How to Create a Retriever in LangChain: Simple Guide

To create a retriever in LangChain, you first set up a vector store with your documents and embeddings, then use the vector store's as_retriever() method to get a retriever object. This retriever can then fetch relevant documents based on queries.

Syntax

Creating a retriever in LangChain involves these parts:

• embedding_function: Converts text to vectors.
• vector_store: Stores document vectors for searching.
• as_retriever(): Method to get a retriever from the vector store.

python

from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS

# Initialize embeddings
embedding_function = OpenAIEmbeddings()

# Create or load vector store
vector_store = FAISS.load_local("faiss_index", embedding_function)

# Create retriever
retriever = vector_store.as_retriever()

Example

This example shows how to create a retriever from a list of texts, then query it to get relevant documents.

python

from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS

# Sample documents
texts = ["LangChain helps build AI apps.", "Retrievers fetch relevant documents.", "Embeddings convert text to vectors."]

# Initialize embeddings
embedding_function = OpenAIEmbeddings()

# Create vector store from texts
vector_store = FAISS.from_texts(texts, embedding_function)

# Create retriever
retriever = vector_store.as_retriever()

# Query retriever
query = "How to get documents related to AI?"
results = retriever.get_relevant_documents(query)

# Print results
for doc in results:
    print(doc.page_content)

Output

LangChain helps build AI apps.
Retrievers fetch relevant documents.
Embeddings convert text to vectors.

Common Pitfalls

Common mistakes when creating retrievers in LangChain include:

• Not initializing the embedding function before creating the vector store.
• Forgetting to use as_retriever() to get the retriever object.
• Using incompatible vector stores or missing required dependencies.
• Not handling empty or very small document sets, which can cause poor retrieval results.

python

from langchain.vectorstores import FAISS

# Wrong: Creating retriever without embeddings
vector_store = FAISS.load_local("faiss_index")  # Missing embedding function
retriever = vector_store.as_retriever()  # This will fail

# Right:
from langchain.embeddings import OpenAIEmbeddings
embedding_function = OpenAIEmbeddings()
vector_store = FAISS.load_local("faiss_index", embedding_function)
retriever = vector_store.as_retriever()

Quick Reference

Tips for creating retrievers in LangChain:

• Always initialize your embedding function first.
• Use vector stores like FAISS, Chroma, or Pinecone for storing vectors.
• Call as_retriever() on your vector store to get a retriever.
• Use retriever.get_relevant_documents(query) to fetch documents.

Key Takeaways

Initialize an embedding function before creating a vector store.

Use the vector store's as_retriever() method to create a retriever.

Retrievers fetch relevant documents based on query similarity.

Ensure your document set is sufficient for meaningful retrieval.

Common errors include missing embeddings or wrong vector store setup.