wxywb/history_rag

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1038 stars · 137 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A Chinese history question-answering application built with RAG (retrieval-augmented generation) over a corpus of Chinese historical texts, using Milvus or Zilliz Cloud as the vector database and LlamaIndex with GPT-4 (or local/alternative LLMs). It includes a CLI and a Gradio web UI for building the knowledge base and querying it.

Use cases

• ask questions about chinese history with cited sources
• build a rag pipeline over historical documents
• reduce llm hallucinations when answering history questions
• index classical chinese texts into a vector database
• demo retrieval-augmented generation with milvus and llamaindex
• chat with the twenty-four histories corpus

When to choose

• you want a working RAG example over Chinese historical texts
• you need a reference implementation combining Milvus, LlamaIndex, and embeddings
• you want to reduce hallucinations by grounding LLM answers in source documents

When to avoid

• you need a production-ready, licensed product (no license is specified)
• you need non-Chinese-language corpora out of the box
• you want a fully local solution without any LLM API key

Facets

application · maturity active

rag vector-database llm-inference search-engine chatbot large-language-models python cli self-hosted milvus llamaindex chinese-history question-answering embeddings gradio zilliz retrieval-augmented-generation natural-language-processing history docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="wxywb/history_rag")