50 lines
1.9 KiB
Python
50 lines
1.9 KiB
Python
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from __future__ import annotations
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from typing import Any, Dict
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from ..registry import NativeToolProvider, ToolDefinition, ToolExecutionContext
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async def vision_analyze_handler(arguments: Dict[str, Any], _context: ToolExecutionContext) -> Dict[str, Any]:
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from ... import main as main_module
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prepared, _images, context_block = await main_module._prepare_chat_message_attachments(
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arguments.get("attachments") or [],
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request_model_supports_vision=False,
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vision_model=arguments.get("vision_model"),
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transcription_model=arguments.get("transcription_model"),
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persist_file_text=False,
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)
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return {
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"context_block": context_block,
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"attachments": [main_module._attachment_history_payload(item) for item in prepared],
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"sources": [],
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}
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def register_vision_tools(registry: NativeToolProvider) -> None:
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registry.register(ToolDefinition(
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name="heimgeist.vision_analyze",
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description="Analyze chat attachments through Heimgeist's existing vision and extraction path.",
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input_schema={
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"type": "object",
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"properties": {
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"attachments": {"type": "array"},
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"vision_model": {"type": "string", "minLength": 1},
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"transcription_model": {"type": ["string", "null"]},
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},
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"required": ["attachments", "vision_model"],
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"additionalProperties": False,
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},
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output_schema={
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"type": "object",
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"properties": {"context_block": {"type": "string"}, "attachments": {"type": "array"}, "sources": {"type": "array"}},
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"required": ["context_block", "attachments", "sources"],
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"additionalProperties": False,
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},
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handler=vision_analyze_handler,
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timeout_seconds=600,
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result_size_limit=140_000,
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llm_call=True,
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))
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