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Python: Move ollama samples to samples getting started dir (#2921)
* Move ollama samples to samples getting started dir * Address feedback
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Evan Mattson
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# Copyright (c) Microsoft. All rights reserved.
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import importlib
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from typing import Any
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IMPORT_PATH = "agent_framework_ollama"
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PACKAGE_NAME = "agent-framework-ollama"
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_IMPORTS = ["__version__", "OllamaChatClient", "OllamaSettings"]
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def __getattr__(name: str) -> Any:
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if name in _IMPORTS:
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try:
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return getattr(importlib.import_module(IMPORT_PATH), name)
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except ModuleNotFoundError as exc:
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raise ModuleNotFoundError(
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f"The '{PACKAGE_NAME}' package is not installed, please do `pip install {PACKAGE_NAME}`"
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) from exc
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raise AttributeError(f"Module {IMPORT_PATH} has no attribute {name}.")
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def __dir__() -> list[str]:
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return _IMPORTS
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# Copyright (c) Microsoft. All rights reserved.
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from agent_framework_ollama import (
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OllamaChatClient,
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OllamaSettings,
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__version__,
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)
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__all__ = [
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"OllamaChatClient",
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"OllamaSettings",
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"__version__",
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]
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@@ -52,6 +52,7 @@ all = [
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"agent-framework-devui",
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"agent-framework-lab",
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"agent-framework-mem0",
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"agent-framework-ollama",
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"agent-framework-purview",
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"agent-framework-redis",
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]
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@@ -1,38 +0,0 @@
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# Ollama Examples
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This folder contains examples demonstrating how to use Ollama models with the Agent Framework.
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## Prerequisites
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1. **Install Ollama**: Download and install Ollama from [ollama.com](https://ollama.com/)
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2. **Start Ollama**: Ensure Ollama is running on your local machine
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3. **Pull a model**: Run `ollama pull mistral` (or any other model you prefer)
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- For function calling examples, use models that support tool calling like `mistral` or `qwen2.5`
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- For reasoning examples, use models that support reasoning like `qwen2.5:8b`
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- For Multimodality you can use models like `gemma3:4b`
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> **Note**: Not all models support all features. Function calling and reasoning capabilities depend on the specific model you're using.
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## Examples
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| File | Description |
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|------|-------------|
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| [`ollama_agent_basic.py`](ollama_agent_basic.py) | Demonstrates basic Ollama agent usage with the native Ollama Chat Client. Shows both streaming and non-streaming responses with tool calling capabilities. |
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| [`ollama_agent_reasoning.py`](ollama_agent_reasoning.py) | Demonstrates Ollama agent with reasoning capabilities using the native Ollama Chat Client. Shows how to enable thinking/reasoning mode. |
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| [`ollama_chat_client.py`](ollama_chat_client.py) | Ollama Chat Client with native Ollama Chat Client |
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| [`ollama_chat_multimodal.py`](ollama_chat_multimodal.py) | Ollama Chat with multimodal native Ollama Chat Client |
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## Configuration
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The examples use environment variables for configuration. Set the appropriate variables based on which example you're running:
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### For Native Ollama Examples (`ollama_agent_basic.py`, `ollama_agent_reasoning.py`)
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Set the following environment variables:
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- `OLLAMA_HOST`: The base URL for your Ollama server (optional, defaults to `http://localhost:11434`)
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- Example: `export OLLAMA_HOST="http://localhost:11434"`
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- `OLLAMA_CHAT_MODEL_ID`: The model name to use
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- Example: `export OLLAMA_CHAT_MODEL_ID="qwen2.5:8b"`
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- Must be a model you have pulled with Ollama
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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from datetime import datetime
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from agent_framework_ollama import OllamaChatClient
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"""
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Ollama Agent Basic Example
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This sample demonstrates implementing a Ollama agent with basic tool usage.
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Ensure to install Ollama and have a model running locally before running the sample
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Not all Models support function calling, to test function calling try llama3.2 or qwen3:4b
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Set the model to use via the OLLAMA_CHAT_MODEL_ID environment variable or modify the code below.
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https://ollama.com/
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"""
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def get_time(location: str) -> str:
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"""Get the current time."""
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return f"The current time in {location} is {datetime.now().strftime('%I:%M %p')}."
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async def non_streaming_example() -> None:
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"""Example of non-streaming response (get the complete result at once)."""
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print("=== Non-streaming Response Example ===")
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agent = OllamaChatClient().create_agent(
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name="TimeAgent",
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instructions="You are a helpful time agent answer in one sentence.",
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tools=get_time,
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)
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query = "What time is it in Seattle? Use a tool call"
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print(f"User: {query}")
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result = await agent.run(query)
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print(f"Result: {result}\n")
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async def streaming_example() -> None:
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"""Example of streaming response (get results as they are generated)."""
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print("=== Streaming Response Example ===")
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agent = OllamaChatClient().create_agent(
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name="TimeAgent",
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instructions="You are a helpful time agent answer in one sentence.",
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tools=get_time,
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)
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query = "What time is it in San Francisco? Use a tool call"
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print(f"User: {query}")
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print("Agent: ", end="", flush=True)
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async for chunk in agent.run_stream(query):
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if chunk.text:
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print(chunk.text, end="", flush=True)
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print("\n")
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async def main() -> None:
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print("=== Basic Ollama Chat Client Agent Example ===")
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await non_streaming_example()
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await streaming_example()
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if __name__ == "__main__":
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asyncio.run(main())
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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from agent_framework import TextReasoningContent
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from agent_framework_ollama import OllamaChatClient
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"""
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Ollama Agent Reasoning Example
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This sample demonstrates implementing a Ollama agent with reasoning.
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Ensure to install Ollama and have a model running locally before running the sample
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Not all Models support reasoning, to test reasoning try qwen3:8b
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Set the model to use via the OLLAMA_CHAT_MODEL_ID environment variable or modify the code below.
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https://ollama.com/
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"""
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async def reasoning_example() -> None:
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print("=== Response Reasoning Example ===")
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agent = OllamaChatClient().create_agent(
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name="TimeAgent",
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instructions="You are a helpful agent answer in one sentence.",
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additional_chat_options={"think": True}, # Enable Reasoning on agent level
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)
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query = "Hey what is 3+4? Can you explain how you got to that answer?"
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print(f"User: {query}")
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# Enable Reasoning on per request level
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result = await agent.run(query)
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reasoning = "".join(c.text for c in result.messages[-1].contents if isinstance(c, TextReasoningContent))
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print(f"Reasoning: {reasoning}")
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print(f"Answer: {result}\n")
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async def main() -> None:
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print("=== Basic Ollama Chat Client Agent Reasoning ===")
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await reasoning_example()
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if __name__ == "__main__":
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asyncio.run(main())
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@@ -1,36 +0,0 @@
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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from datetime import datetime
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from agent_framework_ollama import OllamaChatClient
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# Ensure to install Ollama and have a model running locally before running the sample
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# Not all Models support function calling, to test function calling try llama3.2
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# Set the model to use via the OLLAMA_CHAT_MODEL_ID environment variable or modify the code below.
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# https://ollama.com/
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def get_time():
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"""Get the current time."""
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return f"The current time is {datetime.now().strftime('%I:%M %p')}."
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async def main() -> None:
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client = OllamaChatClient()
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message = "What time is it? Use a tool call"
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stream = False
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print(f"User: {message}")
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if stream:
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print("Assistant: ", end="")
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async for chunk in client.get_streaming_response(message, tools=get_time):
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if str(chunk):
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print(str(chunk), end="")
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print("")
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else:
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response = await client.get_response(message, tools=get_time)
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print(f"Assistant: {response}")
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if __name__ == "__main__":
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asyncio.run(main())
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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from agent_framework import ChatMessage, DataContent, Role, TextContent
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from agent_framework_ollama import OllamaChatClient
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"""
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Ollama Agent Multimodal Example
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This sample demonstrates implementing a Ollama agent with multimodal input capabilities.
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Ensure to install Ollama and have a model running locally before running the sample
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Not all Models support multimodal input, to test multimodal input try gemma3:4b
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Set the model to use via the OLLAMA_CHAT_MODEL_ID environment variable or modify the code below.
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https://ollama.com/
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"""
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def create_sample_image() -> str:
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"""Create a simple 1x1 pixel PNG image for testing."""
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# This is a tiny red pixel in PNG format
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png_data = "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8/5+hHgAHggJ/PchI7wAAAABJRU5ErkJggg=="
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return f"data:image/png;base64,{png_data}"
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async def test_image() -> None:
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"""Test image analysis with Ollama."""
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client = OllamaChatClient()
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image_uri = create_sample_image()
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message = ChatMessage(
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role=Role.USER,
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contents=[
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TextContent(text="What's in this image?"),
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DataContent(uri=image_uri, media_type="image/png"),
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],
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)
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response = await client.get_response(message)
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print(f"Image Response: {response}")
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async def main() -> None:
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print("=== Testing Ollama Multimodal ===")
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await test_image()
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if __name__ == "__main__":
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asyncio.run(main())
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