qixinbo
163 lines
7.0 KiB
Python
163 lines
7.0 KiB
Python
import contextvars
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import json
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from typing import Any, Dict, List, Optional
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from loguru import logger
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from nanobot.providers.litellm_provider import LiteLLMProvider
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from nanobot.providers.base import LLMResponse
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from litellm import acompletion, stream_chunk_builder
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streaming_queue_var = contextvars.ContextVar("streaming_queue", default=None)
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class StreamingLiteLLMProvider(LiteLLMProvider):
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def __init__(self, *args, **kwargs):
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self._provider_name_override = kwargs.get("provider_name")
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super().__init__(*args, **kwargs)
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def _get_active_spec(self, model: str):
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from nanobot.providers.registry import find_by_model, find_by_name
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spec = None
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if self._provider_name_override:
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spec = find_by_name(self._provider_name_override)
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if not spec:
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spec = find_by_model(model)
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return spec
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def _setup_env(self, api_key: str, api_base: str | None, model: str) -> None:
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"""Set environment variables based on detected provider."""
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import os
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spec = self._gateway or self._get_active_spec(model)
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if not spec:
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return
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if not spec.env_key:
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return
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if self._gateway:
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os.environ[spec.env_key] = api_key
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else:
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# os.environ.setdefault 会在已存在且为空字符串时保留空字符串，导致 litellm 无法识别
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# 因此强制更新
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os.environ[spec.env_key] = api_key
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effective_base = api_base or spec.default_api_base
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for env_name, env_val in spec.env_extras:
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resolved = env_val.replace("{api_key}", api_key)
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resolved = resolved.replace("{api_base}", effective_base)
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os.environ[env_name] = resolved
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def _resolve_model(self, model: str) -> str:
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"""Resolve model name by applying provider/gateway prefixes, using override if available."""
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if self._gateway:
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prefix = self._gateway.litellm_prefix
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if self._gateway.strip_model_prefix:
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model = model.split("/")[-1]
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if prefix and not model.startswith(f"{prefix}/"):
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model = f"{prefix}/{model}"
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return model
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spec = self._get_active_spec(model)
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if spec and spec.litellm_prefix:
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model = self._canonicalize_explicit_prefix(model, spec.name, spec.litellm_prefix)
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if not any(model.startswith(s) for s in spec.skip_prefixes):
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model = f"{spec.litellm_prefix}/{model}"
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elif spec and not spec.litellm_prefix and "/" not in model:
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# For standard providers like openai, anthropic, litellm requires the prefix for unknown models
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# but registry sets litellm_prefix="" to rely on native matching.
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# If native matching fails (e.g. non-standard model name), we should force prefix.
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# We only force prefix if provider was explicitly set and model has no prefix.
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if self._provider_name_override:
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model = f"{spec.name}/{model}"
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return model
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def _apply_model_overrides(self, model: str, kwargs: dict[str, Any]) -> None:
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"""Apply model-specific parameter overrides from the registry."""
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model_lower = model.lower()
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spec = self._get_active_spec(model)
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if spec:
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for pattern, overrides in spec.model_overrides:
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if pattern in model_lower:
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kwargs.update(overrides)
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return
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def _extra_msg_keys(self, original_model: str, resolved_model: str) -> frozenset[str]:
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"""Return provider-specific extra keys to preserve in request messages."""
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spec = self._get_active_spec(original_model) or self._get_active_spec(resolved_model)
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if (spec and spec.name == "anthropic") or "claude" in original_model.lower() or resolved_model.startswith("anthropic/"):
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# _ANTHROPIC_EXTRA_KEYS is defined in nanobot.providers.litellm_provider, let's just use the string
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return frozenset({"thinking_blocks"})
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return frozenset()
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async def chat(
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self,
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messages: List[Dict[str, Any]],
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tools: Optional[List[Dict[str, Any]]] = None,
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model: Optional[str] = None,
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temperature: float = 0.7,
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max_tokens: int = 4000,
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reasoning_effort: Optional[str] = None,
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request_timeout: Optional[int] = None,
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num_retries: Optional[int] = None,
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) -> LLMResponse:
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original_model = model or self.default_model
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model_name = self._resolve_model(original_model)
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extra_msg_keys = self._extra_msg_keys(original_model, model_name)
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if self._supports_cache_control(original_model):
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messages, tools = self._apply_cache_control(messages, tools)
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kwargs: Dict[str, Any] = {
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"model": model_name,
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"messages": self._sanitize_messages(self._sanitize_empty_content(messages), extra_keys=extra_msg_keys),
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"temperature": temperature,
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"max_tokens": max(1, max_tokens),
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"stream": True, # 强制开启流式
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}
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self._apply_model_overrides(model_name, kwargs)
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if self.api_key and self.api_key != "no-key":
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kwargs["api_key"] = self.api_key
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if self.api_base:
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kwargs["api_base"] = self.api_base
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if self.extra_headers:
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kwargs["extra_headers"] = self.extra_headers
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if tools:
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kwargs["tools"] = tools
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kwargs["tool_choice"] = "auto"
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if request_timeout is not None:
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kwargs["timeout"] = request_timeout
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if num_retries is not None:
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kwargs["num_retries"] = max(0, int(num_retries))
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if reasoning_effort:
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kwargs["reasoning_effort"] = reasoning_effort
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kwargs["drop_params"] = True
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try:
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response_stream = await acompletion(**kwargs)
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chunks = []
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queue = streaming_queue_var.get()
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async for chunk in response_stream:
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chunks.append(chunk)
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if queue is not None:
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# 提取普通内容或 think 内容
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delta = chunk.choices[0].delta if chunk.choices else None
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if delta:
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content = getattr(delta, "content", None)
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reasoning_content = getattr(delta, "reasoning_content", None)
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if content:
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await queue.put({"type": "delta", "content": content})
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if reasoning_content:
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await queue.put({"type": "progress", "content": reasoning_content, "is_reasoning": True})
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# 还原为完整的 response 对象供 nanobot 处理
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full_response = stream_chunk_builder(chunks, messages=messages)
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return self._parse_response(full_response)
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except Exception as e:
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logger.error("StreamingLiteLLMProvider failed: {}", e)
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raise
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