mirror of
https://github.com/blakeblackshear/frigate.git
synced 2026-06-23 21:01:53 +03:00
0bdf5002a0
* use monotonic clock for detector inference duration to prevent negative values from wall clock steps * add ability to set camera's webui_url from camera management pane * Gemini send thought signature * Update docs * copy face and lpr configs from source camera to replay camera * add guard * improve dummy camera docs * remove version number * fix stale field message after reverting a conditional form field Routes field-level conditional messages through a dedicated React Context instead of merging them into uiSchema. RJSF's Form keeps state.uiSchema sticky across renders during processPendingChange (formData is updated, uiSchema is not), so a previously injected ui:messages array stays attached to a field even after the triggering condition flips back to false. Context propagation re-runs FieldTemplate directly on every provider value change, sidestepping that staleness. * add semantic search field message to note that model_size is irrelevant when embeddings provider is selected --------- Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
84 lines
2.8 KiB
Python
84 lines
2.8 KiB
Python
"""Shared helpers for GenAI providers and chat (OpenAI-style messages, tool call parsing)."""
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import json
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import logging
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from typing import Any, List, Optional
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logger = logging.getLogger(__name__)
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def parse_tool_calls_from_message(
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message: dict[str, Any],
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) -> Optional[list[dict[str, Any]]]:
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"""
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Parse tool_calls from an OpenAI-style message dict.
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Message may have "tool_calls" as a list of:
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{"id": str, "function": {"name": str, "arguments": str}, ...}
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Returns a list of {"id", "name", "arguments"} with arguments parsed as dict,
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or None if no tool_calls. Used by Ollama and LlamaCpp (non-stream) responses.
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"""
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raw = message.get("tool_calls")
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if not raw or not isinstance(raw, list):
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return None
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result = []
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for idx, tool_call in enumerate(raw):
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function_data = tool_call.get("function") or {}
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raw_arguments = function_data.get("arguments") or {}
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if isinstance(raw_arguments, dict):
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arguments = raw_arguments
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elif isinstance(raw_arguments, str):
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try:
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arguments = json.loads(raw_arguments)
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except (json.JSONDecodeError, KeyError, TypeError) as e:
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logger.warning(
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"Failed to parse tool call arguments: %s, tool: %s",
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e,
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function_data.get("name", "unknown"),
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)
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arguments = {}
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else:
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arguments = {}
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result.append(
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{
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"id": tool_call.get("id", "") or f"call_{idx}",
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"name": function_data.get("name", ""),
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"arguments": arguments,
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}
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)
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return result if result else None
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def build_assistant_message_for_conversation(
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content: Any,
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tool_calls_raw: Optional[List[dict[str, Any]]],
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) -> dict[str, Any]:
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"""
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Build the assistant message dict in OpenAI format for appending to a conversation.
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tool_calls_raw: list of {"id", "name", "arguments"} (arguments as dict), or None.
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"""
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msg: dict[str, Any] = {"role": "assistant", "content": content}
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if tool_calls_raw:
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msg["tool_calls"] = [
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{
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"id": tc["id"],
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"type": "function",
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"function": {
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"name": tc["name"],
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"arguments": json.dumps(tc.get("arguments") or {}),
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},
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# Gemini-only: opaque signature that must be echoed back on
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# the same functionCall part in the next turn. Other providers
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# do not set or read this.
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**(
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{"thought_signature": tc["thought_signature"]}
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if tc.get("thought_signature")
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else {}
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),
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}
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for tc in tool_calls_raw
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]
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return msg
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