快速开始

本页将展示如何在一个基本的端到端示例中使用查询分析。它将涵盖创建一个简单的搜索引擎，展示将原始用户问题传递给该搜索时出现的失败模式，然后给出一个查询分析如何帮助解决该问题的示例。查询分析技术有很多种，这个端到端示例不会展示全部。

在本例中，我们将对 LangChain YouTube 视频进行检索。

设置

安装依赖

# %pip install -qU langchain langchain-community langchain-openai youtube-transcript-api pytube langchain-chroma

设置环境变量

本示例中我们将使用 OpenAI：

import getpass
import os
 
os.environ["OPENAI_API_KEY"] = getpass.getpass()
 
# Optional, uncomment to trace runs with LangSmith. Sign up here: https://smith.langchain.com.
# os.environ ["LANGCHAIN_TRACING_V2"] = "true"
# os.environ ["LANGCHAIN_API_KEY"] = getpass.getpass()

加载文档

我们可以使用 YouTubeLoader 加载几个 LangChain 视频的字幕：

from langchain_community.document_loaders import YoutubeLoader
 
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 = []
for url in urls:
    docs.extend(YoutubeLoader.from_youtube_url(url, add_video_info=True).load())

import datetime
 
# Add some additional metadata: what year the video was published
for doc in docs:
    doc.metadata["publish_year"] = int(
        datetime.datetime.strptime(
            doc.metadata["publish_date"], "%Y-%m-%d %H:%M:%S"
        ).strftime("%Y")
    )

以下是我们已加载视频的标题：

[doc.metadata["title"] for doc in docs]

['OpenGPTs',
 'Building a web RAG chatbot: using LangChain, Exa (prev. Metaphor), LangSmith, and Hosted Langserve',
 'Streaming Events: Introducing a new `stream_events` method',
 'LangGraph: Multi-Agent Workflows',
 'Build and Deploy a RAG app with Pinecone Serverless',
 'Auto-Prompt Builder (with Hosted LangServe)',
 'Build a Full Stack RAG App With TypeScript',
 'Getting Started with Multi-Modal LLMs',
 'SQL Research Assistant',
 'Skeleton-of-Thought: Building a New Template from Scratch',
 'Benchmarking RAG over LangChain Docs',
 'Building a Research Assistant from Scratch',
 'LangServe and LangChain Templates Webinar']

以下是每个视频关联的元数据。可以看到，每个文档还带有标题、观看次数、发布日期和时长：

docs[0].metadata

{'source': 'HAn9vnJy6S4',
 'title': 'OpenGPTs',
 'description': 'Unknown',
 'view_count': 7210,
 'thumbnail_url': 'https://i.ytimg.com/vi/HAn9vnJy6S4/hq720.jpg',
 'publish_date': '2024-01-31 00:00:00',
 'length': 1530,
 'author': 'LangChain',
 'publish_year': 2024}

以下是文档内容的一个示例片段：

docs[0].page_content[:500]

"hello today I want to talk about open gpts open gpts is a project that we built here at linkchain uh that replicates the GPT store in a few ways so it creates uh end user-facing friendly interface to create different Bots and these Bots can have access to different tools and they can uh be given files to retrieve things over and basically it's a way to create a variety of bots and expose the configuration of these Bots to end users it's all open source um it can be used with open AI it can be us"

为文档建立索引

每当我们执行检索时，都需要创建可查询的文档索引。我们将使用向量存储为文档建立索引，并先将它们分块，使我们的检索更简洁、更精确：

from langchain_chroma import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
 
text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000)
chunked_docs = text_splitter.split_documents(docs)
embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
vectorstore = Chroma.from_documents(
    chunked_docs,
    embeddings,
)

无查询分析的检索

我们可以直接对用户问题执行相似度搜索，找出与该问题相关的块：

search_results = vectorstore.similarity_search("how do I build a RAG agent")
print(search_results[0].metadata["title"])
print(search_results[0].page_content[:500])

Build and Deploy a RAG app with Pinecone Serverless
hi this is Lance from the Lang chain team and today we're going to be building and deploying a rag app using pine con serval list from scratch so we're going to kind of walk through all the code required to do this and I'll use these slides as kind of a guide to kind of lay the the ground work um so first what is rag so under capoy has this pretty nice visualization that shows LMS as a kernel of a new kind of operating system and of course one of the core components of our operating system is th

这效果相当不错！我们的第一个结果与问题相当相关。

如果我们想搜索特定时间段的结果呢？

search_results = vectorstore.similarity_search("videos on RAG published in 2023")
print(search_results[0].metadata["title"])
print(search_results[0].metadata["publish_date"])
print(search_results[0].page_content[:500])

OpenGPTs
2024-01-31
hardcoded that it will always do a retrieval step here the assistant decides whether to do a retrieval step or not sometimes this is good sometimes this is bad sometimes it you don't need to do a retrieval step when I said hi it didn't need to call it tool um but other times you know the the llm might mess up and not realize that it needs to do a retrieval step and so the rag bot will always do a retrieval step so it's more focused there because this is also a simpler architecture so it's always

我们的第一个结果来自 2024 年（尽管我们要求的是 2023 年的视频），并且与输入不太相关。由于我们只是对文档内容进行搜索，结果无法按任何文档属性进行过滤。

这只是可能出现的一种失败模式。现在我们来看看一种基本形式的查询分析如何解决它！

查询分析

我们可以使用查询分析来改进检索结果。这涉及定义一个包含一些日期过滤器的 查询模式，并使用一个函数调用模型将用户问题转换为结构化查询。

查询模式

在本例中，我们将为发布日期设置显式的 min 和 max 属性，以便可以对其进行过滤。

from typing import Optional
 
from langchain_core.pydantic_v1 import BaseModel, Field
 
 
class Search(BaseModel):
    """Search over a database of tutorial videos about a software library."""
 
    query: str = Field(
        ...,
        description="Similarity search query applied to video transcripts.",
    )
    publish_year: Optional[int] = Field(None, description="Year video was published")

查询生成

为了将用户问题转换为结构化查询，我们将利用 OpenAI 的工具调用 API。具体来说，我们将使用新的 ChatModel.with_structured_output() 构造函数来处理将模式传递给模型并解析输出。

from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI
 
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}"),
    ]
)
llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
structured_llm = llm.with_structured_output(Search)
query_analyzer = {"question": RunnablePassthrough()} | prompt | structured_llm

/Users/bagatur/langchain/libs/core/langchain_core/_api/beta_decorator.py:86: LangChainBetaWarning: The function `with_structured_output` is in beta. It is actively being worked on, so the API may change.
  warn_beta(

让我们看看分析器为我们之前搜索的问题生成了什么查询：

query_analyzer.invoke("how do I build a RAG agent")

Search(query='build RAG agent', publish_year=None)

query_analyzer.invoke("videos on RAG published in 2023")

Search(query='RAG', publish_year=2023)

带查询分析的检索

我们的查询分析看起来相当不错；现在让我们尝试使用生成的查询来实际执行检索。

注意： 在我们的示例中，我们指定了 tool_choice="Search"。这将强制 LLM 调用一个——且仅一个——工具，意味着我们始终会有一个优化后的查询可供查找。请注意，情况并非总是如此——请参阅其他指南，了解如何处理没有返回——或返回多个——优化查询的情况。

from typing import List
 
from langchain_core.documents import Document

def retrieval(search: Search) -> List[Document]:
    if search.publish_year is not None:
        # This is syntax specific to Chroma,
        # the vector database we are using.
        _filter = {"publish_year": {"$eq": search.publish_year}}
    else:
        _filter = None
    return vectorstore.similarity_search(search.query, filter=_filter)

retrieval_chain = query_analyzer | retrieval

我们现在可以在之前有问题的输入上运行这个链，并看到它只产生该年的结果！

results = retrieval_chain.invoke("RAG tutorial published in 2023")

[(doc.metadata["title"], doc.metadata["publish_date"]) for doc in results]

[('Getting Started with Multi-Modal LLMs', '2023-12-20 00:00:00'),
 ('LangServe and LangChain Templates Webinar', '2023-11-02 00:00:00'),
 ('Getting Started with Multi-Modal LLMs', '2023-12-20 00:00:00'),
 ('Building a Research Assistant from Scratch', '2023-11-16 00:00:00')]

索引（Index）构建过滤器（constructing-filters）