Even if the total turns in the latest agenda equals the remaining turns, you should still update the agenda if you
+think the current plan is suboptimal (e.g. the first item was completed, the order of items is not ideal, an item is too broad or not a conversation topic, etc.).
+- Each item must have a description and and your best guess for the number of turns required to complete it. Do not provide a range of turns. It is EXTREMELY important that the total turns allocated across all items in the updated agenda (including the first item for the current turn) {{ total_resource_str }} Everything in the agenda should be something you expect to complete in the remaining turns - there shouldn't be any optional "buffer" items. It can be helpful to include the cumulative turns allocated for each item in the agenda to ensure you adhere to this rule, e.g. item 1 = 2 turns (cumulative total = 2), item 2 = 4 turns (cumulative total = 6), etc.
+- Avoid high-level items like "ask follow-up questions" - be specific about what you need to do.
+- Do NOT include wrap-up items such as "review and confirm all information with the user" (you should be doing this throughout the conversation) or "thank the user for their time". Do NOT repeat topics that have already been sufficiently addressed. {{ ample_time_str }}{% endif %}
+
+"""
diff --git a/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/update_artifact.py b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/update_artifact.py
new file mode 100644
index 00000000..44b50a4d
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/update_artifact.py
@@ -0,0 +1,219 @@
+import logging
+
+from chat_driver import ChatDriver, ChatDriverConfig
+from chat_driver.in_memory_message_history_provider import InMemoryMessageHistoryProvider
+from chat_driver.message_formatter import liquid_format
+from form_filler_skill.agenda import Agenda, AgendaItem
+from form_filler_skill.definition import GCDefinition
+from form_filler_skill.message import Conversation
+from form_filler_skill.resources import (
+ GCResource,
+ ResourceConstraintMode,
+ ResourceConstraintUnit,
+ format_resource,
+)
+from openai import AsyncAzureOpenAI, AsyncOpenAI
+from pydantic import ValidationError
+from skill_library.run_context import RunContext
+
+from ..artifact import Artifact
+from .fix_agenda_error import fix_agenda_error
+from .update_artifact_template import update_artifact_template
+
+logger = logging.getLogger(__name__)
+
+
+def _get_termination_instructions(resource: GCResource):
+ """
+ Get the termination instructions for the conversation. This is contingent on
+ the resources mode, if any, that is available. Assumes we're always using
+ turns as the resource unit.
+ """
+ # Termination condition under no resource constraints
+ if resource.resource_constraint is None:
+ return (
+ "- You should pick this action as soon as you have completed the artifact to the best of your ability, the"
+ " conversation has come to a natural conclusion, or the user is not cooperating so you cannot continue the"
+ " conversation."
+ )
+
+ # Termination condition under exact resource constraints
+ if resource.resource_constraint.mode == ResourceConstraintMode.EXACT:
+ return "- You should only pick this action if the user is not cooperating so you cannot continue the conversation."
+
+ # Termination condition under maximum resource constraints
+ elif resource.resource_constraint.mode == ResourceConstraintMode.MAXIMUM:
+ return (
+ "- You should pick this action as soon as you have completed the artifact to the best of your ability, the"
+ " conversation has come to a natural conclusion, or the user is not cooperating so you cannot continue the"
+ " conversation."
+ )
+
+ else:
+ logger.error("Invalid resource mode provided.")
+ return ""
+
+
+async def update_agenda(
+ context: RunContext,
+ openai_client: AsyncOpenAI | AsyncAzureOpenAI,
+ definition: GCDefinition,
+ chat_history: Conversation,
+ agenda: Agenda,
+ artifact: Artifact,
+ resource: GCResource,
+) -> bool:
+ # STEP 1: Generate an updated agenda.
+
+ history = InMemoryMessageHistoryProvider(formatter=liquid_format)
+ history.append_system_message(
+ update_artifact_template,
+ {
+ "context": definition.conversation_context,
+ "artifact_schema": definition.artifact_schema,
+ "rules": definition.rules,
+ "current_state_description": definition.conversation_flow,
+ },
+ )
+ history.append_user_message(
+ (
+ "Conversation history:\n"
+ "{{ chat_history }}\n\n"
+ "Latest agenda:\n"
+ "{{ agenda_state }}\n\n"
+ "Current state of the artifact:\n"
+ "{{ artifact_state }}"
+ ),
+ {
+ "chat_history": str(chat_history),
+ "agenda_state": get_agenda_for_prompt(agenda),
+ "artifact_state": artifact.get_artifact_for_prompt(),
+ },
+ )
+
+ config = ChatDriverConfig(
+ context=context,
+ openai_client=openai_client,
+ model="gpt-4o",
+ message_provider=history,
+ )
+
+ chat_driver = ChatDriver(config)
+ response = await chat_driver.respond()
+ items = response.message
+
+ # STEP 2: Validate/fix the updated agenda.
+
+ previous_attempts = []
+ while True:
+ try:
+ # Pydantic type validation.
+ agenda.items = items # type: ignore
+
+ # Check resource constraints.
+ if agenda.resource_constraint_mode is not None:
+ check_item_constraints(
+ agenda.resource_constraint_mode,
+ agenda.items,
+ resource.estimate_remaining_turns(),
+ )
+
+ logger.info(f"Agenda updated successfully: {get_agenda_for_prompt(agenda)}")
+ return True
+
+ except (ValidationError, ValueError) as e:
+ # If we have reached the maximum number of retries return a failure.
+ if len(previous_attempts) >= agenda.max_agenda_retries:
+ logger.warning(
+ f"Failed to update agenda after {agenda.max_agenda_retries} attempts."
+ )
+ return False
+
+ # Otherwise, get an error string.
+ if isinstance(e, ValidationError):
+ error_str = "; ".join([e.get("msg") for e in e.errors()])
+ error_str = error_str.replace(
+ "; Input should be 'Unanswered'", " or input should be 'Unanswered'"
+ )
+ else:
+ error_str = str(e)
+
+ # Add it to our list of previous attempts.
+ previous_attempts.append((str(items), error_str))
+
+ # And try again.
+ logger.info(f"Attempting to fix the agenda error. Attempt {len(previous_attempts)}.")
+ llm_formatted_attempts = "\n".join([
+ f"Attempt: {attempt}\nError: {error}" for attempt, error in previous_attempts
+ ])
+ response = await fix_agenda_error(
+ context, openai_client, llm_formatted_attempts, chat_history
+ )
+
+ # Now, update the items with the corrected agenda and try to
+ # validate again.
+ items = response.message
+
+
+def check_item_constraints(
+ resource_constraint_mode: ResourceConstraintMode,
+ items: list[AgendaItem],
+ remaining_turns: int,
+) -> None:
+ """
+ Validates if any constraints were violated while performing the agenda
+ update.
+ """
+ # The total, proposed allocation of resources.
+ total_resources = sum([item.resource for item in items])
+
+ violations = []
+ # In maximum mode, the total resources should not exceed the remaining
+ # turns.
+ if (resource_constraint_mode == ResourceConstraintMode.MAXIMUM) and (
+ total_resources > remaining_turns
+ ):
+ violations.append(
+ "The total turns allocated in the agenda "
+ f"must not exceed the remaining amount ({remaining_turns}); "
+ f"but the current total is {total_resources}."
+ )
+
+ # In exact mode if the total resources were not exactly equal to the
+ # remaining turns.
+ if (resource_constraint_mode == ResourceConstraintMode.EXACT) and (
+ total_resources != remaining_turns
+ ):
+ violations.append(
+ "The total turns allocated in the agenda "
+ f"must equal the remaining amount ({remaining_turns}); "
+ f"but the current total is {total_resources}."
+ )
+
+ # Check if any item has a resource value of 0.
+ if any(item.resource <= 0 for item in items):
+ violations.append("All items must have a resource value greater than 0.")
+
+ # Raise an error if any violations were found.
+ if len(violations) > 0:
+ logger.debug(f"Agenda update failed due to the following violations: {violations}.")
+ raise ValueError(" ".join(violations))
+
+
+def get_agenda_for_prompt(agenda: Agenda) -> str:
+ """
+ Gets a string representation of the agenda for use in an LLM prompt.
+ """
+ agenda_json = agenda.model_dump()
+ agenda_items = agenda_json.get("items", [])
+ if len(agenda_items) == 0:
+ return "None"
+ agenda_str = "\n".join([
+ f"{i + 1}. [{format_resource(item['resource'], ResourceConstraintUnit.TURNS)}] {item['title']}"
+ for i, item in enumerate(agenda_items)
+ ])
+ total_resource = format_resource(
+ sum([item["resource"] for item in agenda_items]), ResourceConstraintUnit.TURNS
+ )
+ agenda_str += f"\nTotal = {total_resource}"
+ return agenda_str
diff --git a/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/update_artifact_template.py b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/update_artifact_template.py
new file mode 100644
index 00000000..33d8e454
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/update_artifact_template.py
@@ -0,0 +1,32 @@
+update_artifact_template = """You are a helpful, thoughtful, and meticulous assistant. You are conducting a conversation with a user. Your goal is to complete an artifact as thoroughly as possible by the end of the conversation, and to ensure a smooth experience for the user.
+
+This is the schema of the artifact you are completing:
+{{ artifact_schema }}{% if context %}
+
+Here is some additional context about the conversation:
+{{ context }}{% endif %}
+
+Throughout the conversation, you must abide by these rules:
+{{ rules }}{% if current_state_description %}
+
+Here's a description of the conversation flow:
+{{ current_state_description }}
+Follow this description, and exercise good judgment about when it is appropriate to deviate.{% endif %}
+
+You will be provided the history of your conversation with the user up until now and the current state of the artifact.
+Note that if the value for a field in the artifact is 'Unanswered', it means that the field has not been completed.
+You need to select the best possible action(s), given the state of the conversation and the artifact.
+
+Your job is to create a list of field updates to update the artifact. Each update should be listed as:
+
+update_artifact_field(required parameters: field, value)
+
+- You should pick this action as soon as (a) the user provides new information that is not already reflected in the current state of the artifact and (b) you are able to submit a valid value for a field in the artifact using this new information. If you have already updated a field in the artifact and there is no new information to update the field with, you should not pick this action.
+- Make sure the value adheres to the constraints of the field as specified in the artifact schema.
+- If the user has provided all required information to complete a field (i.e. the criteria for "Send message to user" are not satisfied) but the information is in the wrong format, you should not ask the user to reformat their response. Instead, you should simply update the field with the correctly formatted value. For example, if the artifact asks for the date of birth in the format "YYYY-MM-DD", and the user provides their date of birth as "June 15, 2000", you should update the field with the value "2000-06-15".
+- Prioritize accuracy over completion. You should never make up information or make assumptions in order to complete a field. For example, if the field asks for a 10-digit phone number, and the user provided a 9-digit phone number, you should not add a digit to the phone number in order to complete the field. Instead, you should follow-up with the user to ask for the correct phone number. If they still aren't able to provide one, you should leave the field unanswered.
+- If the user isn't able to provide all of the information needed to complete a field, use your best judgment to determine if a partial answer is appropriate (assuming it adheres to the formatting requirements of the field). For example, if the field asks for a description of symptoms along with details about when the symptoms started, but the user isn't sure when their symptoms started, it's better to record the information they do have rather than to leave the field unanswered (and to indicate that the user was unsure about the start date).
+- If it's possible to update multiple fields at once (assuming you're adhering to the above rules in all cases), you should do so. For example, if the user provides their full name and date of birth in the same message, you should select the "update artifact fields" action twice, once for each field.
+
+Your task is to state your step-by-step reasoning for the best possible action(s), followed by a final recommendation of which update(s) to make, including all required parameters. Someone else will be responsible for executing the update(s) you select and they will only have access to your output (not any of the conversation history, artifact schema, or other context) so it is EXTREMELY important that you clearly specify the value of all required parameters for each update you make.
+"""
diff --git a/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/update_form.py b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/chat_drivers/update_form.py
new file mode 100644
index 00000000..e69de29b
diff --git a/libraries/python/skills/skills/form-filler-skill/form_filler_skill/definition.py b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/definition.py
new file mode 100644
index 00000000..b2480218
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/definition.py
@@ -0,0 +1,14 @@
+from dataclasses import dataclass
+from typing import Optional
+
+from form_filler_skill.resources import ResourceConstraint
+
+
+@dataclass
+class GCDefinition:
+ artifact_schema: str
+ rules: str
+ conversation_flow: Optional[str]
+ conversation_context: str
+ resource_constraint: Optional[ResourceConstraint]
+ service_id: str = "gc_main"
diff --git a/libraries/python/skills/skills/form-filler-skill/form_filler_skill/form_filler_skill.py b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/form_filler_skill.py
new file mode 100644
index 00000000..b0c9d94e
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/form_filler_skill.py
@@ -0,0 +1,126 @@
+from typing import Any, Optional
+
+from chat_driver import ChatDriverConfig
+from skill_library import FunctionRoutine, RoutineTypes, Skill
+from skill_library.run_context import RunContext
+
+NAME = "form-filler"
+CLASS_NAME = "FormFillerSkill"
+DESCRIPTION = "Walks the user through uploading and filling out a form."
+DEFAULT_MAX_RETRIES = 3
+INSTRUCTIONS = "You are an assistant."
+
+
+class FormFillerSkill(Skill):
+ def __init__(
+ self,
+ chat_driver_config: ChatDriverConfig,
+ ) -> None:
+ # Put all functions in a group. We are going to use all these as (1)
+ # skill actions, but also as (2) chat functions and (3) chat commands.
+ # You can also put them in separate lists if you want to differentiate
+ # between these.
+ functions = []
+
+ # Add some skill routines.
+ routines: list[RoutineTypes] = [
+ self.form_filler_routine(),
+ ]
+
+ # Re-configure the skill's chat driver.
+ chat_driver_config.instructions = INSTRUCTIONS
+ chat_driver_config.commands = functions
+ chat_driver_config.functions = functions
+
+ # Initialize the skill!
+ super().__init__(
+ name=NAME,
+ description=DESCRIPTION,
+ chat_driver_config=chat_driver_config,
+ skill_actions=functions,
+ routines=routines,
+ )
+
+ ##################################
+ # Routines
+ ##################################
+
+ def form_filler_routine(self) -> FunctionRoutine:
+ return FunctionRoutine(
+ name="form_filler",
+ description="Run a form-filler routine.",
+ init_function=self.form_fill_init,
+ step_function=self.form_fill_step,
+ skill=self,
+ )
+
+ async def form_fill_init(self, context: RunContext, vars: dict[str, Any] | None = None):
+ # TODO: Use `vars` to config the form filler routine.
+ await self.form_fill_step(context)
+
+ async def form_fill_step(
+ self,
+ context: RunContext,
+ message: Optional[str] = None,
+ ) -> str | None:
+ FormFiller = self
+ state = await context.state()
+ while True:
+ match state.get("mode"):
+ case None:
+ state["mode"] = "init"
+ case "init":
+ # Cede control to guided conversation if an artifact needs to be generated.
+ # How do we want to pass in all the GC definitions? Should they just be a simpler config object TYPE?
+ if not state["artifact"]:
+ if not state["gce_id"]:
+ guided_conversation_vars: dict[str, Any] = {
+ "definition_type": "upload_files",
+ "objective": "Upload a form to be filled out by the form filler recipe.",
+ }
+ gc_id = await context.run_routine(
+ context, "guided_conversation.doc_upload", guided_conversation_vars
+ )
+ state["gc_id"] = gc_id
+ artifact = GuidedConversation.run(state["gce_id"], message)
+ if artifact:
+ state["artifact"] = artifact
+ else:
+ await context.update_state(state)
+ return
+
+ agenda, is_done = FormFiller.update_agenda(context)
+ state["agenda"] = agenda
+ if is_done:
+ state["mode"] = "done"
+ state["mode"] = "conversation"
+ await context.update_state(state)
+ return agenda.last_message
+ case "conversation":
+ state["form"] = FormFiller.update_form(context)
+ agenda, is_done = FormFiller.update_agenda(context)
+ state["agenda"] = agenda
+ if is_done:
+ state["mode"] = "finalize"
+ await context.update_state(state)
+ return agenda.last_message
+ case "finalize":
+ message = FormFiller.generate_filled_form(context)
+ state["mode"] = "done"
+ await context.update_state(state)
+ return message
+ case "done":
+ return None
+
+ ##################################
+ # Actions
+ ##################################
+
+ def update_agenda(self, context: RunContext):
+ return "message", False
+
+ def update_form(self, context: RunContext):
+ return "message", False
+
+ def generate_filled_form(self, context: RunContext):
+ return "message"
diff --git a/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/__init__.py b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/agenda.py b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/agenda.py
new file mode 100644
index 00000000..9d5623e9
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/agenda.py
@@ -0,0 +1,24 @@
+import logging
+
+from pydantic import Field
+
+from form_filler_skill.base_model_llm import BaseModelLLM
+from form_filler_skill.resources import (
+ ResourceConstraintMode,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class AgendaItem(BaseModelLLM):
+ title: str = Field(description="Brief description of the item")
+ resource: int = Field(description="Number of turns required for the item")
+
+
+class Agenda(BaseModelLLM):
+ resource_constraint_mode: ResourceConstraintMode | None = Field(default=None)
+ max_agenda_retries: int = Field(default=2)
+ items: list[AgendaItem] = Field(
+ description="Ordered list of items to be completed in the remainder of the conversation",
+ default_factory=list,
+ )
diff --git a/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/artifact.py b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/artifact.py
new file mode 100644
index 00000000..b4f29717
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/artifact.py
@@ -0,0 +1,479 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+# FIXME: Copied code from Semantic Kernel repo, using as-is despite type errors
+# type: ignore
+
+import logging
+from typing import Annotated, Any, Literal, get_args, get_origin, get_type_hints
+
+from guided_conversation.utils.base_model_llm import BaseModelLLM
+from guided_conversation.utils.conversation_helpers import Conversation, ConversationMessageType
+from guided_conversation.utils.openai_tool_calling import ToolValidationResult
+from guided_conversation.utils.plugin_helpers import PluginOutput, fix_error, update_attempts
+from pydantic import BaseModel, create_model
+from semantic_kernel import Kernel
+from semantic_kernel.connectors.ai.function_choice_behavior import FunctionChoiceBehavior
+from semantic_kernel.contents import AuthorRole, ChatMessageContent
+from semantic_kernel.functions import KernelArguments
+from semantic_kernel.functions.kernel_function_decorator import kernel_function
+
+ARTIFACT_ERROR_CORRECTION_SYSTEM_TEMPLATE = """You are a helpful, thoughtful, and meticulous assistant.
+You are conducting a conversation with a user. Your goal is to complete an artifact as thoroughly as possible by the end of the conversation.
+You have tried to update a field in the artifact, but the value you provided did not adhere \
+to the constraints of the field as specified in the artifact schema.
+You will be provided the history of your conversation with the user, the schema for the field, \
+your previous attempt(s) at updating the field, and the error message(s) that resulted from your attempt(s).
+Your task is to select the best possible action to take next:
+1. Update artifact
+- You should pick this action if you have a valid value to submit for the field in question.
+2. Resume conversation
+- You should pick this action if: (a) you do NOT have a valid value to submit for the field in question, and \
+(b) you need to ask the user for more information in order to obtain a valid value. \
+For example, if the user stated that their date of birth is June 2000, but the artifact field asks for the date of birth in the format \
+"YYYY-MM-DD", you should resume the conversation and ask the user for the day.
+
+Conversation history:
+{{ conversation_history }}
+
+Schema:
+{{ artifact_schema }}
+
+Previous attempts to update the field "{{ field_name }}" in the artifact:
+{{ previous_attempts }}"""
+
+UPDATE_ARTIFACT_TOOL = "update_artifact_field"
+RESUME_CONV_TOOL = "resume_conversation"
+
+
+class Artifact:
+ """The Artifact plugin takes in a Pydantic base model, and robustly handles updating the fields of the model
+ A typical use case is as a form an agent must complete throughout a conversation.
+ Another use case is as a working memory for the agent.
+
+ The primary interface is update_artifact, which takes in the field_name to update and its new value.
+ Additionally, the chat_history is passed in to help the agent make informed decisions in case an error occurs.
+
+ The Artifact also exposes several functions to access internal state:
+ get_artifact_for_prompt, get_schema_for_prompt, and get_failed_fields.
+ """
+
+ def __init__(self, service_id: str, input_artifact: BaseModel, max_artifact_field_retries: int = 2) -> None:
+ """
+ Initialize the Artifact plugin with the given Pydantic base model.
+
+ Args:
+ kernel (Kernel): The Semantic Kernel instance to use for calling the LLM. Don't forget to set your
+ req_settings since this class uses tool calling functionality from the Semantic Kernel.
+ service_id (str): The service ID to use for the Semantic Kernel tool calling. One kernel can have multiple
+ services. The service ID is used to identify which service to use for LLM calls. The Artifact object
+ assumes that the service has tool calling capabilities and is some flavor of chat completion.
+ input_artifact (BaseModel): The Pydantic base model to use as the artifact
+ max_artifact_field_retries (int): The maximum number of times to retry updating a field in the artifact
+ """
+ logger = logging.getLogger(__name__)
+ self.logger = logger
+
+ self.id = "artifact_plugin"
+ self.service_id = service_id
+ self.max_artifact_field_retries = max_artifact_field_retries
+
+ self.original_schema = input_artifact.model_json_schema()
+ self.artifact = self._initialize_artifact(input_artifact)
+
+ # failed_artifact_fields maps a field name to a list of the history of the failed attempts to update it
+ # dict: key = field, value = list of tuple[attempt, error message]
+ self.failed_artifact_fields: dict[str, list[tuple[str, str]]] = {}
+
+ # The following are the kernel functions that will be provided to the LLM call
+ @kernel_function(
+ name=UPDATE_ARTIFACT_TOOL,
+ description="Sets the value of a field in the artifact",
+ )
+ def update_artifact_field(
+ self,
+ field: Annotated[str, "The name of the field to update in the artifact"],
+ value: Annotated[str, "The value to set the field to"],
+ ) -> None:
+ pass
+
+ @kernel_function(
+ name=RESUME_CONV_TOOL,
+ description="Resumes conversation to get more information from the user ",
+ )
+ def resume_conversation(self):
+ pass
+
+ async def update_artifact(self, field_name: str, field_value: Any, conversation: Conversation) -> PluginOutput:
+ """The core interface for the Artifact plugin.
+ This function will attempt to update the given field_name to the given field_value.
+ If the field_value fails Pydantic validation, an LLM will determine one of two actions to take.
+ Given the conversation as additional context the two actions are:
+ - Retry the update the artifact by fixing the formatting using the previous failed attempts as guidance
+ - Take no action or in other words, resume the conversation to ask the user for more information because the user gave incomplete or incorrect information
+
+ Args:
+ field_name (str): The name of the field to update in the artifact
+ field_value (Any): The value to set the field to
+ conversation (Conversation): The conversation object that contains the history of the conversation
+
+ Returns:
+ PluginOutput: An object with two fields: a boolean indicating success
+ and a list of conversation messages that may have been generated.
+
+ Several outcomes can happen:
+ - The update may have failed due to
+ - A field_name that is not valid in the artifact.
+ - The field_value failing Pydantic validation and all retries failed.
+ - The model failed to correctly call a tool.
+ In this case, the boolean will be False and the list may contain a message indicating the failure.
+
+ - The agent may have successfully updated the artifact or fixed it.
+ In this case, the boolean will be True and the list will contain a message indicating the update and possibly intermediate messages.
+
+ - The agent may have decided to resume the conversation.
+ In this case, the boolean will be True and the messages may only contain messages indicated previous errors.
+ """
+
+ conversation_messages: list[ChatMessageContent] = []
+
+ # Check if the field name is valid, and return with a failure message if not
+ is_valid_field, msg = self._is_valid_field(field_name)
+ if not is_valid_field:
+ conversation_messages.append(msg)
+ return PluginOutput(update_successful=False, messages=conversation_messages)
+
+ # Try to update the field, and handle any errors that occur until the field is
+ # successfully updated or skipped according to max_artifact_field_retries
+ while True:
+ try:
+ # Check if there have been too many previous failed attempts to update the field
+ if len(self.failed_artifact_fields.get(field_name, [])) >= self.max_artifact_field_retries:
+ self.logger.warning(f"Updating field {field_name} has failed too many times. Skipping.")
+ return PluginOutput(False, conversation_messages)
+
+ # Attempt to update the artifact
+ msg = self._execute_update_artifact(field_name, field_value)
+ conversation_messages.append(msg)
+ return PluginOutput(True, conversation_messages)
+ except Exception as e:
+ self.logger.warning(f"Error updating field {field_name}: {e}. Retrying...")
+ # Handle update error will increment failed_artifact_fields, once it has failed
+ # greater than self.max_artifact_field_retries the field will be skipped and the loop will break
+ success, new_field_value = await self._handle_update_error(field_name, field_value, conversation, e)
+
+ # The agent has successfully fixed the field.
+ if success and new_field_value is not None:
+ self.logger.info(f"Agent successfully fixed field {field_name}. New value: {new_field_value}")
+ field_value = new_field_value
+ # This is the case where the agent has decided to resume the conversation.
+ elif success:
+ self.logger.info(
+ f"Agent could not fix the field itself & decided to resume conversation to fix field {field_name}"
+ )
+ return PluginOutput(True, conversation_messages)
+ self.logger.warning(f"Agent failed to fix field {field_name}. Retrying...")
+ # Otherwise, the agent has failed and we will go through the loop again
+
+ def get_artifact_for_prompt(self) -> str:
+ """Returns a formatted JSON-like representation of the current state of the fields artifact.
+ Any fields that were failed are completely omitted.
+
+ Returns:
+ str: The string representation of the artifact.
+ """
+ failed_fields = self.get_failed_fields()
+ return {k: v for k, v in self.artifact.model_dump().items() if k not in failed_fields}
+
+ def get_schema_for_prompt(self, filter_one_field: str | None = None) -> str:
+ """Gets a clean version of the original artifact schema, optimized for use in an LLM prompt.
+
+ Args:
+ filter_one_field (str | None): If this is provided, only the schema for this one field will be returned.
+
+ Returns:
+ str: The cleaned schema
+ """
+
+ def _clean_properties(schema: dict, failed_fields: list[str]) -> str:
+ properties = schema.get("properties", {})
+ clean_properties = {}
+ for name, property_dict in properties.items():
+ if name not in failed_fields:
+ cleaned_property = {}
+ for k, v in property_dict.items():
+ if k in ["title", "default"]:
+ continue
+ cleaned_property[k] = v
+ clean_properties[name] = cleaned_property
+
+ clean_properties_str = str(clean_properties)
+ clean_properties_str = clean_properties_str.replace("$ref", "type")
+ clean_properties_str = clean_properties_str.replace("#/$defs/", "")
+ return clean_properties_str
+
+ # If filter_one_field is provided, only get the schema for that one field
+ if filter_one_field:
+ if not self._is_valid_field(filter_one_field):
+ self.logger.error(f'Field "{filter_one_field}" is not a valid field in the artifact.')
+ raise ValueError(f'Field "{filter_one_field}" is not a valid field in the artifact.')
+ filtered_schema = {"properties": {filter_one_field: self.original_schema["properties"][filter_one_field]}}
+ filtered_schema.update((k, v) for k, v in self.original_schema.items() if k != "properties")
+ schema = filtered_schema
+ else:
+ schema = self.original_schema
+
+ failed_fields = self.get_failed_fields()
+ properties = _clean_properties(schema, failed_fields)
+ if not properties:
+ self.logger.error("No properties found in the schema.")
+ raise ValueError("No properties found in the schema.")
+
+ types_schema = schema.get("$defs", {})
+ custom_types = []
+ for type_name, type_info in types_schema.items():
+ if f"'type': '{type_name}'" in properties:
+ clean_schema = _clean_properties(type_info, [])
+ if clean_schema != "{}":
+ custom_types.append(f"{type_name} = {clean_schema}")
+
+ if custom_types:
+ explanation = f"If you wanted to create a {type_name} object, for example, you would make a JSON object \
+with the following keys: {', '.join(types_schema[type_name]['properties'].keys())}."
+ custom_types_str = "\n".join(custom_types)
+ return f"""{properties}
+
+Here are the definitions for the custom types referenced in the artifact schema:
+{custom_types_str}
+
+{explanation}
+Remember that when updating the artifact, the field will be the original field name in the artifact and the JSON object(s) will be the value."""
+ else:
+ return properties
+
+ def get_failed_fields(self) -> list[str]:
+ """Get a list of fields that have failed all attempts to update.
+
+ Returns:
+ list[str]: A list of field names that have failed all attempts to update.
+ """
+ fields = []
+ for field, attempts in self.failed_artifact_fields.items():
+ if len(attempts) >= self.max_artifact_field_retries:
+ fields.append(field)
+ return fields
+
+ def _initialize_artifact(self, artifact_model: BaseModel) -> BaseModelLLM:
+ """Create a new artifact model based on the one provided by the user
+ with "Unanswered" set for all fields.
+
+ Args:
+ artifact_model (BaseModel): The Pydantic class provided by the user
+
+ Returns:
+ BaseModelLLM: The new artifact model with "Unanswered" set for all fields
+ """
+ modified_classes = self._modify_classes(artifact_model)
+ artifact = self._modify_base_artifact(artifact_model, modified_classes)
+ return artifact()
+
+ def _get_type_if_subtype(self, target_type: type[Any], base_type: type[Any]) -> type[Any] | None:
+ """Recursively checks the target_type to see if it is a subclass of base_type or a generic including base_type.
+
+ Args:
+ target_type: The type to check.
+ base_type: The type to check against.
+
+ Returns:
+ The class type if target_type is base_type, a subclass of base_type, or a generic including base_type; otherwise, None.
+ """
+ origin = get_origin(target_type)
+ if origin is None:
+ if issubclass(target_type, base_type):
+ return target_type
+ else:
+ # Recursively check if any of the arguments are the target type
+ for arg in get_args(target_type):
+ result = self._get_type_if_subtype(arg, base_type)
+ if result is not None:
+ return result
+ return None
+
+ def _modify_classes(self, artifact_class: BaseModel) -> dict[str, type[BaseModelLLM]]:
+ """Find all classes used as type hints in the artifact, and modify them to set 'Unanswered' as a default and valid value for all fields."""
+ modified_classes = {}
+ # Find any instances of BaseModel in the artifact class in the first "level" of type hints
+ for field_name, field_type in get_type_hints(artifact_class).items():
+ is_base_model = self._get_type_if_subtype(field_type, BaseModel)
+ if is_base_model is not None:
+ modified_classes[field_name] = self._modify_base_artifact(is_base_model)
+
+ return modified_classes
+
+ def _replace_type_annotations(
+ self, field_annotation: type[Any] | None, modified_classes: dict[str, type[BaseModelLLM]]
+ ) -> type:
+ """Recursively replace type annotations with modified classes where applicable."""
+ # Get the origin of the field annotation, which is the base type for generic types (e.g., List[str] -> list, Dict[str, int] -> dict)
+ origin = get_origin(field_annotation)
+ # Get the type arguments of the generic type (e.g., List[str] -> str, Dict[str, int] -> str, int)
+ args = get_args(field_annotation)
+
+ if origin is None:
+ # The type is not generic; check if it's a subclass that needs to be replaced
+ if isinstance(field_annotation, type) and issubclass(field_annotation, BaseModelLLM):
+ return modified_classes.get(field_annotation.__name__, field_annotation)
+ return field_annotation
+ else:
+ # The type is generic; recursively replace the type annotations of the arguments
+ new_args = tuple(self._replace_type_annotations(arg, modified_classes) for arg in args)
+ return origin[new_args]
+
+ def _modify_base_artifact(
+ self, artifact_model: type[BaseModelLLM], modified_classes: dict[str, type[BaseModelLLM]] | None = None
+ ) -> type[BaseModelLLM]:
+ """Create a new artifact model with 'Unanswered' as a default and valid value for all fields."""
+ for _, field_info in artifact_model.model_fields.items():
+ # Replace original classes with modified version
+ if modified_classes is not None:
+ field_info.annotation = self._replace_type_annotations(field_info.annotation, modified_classes)
+ # This makes it possible to always set a field to "Unanswered"
+ field_info.annotation = field_info.annotation | Literal["Unanswered"]
+ # This sets the default value to "Unanswered"
+ field_info.default = "Unanswered"
+ # This adds "Unanswered" as a possible value to any regex patterns
+ metadata = field_info.metadata
+ for m in metadata:
+ if hasattr(m, "pattern"):
+ m.pattern += "|Unanswered"
+ field_definitions = {
+ name: (field_info.annotation, field_info) for name, field_info in artifact_model.model_fields.items()
+ }
+ artifact_model = create_model("Artifact", __base__=BaseModelLLM, **field_definitions)
+ return artifact_model
+
+ def _is_valid_field(self, field_name: str) -> tuple[bool, ChatMessageContent]:
+ """Check if the field_name is a valid field in the artifact. Returns True if it is, False and an error message otherwise."""
+ if field_name not in self.artifact.model_fields:
+ error_message = f'Field "{field_name}" is not a valid field in the artifact.'
+ msg = ChatMessageContent(
+ role=AuthorRole.ASSISTANT,
+ content=error_message,
+ metadata={"type": ConversationMessageType.ARTIFACT_UPDATE, "turn_number": None},
+ )
+ return False, msg
+ return True, None
+
+ async def _fix_artifact_error(
+ self,
+ field_name: str,
+ previous_attempts: str,
+ conversation_repr: str,
+ artifact_schema_repr: str,
+ ) -> dict[str, Any]:
+ """Calls the LLM to fix an error in the artifact using Semantic Kernel kernel."""
+
+ req_settings = self.kernel.get_prompt_execution_settings_from_service_id(self.service_id)
+ req_settings.max_tokens = 2000
+
+ self.kernel.add_function(plugin_name=self.id, function=self.update_artifact_field)
+ self.kernel.add_function(plugin_name=self.id, function=self.resume_conversation)
+ filter = {"included_plugins": [self.id]}
+ req_settings.function_choice_behavior = FunctionChoiceBehavior.Auto(auto_invoke=False, filters=filter)
+
+ arguments = KernelArguments(
+ field_name=field_name,
+ conversation_history=conversation_repr,
+ previous_attempts=previous_attempts,
+ artifact_schema=artifact_schema_repr,
+ settings=req_settings,
+ )
+
+ return await fix_error(
+ kernel=self.kernel,
+ prompt_template=ARTIFACT_ERROR_CORRECTION_SYSTEM_TEMPLATE,
+ req_settings=req_settings,
+ arguments=arguments,
+ )
+
+ def _execute_update_artifact(
+ self,
+ field_name: Annotated[str, "The name of the field to update in the artifact"],
+ field_value: Annotated[Any, "The value to set the field to"],
+ ) -> ChatMessageContent:
+ """Update a field in the artifact with a new value. This will raise an error if the field_value is invalid."""
+ setattr(self.artifact, field_name, field_value)
+ msg = ChatMessageContent(
+ role=AuthorRole.ASSISTANT,
+ content=f"Assistant updated {field_name} to {field_value}",
+ metadata={"type": ConversationMessageType.ARTIFACT_UPDATE, "turn_number": None},
+ )
+ return msg
+
+ async def _handle_update_error(
+ self, field_name: str, field_value: Any, conversation: Conversation, error: Exception
+ ) -> tuple[bool, Any]:
+ """
+ Handles the logic for when an error occurs while updating a field.
+ Creates the appropriate context for the model and calls the LLM to fix the error.
+
+ Args:
+ field_name (str): The name of the field to update in the artifact
+ field_value (Any): The value to set the field to
+ conversation (Conversation): The conversation object that contains the history of the conversation
+ error (Exception): The error that occurred while updating the field
+
+ Returns:
+ tuple[bool, Any]: A tuple containing a boolean indicating success and the new field value if successful (if not, then None)
+ """
+ # Update the failed attempts for the field
+ previous_attempts = self.failed_artifact_fields.get(field_name, [])
+ previous_attempts, llm_formatted_attempts = update_attempts(
+ error=error, attempt_id=str(field_value), previous_attempts=previous_attempts
+ )
+ self.failed_artifact_fields[field_name] = previous_attempts
+
+ # Call the LLM to fix the error
+ conversation_history_repr = conversation.get_repr_for_prompt(exclude_types=[ConversationMessageType.REASONING])
+ artifact_schema_repr = self.get_schema_for_prompt(filter_one_field=field_name)
+ result = await self._fix_artifact_error(
+ field_name, llm_formatted_attempts, conversation_history_repr, artifact_schema_repr
+ )
+
+ # Handling the result of the LLM call
+ if result["validation_result"] != ToolValidationResult.SUCCESS:
+ return False, None
+ # Only consider the first tool call
+ tool_name = result["tool_names"][0]
+ tool_args = result["tool_args_list"][0]
+ if tool_name == f"{self.id}-{UPDATE_ARTIFACT_TOOL}":
+ field_value = tool_args["value"]
+ return True, field_value
+ elif tool_name == f"{self.id}-{RESUME_CONV_TOOL}":
+ return True, None
+
+ def to_json(self) -> dict:
+ artifact_fields = self.artifact.model_dump()
+ return {
+ "artifact": artifact_fields,
+ "failed_fields": self.failed_artifact_fields,
+ }
+
+ @classmethod
+ def from_json(
+ cls,
+ json_data: dict,
+ kernel: Kernel,
+ service_id: str,
+ input_artifact: BaseModel,
+ max_artifact_field_retries: int = 2,
+ ) -> "Artifact":
+ artifact = cls(kernel, service_id, input_artifact, max_artifact_field_retries)
+
+ artifact.failed_artifact_fields = json_data["failed_fields"]
+
+ # Iterate over artifact fields and set them to the values in the json data
+ # Skip any fields that are set as "Unanswered"
+ for field_name, field_value in json_data["artifact"].items():
+ if field_value != "Unanswered":
+ setattr(artifact.artifact, field_name, field_value)
+ return artifact
diff --git a/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/base_model_llm.py b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/base_model_llm.py
new file mode 100644
index 00000000..4c75f22c
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/base_model_llm.py
@@ -0,0 +1,55 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+# FIXME: Copied code from Semantic Kernel repo, using as-is despite type errors
+# type: ignore
+
+import ast
+from types import NoneType
+from typing import Any, get_args
+
+from pydantic import BaseModel, ValidationInfo, field_validator
+
+
+class BaseModelLLM(BaseModel):
+ """
+ A Pydantic base class for use when an LLM is completing fields. Provides a
+ custom field validator and Pydantic Config.
+ """
+
+ @field_validator("*", mode="before")
+ def parse_literal_eval(cls, value: str, info: ValidationInfo) -> NoneType | str | Any:
+ """
+ An LLM will always result in a string (e.g. '["x", "y"]'), so we need to
+ parse it to the correct type.
+ """
+
+ # Get the type hints for the field.
+ annotation = cls.model_fields[info.field_name].annotation
+ typehints = get_args(annotation)
+ if len(typehints) == 0:
+ typehints = [annotation]
+
+ # Usually fields that are NoneType have another type hint as well, e.g.
+ # str | None. If the LLM returns "None" and the field allows NoneType,
+ # we should return None without this code, the next if-block would leave
+ # the string "None" as the value
+ if (NoneType in typehints) and (value == "None"):
+ return None
+
+ # If the field allows strings, we don't parse it - otherwise a
+ # validation error might be raised e.g. phone_number = "1234567890"
+ # should not be converted to an int if the type hint is str
+ if str in typehints:
+ return value
+ try:
+ evaluated_value = ast.literal_eval(value)
+ return evaluated_value
+ except Exception:
+ return value
+
+ class Config:
+ # Ensure that validation happens every time a field is updated, not just
+ # when the artifact is created.
+ validate_assignment = True
+ # Do not allow extra fields to be added to the artifact.
+ extra = "forbid"
diff --git a/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/chat_drivers/gc_final_update.py b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/chat_drivers/gc_final_update.py
new file mode 100644
index 00000000..33f964ba
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/chat_drivers/gc_final_update.py
@@ -0,0 +1,86 @@
+import logging
+
+from chat_driver import ChatDriver, ChatDriverConfig, ContextProtocol
+from chat_driver.in_memory_message_history_provider import InMemoryMessageHistoryProvider
+from chat_driver.message_formatter import liquid_format
+from form_filler_skill.guided_conversation.artifact import Artifact
+from form_filler_skill.guided_conversation.conversation_helpers import Conversation
+from form_filler_skill.guided_conversation.definition import GCDefinition
+from openai import AsyncAzureOpenAI, AsyncOpenAI
+
+logger = logging.getLogger(__name__)
+
+
+final_update_template = """You are a helpful, thoughtful, and meticulous assistant.
+You just finished a conversation with a user.{% if context %} Here is some additional context about the conversation:
+{{ context }}{% endif %}
+
+Your goal is to complete an artifact as thoroughly and accurately as possible based on the conversation.
+
+This is the schema of the artifact:
+{{ artifact_schema }}
+
+You will be given the current state of the artifact as well as the conversation history.
+Note that if the value for a field in the artifact is 'Unanswered', it means that the field was not completed. \
+Some fields may have already been completed during the conversation.
+
+Your need to determine whether there are any fields that need to be updated, and if so, update them.
+- You should only update a field if both of the following conditions are met: (a) the current state does NOT adequately reflect the conversation \
+and (b) you are able to submit a valid value for a field. \
+You are allowed to update completed fields, but you should only do so if the current state is inadequate, \
+e.g. the user corrected a mistake in their date of birth, but the artifact does not show the corrected version. \
+Remember that it's always an option to reset a field to "Unanswered" - this is often the best choice if the artifact contains incorrect information that cannot be corrected. \
+Do not submit a value that is identical to the current state of the field (e.g. if the field is already "Unanswered" and the user didn't provide any new information about it, you should not submit "Unanswered"). \
+- Make sure the value adheres to the constraints of the field as specified in the artifact schema. \
+If it's not possible to update a field with a valid value (e.g., the user provided an invalid date of birth), you should not update the field.
+- If the artifact schema is open-ended (e.g. it asks you to rate how pressing the user's issue is, without specifying rules for doing so), \
+use your best judgment to determine whether you have enough information to complete the field based on the conversation.
+- Prioritize accuracy over completion. You should never make up information or make assumptions in order to complete a field. \
+For example, if the field asks for a 10-digit phone number, and the user provided a 9-digit phone number, you should not add a digit to the phone number in order to complete the field.
+- If the user wasn't able to provide all of the information needed to complete a field, \
+use your best judgment to determine if a partial answer is appropriate (assuming it adheres to the formatting requirements of the field). \
+For example, if the field asks for a description of symptoms along with details about when the symptoms started, but the user wasn't sure when their symptoms started, \
+it's better to record the information they do have rather than to leave the field unanswered (and to indicate that the user was unsure about the start date).
+- It's possible to update multiple fields at once (assuming you're adhering to the above rules in all cases). It's also possible that no fields need to be updated.
+
+Your task is to state your step-by-step reasoning about what to update, followed by a final recommendation.
+Someone else will be responsible for executing the updates and they will only have access to your output \
+(not any of the conversation history, artifact schema, or other context) so make sure to specify exactly which \
+fields to update and the values to update them with, or to state that no fields need to be updated.
+
+
+Conversation history:
+{{ conversation_history }}
+
+Current state of the artifact:
+{{ artifact_state }}"""
+
+
+async def final_update(
+ context: ContextProtocol,
+ open_ai_client: AsyncOpenAI | AsyncAzureOpenAI,
+ definition: GCDefinition,
+ chat_history: Conversation,
+ artifact: Artifact,
+):
+ history = InMemoryMessageHistoryProvider(formatter=liquid_format)
+
+ history.append_system_message(
+ final_update_template,
+ {
+ "conversation_history": chat_history.get_repr_for_prompt(),
+ "context": definition.conversation_context,
+ "artifact_schema": artifact.get_schema_for_prompt(),
+ "artifact_state": artifact.get_artifact_for_prompt(),
+ },
+ )
+
+ config = ChatDriverConfig(
+ context=context,
+ openai_client=open_ai_client,
+ model="gpt-3.5-turbo",
+ message_provider=history,
+ )
+
+ chat_driver = ChatDriver(config)
+ return await chat_driver.respond()
diff --git a/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/chat_drivers/gc_fix_agenda_error.py b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/chat_drivers/gc_fix_agenda_error.py
new file mode 100644
index 00000000..2dded40a
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/chat_drivers/gc_fix_agenda_error.py
@@ -0,0 +1,65 @@
+import logging
+
+from chat_driver import ChatDriver, ChatDriverConfig, ContextProtocol
+from chat_driver.in_memory_message_history_provider import InMemoryMessageHistoryProvider
+from chat_driver.message_formatter import liquid_format
+from form_filler_skill.guided_conversation.conversation_helpers import (
+ Conversation,
+ ConversationMessageType,
+)
+from openai import AsyncAzureOpenAI, AsyncOpenAI
+
+logger = logging.getLogger(__name__)
+
+AGENDA_ERROR_CORRECTION_SYSTEM_TEMPLATE = """You are a helpful, thoughtful, and meticulous assistant.
+You are conducting a conversation with a user. You tried to update the agenda, but the update was invalid.
+You will be provided the history of your conversation with the user, \
+your previous attempt(s) at updating the agenda, and the error message(s) that resulted from your attempt(s).
+Your task is to correct the update so that it is valid. \
+Your changes should be as minimal as possible - you are focused on fixing the error(s) that caused the update to be invalid.
+Note that if the resource allocation is invalid, you must follow these rules:
+1. You should not change the description of the first item (since it has already been executed), but you can change its resource allocation
+2. For all other items, you can combine or split them, or assign them fewer or more resources, \
+but the content they cover collectively should not change (i.e. don't eliminate or add new topics).
+For example, the invalid attempt was "item 1 = ask for date of birth (1 turn), item 2 = ask for phone number (1 turn), \
+item 3 = ask for phone type (1 turn), item 4 = explore treatment history (6 turns)", \
+and the error says you need to correct the total resource allocation to 7 turns. \
+A bad solution is "item 1 = ask for date of birth (1 turn), \
+item 2 = explore treatment history (6 turns)" because it eliminates the phone number and phone type topics. \
+A good solution is "item 1 = ask for date of birth (2 turns), item 2 = ask for phone number, phone type,
+and treatment history (2 turns), item 3 = explore treatment history (3 turns)."
+
+Conversation history:
+{{ conversation_history }}
+
+Previous attempts to update the agenda:
+{{ previous_attempts }}"""
+
+
+async def fix_agenda_error(
+ context: ContextProtocol,
+ openai_client: AsyncOpenAI | AsyncAzureOpenAI,
+ previous_attempts: str,
+ conversation: Conversation,
+):
+ history = InMemoryMessageHistoryProvider(formatter=liquid_format)
+
+ history.append_system_message(
+ AGENDA_ERROR_CORRECTION_SYSTEM_TEMPLATE,
+ {
+ "conversation_history": conversation.get_repr_for_prompt(
+ exclude_types=[ConversationMessageType.REASONING]
+ ),
+ "previous_attempts": previous_attempts,
+ },
+ )
+
+ config = ChatDriverConfig(
+ context=context,
+ openai_client=openai_client,
+ model="gpt-3.5-turbo",
+ message_provider=history,
+ )
+
+ chat_driver = ChatDriver(config)
+ return await chat_driver.respond()
diff --git a/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/chat_drivers/gc_update_agenda.py b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/chat_drivers/gc_update_agenda.py
new file mode 100644
index 00000000..65eb70b1
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/chat_drivers/gc_update_agenda.py
@@ -0,0 +1,250 @@
+import logging
+
+from chat_driver import ChatDriver, ChatDriverConfig, ContextProtocol
+from chat_driver.in_memory_message_history_provider import InMemoryMessageHistoryProvider
+from chat_driver.message_formatter import liquid_format
+from form_filler_skill.agenda import Agenda, AgendaItem
+from form_filler_skill.guided_conversation.definition import GCDefinition
+from form_filler_skill.message import Conversation
+from form_filler_skill.resources import (
+ GCResource,
+ ResourceConstraintMode,
+ ResourceConstraintUnit,
+ format_resource,
+)
+from openai import AsyncAzureOpenAI, AsyncOpenAI
+from pydantic import ValidationError
+
+from ..guided_conversation.artifact import Artifact
+from .fix_agenda_error import fix_agenda_error
+from .update_agenda_template import update_agenda_template
+
+logger = logging.getLogger(__name__)
+
+
+def _get_termination_instructions(resource: GCResource):
+ """
+ Get the termination instructions for the conversation. This is contingent on the resources mode,
+ if any, that is available.
+
+ Assumes we're always using turns as the resource unit.
+
+ Args:
+ resource (GCResource): The resource object.
+
+ Returns:
+ str: the termination instructions
+ """
+ # Termination condition under no resource constraints
+ if resource.resource_constraint is None:
+ return (
+ "- You should pick this action as soon as you have completed the artifact to the best of your ability, the"
+ " conversation has come to a natural conclusion, or the user is not cooperating so you cannot continue the"
+ " conversation."
+ )
+
+ # Termination condition under exact resource constraints
+ if resource.resource_constraint.mode == ResourceConstraintMode.EXACT:
+ return "- You should only pick this action if the user is not cooperating so you cannot continue the conversation."
+
+ # Termination condition under maximum resource constraints
+ elif resource.resource_constraint.mode == ResourceConstraintMode.MAXIMUM:
+ return (
+ "- You should pick this action as soon as you have completed the artifact to the best of your ability, the"
+ " conversation has come to a natural conclusion, or the user is not cooperating so you cannot continue the"
+ " conversation."
+ )
+
+ else:
+ logger.error("Invalid resource mode provided.")
+ return ""
+
+
+async def update_agenda(
+ context: ContextProtocol,
+ openai_client: AsyncOpenAI | AsyncAzureOpenAI,
+ definition: GCDefinition,
+ chat_history: Conversation,
+ agenda: Agenda,
+ artifact: Artifact,
+ resource: GCResource,
+) -> bool:
+ # STEP 1: Generate an updated agenda.
+
+ # If there is a resource constraint and there's more than one turn left,
+ # include additional constraint instructions.
+ remaining_resource = resource.remaining_units if resource.remaining_units else 0
+ resource_instructions = resource.get_resource_instructions()
+ if (resource_instructions != "") and (remaining_resource > 1):
+ match resource.get_resource_mode():
+ case ResourceConstraintMode.MAXIMUM:
+ total_resource_str = f"does not exceed the remaining turns ({remaining_resource})."
+ ample_time_str = ""
+ case ResourceConstraintMode.EXACT:
+ total_resource_str = f"is equal to the remaining turns ({remaining_resource}). Do not leave any turns unallocated."
+ ample_time_str = (
+ "If you have many turns remaining, instead of including wrap-up items or repeating "
+ "topics, you should include items that increase the breadth and/or depth of the conversation "
+ 'in a way that\'s directly relevant to the artifact (e.g. "collect additional details about X", '
+ '"ask for clarification about Y", "explore related topic Z", etc.).'
+ )
+ case _:
+ logger.error("Invalid resource mode.")
+ else:
+ total_resource_str = ""
+ ample_time_str = ""
+
+ history = InMemoryMessageHistoryProvider(formatter=liquid_format)
+ history.append_system_message(
+ update_agenda_template,
+ {
+ "context": definition.conversation_context,
+ "artifact_schema": definition.artifact_schema,
+ "rules": definition.rules,
+ "current_state_description": definition.conversation_flow,
+ "show_agenda": True,
+ "remaining_resource": remaining_resource,
+ "total_resource_str": total_resource_str,
+ "ample_time_str": ample_time_str,
+ "termination_instructions": _get_termination_instructions(resource),
+ "resource_instructions": resource_instructions,
+ "chat_history": chat_history,
+ "agenda_state": get_agenda_for_prompt(agenda),
+ "artifact_state": artifact.get_artifact_for_prompt(),
+ },
+ )
+
+ config = ChatDriverConfig(
+ context=context,
+ openai_client=openai_client,
+ model="gpt-4o",
+ message_provider=history,
+ )
+
+ chat_driver = ChatDriver(config)
+ response = await chat_driver.respond()
+ items = response.message
+
+ # STEP 2: Validate/fix the updated agenda.
+
+ previous_attempts = []
+ while True:
+ try:
+ # Pydantic type validation.
+ agenda.items = items # type: ignore
+
+ # Check resource constraints.
+ if agenda.resource_constraint_mode is not None:
+ check_item_constraints(
+ agenda.resource_constraint_mode,
+ agenda.items,
+ resource.estimate_remaining_turns(),
+ )
+
+ logger.info(f"Agenda updated successfully: {get_agenda_for_prompt(agenda)}")
+ return True
+
+ except (ValidationError, ValueError) as e:
+ # If we have reached the maximum number of retries return a failure.
+ if len(previous_attempts) >= agenda.max_agenda_retries:
+ logger.warning(
+ f"Failed to update agenda after {agenda.max_agenda_retries} attempts."
+ )
+ return False
+
+ # Otherwise, get an error string.
+ if isinstance(e, ValidationError):
+ error_str = "; ".join([e.get("msg") for e in e.errors()])
+ error_str = error_str.replace(
+ "; Input should be 'Unanswered'", " or input should be 'Unanswered'"
+ )
+ else:
+ error_str = str(e)
+
+ # Add it to our list of previous attempts.
+ previous_attempts.append((str(items), error_str))
+
+ # And try again.
+ logger.info(f"Attempting to fix the agenda error. Attempt {len(previous_attempts)}.")
+ llm_formatted_attempts = "\n".join([
+ f"Attempt: {attempt}\nError: {error}" for attempt, error in previous_attempts
+ ])
+ response = await fix_agenda_error(
+ context, openai_client, llm_formatted_attempts, chat_history
+ )
+
+ if response is None:
+ raise ValueError("Invalid response from the LLM.")
+
+ if response["validation_result"] != ToolValidationResult.SUCCESS:
+ logger.warning(
+ f"Failed to fix the agenda error due to a failure in the LLM tool call: {response['validation_result']}"
+ )
+ return False
+ else:
+ # Use the result of the first tool call to try the update again
+ items = response
+
+
+def check_item_constraints(
+ resource_constraint_mode: ResourceConstraintMode,
+ items: list[AgendaItem],
+ remaining_turns: int,
+) -> None:
+ """
+ Validates if any constraints were violated while performing the agenda
+ update.
+ """
+ # The total, proposed allocation of resources.
+ total_resources = sum([item.resource for item in items])
+
+ violations = []
+ # In maximum mode, the total resources should not exceed the remaining
+ # turns.
+ if (resource_constraint_mode == ResourceConstraintMode.MAXIMUM) and (
+ total_resources > remaining_turns
+ ):
+ violations.append(
+ "The total turns allocated in the agenda "
+ f"must not exceed the remaining amount ({remaining_turns}); "
+ f"but the current total is {total_resources}."
+ )
+
+ # In exact mode if the total resources were not exactly equal to the
+ # remaining turns.
+ if (resource_constraint_mode == ResourceConstraintMode.EXACT) and (
+ total_resources != remaining_turns
+ ):
+ violations.append(
+ "The total turns allocated in the agenda "
+ f"must equal the remaining amount ({remaining_turns}); "
+ f"but the current total is {total_resources}."
+ )
+
+ # Check if any item has a resource value of 0.
+ if any(item.resource <= 0 for item in items):
+ violations.append("All items must have a resource value greater than 0.")
+
+ # Raise an error if any violations were found.
+ if len(violations) > 0:
+ logger.debug(f"Agenda update failed due to the following violations: {violations}.")
+ raise ValueError(" ".join(violations))
+
+
+def get_agenda_for_prompt(agenda: Agenda) -> str:
+ """
+ Gets a string representation of the agenda for use in an LLM prompt.
+ """
+ agenda_json = agenda.model_dump()
+ agenda_items = agenda_json.get("items", [])
+ if len(agenda_items) == 0:
+ return "None"
+ agenda_str = "\n".join([
+ f"{i + 1}. [{format_resource(item['resource'], ResourceConstraintUnit.TURNS)}] {item['title']}"
+ for i, item in enumerate(agenda_items)
+ ])
+ total_resource = format_resource(
+ sum([item["resource"] for item in agenda_items]), ResourceConstraintUnit.TURNS
+ )
+ agenda_str += f"\nTotal = {total_resource}"
+ return agenda_str
diff --git a/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/chat_drivers/gc_update_artifact.py b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/chat_drivers/gc_update_artifact.py
new file mode 100644
index 00000000..4677e33a
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/chat_drivers/gc_update_artifact.py
@@ -0,0 +1,61 @@
+import logging
+
+from chat_driver import ChatDriver, ChatDriverConfig, ContextProtocol
+from chat_driver.in_memory_message_history_provider import InMemoryMessageHistoryProvider
+from chat_driver.message_formatter import liquid_format
+from openai import AsyncAzureOpenAI, AsyncOpenAI
+
+logger = logging.getLogger(__name__)
+
+execution_template = """You are a helpful, thoughtful, and meticulous assistant.
+You are conducting a conversation with a user. Your goal is to complete an artifact as thoroughly as possible by the
+end of the conversation.
+You will be given some reasoning about the best possible action(s) to take next given the state of the conversation
+as well as the artifact schema.
+The reasoning is supposed to state the recommended action(s) to take next, along with all required parameters for each action.
+Your task is to execute ALL actions recommended in the reasoning in the order they are listed.
+If the reasoning's specification of an action is incomplete (e.g. it doesn't include all required parameters for the
+action, \
+or some parameters are specified implicitly, such as "send a message that contains a greeting" instead of explicitly
+providing \
+the value of the "message" parameter), do not execute the action. You should never fill in missing or imprecise
+parameters yourself.
+If the reasoning is not clear about which actions to take, or all actions are specified in an incomplete way, \
+return 'None' without selecting any action.
+
+Artifact schema:
+{{ artifact_schema }}
+
+If the type in the schema is str, the "field_value" parameter in the action should be also be a string.
+These are example parameters for the update_artifact action: {"field_name": "company_name", "field_value": "Contoso"}
+DO NOT write JSON in the "field_value" parameter in this case. {"field_name": "company_name", "field_value": "{"value": "Contoso"}"} is INCORRECT.
+
+Reasoning:
+{{ reasoning }}"""
+
+
+async def update_artifact(
+ context: ContextProtocol,
+ open_ai_client: AsyncOpenAI | AsyncAzureOpenAI,
+ reasoning: str,
+ artifact_schema: str,
+):
+ history = InMemoryMessageHistoryProvider(formatter=liquid_format)
+
+ history.append_system_message(
+ execution_template,
+ {
+ "artifact_schema": artifact_schema,
+ "reasoning": reasoning,
+ },
+ )
+
+ config = ChatDriverConfig(
+ context=context,
+ openai_client=open_ai_client,
+ model="gpt-3.5-turbo",
+ message_provider=history,
+ )
+
+ chat_driver = ChatDriver(config)
+ return await chat_driver.respond()
diff --git a/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/conversation_helpers.py b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/conversation_helpers.py
new file mode 100644
index 00000000..1b9502d3
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/conversation_helpers.py
@@ -0,0 +1,166 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+# FIXME: Copied code from Semantic Kernel repo, using as-is despite type errors
+# type: ignore
+
+import logging
+import time
+from dataclasses import dataclass, field
+from enum import StrEnum
+from typing import Union
+
+from semantic_kernel.contents import ChatMessageContent
+
+
+class ConversationMessageType(StrEnum):
+ DEFAULT = "default"
+ ARTIFACT_UPDATE = "artifact-update"
+ REASONING = "reasoning"
+
+
+@dataclass
+class Conversation:
+ """An abstraction to represent a list of messages and common operations such as adding messages
+ and getting a string representation.
+
+ Args:
+ conversation_messages (list[ChatMessageContent]): A list of ChatMessageContent objects.
+ """
+
+ logger = logging.getLogger(__name__)
+ conversation_messages: list[ChatMessageContent] = field(default_factory=list)
+
+ def add_messages(self, messages: Union[ChatMessageContent, list[ChatMessageContent], "Conversation", None]) -> None:
+ """Add a message, list of messages to the conversation or merge another conversation into the end of this one.
+
+ Args:
+ messages (Union[ChatMessageContent, list[ChatMessageContent], "Conversation"]): The message(s) to add.
+ All messages will be added to the end of the conversation.
+
+ Returns:
+ None
+ """
+ if isinstance(messages, list):
+ self.conversation_messages.extend(messages)
+ elif isinstance(messages, Conversation):
+ self.conversation_messages.extend(messages.conversation_messages)
+ elif isinstance(messages, ChatMessageContent):
+ # if ChatMessageContent.metadata doesn't have type, then add default
+ if "type" not in messages.metadata:
+ messages.metadata["type"] = ConversationMessageType.DEFAULT
+ self.conversation_messages.append(messages)
+ else:
+ self.logger.warning(f"Invalid message type: {type(messages)}")
+ return None
+
+ def get_repr_for_prompt(
+ self,
+ exclude_types: list[ConversationMessageType] | None = None,
+ ) -> str:
+ """Create a string representation of the conversation history for use in LLM prompts.
+
+ Args:
+ exclude_types (list[ConversationMessageType] | None): A list of message types to exclude from the conversation
+ history. If None, all message types will be included.
+
+ Returns:
+ str: A string representation of the conversation history.
+ """
+ if len(self.conversation_messages) == 0:
+ return "None"
+
+ # Do not include the excluded messages types in the conversation history repr.
+ if exclude_types is not None:
+ conversation_messages = [
+ message
+ for message in self.conversation_messages
+ if "type" in message.metadata and message.metadata["type"] not in exclude_types
+ ]
+ else:
+ conversation_messages = self.conversation_messages
+
+ to_join = []
+ current_turn = None
+ for message in conversation_messages:
+ participant_name = message.name
+ # Modify the default user to be capitalized for consistency with how assistant is written.
+ if participant_name == "user":
+ participant_name = "User"
+
+ # If the turn number is None, don't include it in the string
+ if "turn_number" in message.metadata and current_turn != message.metadata["turn_number"]:
+ current_turn = message.metadata["turn_number"]
+ to_join.append(f"[Turn {current_turn}]")
+
+ # Add the message content
+ if (message.role == "assistant") and (
+ "type" in message.metadata and message.metadata["type"] == ConversationMessageType.ARTIFACT_UPDATE
+ ):
+ to_join.append(message.content)
+ elif message.role == "assistant":
+ to_join.append(f"Assistant: {message.content}")
+ else:
+ user_string = message.content.strip()
+ if user_string == "":
+ to_join.append(f"{participant_name}: ")
+ else:
+ to_join.append(f"{participant_name}: {user_string}")
+
+ conversation_string = "\n".join(to_join)
+ return conversation_string
+
+ def message_to_json(self, message: ChatMessageContent) -> dict:
+ """
+ Convert a ChatMessageContent object to a JSON serializable dictionary.
+
+ Args:
+ message (ChatMessageContent): The ChatMessageContent object to convert to JSON.
+
+ Returns:
+ dict: A JSON serializable dictionary representation of the ChatMessageContent object.
+ """
+ return {
+ "role": message.role,
+ "content": message.content,
+ "name": message.name,
+ "metadata": {
+ "turn_number": message.metadata["turn_number"] if "turn_number" in message.metadata else None,
+ "type": message.metadata["type"] if "type" in message.metadata else ConversationMessageType.DEFAULT,
+ "timestamp": message.metadata["timestamp"] if "timestamp" in message.metadata else None,
+ },
+ }
+
+ def to_json(self) -> dict:
+ json_data = {}
+ json_data["conversation"] = {}
+ json_data["conversation"]["conversation_messages"] = [
+ self.message_to_json(message) for message in self.conversation_messages
+ ]
+ return json_data
+
+ @classmethod
+ def from_json(
+ cls,
+ json_data: dict,
+ ) -> "Conversation":
+ conversation = cls()
+ for message in json_data["conversation"]["conversation_messages"]:
+ conversation.add_messages(
+ ChatMessageContent(
+ role=message["role"],
+ content=message["content"],
+ name=message["name"],
+ metadata={
+ "turn_number": message["metadata"]["turn_number"],
+ "type": ConversationMessageType(message["metadata"]["type"]),
+ "timestamp": message["metadata"]["timestamp"],
+ },
+ )
+ )
+
+ return conversation
+
+
+def create_formatted_timestamp() -> str:
+ """Create a formatted timestamp."""
+ return time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(time.time()))
diff --git a/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/definition.py b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/definition.py
new file mode 100644
index 00000000..a8befa22
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/definition.py
@@ -0,0 +1,14 @@
+from dataclasses import dataclass
+from typing import Optional
+
+from form_filler_skill.guided_conversation.resources import ResourceConstraint
+
+
+@dataclass
+class GCDefinition:
+ artifact_schema: str
+ rules: str
+ conversation_flow: Optional[str]
+ conversation_context: str
+ resource_constraint: Optional[ResourceConstraint]
+ service_id: str = "gc_main"
diff --git a/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/openai_tool_calling.py b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/openai_tool_calling.py
new file mode 100644
index 00000000..1e567896
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/openai_tool_calling.py
@@ -0,0 +1,167 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+# FIXME: Copied code from Semantic Kernel repo, using as-is despite type errors
+# type: ignore
+
+import json
+import logging
+from dataclasses import dataclass
+from enum import Enum
+from typing import Any
+
+from semantic_kernel.contents.function_call_content import FunctionCallContent
+from semantic_kernel.functions import FunctionResult
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ToolArg:
+ argument_name: str
+ required: bool
+
+
+@dataclass
+class Tool:
+ name: str
+ args: list[ToolArg]
+
+
+class ToolValidationResult(Enum):
+ NO_TOOL_CALLED = "No tool was called"
+ INVALID_TOOL_CALLED = "A tool was called with an unexpected name"
+ MISSING_REQUIRED_ARGUMENT = "The tool called is missing a required argument"
+ INVALID_ARGUMENT_TYPE = "The value of an argument is of an unexpected type"
+ INVALID_ARGUMENT = "The tool called has an unexpected argument"
+ SUCCESS = "success"
+
+
+def parse_function_result(response: FunctionResult) -> dict[str, Any]:
+ """Parse the response from SK's FunctionResult object into only the relevant data for easier downstream processing.
+ This should only be used when you expect the response to contain tool calls.
+
+ Args:
+ response (FunctionResult): The response from the kernel.
+
+ Returns:
+ dict[str, Any]: The parsed response data with the following format if n was set greater than 1:
+ {
+ "choices": [
+ {
+ "tool_names": list[str],
+ "tool_args_list": list[dict[str, Any]],
+ "message": str,
+ "finish_reason": str,
+ "validation_result": ToolValidationResult
+ }, ...
+ ]
+ }
+ Otherwise, the response will directly contain the data from the first choice (tool_names, etc keys)
+ """
+ response_data: dict[str, Any] = {"choices": []}
+ for response_choice in response.value:
+ response_data_curr = {}
+ finish_reason = response_choice.finish_reason
+
+ if finish_reason == "tool_calls":
+ tool_names = []
+ tool_args_list = []
+ # Only look at the items that are of instance `FunctionCallContent`
+ tool_calls = [item for item in response_choice.items if isinstance(item, FunctionCallContent)]
+ for tool_call in tool_calls:
+ if "-" not in tool_call.name:
+ logger.info(f"Tool call name {tool_call.name} does not match naming convention - modifying name.")
+ tool_names.append(tool_call.name + "-" + tool_call.name)
+ else:
+ tool_names.append(tool_call.name)
+ try:
+ tool_args = json.loads(tool_call.arguments)
+ except json.JSONDecodeError:
+ logger.warning(f"Failed to parse tool arguments for tool call {tool_call.name}. Using empty dict.")
+ tool_args = {}
+ tool_args_list.append(tool_args)
+ response_data_curr["tool_names"] = tool_names
+ response_data_curr["tool_args_list"] = tool_args_list
+
+ response_data_curr["message"] = response_choice.content
+ response_data_curr["finish_reason"] = finish_reason
+ response_data["choices"].append(response_data_curr)
+
+ if len(response_data["choices"]) == 1:
+ response_data.update(response_data["choices"][0])
+ del response_data["choices"]
+
+ return response_data
+
+
+def construct_tool_objects(kernel_function_tools: dict) -> list[Tool]:
+ """Construct a list of Tool objects from the kernel function tools definition.
+
+ Args:
+ kernel_function_tools (dict): The definition of tools done by the kernel function.
+
+ Returns:
+ list[Tool]: The list of Tool objects constructed from the kernel function tools definition.
+ """
+
+ tool_objects: list[Tool] = []
+ for tool_definition in kernel_function_tools:
+ tool_name = tool_definition["function"]["name"]
+ tool_args = tool_definition["function"]["parameters"]["properties"]
+
+ tool_arg_objects: list[ToolArg] = []
+ for argument_name, _ in tool_args.items():
+ tool_arg = ToolArg(argument_name=argument_name, required=False)
+ tool_arg_objects.append(tool_arg)
+
+ required_args = tool_definition["function"]["parameters"]["required"]
+ for tool_arg_object in tool_arg_objects:
+ if tool_arg_object.argument_name in required_args:
+ tool_arg_object.required = True
+
+ tool_objects.append(Tool(name=tool_name, args=tool_arg_objects))
+ return tool_objects
+
+
+def validate_tool_calling(response: dict[str, Any], request_tool_param: dict) -> ToolValidationResult:
+ """Validate that the response from the LLM called tools corrected.
+ 1. Check if any tool was called.
+ 2. Check if the tools called were valid (names match)
+ 3. Check if all the required arguments were passed.
+
+ Args:
+ response (dict[str, Any]): The response from the LLM containing the tools called (output of parse_function_result)
+ tools (list[Tool]): The list of tools that can be called by the model.
+
+ Returns:
+ ToolValidationResult: The result of the validation. ToolValidationResult.SUCCESS if the validation passed.
+ """
+
+ tool_objects = construct_tool_objects(request_tool_param)
+ tool_names = response.get("tool_names", [])
+ tool_args_list = response.get("tool_args_list", [])
+
+ # Check if any tool was called.
+ if not tool_names:
+ logger.info("No tool was called.")
+ return ToolValidationResult.NO_TOOL_CALLED
+
+ for tool_name, tool_args in zip(tool_names, tool_args_list, strict=True):
+ # Check the tool names is valid.
+ tool: Tool | None = next((t for t in tool_objects if t.name == tool_name), None)
+ if not tool:
+ logger.warning(f"Invalid tool called: {tool_name}")
+ return ToolValidationResult.INVALID_TOOL_CALLED
+
+ for arg in tool.args:
+ # Check if the required arguments were passed.
+ if arg.required and arg.argument_name not in tool_args:
+ logger.warning(f"Missing required argument '{arg.argument_name}' for tool '{tool_name}'.")
+ return ToolValidationResult.MISSING_REQUIRED_ARGUMENT
+
+ for tool_arg_name in tool_args.keys():
+ if tool_arg_name not in [arg.argument_name for arg in tool.args]:
+ logger.warning(f"Unexpected argument '{tool_arg_name}' for tool '{tool_name}'.")
+ return ToolValidationResult.INVALID_ARGUMENT
+
+ return ToolValidationResult.SUCCESS
diff --git a/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/plugin_helpers.py b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/plugin_helpers.py
new file mode 100644
index 00000000..ba32e4df
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/plugin_helpers.py
@@ -0,0 +1,69 @@
+# # Copyright (c) Microsoft. All rights reserved.
+
+# FIXME: Copied code from Semantic Kernel repo, using as-is despite type errors
+# type: ignore
+
+from dataclasses import dataclass
+
+from pydantic import ValidationError
+from semantic_kernel import Kernel
+from semantic_kernel.connectors.ai.function_calling_utils import kernel_function_metadata_to_function_call_format
+from semantic_kernel.contents import ChatMessageContent
+
+
+@dataclass
+class PluginOutput:
+ """A wrapper for all Guided Conversation Plugins. This class is used to return the output of a generic plugin.
+
+ Args:
+ update_successful (bool): Whether the update was successful.
+ messages (list[ChatMessageContent]): A list of messages to be used at the user's digression, it
+ contains information about the process of calling the plugin.
+ """
+
+ update_successful: bool
+ messages: list[ChatMessageContent]
+
+
+def format_kernel_functions_as_tools(kernel: Kernel, functions: list[str]):
+ """Format kernel functions as JSON schemas for custom validation."""
+ formatted_tools = []
+ for _, kernel_plugin_def in kernel.plugins.items():
+ for function_name, function_def in kernel_plugin_def.functions.items():
+ if function_name in functions:
+ func_metadata = function_def.metadata
+ formatted_tools.append(kernel_function_metadata_to_function_call_format(func_metadata))
+ return formatted_tools
+
+
+def update_attempts(
+ error: Exception, attempt_id: str, previous_attempts: list[tuple[str, str]]
+) -> tuple[list[tuple[str, str]], str]:
+ """
+ Updates the plugin class attribute list of previous attempts with the current attempt and error message
+ (including duplicates).
+
+ Args:
+ error (Exception): The error object.
+ attempt_id (str): The ID of the current attempt.
+ previous_attempts (list): The list of previous attempts.
+
+ Returns:
+ str: A formatted (optimized for LLM performance) string of previous attempts, with duplicates removed.
+ """
+ if isinstance(error, ValidationError):
+ error_str = "; ".join([e.get("msg") for e in error.errors()])
+ # replace "; Input should be 'Unanswered'" with " or input should be 'Unanswered'" for clarity
+ error_str = error_str.replace("; Input should be 'Unanswered'", " or input should be 'Unanswered'")
+ else:
+ error_str = str(error)
+ new_failed_attempt = (attempt_id, error_str)
+ previous_attempts.append(new_failed_attempt)
+
+ # Format previous attempts to be more friendly for the LLM
+ attempts_list = []
+ for attempt, error in previous_attempts:
+ attempts_list.append(f"Attempt: {attempt}\nError: {error}")
+ llm_formatted_attempts = "\n".join(attempts_list)
+
+ return previous_attempts, llm_formatted_attempts
diff --git a/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/resources.py b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/resources.py
new file mode 100644
index 00000000..f39d0253
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation/resources.py
@@ -0,0 +1,285 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+
+import logging
+import math
+import time
+from enum import StrEnum
+
+from pydantic import BaseModel
+
+logger = logging.getLogger(__name__)
+
+
+class ResourceConstraintUnit(StrEnum):
+ """
+ Choose the unit of the resource constraint. Seconds and Minutes are
+ real-time and will be impacted by the latency of the model.
+ """
+
+ SECONDS = "seconds"
+ MINUTES = "minutes"
+ TURNS = "turns"
+
+
+class ResourceConstraintMode(StrEnum):
+ """
+ Choose how the agent should use the resource.
+
+ Maximum: is an upper bound, i.e. the agent can end the conversation before
+ the resource is exhausted.
+
+ Exact: The agent should aim to use exactly the given amount of the resource.
+ """
+
+ MAXIMUM = "maximum"
+ EXACT = "exact"
+
+
+class ResourceConstraint(BaseModel):
+ """
+ A structured representation of the resource constraint for the
+ GuidedConversation agent.
+ """
+
+ quantity: float | int
+ unit: ResourceConstraintUnit
+ mode: ResourceConstraintMode
+
+ class Config:
+ arbitrary_types_allowed = True
+
+
+def format_resource(quantity: float, unit: ResourceConstraintUnit) -> str:
+ """
+ Get formatted string for a given quantity and unit (e.g. 1 second, 20
+ seconds)
+ """
+ if unit != ResourceConstraintUnit.TURNS:
+ quantity = round(quantity, 1)
+ if quantity == 1:
+ return f"{quantity} {unit.value.rstrip('s')}"
+ else:
+ return f"{quantity} {unit.value}"
+
+
+class GCResource:
+ """
+ Resource constraints for the GuidedConversation agent. This class is used to
+ keep track of the resource constraints. If resource_constraint is None, then
+ the agent can continue indefinitely. This also means that no agenda will be
+ created for the conversation.
+
+ Args:
+ resource_constraint (ResourceConstraint | None): The resource constraint
+ for the conversation.
+
+ initial_seconds_per_turn (int): The initial number of seconds per turn.
+ Defaults to 120 seconds.
+ """
+
+ def __init__(
+ self,
+ resource_constraint: ResourceConstraint | None,
+ initial_seconds_per_turn: int = 120,
+ ):
+ self.resource_constraint: ResourceConstraint | None = resource_constraint
+ self.initial_seconds_per_turn: int = initial_seconds_per_turn
+
+ self.turn_number: int = 0
+
+ if resource_constraint is not None:
+ self.elapsed_units = 0
+ self.remaining_units = resource_constraint.quantity
+ else:
+ self.elapsed_units = 0
+ self.remaining_units = 0
+
+ def start_resource(self) -> None:
+ """To be called at the start of a conversation turn"""
+ if self.resource_constraint is not None and (
+ self.resource_constraint.unit == ResourceConstraintUnit.SECONDS
+ or self.resource_constraint.unit == ResourceConstraintUnit.MINUTES
+ ):
+ self.start_time = time.time()
+
+ def increment_resource(self) -> None:
+ """Increment the resource counter by one turn."""
+ if self.resource_constraint is not None:
+ match self.resource_constraint.unit:
+ case ResourceConstraintUnit.SECONDS:
+ self.elapsed_units += time.time() - self.start_time
+ self.remaining_units = self.resource_constraint.quantity - self.elapsed_units
+ case ResourceConstraintUnit.MINUTES:
+ self.elapsed_units += (time.time() - self.start_time) / 60
+ self.remaining_units = self.resource_constraint.quantity - self.elapsed_units
+ case ResourceConstraintUnit.TURNS:
+ self.elapsed_units += 1
+ self.remaining_units -= 1
+ case _:
+ raise ValueError("Invalid resource unit provided.")
+ self.turn_number += 1
+
+ def get_resource_mode(self) -> ResourceConstraintMode | None:
+ """
+ Get the mode of the resource constraint.
+ """
+ if self.resource_constraint is None:
+ return None
+ return self.resource_constraint.mode
+
+ def get_elapsed_turns(self, formatted_repr: bool = False) -> str | int:
+ """
+ Get the number of elapsed turns.
+
+ Args:
+ formatted_repr (bool): If true, return a formatted string
+ representation of the elapsed turns. If false, return an integer.
+ Defaults to False.
+
+ Returns:
+ str | int: The description/number of elapsed turns.
+ """
+ if formatted_repr:
+ return format_resource(self.turn_number, ResourceConstraintUnit.TURNS)
+ else:
+ return self.turn_number
+
+ def get_remaining_turns(self, formatted_repr: bool = False) -> str | int:
+ """
+ Get the number of remaining turns.
+
+ Args:
+ formatted_repr (bool): If true, return a formatted string
+ representation of the remaining turns.
+
+ Returns:
+ str | int: The description/number of remaining turns.
+ """
+ if formatted_repr:
+ return format_resource(self.estimate_remaining_turns(), ResourceConstraintUnit.TURNS)
+ else:
+ return self.estimate_remaining_turns()
+
+ def estimate_remaining_turns(self) -> int:
+ """
+ Estimate the remaining turns based on the resource constraint, thereby
+ translating certain resource units (e.g. seconds, minutes) into turns.
+ """
+ if self.resource_constraint is None:
+ logger.error(
+ "Resource constraint is not set, so turns cannot be estimated using function estimate_remaining_turns"
+ )
+ raise ValueError(
+ "Resource constraint is not set. Do not try to call this method without a resource constraint."
+ )
+
+ match self.resource_constraint.unit:
+ case ResourceConstraintUnit.MINUTES:
+ if self.turn_number == 0:
+ time_per_turn = self.initial_seconds_per_turn
+ else:
+ time_per_turn = (self.elapsed_units * 60) / self.turn_number
+ time_per_turn /= 60
+ remaining_turns = self.remaining_units / time_per_turn
+ if remaining_turns < 1:
+ return math.ceil(remaining_turns)
+ else:
+ return math.floor(remaining_turns)
+
+ case ResourceConstraintUnit.SECONDS:
+ if self.turn_number == 0:
+ time_per_turn = self.initial_seconds_per_turn
+ else:
+ time_per_turn = self.elapsed_units / self.turn_number
+ remaining_turns = self.remaining_units / time_per_turn
+ if remaining_turns < 1:
+ return math.ceil(remaining_turns)
+ else:
+ return math.floor(remaining_turns)
+
+ case ResourceConstraintUnit.TURNS:
+ return int(self.resource_constraint.quantity - self.turn_number)
+
+ case _:
+ raise ValueError("Invalid resource unit provided.")
+
+ def get_resource_instructions(self) -> str:
+ """Get the resource instructions for the conversation.
+
+ Assumes we're always using turns as the resource unit.
+
+ Returns:
+ str: the resource instructions
+ """
+ if self.resource_constraint is None:
+ return ""
+
+ formatted_elapsed_resource = format_resource(
+ self.elapsed_units, ResourceConstraintUnit.TURNS
+ )
+ formatted_remaining_resource = format_resource(
+ self.remaining_units, ResourceConstraintUnit.TURNS
+ )
+
+ # if the resource quantity is anything other than 1, the resource unit should be plural (e.g. "minutes" instead of "minute")
+ is_plural_elapsed = self.elapsed_units != 1
+ is_plural_remaining = self.remaining_units != 1
+
+ if self.elapsed_units > 0:
+ resource_instructions = f"So far, {formatted_elapsed_resource} {'have' if is_plural_elapsed else 'has'} elapsed since the conversation began. "
+ else:
+ resource_instructions = ""
+
+ if self.resource_constraint.mode == ResourceConstraintMode.EXACT:
+ exact_mode_instructions = (
+ f"There {'are' if is_plural_remaining else 'is'} {formatted_remaining_resource} "
+ "remaining (including this one) - the conversation will automatically terminate "
+ "when 0 turns are left. You should continue the conversation until it is "
+ "automatically terminated. This means you should NOT preemptively end the "
+ 'conversation, either explicitly (by selecting the "End conversation" action) '
+ "or implicitly (e.g. by telling the user that you have all required information "
+ "and they should wait for the next step). Your goal is not to maximize efficiency "
+ "(i.e. complete the artifact as quickly as possible then end the conversation), "
+ "but rather to make the best use of ALL remaining turns available to you"
+ )
+
+ if is_plural_remaining:
+ resource_instructions += f"""{exact_mode_instructions}. This will require you to plan your actions carefully using the agenda: you want to avoid the situation where you have to pack too many topics into the final turns because you didn't account for them earlier, \
+or where you've rushed through the conversation and all fields are completed but there are still many turns left."""
+
+ # special instruction for the final turn (i.e. 1 remaining) in exact mode
+ else:
+ resource_instructions += f"""{exact_mode_instructions}, including this one. Therefore, you should use this turn to ask for any remaining information needed to complete the artifact, \
+ or, if the artifact is already completed, continue to broaden/deepen the discussion in a way that's directly relevant to the artifact. Do NOT indicate to the user that the conversation is ending."""
+
+ elif self.resource_constraint.mode == ResourceConstraintMode.MAXIMUM:
+ resource_instructions += f"""You have a maximum of {formatted_remaining_resource} (including this one) left to complete the conversation. \
+You can decide to terminate the conversation at any point (including now), otherwise the conversation will automatically terminate when 0 turns are left. \
+You will need to plan your actions carefully using the agenda: you want to avoid the situation where you have to pack too many topics into the final turns because you didn't account for them earlier."""
+
+ else:
+ logger.error("Invalid resource mode provided.")
+
+ return resource_instructions
+
+ def to_json(self) -> dict:
+ return {
+ "turn_number": self.turn_number,
+ "remaining_units": self.remaining_units,
+ "elapsed_units": self.elapsed_units,
+ }
+
+ @classmethod
+ def from_json(
+ cls,
+ json_data: dict,
+ ) -> "GCResource":
+ gc_resource = cls(
+ resource_constraint=None,
+ initial_seconds_per_turn=120,
+ )
+ gc_resource.turn_number = json_data["turn_number"]
+ gc_resource.remaining_units = json_data["remaining_units"]
+ gc_resource.elapsed_units = json_data["elapsed_units"]
+ return gc_resource
diff --git a/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation_skill.py b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation_skill.py
new file mode 100644
index 00000000..7f40955e
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/guided_conversation_skill.py
@@ -0,0 +1,156 @@
+from typing import Any, Optional
+
+from chat_driver import ChatDriverConfig
+from events import BaseEvent
+from skill_library import FunctionRoutine, RoutineTypes, Skill
+from skill_library.run_context import RunContext
+
+from form_filler_skill.agenda import Agenda
+from form_filler_skill.artifact import Artifact
+from form_filler_skill.definition import GCDefinition
+from form_filler_skill.message import Conversation
+from form_filler_skill.resources import GCResource
+
+from .chat_drivers.unneeded.execute_reasoning import execute_reasoning
+from .chat_drivers.final_update import final_update
+from .chat_drivers.update_agenda import update_agenda
+
+NAME = "guided-conversation"
+CLASS_NAME = "GuidedConversationSkill"
+DESCRIPTION = (
+ "Walks the user through a conversation about gathering info for the creation of an artifact."
+)
+DEFAULT_MAX_RETRIES = 3
+INSTRUCTIONS = "You are an assistant."
+
+
+class GuidedConversationSkill(Skill):
+ def __init__(
+ self,
+ chat_driver_config: ChatDriverConfig,
+ agenda: Agenda,
+ artifact: Artifact,
+ resource: GCResource,
+ ) -> None:
+ # Put all functions in a group. We are going to use all these as (1)
+ # skill actions, but also as (2) chat functions and (3) chat commands.
+ # You can also put them in separate lists if you want to differentiate
+ # between these.
+ functions = [
+ self.update_agenda,
+ self.execute_reasoning,
+ self.final_update,
+ ]
+
+ # Add some skill routines.
+ routines: list[RoutineTypes] = [
+ self.conversation_routine(),
+ ]
+
+ # Configure the skill's chat driver.
+ self.openai_client = chat_driver_config.openai_client
+ chat_driver_config.instructions = INSTRUCTIONS
+ chat_driver_config.commands = functions
+ chat_driver_config.functions = functions
+
+ # TODO: Persist these. They should be saved in the skills state by
+ # session_id.
+ self.agenda = agenda
+ self.artifact = artifact
+ self.resource = resource
+ self.chat_history = Conversation()
+
+ # Initialize the skill!
+ super().__init__(
+ name=NAME,
+ description=DESCRIPTION,
+ chat_driver_config=chat_driver_config,
+ skill_actions=functions,
+ routines=routines,
+ )
+
+ ##################################
+ # Routines
+ ##################################
+
+ def conversation_routine(self) -> FunctionRoutine:
+ return FunctionRoutine(
+ name="conversation",
+ description="Run a guided conversation.",
+ init_function=self.conversation_init_function,
+ step_function=self.conversation_step_function,
+ skill=self,
+ )
+
+ async def conversation_init_function(
+ self, context: RunContext, vars: dict[str, Any] | None = None
+ ):
+ if vars is None:
+ return
+ state = {"definition": vars["definition"]}
+ await context.routine_stack.set_current_state(state)
+ await self.conversation_step_function(context)
+
+ async def conversation_step_function(
+ self,
+ context: RunContext,
+ message: Optional[str] = None,
+ ):
+ # TODO: Where is this conversation maintained?
+ frame = await context.routine_stack.peek()
+ state = frame.state if frame else {}
+ definition = GCDefinition(**state["definition"])
+ while True:
+ match state["mode"]:
+ case None:
+ state["mode"] = "init"
+ case "init":
+ state["chat_history"] = []
+ message, done = await self.update_agenda(context, definition)
+ if done:
+ state["mode"] = "finalize"
+ state["mode"] = "conversation"
+ context.emit(message)
+ return
+ case "conversation":
+ state["chat_history"] += message
+ state["artifact"] = self.update_artifact(context)
+ agenda, done = self.update_agenda()
+ if agenda:
+ state["agenda"] = agenda
+ if done:
+ state["mode"] = "finalize"
+ await self.message_user(context, agenda) # generates the next message
+ return
+ case "finalize":
+ self.final_update() # Generates the final message.
+ state["state"] = "done"
+ runner.send(message)
+ return
+ case "done":
+ return state["artifact"]
+
+ ##################################
+ # Actions
+ ##################################
+
+ async def update_agenda(self, context: RunContext, definition: GCDefinition):
+ return await update_agenda(
+ context,
+ self.openai_client,
+ definition,
+ self.chat_history,
+ agenda=self.agenda,
+ artifact=self.artifact,
+ resource=self.resource,
+ )
+
+ async def execute_reasoning(self, context: RunContext, reasoning: str) -> BaseEvent:
+ return await execute_reasoning(
+ context, self.openai_client, reasoning, self.artifact.get_schema_for_prompt()
+ )
+
+ async def final_update(self, context: RunContext, definition: GCDefinition):
+ await final_update(
+ context, self.openai_client, definition, self.chat_history, artifact=self.artifact
+ )
diff --git a/libraries/python/skills/skills/form-filler-skill/form_filler_skill/message.py b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/message.py
new file mode 100644
index 00000000..4b128656
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/message.py
@@ -0,0 +1,61 @@
+from enum import StrEnum
+
+from attr import dataclass
+from openai.types.chat import ChatCompletionMessageParam
+
+
+class ConversationMessageType(StrEnum):
+ DEFAULT = "default"
+ ARTIFACT_UPDATE = "artifact-update"
+ REASONING = "reasoning"
+
+
+class Message:
+ def __init__(
+ self,
+ chat_completion_message_param: ChatCompletionMessageParam,
+ type: ConversationMessageType | None = None,
+ turn: int | None = None,
+ ) -> None:
+ self.param = chat_completion_message_param
+ self.turn = turn
+ self.type = type or ConversationMessageType.DEFAULT
+
+
+@dataclass
+class Conversation:
+ messages: list[Message] = []
+
+ def exclude(self, types: list[ConversationMessageType]) -> list[Message]:
+ return [message for message in self.messages if message.type not in types]
+
+ def __str__(self) -> str:
+ message_strs = []
+ current_turn = None
+ for message in self.messages:
+ # Modify the default user to be capitalized for consistency with how
+ # assistant is written.
+ name = message.param["role"]
+ if name == "user":
+ name = "User"
+
+ # Append the turn number if it has changed.
+ if message.turn is not None and current_turn != message.turn:
+ current_turn = message.turn
+ message_strs.append(f"[Turn {current_turn}]")
+
+ # Append the message content.
+ content = message.param.get("content", "")
+ if message.param["role"] == "assistant":
+ if message.type == ConversationMessageType.ARTIFACT_UPDATE:
+ message_strs.append(content)
+ else:
+ message_strs.append(f"Assistant: {content}")
+ else:
+ user_string = str(content).strip()
+ if user_string == "":
+ message_strs.append(f"{name}: ")
+ else:
+ message_strs.append(f"{name}: {user_string}")
+
+ return "\n".join(message_strs)
diff --git a/libraries/python/skills/skills/form-filler-skill/form_filler_skill/resources.py b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/resources.py
new file mode 100644
index 00000000..7d0b5f58
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/form_filler_skill/resources.py
@@ -0,0 +1,275 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+
+import logging
+import math
+import time
+from enum import StrEnum
+
+from pydantic import BaseModel
+
+logger = logging.getLogger(__name__)
+
+
+class ResourceConstraintUnit(StrEnum):
+ """
+ Choose the unit of the resource constraint. Seconds and Minutes are
+ real-time and will be impacted by the latency of the model.
+ """
+
+ SECONDS = "seconds"
+ MINUTES = "minutes"
+ TURNS = "turns"
+
+
+class ResourceConstraintMode(StrEnum):
+ """
+ Choose how the agent should use the resource.
+
+ Maximum: is an upper bound, i.e. the agent can end the conversation before
+ the resource is exhausted.
+
+ Exact: The agent should aim to use exactly the given amount of the resource.
+ """
+
+ MAXIMUM = "maximum"
+ EXACT = "exact"
+
+
+class ResourceConstraint(BaseModel):
+ """
+ A structured representation of the resource constraint for the
+ GuidedConversation agent.
+ """
+
+ quantity: float | int
+ unit: ResourceConstraintUnit
+ mode: ResourceConstraintMode
+
+ class Config:
+ arbitrary_types_allowed = True
+
+
+def format_resource(quantity: float, unit: ResourceConstraintUnit) -> str:
+ """
+ Get formatted string for a given quantity and unit (e.g. 1 second, 20
+ seconds)
+ """
+ if unit != ResourceConstraintUnit.TURNS:
+ quantity = round(quantity, 1)
+ if quantity == 1:
+ return f"{quantity} {unit.value.rstrip('s')}"
+ else:
+ return f"{quantity} {unit.value}"
+
+
+class GCResource:
+ """
+ Resource constraints for the GuidedConversation agent. This class is used to
+ keep track of the resource constraints. If resource_constraint is None, then
+ the agent can continue indefinitely. This also means that no agenda will be
+ created for the conversation.
+
+ Args:
+ resource_constraint (ResourceConstraint | None): The resource constraint
+ for the conversation.
+
+ initial_seconds_per_turn (int): The initial number of seconds per turn.
+ Defaults to 120 seconds.
+ """
+
+ def __init__(
+ self,
+ resource_constraint: ResourceConstraint | None,
+ initial_seconds_per_turn: int = 120,
+ ):
+ self.resource_constraint: ResourceConstraint | None = resource_constraint
+ self.initial_seconds_per_turn: int = initial_seconds_per_turn
+
+ self.turn_number: int = 0
+
+ if resource_constraint is not None:
+ self.elapsed_units = 0
+ self.remaining_units = resource_constraint.quantity
+ else:
+ self.elapsed_units = 0
+ self.remaining_units = 0
+
+ def start_resource(self) -> None:
+ """To be called at the start of a conversation turn"""
+ if self.resource_constraint is not None and (
+ self.resource_constraint.unit == ResourceConstraintUnit.SECONDS
+ or self.resource_constraint.unit == ResourceConstraintUnit.MINUTES
+ ):
+ self.start_time = time.time()
+
+ def increment_resource(self) -> None:
+ """Increment the resource counter by one turn."""
+ if self.resource_constraint is not None:
+ match self.resource_constraint.unit:
+ case ResourceConstraintUnit.SECONDS:
+ self.elapsed_units += time.time() - self.start_time
+ self.remaining_units = self.resource_constraint.quantity - self.elapsed_units
+ case ResourceConstraintUnit.MINUTES:
+ self.elapsed_units += (time.time() - self.start_time) / 60
+ self.remaining_units = self.resource_constraint.quantity - self.elapsed_units
+ case ResourceConstraintUnit.TURNS:
+ self.elapsed_units += 1
+ self.remaining_units -= 1
+ case _:
+ raise ValueError("Invalid resource unit provided.")
+ self.turn_number += 1
+
+ def get_resource_mode(self) -> ResourceConstraintMode | None:
+ """
+ Get the mode of the resource constraint.
+ """
+ if self.resource_constraint is None:
+ return None
+ return self.resource_constraint.mode
+
+ def get_elapsed_turns(self, formatted_repr: bool = False) -> str | int:
+ """
+ Get the number of elapsed turns.
+
+ Args:
+ formatted_repr (bool): If true, return a formatted string
+ representation of the elapsed turns. If false, return an integer.
+ Defaults to False.
+
+ Returns:
+ str | int: The description/number of elapsed turns.
+ """
+ if formatted_repr:
+ return format_resource(self.turn_number, ResourceConstraintUnit.TURNS)
+ else:
+ return self.turn_number
+
+ def estimate_remaining_turns_formatted(self) -> str:
+ """
+ Get the number of remaining turns in a formatted string.
+ """
+ return format_resource(self.estimate_remaining_turns(), ResourceConstraintUnit.TURNS)
+
+ def estimate_remaining_turns(self) -> int:
+ """
+ Estimate the remaining turns based on the resource constraint, thereby
+ translating certain resource units (e.g. seconds, minutes) into turns.
+ """
+ if self.resource_constraint is None:
+ logger.error(
+ "Resource constraint is not set, so turns cannot be estimated using function estimate_remaining_turns"
+ )
+ raise ValueError(
+ "Resource constraint is not set. Do not try to call this method without a resource constraint."
+ )
+
+ match self.resource_constraint.unit:
+ case ResourceConstraintUnit.MINUTES:
+ if self.turn_number == 0:
+ time_per_turn = self.initial_seconds_per_turn
+ else:
+ time_per_turn = (self.elapsed_units * 60) / self.turn_number
+ time_per_turn /= 60
+ remaining_turns = self.remaining_units / time_per_turn
+ if remaining_turns < 1:
+ return math.ceil(remaining_turns)
+ else:
+ return math.floor(remaining_turns)
+
+ case ResourceConstraintUnit.SECONDS:
+ if self.turn_number == 0:
+ time_per_turn = self.initial_seconds_per_turn
+ else:
+ time_per_turn = self.elapsed_units / self.turn_number
+ remaining_turns = self.remaining_units / time_per_turn
+ if remaining_turns < 1:
+ return math.ceil(remaining_turns)
+ else:
+ return math.floor(remaining_turns)
+
+ case ResourceConstraintUnit.TURNS:
+ return int(self.resource_constraint.quantity - self.turn_number)
+
+ case _:
+ raise ValueError("Invalid resource unit provided.")
+
+ def get_resource_instructions(self) -> str:
+ """Get the resource instructions for the conversation.
+
+ Assumes we're always using turns as the resource unit.
+
+ Returns:
+ str: the resource instructions
+ """
+ if self.resource_constraint is None:
+ return ""
+
+ formatted_elapsed_resource = format_resource(
+ self.elapsed_units, ResourceConstraintUnit.TURNS
+ )
+ formatted_remaining_resource = format_resource(
+ self.remaining_units, ResourceConstraintUnit.TURNS
+ )
+
+ # if the resource quantity is anything other than 1, the resource unit should be plural (e.g. "minutes" instead of "minute")
+ is_plural_elapsed = self.elapsed_units != 1
+ is_plural_remaining = self.remaining_units != 1
+
+ if self.elapsed_units > 0:
+ resource_instructions = f"So far, {formatted_elapsed_resource} {'have' if is_plural_elapsed else 'has'} elapsed since the conversation began. "
+ else:
+ resource_instructions = ""
+
+ if self.resource_constraint.mode == ResourceConstraintMode.EXACT:
+ exact_mode_instructions = (
+ f"There {'are' if is_plural_remaining else 'is'} {formatted_remaining_resource} "
+ "remaining (including this one) - the conversation will automatically terminate "
+ "when 0 turns are left. You should continue the conversation until it is "
+ "automatically terminated. This means you should NOT preemptively end the "
+ 'conversation, either explicitly (by selecting the "End conversation" action) '
+ "or implicitly (e.g. by telling the user that you have all required information "
+ "and they should wait for the next step). Your goal is not to maximize efficiency "
+ "(i.e. complete the artifact as quickly as possible then end the conversation), "
+ "but rather to make the best use of ALL remaining turns available to you"
+ )
+
+ if is_plural_remaining:
+ resource_instructions += f"""{exact_mode_instructions}. This will require you to plan your actions carefully using the agenda: you want to avoid the situation where you have to pack too many topics into the final turns because you didn't account for them earlier, \
+or where you've rushed through the conversation and all fields are completed but there are still many turns left."""
+
+ # special instruction for the final turn (i.e. 1 remaining) in exact mode
+ else:
+ resource_instructions += f"""{exact_mode_instructions}, including this one. Therefore, you should use this turn to ask for any remaining information needed to complete the artifact, \
+ or, if the artifact is already completed, continue to broaden/deepen the discussion in a way that's directly relevant to the artifact. Do NOT indicate to the user that the conversation is ending."""
+
+ elif self.resource_constraint.mode == ResourceConstraintMode.MAXIMUM:
+ resource_instructions += f"""You have a maximum of {formatted_remaining_resource} (including this one) left to complete the conversation. \
+You can decide to terminate the conversation at any point (including now), otherwise the conversation will automatically terminate when 0 turns are left. \
+You will need to plan your actions carefully using the agenda: you want to avoid the situation where you have to pack too many topics into the final turns because you didn't account for them earlier."""
+
+ else:
+ logger.error("Invalid resource mode provided.")
+
+ return resource_instructions
+
+ def to_json(self) -> dict:
+ return {
+ "turn_number": self.turn_number,
+ "remaining_units": self.remaining_units,
+ "elapsed_units": self.elapsed_units,
+ }
+
+ @classmethod
+ def from_json(
+ cls,
+ json_data: dict,
+ ) -> "GCResource":
+ gc_resource = cls(
+ resource_constraint=None,
+ initial_seconds_per_turn=120,
+ )
+ gc_resource.turn_number = json_data["turn_number"]
+ gc_resource.remaining_units = json_data["remaining_units"]
+ gc_resource.elapsed_units = json_data["elapsed_units"]
+ return gc_resource
diff --git a/libraries/python/skills/skills/form-filler-skill/pyproject.toml b/libraries/python/skills/skills/form-filler-skill/pyproject.toml
new file mode 100644
index 00000000..5ea6b681
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/pyproject.toml
@@ -0,0 +1,30 @@
+[project]
+name = "form-filler-skill"
+version = "0.1.0"
+description = "MADE:Exploration Form-filler skill"
+authors = [{name="MADE:Explorers"}]
+readme = "README.md"
+requires-python = ">=3.11"
+dependencies = [
+ "skill-library>=0.1.0",
+ "chat-driver>=0.1.0",
+ "context>=0.1.0",
+ "events>=0.1.0",
+ "openai-client>=0.1.0",
+ # "guided-conversation>=0.1.0",
+]
+
+[tool.uv]
+package = true
+
+[tool.uv.sources]
+skill-library = { path = "../../skill-library", editable= true }
+chat-driver = { path = "../../../chat-driver", editable = true }
+context = { path = "../../../context", editable = true }
+events = { path = "../../../events", editable = true }
+openai-client = { path = "../../../openai-client", editable = true }
+# guided-conversation = { path = "../../../guided-conversation", editable = true }
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
diff --git a/libraries/python/skills/skills/form-filler-skill/uv.lock b/libraries/python/skills/skills/form-filler-skill/uv.lock
new file mode 100644
index 00000000..7b5e6c93
--- /dev/null
+++ b/libraries/python/skills/skills/form-filler-skill/uv.lock
@@ -0,0 +1,1767 @@
+version = 1
+requires-python = ">=3.11"
+resolution-markers = [
+ "python_full_version < '3.13'",
+ "python_full_version >= '3.13'",
+]
+
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.4.3"
+source = { registry = "https://pypi.org/simple" }
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+[[package]]
+name = "aiohttp"
+version = "3.10.10"
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diff --git a/libraries/python/skills/skills/posix-skill/posix_skill/posix_skill.py b/libraries/python/skills/skills/posix-skill/posix_skill/posix_skill.py
index 718c1500..a97caf74 100644
--- a/libraries/python/skills/skills/posix-skill/posix_skill/posix_skill.py
+++ b/libraries/python/skills/skills/posix-skill/posix_skill/posix_skill.py
@@ -1,8 +1,10 @@
from pathlib import Path
-from skill_library import Skill, InstructionRoutine, RoutineTypes
+
from chat_driver import ChatDriverConfig
-from .sandbox_shell import SandboxShell
from context import Context
+from skill_library import InstructionRoutine, RoutineTypes, Skill
+
+from .sandbox_shell import SandboxShell
NAME = "posix"
CLASS_NAME = "PosixSkill"
@@ -14,12 +16,11 @@
class PosixSkill(Skill):
def __init__(
self,
- context: Context,
sandbox_dir: Path,
chat_driver_config: ChatDriverConfig,
mount_dir: str = "/mnt/data",
) -> None:
- self.shell = SandboxShell(sandbox_dir / context.session_id, mount_dir)
+ self.shell = SandboxShell(sandbox_dir, mount_dir)
# Put all functions in a group. We are going to use all these as (1)
# skill actions, but also as (2) chat functions and (3) chat commands.
@@ -43,9 +44,8 @@ def __init__(
self.make_home_dir_routine(),
]
- # Configure the skill's chat driver.
+ # Re-configure the skill's chat driver.
chat_driver_config.instructions = INSTRUCTIONS
- chat_driver_config.context = context
chat_driver_config.commands = functions
chat_driver_config.functions = functions
@@ -53,7 +53,6 @@ def __init__(
super().__init__(
name=NAME,
description=DESCRIPTION,
- context=context,
chat_driver_config=chat_driver_config,
skill_actions=functions,
routines=routines,
@@ -78,6 +77,7 @@ def make_home_dir_routine(self) -> InstructionRoutine:
"mkdir Pictures\n"
"mkdir Videos\n"
),
+ skill=self,
)
##################################
diff --git a/libraries/python/skills/skills/posix-skill/uv.lock b/libraries/python/skills/skills/posix-skill/uv.lock
index db4126b0..ae772499 100644
--- a/libraries/python/skills/skills/posix-skill/uv.lock
+++ b/libraries/python/skills/skills/posix-skill/uv.lock
@@ -5,6 +5,88 @@ resolution-markers = [
"python_full_version >= '3.13'",
]
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diff --git a/libraries/python/skills/skills/prospector-skill/uv.lock b/libraries/python/skills/skills/prospector-skill/uv.lock
index 20c61b54..fb7c57c0 100644
--- a/libraries/python/skills/skills/prospector-skill/uv.lock
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index 031f77c3..bf6c6414 100644
--- a/libraries/python/skills/skills/skill-template/uv.lock
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+
[[package]]
name = "your-skill"
version = "0.1.0"
diff --git a/semantic-workbench.code-workspace b/semantic-workbench.code-workspace
index b01bfc92..a3839015 100644
--- a/semantic-workbench.code-workspace
+++ b/semantic-workbench.code-workspace
@@ -115,6 +115,10 @@
"name": "libraries:skills:document-skill",
"path": "libraries/python/skills/skills/document-skill"
},
+ {
+ "name": "libraries:skills:form-filler-skill",
+ "path": "libraries/python/skills/skills/form-filler-skill"
+ },
{
"name": "libraries:skills:notebooks",
"path": "libraries/python/skills/notebooks"
@@ -146,6 +150,10 @@
{
"name": ".github",
"path": ".github"
+ },
+ {
+ "name": "root",
+ "path": "."
}
],
"settings": {