---
title: Similarity Search Patterns
category: product
entity_type: skill
price: $15
canonical: https://forgehouse.ai/skills/similarity-search-patterns/
lang: en
hreflang_alt: https://forgehouse.ai/tr/skiller/similarity-search-patterns/
last_updated: 2026-06-20
---

# Similarity Search Patterns

> Implement efficient similarity search with vector databases.

Production-ready blueprints for building semantic and vector search that actually scales. It ships working implementations for four vector stores (Pinecone, Qdrant, pgvector, and Weaviate) plus the decision frameworks for index choice, distance metric, and filtering strategy that separate a fast retrieval system from a slow, low-recall one. Stop guessing at HNSW parameters and ship search that returns the right results in under 200ms.

## Use cases
- Building semantic search over millions of documents
- Powering RAG retrieval for AI assistants
- Implementing recommendation engines
- Combining vector and keyword (hybrid) search
- Migrating from flat search to HNSW or IVF+PQ
- Reranking candidate results with a cross-encoder

## Benefits
- Choose the right index for your data size instead of over-engineering small datasets
- Hit sub-200ms search latency with tuned recall/speed tradeoffs
- Avoid the post-filter trap that silently returns too few results
- Cut vector-store costs by matching index type and quantization to scale

## What’s included
- Copy-paste vector store classes for Pinecone, Qdrant, pgvector, and Weaviate
- Index selection guide (Flat vs HNSW vs IVF+PQ) by data size and recall target
- Distance metric reference (cosine, euclidean, dot product) with when-to-use guidance
- Hybrid search templates merging dense vectors with full-text and BM25
- Pre-filter vs post-filter strategy with payload-index optimization notes
- Reranking, batch upsert, and score-threshold calibration patterns

## Who it’s for
Backend and AI engineers building semantic search, RAG retrieval, or recommendation systems that need to stay fast and accurate at scale.

## How it runs
The decision and build sequence the skill walks through when standing up production similarity search, in order:
1. Size the dataset first and let it pick the index family: under 10K vectors flat exact search, 10K to 1M HNSW, beyond that HNSW with quantization or IVF+PQ. Every tier trades a little recall for an order of magnitude in speed.
2. Match the distance metric to the embedding model: cosine for L2-normalized outputs, dot product when magnitude carries meaning. Vectors from different models never share one space; switching models means re-embedding everything.
3. Decide pre-filter vs post-filter for metadata: pre-filter with payload indexes on hot fields like tenant_id and category, because post-filtering 100 results down to 3 starves the user of answers.
4. Load vectors in batches of 100 to 1000 per upsert call for 10-50x throughput; on large pgvector imports, drop the index first and rebuild after the load, which runs 5 to 10x faster.
5. Wire hybrid search where keywords still matter: blend the dense vector score with BM25 or full-text rank at a tuned weight. The skill carries ready templates for Pinecone, Qdrant, pgvector and Weaviate, plus cross-encoder reranking on the over-fetched top 50.
6. Calibrate the score threshold against ground-truth pairs instead of hardcoding a magic 0.85, then track recall weekly as a live metric to catch embedding drift early.

## FAQ
### We only have about 50K vectors, is this overkill for us?
Part of the value is being told not to over-engineer: the index selection guide maps data size to index type, and at 50K vectors a flat index or simple HNSW is usually the right call. The blueprints then scale with you as the dataset grows.

### How does it actually get search under 200ms?
Working store classes for Pinecone, Qdrant, pgvector, and Weaviate, plus the decisions that dominate latency: HNSW parameters, pre-filter versus post-filter strategy with payload indexes, quantization, and score-threshold calibration. The post-filter trap that silently returns too few results is called out specifically.

### Does it include an embedding model or host the vector database?
No. It assumes you bring your own embeddings and a vector store; what it provides are the implementation patterns and decision frameworks on top. Model choice and hosting costs remain yours.

## Price
$15, one-time, no subscription. VAT included.

Related guide: [AI for data analytics](https://forgehouse.ai/guides/ai-data-analytics/)