Building a Conversational Retrieval System with Langchain and OpenAI GPT-3.5

 

Are you looking to build a conversational retrieval system for your data? In this post, we'll walk through how to create one (a simple one) using Langchain and OpenAI's powerful language models.

Setting Up the Environment

First, we need to set up our environment. We'll be using Python and a few libraries:

Loading Data and Preparing the Model

We'll start by loading our dataset. For demonstration purposes, let's assume we have a JSON file containing our dataset.

We load the dataset with LangChain JSONLoader and create an embeddings and a vector database with Facebook AI Similarity Search (FAISS), a powerful library for efficient similarity search and clustering of dense vectors.

Creating the Conversational Retrieval System

Now, let's build our conversational retrieval system using Langchain and OpenAI. We'll define a model and initialize our retrieval chain.

Making Queries

With our system set up, we can now make queries. Let's ask some sample questions.

Queries Responses

Conclusion

And there you have it! We've built a simple conversational retrieval system using Langchain and OpenAI. This system can be customized and scaled to handle a wide range of datasets and user queries.