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Build intelligent document-grounded retrieval and question-answering workflows.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "haiku.rag" yet — see the docs or source repo.
Use haiku.rag to create a RAG assistant for my product docs. Ingest PDF and Markdown files, answer user questions based on the documents, and return source citations.
A retrieval-augmented workflow that answers questions from product documentation with source citations.
Use haiku.rag to ingest a set of research reports and reference documents, build a searchable knowledge base, and let me query key findings, definitions, and sources in natural language.
A knowledge retrieval system for research materials that returns answers with supporting source text.
Use haiku.rag to design a pipeline that parses uploaded technical documents, extracts text, stores it in a vector database, and exposes retrieval and question-answering interfaces.
A complete RAG implementation plan covering document parsing, indexing, retrieval, and question answering.
Give AI agents persistent graph-based memory and long-term semantic recall.
Connect AI to documents, embeddings, and semantic search through PostgREST APIs.
Let AI index and search local and online sources inside your IDE.
Search local knowledge packs and retrieve chunks for stronger AI answers.
Build modular RAG workflows for document Q&A, semantic search, and knowledge bases.
Set up a local RAG server for private knowledge search and QA.