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如何手撸一个自有知识库的RAG系统

如何手撸一个自有知识库的RAG系统 京东云开发者
2024-06-14
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导读：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
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 jsonimport loggingfrom pathlib import Pathfrom typing import Iterator, Optional, Union
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoaderfrom 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 clickhousefrom 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 clickhousefrom 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 FastAPIfrom pydantic import BaseModelfrom singleton_decorator import singletonfrom langchain_community.embeddings import HuggingFaceEmbeddingsimport langchain_community.vectorstores.clickhouse as clickhouseimport uvicornimport 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 FastAPIfrom pydantic import BaseModelfrom langchain_community.llms import VLLMfrom transformers import AutoTokenizerfrom langchain.prompts import PromptTemplateimport requestsimport uvicornimport jsonimport 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,)
    # promptprompt_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 jsonimport gradio as grimport 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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京东云开发者
京东云开发者（Developer of JD Technology）是京东科技集团旗下为AI、云计算、IoT等相关领域开发者提供技术分享交流的平台。平台将发布京东产品技术信息、行业技术内容、技术活动等资讯。拥抱技术，与开发者携手预见未来！
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京东云开发者 京东云开发者（Developer of JD Technology）是京东科技集团旗下为AI、云计算、IoT等相关领域开发者提供技术分享交流的平台。平台将发布京东产品技术信息、行业技术内容、技术活动等资讯。拥抱技术，与开发者携手预见未来！
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