支持基于图片素材的视频生成逻辑
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puke
@@ -12,15 +12,22 @@
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"""
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LLM (Large Language Model) Service - Direct OpenAI SDK implementation
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Supports structured output via response_type parameter (Pydantic model).
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"""
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import os
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from typing import Optional
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import json
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import re
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from typing import Optional, Type, TypeVar, Union
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from openai import AsyncOpenAI
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from pydantic import BaseModel
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from loguru import logger
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T = TypeVar("T", bound=BaseModel)
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class LLMService:
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"""
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LLM (Large Language Model) service
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@@ -114,8 +121,9 @@ class LLMService:
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model: Optional[str] = None,
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temperature: float = 0.7,
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max_tokens: int = 2000,
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response_type: Optional[Type[T]] = None,
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**kwargs
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) -> str:
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) -> Union[str, T]:
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"""
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Generate text using LLM
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@@ -126,24 +134,28 @@ class LLMService:
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model: Model name (optional, uses config if not provided)
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temperature: Sampling temperature (0.0-2.0). Lower is more deterministic.
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max_tokens: Maximum tokens to generate
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response_type: Optional Pydantic model class for structured output.
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If provided, returns parsed model instance instead of string.
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**kwargs: Additional provider-specific parameters
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Returns:
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Generated text
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Generated text (str) or parsed Pydantic model instance (if response_type provided)
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Examples:
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# Use config from config.yaml
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# Basic text generation
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answer = await pixelle_video.llm("Explain atomic habits")
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# Override with custom parameters
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answer = await pixelle_video.llm(
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prompt="Explain atomic habits in 3 sentences",
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api_key="sk-custom-key",
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base_url="https://api.custom.com/v1",
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model="custom-model",
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temperature=0.7,
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max_tokens=500
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# Structured output with Pydantic model
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class MovieReview(BaseModel):
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title: str
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rating: int
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summary: str
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review = await pixelle_video.llm(
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prompt="Review the movie Inception",
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response_type=MovieReview
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)
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print(review.title) # Structured access
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"""
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# Create client (new instance each time to support parameter overrides)
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client = self._create_client(api_key=api_key, base_url=base_url)
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@@ -155,25 +167,143 @@ class LLMService:
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or "gpt-3.5-turbo" # Default fallback
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)
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logger.debug(f"LLM call: model={final_model}, base_url={client.base_url}")
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logger.debug(f"LLM call: model={final_model}, base_url={client.base_url}, response_type={response_type}")
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try:
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response = await client.chat.completions.create(
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model=final_model,
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if response_type is not None:
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# Structured output mode - try beta.chat.completions.parse first
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return await self._call_with_structured_output(
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client=client,
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model=final_model,
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prompt=prompt,
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response_type=response_type,
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temperature=temperature,
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max_tokens=max_tokens,
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**kwargs
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)
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else:
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# Standard text output mode
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response = await client.chat.completions.create(
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model=final_model,
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messages=[{"role": "user", "content": prompt}],
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temperature=temperature,
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max_tokens=max_tokens,
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**kwargs
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)
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result = response.choices[0].message.content
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logger.debug(f"LLM response length: {len(result)} chars")
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return result
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except Exception as e:
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logger.error(f"LLM call error (model={final_model}, base_url={client.base_url}): {e}")
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raise
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async def _call_with_structured_output(
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self,
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client: AsyncOpenAI,
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model: str,
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prompt: str,
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response_type: Type[T],
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temperature: float,
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max_tokens: int,
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**kwargs
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) -> T:
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"""
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Call LLM with structured output support
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Tries OpenAI beta.chat.completions.parse first, falls back to JSON parsing
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if the provider doesn't support structured outputs.
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Args:
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client: OpenAI client
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model: Model name
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prompt: The prompt
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response_type: Pydantic model class
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temperature: Sampling temperature
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max_tokens: Max tokens
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**kwargs: Additional parameters
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Returns:
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Parsed Pydantic model instance
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"""
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# Try OpenAI structured output API first (beta.chat.completions.parse)
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try:
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response = await client.beta.chat.completions.parse(
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model=model,
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messages=[{"role": "user", "content": prompt}],
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response_format=response_type,
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temperature=temperature,
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max_tokens=max_tokens,
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**kwargs
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)
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result = response.choices[0].message.content
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logger.debug(f"LLM response length: {len(result)} chars")
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parsed = response.choices[0].message.parsed
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if parsed is not None:
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logger.debug(f"Structured output parsed successfully via beta API")
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return parsed
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# If parsed is None, fall through to fallback
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logger.warning("Structured output API returned None, falling back to JSON parsing")
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content = response.choices[0].message.content
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return result
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except Exception as e:
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logger.error(f"LLM call error (model={final_model}, base_url={client.base_url}): {e}")
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raise
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# If beta API not supported, fall back to JSON mode
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logger.debug(f"Structured output API not available ({e}), falling back to JSON parsing")
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response = await client.chat.completions.create(
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model=model,
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messages=[{"role": "user", "content": prompt}],
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temperature=temperature,
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max_tokens=max_tokens,
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**kwargs
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)
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content = response.choices[0].message.content
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# Fallback: Parse JSON from response content
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return self._parse_response_as_model(content, response_type)
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def _parse_response_as_model(self, content: str, response_type: Type[T]) -> T:
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"""
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Parse LLM response content as Pydantic model
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Args:
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content: Raw LLM response text
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response_type: Target Pydantic model class
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Returns:
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Parsed model instance
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"""
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# Try direct JSON parsing first
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try:
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data = json.loads(content)
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return response_type.model_validate(data)
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except json.JSONDecodeError:
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pass
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# Try extracting from markdown code block
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json_pattern = r'```(?:json)?\s*([\s\S]+?)\s*```'
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match = re.search(json_pattern, content, re.DOTALL)
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if match:
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try:
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data = json.loads(match.group(1))
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return response_type.model_validate(data)
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except json.JSONDecodeError:
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pass
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# Try to find any JSON object in the text
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brace_start = content.find('{')
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brace_end = content.rfind('}')
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if brace_start != -1 and brace_end > brace_start:
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try:
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json_str = content[brace_start:brace_end + 1]
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data = json.loads(json_str)
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return response_type.model_validate(data)
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except json.JSONDecodeError:
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pass
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raise ValueError(f"Failed to parse LLM response as {response_type.__name__}: {content[:200]}...")
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@property
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def active(self) -> str:
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