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

Key Concepts

Responses API, RAG (Retrieval-Augmented Generation), Vector Store, File Search Tool, Evaluation/Testing Strategy, Recall, Precision, Ragas, LLM-assisted evaluation.

1. Introduction to Responses API

• OpenAI released new developer tools, including the Responses API.
• This API is an alternative to the Assistants API (deprecated mid-2026).
• The Responses API allows the use of tools, differentiating it from the chat completion API.

2. File Search Tool and RAG Implementation

• The Responses API includes a built-in File Search tool.
• Users can upload files to OpenAI servers, which creates a vector store.
• The API retrieves relevant document chunks based on user queries.
• OpenAI handles chunking, embedding, and retrieval pipelines.
• Companies are building custom RAG pipelines using this API.
• Cost: $0.10 per GB of vector storage per day (first GB free), $2.50 per 1000 tool calls.

3. Building a RAG Pipeline with Responses API

• The video demonstrates building a RAG pipeline using the Responses API.
• The example uses OpenAI blog posts as the data source.
• The process includes creating a vector store, uploading files, and querying the store.

3.1. Installation and Setup

• Install the latest version of the OpenAI Python SDK.
• Import necessary packages.
• Store the OpenAI API key securely (e.g., using Google Colab secrets).

3.2. Data Preparation

• Download relevant documents (e.g., OpenAI blog posts) and store them in a folder.
• Supported file types include PDFs and others (a detailed list is available).

3.3. Creating and Populating the Vector Store

• Use client.vector_stores.create to create a vector store, providing a name.
• The function returns the vector store ID, name, creation timestamp, and file count.
• A helper function uploads individual files to the vector store using files.create endpoint.
• Another helper function concurrently uploads all files from a folder to expedite the process.
• OpenAI handles chunking and embedding after file upload.

3.4. Retrieving Documents from the Vector Store

• Use the search endpoint on the vector store to retrieve relevant documents based on a query.
• Provide the vector store ID and the user query.
• The result is a list of documents with relevance scores.
• Example: Querying "what is deep research" returns documents from the "introducing deep research" blog post.
• The system processes documents in terms of characters (approximately 3000 characters per chunk, roughly 800-1000 tokens).

3.5. Generating Responses with the LLM

• Use the response endpoint for end-to-end retrieval and generation.
• Provide the user query, the model to use (e.g., gpt-4-turbo-preview), and the tool to use (e.g., file_search).
• Specify the vector store within the file_search tool.
• The API retrieves relevant documents and generates a response using the LLM.
• Example: Querying about "Elon's case" and "deep research" generates responses for both topics.
• Multiple tools can be combined, such as the web search tool for fallback information retrieval.

4. Evaluation and Testing Strategy

• Evaluating the RAG pipeline is crucial to measure the quality of responses.
• Evaluation should occur at both the retrieval and generation steps.
• Retrieval Step: Measure whether the retrieved documents are relevant to the question.
• Generation Step: Measure whether the LLM generates appropriate responses based on the retrieved documents.
• Libraries like Ragas can be used for advanced evaluation (measuring recall, faithfulness, and correctness).

4.1. Creating an Evaluation Dataset

• Use an LLM to generate questions based on the documents.
• Prompt: "Can you generate a question that can only be answered using this document?"
• Example: "What was the outcome of Elon Musk's request for a preliminary injunction against OpenAI?"
• Human review is essential to validate the generated questions.
• Paper reference: "Who validates the validators: Aligning LLM-assisted evaluation of LLM outputs with human preferences."

4.2. Evaluating Retrieval Accuracy

• Loop through the questions and query the vector store.
• Compare the retrieved document with the original document where the question originated.
• If the documents match, consider it a pass; otherwise, a fail.
• Calculate recall and precision at K (e.g., top 5 or top 10 documents).
• The example uses five questions, resulting in 100% recall.

4.3. Advanced Evaluation Metrics

• Consider metrics like response relevancy and faithfulness for evaluating generation accuracy.
• Ragas is recommended for more advanced evaluation metrics.

5. Conclusion

• The Responses API simplifies the creation of RAG pipelines.
• It's important to compare the pricing with custom implementations or other solutions.
• A robust evaluation strategy is essential for measuring the quality of the RAG pipeline.