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hussein1362andXubin Ren
415e617398
Add an `extra_body` field to `ProviderConfig` that merges arbitrary key-value pairs into every OpenAI-compatible request body. This is the escape hatch for provider-specific features that nanobot does not have first-class fields for. Real-world use cases this unblocks via config alone (no code changes): - vLLM/TGI `chat_template_kwargs` (e.g. `enable_thinking: false`) - vLLM guided decoding (`guided_json`, `guided_regex`) - Local model sampling params (`repetition_penalty`, `top_k`, `min_p`) - Any future provider-specific param without a new PR each time The config extra_body is applied last via recursive deep-merge, so it can extend or override provider-specific defaults (e.g. thinking params) without clobbering sibling keys set by internal logic. Changes: - Add `extra_body: dict[str, Any] | None` to `ProviderConfig` - Pass it through `factory.py` to `OpenAICompatProvider.__init__` - Deep-merge into `_build_kwargs` after all internal extra_body entries - Add `_deep_merge` helper (recursive dict merge, does not mutate inputs) - 21 tests: deep-merge semantics, provider init, _build_kwargs integration, thinking coexistence, real-world patterns (guided_json, repetition_penalty), and schema validation
114 lines
3.9 KiB
Python
114 lines
3.9 KiB
Python
"""Create LLM providers from config."""
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from __future__ import annotations
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from dataclasses import dataclass
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from pathlib import Path
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from nanobot.config.schema import Config
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from nanobot.providers.base import GenerationSettings, LLMProvider
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from nanobot.providers.registry import find_by_name
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@dataclass(frozen=True)
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class ProviderSnapshot:
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provider: LLMProvider
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model: str
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context_window_tokens: int
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signature: tuple[object, ...]
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def make_provider(config: Config) -> LLMProvider:
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"""Create the LLM provider implied by config."""
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model = config.agents.defaults.model
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provider_name = config.get_provider_name(model)
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p = config.get_provider(model)
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spec = find_by_name(provider_name) if provider_name else None
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backend = spec.backend if spec else "openai_compat"
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if backend == "azure_openai":
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if not p or not p.api_key or not p.api_base:
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raise ValueError("Azure OpenAI requires api_key and api_base in config.")
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elif backend == "openai_compat" and not model.startswith("bedrock/"):
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needs_key = not (p and p.api_key)
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exempt = spec and (spec.is_oauth or spec.is_local or spec.is_direct)
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if needs_key and not exempt:
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raise ValueError(f"No API key configured for provider '{provider_name}'.")
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if backend == "openai_codex":
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from nanobot.providers.openai_codex_provider import OpenAICodexProvider
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provider = OpenAICodexProvider(default_model=model)
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elif backend == "azure_openai":
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from nanobot.providers.azure_openai_provider import AzureOpenAIProvider
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provider = AzureOpenAIProvider(
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api_key=p.api_key,
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api_base=p.api_base,
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default_model=model,
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)
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elif backend == "github_copilot":
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from nanobot.providers.github_copilot_provider import GitHubCopilotProvider
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provider = GitHubCopilotProvider(default_model=model)
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elif backend == "anthropic":
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from nanobot.providers.anthropic_provider import AnthropicProvider
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provider = AnthropicProvider(
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api_key=p.api_key if p else None,
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api_base=config.get_api_base(model),
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default_model=model,
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extra_headers=p.extra_headers if p else None,
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)
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else:
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from nanobot.providers.openai_compat_provider import OpenAICompatProvider
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provider = OpenAICompatProvider(
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api_key=p.api_key if p else None,
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api_base=config.get_api_base(model),
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default_model=model,
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extra_headers=p.extra_headers if p else None,
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spec=spec,
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extra_body=p.extra_body if p else None,
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)
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defaults = config.agents.defaults
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provider.generation = GenerationSettings(
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temperature=defaults.temperature,
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max_tokens=defaults.max_tokens,
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reasoning_effort=defaults.reasoning_effort,
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)
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return provider
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def provider_signature(config: Config) -> tuple[object, ...]:
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"""Return the config fields that affect the primary LLM provider."""
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model = config.agents.defaults.model
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defaults = config.agents.defaults
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return (
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model,
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defaults.provider,
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config.get_provider_name(model),
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config.get_api_key(model),
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config.get_api_base(model),
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defaults.max_tokens,
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defaults.temperature,
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defaults.reasoning_effort,
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defaults.context_window_tokens,
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)
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def build_provider_snapshot(config: Config) -> ProviderSnapshot:
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return ProviderSnapshot(
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provider=make_provider(config),
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model=config.agents.defaults.model,
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context_window_tokens=config.agents.defaults.context_window_tokens,
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signature=provider_signature(config),
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)
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def load_provider_snapshot(config_path: Path | None = None) -> ProviderSnapshot:
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from nanobot.config.loader import load_config, resolve_config_env_vars
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return build_provider_snapshot(resolve_config_env_vars(load_config(config_path)))
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