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AI Agents with LangChain & Autonomous Workflows · 课时

用于检索的向量存储

学习使用向量数据库，根据语义相似度存储并高效检索相关文档块，以实现 RAG（检索增强生成）

用于检索的向量存储 是 CoddyKit 上的免费 AI Agents with LangChain & Autonomous Workflows 课时。 这是第 3 节课，共 4 节。 你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。 这是 AI Agents with LangChain & Autonomous Workflows 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 AI Agents with LangChain & Autonomous Workflows 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

Intro to Vector Stores

Welcome to the final lesson on Data Loading & Retrieval! Today, we'll dive into Vector Stores, a crucial component for building intelligent AI agents.

Think of vector stores as specialized databases designed to store and efficiently search through numerical representations of information, called embeddings.

Why Vector Stores for RAG?

Vector stores are the backbone of Retrieval Augmented Generation (RAG). RAG allows Large Language Models (LLMs) to access external, up-to-date information, overcoming their inherent limitations like:

  • Knowledge cutoffs: LLMs only know what they were trained on.
  • Hallucinations: Making up facts when uncertain.

Vector stores provide the relevant context for the LLM to generate accurate responses.

How Vector Stores Work

When you have text documents (which you learned to load and split in previous lessons), they are first converted into numerical embeddings.

These embeddings are then stored in a vector store. When a query comes in, it's also converted into an embedding. The vector store then finds document embeddings that are 'closest' (most similar) to the query embedding.

LangChain's VectorStore Abstraction

LangChain provides a powerful abstraction for interacting with various vector stores. This means you can swap out different vector database providers (like Chroma, Pinecone, FAISS) with minimal code changes.

Key methods include from_documents() to create a store from documents, add_documents() to add more, and similarity_search() to find relevant content.

Local Store: ChromaDB

For our examples, we'll use ChromaDB. It's an open-source, lightweight vector database that can run locally, making it perfect for development and testing without needing cloud services.

First, make sure you have the necessary packages installed:

  • pip install chromadb
  • pip install langchain-community
  • pip install sentence-transformers

Initializing Chroma & Embeddings

Let's set up ChromaDB with a local embedding model. The embedding model converts text into vectors.

This example uses SentenceTransformerEmbeddings, which runs directly on your machine.

from langchain_community.vectorstores import Chroma
from langchain_community.embeddings import SentenceTransformerEmbeddings
import os

def main():
    # Define a path for ChromaDB to store data locally
    # This creates a 'chroma_db' folder if it doesn't exist
    persist_directory = "./chroma_db"
    
    # Initialize a local embedding function
    # 'all-MiniLM-L6-v2' is a small, efficient model
    embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
    
    # Initialize ChromaDB. It will load if exists, or create new.
    vectordb = Chroma(
        persist_directory=persist_directory,
        embedding_function=embeddings
    )
    
    print("ChromaDB initialized successfully!")
    print(f"Database will persist at: {os.path.abspath(persist_directory)}")

if __name__ == "__main__":
    main()

Adding Documents to Chroma

Once initialized, we can add Document objects (which contain page_content and optional metadata) to our vector store. LangChain handles the embedding process automatically.

We'll add a few sample documents to our ChromaDB instance.

from langchain_community.vectorstores import Chroma
from langchain_community.embeddings import SentenceTransformerEmbeddings
from langchain_core.documents import Document
import os

def main():
    persist_directory = "./chroma_db"
    embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
    
    # Load the existing ChromaDB or create a new one
    vectordb = Chroma(
        persist_directory=persist_directory,
        embedding_function=embeddings
    )
    
    # Example documents to add
    documents = [
        Document(page_content="The quick brown fox jumps over the lazy dog."),
        Document(page_content="Artificial intelligence is transforming industries."),
        Document(page_content="Machine learning is a subset of AI."),
        Document(page_content="Dogs are known for their loyalty and companionship.")
    ]
    
    print(f"Adding {len(documents)} documents to ChromaDB...")
    # add_documents handles embedding and storing
    vectordb.add_documents(documents)
    print("Documents added.")

if __name__ == "__main__":
    main()

Performing Similarity Search

Now that documents are stored, we can query the vector store to find content semantically similar to our query. The similarity_search() method returns a list of relevant Document objects.

The k parameter specifies how many top similar documents to retrieve.

from langchain_community.vectorstores import Chroma
from langchain_community.embeddings import SentenceTransformerEmbeddings
import os

def main():
    persist_directory = "./chroma_db"
    embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
    
    # Load the existing ChromaDB
    vectordb = Chroma(
        persist_directory=persist_directory,
        embedding_function=embeddings
    )
    
    query = "What is AI?"
    print(f"Searching for documents similar to: '{query}'")
    
    # Perform similarity search, retrieve top 2 results
    docs = vectordb.similarity_search(query, k=2)
    
    print("\nFound relevant documents:")
    for i, doc in enumerate(docs):
        print(f"--- Document {i+1} ---")
        print(doc.page_content)

if __name__ == "__main__":
    main()

VectorStore as a Retriever

In LangChain, a Retriever is an interface that returns Documents given an unstructured query. A vector store is one of the most common ways to create a retriever.

By converting your vector store into a retriever, you can easily plug it into more complex LangChain chains and agents, especially for RAG applications.

Activating the Retriever

Here's how to convert your ChromaDB instance into a retriever and use it to fetch documents based on a query. This is the final step before integrating it into a full RAG chain.

from langchain_community.vectorstores import Chroma
from langchain_community.embeddings import SentenceTransformerEmbeddings
import os

def main():
    persist_directory = "./chroma_db"
    embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
    
    # Load the existing ChromaDB
    vectordb = Chroma(
        persist_directory=persist_directory,
        embedding_function=embeddings
    )
    
    # Convert the vector store into a retriever
    # search_kwargs allows passing arguments like 'k' to the underlying search
    retriever = vectordb.as_retriever(search_kwargs={"k": 2})
    
    query = "Tell me about AI."
    print(f"Using retriever to find documents for: '{query}'")
    
    # Use the retriever to get relevant documents
    relevant_docs = retriever.get_relevant_documents(query)
    
    print("\nRelevant documents retrieved by the retriever:")
    for i, doc in enumerate(relevant_docs):
        print(f"--- Retrieved Document {i+1} ---")
        print(doc.page_content)

if __name__ == "__main__":
    main()

Quick Check: Vector Stores

Which of the following best describes the primary purpose of a vector store in the context of Retrieval Augmented Generation (RAG)?

Recap: Vector Stores for RAG

Great job! In this lesson, you learned about:

  • The role of vector stores in enhancing LLMs through RAG.
  • How vector stores store embeddings for semantic search.
  • Setting up and interacting with a local ChromaDB.
  • Adding documents and performing similarity searches.
  • Converting a vector store into a LangChain Retriever.

You now have a solid foundation for implementing data retrieval in your AI agents!

常见问题解答

「用于检索的向量存储」课时是免费的吗？

是的 — 「用于检索的向量存储」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 AI Agents with LangChain & Autonomous Workflows 课程的其余内容，请升级到 CoddyKit PRO。 AI Agents with LangChain & Autonomous Workflows 课程共包含 4 节课。

「用于检索的向量存储」这节课中我会学到什么？

学习使用向量数据库，根据语义相似度存储并高效检索相关文档块，以实现 RAG（检索增强生成） 你通过在浏览器中直接运行的动手代码来练习 AI Agents with LangChain & Autonomous Workflows，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 AI Agents with LangChain & Autonomous Workflows 需要有经验吗？

无需任何先前经验。CoddyKit 上的 AI Agents with LangChain & Autonomous Workflows 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。 这是第 3 节课，共 4 节。

「用于检索的向量存储」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 AI Agents with LangChain & Autonomous Workflows 课中编写并运行代码吗？

能。每节 AI Agents with LangChain & Autonomous Workflows 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

此课程中的所有课时

  1. 文档加载器详解
  2. 文本分割器与嵌入
  3. 用于检索的向量存储
  4. 检索器与上下文压缩
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