RAG Movie Info

This repository demonstrates the implementation of a Retrieval-Augmented Generation (RAG) model applied to a movie database. The RAG model efficiently retrieves information from a vector database and uses a language model to generate responses, making it ideal for querying structured and unstructured datasets.

Introduction to RAG

Retrieval-Augmented Generation (RAG) is a hybrid approach that combines the strengths of information retrieval systems and language models. The pipeline involves Three key components:

1. Embedding & Storing : Embeded the DataSet and store into pinecone
2. Retriever: Fetches relevant information from pinecone vector database.
3. Generator: Processes the retrieved data and generates a coherent, context-aware response using LLM(openai).

RAG Pipeline

Below is an overview of the RAG pipeline:

Tech Stack

The following technologies were used to build the RAG Movie Info system:

• LangChain: A framework for building applications powered by language models.
• OpenAI: Provides the LLM (e.g., GPT-3.5-Turbo) for generating responses.
• Hugging Face: Used for embedding text data into vector representations.
• Pinecone: Serves as the vector database for efficient similarity search and retrieval.

Installation Steps

Follow these steps to set up the project locally:

Step 1: Clone the Repository

git clone https://github.com/Dharansh-Neema/RAG-movie-info.git
cd RAG-movie-info

Step 2: Create a Virtual Environment

Create a virtual environment to isolate the dependencies for this project:

python -m venv env

Step 3: Activate the Virtual Environment

• On Windows:

  .\env\Scripts\activate

• On Linux/MacOS:

  source env/bin/activate

Step 4: Install Dependencies

Install all required dependencies listed in dependency.txt:

pip install -r dependency.txt

Step 5: Configure Environment Variables

Create a .env file in the root directory and add the following keys:

OPENAI_API_KEY=your_openai_api_key
PINECONE_API_KEY=your_pinecone_api_key

Replace your_openai_api_key and your_pinecone_api_key with your actual API keys.