Seamlessly Parse, Precisely Retrieve, Intelligently Generate & Effortlessly Deploy, let’s go…
Credit : https://github.com/truefoundry/cognita
Why care, when there’s so many out there?
RAG (Retrieval Augmented Generation) systems are powerful, but building and deploying them can be tricky.
Cognita aims to be a user-friendly and modular solution that addresses common RAG challenges.
In Search of a True Production-Grade Complete RAG framework
Issues with current frameworks:
Chunking & embedding jobs often need separate setup, but not built-in in current frameworks known to Author as of now.
Deploying query services for production can be complex
Handling model deployment (language models, embedding models) lacks built-in support.
Vector databases can be tricky to deploy for scalability.
No single, ready-to-use template for easy adoption
Disclaimer: These issues may have been addressed by other frameworks, not known to Author as of this writing.
How Cognita Solves These Issues
Cognita balances customization with ease of use.
Scalable design to integrate breakthroughs as they happen.
Cognita — A Library for building modular, open source RAG applications for production
Modular Design: Break down RAG into steps for easier management and updates.
Reusable components: Parsers, loaders, etc., to save time across projects.
Streamlined Deployment: Cognita handles the nitty-gritty of production systems.
Scalability: Components scale independently to handle increased traffic.
User-friendly Interface: Even non-technical users can play with RAG setups.
API-Driven: Cognita works well with other systems.
Cognita Components
Indexing Job
- Data Loaders
What: Fetch data from various places (folders, databases, etc.).
Why: RAG needs data to work with! - Parsers
What: Turn different file types into a common format.
Why: Makes it easier for the RAG system to process everything. - Embedders
What: Create code-like representations of text for fast comparison
Why: Helps find the most relevant info for your question.
Metadata Store
What: The ‘brain’ of the system, storing configuration details
Why: Keeps your RAG organized and easy to manage.
LLM Gateway
What: A ‘translator’ for different language models.
Why: Lets you switch between models without re-coding everything.
Vector DB
What: Stores embeddings for super-fast data searches.
Why: Efficient searches are key for large datasets.
API Server
What: The coordinator that handles user questions and generates answers.
Why: It connects all the pieces of the RAG system together.
Excited for Part 2: Coding with Cognita!