rag-pipeline
Use when building Retrieval-Augmented Generation systems. Covers document chunking, embedding generation, vector indexing, semantic search, context building, and integration of retrieval with LLM completion for...
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Use when building Retrieval-Augmented Generation systems. Covers document chunking, embedding generation, vector indexing, semantic search, context building, and integration of retrieval with LLM completion for...
Use when integrating OpenAI GPT models for conversational AI. Covers chat completions, streaming responses, function calling, system prompts, token management, cost optimization, error handling, and building sp...
Use when working with Qdrant vector database for semantic search and RAG. Covers collection setup, embedding generation, vector upsert/search, HNSW indexing, filtering, and integration with OpenAI embeddings fo...
Use when working with Neon serverless PostgreSQL database. Covers database setup, CLI commands, Drizzle ORM integration, schema migrations, connection pooling, and user data storage patterns for authentication...
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.