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577f4d1bdf* Fix tool_choice to always require tools when result_type is set * Consolidate sampling examples with rich output * Replace eval() with explicit add/multiply tools
62 lines
1.3 KiB
Markdown
62 lines
1.3 KiB
Markdown
# Sampling Examples
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These examples demonstrate FastMCP's sampling API, which allows server tools to request LLM completions from the client.
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## Prerequisites
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```bash
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pip install fastmcp[anthropic]
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export ANTHROPIC_API_KEY=your-key
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```
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Or run directly with `uv`:
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```bash
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uv run examples/sampling/text.py
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```
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## Examples
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### Simple Text Sampling (`text.py`)
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Basic sampling flow where a server tool requests an LLM completion:
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```bash
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uv run examples/sampling/text.py
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```
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### Structured Output (`structured_output.py`)
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Uses `result_type` to get validated Pydantic models from the LLM:
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```bash
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uv run examples/sampling/structured_output.py
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```
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### Tool Use (`tool_use.py`)
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Gives the LLM tools to use during sampling (calculator, time, dice):
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```bash
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uv run examples/sampling/tool_use.py
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```
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### Server Fallback (`server_fallback.py`)
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Configures a fallback sampling handler on the server, enabling sampling even when clients don't support it:
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```bash
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uv run examples/sampling/server_fallback.py
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```
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## Using OpenAI Instead
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To use OpenAI instead of Anthropic, change the handler:
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```python
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from fastmcp.client.sampling.handlers.openai import OpenAISamplingHandler
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handler = OpenAISamplingHandler(default_model="gpt-4o-mini")
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```
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And install with `pip install fastmcp[openai]`.
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