diff --git a/assistants/prospector-assistant/.vscode/settings.json b/assistants/prospector-assistant/.vscode/settings.json index c0c57648..4fab9732 100644 --- a/assistants/prospector-assistant/.vscode/settings.json +++ b/assistants/prospector-assistant/.vscode/settings.json @@ -25,6 +25,7 @@ "python.analysis.inlayHints.functionReturnTypes": true, "python.analysis.typeCheckingMode": "standard", "python.defaultInterpreterPath": "${workspaceFolder}/.venv", + "python.testing.pytestEnabled": true, "[python]": { "editor.defaultFormatter": "charliermarsh.ruff", "editor.formatOnSave": true, diff --git a/assistants/prospector-assistant/assistant/agents/artifact_agent.py b/assistants/prospector-assistant/assistant/agents/artifact_agent.py index 57657730..33ddceef 100644 --- a/assistants/prospector-assistant/assistant/agents/artifact_agent.py +++ b/assistants/prospector-assistant/assistant/agents/artifact_agent.py @@ -1,7 +1,7 @@ from pathlib import Path from typing import TYPE_CHECKING, Annotated, Literal, Union -from pydantic import BaseModel, Field +from pydantic import BaseModel, ConfigDict, Field from semantic_workbench_assistant.assistant_app import ( AssistantConversationInspectorStateDataModel, BaseModelAssistantConfig, @@ -67,65 +67,70 @@ class ArtifactAgentConfigModel(BaseModel): class ArtifactMarkdownContent(BaseModel): - class Config: - extra = "forbid" - json_schema_extra = { + model_config = ConfigDict( + extra="forbid", + json_schema_extra={ "description": ( "The content of the artifact in markdown format. Use this type for any general text that" " does not match another, more specific type." ), "required": ["content_type"], - } + }, + ) content_type: Literal["markdown"] = "markdown" class ArtifactCodeContent(BaseModel): - class Config: - extra = "forbid" - json_schema_extra = { + model_config = ConfigDict( + extra="forbid", + json_schema_extra={ "description": ( "The content of the artifact in code format with a specified language for syntax highlighting." ), "required": ["content_type", "language"], - } + }, + ) content_type: Literal["code"] = "code" language: str class ArtifactMermaidContent(BaseModel): - class Config: - extra = "forbid" - json_schema_extra = { + model_config = ConfigDict( + extra="forbid", + json_schema_extra={ "description": "The content of the artifact in mermaid format, which will be rendered as a diagram.", "required": ["content_type"], - } + }, + ) content_type: Literal["mermaid"] = "mermaid" class ArtifactAbcContent(BaseModel): - class Config: - extra = "forbid" - json_schema_extra = { + model_config = ConfigDict( + extra="forbid", + json_schema_extra={ "description": ( "The content of the artifact in abc format, which will be rendered as sheet music, an interactive player," " and available for download." ), "required": ["content_type"], - } + }, + ) content_type: Literal["abc"] = "abc" class ArtifactExcalidrawContent(BaseModel): - class Config: - extra = "forbid" - json_schema_extra = { + model_config = ConfigDict( + extra="forbid", + json_schema_extra={ "description": ("The content of the artifact in Excalidraw format, which will be rendered as a diagram."), "required": ["content_type", "excalidraw"], - } + }, + ) content_type: Literal["excalidraw"] = "excalidraw" @@ -140,9 +145,9 @@ class Config: class Artifact(BaseModel): - class Config: - extra = "forbid" - json_schema_extra = { + model_config = ConfigDict( + extra="forbid", + json_schema_extra={ "description": ( "Data for the artifact, which includes a label, content, filename, type, and version. The filename" " should be unique for each artifact, and the version should start at 1 and increment for each new" @@ -150,7 +155,8 @@ class Config: " additional fields required for that type." ), "required": ["label", "content", "filename", "type", "version"], - } + }, + ) label: str content: str diff --git a/assistants/prospector-assistant/assistant/artifact_creation_extension/_llm.py b/assistants/prospector-assistant/assistant/artifact_creation_extension/_llm.py index 7621f8d9..84b3b73d 100644 --- a/assistants/prospector-assistant/assistant/artifact_creation_extension/_llm.py +++ b/assistants/prospector-assistant/assistant/artifact_creation_extension/_llm.py @@ -1,103 +1,67 @@ import logging -from abc import ABC, abstractmethod +from time import perf_counter from typing import Any, AsyncIterator, Awaitable, Callable, Coroutine, Generic, Iterable, TypeVar +import openai from attr import dataclass -from openai import pydantic_function_tool +from openai import NotGiven, pydantic_function_tool from openai.types.chat import ( ChatCompletionAssistantMessageParam, ChatCompletionMessageParam, ChatCompletionMessageToolCallParam, + ChatCompletionToolChoiceOptionParam, ChatCompletionToolMessageParam, ParsedFunctionToolCall, ) from pydantic import BaseModel -from semantic_workbench_assistant.assistant_app.context import ConversationContext from .config import LLMConfig logger = logging.getLogger(__name__) -class NoResponseChoicesError(Exception): - pass - - -class NoParsedMessageError(Exception): - pass - - -ResponseModelT = TypeVar("ResponseModelT", bound=BaseModel) - - -async def structured_completion( - llm_config: LLMConfig, messages: list[ChatCompletionMessageParam], response_model: type[ResponseModelT] -) -> tuple[ResponseModelT, dict[str, Any]]: - async with llm_config.openai_client_factory() as client: - response = await client.beta.chat.completions.parse( - messages=messages, - model=llm_config.openai_model, - response_format=response_model, - max_tokens=llm_config.max_response_tokens, - ) - - if not response.choices: - raise NoResponseChoicesError() - - if not response.choices[0].message.parsed: - raise NoParsedMessageError() - - metadata = { - "request": { - "model": llm_config.openai_model, - "messages": messages, - "max_tokens": llm_config.max_response_tokens, - "response_format": response_model.model_json_schema(), - }, - "response": response.model_dump(), - } - - return response.choices[0].message.parsed, metadata - - -class ToolArgsModel(ABC, BaseModel): - @abstractmethod - def set_context(self, context: ConversationContext) -> None: ... - - -TToolArgs = TypeVar("TToolArgs", bound=ToolArgsModel) -TToolResult = TypeVar("TToolResult", bound=BaseModel) +TToolArgs = TypeVar("TToolArgs", bound=BaseModel) @dataclass -class CompletionTool(Generic[TToolArgs, TToolResult]): - function: Callable[[TToolArgs], Coroutine[Any, Any, TToolResult]] +class CompletionTool(Generic[TToolArgs]): + function: Callable[[TToolArgs], Coroutine[Any, Any, str]] argument_model: type[TToolArgs] description: str = "" """Description of the tool. If omitted, wil use the docstring of the function.""" -@dataclass -class LLMResponse: +class LLMResponse(BaseModel): metadata: dict[str, Any] -@dataclass class ToolCallResponse(LLMResponse): tool_call: ParsedFunctionToolCall - result: BaseModel + result: str -@dataclass class MessageResponse(LLMResponse): message: str +class CompletionError(Exception): + def __init__(self, message: str, metadata: dict[str, Any]) -> None: + super().__init__(message) + self.message = message + self.metadata = metadata + + def __str__(self) -> str: + return f"CompletionError(message={repr(self.message)}, metadata={repr(self.metadata)})" + + async def completion_with_tools( llm_config: LLMConfig, - context: ConversationContext, - get_messages: Callable[[], Awaitable[Iterable[ChatCompletionMessageParam]]], + head_messages: Callable[[], Awaitable[Iterable[ChatCompletionMessageParam]]], + tail_messages: Callable[[], Awaitable[Iterable[ChatCompletionMessageParam]]], tools: list[CompletionTool] = [], + tool_choice: ChatCompletionToolChoiceOptionParam | None = None, + ignore_tool_calls_after: int = -1, + allow_tool_followup: bool = True, ) -> AsyncIterator[ToolCallResponse | MessageResponse]: openai_tools = [ pydantic_function_tool( @@ -109,60 +73,93 @@ async def completion_with_tools( ] tool_messages: list[ChatCompletionMessageParam] = [] - reasoning_effort = "medium" + tool_attempts = 0 async with llm_config.openai_client_factory() as client: - while True: - completion_messages = list(await get_messages()) + tool_messages - - response = await client.beta.chat.completions.parse( - messages=completion_messages, - model=llm_config.openai_model, - max_completion_tokens=llm_config.max_response_tokens + 25_000, - tools=openai_tools, - reasoning_effort=reasoning_effort, - # parallel_tool_calls=False, - ) + while tool_attempts <= 2: + tool_attempts += 1 + completion_messages = list(await head_messages()) + tool_messages + list(await tail_messages()) + + metadata = { + "request": { + "model": llm_config.openai_model, + "messages": completion_messages, + "tools": openai_tools, + "reasoning_effort": llm_config.reasoning_effort, + "max_completion_tokens": llm_config.max_response_tokens, + }, + } + + start = perf_counter() + try: + response_raw = await client.beta.chat.completions.with_raw_response.parse( + messages=completion_messages, + model=llm_config.openai_model, + tools=openai_tools or NotGiven(), + tool_choice=tool_choice or NotGiven(), + reasoning_effort=llm_config.reasoning_effort or NotGiven(), + max_completion_tokens=llm_config.max_response_tokens, + parallel_tool_calls=False if openai_tools else NotGiven(), + ) + except openai.BadRequestError as e: + raise CompletionError( + message="Failed to parse completion request", + metadata={ + **metadata, + "response_duration": perf_counter() - start, + "error": str(e), + }, + ) from e + + headers = {key: value for key, value in response_raw.headers.items()} + response = response_raw.parse() message = response.choices[0].message - if not message.tool_calls: - metadata = { - "request": { - "model": llm_config.openai_model, - "messages": completion_messages, - "tools": openai_tools, - "reasoning_effort": reasoning_effort, - "max_completion_tokens": llm_config.max_response_tokens, - }, - "response": response.model_dump(), - } + metadata = { + **metadata, + "response": response.model_dump(), + "response_headers": headers, + "response_duration": perf_counter() - start, + } + + if message.content: yield MessageResponse(message=str(message.content), metadata=metadata) + + if not message.tool_calls: return - async with context.set_status("calling tools..."): - logger.info("tool calls: %s", message.tool_calls) - - # append the assistant message with the tool calls for the next iteration - tool_messages.append( - ChatCompletionAssistantMessageParam( - role="assistant", - tool_calls=[ - ChatCompletionMessageToolCallParam( - id=c.id, - function={ - "name": c.function.name, - "arguments": c.function.arguments, - }, - type="function", - ) - for c in message.tool_calls - ], - ) + logger.info("tool calls (%d): %s", len(message.tool_calls), message.tool_calls) + + # append the assistant message with the tool calls for the next iteration + tool_messages.append( + ChatCompletionAssistantMessageParam( + role="assistant", + tool_calls=[ + ChatCompletionMessageToolCallParam( + id=tool_call.id, + function={ + "name": tool_call.function.name, + "arguments": tool_call.function.arguments, + }, + type="function", + ) + for tool_call in message.tool_calls + ], ) - for tool_call in message.tool_calls: - function = tool_call.function + ) + for index, tool_call in enumerate(message.tool_calls): + if ignore_tool_calls_after >= 0 and index > ignore_tool_calls_after: + logger.info("ignoring tool call: %s", tool_call) + if allow_tool_followup: + break + return + + function = tool_call.function + + start = perf_counter() + try: # find the matching tool tool = next((t for t in tools if t.function.__name__ == function.name), None) if tool is None: @@ -170,27 +167,62 @@ async def completion_with_tools( # validate the args and call the tool function args = tool.argument_model.model_validate(function.parsed_arguments) - args.set_context(context) - result: BaseModel = await tool.function(args) - - metadata = { - "request": { - "model": llm_config.openai_model, - "messages": completion_messages, - "tools": openai_tools, - "max_tokens": llm_config.max_response_tokens, - }, - "response": response.model_dump(), + result = await tool.function(args) + + tool_metadata = { + **metadata, + "tool_call": tool_call.model_dump(mode="json"), + "tool_result": result, + "tool_duration": perf_counter() - start, + } + yield ToolCallResponse(tool_call=tool_call, result=result, metadata=tool_metadata) + + # append the tool result to the messages for the next iteration + tool_messages.append( + ChatCompletionToolMessageParam( + content=result, + role="tool", + tool_call_id=tool_call.id, + ) + ) + + if not allow_tool_followup: + logger.info("skipping completion after tool call") + return + break + + except Exception as e: + logger.warning( + "Error calling tool; tool: %s, arguments: %s", + tool_call.function.name, + tool_call.function.parsed_arguments, + exc_info=e, + ) + + tool_metadata = { + **metadata, "tool_call": tool_call.model_dump(mode="json"), - "tool_result": result.model_dump(mode="json"), + "tool_error": str(e), + "tool_duration": perf_counter() - start, } - yield ToolCallResponse(tool_call=tool_call, result=result, metadata=metadata) + + match tool_attempts: + case 1: + result = f"An error occurred while calling the tool: {e}. Please try again." + + case _: + result = f"An error occurred while calling the tool: {e}. Do not try again. Tell the user what you were trying to do and explain that an error occurred." + logger.warning("Fatal error calling tool, exiting tool loop") + yield ToolCallResponse(tool_call=tool_call, result=result, metadata=tool_metadata) # append the tool result to the messages for the next iteration tool_messages.append( ChatCompletionToolMessageParam( - content=result.model_dump_json(), + content=result, role="tool", tool_call_id=tool_call.id, ) ) + + # exit the tool loop + break diff --git a/assistants/prospector-assistant/assistant/artifact_creation_extension/config.py b/assistants/prospector-assistant/assistant/artifact_creation_extension/config.py index 7956fc3a..6efac850 100644 --- a/assistants/prospector-assistant/assistant/artifact_creation_extension/config.py +++ b/assistants/prospector-assistant/assistant/artifact_creation_extension/config.py @@ -2,6 +2,7 @@ from typing import Callable from openai import AsyncOpenAI +from openai.types.chat import ChatCompletionReasoningEffort @dataclass @@ -9,3 +10,4 @@ class LLMConfig: openai_client_factory: Callable[[], AsyncOpenAI] openai_model: str max_response_tokens: int + reasoning_effort: ChatCompletionReasoningEffort | None = None diff --git a/assistants/prospector-assistant/assistant/artifact_creation_extension/document.py b/assistants/prospector-assistant/assistant/artifact_creation_extension/document.py index 698f38d4..31928f40 100644 --- a/assistants/prospector-assistant/assistant/artifact_creation_extension/document.py +++ b/assistants/prospector-assistant/assistant/artifact_creation_extension/document.py @@ -7,6 +7,8 @@ class SectionMetadata(BaseModel): purpose: str = "" """Describes the intent of the section.""" + + # These are for humans created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc)) """Timestamp for when the section was created.""" last_modified_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc)) @@ -47,13 +49,19 @@ class DocumentMetadata(BaseModel): """Describes the intent of the document""" audience: str = "" """Describes the intended audience for the document""" + + # Value of this is still to be determined other_guidelines: str = "" """ - Describes any other guidelines or standards, stylistic, structure, reading level, etc., - that the document should follow + Describes any other guidelines or standards, stylistic, structure, etc., + that the document should follow (tone, style, length) """ + + # Value of this is still to be determined supporting_documents: list[str] = Field(default_factory=list) """List of document titles for supporting documents.""" + + # These are for humans created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc)) """Timestamp for when the document was created.""" last_modified_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc)) @@ -67,12 +75,13 @@ class Document(BaseModel): title: str = "" """Title of the document. Doubles as a unique identifier for the document.""" - sections: list[Section] = Field(default_factory=list) - """Structured content of the document.""" metadata: DocumentMetadata = DocumentMetadata() """Metadata describing the document.""" + sections: list[Section] = Field(default_factory=list) + """Structured content of the document.""" + class DocumentHeader(BaseModel): document_id: str diff --git a/assistants/prospector-assistant/assistant/artifact_creation_extension/extension.py b/assistants/prospector-assistant/assistant/artifact_creation_extension/extension.py index b50e8778..ef5d6269 100644 --- a/assistants/prospector-assistant/assistant/artifact_creation_extension/extension.py +++ b/assistants/prospector-assistant/assistant/artifact_creation_extension/extension.py @@ -1,13 +1,16 @@ import logging +import re +from contextvars import ContextVar +from dataclasses import dataclass from textwrap import dedent +from typing import Any, AsyncIterable, Iterable, Literal import openai_client from openai.types.chat import ( - ChatCompletionAssistantMessageParam, - ChatCompletionDeveloperMessageParam, ChatCompletionMessageParam, - ChatCompletionUserMessageParam, + ChatCompletionMessageToolCallParam, ) +from pydantic import BaseModel, Field from semantic_workbench_api_model.workbench_model import ( ConversationEvent, ConversationMessage, @@ -23,35 +26,39 @@ from . import store, tools from ._llm import CompletionTool, MessageResponse, ToolCallResponse, completion_with_tools from .config import LLMConfig -from .store import ActiveDocumentInspector, DocumentWorkspaceInspector logger = logging.getLogger(__name__) -system_message = dedent( - """ +system_message_document_assistant = dedent(""" You are an assistant. Ultimately, you help users create documents in a document workspace. To do this, you will assist with ideation, drafting, and editing. Documents are can represent a variety of content types, such as reports, articles, blog posts, stories, slide decks or others. You can create, update, and remove - documents, as well as create, update, and remove sections within documents. You can also list all documents - in the workspace. - The documents in the workspace, in their current state, are available here for your reference. + documents, as well as create, update, and remove sections within documents. + When updating the content of sections, by calling create_document_section or update_document_section, + you will always ensure that the purpose, audience and other_guidelines of the document and respective + section are adhered to, if they are set. +""") + +# document_workspace_inspector = store.DocumentWorkspaceInspector() +active_document_inspector = store.AllDocumentsInspector() - Documents: - --- - """ -) + +@dataclass +class LLMs: + fast: LLMConfig + chat: LLMConfig + reasoning_fast: LLMConfig + reasoning_long: LLMConfig class ArtifactCreationExtension: def __init__( self, assistant_app: AssistantAppProtocol, assistant_config: BaseModelAssistantConfig[AssistantConfigModel] ) -> None: - document_workspace_inspector = DocumentWorkspaceInspector() - active_document_inspector = ActiveDocumentInspector() - assistant_app.add_inspector_state_provider( - document_workspace_inspector.display_name, document_workspace_inspector - ) + # assistant_app.add_inspector_state_provider( + # document_workspace_inspector.display_name, document_workspace_inspector + # ) assistant_app.add_inspector_state_provider(active_document_inspector.display_name, active_document_inspector) @assistant_app.events.conversation.message.command.on_created @@ -65,50 +72,35 @@ async def on_message_command_created( match message.content.split(" ")[0]: case "/help": await _send_message( - context, dedent(""" /help - Display this help message. /ls - List all documents in the workspace. - /select - Select the active document. - """), + /select - Select the active document."""), {}, message_type=MessageType.command_response, + generated_content=False, ) case "/ls": args = tools.ListDocumentsArgs() - args.set_context(context) headers = await tools.list_documents(args) document_list = "\n".join( f"{index}. {header.title} ({header.document_id})" for index, header in enumerate(headers.documents) ) await _send_message( - context, f"Documents in the workspace: {headers.count}\n\n{document_list}", {}, message_type=MessageType.command_response, - ) - - case "/select": - index = int(message.content.split(" ")[1]) - list_args = tools.ListDocumentsArgs() - list_args.set_context(context) - headers = await tools.list_documents(list_args) - store.active_document_id = headers.documents[index].document_id - await _send_message( - context, - f"Selected document: {headers.documents[index].title}", - {}, - message_type=MessageType.command_response, + generated_content=False, ) case _: await _send_message( - context, "Unknown command. Use /help to see available commands.", {}, message_type=MessageType.command_response, + generated_content=False, ) @assistant_app.events.conversation.message.chat.on_created @@ -119,135 +111,536 @@ async def on_message_chat_created( if config.guided_workflow != "Long Document Creation": return + tools.current_document_store.set(store.for_context(context)) + current_context.set(context) + + chat_model = "gpt-4o" + fast_model = "gpt-4o-mini" + reasoning_model = "o3-mini" + chat_service_config = config.service_config.model_copy(deep=True) + chat_service_config.azure_openai_deployment = chat_model # type: ignore + fast_service_config = config.service_config.model_copy(deep=True) + fast_service_config.azure_openai_deployment = fast_model # type: ignore + reasoning_fast_service_config = config.service_config.model_copy(deep=True) + reasoning_fast_service_config.azure_openai_deployment = reasoning_model # type: ignore + reasoning_long_service_config = config.service_config.model_copy(deep=True) + reasoning_long_service_config.azure_openai_deployment = reasoning_model # type: ignore + + llms = LLMs( + fast=LLMConfig( + openai_client_factory=lambda: openai_client.create_client(fast_service_config), + openai_model=fast_model, + max_response_tokens=config.request_config.response_tokens, + ), + chat=LLMConfig( + openai_client_factory=lambda: openai_client.create_client(chat_service_config), + openai_model=chat_model, + max_response_tokens=config.request_config.response_tokens, + ), + reasoning_fast=LLMConfig( + openai_client_factory=lambda: openai_client.create_client(reasoning_fast_service_config), + openai_model=reasoning_model, + max_response_tokens=config.request_config.response_tokens, + reasoning_effort="low", + ), + reasoning_long=LLMConfig( + openai_client_factory=lambda: openai_client.create_client(reasoning_long_service_config), + openai_model=reasoning_model, + max_response_tokens=90_000, + reasoning_effort="high", + ), + ) + + messages_response = await context.get_messages(before=message.id) + chat_history = (*(message for message in messages_response.messages), message) + async with context.set_status("responding ..."): - messages_response = await context.get_messages(before=message.id) - - async def message_generator() -> list[ChatCompletionMessageParam]: - messages: list[ChatCompletionMessageParam] = [] - for msg in (*messages_response.messages, message): - match msg.sender.participant_role: - case ParticipantRole.user: - messages.append(ChatCompletionUserMessageParam(content=msg.content, role="user")) - - case ParticipantRole.assistant: - messages.append( - ChatCompletionAssistantMessageParam(content=msg.content, role="assistant") - ) - - list_args = tools.ListDocumentsArgs() - list_args.set_context(context) - headers = await tools.list_documents(list_args) - document_content_list = "" - if not headers: - document_content_list = "There are currently documents in the workspace." - - for header in headers.documents: - get_document_args = tools.GetDocumentArgs(document_id=header.document_id) - get_document_args.set_context(context) - document = await tools.get_document(get_document_args) - - document_content_list += f"\n\n```json\n{document.model_dump_json()}\n```" - - messages.append( - # ChatCompletionSystemMessageParam(content=system_message + document_content_list, role="system") - ChatCompletionDeveloperMessageParam( - content=system_message + document_content_list, role="developer" + await respond_to_message(llms=llms, conversation_history=chat_history) + + +completion_tools: list[CompletionTool] = [ + CompletionTool( + function=tools.create_document, + argument_model=tools.CreateDocumentArgs, + ), + CompletionTool( + function=tools.update_document, + argument_model=tools.UpdateDocumentArgs, + ), + CompletionTool( + function=tools.remove_document, + argument_model=tools.RemoveDocumentArgs, + ), + CompletionTool( + function=tools.create_document_section, + argument_model=tools.CreateDocumentSectionArgs, + ), + CompletionTool( + function=tools.update_document_section, + argument_model=tools.UpdateDocumentSectionArgs, + ), + CompletionTool( + function=tools.remove_document_section, + argument_model=tools.RemoveDocumentSectionArgs, + ), + # CompletionTool( + # function=tools.get_document, + # argument_model=tools.GetDocumentArgs, + # ), + # CompletionTool( + # function=tools.list_documents, + # argument_model=tools.ListDocumentsArgs, + # ), +] + +tool_list_for_plan = "\n".join( + f"- Function: {tool.function.__name__}; Args: {tool.argument_model.model_json_schema()}" + for tool in completion_tools +) + +system_message_plan_for_turn = dedent(f""" + You are responsible for recommending the next action to take in the conversation. + This action will be executed by another assistant. + + # RECOMMENDED NEXT ACTION + You will recommend either clarifying the document change request, making the requested document changes, + or continuing the conversation. + + ## continue_conversation + Continuing the conversation means that you have determined that the user is not requesting changes to + the document, and the conversation should be continued. For example, the user may be asking a question + about the document, asking a question general knowledge, chatting, or providing additional information. + + ## clarify_document_change_request + Clarifying the document change request means that you have determined that the user is requesting changes, + however the request could use clarification. Or it is possible that the user message coulud be interpreted + as a request to make changes, but it is not clear. You will set 'clarification_question' to instruct + the assistant on what clarifications to make with the user. + + ## make_document_changes + Making the requested document changes means that you have determined that the user is requesting changes to + the document, or documents, and there is clarity on what changes are needed. + When considering what changes to make, make sure you review the content of the document and all sections + within the document to determine if they need to be changed. + You will set 'document_changes_explanation' to explain the changes and 'document_changes_tool_calls' to list + the tool calls that you recommend to make the requested changes. + + Document changes explanation must: + - Explain the changes that are needed in the document and how they should be implemented. + - Speak in the first person, as if you are the assistant that will execute the plan. For example, do not use + "We will", "We are going to" or "The assistant will". + + When recommending an action of make_document_changes, and recommending calls to `create_document_section` or + `update_document_section, you must: + - specify the `content` argument as "", the content placeholder, to indicate that the content should + be generated by the assistant that will execute the plan. + - specify the `document_id` argument as the id for the appropriate document, if it exists at the time this plan + is being created. + - specify the `document_id` as "", the document id placeholder, if the document is being created + in this plan. The placeholder will be replaced with the result of the `create_document` tool call when the + plan is executed. + + Tool call explanations, for the assistant that will execute the plan, must: + - Explain the intent of the tool call. + - Ensure the explanation is thorough and clear. + - The explanation is for an LLM that will execute the plan - the explanation is not for the user. + + Content placeholder replacement instructions, for the assistant that will execute the plan, must: + - Explain in specific detail what content to generate. + - Include relevant considerations based on the document purpose. + - Include relevant considerations based on the document other_guidelines. + - Include relevant considerations based on the section purpose. + - Include all context, as the assistant that will execute the plan will not have access to the user's request. + + Example of a BAD content placeholder replacement instruction: + "Replace the placeholder with content." + + Example of a GOOD explanation: (for a document with purpose "to summarize findings from another document" and other_guidelines "use bullet points") + "Replace the placeholder with a detailed summary of the document, including the main points in a bulleted list." + + # TOOLS AVAILABLE FOR DOCUMENT CHANGES + + {tool_list_for_plan} +""") + + +system_message_clarify_document_change_request = dedent(""" + It seems likely that the user is requesting changes to the document. + A question has been posed by another AI assistant to clarify the request. + + Ask the user, in you own words, the question posed by the other AI assistant. + + # QUESTION TO ASK THE USER + {clarification_question} +""") + +system_message_make_document_changes = dedent(""" + The user is requesting changes to the document. + A multi-step plan has been created to implement the changes. + You will execute a single step in the plan, by calling the recommended tool. + + If you decide to share a text message in addition to the tool call, do not refer + to the plan, but rather explain the changes in your own words, and in the context + of what the user requested in their last message. + + # EXPLANATION OF THE FULL PLAN + + NOTE: This full plan is provided for context. You do not need to execute on it. You will execute + only the current step. + + {plan_explanation} + + # CURRENT STEP TO EXECUTE + + NOTE: This is the step you will execute. + NOTE: The "" placeholder in the recommended tool call must be replaced with generated + content based on the content placeholder replacement instructions. + + {step_plan} +""") + +system_message_make_document_changes_complete = dedent(""" + Congratulations! + + You have just now completed the changes requested by the user according to a plan. + The document in the workspace reflects the changes you've made. + + Your job now is to explain what you've done to complete the user's request. + Explain in your own words and taking into consideration the plan below and the + user's request in their last message. + + - Do NOT refer to the plan. + - Explain the changes as though you just did them + - Do NOT explain them as though they are already done. + + For example: + - say "I have created a new document ..." instead of "The document already existed ..." + - say "I have added a new section ..." instead of "The section already existed ..." + - say "I have updated the document ..." instead of "The document was already updated ..." + + # THE PLAN YOU JUST COMPLETED + + {plan_explanation} +""") + + +class ToolCall(BaseModel): + call: str = Field( + description="The recommended tool call to make. Example format: function_name({arg1: value1, arg2: value2})" + ) + explanation: str = Field( + description="An explanation of why this tool call is being made and what it is trying to accomplish." + ) + content_instructions: str = Field( + description="Specific instructions on how to replace the placeholder in the tool call with generated content." + ) + + +class PlanForTurn(BaseModel): + recommended_next_action: Literal[ + "clarify_document_change_request", "make_document_changes", "continue_conversation" + ] = Field( + description=dedent(""" + The recommended next action to take. + If 'clarify_document_change_request', you have determined that the user is requesting changes to the document, however + the request could use clarification; additionally, you will set 'clarification_question' to prompt the user for more + information. + If 'make_document_changes', you have determined that the user is requesting changes to the document and there is + clarity on what changes are needed; additionally, you will set 'document_changes_explanation' to explain the changes + and 'document_changes_tool_calls' to list the tool calls that you recommend to make the changes. + If 'continue_conversation', you have determined that the user is not requesting changes to the document and the conversation + should be continued. + """) + ) + clarification_question: str | None = Field( + description=dedent(""" + A question to prompt the user for more information about the document change request - should be set only if + 'recommended_next_action' is 'clarify_document_change_request'. + """) + ) + document_changes_explanation: str | None = Field( + description=dedent(""" + An explanation of the changes that are needed in the document - should be set only if 'recommended_next_action + is 'make_document_changes'. + """) + ) + document_changes_tool_calls: list[ToolCall] | None = Field( + description=dedent(""" + A list of tool calls that you recommend to make the changes in the document - should be set only if + 'recommended_next_action' is 'make_document_changes'. + """) + ) + + +async def system_message_generator(message: str) -> list[ChatCompletionMessageParam]: + return [ + openai_client.create_developer_message(content=message), + ] + + +async def chat_message_generator(history: Iterable[ConversationMessage]) -> list[ChatCompletionMessageParam]: + messages: list[ChatCompletionMessageParam] = [] + for msg in history: + match msg.sender.participant_role: + case ParticipantRole.user: + messages.append(openai_client.create_user_message(content=msg.content)) + + case ParticipantRole.assistant: + messages.append(openai_client.create_assistant_message(content=msg.content)) + + headers = await tools.list_documents(tools.ListDocumentsArgs()) + document_content_list: list[str] = [] + document_content_list.append(f"There are currently {len(headers.documents)} documents in the workspace.") + + for header in headers.documents: + document = await tools.get_document(tools.GetDocumentArgs(document_id=header.document_id)) + document_content_list.append(f"```json\n{document.model_dump_json()}\n```") + + document_content = "\n\n".join(document_content_list) + + last_assistant_index = 0 + for i in range(1, len(messages)): + if messages[-i].get("role") == "assistant": + last_assistant_index = len(messages) - i + break + + if last_assistant_index >= 0: + messages = ( + messages[:last_assistant_index] + + [ + openai_client.create_assistant_message( + content=None, + tool_calls=[ + ChatCompletionMessageToolCallParam( + id="call_1", + function={ + "name": "get_all_documents", + "arguments": "{}", + }, + type="function", ) - ) - return messages + ], + ), + openai_client.create_tool_message( + tool_call_id="call_1", + content=document_content, + ), + ] + + messages[last_assistant_index:] + ) - completion_tools = [ - CompletionTool( - function=tools.create_document, - argument_model=tools.CreateDocumentArgs, - ), - CompletionTool( - function=tools.update_document, - argument_model=tools.UpdateDocumentArgs, - ), - CompletionTool( - function=tools.remove_document, - argument_model=tools.RemoveDocumentArgs, - ), - CompletionTool( - function=tools.get_document, - argument_model=tools.GetDocumentArgs, - ), - CompletionTool( - function=tools.create_document_section, - argument_model=tools.CreateDocumentSectionArgs, - ), - CompletionTool( - function=tools.update_document_section, - argument_model=tools.UpdateDocumentSectionArgs, - ), - CompletionTool( - function=tools.remove_document_section, - argument_model=tools.RemoveDocumentSectionArgs, - ), - CompletionTool( - function=tools.list_documents, - argument_model=tools.ListDocumentsArgs, - ), - ] + return messages - config = await assistant_config.get(context.assistant) - config.service_config.azure_openai_deployment = "o3-mini" # type: ignore - llm_config = LLMConfig( - openai_client_factory=lambda: openai_client.create_client(config.service_config), - openai_model="o3-mini", # config.request_config.openai_model, - max_response_tokens=config.request_config.response_tokens, + +async def respond_to_message( + llms: LLMs, + conversation_history: Iterable[ConversationMessage], +) -> None: + async with current_context.get().set_status("planning..."): + try: + plan_for_turn = await build_plan_for_turn(llms=llms, history=conversation_history) + except Exception as e: + logger.exception("Failed to generate plan.") + await _send_error_message("Failed to generate plan.", {"error": str(e)}) + return + + try: + await execute_plan(llms=llms, history=conversation_history, plan_for_turn=plan_for_turn) + except Exception as e: + logger.exception("Failed to generate completion.") + await _send_error_message("Failed to generate completion.", {"error": str(e)}) + return + + +async def build_plan_for_turn(llms: LLMs, history: Iterable[ConversationMessage]) -> PlanForTurn: + async with llms.reasoning_fast.openai_client_factory() as client: + logger.info("generating plan") + structured_response = await openai_client.completion_structured( + async_client=client, + messages=await chat_message_generator(history) + + await system_message_generator(system_message_plan_for_turn), + response_model=PlanForTurn, + openai_model=llms.reasoning_fast.openai_model, + max_completion_tokens=llms.reasoning_fast.max_response_tokens, + reasoning_effort=llms.reasoning_fast.reasoning_effort, + ) + plan_for_turn = structured_response.response + metadata = structured_response.metadata + logger.info("plan_for_turn: %s", plan_for_turn) + + await _send_message( + ( + f"Recommended next action: {plan_for_turn.recommended_next_action}; " + f"{plan_for_turn.document_changes_explanation or plan_for_turn.clarification_question or ''}" + ), + { + **metadata, + "plan_for_turn": plan_for_turn.model_dump(mode="json"), + }, + message_type=MessageType.notice, + ) + + return plan_for_turn + + +async def execute_plan(llms: LLMs, history: Iterable[ConversationMessage], plan_for_turn: PlanForTurn) -> None: + async def generate_steps_from_plan() -> AsyncIterable[tuple[str, str, int, bool, list[CompletionTool]]]: + match plan_for_turn.recommended_next_action: + case "continue_conversation": + yield "", "", -1, True, [] + + case "clarify_document_change_request": + step_plan = system_message_clarify_document_change_request.replace( + "{clarification_question}", plan_for_turn.clarification_question or "" + ) + yield step_plan, "", -1, True, [] + + case "make_document_changes": + for recommendation in plan_for_turn.document_changes_tool_calls or []: + document_id = "" + headers = await tools.list_documents(tools.ListDocumentsArgs()) + if headers.documents: + document_id = headers.documents[0].document_id + + call_with_document_id = re.sub(r"", document_id, recommendation.call, 1, re.IGNORECASE) + tool_plan = "\n\n".join([ + f"Explanation for tool call:\n{recommendation.explanation}", + f"Content placeholder replacement instructions:\n{recommendation.content_instructions}", + f"Call:\n{call_with_document_id}", + ]) + step_plan = system_message_make_document_changes.replace( + "{plan_explanation}", plan_for_turn.document_changes_explanation or "" + ).replace("{step_plan}", tool_plan) + tool_choice = recommendation.call.split("(")[0] + step_tools = [tool for tool in completion_tools if tool.function.__name__ == tool_choice] + yield (step_plan, recommendation.call.split("(")[0], 0, False, step_tools) + + yield ( + system_message_make_document_changes_complete.replace( + "{plan_explanation}", plan_for_turn.document_changes_explanation or "" + ), + "", + -1, + True, + [], ) - try: - async for response in completion_with_tools( - llm_config=llm_config, - context=context, - get_messages=lambda: message_generator(), - tools=completion_tools, - ): - match response: - case MessageResponse(): - await _send_message(context, response.message, response.metadata) - - case ToolCallResponse(): - async with ( - context.state_updated_event_after(document_workspace_inspector.display_name), - context.state_updated_event_after(active_document_inspector.display_name), - ): - await _send_message( - context, - f"Called {response.tool_call.function.name}", - response.metadata, - message_type=MessageType.notice, - ) - - except Exception as e: - logger.exception("Failed to generate completion.") - await _send_error_message(context, "Failed to generate completion.", {"error": str(e)}) - return + case _: + raise ValueError(f"Unsupported recommended_next_action: {plan_for_turn.recommended_next_action}") + + additional_messages: list[ConversationMessage] = [] + + async for ( + step_plan, + tool_choice, + ignore_tool_calls_after, + allow_tool_followup, + plan_tools, + ) in generate_steps_from_plan(): + logger.info("executing step plan: %s", step_plan) + async for response in completion_with_tools( + llm_config=llms.chat, + head_messages=lambda: chat_message_generator((*history, *additional_messages)), + tail_messages=lambda: system_message_generator(system_message_document_assistant + step_plan), + tools=plan_tools, + tool_choice={"function": {"name": tool_choice}, "type": "function"} if tool_choice else None, + ignore_tool_calls_after=ignore_tool_calls_after, + allow_tool_followup=allow_tool_followup, + ): + match response: + case MessageResponse(): + message = await _send_message(response.message, debug=response.metadata) + if message is not None: + additional_messages.append(message) + + case ToolCallResponse(): + # async with ( + # current_context.get().state_updated_event_after(document_workspace_inspector.display_name), + # current_context.get().state_updated_event_after(active_document_inspector.display_name), + # ): + async with current_context.get().state_updated_event_after(active_document_inspector.display_name): + await _send_message( + response.result, + response.metadata, + message_type=MessageType.notice, + generated_content=False, + ) async def _send_message( - context: ConversationContext, message: str, debug: dict, message_type: MessageType = MessageType.chat -) -> None: + message: str, + debug: dict[str, Any], + message_type: MessageType = MessageType.chat, + metadata: dict[str, Any] | None = None, + generated_content: bool = True, +) -> ConversationMessage | None: if not message: - return + return None + + if not generated_content: + metadata = {"generated_content": False, **(metadata or {})} - await context.send_messages( + footer_items = _footer_items_for(debug) + if footer_items: + metadata = {"footer_items": footer_items, **(metadata or {})} + + message_list = await current_context.get().send_messages( NewConversationMessage( content=message, message_type=message_type, + metadata=metadata, debug_data=debug, ) ) + return message_list.messages[0] if message_list.messages else None -async def _send_error_message(context: ConversationContext, message: str, debug: dict) -> None: - await context.send_messages( - NewConversationMessage( - content=message, - message_type=MessageType.notice, - debug_data=debug, - ) + +def _footer_items_for(debug: dict[str, Any]) -> list[str]: + footer_items = [] + + def format_duration(duration: float) -> str: + if duration < 1: + return f"{duration * 1_000:.0f} milliseconds" + if duration < 60: + return f"{duration:.2f} seconds" + if duration < 3600: + return f"{duration / 60:.2f} minutes" + return f"{duration / 3600:.2f} hours" + + if "response_duration" in debug: + footer_items.append(f"Response time: {format_duration(debug['response_duration'])}") + + if "tool_duration" in debug: + footer_items.append(f"Tool time: {format_duration(debug['tool_duration'])}") + + def format_tokens(tokens: int) -> str: + if tokens < 1_000: + return f"{tokens:,}" + if tokens < 1_000_000: + return f"{tokens / 1_000:.1f}K" + return f"{tokens / 1_000_000:.1f}M" + + if "response" in debug: + if "usage" in debug["response"]: + usage = debug["response"]["usage"] + footer_items.append( + f"Tokens: {format_tokens(usage['total_tokens'])} ({format_tokens(usage['prompt_tokens'])} in, {format_tokens(usage['completion_tokens'])} out)" + ) + + if "model" in debug["response"]: + footer_items.append(f"Model: {debug['response']['model']}") + + return footer_items + + +async def _send_error_message(message: str, debug: dict[str, Any]) -> None: + await _send_message( + message=message, + debug=debug, + message_type=MessageType.notice, + generated_content=False, ) + + +current_context: ContextVar[ConversationContext] = ContextVar("current_conversation_context") diff --git a/assistants/prospector-assistant/assistant/artifact_creation_extension/store.py b/assistants/prospector-assistant/assistant/artifact_creation_extension/store.py index 3d115b69..415b4413 100644 --- a/assistants/prospector-assistant/assistant/artifact_creation_extension/store.py +++ b/assistants/prospector-assistant/assistant/artifact_creation_extension/store.py @@ -82,33 +82,35 @@ async def get(self, context: ConversationContext) -> AssistantConversationInspec return AssistantConversationInspectorStateDataModel(data={"content": projected}) -active_document_id: str | None = None - - -class ActiveDocumentInspector(ReadOnlyAssistantConversationInspectorStateProvider): +class AllDocumentsInspector(ReadOnlyAssistantConversationInspectorStateProvider): @property def display_name(self) -> str: - return "Active Document" + return "Documents" @property def description(self) -> str: - return "The active document." + return "All documents." async def get(self, context: ConversationContext) -> AssistantConversationInspectorStateDataModel: - global active_document_id store = for_context(context) headers = store.list_documents() if not headers: return AssistantConversationInspectorStateDataModel(data={"content": "No active document."}) - if active_document_id is None: - active_document_id = headers[0].document_id + toc: list[str] = [] + content: list[str] = [] - doc = store.read(active_document_id) + headers = store.list_documents() + for header in headers: + doc = store.read(header.document_id) + toc.append(f"- [{doc.title}](#{doc.title.lower().replace(' ', '-')})") + content.append(project_document_to_markdown(doc)) - projected = project_document_to_markdown(doc) + tocs = "\n".join(toc) + contents = "\n".join(content) + projection = f"```markdown\nDocuments:\n\n{tocs}\n\n{contents}\n```" - return AssistantConversationInspectorStateDataModel(data={"content": projected}) + return AssistantConversationInspectorStateDataModel(data={"content": projection}) def project_document_to_markdown(doc: Document) -> str: @@ -117,7 +119,7 @@ def project_document_to_markdown(doc: Document) -> str: """ markdown = f"# {doc.title}\n\n***{doc.metadata.purpose}***\n\n" for section in doc.sections: - markdown += f"{'#' * section.heading_level} {section.section_number} {section.title}\n\n{section.content}\n\n" + markdown += f"{'#' * section.heading_level} {section.section_number} {section.title}\n\n***{section.metadata.purpose}***\n\n{section.content}\n\n" markdown += "-" * 3 + "\n\n" - return f"```markdown\n{markdown}\n```" + return markdown diff --git a/assistants/prospector-assistant/assistant/artifact_creation_extension/test/conftest.py b/assistants/prospector-assistant/assistant/artifact_creation_extension/test/conftest.py new file mode 100644 index 00000000..913e52f3 --- /dev/null +++ b/assistants/prospector-assistant/assistant/artifact_creation_extension/test/conftest.py @@ -0,0 +1,100 @@ +import tempfile +from typing import Iterable +from unittest.mock import AsyncMock, MagicMock, Mock + +import dotenv +import openai_client +import pytest +from assistant.artifact_creation_extension import store +from assistant.artifact_creation_extension.config import LLMConfig +from assistant.artifact_creation_extension.extension import LLMs +from openai.types.chat import ChatCompletionReasoningEffort +from pydantic import HttpUrl +from semantic_workbench_assistant import logging_config, settings, storage +from semantic_workbench_assistant.assistant_app.context import AssistantContext, ConversationContext + +logging_config.config(settings=settings.logging) + + +@pytest.fixture(autouse=True, scope="function") +def assistant_settings(monkeypatch: pytest.MonkeyPatch) -> Iterable[None]: + """Fixture that sets up a temporary directory for the assistant storage.""" + with tempfile.TemporaryDirectory() as temp_dir: + monkeypatch.setattr(settings, "storage", storage.FileStorageSettings(root=temp_dir)) + yield + + +@pytest.fixture +def assistant_context() -> AssistantContext: + """Fixture that provides an assistant context for tests.""" + return AssistantContext( + id="test", + name="test-assistant", + _assistant_service_id="test", + ) + + +@pytest.fixture +def mock_conversation_context(assistant_context: AssistantContext) -> Mock: + """Fixture that provides a mock conversation context for tests.""" + mock_context = Mock(spec=ConversationContext) + mock_context.id = "test" + mock_context.title = "test-conversation" + mock_context.assistant = assistant_context + mock_context.set_status = MagicMock() + mock_context.state_updated_event_after = MagicMock() + mock_context.send_messages = AsyncMock() + + from assistant.artifact_creation_extension.extension import current_context + + current_context.set(mock_context) + + return mock_context + + +@pytest.fixture +def llms() -> LLMs: + """Fixture that provides LLM configurations for tests.""" + + endpoint_env_var = dotenv.dotenv_values().get("ASSISTANT__AZURE_OPENAI_ENDPOINT") or "" + if not endpoint_env_var: + pytest.skip("ASSISTANT__AZURE_OPENAI_ENDPOINT not set") + + def build_llm_config( + deployment: str, + model: str, + max_response_tokens: int = 16_000, + reasoning_effort: ChatCompletionReasoningEffort | None = None, + ) -> LLMConfig: + """Build LLM configuration for the specified deployment and model.""" + return LLMConfig( + openai_client_factory=lambda: openai_client.create_client( + openai_client.AzureOpenAIServiceConfig( + auth_config=openai_client.AzureOpenAIAzureIdentityAuthConfig(), + azure_openai_endpoint=HttpUrl(endpoint_env_var), + azure_openai_deployment=deployment, + ) + ), + openai_model=model, + max_response_tokens=max_response_tokens, + reasoning_effort=reasoning_effort, + ) + + return LLMs( + fast=build_llm_config("gpt-4o-mini", "gpt-4o-mini"), + chat=build_llm_config("gpt-4o", "gpt-4o"), + reasoning_fast=build_llm_config("o3-mini", "o3-mini", max_response_tokens=50_000, reasoning_effort="low"), + reasoning_long=build_llm_config("o3-mini", "o3-mini", max_response_tokens=50_000, reasoning_effort="high"), + ) + + +@pytest.fixture(autouse=True, scope="function") +def document_store(mock_conversation_context: Mock) -> store.DocumentStore: + """Fixture that provides a document store for tests.""" + document_store = store.for_context(mock_conversation_context) + + from assistant.artifact_creation_extension import tools + + tools.current_document_store.set(document_store) + + return document_store diff --git a/assistants/prospector-assistant/assistant/artifact_creation_extension/test/evaluation.py b/assistants/prospector-assistant/assistant/artifact_creation_extension/test/evaluation.py new file mode 100644 index 00000000..2b492439 --- /dev/null +++ b/assistants/prospector-assistant/assistant/artifact_creation_extension/test/evaluation.py @@ -0,0 +1,17 @@ +from sentence_transformers import SimilarityFunction + + +def sentence_cosine_similarity(sentence1: str, sentence2: str) -> float: + """Calculate the cosine similarity between two sentences.""" + + from sentence_transformers import SentenceTransformer + + model = SentenceTransformer("all-mpnet-base-v2", similarity_fn_name=SimilarityFunction.COSINE) + + sentence1_embeddings = model.encode([sentence1]) + sentence2_embeddings = model.encode([sentence2]) + + similarities = model.similarity(sentence1_embeddings[0], sentence2_embeddings[0]) + similarity = similarities[0][0] + + return similarity.item() diff --git a/assistants/prospector-assistant/assistant/artifact_creation_extension/test/test_completion_with_tools.py b/assistants/prospector-assistant/assistant/artifact_creation_extension/test/test_completion_with_tools.py new file mode 100644 index 00000000..28bbd39e --- /dev/null +++ b/assistants/prospector-assistant/assistant/artifact_creation_extension/test/test_completion_with_tools.py @@ -0,0 +1,83 @@ +import logging +from typing import Iterable +from unittest.mock import AsyncMock +from uuid import uuid4 + +import openai_client +import pytest +from assistant.artifact_creation_extension._llm import ( + CompletionTool, + MessageResponse, + ToolCallResponse, + completion_with_tools, +) +from assistant.artifact_creation_extension.extension import LLMs +from openai.types.chat import ChatCompletionMessageParam +from pydantic import BaseModel + +logger = logging.getLogger(__name__) + + +@pytest.mark.repeat(5) +async def test_completion_with_tools_error_handling(llms: LLMs): + class MockToolArgs(BaseModel): + arg1: str + + mock_tool_function = AsyncMock() + mock_tool_function.__name__ = "mock_tool_function" + mock_tool_function.__doc__ = "The only tool you'll ever need" + + success_message = "Success on the second call!" + uuid4().hex + # Simulate a tool that fails once before succeeding + mock_tool_function.side_effect = [ + Exception("An error occurred on the first call"), + success_message, + ] + + tools = [ + CompletionTool( + function=mock_tool_function, + argument_model=MockToolArgs, + ) + ] + + async def head_messages() -> Iterable[ChatCompletionMessageParam]: + return [openai_client.create_system_message("Call the tool. Once it succeeds, let me know.")] + + async def tail_messages() -> Iterable[ChatCompletionMessageParam]: + return [] + + # Call the function and collect responses + responses = [] + async for response in completion_with_tools( + llm_config=llms.chat, + tools=tools, + head_messages=head_messages, + tail_messages=tail_messages, + ): + logger.info("Response: %s", response) + responses.append(response) + + assert responses, "Expected at least one response" + + tool_responses = [] + message_responses = [] + for response in responses: + if isinstance(response, ToolCallResponse): + tool_responses.append(response) + continue + + if isinstance(response, MessageResponse): + message_responses.append(response) + continue + + pytest.fail(f"Unexpected response type: {type(response)}") + + assert len(tool_responses) == 1, "Expected one tool response" + tool_response = tool_responses[0] + assert tool_response.tool_call.function.name == mock_tool_function.__name__ + assert tool_response.result == success_message + + assert len(message_responses) >= 1, "Expected at least one message response" + for message_response in message_responses: + logger.info("Message: %s", message_response.message) diff --git a/assistants/prospector-assistant/assistant/artifact_creation_extension/test/test_extension.py b/assistants/prospector-assistant/assistant/artifact_creation_extension/test/test_extension.py new file mode 100644 index 00000000..286b71cb --- /dev/null +++ b/assistants/prospector-assistant/assistant/artifact_creation_extension/test/test_extension.py @@ -0,0 +1,320 @@ +import datetime +import json +import logging +import uuid +from textwrap import dedent +from unittest.mock import Mock, _CallList + +import pytest +from assistant.artifact_creation_extension import store +from assistant.artifact_creation_extension.extension import LLMs, ToolCall, build_plan_for_turn +from assistant.artifact_creation_extension.test import evaluation +from semantic_workbench_api_model.workbench_model import ( + ConversationMessage, + MessageSender, + MessageType, + NewConversationMessage, + ParticipantRole, +) + + +async def test_create_simple_document( + mock_conversation_context: Mock, llms: LLMs, document_store: store.DocumentStore +) -> None: + from assistant.artifact_creation_extension.extension import respond_to_message + + conversation_history = [ + user_message( + content="can you create a new software project plan document? and populate all sections as you see fit?" + ) + ] + + await respond_to_message(llms=llms, conversation_history=conversation_history) + + assert mock_conversation_context.send_messages.call_count > 0 + + calls: _CallList = mock_conversation_context.send_messages.call_args_list + for call in calls: + message = call.args[0] + assert isinstance(message, NewConversationMessage) + if message.message_type != MessageType.chat: + continue + + logging.info("message type: %s, content: %s", message.message_type, message.content) + + headers = document_store.list_documents() + assert len(headers) == 1 + + document = document_store.read(headers[0].document_id) + logging.info("document: %s", document.title) + assert document.title == "Software Project Plan" + + assert len(document.sections) > 0 + for section in document.sections: + logging.info("section: %s", section.title) + + +@pytest.mark.repeat(3) +async def test_create_us_constitution( + mock_conversation_context: Mock, llms: LLMs, document_store: store.DocumentStore +) -> None: + from assistant.artifact_creation_extension.extension import respond_to_message + + conversation_history = [ + user_message( + content=dedent(""" + please create a new document for the United States Constitution. + populate it with the preamble, all articles (I through VII), and all amendments (I through XXVII). + the preamble, each article, and each amendment, should be in a separate section. + ensure that all content matches that of the actual constitution. + """).strip() + ) + ] + + await respond_to_message(llms=llms, conversation_history=conversation_history) + + assert mock_conversation_context.send_messages.call_count > 0 + + calls: _CallList = mock_conversation_context.send_messages.call_args_list + assert len(calls) > 0 + + for call in reversed(calls): + message = call.args[0] + assert isinstance(message, NewConversationMessage) + + if message.message_type != MessageType.chat: + continue + + conversation_history.append(assistant_message(content=message.content)) + logging.info("message type: %s, content: %s", message.message_type, message.content) + + headers = document_store.list_documents() + assert len(headers) == 1 + + document = document_store.read(headers[0].document_id) + logging.info("document: %s", document.title) + assert "united states constitution" in document.title.lower() + + assert len(document.sections) > 0 + + markdown_document = store.project_document_to_markdown(document) + logging.info("markdown document:\n%s", markdown_document) + + preamble = document.sections[0] + assert preamble.title == "Preamble" + assert ( + evaluation.sentence_cosine_similarity( + preamble.content, + """ + We the People of the United States, in Order to form a more perfect Union, establish Justice, insure domestic Tranquility, + provide for the common defence, promote the general Welfare, and secure the Blessings of Liberty to ourselves and our Posterity, + do ordain and establish this Constitution for the United States of America. + """, + ) + > 0.99 + ) + + titles = [section.title for section in document.sections] + assert titles == [ + "Preamble", + "Article I", + "Article II", + "Article III", + "Article IV", + "Article V", + "Article VI", + "Article VII", + "Amendment I", + "Amendment II", + "Amendment III", + "Amendment IV", + "Amendment V", + "Amendment VI", + "Amendment VII", + "Amendment VIII", + "Amendment IX", + "Amendment X", + "Amendment XI", + "Amendment XII", + "Amendment XIII", + "Amendment XIV", + "Amendment XV", + "Amendment XVI", + "Amendment XVII", + "Amendment XVIII", + "Amendment XIX", + "Amendment XX", + "Amendment XXI", + "Amendment XXII", + "Amendment XXIII", + "Amendment XXIV", + "Amendment XXV", + "Amendment XXVI", + "Amendment XXVII", + ] + + +@pytest.mark.repeat(10) +async def test_build_plan_for_us_constitution(llms: LLMs) -> None: + conversation_history = [ + user_message( + content=dedent(""" + please create a new document for the United States Constitution. + populate it with the preamble, all articles (I through VII), and all amendments (I through XXVII). + the preamble, each article, and each amendment, should be in a separate section. + ensure that all content matches that of the actual constitution. + """).strip() + ) + ] + plan = await build_plan_for_turn(llms, conversation_history) + + assert plan.recommended_next_action == "make_document_changes" + assert plan.document_changes_tool_calls is not None, ( + "document_changes_tool_calls is not set, even though recommended_next_action is set to make_document_changes" + ) + + def tool_name(call: ToolCall) -> str: + return call.call.split("(")[0] + + def tool_args(call: ToolCall) -> dict: + args_json = call.call.strip(tool_name(call) + "(").strip(")") + args = json.loads(args_json) + # the completion sometimes returns the arguments nested in a "properties" field + if "properties" in args: + args = args["properties"] + return args + + assert len(plan.document_changes_tool_calls) > 0, ( + "document_changes_tool_calls is empty, expected at least one element" + ) + possible_create_document_call = plan.document_changes_tool_calls.pop(0) + assert tool_name(possible_create_document_call) == "create_document" + assert tool_args(possible_create_document_call).get("title") == "United States Constitution" + + create_document_section_calls = [] + while ( + len(plan.document_changes_tool_calls) > 0 + and tool_name(plan.document_changes_tool_calls[0]) == "create_document_section" + ): + create_document_section_calls.append(plan.document_changes_tool_calls.pop(0)) + + # collect these for including in assertion messages + create_document_section_titles = [tool_args(call).get("section_title") for call in create_document_section_calls] + + assert len(plan.document_changes_tool_calls) == 0, ( + f"Remaining tool calls are unexpected: {plan.document_changes_tool_calls}" + ) + + assert len(create_document_section_calls) > 0, ( + "create_document_section_calls is empty, expected at least one element" + ) + possible_preamble_call = create_document_section_calls.pop(0) + assert tool_args(possible_preamble_call).get("section_title") == "Preamble" + + # collect calls for articles based on title prefix + create_article_calls = [] + while len(create_document_section_calls) > 0 and ( + tool_args(create_document_section_calls[0]).get("section_title") or "" + ).startswith("Article"): + create_article_calls.append(create_document_section_calls.pop(0)) + + # collect calls for amendments based on title prefix + create_amendment_calls = [] + while len(create_document_section_calls) > 0 and ( + tool_args(create_document_section_calls[0]).get("section_title") or "" + ).startswith("Amendment"): + create_amendment_calls.append(create_document_section_calls.pop(0)) + + assert len(create_document_section_calls) == 0, ( + f"Remaining tool calls have unexpected titles: {create_document_section_calls}" + ) + + article_titles = [tool_args(call).get("section_title") for call in create_article_calls] + + assert article_titles == [ + "Article I", + "Article II", + "Article III", + "Article IV", + "Article V", + "Article VI", + "Article VII", + ] or article_titles == [ + "Article I - The Legislative Branch", + "Article II - The Executive Branch", + "Article III - The Judicial Branch", + "Article IV - States' Powers and Limits", + "Article V - Amendment Process", + "Article VI - Federal Powers", + "Article VII - Ratification", + ], f"Unexpected article titles. Titles for all sections: {create_document_section_titles}" + + amendment_titles = [tool_args(call).get("section_title") for call in create_amendment_calls] + + # allow for Amendments separator section to be included + if len(amendment_titles) and amendment_titles[0] == "Amendments": + amendment_titles.pop(0) + + assert amendment_titles == [ + "Amendment I", + "Amendment II", + "Amendment III", + "Amendment IV", + "Amendment V", + "Amendment VI", + "Amendment VII", + "Amendment VIII", + "Amendment IX", + "Amendment X", + "Amendment XI", + "Amendment XII", + "Amendment XIII", + "Amendment XIV", + "Amendment XV", + "Amendment XVI", + "Amendment XVII", + "Amendment XVIII", + "Amendment XIX", + "Amendment XX", + "Amendment XXI", + "Amendment XXII", + "Amendment XXIII", + "Amendment XXIV", + "Amendment XXV", + "Amendment XXVI", + "Amendment XXVII", + ], f"Unexpected amendment titles. Titles for all sections: {create_document_section_titles}" + + +def assistant_message(content: str, message_type: MessageType = MessageType.chat) -> ConversationMessage: + return ConversationMessage( + id=uuid.uuid4(), + sender=MessageSender( + participant_id="assistant", + participant_role=ParticipantRole.assistant, + ), + content=content, + timestamp=datetime.datetime.now(datetime.UTC), + content_type="text/plain", + message_type=message_type, + filenames=[], + metadata={}, + has_debug_data=False, + ) + + +def user_message(content: str, message_type: MessageType = MessageType.chat) -> ConversationMessage: + return ConversationMessage( + id=uuid.uuid4(), + sender=MessageSender( + participant_id="user", + participant_role=ParticipantRole.user, + ), + content=content, + timestamp=datetime.datetime.now(datetime.UTC), + content_type="text/plain", + message_type=message_type, + filenames=[], + metadata={}, + has_debug_data=False, + ) diff --git a/assistants/prospector-assistant/assistant/artifact_creation_extension/tools.py b/assistants/prospector-assistant/assistant/artifact_creation_extension/tools.py index 83112cf1..c579a418 100644 --- a/assistants/prospector-assistant/assistant/artifact_creation_extension/tools.py +++ b/assistants/prospector-assistant/assistant/artifact_creation_extension/tools.py @@ -1,25 +1,15 @@ from collections import defaultdict +from contextvars import ContextVar from datetime import datetime, timezone from typing import Optional from pydantic import BaseModel, Field -from semantic_workbench_assistant.assistant_app.context import ConversationContext -from ._llm import ToolArgsModel from .document import Document, DocumentHeader, DocumentMetadata, Section, SectionMetadata -from .store import DocumentStore, for_context +from .store import DocumentStore -class ArgsWithDocumentStore(ToolArgsModel): - def set_context(self, context: ConversationContext) -> None: - self._context = context - - @property - def store(self) -> DocumentStore: - return for_context(self._context) - - -class CreateDocumentArgs(ArgsWithDocumentStore): +class CreateDocumentArgs(BaseModel): title: str = Field(description="Document title") purpose: Optional[str] = Field(description="Describes the intent of the document.") audience: Optional[str] = Field(description="Describes the intended audience for the document.") @@ -28,7 +18,7 @@ class CreateDocumentArgs(ArgsWithDocumentStore): ) -async def create_document(args: CreateDocumentArgs) -> DocumentMetadata: +async def create_document(args: CreateDocumentArgs) -> str: """ Create a new document with the specified metadata. """ @@ -41,12 +31,12 @@ async def create_document(args: CreateDocumentArgs) -> DocumentMetadata: metadata.other_guidelines = args.other_guidelines document = Document(title=args.title, metadata=metadata) - args.store.write(document) + current_document_store.get().write(document) - return document.metadata + return f"Document with id {document.metadata.document_id} created successfully" -class UpdateDocumentArgs(ArgsWithDocumentStore): +class UpdateDocumentArgs(BaseModel): document_id: str = Field(description="The id of the document to update.") title: Optional[str] = Field(description="The updated title of the document. Pass None to leave unchanged.") purpose: Optional[str] = Field( @@ -60,11 +50,11 @@ class UpdateDocumentArgs(ArgsWithDocumentStore): ) -async def update_document(args: UpdateDocumentArgs) -> DocumentMetadata: +async def update_document(args: UpdateDocumentArgs) -> str: """ Update the metadata of an existing document. """ - with args.store.checkout(args.document_id) as document: + with current_document_store.get().checkout(args.document_id) as document: if args.title is not None: document.title = args.title if args.purpose is not None: @@ -76,10 +66,10 @@ async def update_document(args: UpdateDocumentArgs) -> DocumentMetadata: document.metadata.last_modified_at = datetime.now(timezone.utc) - return document.metadata + return f"Document with id {args.document_id} updated successfully" -class GetDocumentArgs(ArgsWithDocumentStore): +class GetDocumentArgs(BaseModel): document_id: str = Field(description="The id of the document to retrieve.") @@ -87,23 +77,23 @@ async def get_document(args: GetDocumentArgs) -> Document: """ Retrieve a document by its id. """ - return args.store.read(id=args.document_id) + return current_document_store.get().read(id=args.document_id) -class RemoveDocumentArgs(ArgsWithDocumentStore): +class RemoveDocumentArgs(BaseModel): document_id: str = Field(description="The id of the document to remove.") -async def remove_document(args: RemoveDocumentArgs) -> Document: +async def remove_document(args: RemoveDocumentArgs) -> str: """ Remove a document from the workspace. """ - document = args.store.read(id=args.document_id) - args.store.delete(id=args.document_id) - return document + document = current_document_store.get().read(id=args.document_id) + current_document_store.get().delete(id=args.document_id) + return f"Document with id {document.metadata.document_id} removed successfully" -class CreateDocumentSectionArgs(ArgsWithDocumentStore): +class CreateDocumentSectionArgs(BaseModel): document_id: str = Field(description="The id of the document to add the section to.") insert_before_section_number: Optional[str] = Field( description="The section number of the section to insert the new section ***before***." @@ -117,12 +107,12 @@ class CreateDocumentSectionArgs(ArgsWithDocumentStore): section_content: str = Field(description="The content of the new section. Can be left blank.") -async def create_document_section(args: CreateDocumentSectionArgs) -> Section: +async def create_document_section(args: CreateDocumentSectionArgs) -> str: """ Create a new section in an existing document. """ - with args.store.checkout(args.document_id) as document: + with current_document_store.get().checkout(args.document_id) as document: document.metadata.last_modified_at = datetime.now(timezone.utc) metadata = SectionMetadata() @@ -138,6 +128,8 @@ async def create_document_section(args: CreateDocumentSectionArgs) -> Section: f"Section {args.insert_before_section_number} not found in document {args.document_id}" ) + _validate_content(args.section_content) + section = Section( title=args.section_title, content=args.section_content, @@ -150,10 +142,10 @@ async def create_document_section(args: CreateDocumentSectionArgs) -> Section: _renumber_sections(document.sections) - return section + return f"Section with number {section.section_number} added to document {args.document_id} successfully" -class UpdateDocumentSectionArgs(ArgsWithDocumentStore): +class UpdateDocumentSectionArgs(BaseModel): document_id: str = Field(description="The id of the document containing the section to update.") section_number: str = Field(description="The number of the section to update.") section_heading_level: Optional[int] = Field( @@ -168,11 +160,11 @@ class UpdateDocumentSectionArgs(ArgsWithDocumentStore): ) -async def update_document_section(args: UpdateDocumentSectionArgs) -> Section: +async def update_document_section(args: UpdateDocumentSectionArgs) -> str: """ Update the content of a section in an existing document. """ - with args.store.checkout(args.document_id) as document: + with current_document_store.get().checkout(args.document_id) as document: section, _ = _find_section(args.section_number, document) if section is None: raise ValueError(f"Section {args.section_number} not found in document {args.document_id}") @@ -184,24 +176,25 @@ async def update_document_section(args: UpdateDocumentSectionArgs) -> Section: if args.section_purpose is not None: section.metadata.purpose = args.section_purpose if args.section_content is not None: + _validate_content(args.section_content) section.content = args.section_content document.metadata.last_modified_at = datetime.now(timezone.utc) _renumber_sections(document.sections) - return section + return f"Section with number {args.section_number} updated successfully" -class RemoveDocumentSectionArgs(ArgsWithDocumentStore): +class RemoveDocumentSectionArgs(BaseModel): document_id: str = Field(description="The id of the document containing the section to remove.") section_number: str = Field(description="The section number of the section to remove.") -async def remove_document_section(args: RemoveDocumentSectionArgs) -> Section: +async def remove_document_section(args: RemoveDocumentSectionArgs) -> str: """ Remove a section from an existing document. Note that removing a section will also remove all nested sections. """ - with args.store.checkout(args.document_id) as document: + with current_document_store.get().checkout(args.document_id) as document: section, _ = _find_section(args.section_number, document) if section is None: raise ValueError(f"Section with number {args.section_number} not found in document {args.document_id}") @@ -212,7 +205,7 @@ async def remove_document_section(args: RemoveDocumentSectionArgs) -> Section: document.metadata.last_modified_at = datetime.now(timezone.utc) - return section + return f"Section with number {args.section_number} removed successfully" class DocumentList(BaseModel): @@ -220,7 +213,7 @@ class DocumentList(BaseModel): count: int = Field(description="The number of documents in the workspace.") -class ListDocumentsArgs(ArgsWithDocumentStore): +class ListDocumentsArgs(BaseModel): pass @@ -228,7 +221,7 @@ async def list_documents(args: ListDocumentsArgs) -> DocumentList: """ List the titles of all documents in the workspace. """ - headers = args.store.list_documents() + headers = current_document_store.get().list_documents() return DocumentList(documents=headers, count=len(headers)) @@ -270,3 +263,14 @@ def _renumber_sections(sections: list[Section]) -> None: current_section_number_parts.append(str(sections_at_level[current_heading_level])) section.section_number = ".".join(current_section_number_parts) + + +def _validate_content(content: str) -> None: + """ + Validate the content of a section. + """ + if "" in content.lower(): + raise ValueError("Content placeholder was not replaced according to instructions") + + +current_document_store: ContextVar[DocumentStore] = ContextVar("current_document_store") diff --git a/assistants/prospector-assistant/assistant/legacy.py b/assistants/prospector-assistant/assistant/legacy.py index a4625234..38a34a17 100644 --- a/assistants/prospector-assistant/assistant/legacy.py +++ b/assistants/prospector-assistant/assistant/legacy.py @@ -15,6 +15,7 @@ async def provide_guidance_if_necessary(context: ConversationContext) -> None: if marker_path.exists(): return + marker_path.parent.mkdir(parents=True, exist_ok=True) marker_path.touch() conversation_response = await context.get_conversation() diff --git a/assistants/prospector-assistant/pyproject.toml b/assistants/prospector-assistant/pyproject.toml index 370760ea..fac030a5 100644 --- a/assistants/prospector-assistant/pyproject.toml +++ b/assistants/prospector-assistant/pyproject.toml @@ -31,4 +31,19 @@ requires = ["hatchling"] build-backend = "hatchling.build" [dependency-groups] -dev = ["pyright>=1.1.389"] +dev = [ + "pyright>=1.1.389", + "pytest>=8.3.4", + "pytest-asyncio>=0.25.3", + "pytest-httpx>=0.35.0", + "pytest-repeat>=0.9.3", + "sentence-transformers>=3.4.1", +] + +[tool.pytest.ini_options] +addopts = "-vv --color=yes" +log_cli = true +log_cli_level = "INFO" +log_cli_format = "%(asctime)s | %(levelname)-7s | %(name)s | %(message)s" +asyncio_mode = "auto" +asyncio_default_fixture_loop_scope = "function" diff --git a/assistants/prospector-assistant/uv.lock b/assistants/prospector-assistant/uv.lock index 32364075..4f435658 100644 --- a/assistants/prospector-assistant/uv.lock +++ b/assistants/prospector-assistant/uv.lock @@ -1,23 +1,22 @@ version = 1 requires-python = ">=3.11" resolution-markers = [ + "python_full_version >= '3.12'", "python_full_version < '3.12'", - "python_full_version == '3.12.*'", - "python_full_version >= '3.13'", ] [[package]] name = "aiohappyeyeballs" -version = "2.4.3" +version = "2.4.6" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/bc/69/2f6d5a019bd02e920a3417689a89887b39ad1e350b562f9955693d900c40/aiohappyeyeballs-2.4.3.tar.gz", hash = "sha256:75cf88a15106a5002a8eb1dab212525c00d1f4c0fa96e551c9fbe6f09a621586", size = 21809 } +sdist = { url = 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a/libraries/python/anthropic-client/.vscode/settings.json b/libraries/python/anthropic-client/.vscode/settings.json index 04d3b790..5be4f6a8 100644 --- a/libraries/python/anthropic-client/.vscode/settings.json +++ b/libraries/python/anthropic-client/.vscode/settings.json @@ -19,7 +19,6 @@ "python.analysis.inlayHints.functionReturnTypes": true, "python.analysis.typeCheckingMode": "standard", "python.defaultInterpreterPath": "${workspaceFolder}/.venv", - "python.testing.pytestEnabled": true, "[python]": { "editor.defaultFormatter": "charliermarsh.ruff", "editor.formatOnSave": true, diff --git a/libraries/python/llm-client/.vscode/settings.json b/libraries/python/llm-client/.vscode/settings.json index 04d3b790..5be4f6a8 100644 --- a/libraries/python/llm-client/.vscode/settings.json +++ b/libraries/python/llm-client/.vscode/settings.json @@ -19,7 +19,6 @@ "python.analysis.inlayHints.functionReturnTypes": true, "python.analysis.typeCheckingMode": "standard", "python.defaultInterpreterPath": "${workspaceFolder}/.venv", - "python.testing.pytestEnabled": true, "[python]": { "editor.defaultFormatter": "charliermarsh.ruff", "editor.formatOnSave": true, diff --git a/libraries/python/openai-client/openai_client/completion.py b/libraries/python/openai-client/openai_client/completion.py index d2dc3c7b..6203ccc1 100644 --- a/libraries/python/openai-client/openai_client/completion.py +++ b/libraries/python/openai-client/openai_client/completion.py @@ -121,7 +121,7 @@ async def completion_structured( }, "response": response.model_dump(), "response_headers": headers, - "duration": perf_counter() - start, + "response_duration": perf_counter() - start, } return StructuredResponse(response=response.choices[0].message.parsed, metadata=metadata)