A user is seeking guidance on building a local, offline Retrieval-Augmented Generation (RAG) system for document processing. The system aims to handle various file types, ingest documents automatically, and perform structured and comparative queries. Key challenges include selecting an appropriate vector database (like Qdrant or pgvector), determining the feasibility of running GraphRAG systems such as Neo4j or Microsoft GraphRAG locally, and implementing a user interface that highlights specific text segments and provides citations, similar to plagiarism detection tools. AI
IMPACT Guidance sought on building a local RAG system with advanced features like document highlighting and citation.
RANK_REASON User seeking advice on implementing a specific technical system using various tools.
- AnythingLLM
- Kotaemon
- LlamaIndex LiteParse
- Microsoft GraphRAG
- Neo4j
- pgvector
- Postgres
- Qdrant
- retrieval-augmented generation
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