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langchain-embeddings-search

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Build and query vector stores with LangChain 1.0 without getting burned by flipped score semantics, embedding-dim mismatches, reranker quirks, and chunk-splitter bugs. Use when building a RAG pipeline, choosing between FAISS / Pinecone / Chroma / PGVector, filtering by similarity score, or adding a reranker. Trigger with "langchain embeddings", "vector store similarity search", "langchain RAG retrieval", "FAISS score", "Pinecone score", "reranker score".

plugins/saas-packs/langchain-py-pack/skills/langchain-embeddings-search/SKILL.md

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Nov 12, 2026
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# LangChain Embeddings and Vector Search (Python)

## Overview

`FAISS.similarity_search_with_score()` returns L2 distance — **lower is better**.
`Pinecone.similarity_search_with_score()` returns cosine similarity — **higher is
better**. Swap your vector store and your `if score > 0.8` filter now keeps the
garbage and drops the good results, silently. This is pain-catalog entry P12,
and it is the single most common reason a "we migrated from FAISS to Pinecone
for scale" project loses retrieval quality overnight.

The sibling gotchas:

- P13 — `RecursiveCharacterTextSplitter` default separators break inside code
  fences, so RAG over Markdown docs truncates code examples mid-function
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