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RAG (Retrieval Augmented Generation) Framework for building modular, open source applications for production by TrueFoundry
- Added support for Weaviate vector database - Implemented the functionality of document insertion and incremental insertion. Tested the changes locally using weaviate cloud instance. Initial Insertion(Added two files, file1...
Bumps [rollup](https://github.com/rollup/rollup) from 4.21.3 to 4.22.4. Release notes Sourced from rollup's releases. v4.22.4 4.22.4 2024-09-21 Bug Fixes Fix a vulnerability in generated code that affects IIFE, UMD and CJS bundles...
Steps to reproduce: 1. Create a collection 2. Link 2 data sources to it. Excepted Behaviour: - when one of the data sources has completed ingestion, its status changes to...
Currently unstructured parser api calls are sync calls made with requests library. We should just use httpx / aiohttp to make them async.
Unassociate datasource from collection API should remove vector points from the required collection.
Current, `aunassociate_data_source_with_collection` function in `prismastore.py` removes only the datasource from the collection but does not remove the associated data points ingested in the vector DB. Ideally it should also remove...
Hi, We would like to enhance Cognita RAG use case by integrating [MirrorSecurity](https://mirrorsecurity.io/) SDK. The MirrorSecurity SDK provides the following key features that we believe will significantly benefit Cognita: **VectaX**:...
Fix typo issue The rerank model rerank-english-v2.0 is proved by cohere not OpenAI.
Dear Team, it is to update you that I gave been trying to deploy the app locally using my windows system & docker desktop. The following has been the output...
In readme file, it says "you can register your custom embeddings in backend/modules/embedder/__init__.py". However there is no such folder or file.