create RagAgent.md

docs_building/docs_ch/Oxy/Agents/RagAgent.md

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+检索增强生成，是一种结合信息检索（Retrieval）和文本生成（Generation）的技术。
+RAG技术通过实时检索相关文档或信息，并将其作为上下文输入到生成模型中，从而提高生成结果的时效性和准确性。
+## Overview
+本模块提供了 ChatAgent 类，该类通过管理对话记忆、处理用户查询，并协调语言模型生成回复，从而实现对话式人工智能的交互。
+## Quick Start
+### Basic Usage
+OxyGent支持通过knowledge参数向prompts注入知识。以下将展示一个最简单的RAG示例：
+您需要先创建一个retrieval方法：
+```python
+def retrieval(query):
+    # 替换为实际的数据库
+    return "\n".join(["knowledge1", "knowledge2", "knowledge3"])
+```
+然后，需要在update_query中将检索的知识进行注入：
+```python
+def update_query(oxy_request: OxyRequest):
+    current_query = oxy_request.get_query()
+
+    def retrieval(query):
+        return "\n".join(["knowledge1", "knowledge2", "knowledge3"])
+
+    oxy_request.arguments["knowledge"] = retrieval(current_query) # Key Method
+    return oxy_request
+```
+## Complete runnable example
+以下是可运行的完整代码示例：
+```python
+import asyncio
+
+from oxygent import MAS, OxyRequest, oxy
+from oxygent.utils.env_utils import get_env_var
+
+INSTRUCTION = """
+You are a helpful assistant and can use these tools:
+${tools_description}
+
+Experience in choosing tools:
+${knowledge}
+
+Select the appropriate tool based on the user's question.
+If no tool is needed, reply directly.
+If answering the user's question requires calling multiple tools, call only one tool at a time. After the user receives the tool result, they will give you feedback on the tool call result.
+
+Important notes:
+1. When you have collected enough information to answer the user's question, please respond in the following format:
+<think>Your reasoning (if analysis is needed)</think>
+Your response content
+2. When you find that the user's question lacks certain conditions, you can ask them back. Please respond in the following format:
+<think>Your reasoning (if analysis is needed)</think>
+Your follow-up question to the user
+3. When you need to use a tool, you must respond **only** with the following exact JSON object format, and nothing else:
+
+{
+    "think": "Your reasoning (if analysis is needed)",
+    "tool_name": "Tool name",
+    "arguments": {
+        "Parameter name": "Parameter value"
+    }
+}
+"""
+
+
+def update_query(oxy_request: OxyRequest):
+    current_query = oxy_request.get_query()
+
+    def retrieval(query):
+        return "\n".join(["knowledge1", "knowledge2", "knowledge3"])
+
+    oxy_request.arguments["knowledge"] = retrieval(current_query)
+    return oxy_request
+
+
+oxy_space = [
+    oxy.HttpLLM(
+        name="default_llm",
+        api_key=get_env_var("DEFAULT_LLM_API_KEY"),
+        base_url=get_env_var("DEFAULT_LLM_BASE_URL"),
+        model_name=get_env_var("DEFAULT_LLM_MODEL_NAME"),
+        llm_params={"temperature": 0.01},
+        semaphore=4,
+    ),
+    oxy.ReActAgent(
+        name="master_agent",
+        is_master=True,
+        llm_model="default_llm",
+        timeout=100,
+        prompt=INSTRUCTION,
+        func_process_input=update_query,
+    ),
+]
+
+
+async def main():
+    async with MAS(oxy_space=oxy_space) as mas:
+        await mas.start_web_service(
+            first_query="This is an example for rag. Please modify it according to the specific needs",
+        )
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+## Configuration Options
+### Core Parameters
+#### knowledge
+1.调用外部注入的检索函数，根据请求内容获取相关知识。
+2.将检索到的知识写入 oxy_request.arguments 中的占位符键（knowledge_placeholder），以便后续进行模板渲染或提示词组合。
+#### retrieval(str)
+根据当前请求执行知识检索（例如，从向量数据库、搜索服务或自托管知识库中检索）。

docs_building/docs_en/Oxy/Agents/RagAgent.md

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+Rag_agent  (Retrieval-Augmented Generation) conversational agent.
+Before generating a response with a large language model, relevant external knowledge is retrieved through a search function and injected into the prompt to improve the accuracy and controllability of the answers.
+## Overview
+This module provides the ChatAgent class, which handles conversational AI interactions by managing conversation memory, processing user queries, and coordinating with language models to generate responses.
+## Quick Start
+### Basic Usage
+OxyGent supports injecting knowledge into prompts via the knowledge parameter. Below is a simple example of RAG:
+You need to first create a retrieval method:
+```python
+def retrieval(query):
+    # Replace with the actual database
+    return "\n".join(["knowledge1", "knowledge2", "knowledge3"])
+```
+Next, you need to inject the retrieved knowledge in update_query:
+```python
+def update_query(oxy_request: OxyRequest):
+    current_query = oxy_request.get_query()
+
+    def retrieval(query):
+        return "\n".join(["knowledge1", "knowledge2", "knowledge3"])
+
+    oxy_request.arguments["knowledge"] = retrieval(current_query) # Key Method
+    return oxy_request
+```
+## Complete runnable example
+Below is a complete runnable code example:
+```python
+import asyncio
+
+from oxygent import MAS, OxyRequest, oxy
+from oxygent.utils.env_utils import get_env_var
+
+INSTRUCTION = """
+You are a helpful assistant and can use these tools:
+${tools_description}
+
+Experience in choosing tools:
+${knowledge}
+
+Select the appropriate tool based on the user's question.
+If no tool is needed, reply directly.
+If answering the user's question requires calling multiple tools, call only one tool at a time. After the user receives the tool result, they will give you feedback on the tool call result.
+
+Important notes:
+1. When you have collected enough information to answer the user's question, please respond in the following format:
+<think>Your reasoning (if analysis is needed)</think>
+Your response content
+2. When you find that the user's question lacks certain conditions, you can ask them back. Please respond in the following format:
+<think>Your reasoning (if analysis is needed)</think>
+Your follow-up question to the user
+3. When you need to use a tool, you must respond **only** with the following exact JSON object format, and nothing else:
+
+{
+    "think": "Your reasoning (if analysis is needed)",
+    "tool_name": "Tool name",
+    "arguments": {
+        "Parameter name": "Parameter value"
+    }
+}
+"""
+
+
+def update_query(oxy_request: OxyRequest):
+    current_query = oxy_request.get_query()
+
+    def retrieval(query):
+        return "\n".join(["knowledge1", "knowledge2", "knowledge3"])
+
+    oxy_request.arguments["knowledge"] = retrieval(current_query)
+    return oxy_request
+
+
+oxy_space = [
+    oxy.HttpLLM(
+        name="default_llm",
+        api_key=get_env_var("DEFAULT_LLM_API_KEY"),
+        base_url=get_env_var("DEFAULT_LLM_BASE_URL"),
+        model_name=get_env_var("DEFAULT_LLM_MODEL_NAME"),
+        llm_params={"temperature": 0.01},
+        semaphore=4,
+    ),
+    oxy.ReActAgent(
+        name="master_agent",
+        is_master=True,
+        llm_model="default_llm",
+        timeout=100,
+        prompt=INSTRUCTION,
+        func_process_input=update_query,
+    ),
+]
+
+
+async def main():
+    async with MAS(oxy_space=oxy_space) as mas:
+        await mas.start_web_service(
+            first_query="This is an example for rag. Please modify it according to the specific needs",
+        )
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+## Configuration Options
+### Core Parameters
+#### knowledge
+1.Call the externally injected retrieval function to obtain relevant knowledge based on the request content.
+2.Write theretrieved knowledge into oxy_request.arguments under the placeholder key(knowledge_placeholder) for subsequent template rendering/prompt composition.
+#### retrieval(str)
+Perform knowledge retrieval based on the current request(e.g., from a vector database, searchservice, or self-hosted knowledgebase)