OpenAI’s Responses API: The Easiest Way to Build a RAG System?


Summary

The video introduces new developer tools and a series focusing on each tool in detail. It gives an overview of the Responses API, emphasizing its differences from the Chart Completion API and its benefits over the old API set to be deprecated in mid-2026. The video explores creating custom tools using the Responses API, including the File Search tool for creating a vector store and using responses to search documents. It discusses using Open AI for embeddings and considerations for using the Vector Store solution, such as pricing and features for document uploads. The video also covers steps to build a Rack pipeline, setting up an evaluation pipeline for the Rack system, and evaluating responses for accuracy and relevance.


Introduction to Developer Tools

Introducing new developer tools and a new series focusing on each tool in detail to help developers start building with these tools.

Responses API Overview

Overview of the Responses API, highlighting its differences from the Chart Completion API and its benefits over the old API which is set to be deprecated in mid-2026.

Creating Tools with the Responses API

Exploring the ability to create custom tools using the Responses API, with a focus on the File Search tool for creating a vector store and using responses to search documents.

Working with Open AI

Discussion on using Open AI for embeddings and considerations for using the Vector Store solution, including pricing and features for document uploads.

Building a Rack Pipeline

Steps to build a Rack pipeline, starting with creating a Vector Store, uploading files, and implementing a custom chunking strategy for model training.

Evaluation Pipeline Setup

Setting up an evaluation pipeline for the Rack system, including creating a dataset, generating prompts for LLMs, and evaluating responses for accuracy and relevance.

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