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

# File Upload

File upload enables agents to process and understand various file types including documents, images, code files, and data formats. By providing files alongside text queries, agents can analyze visual content, extract information from documents, review code, and work with structured data.

This multimodal capability allows agents to go beyond text-only interactions, enabling them to understand and respond to questions about file contents, extract structured data, and combine visual or document context with natural language queries.

## Supported File Types

Agents can process a wide variety of file formats:

| Category      | Supported Formats         | Use Cases                             |
| ------------- | ------------------------- | ------------------------------------- |
| **Documents** | PDF, TXT, DOCX, MD, RTF   | Reports, articles, documentation      |
| **Images**    | PNG, JPG, JPEG, GIF, WEBP | Photos, diagrams, screenshots, charts |
| **Audio**     | MP3, WAV, M4A, OGG        | Speech transcription, audio analysis  |
| **Video**     | MP4, MOV, AVI, WEBM       | Video analysis, frame extraction      |

<Note>
  Supported file types can change depending on the model. Check your model provider's documentation for specific file type support and limitations.
</Note>

## Non-Streaming

Upload and process files using the `files` parameter with `run()` or `arun()`. Files can be local paths or URLs:

<CodeGroup>
  ```python Sync theme={null} theme={null}
  from hypertic.agents import Agent
  from hypertic.models import OpenAIChat

  model = OpenAIChat(model="gpt-5.2")

  agent = Agent(
      model=model
  )

  # Non-streaming with files
  response = agent.run(
      query="What's in this image and the document?",
      files=[
          "https://yavuzceliker.github.io/sample-images/image-1021.jpg",
          "data/index.pdf"
      ]
  )
  print(f"Response: {response.content}")
  print(f"Metadata: {response.metadata}")
  ```

  ```python Async theme={null} theme={null}
  import asyncio
  from hypertic.agents import Agent
  from hypertic.models import OpenAIChat

  model = OpenAIChat(model="gpt-5.2")

  agent = Agent(
      model=model
  )

  async def main():
      # Non-streaming with files
      response = await agent.arun(
          query="What's in this image and the document?",
          files=[
              "https://yavuzceliker.github.io/sample-images/image-1021.jpg",
              "data/index.pdf"
          ]
      )
      print(f"Response: {response.content}")
      print(f"Metadata: {response.metadata}")

  asyncio.run(main())
  ```
</CodeGroup>

## Streaming

Stream responses when processing files using `stream()` or `astream()`:

<CodeGroup>
  ```python Sync theme={null} theme={null}
  from hypertic.agents import Agent
  from hypertic.models import OpenAIChat

  model = OpenAIChat(model="gpt-5.2")

  agent = Agent(
      model=model
  )

  # Streaming with files
  for event in agent.stream(
      query="What is in the image and the document?",
      files=["data/image.jpg", "https://www.berkshirehathaway.com/letters/2024ltr.pdf"]
  ):
      if event.type == "content":
          print(event.content, end="", flush=True)
      elif event.type == "tool_calls":
          print(f"\nTool Calls: {event.tool_calls}")
      elif event.type == "tool_outputs":
          print(f"\nTool Outputs: {event.tool_outputs}")
      elif event.type == "metadata":
          print(f"\nMetadata: {event.metadata}")
  ```

  ```python Async theme={null} theme={null}
  import asyncio
  from hypertic.agents import Agent
  from hypertic.models import OpenAIChat

  model = OpenAIChat(model="gpt-5.2")

  agent = Agent(
      model=model
  )

  async def main():
      # Streaming with files
      async for event in agent.astream(
          query="What is in the image and the document?",
          files=["data/image.jpg", "https://www.berkshirehathaway.com/letters/2024ltr.pdf"]
      ):
          if event.type == "content":
              print(event.content, end="", flush=True)
          elif event.type == "tool_calls":
              print(f"\nTool Calls: {event.tool_calls}")
          elif event.type == "tool_outputs":
              print(f"\nTool Outputs: {event.tool_outputs}")
          elif event.type == "metadata":
              print(f"\nMetadata: {event.metadata}")

  asyncio.run(main())
  ```
</CodeGroup>