Tag: embeddings

Generate text embeddings with OpenAI and compute cosine similarity to measure semantic similarity between texts for search, dedup, and clustering

Use Pinecone to store, query, and filter vector embeddings for semantic search with metadata filtering and namespace isolation

How to build a Retrieval-Augmented Generation (RAG) pipeline using LangChain and vector databases for AI-powered search

How to implement semantic search using text embeddings and vector similarity search for intelligent document retrieval

Cache LLM embeddings to reduce API calls and cost. Store embeddings with a content hash key and serve from cache on repeated inputs.

Combine keyword (BM25) and semantic (vector) search to improve retrieval accuracy in RAG pipelines. Fuse ranked results using reciprocal rank fusion.

Checklist for preparing data for LLM and RAG systems: data collection, cleaning, chunking, embedding, deduplication, PII removal, format validation, quality scoring, and indexing with metrics and thresholds.

Master the OpenAI API in production. Covers chat completions, streaming, function calling, structured outputs, embeddings, fine-tuning, batch API, assistants API, rate limits, error handling, and cost optimization patterns.

Build production RAG systems. Covers chunking strategies, embedding models, vector stores, retrieval optimization, reranking, hybrid search, evaluation, and deployment patterns for reliable retrieval-augmented generation.

A practical guide to vector databases: embeddings, similarity search, approximate nearest neighbors, and choosing between Pinecone, Weaviate, pgvector, and Chroma.