mirror of
https://github.com/blakeblackshear/frigate.git
synced 2026-03-12 11:27:34 +03:00
65db9b0aec
Some checks are pending
CI / AMD64 Build (push) Waiting to run
CI / ARM Build (push) Waiting to run
CI / Jetson Jetpack 6 (push) Waiting to run
CI / AMD64 Extra Build (push) Blocked by required conditions
CI / ARM Extra Build (push) Blocked by required conditions
CI / Synaptics Build (push) Blocked by required conditions
CI / Assemble and push default build (push) Blocked by required conditions
* fix ollama chat tool calling handle dict arguments, streaming fallback, and message format * pin setuptools<81 to ensure pkg_resources remains available When ensure_torch_dependencies() installs torch/torchvision via pip, it can upgrade setuptools to >=81.0.0, which removed the pkg_resources module. rknn-toolkit2 depends on pkg_resources internally, so subsequent RKNN conversion fails with No module named 'pkg_resources'.
76 lines
2.4 KiB
Python
76 lines
2.4 KiB
Python
"""Shared helpers for GenAI providers and chat (OpenAI-style messages, tool call parsing)."""
|
|
|
|
import json
|
|
import logging
|
|
from typing import Any, List, Optional
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
def parse_tool_calls_from_message(
|
|
message: dict[str, Any],
|
|
) -> Optional[list[dict[str, Any]]]:
|
|
"""
|
|
Parse tool_calls from an OpenAI-style message dict.
|
|
|
|
Message may have "tool_calls" as a list of:
|
|
{"id": str, "function": {"name": str, "arguments": str}, ...}
|
|
|
|
Returns a list of {"id", "name", "arguments"} with arguments parsed as dict,
|
|
or None if no tool_calls. Used by Ollama and LlamaCpp (non-stream) responses.
|
|
"""
|
|
raw = message.get("tool_calls")
|
|
if not raw or not isinstance(raw, list):
|
|
return None
|
|
result = []
|
|
for idx, tool_call in enumerate(raw):
|
|
function_data = tool_call.get("function") or {}
|
|
raw_arguments = function_data.get("arguments") or {}
|
|
if isinstance(raw_arguments, dict):
|
|
arguments = raw_arguments
|
|
elif isinstance(raw_arguments, str):
|
|
try:
|
|
arguments = json.loads(raw_arguments)
|
|
except (json.JSONDecodeError, KeyError, TypeError) as e:
|
|
logger.warning(
|
|
"Failed to parse tool call arguments: %s, tool: %s",
|
|
e,
|
|
function_data.get("name", "unknown"),
|
|
)
|
|
arguments = {}
|
|
else:
|
|
arguments = {}
|
|
result.append(
|
|
{
|
|
"id": tool_call.get("id", "") or f"call_{idx}",
|
|
"name": function_data.get("name", ""),
|
|
"arguments": arguments,
|
|
}
|
|
)
|
|
return result if result else None
|
|
|
|
|
|
def build_assistant_message_for_conversation(
|
|
content: Any,
|
|
tool_calls_raw: Optional[List[dict[str, Any]]],
|
|
) -> dict[str, Any]:
|
|
"""
|
|
Build the assistant message dict in OpenAI format for appending to a conversation.
|
|
|
|
tool_calls_raw: list of {"id", "name", "arguments"} (arguments as dict), or None.
|
|
"""
|
|
msg: dict[str, Any] = {"role": "assistant", "content": content}
|
|
if tool_calls_raw:
|
|
msg["tool_calls"] = [
|
|
{
|
|
"id": tc["id"],
|
|
"type": "function",
|
|
"function": {
|
|
"name": tc["name"],
|
|
"arguments": json.dumps(tc.get("arguments") or {}),
|
|
},
|
|
}
|
|
for tc in tool_calls_raw
|
|
]
|
|
return msg
|