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Production-ready RAG Framework for Building Scalable Applications

Cognita is a versatile open-source RAG framework designed to enable Data Science, Machine Learning and Platform Engineering leaders to quickly build and deploy scalable RAG applications. It features a fully modular, user-friendly, and adaptable architecture, ensuring complete security and compliance.

In partnership with
Cognita by Truefoundry, a platform for cognitive solutions and AI-driven technologies.
Fully modular
Production Ready
Reusable

Simple yet powerful RAG framework

Fully modular

Fully API driven - which allows integration with other systems

Backend API endpoints for RAG system, including health check, components, data sources, and collections management.

Production Ready

Manage concurrent traffic and auto-scale during traffic spikes

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Reusable

A central reusable repository of parsers, loaders, embedders and retrievers

Cognita interface with data sources and embedder configuration for estater collection displayed on screen.

Diverse integrations and connections

Parsers

Work with different kinds of data, like regular text files, PDFs, and even Markdown files

Data Loaders

load the data from different sources like local directories, S3 buckets, databases, Truefoundry artifacts, etc

Embedders

Support for various pre-trained models available to embed the data such as models from OpenAI, Cohere, etc

Rerankers

Support for SOTA reranker (as of April, 2024) from mixedbread-ai

Vector DBs

Support for various available vector databases in the market, like Qdrant, Singlestore, Chroma, Weaviate, etc

Query Controllers

Inference with best support for concurrent requests, autoscaling, etc

Session

Deep dive into Cognita with our Founders

  • Real-life challenges in putting RAG into production
  • RAG use cases and impact we are seeing with enterprises
  • How can you build RAG with less fuss and more impact
Watch now
Truefoundry Cognita webinar with Nikunj Bajaj and Abhishek Choudhary on enterprise-scale RAG applications.

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Explore the advanced use cases of RAG with us

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