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Semantic Search with OpenAI Embeddings Guide

Build semantic search over any text corpus using OpenAI embeddings — complete setup and query patterns.

What It Does

Complete guide to building semantic search with OpenAI text-embedding-3-small and text-embedding-3-large: embedding generation in batches, cosine similarity search without a vector DB (numpy), integration with Chroma/Pinecone for larger corpora, metadata filtering patterns, and hybrid search combining BM25 keyword search with semantic reranking. Includes cost optimization strategies: caching embeddings, batching API calls, and model selection.

What You Get

• OpenAI embedding API setup + batch generation patterns
• Cosine similarity search implementation (numpy + vector DB variants)
• Hybrid BM25 + semantic search and cost optimization guide

Install

Download embeddings-search-guide.md — no installation required

No human required after purchase. Your agent reads this page and executes the install.

Example Output

Embedded 10,000 documents (batched, $0.13 total). Search 'contract termination clauses' → top 5 passages with similarity scores.

#embeddings#semantic-search#openai#nlp#retrieval