0988f636d0
Add pitrader_script executor for running PITrader scripts (pi-scan, playbook, execute_trades) as pipeline steps with vault credential injection and JSON output capture. Extend claude_prompt step with vision support (image_b64 in trigger context). Add image pipeline trigger to on_image_message handler. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
63 lines
1.8 KiB
Python
63 lines
1.8 KiB
Python
"""Claude prompt step — call LLM via LiteLLM proxy."""
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import logging
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logger = logging.getLogger(__name__)
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async def execute_claude_prompt(
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config: dict,
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context: dict | None = None,
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llm=None,
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default_model: str = "claude-haiku",
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escalation_model: str = "claude-sonnet",
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**_kwargs,
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) -> str:
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"""Send a prompt to Claude and return the response.
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Supports vision: if config contains 'image_b64' or trigger context has
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'image_b64', the image is included as a vision content block.
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"""
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if not llm:
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raise RuntimeError("LLM client not configured")
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prompt = config.get("prompt", "")
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if not prompt:
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raise ValueError("claude_prompt step requires 'prompt' field")
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model_name = config.get("model", "default")
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model = escalation_model if model_name == "escalation" else default_model
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# Check for image data (from config or trigger context)
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image_b64 = config.get("image_b64", "")
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image_mime = config.get("image_mime", "image/png")
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if not image_b64 and context:
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trigger = context.get("trigger", {})
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image_b64 = trigger.get("image_b64", "")
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image_mime = trigger.get("mime_type", "image/png")
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# Build message content
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if image_b64:
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content = [
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{
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"type": "image_url",
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"image_url": {
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"url": f"data:{image_mime};base64,{image_b64}",
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},
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},
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{
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"type": "text",
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"text": prompt,
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},
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]
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else:
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content = prompt
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response = await llm.chat.completions.create(
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model=model,
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messages=[{"role": "user", "content": content}],
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max_tokens=4096,
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)
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return response.choices[0].message.content or ""
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