How to improve the index vector search for QA over docs?

• qdrant

  140 17,839 9.9 Rust

  Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/

  

You should try Qdrant or any other open-source Vector Database. Chrome isn't one. However, the quality also heavily depends on the model.

• marqo

  114 4,111 9.3 Python

  Unified embedding generation and search engine. Also available on cloud - cloud.marqo.ai

  

Have you tried Marqo (https://github.com/marqo-ai/marqo). Good thing is it's end to end with both storage and inference (and is OS with an option for managed hosting). Here's an article about doing Q&A over docs with it.

• WorkOS

  workos.com sponsored

  The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

  

• qdrant-js

  2 194 7.0 TypeScript

  JavaScript/Typescript SDK for Qdrant Vector Database

  

Qdrant JS SDK was introduced just recently https://github.com/qdrant/qdrant-js

Suggest a related project