name: langchain-embeddings-search description: '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".

' allowed-tools: Read, Write, Edit, Bash(python:), Bash(pip:), Grep version: 2.0.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io tags:

• saas
• langchain
• python
• langchain-1.0
• embeddings
• rag
• vector-store compatibility: Designed for Claude Code, also compatible with Codex

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
• P14 — Embedding-dim mismatch crashes at insert time (after 10 minutes of processing), not at VectorStore.__init__; the failure blames "dim mismatch: 1536 != 3072" and no earlier error
• P15 — Cohere/Jina reranker scores are within-query relative, so a 0.34 top-1 is not worse than a 0.92 top-1 on a different query; filtering by threshold is the wrong heuristic

This skill walks through embedding model selection, vector store creation with the version-safe dim guard, score normalization, hybrid keyword+vector search, and rerankers with the correct filter-by-rank pattern. Pin: langchain-core 1.0.x, langchain-community 1.0.x, langchain-openai 1.0.x, faiss-cpu, pinecone-client. Pain-catalog anchors: P12, P13, P14, P15, P49, P50.

Prerequisites

• Python 3.10+
• langchain-core >= 1.0, < 2.0 and langchain-community >= 1.0, < 2.0
• Embedding provider: pip install langchain-openai (text-embedding-3-small/large)
• Vector store: pip install faiss-cpu OR pip install langchain-pinecone
• Provider API keys: OPENAI_API_KEY, PINECONE_API_KEY

Instructions

Step 1 — Initialize embeddings with an explicit dim

from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(
    model="text-embedding-3-small",  # 1536 dims
    # For text-embedding-3-large, use 3072 dims — must match index
)

# Assert dim at startup (prevents P14)
assert len(embeddings.embed_query("test")) == 1536, "embedding dim drifted"

Swapping models (-small 1536 → -large 3072) is a migration, not a swap. Plan it — back-fill the index, not just the config.

Step 2 — Choose a vector store

from langchain_community.vectorstores import FAISS

store = FAISS.from_documents(docs, embedding=embeddings)
results = store.similarity_search_with_score("query", k=5)
# FAISS: [(doc, 0.31), (doc, 0.42), ...] — LOWER IS MORE SIMILAR

vs.

from langchain_pinecone import PineconeVectorStore

store = PineconeVectorStore(index_name="prod", embedding=embeddings)
results = store.similarity_search_with_score("query", k=5)
# Pinecone: [(doc, 0.91), (doc, 0.87), ...] — HIGHER IS MORE SIMILAR

See Vector Store Comparison for the feature matrix and the migration gotchas.

Step 3 — Normalize scores before any threshold filter

Write a normalizer at the retriever boundary, so downstream code never sees raw store-specific scores:

def normalize(score: float, store_type: str) -> float:
    """Return similarity in [0, 1] where 1 = identical, 0 = unrelated."""
    if store_type == "faiss_l2":
        return 1.0 / (1.0 + score)  # collapse L2 distance into similarity
    if store_type in {"pinecone", "chroma", "pgvector"}:
        return max(0.0, min(1.0, score))  # already similarity, clamp just in case
    raise ValueError(f"Unknown store type: {store_type}")

Now score > 0.7 means the same thing regardless of backend. See Score Semantics for the per-store derivation.

Step 4 — Chunk text with language-aware splitters

from langchain_text_splitters import RecursiveCharacterTextSplitter, Language

# BAD — breaks inside Markdown code fences (P13)
bad = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)

# GOOD — respects Markdown structure
md_splitter = RecursiveCharacterTextSplitter.from_language(
    Language.MARKDOWN, chunk_size=1000, chunk_overlap=100,
)

# For Python source files
py_splitter = RecursiveCharacterTextSplitter.from_language(
    Language.PYTHON, chunk_size=1500, chunk_overlap=150,
)

PDF pipelines have their own pain: PyPDFLoader splits by page, tearing tables in half (P49). Use PyMuPDFLoader or UnstructuredPDFLoader for documents with tables.

Step 5 — Hybrid search (keyword + vector)

Pure vector search misses exact-match keywords (product SKUs, error codes, function names). Combine BM25 + vector:

from langchain.retrievers import EnsembleRetriever
from langchain_community.retrievers import BM25Retriever

bm25 = BM25Retriever.from_documents(docs); bm25.k = 5
vector = store.as_retriever(search_kwargs={"k": 5})

ensemble = EnsembleRetriever(
    retrievers=[bm25, vector],
    weights=[0.4, 0.6],  # tune on your eval set
)

See Hybrid Search for the eval harness and the weight-tuning procedure.

Step 6 — Rerank by rank, not by score

from langchain_cohere import CohereRerank

reranker = CohereRerank(top_n=3, model="rerank-v3.5")
reranked = reranker.compress_documents(
    documents=candidates, query=query,
)
# reranked[0].metadata["relevance_score"] is query-relative — 0.34 may be the best
# WRONG: [d for d in reranked if d.metadata["relevance_score"] > 0.5]
# RIGHT: reranked[:top_n]  — trust the rank order

Filter by rank (keep top-k) not threshold. Calibration per-query is possible but rarely worth the engineering cost.

Output

• Embeddings initialized with dim assertion at startup
• Vector store chosen from the comparison matrix with score-semantics awareness
• Score normalizer applied at retriever boundary (no raw scores downstream)
• Language-aware text splitter that respects code fences and PDF structure
• Hybrid retriever combining BM25 and vector with tuned weights
• Reranker filtering by rank, not threshold

Error Handling

Examples

Building a RAG retriever with hybrid search

End-to-end: load Markdown docs with language-aware chunking, embed with OpenAI text-embedding-3-small, index in FAISS for local dev, wrap in an EnsembleRetriever with BM25 at 0.4 weight and vector at 0.6.

See Hybrid Search for the full builder and the weight-tuning procedure on a golden set.

Migrating from FAISS to Pinecone without quality regression

The three gotchas: (a) score semantics flip (P12), (b) the migration needs a re-embed unless the source embedding is stable, (c) threshold filters must be retuned on the new score scale.

See Vector Store Comparison for the migration checklist.

Per-tenant vector stores without leakage

Use Pinecone namespaces or PGVector row-level security. Construct the retriever per-request with the tenant ID — never bind a retriever at import time (P33).

See the pack's langchain-enterprise-rbac skill for the tenant-isolation pattern.

Resources

• LangChain Python: Vector stores
• LangChain Python: Retrievers
• LangChain: Text splitters
• FAISS docs (score is L2 distance)
• Pinecone metrics (cosine default)
• Cohere Rerank (score per-query relative)
• Pack pain catalog: docs/pain-catalog.md (entries P12, P13, P14, P15, P49, P50)