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60 lines
2.3 KiB
Python
60 lines
2.3 KiB
Python
"""Factory for services-layer provider runtime objects."""
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from __future__ import annotations
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from deeptutor.services.llm.config import LLMConfig, get_llm_config
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from deeptutor.services.llm.provider_core import (
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AnthropicProvider,
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AzureOpenAIProvider,
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GenerationSettings,
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GitHubCopilotProvider,
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LLMProvider,
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OpenAICodexProvider,
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OpenAICompatProvider,
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)
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from deeptutor.services.provider_registry import find_by_name
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def get_runtime_provider(config: LLMConfig | None = None) -> LLMProvider:
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"""Build the authoritative services-layer provider for the supplied config."""
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llm_config = config or get_llm_config()
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provider_name = llm_config.provider_name or llm_config.binding
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spec = find_by_name(provider_name)
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backend = spec.backend if spec else "openai_compat"
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if backend == "openai_codex":
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provider: LLMProvider = OpenAICodexProvider(default_model=llm_config.model)
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elif backend == "github_copilot":
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provider = GitHubCopilotProvider(default_model=llm_config.model)
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elif backend == "azure_openai":
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provider = AzureOpenAIProvider(
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api_key=llm_config.api_key or "",
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api_base=llm_config.effective_url or llm_config.base_url or "",
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default_model=llm_config.model,
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extra_headers=llm_config.extra_headers or None,
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)
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elif backend == "anthropic":
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provider = AnthropicProvider(
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api_key=llm_config.api_key or None,
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api_base=llm_config.effective_url or llm_config.base_url or None,
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default_model=llm_config.model,
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extra_headers=llm_config.extra_headers or None,
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supports_prompt_caching=bool(spec and spec.supports_prompt_caching),
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)
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else:
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provider = OpenAICompatProvider(
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api_key=llm_config.api_key or None,
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api_base=llm_config.effective_url or llm_config.base_url or None,
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default_model=llm_config.model,
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extra_headers=llm_config.extra_headers or None,
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spec=spec,
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provider_name=provider_name,
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)
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provider.generation = GenerationSettings(
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temperature=llm_config.temperature,
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max_tokens=llm_config.max_tokens,
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reasoning_effort=llm_config.reasoning_effort,
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)
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return provider
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