GanymedeNil/document.ai

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

3670 stars · 324 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A universal local knowledge base solution that stores documents as vectors in a vector database and uses GPT3.5 to generate answers from retrieved similar content. It includes example code and write-ups on improving retrieval accuracy via question-answer splitting, topic extraction, and custom-trained embedding models.

Use cases

• build a local knowledge base question answering system
• chat with my own documents using GPT
• semantic search over a private FAQ dataset
• improve retrieval accuracy for domain-specific Q&A
• combine vector search with LLM generation for customer support
• train custom Chinese embedding models for similarity search

When to choose

• you want a reference implementation of the classic RAG pipeline with GPT3.5
• you need Chinese-language embeddings and Q&A retrieval guidance
• you want example code plus design notes on improving retrieval quality

When to avoid

• you need a production-ready maintained product - the project is a demo/exploration and last released in 2023
• you want fully local inference without OpenAI API dependency
• you need fine-grained document ingestion features out of the box

Facets

application · maturity maintenance

rag vector-database search-engine llm-inference nlp machine-learning large-language-models artificial-intelligence databases python self-hosted cross-platform local-knowledge-base embeddings gpt3.5 question-answering semantic-search chinese-nlp retrieval-augmented-generation natural-language-processing

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markdown · JSON · MCP: product_card(name="GanymedeNil/document.ai")