Create an AI model that combines language models with your business data

As we explore how we can use AI in our business let’s get started with creating a simple ChatGPT model that we can talk to. You can already do that you say? Well what if it’s it was trained on your business data specificly. Below is the outline of how we’ll create an API to an ChatGPT model trained on your business data.

How It Works

Step-by-Step Guide

Document Loading:

• Use third party such as LangChain’s DirectoryLoader to load your business data from text files
• Supports multiple file types and nested directory structures

Text Processing:

• Split documents into manageable chunks with overlap to maintain context
• Uses recursive character splitting for better context preservation

Vector Database:

• Creates embeddings using OpenAI’s embedding model
• Store them in a Chroma vector database for efficient retrieval
• Persists the database locally for reuse

Chat Interface:

• Uses GPT-4 as the base language model
• Implements conversation memory to maintain context
• Returns both answers and source documents for transparency

API Endpoint

We can also include error handling, creates a FastAPI endpoint for easy integration, and handle chat history to return structured responses.

Further Investigation

Don’t worry this is just an outline and we’ll be digging deep into this later.