bshzzle

Yea it's a similar idea - DeepWiki has the generated "wiki" part which we think is really cool (and maybe we'll add something similar in the future). The core chat experience is the same idea - we had some UX inspiration since we think they nailed the experience.

Deepwiki's context retrieval seems to be more sophisticated. I'm speculating, but I imagine they are using the generated wiki + embeddings which probably gives them higher recall over the codebase, vs. how we are using precision search.

Sourcebot has more "IDE" features built into the product like a file explorer and code navigation, which makes it easier to use the AI-generated answer as a jumping off point for further code exploration.

We went with Zoekt because it is full-featured (it's fast, supports regex, search filters, streamed search, etc.), and is a mature project. Sourcegraph, GitLab, and other large companies use it, so it felt like a safe choice. Overall our experience has been positive - maybe I'll write a blog post about it :)

awesome glad to hear! We are monetizing enterprise features like audit logging and SSO. The core product will remain free and under a FSL license.

Yea they are currently separate - the MCP server exposes out the same tools that Ask Sourcebot uses, but the actual LLMs call is on the MCP client. It would be interesting to merge them though - maybe have a Exa style MCP tool that lets MCP clients ask questions similar to how we are doing it with Ask Sourcebot.

Would be great to hear more about your use case though.

You certainly could create an embedding of your code and then hooking it up to OpenWeb UI or equivalent as a chat interface - we've actually spoked to some teams that have rolled their own custom solution like that!

From a product POV: our main focus with Sourcebot is providing a world-class DX and UX so that it is really easy to use. Practically speaking, for DX: a sys-admin should be able to throw Sourcebot up into their cluster in minutes with minimal maintenance overhead. For UX: provide a snappy interface that is minimal and gets out of your way.

From a technology POV: vector embeddings (and techniques like graph-RAG) are definitely something we are going to investigate as a means of improving the agent's ability to find relevant context fast. Bringing in additional context sources (like git history, logs, GitHub issues, etc.) is also something we plan to investigate. It's a really fascinating problem :)

We used Perplexity as a mental mapping since there is some overlap, e.g., LLMs using search and citing its sources, it's a webapp, etc.