How to Integrate FAISS with LangChain

What this does

LangChain provides a vector store abstraction that wraps FAISS, eliminating boilerplate for index creation, persistence, and similarity search. By combining LangChain's document loading and text splitting utilities with a FAISS vector store backed by Ollama embeddings, you can build a complete retrieval pipeline efficiently.

Steps

1. Configure the embedding model.

   from langchain_ollama import OllamaEmbeddings
   
   embeddings = OllamaEmbeddings(
       model="nomic-embed-text",
       base_url="http://localhost:11434"
   )
   sample = embeddings.embed_query("test embedding")
   print(f"Embedding dimension: {len(sample)}")
   # Expected: integer vector length (e.g., 768)
   

2. Load and split documents.

   from langchain.text_splitter import RecursiveCharacterTextSplitter
   
   text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
   corpus = "Retrieval-augmented generation combines LLMs with vector databases."
   docs = text_splitter.create_documents([corpus])
   print(f"Created {len(docs)} document chunks")
   

3. Create the FAISS vector store.

   from langchain_community.vectorstores import FAISS
   
   vectorstore = FAISS.from_documents(documents=docs, embedding=embeddings)
   print(f"FAISS index size: {vectorstore.index.ntotal} vectors")
   

4. Save and reload the vector store.

   vectorstore.save_local("faiss_index_store")
   loaded_store = FAISS.load_local(
       "faiss_index_store",
       embeddings,
       allow_dangerous_deserialization=True
   )
   print(f"Reloaded index size: {loaded_store.index.ntotal}")
   

5. Perform similarity search.

   retriever = vectorstore.as_retriever(search_kwargs={"k": 2})
   results = retriever.invoke("How does RAG work?")
   for doc in results:
       print(doc.page_content[:80])
   

Verification

python3 -c "
from langchain_ollama import OllamaEmbeddings
emb = OllamaEmbeddings(model='nomic-embed-text')
print('LangChain OllamaEmbeddings loaded OK')
"
# Expected: LangChain OllamaEmbeddings loaded OK

Common failures

• Embedding model not found. Run ollama pull nomic-embed-text if missing.
• Mismatched embedding dimensions on load. Always reload with the identical embedding configuration used during creation.
• allow_dangerous_deserialization required. The flag is needed when loading FAISS indexes with pickle-serialized metadata.
• Chunk size too large for embedding context. Set chunk_size below 512 tokens as a safe default.
• Empty search results after reload. Verify the directory contains index.faiss and index.pkl files.
• Version mismatch - The installed package or runtime differs from the command shown; check the version first and rerun the smallest verification command.
• Local environment drift - Another service, virtual environment, model, or path is being used; print the active binary path and configuration before changing the guide steps.

Related guides

• setup-faiss-index-similarity-search
• preprocess-text-vector-ingestion