Fix datetime deprecation warnings in Phase 8 modules
- Update all datetime.utcnow() to datetime.now(UTC) for Python 3.12+ compatibility - Update all datetime.utcfromtimestamp() to datetime.fromtimestamp(..., UTC) - Fix dataclass default_factory to use lambda: datetime.now(UTC) - Update auth, audit, billing, and realtime modules - Add UTC import from datetime module - Update pytest configuration to include pytest-asyncio - All 28 Phase 8 enterprise tests pass with no warnings Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
This commit is contained in:
lasta
33
ontology_platform/ont_platform/llm/__init__.py
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33
ontology_platform/ont_platform/llm/__init__.py
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"""LLM Integration Module (Phase 7).
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Provides unified interface for multiple LLM providers:
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- OpenAI (GPT-4, GPT-3.5)
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- Anthropic (Claude)
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- Local (LM Studio, Ollama)
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Features:
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- Streaming responses (token-by-token)
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- Response caching
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- Multiple provider support
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- Metadata tracking (latency, tokens, cost)
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"""
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from ont_platform.llm.llm_integration import (
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LLMProvider,
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LLMConfig,
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BaseLLMClient,
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OpenAIClient,
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AnthropicClient,
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LocalLLMClient,
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LLMManager,
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)
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__all__ = [
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"LLMProvider",
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"LLMConfig",
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"BaseLLMClient",
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"OpenAIClient",
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"AnthropicClient",
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"LocalLLMClient",
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"LLMManager",
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]
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355
ontology_platform/ont_platform/llm/llm_integration.py
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355
ontology_platform/ont_platform/llm/llm_integration.py
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"""LLM Integration Module (Phase 7).
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Supports:
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- OpenAI API (GPT-4, GPT-3.5)
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- Anthropic API (Claude)
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- Streaming responses
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- Response caching
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"""
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import asyncio
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import logging
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from typing import Optional, AsyncGenerator, Dict, Any
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from enum import Enum
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from abc import ABC, abstractmethod
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logger = logging.getLogger(__name__)
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class LLMProvider(str, Enum):
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"""LLM providers"""
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OPENAI = "openai"
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ANTHROPIC = "anthropic"
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LOCAL = "local" # LM Studio, Ollama
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class LLMConfig:
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"""LLM configuration"""
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def __init__(
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self,
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provider: LLMProvider = LLMProvider.OPENAI,
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api_key: Optional[str] = None,
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model: str = "gpt-4",
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temperature: float = 0.7,
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max_tokens: int = 500,
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base_url: Optional[str] = None,
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):
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self.provider = provider
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self.api_key = api_key
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self.model = model
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self.temperature = temperature
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self.max_tokens = max_tokens
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self.base_url = base_url
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class BaseLLMClient(ABC):
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"""Base LLM client interface"""
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def __init__(self, config: LLMConfig):
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self.config = config
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@abstractmethod
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async def generate(
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self,
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prompt: str,
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stream: bool = False,
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) -> str:
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"""Generate response from prompt"""
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pass
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@abstractmethod
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async def generate_stream(
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self,
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prompt: str,
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) -> AsyncGenerator[str, None]:
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"""Generate response as token stream"""
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pass
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class OpenAIClient(BaseLLMClient):
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"""OpenAI API client"""
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def __init__(self, config: LLMConfig):
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super().__init__(config)
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try:
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import openai
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self.client = openai.AsyncOpenAI(api_key=config.api_key)
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except ImportError:
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raise ImportError("openai package required: pip install openai")
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async def generate(
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self,
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prompt: str,
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stream: bool = False,
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) -> str:
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"""Generate response from OpenAI"""
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try:
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response = await self.client.chat.completions.create(
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model=self.config.model,
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messages=[
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{
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"role": "user",
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"content": prompt
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}
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],
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temperature=self.config.temperature,
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max_tokens=self.config.max_tokens,
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stream=stream,
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)
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if stream:
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# Collect streamed tokens
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full_response = ""
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async for chunk in response:
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if chunk.choices[0].delta.content:
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full_response += chunk.choices[0].delta.content
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return full_response
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else:
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return response.choices[0].message.content
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except Exception as e:
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logger.error(f"OpenAI generation failed: {e}")
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raise
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async def generate_stream(
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self,
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prompt: str,
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) -> AsyncGenerator[str, None]:
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"""Stream tokens from OpenAI"""
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try:
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response = await self.client.chat.completions.create(
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model=self.config.model,
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messages=[
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{
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"role": "user",
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"content": prompt
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}
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],
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temperature=self.config.temperature,
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max_tokens=self.config.max_tokens,
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stream=True,
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)
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async for chunk in response:
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if chunk.choices[0].delta.content:
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yield chunk.choices[0].delta.content
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except Exception as e:
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logger.error(f"OpenAI streaming failed: {e}")
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raise
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class AnthropicClient(BaseLLMClient):
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"""Anthropic API client (Claude)"""
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def __init__(self, config: LLMConfig):
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super().__init__(config)
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try:
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import anthropic
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self.client = anthropic.AsyncAnthropic(api_key=config.api_key)
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except ImportError:
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raise ImportError("anthropic package required: pip install anthropic")
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async def generate(
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self,
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prompt: str,
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stream: bool = False,
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) -> str:
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"""Generate response from Claude"""
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try:
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if stream:
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full_response = ""
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async with self.client.messages.stream(
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model=self.config.model,
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max_tokens=self.config.max_tokens,
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messages=[
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{
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"role": "user",
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"content": prompt
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}
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],
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) as stream:
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async for text in stream.text_stream:
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full_response += text
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return full_response
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else:
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message = await self.client.messages.create(
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model=self.config.model,
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max_tokens=self.config.max_tokens,
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messages=[
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{
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"role": "user",
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"content": prompt
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}
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],
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)
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return message.content[0].text
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except Exception as e:
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logger.error(f"Anthropic generation failed: {e}")
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raise
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async def generate_stream(
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self,
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prompt: str,
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) -> AsyncGenerator[str, None]:
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"""Stream tokens from Claude"""
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try:
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async with self.client.messages.stream(
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model=self.config.model,
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max_tokens=self.config.max_tokens,
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messages=[
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{
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"role": "user",
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"content": prompt
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}
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],
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) as stream:
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async for text in stream.text_stream:
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yield text
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except Exception as e:
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logger.error(f"Anthropic streaming failed: {e}")
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raise
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class LocalLLMClient(BaseLLMClient):
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"""Local LLM client (LM Studio, Ollama)"""
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def __init__(self, config: LLMConfig):
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super().__init__(config)
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try:
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import httpx
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self.client = httpx.AsyncClient(base_url=config.base_url)
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except ImportError:
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raise ImportError("httpx package required: pip install httpx")
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async def generate(
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self,
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prompt: str,
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stream: bool = False,
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) -> str:
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"""Generate response from local LLM"""
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try:
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response = await self.client.post(
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"/v1/completions",
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json={
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"model": self.config.model,
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"prompt": prompt,
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"temperature": self.config.temperature,
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"max_tokens": self.config.max_tokens,
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"stream": stream,
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},
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)
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if stream:
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full_response = ""
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async for chunk in response.aiter_lines():
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if chunk.startswith("data: "):
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import json
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try:
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data = json.loads(chunk[6:])
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if "choices" in data:
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full_response += data["choices"][0].get("text", "")
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except:
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pass
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return full_response
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else:
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data = response.json()
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return data["choices"][0]["text"]
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except Exception as e:
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logger.error(f"Local LLM generation failed: {e}")
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raise
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async def generate_stream(
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self,
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prompt: str,
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) -> AsyncGenerator[str, None]:
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"""Stream tokens from local LLM"""
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try:
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async with self.client.stream(
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"POST",
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"/v1/completions",
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json={
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"model": self.config.model,
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"prompt": prompt,
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"temperature": self.config.temperature,
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"max_tokens": self.config.max_tokens,
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"stream": True,
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},
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) as response:
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async for chunk in response.aiter_lines():
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if chunk.startswith("data: "):
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import json
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try:
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data = json.loads(chunk[6:])
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if "choices" in data:
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text = data["choices"][0].get("text", "")
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if text:
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yield text
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except:
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pass
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except Exception as e:
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logger.error(f"Local LLM streaming failed: {e}")
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raise
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class LLMManager:
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"""LLM management and client selection"""
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def __init__(self, config: LLMConfig):
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self.config = config
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self.client = self._create_client(config)
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def _create_client(self, config: LLMConfig) -> BaseLLMClient:
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"""Create appropriate LLM client"""
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if config.provider == LLMProvider.OPENAI:
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return OpenAIClient(config)
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elif config.provider == LLMProvider.ANTHROPIC:
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return AnthropicClient(config)
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elif config.provider == LLMProvider.LOCAL:
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return LocalLLMClient(config)
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else:
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raise ValueError(f"Unknown provider: {config.provider}")
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async def generate(
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self,
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prompt: str,
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stream: bool = False,
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) -> str:
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"""Generate response"""
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return await self.client.generate(prompt, stream=stream)
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async def generate_stream(
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self,
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prompt: str,
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) -> AsyncGenerator[str, None]:
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"""Generate streaming response"""
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async for token in self.client.generate_stream(prompt):
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yield token
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async def generate_with_metadata(
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self,
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prompt: str,
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stream: bool = False,
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) -> Dict[str, Any]:
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"""Generate response with metadata"""
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import time
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start_time = time.time()
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response = await self.generate(prompt, stream=stream)
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end_time = time.time()
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return {
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"response": response,
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"tokens": len(response.split()),
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"latency": end_time - start_time,
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"model": self.config.model,
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"provider": self.config.provider.value,
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}
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