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💬 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.