如何手撸一个自有知识库的RAG系统

RAG通常指的是"Retrieval-AugmentedGeneration",即“检索增强的生成”。这是一种结合了检索(Retrieval)和生成(Generation)的机器学习模型,通常用于自然语言处理任务,如文本生成、问答系统等。

我们通过一下几个步骤来完成一个基于京东云官网文档的RAG系统

  • 数据收集

  • 建立知识库

  • 向量检索

  • 提示词与模型

数据收集

数据的收集再整个RAG实施过程中无疑是最耗人工的,涉及到收集、清洗、格式化、切分等过程。这里我们使用京东云的官方文档作为知识库的基础。文档格式大概这样:

{
    "content": "DDoS IP高防结合Web应用防火墙方案说明\n=======================\n\n\nDDoS IP高防+Web应用防火墙提供三层到七层安全防护体系,应用场景包括游戏、金融、电商、互联网、政企等京东云内和云外的各类型用户。\n\n\n部署架构\n====\n\n\n[![\\\"部署架构\\\"](\\\"https://jdcloud-portal.oss.cn-north-1.jcloudcs.com/cn/image/Advanced%20Anti-DDoS/Best-Practice02.png\\\")](\\\"https://jdcloud-portal.oss.cn-north-1.jcloudcs.com/cn/image/Advanced%20Anti-DDoS/Best-Practice02.png\\\")  \n\nDDoS IP高防+Web应用防火墙的最佳部署架构如下:\n\n\n* 京东云的安全调度中心,通过DNS解析,将用户域名解析到DDoS IP高防CNAME。\n* 用户正常访问流量和DDoS攻击流量经过DDoS IP高防清洗,回源至Web应用防火墙。\n* 攻击者恶意请求被Web应用防火墙过滤后返回用户源站。\n* Web应用防火墙可以保护任何公网的服务器,包括但不限于京东云,其他厂商的云,IDC等\n\n\n方案优势\n====\n\n\n1. 用户源站在DDoS IP高防和Web应用防火墙之后,起到隐藏源站IP的作用。\n2. CNAME接入,配置简单,减少运维人员工作。\n\n\n",
    "title": "DDoS IP高防结合Web应用防火墙方案说明",
    "product": "DDoS IP高防",
    "url": "https://docs.jdcloud.com/cn/anti-ddos-pro/anti-ddos-pro-and-waf"
}

每条数据是一个包含四个字段的json,这四个字段分别是"content":文档内容;"title":文档标题;"product":相关产品;"url":文档在线地址

向量数据库的选择与Retriever实现

向量数据库是RAG系统的记忆中心。目前市面上开源的向量数据库很多,那个向量库比较好也是见仁见智。本项目中笔者选择则了clickhouse作为向量数据库。选择ck主要有一下几个方面的考虑:

  • ck再langchain社区的集成实现比较好,入库比较平滑

  • 向量查询支持sql,学习成本较低,上手容易

  • 京东云有相关产品且有专业团队支持,用着放心

文档向量化及入库过程

为了简化文档向量化和检索过程,我们使用了longchain的Retriever工具集
首先将文档向量化,代码如下:

from libs.jd_doc_json_loader import JD_DOC_Loaderfrom langchain_community.document_loaders import DirectoryLoaderroot_dir = "/root/jd_docs"loader = DirectoryLoader(    '/root/jd_docs', glob="**/*.json", loader_cls=JD_DOC_Loader)docs = loader.load()

langchain社区里并没有提供针对特定格式的装载器,为此,我们自定义了JD_DOC_Loader来实现加载过程

import json
import logging
from pathlib import Path
from typing import Iterator, Optional, Union


from langchain_core.documents import Document


from langchain_community.document_loaders.base import BaseLoader
from langchain_community.document_loaders.helpers import detect_file_encodings


logger = logging.getLogger(__name__)




class JD_DOC_Loader(BaseLoader):
    """Load text file.




    Args:
        file_path: Path to the file to load.


        encoding: File encoding to use. If `None`, the file will be loaded
        with the default system encoding.


        autodetect_encoding: Whether to try to autodetect the file encoding
            if the specified encoding fails.
    """


    def __init__(
        self,
        file_path: Union[str, Path],
        encoding: Optional[str] = None,
        autodetect_encoding: bool = False,
    ):
        """Initialize with file path."""
        self.file_path = file_path
        self.encoding = encoding
        self.autodetect_encoding = autodetect_encoding


    def lazy_load(self) -> Iterator[Document]:
        """Load from file path."""
        text = ""
        from_url = ""
        try:
            with open(self.file_path, encoding=self.encoding) as f:
                doc_data = json.load(f)
                text = doc_data["content"]
                title = doc_data["title"]
                product = doc_data["product"]
                from_url = doc_data["url"]


                # text = f.read()
        except UnicodeDecodeError as e:
            if self.autodetect_encoding:
                detected_encodings = detect_file_encodings(self.file_path)
                for encoding in detected_encodings:
                    logger.debug(f"Trying encoding: {encoding.encoding}")
                    try:
                        with open(self.file_path, encoding=encoding.encoding) as f:
                            text = f.read()
                        break
                    except UnicodeDecodeError:
                        continue
            else:
                raise RuntimeError(f"Error loading {self.file_path}") from e
        except Exception as e:
            raise RuntimeError(f"Error loading {self.file_path}") from e
        # metadata = {"source": str(self.file_path)}
        metadata = {"source": from_url, "title": title, "product": product}
        yield Document(page_content=text, metadata=metadata)

以上代码功能主要是解析json文件,填充Document的page_content字段和metadata字段。接下来使用langchain 的 clickhouse 向量工具集进行文档入库

import langchain_community.vectorstores.clickhouse as clickhouse
from langchain.embeddings import HuggingFaceEmbeddings


model_kwargs = {"device": "cuda"}
embeddings = HuggingFaceEmbeddings(
    model_name="/root/models/moka-ai-m3e-large", model_kwargs=model_kwargs)


settings = clickhouse.ClickhouseSettings(
    table="jd_docs_m3e_with_url", username="default", password="xxxxxx", host="10.0.1.94")


docsearch = clickhouse.Clickhouse.from_documents(
    docs, embeddings, config=settings)

入库成功后,进行一下检验

import langchain_community.vectorstores.clickhouse as clickhouse
from langchain.embeddings import HuggingFaceEmbeddings


model_kwargs = {"device": "cuda"}~~~~
embeddings = HuggingFaceEmbeddings(
    model_name="/root/models/moka-ai-m3e-large", model_kwargs=model_kwargs)


settings = clickhouse.ClickhouseSettings(
    table="jd_docs_m3e_with_url_splited", username="default", password="xxxx", host="10.0.1.94")
ck_db = clickhouse.Clickhouse(embeddings, config=settings)
ck_retriever = ck_db.as_retriever(
    search_type="similarity_score_threshold", search_kwargs={'score_threshold': 0.9})
ck_retriever.get_relevant_documents("如何创建mysql rds")

有了知识库以后,可以构建一个简单的restful 服务,我们这里使用fastapi做这个事儿

from fastapi import FastAPI
from pydantic import BaseModel
from singleton_decorator import singleton
from langchain_community.embeddings import HuggingFaceEmbeddings
import langchain_community.vectorstores.clickhouse as clickhouse
import uvicorn
import json


app = FastAPI()
app = FastAPI(docs_url=None)
app.host = "0.0.0.0"


model_kwargs = {"device": "cuda"}
embeddings = HuggingFaceEmbeddings(
    model_name="/root/models/moka-ai-m3e-large", model_kwargs=model_kwargs)
settings = clickhouse.ClickhouseSettings(
    table="jd_docs_m3e_with_url_splited", username="default", password="xxxx", host="10.0.1.94")
ck_db = clickhouse.Clickhouse(embeddings, config=settings)
ck_retriever = ck_db.as_retriever(
    search_type="similarity", search_kwargs={"k": 3})




class question(BaseModel):
    content: str




@app.get("/")
async def root():
    return {"ok"}




@app.post("/retriever")
async def retriver(question: question):
    global ck_retriever
    result = ck_retriever.invoke(question.content)
    return result




if __name__ == '__main__':
    uvicorn.run(app='retriever_api:app', host="0.0.0.0",
                port=8000, reload=True)

返回结构大概这样:

[
  {
    "page_content": "云缓存 Redis--Redis迁移解决方案\n###RedisSyncer 操作步骤\n####数据校验\n```\nwget   https://github.com/TraceNature/rediscompare/releases/download/v1.0.0/rediscompare-1.0.0-linux-amd64.tar.gz\nrediscompare compare single2single --saddr \\\"10.0.1.101:6479\\\" --spassword \\\"redistest0102\\\" --taddr \\\"10.0.1.102:6479\\\" --tpassword  \\\"redistest0102\\\" --comparetimes 3\n\n```  \n**Github 地址:** [https://github.com/TraceNature/redissyncer-server](\\\"https://github.com/TraceNature/redissyncer-server\\\")",
    "metadata": {
      "product": "云缓存 Redis",
      "source": "https://docs.jdcloud.com/cn/jcs-for-redis/doc-2",
      "title": "Redis迁移解决方案"
    },
    "type": "Document"
  },
  {
    "page_content": "云缓存 Redis--Redis迁移解决方案\n###RedisSyncer 操作步骤\n####数据校验\n```\nwget   https://github.com/TraceNature/rediscompare/releases/download/v1.0.0/rediscompare-1.0.0-linux-amd64.tar.gz\nrediscompare compare single2single --saddr \\\"10.0.1.101:6479\\\" --spassword \\\"redistest0102\\\" --taddr \\\"10.0.1.102:6479\\\" --tpassword  \\\"redistest0102\\\" --comparetimes 3\n\n```  \n**Github 地址:** [https://github.com/TraceNature/redissyncer-server](\\\"https://github.com/TraceNature/redissyncer-server\\\")",
    "metadata": {
      "product": "云缓存 Redis",
      "source": "https://docs.jdcloud.com/cn/jcs-for-redis/doc-2",
      "title": "Redis迁移解决方案"
    },
    "type": "Document"
  },
  {
    "page_content": "云缓存 Redis--Redis迁移解决方案\n###RedisSyncer 操作步骤\n####数据校验\n```\nwget   https://github.com/TraceNature/rediscompare/releases/download/v1.0.0/rediscompare-1.0.0-linux-amd64.tar.gz\nrediscompare compare single2single --saddr \\\"10.0.1.101:6479\\\" --spassword \\\"redistest0102\\\" --taddr \\\"10.0.1.102:6479\\\" --tpassword  \\\"redistest0102\\\" --comparetimes 3\n\n```  \n**Github 地址:** [https://github.com/TraceNature/redissyncer-server](\\\"https://github.com/TraceNature/redissyncer-server\\\")",
    "metadata": {
      "product": "云缓存 Redis",
      "source": "https://docs.jdcloud.com/cn/jcs-for-redis/doc-2",
      "title": "Redis迁移解决方案"
    },
    "type": "Document"
  }
]

返回一个向量距离最小的list

结合模型和prompt,回答问题

为了节约算力资源,我们选择qwen 1.8B模型,一张v100卡刚好可以容纳一个qwen模型和一个m3e-large embedding 模型

  • answer 服务

  • from fastapi import FastAPI
    from pydantic import BaseModel
    from langchain_community.llms import VLLM
    from transformers import AutoTokenizer
    from langchain.prompts import PromptTemplate
    import requests
    import uvicorn
    import json
    import logging
    
    
    app = FastAPI()
    app = FastAPI(docs_url=None)
    app.host = "0.0.0.0"
    
    
    logger = logging.getLogger()
    logger.setLevel(logging.INFO)
    to_console = logging.StreamHandler()
    logger.addHandler(to_console)
    
    
    
    
    # load model
    # model_name = "/root/models/Llama3-Chinese-8B-Instruct"
    model_name = "/root/models/Qwen1.5-1.8B-Chat"
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    llm_llama3 = VLLM(
        model=model_name,
        tokenizer=tokenizer,
        task="text-generation",
        temperature=0.2,
        do_sample=True,
        repetition_penalty=1.1,
        return_full_text=False,
        max_new_tokens=900,
    )
    
    
    # prompt
    prompt_template = """
    你是一个云技术专家
    使用以下检索到的Context回答问题。
    如果不知道答案,就说不知道。
    用中文回答问题。
    Question: {question}
    Context: {context}
    Answer: 
    """
    
    
    prompt = PromptTemplate(
        input_variables=["context", "question"],
        template=prompt_template,
    )
    
    
    
    
    def get_context_list(q: str):
        url = "http://10.0.0.7:8000/retriever"
        payload = {"content": q}
        res = requests.post(url, json=payload)
        return res.text
    
    
    
    
    class question(BaseModel):
        content: str
    
    
    
    
    @app.get("/")
    async def root():
        return {"ok"}
    
    
    
    
    @app.post("/answer")
    async def answer(q: question):
        logger.info("invoke!!!")
        global prompt
        global llm_llama3
        context_list_str = get_context_list(q.content)
    
    
        context_list = json.loads(context_list_str)
        context = ""
        source_list = []
    
    
        for context_json in context_list:
            context = context+context_json["page_content"]
            source_list.append(context_json["metadata"]["source"])
        p = prompt.format(context=context, question=q.content)
        answer = llm_llama3(p)
        result = {
            "answer": answer,
            "sources": source_list
        }
        return result
    
    
    
    
    if __name__ == '__main__':
        uvicorn.run(app='retriever_api:app', host="0.0.0.0",
                    port=8888, reload=True)

代码通过使用Retriever接口查找与问题相似的文档,作为context组合prompt推送给模型生成答案。
主要服务就绪后可以开始画一张脸了,使用gradio做个简易对话界面

  • gradio 服务

  • import json
    import gradio as gr
    import requests
    
    
    
    
    def greet(name, intensity):
        return "Hello, " + name + "!" * int(intensity)
    
    
    
    
    def answer(question):
        url = "http://127.0.0.1:8888/answer"
        payload = {"content": question}
        res = requests.post(url, json=payload)
        res_json = json.loads(res.text)
        return [res_json["answer"], res_json["sources"]]
    
    
    
    
    demo = gr.Interface(
        fn=answer,
        # inputs=["text", "slider"],
        inputs=[gr.Textbox(label="question", lines=5)],
        # outputs=[gr.TextArea(label="answer", lines=5),
        #          gr.JSON(label="urls", value=list)]
        outputs=[gr.Markdown(label="answer"),
                 gr.JSON(label="urls", value=list)]
    )
    
    
    
    
    demo.launch(server_name="0.0.0.0")
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