Results for “semantic-caching”

5 skills

Provides guidance and code for production AI workflows including RAG pipelines, vector databases, embedding indexing, prompt engineering, cost estimation, semantic caching, and quality evaluation.

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Provides persistent, self-hosted semantic memory for AI agents via MCP, storing worklogs, decisions, and notes in PostgreSQL with pgvector for meaning-based retrieval across sessions.

Index local folders and search documents with BM25, vector, or hybrid queries, plus AI answers with citations and a web UI.

Enables semantic file search across a codebase using a vector-powered CLI, with integration for Claude and Codex agents.

Implements production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, advanced prompt engineering, cost estimation, quality evals, semantic caching, streaming, and agents.