Hands-On with Cognita : modular, open source RAG application for prod, built w/ scalability in mind.
This comprehensive guide provides a hands-on approach to building and deploying Retrieval Augmented Generation (RAG) systems using Cognita, a modular and open-source framework designed with scalability in mind. The article walks you through the process of setting up Cognita, ingesting your knowledge base, and leveraging its core features to develop production-ready RAG applications.
🔹 Seamless Setup: Step-by-step instructions guide you through cloning the Cognita repository, creating a virtual environment, installing dependencies, and configuring the necessary files.
🔹 Knowledge Base Ingestion: Learn how to ingest and process your knowledge base, including chunking documents, generating embeddings, and indexing into a vector database.
🔹 Modular Architecture: Cognita promotes separation of concerns by decoupling the data ingestion process from the query service, enabling independent scaling and efficient updates.
🔹 Scalable Deployment: Explore how Cognita facilitates scalable deployments by integrating with cloud infrastructure, allowing independent scaling of components based on resource requirements.
🔹 Production-Ready: Discover the key challenges addressed by Cognita when transitioning a RAG system from a development environment to production, such as chunking and embedding large datasets, building scalable query services, deploying language models and embedding models as separate services, and setting up robust vector databases.
🔹 Hands-On Example: Follow along with a practical example that demonstrates ingesting a credit card knowledge base, configuring the RAG system, and testing the question-answering capabilities using cURL requests.
Whether you’re a seasoned developer or new to RAG systems, this article equips you with the knowledge and tools to build modular, flexible, and scalable production-ready RAG applications using the Cognita framework.