> ## Documentation Index
> Fetch the complete documentation index at: https://docs.upsonic.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# FileSearchTool

> A built-in tool that allows models to search through uploaded files using vector search

## Overview

A built-in tool that provides a fully managed Retrieval-Augmented Generation (RAG) system. It handles file storage, chunking, embedding generation, and context injection into prompts, allowing your agent to search through uploaded files using vector search.

## Usage

```python theme={null}
from upsonic import Agent, Task
from upsonic.tools.builtin_tools import FileSearchTool
from upsonic.models.openai import OpenAIResponsesModel

# Create model with OpenAI Responses API
model = OpenAIResponsesModel(
    model_name="gpt-4o",
    provider="openai"
)

# Create file search tool with vector store IDs
# Vector stores must be created via the OpenAI API beforehand
file_search = FileSearchTool(
    file_store_ids=["vs_abc123"]
)

# Create task
task = Task(
    description="Search through the uploaded documents and find information about quarterly revenue",
    tools=[file_search]
)

# Create agent
agent = Agent(model=model, name="Document Search Agent")

# Execute
result = agent.print_do(task)
print("Result:", result)
```

## Advanced Example

```python theme={null}
from upsonic.tools.builtin_tools import FileSearchTool
from upsonic import Agent, Task
from upsonic.models.openai import OpenAIResponsesModel

model = OpenAIResponsesModel(
    model_name="gpt-4o",
    provider="openai"
)

# Search across multiple vector stores (max 2 for OpenAI)
file_search = FileSearchTool(
    file_store_ids=["vs_policies", "vs_procedures"]
)

task = Task(
    description="""
    Search through the company documents and answer the following:
    1. What is the remote work policy?
    2. What are the guidelines for expense reporting?
    """,
    tools=[file_search]
)

agent = Agent(model=model, name="HR Knowledge Agent")
result = agent.print_do(task)
print("Result:", result)
```

## Parameters

* `file_store_ids` (Sequence\[str], **required**): The file store IDs to search through (max 2 for OpenAI)
  * For OpenAI: Vector store IDs created via the [OpenAI API](https://platform.openai.com/docs/api-reference/vector-stores)
  * For Google: File search store names uploaded and processed via the Gemini Files API

## Provider Support

* OpenAI Responses: ✅ Full support
* Google (Gemini): ✅ Full support
* Anthropic: ❌ Not supported

## Characteristics

* Fully managed RAG pipeline (chunking, embedding, retrieval)
* Vector-based semantic search across uploaded documents
* Provider-managed file storage and indexing
* Automatic context injection into model prompts