Nice, we're on Pinecone so maybe we can use this in the future. Could you see any way of integrating this with a token manager like Nango so our end users could integrate their own accounts?
Thanks for the feedback Sébastien!
1. Are you adding documents through the API with URLs? URL parsing is only supported through the UI right now, through the API it will just add that url as the metadata.
2. Will add md support soon!
3/4. We are working on ways to give more configuration to how the context is formatted like you mentioned, keep an eye out for updates!
5. The response from the completion API should return a "search_results" array which includes the chunks and other columns/metadata/images. If the array is empty there may be something wrong in the search config, happy to hop on a call some time or help in our Discord
I would love it if we get more customization on the context. I just tested on a use case that is close to what our app needs for the back-end, and I'm getting chunks that are too short. It seems I can set how many chunks come up in the context, but not their length.
Other than that, you've got a subscriber. Congrats! I've just entered our CC number.
Yea! Our FE is React/Next and BE is a mix of Supabase, Pinecone, BigQuery and QStash. and mix of HF endpoints and PoplarML in our batch for some embedding models
Are you using it to store the embeddings (pgvector)? I'm asking to see where you're differentiating between supabase & pinecone. We get this question often and it's interesting to hear what others are doing and where they feel pgvector isn't appropriate
Hey! Loving the experience so far - we started the project before pgvector was supported on Supabase, and we want to support hybrid search as well (don't think pgvector supports this?)
Yes, I believe it can be done but it requires a bit of work on your side. I'll see if we can come up with a demo and/or postgres extension which handles this for you
looping back here. The langchain community just released a version of Hybrid Search which using postgres full text search. It's clever, and IMO probably a better approach than just sparse/dense vectors:
Will check this out - it seems like its doing two separate searches where you specify two separate top K values. Curious what the trade offs are between this approach and weighting sparse/dense vectors
Thanks guys for sharing it. This really helps us in the community to look for the architectures that we should focus on and the new interesting developments.
Right now typical usage is < 100 MB per org, but theoretically it could infinitely scale with auto-scaling. And yes heres a new link: https://discord.gg/JCqEzCZ4FM, should be updated on our site as well
We think our current product makes it alot easier to work with documents/embeddings to help build that initial prototype, and once its deployed, tools for versioning and logging. One example use case is ingesting previous support chats and docs to build a "copilot" for your support team.
For standard datasets we use OpenAI ada embeddings, for hybrid its instructor + SPLADE. The HyDE toggle in the context variable feeds the query to a prompt first ("Generate a document that answers..."), before embedding. I think in the paper they use the contriever embedding model but we just use the ones supported on our platform
