kapa is a retrieval augmentented generation system (RAG). To set up an instance of kapa you have to ingest all relevant technical knowledge which is required to answer the questions of the intended audience. To answer questions kapa first performs search over an index created from the ingested knowledge sources and then uses LLMs to generate the final output. Users can manage their data sources in the kapa dashboard.
What knowledge sources does kapa.ai support? 📚
kapa.ai supports a wide range of technical knowledge sources including:
|Supported Knowledge Sources
|Docusaurus, Readme, Sphinx, ReadTheDocs, MkDocs, Mintlify, GitBook, Hugo, Jekyll, Gatsby, Docsify
|Discourse, Stackoverflow, GitHub Discussions, StackExchange
|GitHub Issues/PRs, GitHub Files, GitHub Discussions
|📦 API Specs
|OpenAPI, GraphQL, gRPC, Swagger, Postman
|🧠 Knowledge Bases
|Zendesk, Intercom, Hubspot, Notion, Confluence
|💬 Chat Histories
|Slack, Discord, Telegram
|📚 Other Content
|Blogs, Tutorials, YouTube, Books, PDFs
You are always welcome to reach out to the team to request a new source.
How do I ingest sources? 📥
Currently, the kapa.ai team will help you onboard onto the platform and ingest sources for you. Once you sign up to the kapa.ai beta, there's usually a 12-24hr turnaround time for this process. After the kapa team initially gets you onboarded, you can manage their data sources in the kapa dashboard (access granted after sign-up).
Want access within 12-24 hrs? Head to kapa.ai to sign up and the team will be in touch within a few hours to get you set up with a demo instance.