mirror of
https://github.com/blakeblackshear/frigate.git
synced 2026-03-07 02:18:07 +01:00
4232cc483d
* Improve GenAI docs * Clarify * Fix config updating * Implement streaming for other providers * Set openai base url if applied * Cast context size
290 lines
10 KiB
Python
290 lines
10 KiB
Python
"""Azure OpenAI Provider for Frigate AI."""
|
|
|
|
import base64
|
|
import json
|
|
import logging
|
|
from typing import Any, Optional
|
|
from urllib.parse import parse_qs, urlparse
|
|
|
|
from openai import AzureOpenAI
|
|
|
|
from frigate.config import GenAIProviderEnum
|
|
from frigate.genai import GenAIClient, register_genai_provider
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
@register_genai_provider(GenAIProviderEnum.azure_openai)
|
|
class OpenAIClient(GenAIClient):
|
|
"""Generative AI client for Frigate using Azure OpenAI."""
|
|
|
|
provider: AzureOpenAI
|
|
|
|
def _init_provider(self):
|
|
"""Initialize the client."""
|
|
try:
|
|
parsed_url = urlparse(self.genai_config.base_url)
|
|
query_params = parse_qs(parsed_url.query)
|
|
api_version = query_params.get("api-version", [None])[0]
|
|
azure_endpoint = f"{parsed_url.scheme}://{parsed_url.netloc}/"
|
|
|
|
if not api_version:
|
|
logger.warning("Azure OpenAI url is missing API version.")
|
|
return None
|
|
|
|
except Exception as e:
|
|
logger.warning("Error parsing Azure OpenAI url: %s", str(e))
|
|
return None
|
|
|
|
return AzureOpenAI(
|
|
api_key=self.genai_config.api_key,
|
|
api_version=api_version,
|
|
azure_endpoint=azure_endpoint,
|
|
)
|
|
|
|
def _send(self, prompt: str, images: list[bytes]) -> Optional[str]:
|
|
"""Submit a request to Azure OpenAI."""
|
|
encoded_images = [base64.b64encode(image).decode("utf-8") for image in images]
|
|
try:
|
|
result = self.provider.chat.completions.create(
|
|
model=self.genai_config.model,
|
|
messages=[
|
|
{
|
|
"role": "user",
|
|
"content": [{"type": "text", "text": prompt}]
|
|
+ [
|
|
{
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": f"data:image/jpeg;base64,{image}",
|
|
"detail": "low",
|
|
},
|
|
}
|
|
for image in encoded_images
|
|
],
|
|
},
|
|
],
|
|
timeout=self.timeout,
|
|
**self.genai_config.runtime_options,
|
|
)
|
|
except Exception as e:
|
|
logger.warning("Azure OpenAI returned an error: %s", str(e))
|
|
return None
|
|
if len(result.choices) > 0:
|
|
return result.choices[0].message.content.strip()
|
|
return None
|
|
|
|
def get_context_size(self) -> int:
|
|
"""Get the context window size for Azure OpenAI."""
|
|
return 128000
|
|
|
|
def chat_with_tools(
|
|
self,
|
|
messages: list[dict[str, Any]],
|
|
tools: Optional[list[dict[str, Any]]] = None,
|
|
tool_choice: Optional[str] = "auto",
|
|
) -> dict[str, Any]:
|
|
try:
|
|
openai_tool_choice = None
|
|
if tool_choice:
|
|
if tool_choice == "none":
|
|
openai_tool_choice = "none"
|
|
elif tool_choice == "auto":
|
|
openai_tool_choice = "auto"
|
|
elif tool_choice == "required":
|
|
openai_tool_choice = "required"
|
|
|
|
request_params = {
|
|
"model": self.genai_config.model,
|
|
"messages": messages,
|
|
"timeout": self.timeout,
|
|
}
|
|
|
|
if tools:
|
|
request_params["tools"] = tools
|
|
if openai_tool_choice is not None:
|
|
request_params["tool_choice"] = openai_tool_choice
|
|
|
|
result = self.provider.chat.completions.create(**request_params)
|
|
|
|
if (
|
|
result is None
|
|
or not hasattr(result, "choices")
|
|
or len(result.choices) == 0
|
|
):
|
|
return {
|
|
"content": None,
|
|
"tool_calls": None,
|
|
"finish_reason": "error",
|
|
}
|
|
|
|
choice = result.choices[0]
|
|
message = choice.message
|
|
|
|
content = message.content.strip() if message.content else None
|
|
|
|
tool_calls = None
|
|
if message.tool_calls:
|
|
tool_calls = []
|
|
for tool_call in message.tool_calls:
|
|
try:
|
|
arguments = json.loads(tool_call.function.arguments)
|
|
except (json.JSONDecodeError, AttributeError) as e:
|
|
logger.warning(
|
|
f"Failed to parse tool call arguments: {e}, "
|
|
f"tool: {tool_call.function.name if hasattr(tool_call.function, 'name') else 'unknown'}"
|
|
)
|
|
arguments = {}
|
|
|
|
tool_calls.append(
|
|
{
|
|
"id": tool_call.id if hasattr(tool_call, "id") else "",
|
|
"name": tool_call.function.name
|
|
if hasattr(tool_call.function, "name")
|
|
else "",
|
|
"arguments": arguments,
|
|
}
|
|
)
|
|
|
|
finish_reason = "error"
|
|
if hasattr(choice, "finish_reason") and choice.finish_reason:
|
|
finish_reason = choice.finish_reason
|
|
elif tool_calls:
|
|
finish_reason = "tool_calls"
|
|
elif content:
|
|
finish_reason = "stop"
|
|
|
|
return {
|
|
"content": content,
|
|
"tool_calls": tool_calls,
|
|
"finish_reason": finish_reason,
|
|
}
|
|
|
|
except Exception as e:
|
|
logger.warning("Azure OpenAI returned an error: %s", str(e))
|
|
return {
|
|
"content": None,
|
|
"tool_calls": None,
|
|
"finish_reason": "error",
|
|
}
|
|
|
|
async def chat_with_tools_stream(
|
|
self,
|
|
messages: list[dict[str, Any]],
|
|
tools: Optional[list[dict[str, Any]]] = None,
|
|
tool_choice: Optional[str] = "auto",
|
|
):
|
|
"""
|
|
Stream chat with tools; yields content deltas then final message.
|
|
|
|
Implements streaming function calling/tool usage for Azure OpenAI models.
|
|
"""
|
|
try:
|
|
openai_tool_choice = None
|
|
if tool_choice:
|
|
if tool_choice == "none":
|
|
openai_tool_choice = "none"
|
|
elif tool_choice == "auto":
|
|
openai_tool_choice = "auto"
|
|
elif tool_choice == "required":
|
|
openai_tool_choice = "required"
|
|
|
|
request_params = {
|
|
"model": self.genai_config.model,
|
|
"messages": messages,
|
|
"timeout": self.timeout,
|
|
"stream": True,
|
|
}
|
|
|
|
if tools:
|
|
request_params["tools"] = tools
|
|
if openai_tool_choice is not None:
|
|
request_params["tool_choice"] = openai_tool_choice
|
|
|
|
# Use streaming API
|
|
content_parts: list[str] = []
|
|
tool_calls_by_index: dict[int, dict[str, Any]] = {}
|
|
finish_reason = "stop"
|
|
|
|
stream = self.provider.chat.completions.create(**request_params)
|
|
|
|
for chunk in stream:
|
|
if not chunk or not chunk.choices:
|
|
continue
|
|
|
|
choice = chunk.choices[0]
|
|
delta = choice.delta
|
|
|
|
# Check for finish reason
|
|
if choice.finish_reason:
|
|
finish_reason = choice.finish_reason
|
|
|
|
# Extract content deltas
|
|
if delta.content:
|
|
content_parts.append(delta.content)
|
|
yield ("content_delta", delta.content)
|
|
|
|
# Extract tool calls
|
|
if delta.tool_calls:
|
|
for tc in delta.tool_calls:
|
|
idx = tc.index
|
|
fn = tc.function
|
|
|
|
if idx not in tool_calls_by_index:
|
|
tool_calls_by_index[idx] = {
|
|
"id": tc.id or "",
|
|
"name": fn.name if fn and fn.name else "",
|
|
"arguments": "",
|
|
}
|
|
|
|
t = tool_calls_by_index[idx]
|
|
if tc.id:
|
|
t["id"] = tc.id
|
|
if fn and fn.name:
|
|
t["name"] = fn.name
|
|
if fn and fn.arguments:
|
|
t["arguments"] += fn.arguments
|
|
|
|
# Build final message
|
|
full_content = "".join(content_parts).strip() or None
|
|
|
|
# Convert tool calls to list format
|
|
tool_calls_list = None
|
|
if tool_calls_by_index:
|
|
tool_calls_list = []
|
|
for tc in tool_calls_by_index.values():
|
|
try:
|
|
# Parse accumulated arguments as JSON
|
|
parsed_args = json.loads(tc["arguments"])
|
|
except (json.JSONDecodeError, Exception):
|
|
parsed_args = tc["arguments"]
|
|
|
|
tool_calls_list.append(
|
|
{
|
|
"id": tc["id"],
|
|
"name": tc["name"],
|
|
"arguments": parsed_args,
|
|
}
|
|
)
|
|
finish_reason = "tool_calls"
|
|
|
|
yield (
|
|
"message",
|
|
{
|
|
"content": full_content,
|
|
"tool_calls": tool_calls_list,
|
|
"finish_reason": finish_reason,
|
|
},
|
|
)
|
|
|
|
except Exception as e:
|
|
logger.warning("Azure OpenAI streaming returned an error: %s", str(e))
|
|
yield (
|
|
"message",
|
|
{
|
|
"content": None,
|
|
"tool_calls": None,
|
|
"finish_reason": "error",
|
|
},
|
|
)
|