By juanre
Build secure multi-tenant RAG pipelines in PostgreSQL: ingest documents into pgvector store with owner isolation, retrieve via hybrid vector+BM25 search, multi-query generation and RRF fusion, then rerank and augment LLM prompts for grounded responses.
Use when getting started with llmemory document storage and search - covers installation, initialization, adding documents, vector search, hybrid search, semantic search, BM25 full-text search, document management, and building RAG systems with multi-tenant support
Use when building search systems that need both semantic similarity and keyword matching - covers combining vector and BM25 search with Reciprocal Rank Fusion, alpha tuning for search weight control, and optimizing retrieval quality
Use when search queries need better recall through query expansion - generates multiple query variants, retrieves with each, and fuses results using RRF for improved retrieval quality especially with ambiguous or under-specified queries
Use when building SaaS applications needing data isolation between customers - implements owner-based filtering for secure multi-tenant document storage and search with workspace, organization, or tenant-level separation
Use when building Retrieval-Augmented Generation systems - covers document ingestion, hybrid search retrieval, reranking results, and prompt augmentation for accurate LLM responses grounded in your knowledge base
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npx claudepluginhub juanre/ai-tools --plugin llmemoryUnified Python interface for OpenAI, Anthropic, Google, and Ollama LLMs - skills for chat, streaming, structured output, tools, providers, and lockfiles
Production-ready async PostgreSQL utilities with connection pooling, migrations, and testing support - skills for database patterns, migrations, testing, and architecture
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