How Can I Integrate a Custom Knowledge Base with the GPT API?

Below is an explanation of how you can integrate a custom knowledge base with the GPT API:

Retrieval Augmented Generation (RAG)

• What is RAG?
  RAG (Retrieval Augmented Generation) is a strategy where you retrieve relevant information from your own data sources (like a knowledge base) and then include that retrieved context in your API call. This effectively “augments” the model’s prompt to ensure it has the necessary domain-specific data when generating a response.

• How It Works:

  1. Build a Retrieval System:
     Set up a system (using vector databases, search engines, etc.) to index and query your documents.
  2. Query Retrieval:
     When a user question is received, query your knowledge base to extract the most relevant documents or passages.
  3. Augment the Prompt:
     Include these retrieved documents in your prompt so that the GPT model has access to that extra context when generating answers.

• Built-In Support:
  There isn’t a built-in “knowledge base” or out-of-the-box RAG feature within the GPT API itself. Instead, the process is implemented on your end—retrieving the data and then passing it along in the prompt. The API simply consumes the prompt that you provide.

Fine-Tuning vs. Adding Knowledge

• Fine-Tuning:
  Fine-tuning involves training a custom model to exhibit specific behaviors or focus on a particular domain. It isn’t designed to dynamically add new knowledge. Instead, it adjusts the model’s tendencies and responses based on the data you use to fine-tune.

• Why Use RAG for Knowledge Bases:
  Since fine-tuning doesn’t “add” knowledge on the fly, RAG is generally more suitable for cases where you need up-to-date or domain-specific information. Using retrieval, you can supply the latest information from your knowledge base without needing to retrain or update the model itself.

The Assistant API and Custom Models

• Assistant API:
  The assistant API you noticed is associated with creating variants of the model or fine-tuned models that are more focused or exhibit particular behaviors. It’s not specifically intended for integrating an external knowledge base.

Summary

If you need to incorporate a custom knowledge base, the recommended approach is to implement retrieval augmented generation:

1. Query your knowledge base to fetch relevant data.
2. Prepend or incorporate that data into your GPT API prompt.
3. Let the model use that context for generating accurate and domain-specific responses.

This way, you can effectively “add” external knowledge without fine-tuning the model.