File search is a tool available in the Responses API. It enables models to retrieve information in a knowledge base of previously uploaded files through semantic and keyword search. By creating vector stores and uploading files to them, you can augment the models’ inherent knowledge by giving them access to these knowledge bases or vector_stores.

To learn more about how vector stores and semantic search work, refer to our retrieval guide.

This is a hosted tool managed by OpenAI, meaning you don’t have to implement code on your end to handle its execution. When the model decides to use it, it will automatically call the tool, retrieve information from your files, and return an output.

How to use

Prior to using file search with the Responses API, you need to have set up a knowledge base in a vector store and uploaded files to it.

Create a vector store and upload a file

Follow these steps to create a vector store and upload a file to it. You can use this example file or upload your own.

Upload the file to the File API

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import requests
from io import BytesIO
from openai import OpenAI
client = OpenAI()
defcreate_file(client, file_path):if file_path.startswith("http://") or file_path.startswith("https://"):
        response = requests.get(file_path)
        file_content = BytesIO(response.content)
        file_name = file_path.split("/")[-1]
        file_tuple = (file_name, file_content)
        result = client.files.create(
            file=file_tuple,
            purpose="assistants"        )
else:
withopen(file_path, "rb") as file_content:
            result = client.files.create(
                file=file_content,
                purpose="assistants"            )
print(result.id)
return result.idfile_id = create_file(client, "https://cdn.openai.com/API/docs/deep_research_blog.pdf")
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import fs from"fs";
import OpenAI from"openai";
const openai = new OpenAI();
asyncfunctioncreateFile(filePath) {
let result;
if (filePath.startsWith("http://") || filePath.startsWith("https://")) {
const res = await fetch(filePath);
const buffer = await res.arrayBuffer();
const urlParts = filePath.split("/");
const fileName = urlParts[urlParts.length - 1];
const file = new File([buffer], fileName);
    result = await openai.files.create({
file: file,
purpose: "assistants",
    });
  } else {
const fileContent = fs.createReadStream(filePath);
    result = await openai.files.create({
file: fileContent,
purpose: "assistants",
    });
  }
return result.id;
}
const fileId = await createFile(
"https://cdn.openai.com/API/docs/deep_research_blog.pdf");
console.log(fileId);

Create a vector store

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vector_store = client.vector_stores.create(
    name="knowledge_base")
print(vector_store.id)
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const vectorStore = await openai.vectorStores.create({
name: "knowledge_base",
});
console.log(vectorStore.id);

Add the file to the vector store

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result = client.vector_stores.files.create(
    vector_store_id=vector_store.id,
    file_id=file_id
)
print(result)
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await openai.vectorStores.files.create(
    vectorStore.id,
    {
file_id: fileId,
    }
});

Check status

Run this code until the file is ready to be used (i.e., when the status is completed).

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result = client.vector_stores.files.list(
    vector_store_id=vector_store.id)
print(result)
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const result = await openai.vectorStores.files.list({
vector_store_id: vectorStore.id,
});
console.log(result);

Once your knowledge base is set up, you can include the file_search tool in the list of tools available to the model, along with the list of vector stores in which to search.

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from openai import OpenAI
client = OpenAI()
response = client.responses.create(
    model="gpt-4.1",
input="What is deep research by OpenAI?",
    tools=[{
"type": "file_search",
"vector_store_ids": ["<vector_store_id>"]
    }]
)
print(response)
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import OpenAI from"openai";
const openai = new OpenAI();
const response = await openai.responses.create({
model: "gpt-4.1",
input: "What is deep research by OpenAI?",
tools: [
        {
type: "file_search",
vector_store_ids: ["<vector_store_id>"],
        },
    ],
});
console.log(response);
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using OpenAI.Responses;
string key = Environment.GetEnvironmentVariable("OPENAI_API_KEY")!;
OpenAIResponseClient client = new(model: "gpt-5", apiKey: key);
ResponseCreationOptions options = new();
options.Tools.Add(ResponseTool.CreateFileSearchTool(["<vector_store_id>"]));
OpenAIResponse response = (OpenAIResponse)client.CreateResponse([
    ResponseItem.CreateUserMessageItem([
        ResponseContentPart.CreateInputTextPart("What is deep research by OpenAI?"),
    ]),
], options);
Console.WriteLine(response.GetOutputText());

When this tool is called by the model, you will receive a response with multiple outputs:

  1. A file_search_call output item, which contains the id of the file search call.
  2. A message output item, which contains the response from the model, along with the file citations.
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{
"output": [
    {
"type": "file_search_call",
"id": "fs_67c09ccea8c48191ade9367e3ba71515",
"status": "completed",
"queries": ["What is deep research?"],
"search_results": null    },
    {
"id": "msg_67c09cd3091c819185af2be5d13d87de",
"type": "message",
"role": "assistant",
"content": [
        {
"type": "output_text",
"text": "Deep research is a sophisticated capability that allows for extensive inquiry and synthesis of information across various domains. It is designed to conduct multi-step research tasks, gather data from multiple online sources, and provide comprehensive reports similar to what a research analyst would produce. This functionality is particularly useful in fields requiring detailed and accurate information...",
"annotations": [
            {
"type": "file_citation",
"index": 992,
"file_id": "file-2dtbBZdjtDKS8eqWxqbgDi",
"filename": "deep_research_blog.pdf"            },
            {
"type": "file_citation",
"index": 992,
"file_id": "file-2dtbBZdjtDKS8eqWxqbgDi",
"filename": "deep_research_blog.pdf"            },
            {
"type": "file_citation",
"index": 1176,
"file_id": "file-2dtbBZdjtDKS8eqWxqbgDi",
"filename": "deep_research_blog.pdf"            },
            {
"type": "file_citation",
"index": 1176,
"file_id": "file-2dtbBZdjtDKS8eqWxqbgDi",
"filename": "deep_research_blog.pdf"            }
          ]
        }
      ]
    }
  ]
}

Retrieval customization

Limiting the number of results

Using the file search tool with the Responses API, you can customize the number of results you want to retrieve from the vector stores. This can help reduce both token usage and latency, but may come at the cost of reduced answer quality.

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response = client.responses.create(
    model="gpt-4.1",
input="What is deep research by OpenAI?",
    tools=[{
"type": "file_search",
"vector_store_ids": ["<vector_store_id>"],
"max_num_results": 2    }]
)
print(response)
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const response = await openai.responses.create({
model: "gpt-4.1",
input: "What is deep research by OpenAI?",
tools: [{
type: "file_search",
vector_store_ids: ["<vector_store_id>"],
max_num_results: 2,
    }],
});
console.log(response);

Include search results in the response

While you can see annotations (references to files) in the output text, the file search call will not return search results by default.

To include search results in the response, you can use the include parameter when creating the response.

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response = client.responses.create(
    model="gpt-4.1",
input="What is deep research by OpenAI?",
    tools=[{
"type": "file_search",
"vector_store_ids": ["<vector_store_id>"]
    }],
    include=["file_search_call.results"]
)
print(response)
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const response = await openai.responses.create({
model: "gpt-4.1",
input: "What is deep research by OpenAI?",
tools: [{
type: "file_search",
vector_store_ids: ["<vector_store_id>"],
    }],
include: ["file_search_call.results"],
});
console.log(response);

Metadata filtering

You can filter the search results based on the metadata of the files. For more details, refer to our retrieval guide, which covers:

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response = client.responses.create(
    model="gpt-4.1",
input="What is deep research by OpenAI?",
    tools=[{
"type": "file_search",
"vector_store_ids": ["<vector_store_id>"],
"filters": {
"type": "in",
"key": "category",
"value": ["blog", "announcement"]
        }
    }]
)
print(response)
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const response = await openai.responses.create({
model: "gpt-4.1",
input: "What is deep research by OpenAI?",
tools: [{
type: "file_search",
vector_store_ids: ["<vector_store_id>"],
filters: {
type: "in",
key: "category",
value: ["blog", "announcement"]
        }
    }]
});
console.log(response);

Supported files

For text/ MIME types, the encoding must be one of utf-8, utf-16, or ascii.

File formatMIME type
.ctext/x-c
.cpptext/x-c++
.cstext/x-csharp
.csstext/css
.docapplication/msword
.docxapplication/vnd.openxmlformats-officedocument.wordprocessingml.document
.gotext/x-golang
.htmltext/html
.javatext/x-java
.jstext/javascript
.jsonapplication/json
.mdtext/markdown
.pdfapplication/pdf
.phptext/x-php
.pptxapplication/vnd.openxmlformats-officedocument.presentationml.presentation
.pytext/x-python
.pytext/x-script.python
.rbtext/x-ruby
.shapplication/x-sh
.textext/x-tex
.tsapplication/typescript
.txttext/plain

Usage notes

API AvailabilityRate limitsNotes
Responses
Chat Completions
Assistants

Tier 1
100 RPM

Tier 2 and 3
500 RPM

Tier 4 and 5
1000 RPM

Pricing
ZDR and data residency