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"""Centralized LLM client factory. |
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This module provides factory functions for creating LLM clients, |
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ensuring consistent configuration and clear error messages. |
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Why Magentic requires OpenAI: |
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- Magentic agents use the @ai_function decorator for tool calling |
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- This requires structured function calling protocol (tools, tool_choice) |
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- OpenAI's API supports this natively |
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- Anthropic/HuggingFace Inference APIs are text-in/text-out only |
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""" |
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from typing import TYPE_CHECKING, Any |
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from src.utils.config import settings |
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from src.utils.exceptions import ConfigurationError |
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if TYPE_CHECKING: |
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from agent_framework.openai import OpenAIChatClient |
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def get_magentic_client() -> "OpenAIChatClient": |
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""" |
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Get the OpenAI client for Magentic agents. |
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Magentic requires OpenAI because it uses function calling protocol: |
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- @ai_function decorators define callable tools |
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- LLM returns structured tool calls (not just text) |
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- Requires OpenAI's tools/function_call API support |
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Raises: |
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ConfigurationError: If OPENAI_API_KEY is not set |
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Returns: |
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Configured OpenAIChatClient for Magentic agents |
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""" |
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from agent_framework.openai import OpenAIChatClient |
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api_key = settings.get_openai_api_key() |
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return OpenAIChatClient( |
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model_id=settings.openai_model, |
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api_key=api_key, |
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) |
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def get_pydantic_ai_model() -> Any: |
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""" |
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Get the appropriate model for pydantic-ai based on configuration. |
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Uses the configured LLM_PROVIDER to select between OpenAI and Anthropic. |
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This is used by simple mode components (JudgeHandler, etc.) |
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Returns: |
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Configured pydantic-ai model |
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""" |
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from pydantic_ai.models.anthropic import AnthropicModel |
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from pydantic_ai.models.openai import OpenAIChatModel |
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from pydantic_ai.providers.anthropic import AnthropicProvider |
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from pydantic_ai.providers.openai import OpenAIProvider |
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if settings.llm_provider == "openai": |
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if not settings.openai_api_key: |
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raise ConfigurationError("OPENAI_API_KEY not set for pydantic-ai") |
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provider = OpenAIProvider(api_key=settings.openai_api_key) |
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return OpenAIChatModel(settings.openai_model, provider=provider) |
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if settings.llm_provider == "anthropic": |
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if not settings.anthropic_api_key: |
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raise ConfigurationError("ANTHROPIC_API_KEY not set for pydantic-ai") |
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anthropic_provider = AnthropicProvider(api_key=settings.anthropic_api_key) |
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return AnthropicModel(settings.anthropic_model, provider=anthropic_provider) |
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raise ConfigurationError(f"Unknown LLM provider: {settings.llm_provider}") |
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def check_magentic_requirements() -> None: |
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""" |
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Check if Magentic mode requirements are met. |
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Raises: |
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ConfigurationError: If requirements not met |
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""" |
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if not settings.has_openai_key: |
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raise ConfigurationError( |
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"Magentic mode requires OPENAI_API_KEY for function calling support. " |
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"Anthropic and HuggingFace Inference do not support the structured " |
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"function calling protocol that Magentic agents require. " |
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"Use mode='simple' for other LLM providers." |
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) |
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def check_simple_mode_requirements() -> None: |
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""" |
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Check if simple mode requirements are met. |
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Simple mode supports both OpenAI and Anthropic. |
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Raises: |
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ConfigurationError: If no LLM API key is configured |
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""" |
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if not settings.has_any_llm_key: |
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raise ConfigurationError( |
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"No LLM API key configured. Set OPENAI_API_KEY or ANTHROPIC_API_KEY." |
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) |
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