How to choose FAISS indexer?

[davidshen84]

Hi,

On https://python.langchain.com/docs/integrations/vectorstores/faiss/, it only give a simple code sample that uses the faiss.IndexFlatL2 indexer. It doesn't provide any discussion about how to choose those idexers.

For example, FAISS provides cosine similarity and dot-product similarity indexers, indexers also expect the vectors are normalised before adding to the indexer.

Do I need to take care of these concerns when using vector store with FAISS?

Thanks

[onestardao]

Great question — the short answer is yes, FAISS index type absolutely matters depending on your use case and embedding setup.

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🧠 Cosine vs Dot Product:

• IndexFlatL2 uses L2 (Euclidean) distance, not cosine similarity.
• If you're using cosine-based embeddings (like all-MiniLM-L6-v2), then you should either:
  • Normalize your vectors before adding them, or
  • Switch to IndexFlatIP (inner product), which behaves like cosine if vectors are normalized.

⚠️ Without normalization, IndexFlatIP becomes magnitude-sensitive and can give incorrect ranking.

---

💡 When to normalize:

import numpy as np
normalized_vectors = vectors / np.linalg.norm(vectors, axis=1, keepdims=True)

If you use HuggingFaceEmbeddings from LangChain, you can also override the embedding method to include normalization.

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📌 TL;DR
If you're:

• Using cosine-similarity-based embeddings
• And staying with IndexFlatL2
  → Yes, normalize vectors before inserting.

---

🧩 We actually tracked this exact pain point as AI Problem #3 (Embedding/Index mismatch)
in our WFGY Problem Map →
Might be useful if you're building anything RAG-related and want to avoid silent vector bugs.

[davidshen84]

In the code example, it uses the text-embedding-3-large model which doesn't specify what kind of distance it use. But in another document, OpenAI recommend using cosine similarity distance function. So...safe to guess "text-embedding-3-large" uses cosine similarity distance?

[onestardao]

Thanks for the thoughtful follow-up!

Yes — the example you mentioned (bge-m3) uses cosine similarity by default, and doesn’t explicitly expose the distance type, which makes index selection a bit tricky.

To ensure accurate retrieval ranking, you should always check the distance metric expected by your embedding model.

In practice:

• For cosine-based models (e.g., BGE, OpenAI, etc.), you should normalize vectors before adding to FAISS and use IndexFlatIP.
• For dot-product models, either normalize or switch to an index that supports cosine or euclidean distance (e.g., IndexFlatL2 after norm).

---

✅ Quick checklist for FAISS + LangChain:

1. Check whether your embedding model returns normalized vectors.
2. If not, explicitly normalize them before insertion:

   import numpy as np
   vectors = np.array(vectors)
   vectors = vectors / np.linalg.norm(vectors, axis=1, keepdims=True)
   

3. Choose index type accordingly (FlatIP, FlatL2, etc).

If helpful, I’ve compiled more of these insights into a larger [RAG problem map + solution toolkit]

(https://github.com/onestardao/WFGY/blob/main/ProblemMap/README.md)

which also comes with a fully open-source reasoning engine I’ve been developing (with endorsement from the creator of Tesseract.js).

You're welcome to explore it — if it helps your work, a ⭐ would be appreciated!