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.
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.
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34import fs from "fs";
import OpenAI from "openai";
const openai = new OpenAI();
async function createFile(filePath) {
let result;
if (filePath.startsWith("http://") || filePath.startsWith("https://")) {
// Download the file content from the URL
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 {
// Handle local file path
const fileContent = fs.createReadStream(filePath);
result = await openai.files.create({
file: fileContent,
purpose: "assistants",
});
}
return result.id;
}
// Replace with your own file path or URL
const fileId = await createFile(
"https://cdn.openai.com/API/docs/deep_research_blog.pdf"
);
console.log(fileId);
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32from io import BytesIO
import requests
from openai import OpenAI
client = OpenAI()
def create_file(client, file_path):
if file_path.startswith(("http://", "https://")):
response = requests.get(file_path, timeout=30)
response.raise_for_status()
file_content = BytesIO(response.content)
file_name = file_path.rsplit("/", 1)[-1]
result = client.files.create(
file=(file_name, file_content),
purpose="assistants",
)
else:
with open(file_path, "rb") as file_content:
result = client.files.create(
file=file_content,
purpose="assistants",
)
return result.id
file_id = create_file(
client,
"https://cdn.openai.com/API/docs/deep_research_blog.pdf",
)
print(file_id)
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27package main
import (
"context"
"fmt"
"os"
"github.com/openai/openai-go/v3"
)
func main() {
file, err := os.Open("customer_policies.txt")
if err != nil {
panic(err)
}
defer file.Close()
client := openai.NewClient()
result, err := client.Files.New(context.Background(), openai.FileNewParams{
File: openai.File(file, "customer_policies.txt", "text/plain"),
Purpose: openai.FilePurposeAssistants,
})
if err != nil {
panic(err)
}
fmt.Println(result.ID)
}
const vectorStore = await openai.vectorStores.create({
name: "knowledge_base",
});
console.log(vectorStore.id);
vector_store = client.vector_stores.create(name="knowledge_base")
print(vector_store.id)
package main
import (
"context"
"fmt"
"github.com/openai/openai-go/v3"
)
func main() {
client := openai.NewClient()
vectorStore, err := client.VectorStores.New(context.Background(), openai.VectorStoreNewParams{
Name: openai.String("knowledge_base"),
})
if err != nil {
panic(err)
}
fmt.Println(vectorStore.ID)
}
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3await openai.vectorStores.files.create(vectorStore.id, {
file_id: fileId,
});
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5result = client.vector_stores.files.create(
vector_store_id=vector_store.id,
file_id=file_id,
)
print(result)
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19package main
import (
"context"
"fmt"
"github.com/openai/openai-go/v3"
)
func main() {
client := openai.NewClient()
file, err := client.VectorStores.Files.New(context.Background(), "<vector_store_id>", openai.VectorStoreFileNewParams{
FileID: "file_abc123",
})
if err != nil {
panic(err)
}
fmt.Println(file.ID)
}
Run this code until the file is ready to be used (i.e., when the status is completed).
const result = await openai.vectorStores.files.list(vectorStore.id);
console.log(result);
result = client.vector_stores.files.list(vector_store_id=vector_store.id)
print(result)
package main
import (
"context"
"fmt"
"github.com/openai/openai-go/v3"
)
func main() {
client := openai.NewClient()
files, err := client.VectorStores.Files.List(context.Background(), "<vector_store_id>", openai.VectorStoreFileListParams{})
if err != nil {
panic(err)
}
fmt.Println(files.Data)
}
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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14import OpenAI from "openai";
const openai = new OpenAI();
const response = await openai.responses.create({
model: "gpt-5.6",
input: "What is deep research by OpenAI?",
tools: [
{
type: "file_search",
vector_store_ids: ["<vector_store_id>"],
},
],
});
console.log(response);
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10from openai import OpenAI
client = OpenAI()
response = client.responses.create(
model="gpt-5.6",
input="What is deep research by OpenAI?",
tools=[{"type": "file_search", "vector_store_ids": ["<vector_store_id>"]}],
)
print(response)
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22package main
import (
"context"
"fmt"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/responses"
)
func main() {
client := openai.NewClient()
response, err := client.Responses.New(context.Background(), responses.ResponseNewParams{
Model: "gpt-5.6",
Input: responses.ResponseNewParamsInputUnion{OfString: openai.String("What is deep research by OpenAI?")},
Tools: []responses.ToolUnionParam{responses.ToolParamOfFileSearch([]string{"<vector_store_id>"})},
})
if err != nil {
panic(err)
}
fmt.Println(response)
}
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17using OpenAI.Responses;
#pragma warning disable OPENAI001
string key = Environment.GetEnvironmentVariable("OPENAI_API_KEY")!;
ResponsesClient client = new(key);
CreateResponseOptions options = new() { Model = "gpt-5.6" };
options.Tools.Add(
ResponseTool.CreateFileSearchTool(["<vector_store_id>"])
);
options.InputItems.Add(
ResponseItem.CreateUserMessageItem("What is deep research by OpenAI?")
);
ResponseResult response = await client.CreateResponseAsync(options);
Console.WriteLine(response.GetOutputText());
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16require "openai"
openai = OpenAI::Client.new
response = openai.responses.create(
model: "gpt-5.6",
input: "What is deep research by OpenAI?",
tools: [
{
type: "file_search",
vector_store_ids: ["<vector_store_id>"]
}
]
)
puts(response)
When this tool is called by the model, you will receive a response with multiple outputs:
- A
file_search_call output item, which contains the id of the file search call.
- A
message output item, which contains the response from the model, along with the file citations.
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48{
"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"
}
]
}
]
}
]
}
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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12const response = await openai.responses.create({
model: "gpt-5.6",
input: "What is deep research by OpenAI?",
tools: [
{
type: "file_search",
vector_store_ids: ["<vector_store_id>"],
max_num_results: 2,
},
],
});
console.log(response);
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12response = client.responses.create(
model="gpt-5.6",
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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24package main
import (
"context"
"fmt"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/responses"
)
func main() {
client := openai.NewClient()
tool := responses.ToolParamOfFileSearch([]string{"<vector_store_id>"})
tool.OfFileSearch.MaxNumResults = openai.Int(2)
response, err := client.Responses.New(context.Background(), responses.ResponseNewParams{
Model: "gpt-5.6",
Input: responses.ResponseNewParamsInputUnion{OfString: openai.String("What is deep research by OpenAI?")},
Tools: []responses.ToolUnionParam{tool},
})
if err != nil {
panic(err)
}
fmt.Println(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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12const response = await openai.responses.create({
model: "gpt-5.6",
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);
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12response = client.responses.create(
model="gpt-5.6",
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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23package main
import (
"context"
"fmt"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/responses"
)
func main() {
client := openai.NewClient()
response, err := client.Responses.New(context.Background(), responses.ResponseNewParams{
Model: "gpt-5.6",
Input: responses.ResponseNewParamsInputUnion{OfString: openai.String("What is deep research by OpenAI?")},
Tools: []responses.ToolUnionParam{responses.ToolParamOfFileSearch([]string{"<vector_store_id>"})},
Include: []responses.ResponseIncludable{responses.ResponseIncludableFileSearchCallResults},
})
if err != nil {
panic(err)
}
fmt.Println(response)
}
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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16const response = await openai.responses.create({
model: "gpt-5.6",
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);
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16response = client.responses.create(
model="gpt-5.6",
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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34package main
import (
"context"
"fmt"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/responses"
"github.com/openai/openai-go/v3/shared"
)
func main() {
client := openai.NewClient()
tool := responses.ToolParamOfFileSearch([]string{"<vector_store_id>"})
tool.OfFileSearch.Filters = responses.FileSearchToolFiltersUnionParam{
OfComparisonFilter: &shared.ComparisonFilterParam{
Type: shared.ComparisonFilterTypeIn,
Key: "category",
Value: shared.ComparisonFilterValueUnionParam{OfComparisonFilterValueArray: []shared.ComparisonFilterValueArrayItemUnionParam{
{OfString: openai.String("blog")},
{OfString: openai.String("announcement")},
}},
},
}
response, err := client.Responses.New(context.Background(), responses.ResponseNewParams{
Model: "gpt-5.6",
Input: responses.ResponseNewParamsInputUnion{OfString: openai.String("What is deep research by OpenAI?")},
Tools: []responses.ToolUnionParam{tool},
})
if err != nil {
panic(err)
}
fmt.Println(response)
}
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