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Your Own Private AI Assistant
PythonOllamaLangChainRAGVector DB
Securely chat with your private documents without data leaving your machine. A privacy-focused RAG system powered by local LLMs via Ollama.
Project Demo
Project Overview
An intelligent question-answering system that combines the power of Large Language Models (LLMs) with private knowledge bases. It allows users to query their own documents without exposing data to external APIs.
How It Works
1.**Ingestion**: Documents are parsed and split into chunks.
2.**Embedding**: Text chunks are converted into vector embeddings.
3.**Retrieval**: Relevant chunks are retrieved based on query similarity.
4.**Generation**: A local LLM (via Ollama) generates a response using the retrieved context.
Tech Stack
•LangChain: For orchestration and RAG pipeline construction.
•Ollama: To run open-source LLMs like Llama 3 locally.
•ChromaDB: As the vector store for efficient similarity search.