update latest code

CHANGELOG.md

@@ -23,6 +23,15 @@ Security - Patches and security improvements.
 ***************************************************
 
 # Version Update History
+## [0.0.4] - 2024-04-26
+### Added
+- Add version management capability to the prompt.
+
+### Fixed
+- Fixed compatibility issues on Windows
+  * Due to compatibility issues of Gunicorn with Windows systems, automatically identify the kernel version to select the web startup method.
+  * Specified YAML reading as UTF-8 encoding method.
+
 ## [0.0.3] - 2024-04-19
 ### Init
 - The official release version of AgentUniverse has been initialized. Enjoy using it!

CHANGELOG_zh.md

@@ -23,6 +23,15 @@ Security - 补丁与安全改进。
 ***************************************************
 
 # 版本更新记录
+## [0.0.4] - 2024-04-26
+### Added
+- 新增prompt版本管理能力
+
+### Fixed
+- Windows版本下的兼容问题修复
+  * 由于Gunicore对于windows系统的兼容问题，自动识别内核版本自动选择web启动方式
+  * yaml读取指定为utf-8 encode方式
+
 ## [0.0.3] - 2024-04-19
 ### Init
 - agentUniverse正式外发版本初始化,祝您使用愉快！

README.md

@@ -5,9 +5,9 @@ Language version: [English](./README.md) | [中文](./README_zh.md)
 ![](https://img.shields.io/badge/framework-agentUniverse-pink)
 ![](https://img.shields.io/badge/python-3.10%2B-blue?logo=Python)
 [![](https://img.shields.io/badge/%20license-Apache--2.0-yellow)](LICENSE)
-[![Static Badge](https://img.shields.io/badge/pypi-v0.0.3-blue?logo=pypi)](https://pypi.org/project/agentUniverse/)
+[![Static Badge](https://img.shields.io/badge/pypi-v0.0.4-blue?logo=pypi)](https://pypi.org/project/agentUniverse/)
 
-![](https://github.com/alipay/agentUniverse/blob/master/docs/guidebook/_picture/logo_bar.png)
+![](docs/guidebook/_picture/logo_bar.jpg)
 ****************************************
 
 ## Overview
@@ -24,10 +24,10 @@ This pattern consists of three agents: Data-fining agent, which is designed to s
 
 More patterns are coming soon...
 
-![](https://github.com/alipay/agentUniverse/blob/master/docs/guidebook/_picture/agent_universe_framework_resize.jpg)
+![](docs/guidebook/_picture/agent_universe_framework_resize.jpg)
 
 ## agentUniverseSample Project
-[agentUniverse Sample Project](https://github.com/alipay/agentUniverse/blob/master/sample_standard_app/README.md)
+[agentUniverse Sample Project](sample_standard_app/README.md)
 
 ## Quick Installation
 Using pip:
@@ -42,7 +42,20 @@ We will show you how to:
 * Use pattern components to complete multi-agent collaboration
 * Test and optimize the performance of the agent
 * Quickly serve the agent
-For details, please read [Quick Start](https://github.com/alipay/agentUniverse/blob/master/docs/guidebook/en/1_3_Quick_Start.md).
+For details, please read [Quick Start](docs/guidebook/en/1_3_Quick_Start.md).
 
 ## Guidebook
-For more detailed information, please refer to the [Guidebook](https://github.com/alipay/agentUniverse/blob/master/docs/guidebook/en/0_index.md).
+For more detailed information, please refer to the [Guidebook](docs/guidebook/en/0_index.md).
+
+## API Reference
+[readthedocs](https://agentuniverse.readthedocs.io/en/latest/)
+
+## More Ways to Contact Us
+* github: https://github.com/alipay/agentUniverse
+* gitee: https://gitee.com/agentUniverse/agentUniverse
+* gitcode: https://gitcode.com/agentUniverse
+* Stack Overflow: https://stackoverflowteams.com/c/agentuniverse/questions
+* Discord: https://discord.gg/VfhEvJzQ
+* WeChat Official Account: agentUniverse智多星
+* DingTalk Group:
+![](./docs/guidebook/_picture/dingtalk_util20250429.png)

README_zh.md

@@ -5,9 +5,9 @@
 ![](https://img.shields.io/badge/framework-agentUniverse-pink)
 ![](https://img.shields.io/badge/python-3.10%2B-blue?logo=Python)
 [![](https://img.shields.io/badge/%20license-Apache--2.0-yellow)](LICENSE)
-[![Static Badge](https://img.shields.io/badge/pypi-v0.0.3-blue?logo=pypi)](https://pypi.org/project/agentUniverse/)
+[![Static Badge](https://img.shields.io/badge/pypi-v0.0.4-blue?logo=pypi)](https://pypi.org/project/agentUniverse/)
 
-![](https://github.com/alipay/agentUniverse/blob/master/docs/guidebook/_picture/logo_bar.png)
+![](docs/guidebook/_picture/logo_bar.jpg)
 ****************************************
 
 ## 项目介绍
@@ -25,11 +25,11 @@
 
 更多模式组件持续推出中...
 
-![](https://github.com/alipay/agentUniverse/blob/master/docs/guidebook/_picture/agent_universe_framework_resize.jpg)
+![](docs/guidebook/_picture/agent_universe_framework_resize.jpg)
 
 
 ## agentUniverse 示例项目
-[agentUniverse 示例项目](https://github.com/alipay/agentUniverse/tree/master/sample_standard_app)
+[agentUniverse 示例项目](sample_standard_app/README_zh.md)
 
 ## 快速安装
 使用pip：
@@ -45,7 +45,20 @@ pip install agentUniverse
 * 对agent执行效果进行测试调优
 * 对agent进行快速服务化
 
-详情请阅读[快速开始](https://github.com/alipay/agentUniverse/blob/master/docs/guidebook/zh/1_3_%E5%BF%AB%E9%80%9F%E5%BC%80%E5%A7%8B.md) 。
+详情请阅读[快速开始](docs/guidebook/zh/1_3_%E5%BF%AB%E9%80%9F%E5%BC%80%E5%A7%8B.md) 。
 
 ## 用户指南
-更多详细信息，请参阅[指南](https://github.com/alipay/agentUniverse/blob/master/docs/guidebook/zh/0_%E7%9B%AE%E5%BD%95.md) 。
+更多详细信息，请参阅[指南](docs/guidebook/zh/0_%E7%9B%AE%E5%BD%95.md) 。
+
+## API 参考
+[readthedocs](https://agentuniverse.readthedocs.io/en/latest/)
+
+## 更多方式联系我们
+* github: https://github.com/alipay/agentUniverse
+* gitee: https://gitee.com/agentUniverse/agentUniverse
+* gitcode: https://gitcode.com/agentUniverse
+* Stack Overflow: https://stackoverflowteams.com/c/agentuniverse/questions
+* Discord: https://discord.gg/VfhEvJzQ
+* 微信公众号: agentUniverse智多星
+* 钉钉答疑群:
+![](./docs/guidebook/_picture/dingtalk_util20250429.png)

agentuniverse/agent/action/knowledge/embedding/openai_embedding.py

@@ -24,14 +24,6 @@ class OpenAIEmbedding(Embedding):
     async_client: Any = None
     dimensions: Optional[int] = None
 
-    def __init__(self, **kwargs):
-        """Initialize the openai embedding class."""
-        super().__init__(**kwargs)
-        if self.client is None:
-            self.client = OpenAI(api_key=self.openai_api_key, **self.openai_client_args or {})
-        if self.async_client is None:
-            self.async_client = AsyncOpenAI(api_key=self.openai_api_key, **self.openai_client_args or {})
-
     def get_embeddings(self, texts: List[str]) -> List[List[float]]:
         """Get the OpenAI embeddings.
 
@@ -48,7 +40,7 @@ def get_embeddings(self, texts: List[str]) -> List[List[float]]:
          Raises:
              ValueError: If texts exceed the embedding model token limit or missing some required parameters.
          """
-
+        self.client = OpenAI(api_key=self.openai_api_key, **self.openai_client_args or {})
         if self.embedding_model_name is None:
             raise ValueError("Must provide `embedding_model_name`")
         try:
@@ -81,7 +73,7 @@ async def async_get_embeddings(self, texts: List[str]) -> List[List[float]]:
          Raises:
              ValueError: If texts exceed the embedding model token limit or missing some required parameters.
          """
-
+        self.async_client = AsyncOpenAI(api_key=self.openai_api_key, **self.openai_client_args or {})
         if self.embedding_model_name is None:
             raise ValueError("Must provide `embedding_model_name`")
         try:

agentuniverse/agent/action/knowledge/store/chroma_store.py

@@ -99,6 +99,32 @@ def insert_documents(self, documents: List[Document], **kwargs: Any):
                 ids=[document.id]
             )
 
+    def upsert_document(self, documents: List[Document], **kwargs):
+        """Upsert document into the store."""
+        for document in documents:
+            embedding = document.embedding
+            if self.embedding_model is not None and len(embedding) == 0:
+                embedding = self.embedding_model.get_embeddings([document.text])[0]
+            self.collection.upsert(
+                documents=[document.text],
+                metadatas=[document.metadata],
+                embeddings=[embedding] if embedding is not None else None,
+                ids=[document.id]
+            )
+
+    def update_document(self, documents: List[Document], **kwargs):
+        """Update document into the store."""
+        for document in documents:
+            embedding = document.embedding
+            if self.embedding_model is not None and len(embedding) == 0:
+                embedding = self.embedding_model.get_embeddings([document.text])[0]
+            self.collection.update(
+                documents=[document.text],
+                metadatas=[document.metadata],
+                embeddings=[embedding] if embedding is not None else None,
+                ids=[document.id]
+            )
+
     @staticmethod
     def to_documents(query_result: QueryResult) -> List[Document]:
         """Convert the query results of ChromaDB to the AgentUniverse(AU) document format."""

agentuniverse/agent/action/knowledge/store/document.py

@@ -8,7 +8,7 @@
 from typing import Dict, Any, Optional, List
 
 from langchain_core.documents.base import Document as LCDocument
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, model_validator
 
 
 class Document(BaseModel):
@@ -21,11 +21,18 @@ class Document(BaseModel):
         embedding (List[float]): Embedding data associated with the document
     """
 
-    id: str = Field(default_factory=lambda: str(uuid.uuid4()))
+    id: str = None
     text: Optional[str] = ""
     metadata: Optional[Dict[str, Any]] = None
     embedding: List[float] = Field(default_factory=list)
 
+    @model_validator(mode='before')
+    def create_id(cls, values):
+        text: str = values.get('text', '')
+        if not values.get('id'):
+            values['id'] = str(uuid.uuid5(uuid.NAMESPACE_URL, text))
+        return values
+
     def as_langchain(self) -> LCDocument:
         """Convert to LangChain document format."""
         metadata = self.metadata or {}

agentuniverse/agent/action/knowledge/store/store.py

@@ -87,3 +87,19 @@ def delete_document(self, document_id: str, **kwargs):
     async def async_delete_document(self, document_id: str, **kwargs):
         """Asynchronously delete the specific document by the document id."""
         raise NotImplementedError
+
+    def upsert_document(self, documents: List[Document], **kwargs):
+        """Upsert document into the store."""
+        raise NotImplementedError
+
+    async def async_upsert_document(self, documents: List[Document], **kwargs):
+        """Asynchronously upsert documents into the store."""
+        raise NotImplementedError
+
+    def update_document(self, documents: List[Document], **kwargs):
+        """Update document into the store."""
+        raise NotImplementedError
+
+    async def async_update_document(self, documents: List[Document], **kwargs):
+        """Asynchronously update documents into the store."""
+        raise NotImplementedError

agentuniverse/agent/default/peer_agent.py

@@ -0,0 +1,43 @@
+# !/usr/bin/env python3
+# -*- coding:utf-8 -*-
+
+# @Time    : 2024/4/29 16:00
+# @Author  : wangchongshi
+# @Email   : [email protected]
+# @FileName: peer_agent.py
+from agentuniverse.agent.agent import Agent
+from agentuniverse.agent.input_object import InputObject
+
+
+class PeerAgent(Agent):
+    """Peer Agent class."""
+
+    def input_keys(self) -> list[str]:
+        """Return the input keys of the Agent."""
+        return ['input']
+
+    def output_keys(self) -> list[str]:
+        """Return the output keys of the Agent."""
+        return ['output']
+
+    def parse_input(self, input_object: InputObject, planner_input: dict) -> dict:
+        """Planner parameter parsing.
+
+        Args:
+            input_object(InputObject): agent parameter object
+            planner_input(dict): Planner input
+        Returns:
+            dict: Planner input
+        """
+        planner_input['input'] = input_object.get_data('input')
+        return planner_input
+
+    def parse_result(self, planner_result: dict) -> dict:
+        """Planner result parser.
+
+        Args:
+            planner_result(dict): Planner result
+        Returns:
+            dict: Agent result object.
+        """
+        return {"output": planner_result.get('result')[0].get('expressing_result').get_data('output')}

agentuniverse/agent/default/peer_agent.yaml

@@ -0,0 +1,16 @@
+info:
+  name: 'PeerAgent'
+  description: 'peer agent'
+plan:
+  planner:
+    name: 'peer_planner'
+    eval_threshold: 60
+    retry_count: 2
+    planning: 'PlanningAgent'
+    executing: 'ExecutingAgent'
+    expressing: 'ExpressingAgent'
+    reviewing: 'ReviewingAgent'
+metadata:
+  type: 'AGENT'
+  module: 'agentuniverse.agent.default.peer_agent'
+  class: 'PeerAgent'

agentuniverse/agent/plan/planner/executing_planner/executing_planner.py

@@ -6,9 +6,10 @@
 # @FileName: executing_planner.py
 """Execution planner module."""
 import asyncio
+
 from langchain.chains import LLMChain
 from langchain_core.memory import BaseMemory
-from langchain_core.prompts import PromptTemplate
+
 from agentuniverse.agent.agent_model import AgentModel
 from agentuniverse.agent.input_object import InputObject
 from agentuniverse.agent.plan.planner.planner import Planner
@@ -39,21 +40,21 @@ def invoke(self, agent_model: AgentModel, planner_input: dict, input_object: Inp
 
         llm: LLM = self.handle_llm(agent_model)
 
-        self.handle_prompt(agent_model, planner_input)
+        prompt: Prompt = self.handle_prompt(agent_model, planner_input)
 
         llm_chain = LLMChain(llm=llm.as_langchain(),
-                             prompt=self.prompt.as_langchain(),
+                             prompt=prompt.as_langchain(),
                              output_key=self.output_key, memory=memory)
         return asyncio.run(llm_chain.acall(inputs=planner_input))
 
-    def handle_prompt(self, agent_model: AgentModel, planner_input: dict):
+    def handle_prompt(self, agent_model: AgentModel, planner_input: dict) -> Prompt:
         """Prompt module processing.
 
         Args:
             agent_model (AgentModel): Agent model object.
             planner_input (dict): Planner input object.
         Returns:
-            PromptTemplate: The prompt template.
+            Prompt: The prompt instance.
         """
         expert_framework = planner_input.pop('expert_framework', '') or ''
 
@@ -74,6 +75,7 @@ def handle_prompt(self, agent_model: AgentModel, planner_input: dict):
                                                                  target=prompt.target,
                                                                  instruction=expert_framework + prompt.instruction)
 
-        self.prompt.build_prompt(user_prompt_model, system_prompt_model,
-                                 self.prompt_assemble_order)
-        process_llm_token(self.prompt.as_langchain(), profile, planner_input)
+        prompt: Prompt = Prompt().build_prompt(user_prompt_model, system_prompt_model,
+                                               self.prompt_assemble_order)
+        process_llm_token(prompt.as_langchain(), profile, planner_input)
+        return prompt