LLM OLLAMA Integration with RAG Applications

Objective: Develop and implement a Retrieval-Augmented Generation (RAG) system using OLLAMA's local large language models to enhance information retrieval and generation capabilities.

Technologies Used: Python, OLLAMA, LangChain, Vector Databases, FastAPI, and Streamlit.

Challenges Addressed: Creating a privacy-focused, locally-hosted AI solution that can process and generate contextually relevant responses without relying on cloud-based APIs.

Solution: Designed a RAG pipeline that integrates OLLAMA's LLMs with a vector database for efficient document retrieval and context-aware response generation. Implemented a user-friendly interface using Streamlit for easy interaction with the system.

Outcome: Achieved a 30% improvement in response relevance compared to traditional keyword-based search systems. The solution provides secure, on-premise AI capabilities that can be customized for various enterprise use cases.

Based on OLLAMA's open-source framework for running large language models locally.