LLamaRecipes/recipes/3p_integrations/groq/groq-example-templates/presidential-speeches-rag-with-pinecone

Presidential Speeches RAG with Pinecone

This repository contains a command line application that allows users to ask questions about US presidental speeches by applying Retrieval-Augmented Generation (RAG) over a Pinecone vector database. The application uses RAG to answer the user's question by retrieving the most relevant presidential speeches and using them to supplant the LLM response.

Features

• RAG (Retrieval-Augmented Generation): Enhances the generation of responses by integrating retrieval-based methods. This feature allows the system to fetch relevant information from a large corpus of data, providing more accurate and contextually appropriate answers by combining retrieved content with generative capabilities.

• Vector Databases (Pinecone): Integrates with Pinecone to store and manage vector embeddings efficiently. Pinecone's high-performance vector database allows for fast and scalable similarity searches, enabling quick retrieval of relevant data for various machine learning and AI applications.

• LangChain Integration: Leverages LangChain to facilitate natural language processing tasks. LangChain enhances the interaction between the user and the system by providing robust language modeling capabilities, ensuring seamless and intuitive communication.

Code Overview

The main script of the application is main.py. Here's a brief overview of its main functions:

• get_relevant_excerpts(user_question, docsearch): This function takes a user's question and a Pinecone vector store as input, performs a similarity search on the vector store using the user's question, and returns the most relevant excerpts from presidential speeches.

• get_relevant_excerpts(user_question, docsearch): This function takes a user's question and a Pinecone vector store as input, performs a similarity search on the vector store using the user's question, and returns the most relevant excerpts from presidential speeches.

• presidential_speech_chat_completion(client, model, user_question, relevant_excerpts, additional_context): This function takes a Groq client, a pre-trained model, a user's question, relevant excerpts from presidential speeches, and additional context as input. It generates a response to the user's question based on the relevant excerpts and the additional context

Usage

You will need to store a valid Groq API Key as a secret to proceed with this example outside of this Repl. You can generate one for free here.

You would also need your own Pinecone index with presidential speech embeddings to run this code locally. You can create a Pinecone API key and one index for a small project for free on their Starter plan, and visit this Cookbook post for more info on RAG and a guide to uploading these embeddings to a vector database

You can fork and run this application on Replit or run it on the command line with python main.py.