Building a chatbot using Cohere and MongoDB: A project on RAG (Retrieval Augmented Generation)
By Arunabh Bora
Categories: Artificial Intelligence
About Course
The next frontier in generative AI involves leveraging it to enhance business efficiency and competitiveness across various domains such as customer service, online sales, employee empowerment, and manufacturing. This requires AI-powered applications that understand your business and utilize your data effectively.
In this project, I will show you how MongoDB Atlas and Cohere Command R+ work together to:
- Empower users with seamless semantic search on customer or operational data in MongoDB Atlas.
- Pass retrieved data to Cohere’s Command R+ generative model for retrieval-augmented generation (RAG).
- Develop and deploy a RAG-optimized user interface for your app.
We’ll go through the typical steps such as data load-in, embedding generation, how to handle user queries and save your chat history.
Course Content
Introduction
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Introduction to Cohere and MongoDB
00:00 -
Creating a Cohere account and a getting a free trial API Key
00:00
Installing requirements
Data loading and data preparation
Generating embeddings with Cohere
Setting up the MongoDB Vector Database
Data Ingestion
Building the pipeline for querying and vector search
Adding the Cohere reranker
Handling user queries
Using MongoDB for storing chat history
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