Vector Databases
Vector databases store embeddings and enable fast similarity search. They are the retrieval component in RAG.
Chroma (Beginner‑Friendly)
In‑memory or persistent, easy to use.
from langchain.vectorstores import Chroma
vectorstore = Chroma.from_documents(documents, embeddings)
retriever = vectorstore.as_retriever()vectorstore = Chroma.from_documents(documents, embeddings, persist_directory="./chroma_db")
vectorstore.persist()FAISS (Fast, Local)
Developed by Facebook, very fast approximate nearest neighbour search.
from langchain.vectorstores import FAISS
vectorstore = FAISS.from_documents(documents, embeddings)Pinecone (Cloud, Scalable)
Managed vector database, good for production at scale. Requires API key and index creation.
Which One to Choose?
- Learning / prototyping: Chroma (in‑memory).
- Local production: FAISS.
- Cloud production: Pinecone or Weaviate.
Two Minute Drill
- Vector databases store embeddings for fast retrieval.
- Chroma: easy, persistent, good for beginners.
- FAISS: fast, local, memory‑efficient.
- Pinecone: managed cloud service.
Need more clarification?
Drop us an email at career@quipoinfotech.com