💬 Chat with PDF using LLM(Local)

Interact with PDF documents using natural language! This project leverages local large language models (LLMs) and embedding-based vector search to answer questions about PDF files efficiently and privately.

🚀 Features

• Ask natural language questions about the content of your PDFs.
• Local inference using llama3:8b via Ollama — no data leaves your machine.
• Fast and lightweight vector search with DocArrayInMemorySearch.
• Embedding powered by nomic-embed-text for semantic understanding.

🧠 Tech Stack

• LLM: llama3:8b via Ollama
• PDF Loader: PyPDFLoader from LangChain
• Embeddings: nomic-embed-text
• Vector Store: DocArrayInMemorySearch
• Framework: Python + LangChain

📦 Installation

Prerequisites

• Python 3.10+
• Ollama installed and running
• llama3 model pulled via Ollama
• Required Python packages installed (see requirements.txt or instructions in the Usage section)

🗂️ Usage

1. Load a PDF document using PyPDFLoader.
2. Generate embeddings with nomic-embed-text.
3. Store and search using DocArrayInMemorySearch.
4. Query using llama3:8b for context-aware responses.

✅ To-Do

• Add a simple web UI using Streamlit or Gradio
• Enable support for querying multiple PDFs
• Add persistent vector store option (e.g., FAISS or Chroma)
• Improve context retention and memory in conversations

📄 License

This project is licensed under the MIT License. See the LICENSE file for details.