# Best practice for using multiple documents with File Search in a single Responses API call?

**URL:** <https://community.openai.com/t/best-practice-for-using-multiple-documents-with-file-search-in-a-single-responses-api-call/1359687>\
**Category:** API\
**Tags:** file-search, responses-api\
**Created:** [September 24, 2025, 12:44pm UTC](https://community.openai.com/t/best-practice-for-using-multiple-documents-with-file-search-in-a-single-responses-api-call/1359687 "2025-09-24T12:44:42Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![Taci](https://sea2.discourse-cdn.com/openai1/user_avatar/community.openai.com/taci/32/448924_2.png) [@Taci](https://community.openai.com/u/Taci)\
**Post date:** [September 24, 2025, 12:44pm UTC](https://community.openai.com/t/best-practice-for-using-multiple-documents-with-file-search-in-a-single-responses-api-call/1359687/1 "2025-09-24T12:44:42Z")

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**TL;DR:** _I want the model (GPT-5) to, in one Responses API call, to search through 3 files, each for a distinct separate purpose. What’s the best way to implement this? One vector store with all three? Three vector stores, one file for each? Or something else?_

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Hi everyone,

I’m building a workflow where the model (GPT-5 - high reasoning effort) needs to, in one **single Responses API call** :

1. **Check against one document** to avoid duplication.
2. **Ground new content** only in another document.
3. **Validate/QA** against a third reference document.

I’m using the **Responses API** (not Azure) and I use the Responses API **n8n**. I’m not a software engineer — I mostly work with no/low-code tools — so I’d like to confirm the most reliable way to structure this from those who are more well-versed in this than me.

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### What I asked ChatGPT

I asked ChatGPT how to achieve this, and it suggested:

- Put all required documents into **one vector store** and attach that store to the `file_search` tool in the Responses API call.
- In the prompt, clearly define the “role” of each file (e.g. one is for deduplication only, another for grounding only, another for QA only).
- Require the model to self-report which `file_id` it used for each step, and forbid using the wrong file.
- After receiving the response, programmatically validate the **annotations** and `file_id`s in the `file_search_call.results` to make sure each step used the correct document. If not, treat it as a violation.
- Optionally: configure ranking options / score thresholds to control retrieval behavior.

This way, the whole multi-step reasoning happens in **one call** , but with post-validation of which document was actually used.

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### My question to the community

- Is this the correct / recommended way to handle a multi-document scenario in a **single Responses API call**?
- Is there any downside to combining multiple files into one vector store versus using multiple vector stores?
- Are there official limits on how many vector stores can be attached to a single call (I’ve seen conflicting info about “up to two” vs. `vector_store_ids` as an array)?
- Is there a more reliable way to enforce “Doc A only for dedupe, Doc B only for grounding, Doc C only for QA” _within one call_?

Would really appreciate input from those of you who’ve worked directly with File Search and the Responses API:  
**Is ChatGPT’s proposed solution correct, and how would you approach this in practice?**

Thanks!