AI Conversational Retrieval

Python Notebook Setup

In this guide, you'll see how to process text files into a vector database using embeddings.

Then you can ask questions and the chat agent will respond with relevant pieces of your docs as context.

This notebook serves as a prompt template testing kit.

Once you find a prompt you like, you can turn this into a looping script you can run in a terminal. Like this example.

Prerequisites

1. LangChain and requisites installed. See LangChain installation docs
2. A folder named data with the .txt files you want to query or a single file, named data.txt.
3. An API key from OpenAI
4. A file named constants.py to store the API key

Then import the required libraries and the API key.

Next, create the vector store.

Persist = True creates, if it doesn't exist, and reuses a vector store.

Persist = False creates a new vector store each time.

Now, we'll tell it which model to use.

And run the chain.

The result...