Efficient Document Retrival using Ollama and PrivateGPT

This project aims to enhance document search and retrieval processes, ensuring privacy and accuracy in data handling.

Getting Started

Step 1: Setup a Virtual Env

Step 2: Install Dependencies using Pip

pip install -r requirements.txt

Step 3: Ensure Ollama and Mistral is installed

ollama pull mistral

Step 4: Create source_documents Folder and Add your Documents

mkdir source_documents

Supported file types:

• .csv: CSV,
• .docx: Word Document,
• .doc: Word Document,
• .enex: EverNote,
• .eml: Email,
• .epub: EPub,
• .html: HTML File,
• .md: Markdown,
• .msg: Outlook Message,
• .odt: Open Document Text,
• .pdf: Portable Document Format (PDF),
• .pptx : PowerPoint Document,
• .ppt : PowerPoint Document,
• .txt: Text file (UTF-8),

Step 5: Ingest Documents to Store in Vector Database for Query

python ingest.py

Step 6: Chat using PrivateGPT

python privateGPT.py

To use a different model try

ollama pull llama2:13b
MODEL=llama2:13b python privateGPT.py