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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.
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