AI Agents with LangChain & Autonomous Workflows · Lektion

Vector Stores für Retrieval

Lernen Sie, Vektordatenbanken zu verwenden, um relevante Dokumentabschnitte anhand semantischer Ähnlichkeit für RAG (Retrieval Augmented Generation) zu speichern und effizient abzurufen.

Vector Stores für Retrieval ist eine kostenlose AI Agents with LangChain & Autonomous Workflows-Lektion auf CoddyKit. Dies ist Lektion 3 von 4. Du kannst die komplette Lektion unten kostenlos lesen – dann übst du sie direkt im Browser mit einem integrierten Code-Editor und einem KI-Tutor rund um die Uhr. Sie ist Teil des AI Agents with LangChain & Autonomous Workflows-Lernpfads, und dein Fortschritt wird über Web und CoddyKit-App synchronisiert. Der AI Agents with LangChain & Autonomous Workflows-Kurs umfasst insgesamt 4 Lektionen.

Teile dieser Lektion wurden noch nicht übersetzt und werden auf Englisch angezeigt.

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!

Häufig gestellte Fragen

Ist die Lektion „Vector Stores für Retrieval“ kostenlos?

Ja — der vollständige Text von „Vector Stores für Retrieval“ ist hier im Web kostenlos zu lesen. Um sie interaktiv zu üben (integrierter Code-Editor und 24/7 KI-Tutor) und den Rest des AI Agents with LangChain & Autonomous Workflows-Kurses freizuschalten, upgrade auf CoddyKit PRO. Der AI Agents with LangChain & Autonomous Workflows-Kurs umfasst insgesamt 4 Lektionen.

Was lerne ich in „Vector Stores für Retrieval“?

Lernen Sie, Vektordatenbanken zu verwenden, um relevante Dokumentabschnitte anhand semantischer Ähnlichkeit für RAG (Retrieval Augmented Generation) zu speichern und effizient abzurufen. Du übst AI Agents with LangChain & Autonomous Workflows mit praktischem Code, den du direkt im Browser ausführst, und ein 24/7 KI-Tutor beantwortet deine Fragen während du die Lektion bearbeitest.

Brauche ich Erfahrung, um AI Agents with LangChain & Autonomous Workflows zu starten?

Keine Vorkenntnisse erforderlich. AI Agents with LangChain & Autonomous Workflows auf CoddyKit ist für Anfänger bis fortgeschrittene Lernende strukturiert, sodass du hier starten oder von Anfang an beginnen und in deinem eigenen Tempo voranschreiten kannst. Dies ist Lektion 3 von 4.

Wie lange dauert die Lektion „Vector Stores für Retrieval“?

Die meisten CoddyKit-Lektionen dauern etwa 5–10 Minuten. Jede ist kompakt und interaktiv, sodass du stetig Fortschritte machst und genau dort weitermachst, wo du aufgehört hast – im Web und in der App.

Kann ich in dieser AI Agents with LangChain & Autonomous Workflows-Lektion Code schreiben und ausführen?

Ja. Jede AI Agents with LangChain & Autonomous Workflows-Lektion enthält einen integrierten Code-Editor, sodass du echten Code direkt in deinem Browser schreibst und ausführst und sofort KI-Feedback erhältst — ohne lokale Einrichtung erforderlich.

Alle Lektionen in diesem Kurs

Document Loader erklärt
Text-Splitter und Embeddings
Vector Stores für Retrieval
Retriever und kontextbezogene Komprimierung

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