LANGCHAIN

Embeddings and Vector Stores: OpenAI Embeddings, FAISS, Chroma, and Similarity Search

Vector stores enable semantic search. Load embeddings, store vectors in FAISS or Chroma, and query with similarity search to retrieve relevant documents for RAG.

Soham Sharma

AI Engineer, Botmartz · July 17, 2026 · 1 min read

◈ Open in Google Colab ⌥ View Code on GitHub

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Embeddings and Vector Stores: OpenAI Embeddings, FAISS, Chroma, and Similarity Search

Embeddings Vector Stores Conclusion

Embeddings convert text to vectors. Vector stores enable semantic search by finding the nearest neighbors. This post covers embedding models, vector databases, and similarity search queries.

Embeddings

Code Snippet

from langchain.embeddings import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
vector = embeddings.embed_query("What is semantic search?")
print(f"Embedding dimension: {len(vector)}")

Output:

Code Snippet

Embedding dimension: 1536

Vector Stores

Code Snippet

from langchain_community.vectorstores import FAISS, Chroma
from langchain.embeddings import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()

# FAISS for in-memory search
vector_store = FAISS.from_documents(documents, embeddings)

# Query
results = vector_store.similarity_search("What is the main topic?", k=3)
for doc in results:
    print(f"- {doc.page_content[:100]}...")

Output:

Code Snippet

- The main topic is semantic search and retrieval-augmented generation...

FAISS is fast and lightweight; Chroma persists to disk.

Conclusion

Embeddings and vector stores are the retrieval backbone of RAG. OpenAI embeddings are high-quality; FAISS and Chroma offer flexible storage and search. Understanding vector databases enables building effective semantic search. Next: we'll build a complete RAG pipeline from data ingestion to generation.