stanford-oval/WikiChat

Health v2 · maintenance only

Adoption not part of the score

1614 stars · 146 forks observed · 2026-08-28

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

WikiChat is a retrieval-augmented generation (RAG) framework that grounds LLM chatbot responses in a corpus (Wikipedia by default) to reduce hallucination. It supports custom document indexing with Qdrant, a free Wikipedia search API, distilled lower-latency models, and multi-user deployment via Chainlit.

Use cases

• build a chatbot that answers factually from wikipedia
• reduce llm hallucination with retrieval-augmented generation
• index my own documents and chat over them
• create a grounded question answering bot over a custom corpus
• distill a smaller model for cheaper factual chatbot responses
• simulate conversations to evaluate chatbot factuality

When to choose

• you need a chatbot whose answers are grounded in Wikipedia or your own documents
• you want a research-backed RAG pipeline focused on factuality
• you want to build a custom vector index with Qdrant and query it with an LLM

When to avoid

• you need a general-purpose agent framework with tool use beyond retrieval
• you want a fully hosted turnkey product rather than a self-hosted Python system
• your use case requires real-time data beyond your indexed corpus

Facets

library · maturity active

rag chatbot nlp search-engine llm-inference large-language-models chatbots python self-hosted hallucination-reduction wikipedia factuality qdrant retrieval-grounding retrieval-augmented-generation natural-language-processing docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="stanford-oval/WikiChat")