As someone who's been building with OpenAI's file search capabilities, I've noticed two missing features that would make a huge difference for developers:

Current workarounds are inefficient

Right now, if you want to do anything sophisticated with document metadata in the OpenAI ecosystem, you have to resort to this kind of double-call pattern:

1. First call to retrieve chunks
2. Manual metadata enhancement
3. Second call to get the actual answer

This wastes tokens, adds latency, and makes our code more complex than it needs to be.

Feature #1: Pre-search filtering via extended metadata filtering

OpenAI already has basic attribute filtering, but it could be greatly enhanced:

“`python

What we want – native support for filtering on rich metadata

search_response = client.responses.create( model="gpt-4o-mini", input=query, tools=[{ "type": "file_search", "vector_store_ids": [vector_store_id], "metadata_filters": { # Filter documents by publication date range "publication_date": {"range": ["01-01-2024", "01-03-2025"]}, # Filter by document type "publication_type": {"equals": "Notitie"}, # Filter by author (partial match) "authors": {"contains": "Jonkeren"} } }] ) “`

This would let us narrow down the search space before doing the semantic search, which would: – Speed up searches dramatically – Reduce irrelevant results – Allow for time-based, author-based or category-based filtering

Feature #2: Native metadata insertion in results

Currently, we have to manually extract the metadata, format it, and include it in a second API call. OpenAI could make this native:

python search_response = client.responses.create( model="gpt-4o-mini", input=query, tools=[{ "type": "file_search", "vector_store_ids": [vector_store_id], "include_metadata": ["title", "authors", "publication_date", "url"], "metadata_format": "DOCUMENT: {filename}\nTITLE: {title}\nAUTHORS: {authors}\nDATE: {publication_date}\nURL: {url}\n\n{text}" }] )

Benefits: – Single API call instead of two – Let OpenAI handle the formatting consistently – Reduce token usage and latency – Simplify client-side code

Why this matters

For anyone building RAG applications, these features would: 1. Reduce costs (fewer API calls, fewer tokens) 2. Improve UX (faster responses) 3. Give more control over search results 4. Simplify code and maintenance

The current workarounds force us to manage two separate API calls and handle all the metadata formatting manually, which is error-prone and inefficient.

What do you all think? Anyone else building with file search and experiencing similar pain points?