Merge pull request #109 from alipay/dev

feat: Version 0.0.10 Release

Author: LandJerry

CHANGELOG.md

@@ -24,6 +24,22 @@ Note - Additional remarks regarding the version.
 ***************************************************
 
 # Version Update History
+## [0.0.10] - 2024-06-28
+### Added
+- Added standard integration for the DeepSeek model in the LLM module.
+- Added a new OpenAI general protocol wrapper class, OpenAIStyleLLM.
+  - Models using the OpenAI protocol can be configured directly.
+- Added a new LangChain tool wrapper class, LangChainTool, with several example tools for search and execution.
+  - LangChain tools can be configured directly.
+- Added Agent information collection capability in the monitor module.
+
+### Note
+- Added use cases.
+  - Supplemented documentation with a financial event analysis case study using PEER collaborative mode.
+- Added several new documents for LLM components, tool components, and the Monitor module.
+- Updated the new README.
+- Some code optimizations and documentation updates.
+
 ## [0.0.9] - 2024-06-14
 ### Added
 - Added standard integration for Claude and Ollama LLM components

CHANGELOG_zh.md

@@ -24,6 +24,22 @@ Note - 对于版本的额外说明。
 ***************************************************
 
 # 版本更新记录
+## [0.0.10] - 2024-06-28
+### Added
+- LLM组件新增DeepSeek模型标准接入
+- 新增OpenAI通用协议包装类OpenAIStyleLLM
+  - openai协议类模型接入可直接配置
+- 新增LangChain工具包装类LangChainTool，新增搜索类、执行类示例工具若干
+  - LangChain工具接入可直接配置
+- monitor模块新增Agent纬度信息采集能力
+
+### Note
+- 新增使用案例
+  - PEER协同模式的金融事件分析案例文档补充
+- 新增若干LLM组件、工具组件、Monitor模块文档
+- 新版README更新
+- 部分代码优化与文档更新
+
 ## [0.0.9] - 2024-06-14
 ### Added
 - LLM组件新增claude、ollama标准接入

README.md

@@ -5,7 +5,7 @@ Language version: [English](./README.md) | [中文](./README_zh.md) | [日本語
 ![](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-v0.0.9-blue?logo=pypi)](https://pypi.org/project/agentUniverse/)
+[![Static Badge](https://img.shields.io/badge/pypi-v0.0.10-blue?logo=pypi)](https://pypi.org/project/agentUniverse/)
 
 ![](docs/guidebook/_picture/logo_bar.jpg)
 ****************************************

README_jp.md

@@ -5,7 +5,7 @@
 ![](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-v0.0.9-blue?logo=pypi)](https://pypi.org/project/agentUniverse/)
+[![Static Badge](https://img.shields.io/badge/pypi-v0.0.10-blue?logo=pypi)](https://pypi.org/project/agentUniverse/)
 
 ![](docs/guidebook/_picture/logo_bar.jpg)
 ****************************************

README_zh.md

@@ -5,7 +5,7 @@
 ![](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-v0.0.9-blue?logo=pypi)](https://pypi.org/project/agentUniverse/)
+[![Static Badge](https://img.shields.io/badge/pypi-v0.0.10-blue?logo=pypi)](https://pypi.org/project/agentUniverse/)
 
 ![](docs/guidebook/_picture/logo_bar.jpg)
 ****************************************

agentuniverse/agent/action/knowledge/knowledge.py

@@ -5,7 +5,10 @@
 # @Author  : wangchongshi
 # @Email   : wangchongshi.wcs@antgroup.com
 # @FileName: knowledge.py
-from typing import Optional, Dict, List, Any
+from typing import Optional, Dict, List
+
+from langchain_core.utils.json import parse_json_markdown
+from langchain.tools import Tool as LangchainTool
 
 from agentuniverse.agent.action.knowledge.reader.reader import Reader
 from agentuniverse.agent.action.knowledge.store.document import Document
@@ -77,3 +80,37 @@ def initialize_by_component_configer(self, component_configer: KnowledgeConfiger
         if component_configer.ext_info:
             self.ext_info = component_configer.ext_info
         return self
+
+    def langchain_query(self, query: str) -> str:
+        """Query the knowledge using LangChain.
+
+        Query documents from the store and return the results.
+        """
+        parse_query = parse_json_markdown(query)
+        query = Query(**parse_query)
+        knowledge = self.store.query(query)
+        res = ['This is Query Result']
+        for doc in knowledge:
+            res.append(doc.text)
+        return "\n=========================================\n".join(res)
+
+    def as_langchain_tool(self) -> LangchainTool:
+        """Convert the Knowledge object to a LangChain tool.
+
+        Returns:
+            Any: the LangChain tool object
+        """
+        args_description = """
+        This is a knowledge base tool, which stores the content you may need. To use this tool, you need to give a json string with the following format:
+        ```json
+        {
+            "query_str": "<your query here>",
+            "top_k": <number of results to return>,
+        }
+        ```
+        """
+        return LangchainTool(
+            name=self.name,
+            description=self.description + args_description,
+            func=self.langchain_query,
+        )

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

@@ -6,10 +6,11 @@
 # @Email   : wangchongshi.wcs@antgroup.com
 # @FileName: chroma_store.py
 from typing import List, Any, Optional
+from pydantic import SkipValidation
 
 import chromadb
 from chromadb import QueryResult
-from chromadb.api.models import Collection
+from chromadb.api.models.Collection import Collection
 
 from agentuniverse.agent.action.knowledge.store.document import Document
 from agentuniverse.agent.action.knowledge.store.query import Query
@@ -28,7 +29,7 @@ class ChromaStore(Store):
     """
 
     collection_name: Optional[str] = 'chroma_db'
-    collection: Collection = None
+    collection: SkipValidation[Collection] = None
     persist_path: Optional[str] = None
 
     def __init__(self, **kwargs):

agentuniverse/agent/agent.py

@@ -6,21 +6,26 @@
 # @Email   : lc299034@antgroup.com
 # @FileName: agent.py
 """The definition of agent paradigm."""
+import json
 from abc import abstractmethod
 from datetime import datetime
 from typing import Optional
 
+from langchain_core.utils.json import parse_json_markdown
+
 from agentuniverse.agent.agent_model import AgentModel
 from agentuniverse.agent.input_object import InputObject
 from agentuniverse.agent.output_object import OutputObject
 from agentuniverse.agent.plan.planner.planner import Planner
 from agentuniverse.agent.plan.planner.planner_manager import PlannerManager
+from agentuniverse.base.annotation.trace import trace_agent
 from agentuniverse.base.component.component_base import ComponentBase
 from agentuniverse.base.component.component_enum import ComponentEnum
 from agentuniverse.base.config.application_configer.application_config_manager \
     import ApplicationConfigManager
 from agentuniverse.base.config.component_configer.configers.agent_configer \
     import AgentConfiger
+from agentuniverse.base.util.logging.logging_util import LOGGER
 from agentuniverse.llm.llm import LLM
 
 
@@ -66,6 +71,7 @@ def parse_result(self, planner_result: dict) -> dict:
         """
         pass
 
+    @trace_agent
     def run(self, **kwargs) -> OutputObject:
         """Agent instance running entry.
 
@@ -156,3 +162,35 @@ def initialize_by_component_configer(self, component_configer: AgentConfiger) ->
                                                        plan=plan, memory=memory, action=action)
         self.agent_model = agent_model
         return self
+
+    def langchain_run(self, input: str, callbacks=None, **kwargs):
+        """Run the agent model using LangChain."""
+        try:
+            parse_result = parse_json_markdown(input)
+        except Exception as e:
+            LOGGER.error(f"langchain run parse_json_markdown error,input(parse_result) error({str(e)})")
+            return "Error , Your Action Input is not a valid JSON string"
+        output_object = self.run(**parse_result, callbacks=callbacks, **kwargs)
+        result_dict = {}
+        for key in self.output_keys():
+            result_dict[key] = output_object.get_data(key)
+        return result_dict
+
+    def as_langchain_tool(self):
+        """Convert to LangChain tool."""
+        from langchain.agents.tools import Tool
+        format_dict = {}
+        for key in self.input_keys():
+            format_dict.setdefault(key, "input val")
+        format_str = json.dumps(format_dict)
+
+        args_description = f"""
+        to use this tool,your input must be a json string,must contain all keys of {self.input_keys()},
+        and the value of the key must be a json string,the format of the json string is as follows:
+        ```{format_str}```
+        """
+        return Tool(
+            name=self.agent_model.info.get("name"),
+            func=self.langchain_run,
+            description=self.agent_model.info.get("description") + args_description
+        )

agentuniverse/agent/default/executing_agent/executing_agent.py

@@ -8,8 +8,11 @@
 import copy
 from concurrent.futures import ThreadPoolExecutor, wait, ALL_COMPLETED
 from typing import Optional, Any
+
+from agentuniverse.agent.action.tool.tool_manager import ToolManager
 from agentuniverse.agent.agent import Agent
 from agentuniverse.agent.input_object import InputObject
+from agentuniverse.agent.plan.planner.planner import Planner
 from agentuniverse.agent.plan.planner.planner_manager import PlannerManager
 
 
@@ -75,9 +78,35 @@ def execute(self, input_object: InputObject, agent_input: dict) -> dict:
         for task in framework:
             agent_input_copy: dict = copy.deepcopy(agent_input)
             agent_input_copy['input'] = task
+            planner: Planner = PlannerManager().get_instance_obj(self.agent_model.plan.get('planner').get('name'))
             futures.append(
-                self.executor.submit(
-                    PlannerManager().get_instance_obj(self.agent_model.plan.get('planner').get('name')).invoke,
-                    self.agent_model, agent_input_copy, input_object))
+                self.executor.submit(planner.invoke, self.agent_model, agent_input_copy,
+                                     self.process_intput_object(input_object, task, planner.input_key)))
         wait(futures, return_when=ALL_COMPLETED)
         return {'futures': futures}
+
+    def process_intput_object(self, input_object: InputObject, subtask: str, planner_input_key: str) -> InputObject:
+        """Process input object for the executing agent.
+
+        Args:
+            input_object (InputObject): input parameters passed by the user.
+            subtask (str): subtask to be executed.
+            planner_input_key (str): planner input key.
+
+        Returns:
+            input_object (InputObject): processed input object.
+        """
+        # get agent toolsets.
+        action: dict = self.agent_model.action or dict()
+        tools: list = action.get('tool') or list()
+        input_object_copy: InputObject = copy.deepcopy(input_object)
+        # wrap input_object for agent knowledge.
+        input_object_copy.add_data(planner_input_key, subtask)
+        # wrap input_object for agent toolsets.
+        for tool_name in tools:
+            tool = ToolManager().get_instance_obj(tool_name)
+            if tool is None:
+                continue
+            # note: only insert the first key of tool input.
+            input_object_copy.add_data(tool.input_keys[0], subtask)
+        return input_object_copy

agentuniverse/agent/memory/langchain_instance.py

@@ -9,6 +9,7 @@
 
 from langchain_core.messages import BaseMessage, get_buffer_string
 from langchain.memory import ConversationSummaryBufferMemory, ConversationTokenBufferMemory
+from langchain_core.output_parsers import StrOutputParser
 
 from agentuniverse.agent.memory.enum import ChatMessageEnum
 from agentuniverse.agent.memory.message import Message
@@ -115,9 +116,8 @@ def predict_new_summary(
         )
         prompt_version = self.prompt_version if self.prompt_version else 'chat_memory.summarizer_cn'
         prompt: Prompt = PromptManager().get_instance_obj(prompt_version)
-        chain = prompt.as_langchain() | self.llm
-        res = chain.invoke(input={'summary': existing_summary, 'new_lines': new_lines})
-        return res.content
+        chain = prompt.as_langchain() | self.llm | StrOutputParser()
+        return chain.invoke(input={'summary': existing_summary, 'new_lines': new_lines})
 
 
 class AuConversationTokenBufferMemory(ConversationTokenBufferMemory):