PDF-based-RAG-application

A Python-based Retrieval-Augmented Generation (RAG) application for querying PDF documents. This project demonstrates advanced RAG techniques, including local LLM integration, vector database updates, and response quality testing.

Using OllamaEmbeddings on a Local PC

This guide provides step-by-step instructions to set up and use OllamaEmbeddings from LangChain to generate text embeddings locally on your PC. This is useful for Retrieval-Augmented Generation (RAG) applications, such as indexing and querying documents in a vector database.

Installation and Setup

1. Install Ollama

• Visit ollama.com and download the installer for your operating system.
• Run the installer and follow the prompts to set up Ollama.

2. Start the Ollama Server

• Open a terminal or command prompt.
• Run the following command to start the Ollama server:

  ollama serve

• This launches a local REST API server at http://localhost:11434. Keep the terminal open while using Ollama.

3. Pull an Embedding Model

• Choose a lightweight embedding model, such as nomic-embed-text.
• Download the model by running:

  ollama pull nomic-embed-text

• Verify the model is available:

  ollama list

4. Install Python Dependencies

• Install the required Python packages using pip:

  pip install langchain ollama

• These packages provide the OllamaEmbeddings module and ensure compatibility with the Ollama server.

Using OllamaEmbeddings in Python

5. Configure OllamaEmbeddings

• Create a Python script (e.g., embeddings.py) and add the following code to initialize the embedding function:

  from langchain.embeddings import OllamaEmbeddings
  
  # Initialize the embedding function
  embedding_function = OllamaEmbeddings(model="nomic-embed-text")