Files
lang-agent/lang_agent/rag/simple.py
2025-10-11 15:12:26 +08:00

73 lines
2.5 KiB
Python

from dataclasses import dataclass, field
from typing import Type
import tyro
from mcp.server.fastmcp import FastMCP
from typing import List
import tyro
from langchain_community.vectorstores import FAISS
from langchain_core.documents.base import Document
from lang_agent.rag.emb import QwenEmbeddings
from lang_agent.config import InstantiateConfig
mcp = FastMCP("Rag")
@tyro.conf.configure(tyro.conf.SuppressFixed)
@dataclass
class SimpleRagConfig(InstantiateConfig):
_target: Type = field(default_factory=lambda: SimpleRag)
model_name:str = "text-embedding-v4"
"""embedding model name"""
api_key:str = "wrong-key"
"""api_key for model; for generic text splitting; give a wrong key"""
folder_path:str = "assets/xiaozhan_emb"
"""path to local database"""
class SimpleRag:
def __init__(self, config:SimpleRagConfig):
self.config = config
self.emb = QwenEmbeddings(self.config.api_key,
self.config.model_name)
self.vec_store = FAISS.load_local(
folder_path=self.config.folder_path,
embeddings=self.emb,
allow_dangerous_deserialization=True # Required for LangChain >= 0.1.1
)
# self.retriever = self.vec_store.as_retriever(search_kwargs={"k":3})
@mcp.tool()
def retrieve(self, query:str):
"""
检索与给定查询相关的文档,并将其序列化为字符串格式。
参数:
query (str): 用户输入的查询字符串。
返回:
Tuple[str, List[Document]]:
- 序列化后的文档内容字符串,每个文档包含来源和内容。
- 检索到的 Document 对象列表。
该工具用于基于向量存储检索相关文档,适用于问答和知识检索场景。
用例示例:
1. 用户询问“推荐一些辣味食物”,系统会检索并返回相关的辣味美食推荐文档。
2. 用户搜索“适合夏天的清爽饮品”,系统会检索并返回相关饮品推荐及其来源信息。
"""
retrieved_docs:List[Document] = self.vec_store.search(query, search_kwargs={"k":3})
serialized = "\n\n".join(
(f"Source: {doc.metadata}\nContent: {doc.page_content}")
for doc in retrieved_docs
)
return serialized, retrieved_docs
if __name__ == "__main__":
# config = tyro.cli(SimpleRagConfig)
config = SimpleRagConfig()
rag:SimpleRag = config.setup()
mcp.run(transport="stdio")