11. FAISS with LangChain

Chapter 11 of 18 · 20 min

KEY INSIGHT

LangChain provides unified abstractions over ChromaDB and FAISSâ€”same code works with either backend. LangChain's `VectorStore` abstraction lets you swap backends without changing application code. ### ChromaDB via LangChain ```python from langchain.vectorstores import Chroma from langchain.embeddings import SentenceTransformerEmbeddings embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2") # LangChain handles collection management vectorstore = Chroma( client=client, # Your ChromaDB client collection_name="langchain_docs", embedding_function=embeddings ) # Add documents vectorstore.add_texts( texts=["Document one content", "Document two content"], ids=["id1", "id2"] ) # Similarity search results = vectorstore.similarity_search("query text", k=3) ``` ### FAISS via LangChain ```python from langchain.vectorstores import FAISS from langchain.embeddings import SentenceTransformerEmbeddings embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2") # Create empty index, then add vectors vectorstore = FAISS.from_texts( texts=["Document one content", "Document two content"], embedding=embeddings, metadatas=[{"source": "doc1"}, {"source": "doc2"}] ) # Save to disk vectorstore.save_local("./faiss_index") # Load from disk loaded = FAISS.load_local("./faiss_index", embeddings) ``` ### Unified Interface Both backends share the same interface: ```python # Same code works for ChromaDB and FAISS def search_vectorstore(vectorstore, query, k=5): """Search both ChromaDB and FAISS with identical code.""" return vectorstore.similarity_search(query, k=k) # Works with either backend chroma_results = search_vectorstore(chroma_store, "password reset") faiss_results = search_vectorstore(faiss_store, "password reset") ``` LangChain also provides `as_retriever()` for integration with chains: ```python retriever = vectorstore.as_retriever( search_type="similarity", search_kwargs={"k": 5, "filter": {"category": "support"}} ) # Use in a chain from langchain.chains import RetrievalQA qa_chain = RetrievalQA.from_chain_type( llm=llm, chain_type="stuff", retriever=retriever ) ``` The abstraction is convenient but adds a dependency. For production systems where you need specific features (like ChromaDB's metadata filtering), use the native client directly.

Local verification checkpoint

Run the smallest example from this chapter in a local workspace and record the package version, runtime, data path, and observed output. If the result depends on model size, vector count, CPU/GPU backend, or available memory, note that constraint beside the exercise so the lesson remains reproducible.

Local verification checkpoint

EXERCISE

Create the same 20 documents in both ChromaDB and FAISS via LangChain. Run the same queries against both. Compare results to ensure consistency.