Stream RAG chat answers with source citations from vector search.
npx skills add https://github.com/mattwoodco/skills --skill ai-rag-chat
Please help me install this Agent Skill. Skill: ai-rag-chat Source: https://github.com/mattwoodco/skills/tree/main/skills/ai-rag-chat Command: npx skills add https://github.com/mattwoodco/skills --skill ai-rag-chat
This Skill enables users to have natural language conversations with their documents, retrieving specific information and summaries without needing to manually read through them.
Use the ai-rag-chat skill to ask "What are the main conclusions of the provided documents?".
High-intent search queries and answers about installing and using this skill.
Retrieval-augmented generation works by using vector search to retrieve relevant document chunks and then streaming answers with source citations. This grounds the chat response in your specific uploaded content.
Yes, you can get source citations when chatting with your documents. Responses include direct citations to the specific documents and page numbers used to generate the answer.
To set up a RAG chat interface, you must integrate it with existing AI chat, AI RAG vector search, and authentication services. This extends your current AI chat functionalities to support document-based conversations.
Yes, the RAG chat saves your conversation history. Conversation persistence allows you to revisit and continue previous conversations with your documents at any time.
You need existing AI chat, AI RAG vector search, and authentication services to use a vector search chat. These integrations are required to retrieve document chunks and stream grounded answers.
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