西安做网站哪家最便宜营销手段和营销方式

创建一个简单搜索引擎，将用户原始问题传递该搜索系统

本文重点：获取保存文档——保存向量数据库——加载向量数据库

专注于youtube的字幕，利用youtube的公开接口，获取元数据

pip install youtube-transscript-api pytube

初始化

import datetime
import os
from operator import itemgetter
from typing import Optional, Listimport bs4
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain.chains.history_aware_retriever import create_history_aware_retriever
from langchain.chains.retrieval import create_retrieval_chain
from langchain.chains.sql_database.query import create_sql_query_chain
from langchain_chroma import Chroma
from langchain_community.agent_toolkits import SQLDatabaseToolkit
from langchain_community.document_loaders import WebBaseLoader, YoutubeLoader
from langchain_community.tools import QuerySQLDataBaseTool
from langchain_community.utilities import SQLDatabase
from langchain_core.documents import Document
from langchain_core.messages import SystemMessage, HumanMessage
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder, PromptTemplate
from langchain_core.runnables import RunnableWithMessageHistory, RunnablePassthrough
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.chat_message_histories import ChatMessageHistory
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langgraph.prebuilt import chat_agent_executor
from pydantic.v1 import BaseModel, Fieldos.environ['http_proxy'] = '127.0.0.1:7890'
os.environ['https_proxy'] = '127.0.0.1:7890'os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_PROJECT"] = "LangchainDemo"
os.environ["LANGCHAIN_API_KEY"] = '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'
# os.environ["TAVILY_API_KEY"] = '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'# 聊天机器人案例
# 创建模型
model = ChatOpenAI(model='gpt-4-turbo')
embeddings = OpenAIEmbeddings(model='text-embedding-3-small')

 创建持久化向量数据库

存放向量数据库的目录

persist_dir = 'chroma_data_dir'  # 存放向量数据库的目录

获取&保存文档

YoutubeLoader是 langchain_community.document_loaders

 ##一个Youtube的视频对应一个document

# 一些YouTube的视频连接
urls = ["https://www.youtube.com/watch?v=HAn9vnJy6S4","https://www.youtube.com/watch?v=dA1cHGACXCo","https://www.youtube.com/watch?v=ZcEMLz27sL4","https://www.youtube.com/watch?v=hvAPnpSfSGo","https://www.youtube.com/watch?v=EhlPDL4QrWY","https://www.youtube.com/watch?v=mmBo8nlu2j0","https://www.youtube.com/watch?v=rQdibOsL1ps","https://www.youtube.com/watch?v=28lC4fqukoc","https://www.youtube.com/watch?v=es-9MgxB-uc","https://www.youtube.com/watch?v=wLRHwKuKvOE","https://www.youtube.com/watch?v=ObIltMaRJvY","https://www.youtube.com/watch?v=DjuXACWYkkU","https://www.youtube.com/watch?v=o7C9ld6Ln-M",
]docs = []  # document的数组
for url in urls:
#     # 一个Youtube的视频对应一个documentdocs.extend(YoutubeLoader.from_youtube_url(url, add_video_info=True).load())print(len(docs))
print(docs[0]) #具体document参数，可以在此查看# # 给doc添加额外的元数据： 视频发布的年份
# for doc in docs:
#     doc.metadata['publish_year'] = int(
#         datetime.datetime.strptime(doc.metadata['publish_date'], '%Y-%m-%d %H:%M:%S').strftime('%Y'))
#
#
# print(docs[0].metadata)
# print(docs[0].page_content[:500])  # 第一个视频的字幕内容
#

len、docs[0]、docs[0].metadata、docs[0].page_content[:500]

构建向量数据库

文档切割——》向量数据库持久化

persist_directory=persist_dir 持久化到磁盘

# # 根据多个doc构建向量数据库
text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=30)
split_doc = text_splitter.split_documents(docs)# # 向量数据库的持久化 # 并且把向量数据库持久化到磁盘
vectorstore = Chroma.from_documents(split_doc, embeddings, persist_directory=persist_dir)  

---

加载磁盘向量数据库

#vectorstore = Chroma(persist_directory=persist_dir, embedding_function=embeddings)

此处persist_dir、Embedding要和上文一致。

## 相似检索结果result【0】【0】才是一个结果。result【0】=（Document，score）

## 若不需要分数，使用.similarity_search

vectorstore = Chroma(persist_directory=persist_dir, embedding_function=embeddings)# 测试向量数据库的相似检索
# result = vectorstore.similarity_search_with_score('how do I build a RAG agent')
# print(result[0])
# print(result[0][0].metadata['publish_year'])

# 测试结果

整合LLM

Prompt

system = """You are an expert at converting user questions into database queries. \
You have access to a database of tutorial videos about a software library for building LLM-powered applications. \
Given a question, return a list of database queries optimized to retrieve the most relevant results.If there are acronyms or words you are not familiar with, do not try to rephrase them."""
prompt = ChatPromptTemplate.from_messages([("system", system),("human", "{question}"),]
)

pydantic Model

# 如果chain中仅用model（不使用.with_structured_output）；那么只能检索content；但我们还需要进行metadata检索，提取结构化参数。那么可以采用pydantic（用于数据管理的库）；

这里Search是数据模型、pydantic的model，非LLM；

Search 的核心作用​​

• ​​标准化检索参数​​：将非结构化文本（用户问题）转化为结构化的 query 和 publish_year，确保后续检索系统能精准处理。
• ​​错误预防​​：通过类型检查（如 publish_year 必须为整数）避免无效参数传递。
• ​​可解释性​​：明确每个字段的用途（通过 description），便于团队协作和维护

Optional：表示可选字段

# pydantic
class Search(BaseModel):"""定义了一个数据模型进行metadata检索"""# 内容的相似性和发布年份query: str = Field(None, description='Similarity search query applied to video transcripts.')publish_year: Optional[int] = Field(None, description='Year video was published')

chain

RunnablePassthrough：让值 可以滞后传递。

model.with_structured_output(Search)：要求模型生成符合 Search 类定义的结构化输出（而非自由文本）

chain = {'question': RunnablePassthrough()} | prompt | model.with_structured_output(Search)# resp1 = chain.invoke('how do I build a RAG agent?')
# print(resp1)
# resp2 = chain.invoke('videos on RAG published in 2023')
# print(resp2)

chain简单测试：这里只是生成指令，并未搜索

执行搜索

自定义一个检索器。

#" $eq"是Chroma向量数据库的固定语法： search.publish_year==向量数据库'publish_year'

def retrieval(search: Search) -> List[Document]:_filter = Noneif search.publish_year:# 根据publish_year，存在得到一个检索条件# "$eq"是Chroma向量数据库的固定语法_filter = {'publish_year': {"$eq": search.publish_year}}return vectorstore.similarity_search(search.query, filter=_filter)new_chain = chain | retrieval# result = new_chain.invoke('videos on RAG published in 2023')
result = new_chain.invoke('RAG tutorial')
print([(doc.metadata['title'], doc.metadata['publish_year']) for doc in result])

结构化数据检索

非结构化检索