Doing RAG on PDFs Using File Search in the Responses API (cookbook.openai.com)

OpenAI has announced the integration of its file search feature with the newly launched Responses API, simplifying the process of searching and retrieving information from PDF documents within AI workflows. Previously part of the Assistants API, this tool allows users to conduct targeted searches across a vector store where PDF content is stored post-embedding. By facilitating direct queries to the vector store, users can now efficiently generate responses from large document collections, significantly enhancing the capability of language models in handling complex queries. This development is particularly significant for the AI/ML community as it streamlines content retrieval, making it easier to extract relevant information from extensive knowledge bases. By leveraging embeddings and hybrid search techniques, users can achieve higher relevance and accuracy in content retrieval. The file search capability, coupled with the ability to analyze and evaluate retrieved content in response generation, paves the way for more robust research workflows and automatic information synthesis, marking a step closer to achieving advanced automated intelligence.