mirror of
https://github.com/langbot-app/LangBot.git
synced 2026-08-28 05:07:14 +00:00
fishzjp
95b8736e93
SessionManager clears image_base64 on past turns to save memory, and
exclude_none serialization drops the hollowed field entirely, so a
replayed history part can arrive as {'type': 'image_base64'} with no
payload. The converter accessed the missing key unconditionally and
raised KeyError on every turn after an image was sent.
Prefer the base64 payload when present, fall back to an image_url that
survived on the same element, and drop hollow parts otherwise (same
strategy as the existing file-part handling). Fixes #2469.
1565 lines
65 KiB
Python
1565 lines
65 KiB
Python
"""LiteLLM unified requester for chat, embedding, and rerank."""
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from __future__ import annotations
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import typing
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import litellm
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from litellm import acompletion, aembedding, arerank
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from .. import errors, reasoning, requester
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from ....utils import httpclient
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import langbot_plugin.api.entities.builtin.resource.tool as resource_tool
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import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
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import langbot_plugin.api.entities.builtin.provider.message as provider_message
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class _ThinkStripState:
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"""Stateful filter that drops think blocks across chunks."""
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_THINK_OPEN = '<think>'
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_THINK_CLOSE = '</think>'
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_LEGACY_OPEN = 'CRETIRE_REASONING_BEGINk'
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_LEGACY_CLOSE = 'CRETIRE_REASONING_ENDk'
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def __init__(self) -> None:
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self._pairs: tuple[tuple[str, str], ...] = (
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(self._THINK_OPEN, self._THINK_CLOSE),
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(self._LEGACY_OPEN, self._LEGACY_CLOSE),
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)
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self._open_tags = tuple(open_tag for open_tag, _close_tag in self._pairs)
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self._buf = ''
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self._close_tag: str | None = None
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self._pending_initial = True
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def feed(self, chunk: str) -> str:
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"""Feed a streaming delta and return user-visible content."""
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if not chunk:
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return chunk
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text = self._buf + chunk
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if self._close_tag is not None:
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return self._consume_think_body(text)
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return self._process_visible_text(text)
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def flush(self) -> str:
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"""Release buffered visible content when the stream ends."""
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if self._close_tag is not None:
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self._buf = ''
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self._close_tag = None
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return ''
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pending, self._buf = self._buf, ''
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self._close_tag = None
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return pending
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def _consume_think_body(self, text: str) -> str:
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close_tag = self._close_tag
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if close_tag is None:
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return text
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close_idx = text.find(close_tag)
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if close_idx != -1:
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self._close_tag = None
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self._buf = ''
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self._pending_initial = False
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return self._process_visible_text(text[close_idx + len(close_tag) :])
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self._buf = self._close_prefix(text, close_tag)
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return ''
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def _process_visible_text(self, text: str) -> str:
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out: list[str] = []
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index = 0
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while index < len(text):
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if self._pending_initial:
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open_idx, open_tag, close_tag = self._find_next_open(text, index)
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orphan_close_idx, orphan_close_tag = self._find_next_close(text, index)
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if orphan_close_idx != -1 and (open_idx == -1 or orphan_close_idx < open_idx):
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self._pending_initial = False
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index = orphan_close_idx + len(orphan_close_tag)
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continue
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if open_idx == -1:
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self._buf = text[index:]
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return ''.join(out)
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if open_idx > index:
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self._pending_initial = False
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out.append(text[index:open_idx])
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index = open_idx
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continue
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open_idx, open_tag, close_tag = self._find_next_open(text, index)
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if open_idx == -1:
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emit_end = self._visible_emit_end(text, index)
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out.append(text[index:emit_end])
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if emit_end > index:
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self._pending_initial = False
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self._buf = text[emit_end:]
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return ''.join(out)
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out.append(text[index:open_idx])
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if open_idx > index:
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self._pending_initial = False
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body_start = open_idx + len(open_tag)
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close_idx = text.find(close_tag, body_start)
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if close_idx == -1:
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self._close_tag = close_tag
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self._buf = self._close_prefix(text[body_start:], close_tag)
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return ''.join(out)
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self._pending_initial = False
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index = close_idx + len(close_tag)
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self._buf = ''
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return ''.join(out)
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def _find_next_open(self, text: str, start: int) -> tuple[int, str, str]:
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best_idx = -1
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best_open = ''
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best_close = ''
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for open_tag, close_tag in self._pairs:
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idx = text.find(open_tag, start)
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if idx != -1 and (best_idx == -1 or idx < best_idx):
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best_idx = idx
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best_open = open_tag
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best_close = close_tag
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return best_idx, best_open, best_close
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def _find_next_close(self, text: str, start: int) -> tuple[int, str]:
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best_idx = -1
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best_close = ''
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for _open_tag, close_tag in self._pairs:
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idx = text.find(close_tag, start)
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if idx != -1 and (best_idx == -1 or idx < best_idx):
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best_idx = idx
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best_close = close_tag
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return best_idx, best_close
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def _visible_emit_end(self, text: str, start: int) -> int:
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visible = text[start:]
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limit = min(len(visible), max(len(open_tag) for open_tag in self._open_tags) - 1)
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for keep in range(limit, 0, -1):
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suffix = visible[-keep:]
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if any(open_tag.startswith(suffix) for open_tag in self._open_tags):
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return len(text) - keep
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return len(text)
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@staticmethod
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def _close_prefix(text: str, close_tag: str) -> str:
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limit = min(len(text), len(close_tag) - 1)
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for keep in range(limit, 0, -1):
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suffix = text[-keep:]
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if close_tag.startswith(suffix):
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return suffix
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return ''
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class LiteLLMRequester(requester.ProviderAPIRequester):
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"""LiteLLM unified API requester supporting chat, embedding, and rerank."""
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_EMBEDDING_MODEL_HINTS = ('embedding', 'embed', 'bge-', 'e5-', 'm3e', 'gte-', 'text-embedding')
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_RERANK_MODEL_HINTS = ('rerank', 're-rank', 're_rank')
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_QWEN_DEDICATED_THINKING_MODELS = frozenset(
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{
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'qwen3.7-max-preview',
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'qwen3.7-max-2026-05-17',
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}
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)
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_QWEN_REASONING_BUDGETS = {
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'low': 1024,
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'medium': 4096,
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'high': 8192,
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}
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_INFERRED_EFFORT_PROVIDERS = frozenset(
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{
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'anthropic',
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'gemini',
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'groq',
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'mistral',
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'openai',
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'openrouter',
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'together_ai',
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'xai',
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}
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)
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_REQUESTER_REASONING_FAMILIES = {
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'openai-chat-completions': 'openai',
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'anthropic-messages': 'anthropic',
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'deepseek-chat-completions': 'deepseek',
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'moonshot-chat-completions': 'kimi',
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'moonshot-cn-chat-completions': 'kimi',
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'bailian-chat-completions': 'qwen',
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'doubao-chat-completions': 'doubao',
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'mimo-chat-completions': 'mimo',
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}
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default_config: dict[str, typing.Any] = {
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'base_url': '',
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'timeout': 120,
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'custom_llm_provider': '',
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'drop_params': False,
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'num_retries': 0,
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'api_version': '',
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'requester_name': '',
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}
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async def initialize(self):
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"""Initialize LiteLLM client settings."""
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# LiteLLM doesn't require explicit client initialization
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# Configuration is passed per-request via litellm params
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pass
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def _build_litellm_model_name(self, model_name: str, custom_llm_provider: str | None = None) -> str:
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"""Build LiteLLM model name with provider prefix if needed."""
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provider = custom_llm_provider or self.requester_cfg.get('custom_llm_provider', '')
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if provider:
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# LiteLLM format: provider/model_name
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if model_name.startswith(f'{provider}/'):
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return model_name
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return f'{provider}/{model_name}'
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# If no custom provider, assume model_name already includes prefix or is OpenAI-compatible
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return model_name
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def _get_custom_llm_provider(self) -> str | None:
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return self.requester_cfg.get('custom_llm_provider') or None
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def _safe_litellm_bool_helper(self, helper_name: str, model_name: str) -> bool:
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"""Call a LiteLLM boolean capability helper without letting metadata gaps fail requests."""
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helper = getattr(litellm, helper_name, None)
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if not callable(helper):
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return False
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provider = self._get_custom_llm_provider()
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candidates: list[tuple[str, str | None]] = [
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(candidate, None) for candidate in self._metadata_model_candidates(model_name)
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]
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candidates.append((model_name, provider))
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litellm_model_name = self._build_litellm_model_name(model_name)
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if litellm_model_name != model_name:
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candidates.append((litellm_model_name, None))
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for metadata_provider in self._metadata_provider_candidates(model_name):
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candidates.append((f'{metadata_provider}/{model_name}', None))
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tried_candidates: set[tuple[str, str | None]] = set()
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for candidate_model, candidate_provider in candidates:
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candidate_key = (candidate_model, candidate_provider)
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if candidate_key in tried_candidates:
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continue
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tried_candidates.add(candidate_key)
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try:
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if bool(helper(model=candidate_model, custom_llm_provider=candidate_provider)):
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return True
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except Exception:
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continue
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return False
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@staticmethod
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def _positive_int(value: typing.Any) -> int | None:
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if isinstance(value, bool):
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return None
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if isinstance(value, int) and value > 0:
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return value
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if isinstance(value, str) and value.isdigit():
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parsed_value = int(value)
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if parsed_value > 0:
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return parsed_value
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return None
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def _context_length_from_scan_payload(self, model_payload: dict[str, typing.Any] | None) -> int | None:
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if not model_payload:
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return None
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for field_name in ('context_length', 'context_window', 'max_context_length'):
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context_length = self._positive_int(model_payload.get(field_name))
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if context_length is not None:
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return context_length
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return None
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def _context_length_from_litellm_model_info(self, model_info: typing.Any) -> int | None:
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if isinstance(model_info, dict):
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return self._positive_int(model_info.get('max_input_tokens'))
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return self._positive_int(getattr(model_info, 'max_input_tokens', None))
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def _metadata_provider_candidates(self, model_name: str) -> list[str]:
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normalized_model_name = (model_name or '').lower()
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candidates = []
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if normalized_model_name.startswith(('moonshot-', 'kimi-')):
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candidates.append('moonshot')
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if normalized_model_name.startswith('deepseek-'):
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candidates.append('deepseek')
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base_url = self.requester_cfg.get('base_url', '').lower()
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if 'moonshot' in base_url:
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candidates.append('moonshot')
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if 'deepseek' in base_url:
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candidates.append('deepseek')
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deduped_candidates = []
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for candidate in candidates:
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if candidate not in deduped_candidates:
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deduped_candidates.append(candidate)
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return deduped_candidates
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@staticmethod
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def _metadata_model_candidates(model_name: str) -> list[str]:
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"""Return known equivalent model IDs used only for LiteLLM metadata lookup."""
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normalized_model_name = (model_name or '').lower()
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if normalized_model_name.startswith('mimo-v2.5'):
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return [f'openrouter/xiaomi/{normalized_model_name}']
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return []
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def _known_context_length_fallback(self, model_name: str) -> int | None:
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normalized_model_name = (model_name or '').lower()
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if normalized_model_name.startswith('deepseek-v4-'):
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return 1_000_000
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if normalized_model_name.startswith(('kimi-k2.5', 'kimi-k2.6')):
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return 256 * 1024
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if normalized_model_name.startswith('moonshot-v1-8k'):
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return 8 * 1024
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if normalized_model_name.startswith('moonshot-v1-32k'):
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return 32 * 1024
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if normalized_model_name.startswith('moonshot-v1-128k') or normalized_model_name == 'moonshot-v1-auto':
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return 128 * 1024
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return None
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def _safe_context_length(self, model_name: str) -> int | None:
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helper = getattr(litellm, 'get_model_info', None)
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if not callable(helper):
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return self._known_context_length_fallback(model_name)
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candidates = self._metadata_model_candidates(model_name)
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candidates.append(model_name)
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litellm_model_name = self._build_litellm_model_name(model_name)
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if litellm_model_name != model_name:
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candidates.append(litellm_model_name)
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for provider in self._metadata_provider_candidates(model_name):
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candidates.append(f'{provider}/{model_name}')
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tried_candidates = []
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for candidate in candidates:
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if candidate in tried_candidates:
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continue
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tried_candidates.append(candidate)
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try:
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model_info = helper(candidate)
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except Exception:
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continue
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context_length = self._context_length_from_litellm_model_info(model_info)
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if context_length is not None:
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return context_length
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return self._known_context_length_fallback(model_name)
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def _supports_function_calling(self, model_name: str) -> bool:
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return self._safe_litellm_bool_helper('supports_function_calling', model_name)
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def _supports_vision(self, model_name: str) -> bool:
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return self._safe_litellm_bool_helper('supports_vision', model_name)
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def _supports_reasoning(self, model_name: str) -> bool:
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return self._safe_litellm_bool_helper('supports_reasoning', model_name)
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def _requester_name(self, model: requester.RuntimeLLMModel | None = None) -> str:
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if model is not None:
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provider_entity = getattr(getattr(model, 'provider', None), 'provider_entity', None)
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name = getattr(provider_entity, 'requester', None)
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if isinstance(name, str) and name:
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return name.lower()
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return str(self.requester_cfg.get('requester_name') or '').lower()
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@staticmethod
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def _infer_reasoning_family_from_model_name(model_name: str) -> str:
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normalized_name = (model_name or '').lower()
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basename = normalized_name.rsplit('/', 1)[-1]
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if basename.startswith(('gpt-', 'chatgpt-', 'o1', 'o3', 'o4')):
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return 'openai'
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if basename.startswith('claude-'):
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return 'anthropic'
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if basename.startswith('deepseek-'):
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return 'deepseek'
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if basename.startswith(('kimi-', 'moonshot-')):
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return 'kimi'
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if basename.startswith(('qwen-', 'qwen3', 'qwq')):
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return 'qwen'
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if basename.startswith(('doubao-', 'seed-')):
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return 'doubao'
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if basename.startswith('mimo-'):
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return 'mimo'
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return ''
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def _reasoning_family(
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self,
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model_name: str,
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model: requester.RuntimeLLMModel | None = None,
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) -> str:
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requester_name = self._requester_name(model)
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if requester_name in {'new-api-chat-completions', 'volcark-chat-completions'}:
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inferred_family = self._infer_reasoning_family_from_model_name(model_name)
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if inferred_family:
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return inferred_family
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return 'volcengine' if requester_name == 'volcark-chat-completions' else ''
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# Bailian's compatible endpoint also hosts Kimi models. Keep those
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# models on Kimi's ``thinking`` protocol instead of Qwen's
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# ``enable_thinking`` protocol.
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if requester_name == 'bailian-chat-completions':
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inferred_family = self._infer_reasoning_family_from_model_name(model_name)
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if inferred_family == 'kimi':
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return inferred_family
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requester_family = self._REQUESTER_REASONING_FAMILIES.get(requester_name)
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if requester_family:
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return requester_family
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inferred_family = self._infer_reasoning_family_from_model_name(model_name)
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provider = (self._get_custom_llm_provider() or '').lower()
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if provider == 'openai':
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return inferred_family or ('openai' if requester_name in {'', 'openai'} else '')
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if provider:
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return provider
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return inferred_family
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@staticmethod
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def _is_anthropic_adaptive_model(model_name: str) -> bool:
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basename = model_name.lower().rsplit('/', 1)[-1]
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if 'mythos-preview' in basename:
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return True
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parts = basename.split('-')
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if len(parts) < 3 or parts[0] != 'claude':
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return False
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model_families = {'opus', 'sonnet', 'fable', 'mythos'}
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if parts[1] in model_families:
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if parts[2] == '5':
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return True
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return len(parts) >= 4 and parts[2] == '4' and parts[3] in {'6', '7', '8'}
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return parts[1] == '5' and parts[2] in model_families
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@staticmethod
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def _is_anthropic_always_thinking_model(model_name: str) -> bool:
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normalized_name = model_name.lower()
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return any(marker in normalized_name for marker in ('fable-5', 'mythos-5', 'mythos-preview'))
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@staticmethod
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def _is_dedicated_qwen_thinking_model(model_name: str) -> bool:
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normalized_name = model_name.lower().rsplit('/', 1)[-1]
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return (
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|
normalized_name in LiteLLMRequester._QWEN_DEDICATED_THINKING_MODELS
|
|
or normalized_name.startswith('qwq')
|
|
or '-thinking' in normalized_name
|
|
)
|
|
|
|
@staticmethod
|
|
def _supports_qwen_thinking_budget(model_name: str) -> bool:
|
|
"""Return whether the documented Qwen3 family supports thinking_budget."""
|
|
normalized_name = model_name.lower().rsplit('/', 1)[-1]
|
|
return normalized_name.startswith('qwen3')
|
|
|
|
def _known_reasoning_levels(self, model_name: str, family: str) -> list[str] | None:
|
|
normalized_name = model_name.lower().rsplit('/', 1)[-1]
|
|
|
|
if family == 'deepseek' and normalized_name.startswith('deepseek-'):
|
|
if normalized_name.startswith('deepseek-v4-'):
|
|
return ['provider_default', 'disabled', 'low', 'high', 'xhigh', 'max']
|
|
if 'reasoner' in normalized_name or '-r1' in normalized_name:
|
|
return ['provider_default']
|
|
return ['provider_default', 'disabled', 'enabled']
|
|
|
|
if family == 'kimi':
|
|
if normalized_name.startswith('kimi-k3'):
|
|
return ['provider_default', 'low', 'high', 'max']
|
|
if normalized_name.startswith('kimi-k2.7-code'):
|
|
return ['provider_default']
|
|
if normalized_name.startswith(('kimi-k2.5', 'kimi-k2.6')):
|
|
return ['provider_default', 'disabled', 'enabled']
|
|
if 'thinking' in normalized_name:
|
|
return ['provider_default']
|
|
|
|
if family == 'qwen' and normalized_name.startswith(('qwen-', 'qwen3', 'qwq')):
|
|
if self._is_dedicated_qwen_thinking_model(normalized_name):
|
|
if self._supports_qwen_thinking_budget(normalized_name):
|
|
return ['provider_default', 'low', 'medium', 'high']
|
|
return ['provider_default']
|
|
if self._supports_qwen_thinking_budget(normalized_name):
|
|
return ['provider_default', 'disabled', 'low', 'medium', 'high']
|
|
return ['provider_default', 'disabled', 'enabled']
|
|
|
|
if family == 'doubao' and normalized_name.startswith(('doubao-', 'seed-')):
|
|
return ['provider_default', 'disabled', 'low', 'medium', 'high']
|
|
|
|
if family == 'mimo' and normalized_name.startswith(('mimo-v2.5',)):
|
|
return ['provider_default', 'disabled', 'enabled']
|
|
|
|
if family == 'anthropic' and normalized_name.startswith('claude-'):
|
|
levels = ['provider_default']
|
|
adaptive = self._is_anthropic_adaptive_model(normalized_name)
|
|
if adaptive and not self._is_anthropic_always_thinking_model(normalized_name):
|
|
levels.append('disabled')
|
|
levels.extend(['low', 'medium', 'high'])
|
|
if adaptive:
|
|
levels.extend(['xhigh', 'max'])
|
|
return levels
|
|
|
|
if family == 'openai' and normalized_name.startswith(('gpt-5', 'o1', 'o3', 'o4')):
|
|
return ['provider_default', 'low', 'medium', 'high']
|
|
|
|
return None
|
|
|
|
def _openai_reasoning_levels(self, model_name: str) -> list[str]:
|
|
model_info = self._safe_model_info(model_name)
|
|
levels = ['provider_default']
|
|
if model_info.get('supports_none_reasoning_effort') is True:
|
|
levels.append('disabled')
|
|
if model_info.get('supports_minimal_reasoning_effort') is True:
|
|
levels.append('minimal')
|
|
for level in ('low', 'medium', 'high'):
|
|
if model_info.get(f'supports_{level}_reasoning_effort') is not False:
|
|
levels.append(level)
|
|
for level in ('xhigh', 'max'):
|
|
if model_info.get(f'supports_{level}_reasoning_effort') is True:
|
|
levels.append(level)
|
|
return levels
|
|
|
|
def _safe_model_info(self, model_name: str) -> dict[str, typing.Any]:
|
|
helper = getattr(litellm, 'get_model_info', None)
|
|
if not callable(helper):
|
|
return {}
|
|
|
|
candidates = [
|
|
*self._metadata_model_candidates(model_name),
|
|
model_name,
|
|
self._build_litellm_model_name(model_name),
|
|
]
|
|
for candidate in candidates:
|
|
try:
|
|
info = helper(candidate)
|
|
except Exception:
|
|
continue
|
|
if isinstance(info, dict):
|
|
return info
|
|
model_dump = getattr(info, 'model_dump', None)
|
|
if callable(model_dump):
|
|
try:
|
|
dumped = model_dump()
|
|
if isinstance(dumped, dict):
|
|
return dumped
|
|
except Exception:
|
|
continue
|
|
return {}
|
|
|
|
def get_reasoning_capabilities(self, model: requester.RuntimeLLMModel) -> dict[str, typing.Any]:
|
|
model_name = model.model_entity.name
|
|
abilities = model.model_entity.abilities or []
|
|
detected = self._supports_reasoning(model_name)
|
|
declared = 'reasoning' in abilities
|
|
family = self._reasoning_family(model_name, model)
|
|
known_levels = self._known_reasoning_levels(model_name, family)
|
|
supported = detected or declared or known_levels is not None
|
|
if not supported:
|
|
return reasoning.default_reasoning_capabilities()
|
|
|
|
normalized_name = model_name.lower()
|
|
if family == 'openai':
|
|
levels = self._openai_reasoning_levels(model_name)
|
|
elif known_levels is not None:
|
|
levels = known_levels
|
|
elif family == 'anthropic':
|
|
levels = ['provider_default', 'low', 'medium', 'high']
|
|
elif family in {'deepseek', 'qwen', 'mimo', 'volcengine'}:
|
|
levels = ['provider_default', 'disabled', 'enabled']
|
|
elif family == 'doubao':
|
|
levels = ['provider_default', 'disabled', 'low', 'medium', 'high']
|
|
elif family == 'ollama':
|
|
levels = ['provider_default']
|
|
levels.append('disabled')
|
|
if normalized_name.startswith('gpt-oss') or '/gpt-oss' in normalized_name:
|
|
levels.extend(['low', 'medium', 'high'])
|
|
else:
|
|
levels.append('enabled')
|
|
elif family in self._INFERRED_EFFORT_PROVIDERS:
|
|
levels = ['provider_default', 'low', 'medium', 'high']
|
|
else:
|
|
levels = ['provider_default']
|
|
|
|
capabilities = {
|
|
'supported': True,
|
|
'levels': list(dict.fromkeys(levels)),
|
|
'source': 'litellm' if detected else ('provider' if known_levels is not None else 'manual'),
|
|
}
|
|
if family == 'qwen' and 'disabled' in capabilities['levels'] and 'enabled' not in capabilities['levels']:
|
|
capabilities['legacy_levels'] = ['enabled']
|
|
return capabilities
|
|
|
|
def _build_reasoning_args(self, model: requester.RuntimeLLMModel) -> dict[str, typing.Any]:
|
|
level = self._reasoning_level(model)
|
|
if level == 'provider_default':
|
|
return {}
|
|
|
|
config = {'level': level}
|
|
capabilities = self.get_reasoning_capabilities(model)
|
|
try:
|
|
reasoning.validate_reasoning_capabilities(config, capabilities, model.model_entity.name)
|
|
except ValueError as exc:
|
|
raise errors.RequesterError(str(exc)) from exc
|
|
|
|
family = self._reasoning_family(model.model_entity.name, model)
|
|
if level == 'disabled':
|
|
if family in {'deepseek', 'kimi', 'mimo', 'doubao'}:
|
|
return {'extra_body': {'thinking': {'type': 'disabled'}}}
|
|
if family == 'qwen':
|
|
return {'extra_body': {'enable_thinking': False}}
|
|
if family == 'volcengine':
|
|
return {'extra_body': {'thinking': {'type': 'disabled'}}}
|
|
if family == 'anthropic':
|
|
return {'thinking': {'type': 'disabled'}}
|
|
return {'reasoning_effort': 'none'}
|
|
if level == 'enabled':
|
|
if family in {'deepseek', 'kimi', 'mimo', 'volcengine'}:
|
|
return {'extra_body': {'thinking': {'type': 'enabled'}}}
|
|
if family == 'qwen':
|
|
return {'extra_body': {'enable_thinking': True}}
|
|
return {'reasoning_effort': 'low'}
|
|
if family == 'qwen' and level in self._QWEN_REASONING_BUDGETS:
|
|
return {
|
|
'extra_body': {
|
|
'enable_thinking': True,
|
|
'thinking_budget': self._QWEN_REASONING_BUDGETS[level],
|
|
}
|
|
}
|
|
if family == 'deepseek':
|
|
return {
|
|
'extra_body': {
|
|
'thinking': {'type': 'enabled'},
|
|
'reasoning_effort': level,
|
|
}
|
|
}
|
|
return {'reasoning_effort': level}
|
|
|
|
@staticmethod
|
|
def _reasoning_config_value(model: requester.RuntimeLLMModel) -> typing.Any:
|
|
raw_config = getattr(model, 'reasoning_config_override', None)
|
|
if raw_config is None:
|
|
raw_config = getattr(model.model_entity, 'reasoning_config', None)
|
|
if not isinstance(raw_config, dict):
|
|
return None
|
|
return raw_config
|
|
|
|
def _reasoning_level(self, model: requester.RuntimeLLMModel) -> str:
|
|
return reasoning.normalize_reasoning_config(self._reasoning_config_value(model))['level']
|
|
|
|
def _infer_model_type(self, model_id: str) -> str:
|
|
normalized_id = (model_id or '').lower()
|
|
if any(kw in normalized_id for kw in self._RERANK_MODEL_HINTS):
|
|
return 'rerank'
|
|
if any(kw in normalized_id for kw in self._EMBEDDING_MODEL_HINTS):
|
|
return 'embedding'
|
|
return 'llm'
|
|
|
|
def _enrich_scanned_model(
|
|
self,
|
|
model_id: str,
|
|
model_payload: dict[str, typing.Any] | None = None,
|
|
) -> dict[str, typing.Any]:
|
|
model_type = self._infer_model_type(model_id)
|
|
scanned_model: dict[str, typing.Any] = {
|
|
'id': model_id,
|
|
'name': model_id,
|
|
'type': model_type,
|
|
}
|
|
|
|
if model_type == 'llm':
|
|
abilities = []
|
|
if self._supports_function_calling(model_id):
|
|
abilities.append('func_call')
|
|
supports_provider_reported_vision = bool(
|
|
model_payload
|
|
and (model_payload.get('supports_image_in') is True or model_payload.get('supports_vision') is True)
|
|
)
|
|
if supports_provider_reported_vision or self._supports_vision(model_id):
|
|
abilities.append('vision')
|
|
supports_provider_reported_reasoning = bool(
|
|
model_payload and model_payload.get('supports_reasoning') is True
|
|
)
|
|
family = self._reasoning_family(model_id)
|
|
supports_known_reasoning = self._known_reasoning_levels(model_id, family) is not None
|
|
if supports_provider_reported_reasoning or supports_known_reasoning or self._supports_reasoning(model_id):
|
|
abilities.append('reasoning')
|
|
scanned_model['abilities'] = abilities
|
|
|
|
context_length = self._context_length_from_scan_payload(model_payload)
|
|
if context_length is None:
|
|
context_length = self._safe_context_length(model_id)
|
|
if context_length is not None:
|
|
scanned_model['context_length'] = context_length
|
|
|
|
return scanned_model
|
|
|
|
def _convert_messages(
|
|
self,
|
|
messages: typing.List[provider_message.Message],
|
|
reasoning_family: str = '',
|
|
include_reasoning_context: bool = True,
|
|
) -> list[dict]:
|
|
"""Convert LangBot messages to LiteLLM/OpenAI format."""
|
|
req_messages = []
|
|
for m in messages:
|
|
msg_dict = m.dict(exclude_none=True)
|
|
content = msg_dict.get('content')
|
|
|
|
if msg_dict.get('role') == 'assistant' and reasoning_family:
|
|
provider_fields = msg_dict.get('provider_specific_fields')
|
|
if isinstance(provider_fields, dict):
|
|
cleaned_provider_fields = dict(provider_fields)
|
|
reasoning_content = cleaned_provider_fields.pop('reasoning_content', None)
|
|
thinking_blocks = cleaned_provider_fields.pop('thinking_blocks', None)
|
|
|
|
# ``content`` is also used for the user-facing rendering.
|
|
# Do not replay that rendered <think> wrapper alongside the
|
|
# structured provider reasoning on the next request.
|
|
if reasoning_content or thinking_blocks:
|
|
content = msg_dict.get('content')
|
|
if isinstance(content, str):
|
|
msg_dict['content'] = self._strip_think(content)
|
|
|
|
if include_reasoning_context:
|
|
if reasoning_family == 'anthropic' and thinking_blocks:
|
|
msg_dict['thinking_blocks'] = thinking_blocks
|
|
elif reasoning_family in {
|
|
'deepseek',
|
|
'kimi',
|
|
'qwen',
|
|
'doubao',
|
|
'mimo',
|
|
'volcengine',
|
|
} and isinstance(reasoning_content, str):
|
|
msg_dict['reasoning_content'] = reasoning_content
|
|
|
|
if cleaned_provider_fields:
|
|
msg_dict['provider_specific_fields'] = cleaned_provider_fields
|
|
else:
|
|
msg_dict.pop('provider_specific_fields', None)
|
|
|
|
if isinstance(content, list):
|
|
converted_parts = []
|
|
for part in content:
|
|
if isinstance(part, dict) and part.get('type') == 'image_base64':
|
|
# History trimming (SessionManager) clears image_base64
|
|
# on past turns and exclude_none serialization drops
|
|
# the key entirely, so the replayed part may carry no
|
|
# payload. Prefer the base64 payload; fall back to an
|
|
# image_url that survived on the same element; drop
|
|
# hollow parts instead of raising KeyError (#2469).
|
|
image_b64 = part.get('image_base64')
|
|
fallback_url = None
|
|
if not image_b64:
|
|
raw_image_url = part.get('image_url')
|
|
if isinstance(raw_image_url, dict):
|
|
fallback_url = raw_image_url.get('url')
|
|
if image_b64 or fallback_url:
|
|
part['image_url'] = {'url': image_b64 or fallback_url}
|
|
part['type'] = 'image_url'
|
|
part.pop('image_base64', None)
|
|
else:
|
|
continue
|
|
# OpenAI-compatible chat models reject non-image file parts
|
|
# (audio/document base64 or url). These originate from Voice /
|
|
# File attachments — including ones replayed from conversation
|
|
# history — and the agent already accesses their bytes via the
|
|
# sandbox. Drop them from the model payload to avoid
|
|
# "Invalid user message ... invalid content type=file_base64".
|
|
if isinstance(part, dict) and part.get('type') in ('file_base64', 'file_url'):
|
|
continue
|
|
converted_parts.append(part)
|
|
msg_dict['content'] = converted_parts
|
|
|
|
req_messages.append(msg_dict)
|
|
|
|
return req_messages
|
|
|
|
_THINK_PATTERNS: tuple[str, ...] = (
|
|
r'^\s*(?:(?!<think>).)*?</think>\s*',
|
|
r'^\s*(?:(?!CRETIRE_REASONING_BEGINk).)*?CRETIRE_REASONING_ENDk\s*',
|
|
r'<think>.*?</think>',
|
|
r'CRETIRE_REASONING_BEGINk.*?CRETIRE_REASONING_ENDk',
|
|
)
|
|
|
|
@classmethod
|
|
def _strip_think(cls, content: str) -> str:
|
|
"""Strip chain-of-thought blocks from ``content``."""
|
|
if not content:
|
|
return content
|
|
|
|
import re
|
|
|
|
for pattern in cls._THINK_PATTERNS:
|
|
content = re.sub(pattern, '', content, flags=re.DOTALL)
|
|
return content.strip()
|
|
|
|
def _process_thinking_content(self, content: str, reasoning_content: str | None, remove_think: bool) -> str:
|
|
"""Process thinking/reasoning content.
|
|
|
|
Args:
|
|
content: The main content from response
|
|
reasoning_content: Separate reasoning content from model
|
|
remove_think: If True, remove thinking markers; if False, preserve them
|
|
|
|
Returns:
|
|
Processed content string
|
|
"""
|
|
if remove_think and content:
|
|
content = self._strip_think(content)
|
|
|
|
if reasoning_content and not remove_think:
|
|
content = f'<think>\n{reasoning_content}\n</think>\n{content or ""}'.strip()
|
|
|
|
return content or ''
|
|
|
|
@staticmethod
|
|
def _thinking_blocks_text(thinking_blocks: typing.Any) -> str:
|
|
if not isinstance(thinking_blocks, list):
|
|
return ''
|
|
parts = []
|
|
for block in thinking_blocks:
|
|
if isinstance(block, dict):
|
|
text = block.get('thinking')
|
|
else:
|
|
text = getattr(block, 'thinking', None)
|
|
if isinstance(text, str) and text:
|
|
parts.append(text)
|
|
return ''.join(parts)
|
|
|
|
@classmethod
|
|
def _merge_thinking_blocks(
|
|
cls,
|
|
current: list[dict[str, typing.Any]],
|
|
incoming: typing.Any,
|
|
) -> list[dict[str, typing.Any]]:
|
|
"""Merge Anthropic thinking block fragments emitted by a stream."""
|
|
if not isinstance(incoming, list):
|
|
return current
|
|
merged = [dict(block) for block in current]
|
|
for raw_block in incoming:
|
|
block = cls._as_dict(raw_block)
|
|
if not block:
|
|
continue
|
|
block_type = block.get('type')
|
|
if block_type == 'redacted_thinking':
|
|
merged.append(block)
|
|
continue
|
|
|
|
text = block.get('thinking') if isinstance(block.get('thinking'), str) else ''
|
|
signature = block.get('signature')
|
|
if merged and merged[-1].get('type') == 'thinking' and not merged[-1].get('signature'):
|
|
merged[-1]['thinking'] = f'{merged[-1].get("thinking", "")}{text}'
|
|
if signature:
|
|
merged[-1]['signature'] = signature
|
|
elif merged and signature and merged[-1].get('signature') == signature:
|
|
if text and text != merged[-1].get('thinking', ''):
|
|
merged[-1]['thinking'] = f'{merged[-1].get("thinking", "")}{text}'
|
|
else:
|
|
merged.append(block)
|
|
return merged
|
|
|
|
@staticmethod
|
|
def _normalize_usage(usage: typing.Any) -> dict:
|
|
"""Normalize a LiteLLM/OpenAI usage object into a plain token dict.
|
|
|
|
Handles several real-world shapes returned by different upstreams:
|
|
- object with ``prompt_tokens`` / ``completion_tokens`` / ``total_tokens`` attrs
|
|
- dict with the same keys
|
|
- missing ``total_tokens`` (derived from prompt + completion)
|
|
- ``None`` / partially-populated usage (defaults to 0)
|
|
- provider-specific token details, including cache token counters
|
|
"""
|
|
|
|
def _plain_value(value: typing.Any) -> typing.Any:
|
|
if value is None:
|
|
return None
|
|
if isinstance(value, dict):
|
|
return {k: _plain_value(v) for k, v in value.items() if v is not None}
|
|
if isinstance(value, (list, tuple)):
|
|
return [_plain_value(v) for v in value]
|
|
|
|
model_dump = getattr(value, 'model_dump', None)
|
|
if callable(model_dump):
|
|
try:
|
|
dumped = model_dump()
|
|
if isinstance(dumped, dict):
|
|
return _plain_value(dumped)
|
|
except Exception:
|
|
pass
|
|
|
|
return value
|
|
|
|
def _usage_dict(value: typing.Any) -> dict[str, typing.Any]:
|
|
if value is None:
|
|
return {}
|
|
plain = _plain_value(value)
|
|
if isinstance(plain, dict):
|
|
return plain
|
|
|
|
def _is_mock_attr(attr: typing.Any) -> bool:
|
|
return type(attr).__module__.startswith('unittest.mock')
|
|
|
|
data: dict[str, typing.Any] = {}
|
|
for key in (
|
|
'prompt_tokens',
|
|
'completion_tokens',
|
|
'total_tokens',
|
|
'prompt_tokens_details',
|
|
'completion_tokens_details',
|
|
'cache_creation_input_tokens',
|
|
'cache_read_input_tokens',
|
|
'input_token_details',
|
|
'output_token_details',
|
|
):
|
|
attr_value = getattr(value, key, None)
|
|
if attr_value is not None and not _is_mock_attr(attr_value):
|
|
data[key] = _plain_value(attr_value)
|
|
return data
|
|
|
|
def _to_int(value: typing.Any) -> int:
|
|
try:
|
|
return int(value or 0)
|
|
except (TypeError, ValueError):
|
|
return 0
|
|
|
|
normalized = _usage_dict(usage)
|
|
|
|
prompt_tokens = _to_int(normalized.get('prompt_tokens'))
|
|
completion_tokens = _to_int(normalized.get('completion_tokens'))
|
|
total_tokens = _to_int(normalized.get('total_tokens'))
|
|
|
|
# Some providers omit total_tokens in streaming usage; derive it.
|
|
if not total_tokens:
|
|
total_tokens = prompt_tokens + completion_tokens
|
|
|
|
normalized['prompt_tokens'] = prompt_tokens
|
|
normalized['completion_tokens'] = completion_tokens
|
|
normalized['total_tokens'] = total_tokens
|
|
return normalized
|
|
|
|
def _extract_usage(self, response) -> dict | None:
|
|
"""Extract usage info from a non-streaming LiteLLM response."""
|
|
usage = getattr(response, 'usage', None)
|
|
if usage is None:
|
|
return None
|
|
return self._normalize_usage(usage)
|
|
|
|
@staticmethod
|
|
def _as_dict(value: typing.Any) -> dict:
|
|
if value is None:
|
|
return {}
|
|
if isinstance(value, dict):
|
|
return value
|
|
if hasattr(value, 'model_dump'):
|
|
return value.model_dump()
|
|
return {}
|
|
|
|
def _normalize_stream_tool_calls(
|
|
self,
|
|
raw_tool_calls: typing.Any,
|
|
tool_call_state: dict[int, dict[str, typing.Any]],
|
|
) -> list[dict] | None:
|
|
"""Fill OpenAI-style streaming tool-call deltas so MessageChunk can validate them.
|
|
|
|
Also preserves provider_specific_fields (e.g., Gemini thought_signature) for
|
|
round-tripping to the next request.
|
|
"""
|
|
if not raw_tool_calls:
|
|
return None
|
|
|
|
normalized = []
|
|
for fallback_index, raw_tool_call in enumerate(raw_tool_calls):
|
|
tool_call = self._as_dict(raw_tool_call)
|
|
index = tool_call.get('index')
|
|
if not isinstance(index, int):
|
|
index = fallback_index
|
|
|
|
state = tool_call_state.setdefault(
|
|
index,
|
|
{
|
|
'id': '',
|
|
'type': 'function',
|
|
'name': '',
|
|
'provider_specific_fields': None,
|
|
},
|
|
)
|
|
if tool_call.get('id'):
|
|
state['id'] = tool_call['id']
|
|
if tool_call.get('type'):
|
|
state['type'] = tool_call['type']
|
|
|
|
# Preserve provider_specific_fields from the raw tool call
|
|
if 'provider_specific_fields' in tool_call:
|
|
state['provider_specific_fields'] = tool_call['provider_specific_fields']
|
|
|
|
function = self._as_dict(tool_call.get('function'))
|
|
if function.get('name'):
|
|
state['name'] = function['name']
|
|
|
|
# Also check function-level provider_specific_fields
|
|
if 'provider_specific_fields' in function:
|
|
# Merge function-level into tool-level, function-level takes precedence
|
|
func_psf = function['provider_specific_fields']
|
|
if state['provider_specific_fields']:
|
|
merged = {**state['provider_specific_fields'], **func_psf}
|
|
state['provider_specific_fields'] = merged
|
|
else:
|
|
state['provider_specific_fields'] = func_psf
|
|
|
|
arguments = function.get('arguments')
|
|
if arguments is None:
|
|
arguments = ''
|
|
elif not isinstance(arguments, str):
|
|
arguments = str(arguments)
|
|
|
|
# Some OpenAI-compatible providers (notably Ollama's
|
|
# /v1/chat/completions) stream a tool-call delta with an `index` and
|
|
# a `function` payload but never emit an OpenAI-style `id`. Without
|
|
# an id the call used to be dropped here, so the whole tool call
|
|
# silently vanished: a tool-only turn then yielded no content and no
|
|
# tool call, the stream "completed" with 0 chars, and the chat
|
|
# appeared stuck. Synthesize a stable per-index id so named-but-idless
|
|
# tool calls survive. Providers that do send ids keep theirs.
|
|
if not state['id'] and state['name']:
|
|
state['id'] = f'call_{index}'
|
|
|
|
if not state['id'] or not state['name']:
|
|
continue
|
|
|
|
tool_call_dict: dict[str, typing.Any] = {
|
|
'id': state['id'],
|
|
'type': state['type'] or 'function',
|
|
'function': {
|
|
'name': state['name'],
|
|
'arguments': arguments,
|
|
},
|
|
}
|
|
|
|
# Include provider_specific_fields if present
|
|
if state['provider_specific_fields']:
|
|
tool_call_dict['provider_specific_fields'] = state['provider_specific_fields']
|
|
|
|
normalized.append(tool_call_dict)
|
|
|
|
return normalized or None
|
|
|
|
def _build_common_args(self, args: dict, include_retry_params: bool = True) -> dict:
|
|
"""Apply common requester config to args dict."""
|
|
if self.requester_cfg.get('base_url'):
|
|
args['api_base'] = self.requester_cfg['base_url']
|
|
if self.requester_cfg.get('timeout'):
|
|
args['timeout'] = self.requester_cfg['timeout']
|
|
if include_retry_params:
|
|
if self.requester_cfg.get('drop_params'):
|
|
args['drop_params'] = self.requester_cfg['drop_params']
|
|
if self.requester_cfg.get('num_retries'):
|
|
args['num_retries'] = self.requester_cfg['num_retries']
|
|
if self.requester_cfg.get('api_version'):
|
|
args['api_version'] = self.requester_cfg['api_version']
|
|
return args
|
|
|
|
def _handle_litellm_error(self, e: Exception) -> None:
|
|
"""Convert LiteLLM exceptions to RequesterError. Never returns, always raises."""
|
|
# Check more specific exceptions first (they inherit from base exceptions)
|
|
if isinstance(e, litellm.ContextWindowExceededError):
|
|
raise errors.RequesterError(f'上下文长度超限: {str(e)}')
|
|
if isinstance(e, litellm.BadRequestError):
|
|
raise errors.RequesterError(f'请求参数错误: {str(e)}')
|
|
if isinstance(e, litellm.AuthenticationError):
|
|
raise errors.RequesterError(f'API key 无效: {str(e)}')
|
|
if isinstance(e, litellm.NotFoundError):
|
|
raise errors.RequesterError(f'模型或路径无效: {str(e)}')
|
|
if isinstance(e, litellm.RateLimitError):
|
|
raise errors.RequesterError(f'请求过于频繁或余额不足: {str(e)}')
|
|
if isinstance(e, litellm.Timeout):
|
|
raise errors.RequesterError(f'请求超时: {str(e)}')
|
|
if isinstance(e, litellm.APIConnectionError):
|
|
raise errors.RequesterError(f'连接错误: {str(e)}')
|
|
if isinstance(e, litellm.APIError):
|
|
raise errors.RequesterError(f'API 错误: {str(e)}')
|
|
raise errors.RequesterError(f'未知错误: {str(e)}')
|
|
|
|
async def _build_completion_args(
|
|
self,
|
|
model: requester.RuntimeLLMModel,
|
|
messages: typing.List[provider_message.Message],
|
|
funcs: typing.List[resource_tool.LLMTool] = None,
|
|
extra_args: dict[str, typing.Any] = {},
|
|
stream: bool = False,
|
|
) -> dict:
|
|
"""Build common completion arguments for invoke_llm and invoke_llm_stream."""
|
|
reasoning_family = self._reasoning_family(model.model_entity.name, model)
|
|
reasoning_level = self._reasoning_level(model)
|
|
req_messages = self._convert_messages(
|
|
messages,
|
|
reasoning_family=reasoning_family,
|
|
include_reasoning_context=reasoning_level != 'disabled',
|
|
)
|
|
model_name = self._build_litellm_model_name(model.model_entity.name)
|
|
api_key = model.provider.token_mgr.get_token()
|
|
|
|
args = {
|
|
'model': model_name,
|
|
'messages': req_messages,
|
|
'api_key': api_key,
|
|
}
|
|
if stream:
|
|
args['stream'] = True
|
|
args['stream_options'] = {'include_usage': True}
|
|
self._build_common_args(args)
|
|
|
|
# Apply model-level extra_args first, then call-level extra_args
|
|
if model.model_entity.extra_args:
|
|
args.update(model.model_entity.extra_args)
|
|
args.update(extra_args)
|
|
|
|
reasoning_args = self._build_reasoning_args(model)
|
|
if reasoning_args:
|
|
conflicts = reasoning.find_reasoning_arg_conflicts(model.model_entity.extra_args)
|
|
conflicts.extend(reasoning.find_reasoning_arg_conflicts(extra_args))
|
|
if conflicts:
|
|
raise errors.RequesterError(
|
|
'reasoning_config conflicts with advanced parameters: ' + ', '.join(dict.fromkeys(conflicts))
|
|
)
|
|
reasoning_extra_body = reasoning_args.get('extra_body')
|
|
if isinstance(reasoning_extra_body, dict):
|
|
existing_extra_body = args.get('extra_body') or {}
|
|
if not isinstance(existing_extra_body, dict):
|
|
raise errors.RequesterError('extra_body must be an object')
|
|
args.update({key: value for key, value in reasoning_args.items() if key != 'extra_body'})
|
|
args['extra_body'] = {**existing_extra_body, **reasoning_extra_body}
|
|
else:
|
|
args.update(reasoning_args)
|
|
if 'reasoning_effort' in reasoning_args and self._get_custom_llm_provider() == 'openai':
|
|
allowed_openai_params = args.get('allowed_openai_params') or []
|
|
if not isinstance(allowed_openai_params, (list, tuple, set)):
|
|
raise errors.RequesterError('allowed_openai_params must be an array')
|
|
args['allowed_openai_params'] = list(dict.fromkeys([*allowed_openai_params, 'reasoning_effort']))
|
|
|
|
if funcs:
|
|
tools = await self.ap.tool_mgr.generate_tools_for_openai(funcs)
|
|
if tools:
|
|
args['tools'] = tools
|
|
args.setdefault('tool_choice', 'auto')
|
|
|
|
return args
|
|
|
|
async def invoke_llm(
|
|
self,
|
|
query: pipeline_query.Query,
|
|
model: requester.RuntimeLLMModel,
|
|
messages: typing.List[provider_message.Message],
|
|
funcs: typing.List[resource_tool.LLMTool] = None,
|
|
extra_args: dict[str, typing.Any] = {},
|
|
remove_think: bool = False,
|
|
) -> tuple[provider_message.Message, dict]:
|
|
"""Invoke LLM and return message with usage info."""
|
|
args = await self._build_completion_args(model, messages, funcs, extra_args, stream=False)
|
|
|
|
try:
|
|
response = await acompletion(**args)
|
|
|
|
message_data = response.choices[0].message.model_dump()
|
|
if 'role' not in message_data or message_data['role'] is None:
|
|
message_data['role'] = 'assistant'
|
|
|
|
content = message_data.get('content', '')
|
|
reasoning_content = message_data.get('reasoning_content', None)
|
|
thinking_blocks = message_data.get('thinking_blocks')
|
|
if reasoning_content or thinking_blocks:
|
|
provider_fields = dict(message_data.get('provider_specific_fields') or {})
|
|
if reasoning_content:
|
|
provider_fields['reasoning_content'] = reasoning_content
|
|
if thinking_blocks:
|
|
provider_fields['thinking_blocks'] = thinking_blocks
|
|
message_data['provider_specific_fields'] = provider_fields
|
|
display_reasoning = reasoning_content or self._thinking_blocks_text(thinking_blocks) or None
|
|
message_data['content'] = self._process_thinking_content(content, display_reasoning, remove_think)
|
|
|
|
if 'reasoning_content' in message_data:
|
|
del message_data['reasoning_content']
|
|
if 'thinking_blocks' in message_data:
|
|
del message_data['thinking_blocks']
|
|
|
|
message = provider_message.Message(**message_data)
|
|
usage_info = self._extract_usage(response)
|
|
|
|
return message, usage_info
|
|
|
|
except Exception as e:
|
|
self._handle_litellm_error(e)
|
|
|
|
async def invoke_llm_stream(
|
|
self,
|
|
query: pipeline_query.Query,
|
|
model: requester.RuntimeLLMModel,
|
|
messages: typing.List[provider_message.Message],
|
|
funcs: typing.List[resource_tool.LLMTool] = None,
|
|
extra_args: dict[str, typing.Any] = {},
|
|
remove_think: bool = False,
|
|
) -> provider_message.MessageChunk:
|
|
"""Invoke LLM streaming and yield chunks."""
|
|
args = await self._build_completion_args(model, messages, funcs, extra_args, stream=True)
|
|
|
|
chunk_idx = 0
|
|
role = 'assistant'
|
|
tool_call_state: dict[int, dict[str, typing.Any]] = {}
|
|
think_state = _ThinkStripState() if remove_think else None
|
|
reasoning_started = False
|
|
reasoning_closed = False
|
|
thinking_blocks_state: list[dict[str, typing.Any]] = []
|
|
|
|
try:
|
|
response = await acompletion(**args)
|
|
async for chunk in response:
|
|
# Capture usage whenever a chunk carries it.
|
|
#
|
|
# Important: many OpenAI-compatible gateways (e.g. new-api) and
|
|
# providers send the final usage payload in a chunk that STILL
|
|
# contains a (empty-delta) choice, not an empty `choices` list.
|
|
# The previous implementation only captured usage when `choices`
|
|
# was empty, so streamed calls always recorded 0 tokens.
|
|
# We therefore capture usage independently of `choices`, and then
|
|
# fall through to also process any content this chunk may carry.
|
|
if getattr(chunk, 'usage', None):
|
|
usage_info = self._normalize_usage(chunk.usage)
|
|
if query is not None:
|
|
if query.variables is None:
|
|
query.variables = {}
|
|
query.variables[requester.STREAM_USAGE_QUERY_VARIABLE] = usage_info
|
|
|
|
if not hasattr(chunk, 'choices') or not chunk.choices:
|
|
continue
|
|
|
|
choice = chunk.choices[0]
|
|
delta = choice.delta.model_dump() if hasattr(choice, 'delta') else {}
|
|
finish_reason = getattr(choice, 'finish_reason', None)
|
|
|
|
if 'role' in delta and delta['role']:
|
|
role = delta['role']
|
|
|
|
delta_content = delta.get('content') or ''
|
|
reasoning_content = delta.get('reasoning_content') or ''
|
|
provider_fields = dict(delta.get('provider_specific_fields') or {})
|
|
raw_thinking_blocks = delta.get('thinking_blocks')
|
|
if raw_thinking_blocks:
|
|
thinking_blocks_state = self._merge_thinking_blocks(thinking_blocks_state, raw_thinking_blocks)
|
|
provider_fields['thinking_blocks'] = thinking_blocks_state
|
|
thinking_blocks_text = self._thinking_blocks_text(raw_thinking_blocks)
|
|
display_reasoning_content = reasoning_content or thinking_blocks_text
|
|
|
|
# Handle reasoning_content based on remove_think flag
|
|
if reasoning_content:
|
|
provider_fields['reasoning_content'] = reasoning_content
|
|
if remove_think:
|
|
delta_content = delta_content or None
|
|
else:
|
|
# Stream explicit markers so downstream adapters and
|
|
# the debug page see the same format as non-streaming
|
|
# responses.
|
|
if not reasoning_started:
|
|
delta_content = '<think>\n'
|
|
reasoning_started = True
|
|
else:
|
|
delta_content = ''
|
|
delta_content += display_reasoning_content
|
|
if delta.get('content'):
|
|
delta_content += f'\n</think>\n{delta.get("content")}'
|
|
reasoning_closed = True
|
|
|
|
elif display_reasoning_content:
|
|
if remove_think:
|
|
delta_content = delta_content or None
|
|
else:
|
|
if not reasoning_started:
|
|
delta_content = '<think>\n'
|
|
reasoning_started = True
|
|
else:
|
|
delta_content = ''
|
|
delta_content += display_reasoning_content
|
|
if delta.get('content'):
|
|
delta_content += f'\n</think>\n{delta.get("content")}'
|
|
reasoning_closed = True
|
|
|
|
elif delta_content and not remove_think and reasoning_started and not reasoning_closed:
|
|
delta_content = f'\n</think>\n{delta_content}'
|
|
reasoning_closed = True
|
|
|
|
if finish_reason and not remove_think and reasoning_started and not reasoning_closed:
|
|
delta_content = f'{delta_content}\n</think>\n'
|
|
reasoning_closed = True
|
|
|
|
if think_state is not None and delta_content:
|
|
delta_content = think_state.feed(delta_content)
|
|
|
|
tool_calls = self._normalize_stream_tool_calls(delta.get('tool_calls'), tool_call_state)
|
|
|
|
if not delta_content and not tool_calls and not provider_fields and not finish_reason:
|
|
chunk_idx += 1
|
|
continue
|
|
|
|
chunk_data: dict[str, typing.Any] = {
|
|
'role': role,
|
|
'content': delta_content if delta_content else None,
|
|
'tool_calls': tool_calls,
|
|
'is_final': bool(finish_reason),
|
|
}
|
|
|
|
# Preserve provider_specific_fields from delta (e.g., Gemini thought_signatures)
|
|
if provider_fields:
|
|
chunk_data['provider_specific_fields'] = provider_fields
|
|
|
|
chunk_data = {k: v for k, v in chunk_data.items() if v is not None}
|
|
yield provider_message.MessageChunk(**chunk_data)
|
|
chunk_idx += 1
|
|
|
|
if reasoning_started and not reasoning_closed:
|
|
yield provider_message.MessageChunk(
|
|
role=role,
|
|
content='\n</think>\n',
|
|
is_final=True,
|
|
)
|
|
|
|
if think_state is not None:
|
|
pending_content = think_state.flush()
|
|
if pending_content:
|
|
yield provider_message.MessageChunk(
|
|
role=role,
|
|
content=pending_content,
|
|
is_final=True,
|
|
)
|
|
|
|
except Exception as e:
|
|
self._handle_litellm_error(e)
|
|
|
|
async def invoke_embedding(
|
|
self,
|
|
model: requester.RuntimeEmbeddingModel,
|
|
input_text: list[str],
|
|
extra_args: dict[str, typing.Any] = {},
|
|
) -> tuple[list[list[float]], dict]:
|
|
"""Invoke embedding and return vectors with usage info."""
|
|
model_name = self._build_litellm_model_name(model.model_entity.name)
|
|
api_key = model.provider.token_mgr.get_token()
|
|
|
|
args = {
|
|
'model': model_name,
|
|
'input': input_text,
|
|
'api_key': api_key,
|
|
}
|
|
self._build_common_args(args, include_retry_params=False)
|
|
|
|
if model.model_entity.extra_args:
|
|
args.update(model.model_entity.extra_args)
|
|
|
|
args.update(extra_args)
|
|
|
|
try:
|
|
response = await aembedding(**args)
|
|
|
|
# LiteLLM returns response.data entries either as objects with an
|
|
# `.embedding` attribute or as plain dicts (many OpenAI-compatible
|
|
# gateways, e.g. new-api, yield dict-shaped entries). Handle both.
|
|
embeddings = [d['embedding'] if isinstance(d, dict) else d.embedding for d in response.data]
|
|
usage_info = self._extract_usage(response)
|
|
|
|
return embeddings, usage_info
|
|
|
|
except Exception as e:
|
|
self._handle_litellm_error(e)
|
|
|
|
async def invoke_rerank(
|
|
self,
|
|
model: requester.RuntimeRerankModel,
|
|
query: str,
|
|
documents: typing.List[str],
|
|
extra_args: dict[str, typing.Any] = {},
|
|
) -> typing.List[dict]:
|
|
"""Invoke rerank and return relevance scores."""
|
|
model_name = self._build_litellm_model_name(model.model_entity.name)
|
|
api_key = model.provider.token_mgr.get_token()
|
|
|
|
top_n = min(len(documents), 64)
|
|
|
|
provider = self._get_custom_llm_provider()
|
|
|
|
try:
|
|
# LiteLLM's rerank API does not support the `openai` provider
|
|
# (litellm/rerank_api/main.py raises "Unsupported provider: openai").
|
|
# OpenAI-compatible gateways (newapi / one-api / vLLM / Xinference, etc.)
|
|
# expose the standard Jina/Cohere-style POST /v1/rerank endpoint, so
|
|
# call it directly over HTTP for openai-compatible (or unspecified) providers.
|
|
if provider in (None, '', 'openai'):
|
|
results = await self._invoke_rerank_openai_compatible(
|
|
model_name=model.model_entity.name,
|
|
query=query,
|
|
documents=documents,
|
|
api_key=api_key,
|
|
top_n=top_n,
|
|
extra_args={**(model.model_entity.extra_args or {}), **extra_args},
|
|
)
|
|
else:
|
|
args = {
|
|
'model': model_name,
|
|
'query': query,
|
|
'documents': documents,
|
|
'api_key': api_key,
|
|
'top_n': top_n,
|
|
}
|
|
self._build_common_args(args, include_retry_params=False)
|
|
|
|
if model.model_entity.extra_args:
|
|
args.update(model.model_entity.extra_args)
|
|
|
|
args.update(extra_args)
|
|
|
|
response = await arerank(**args)
|
|
|
|
results = []
|
|
for r in response.results:
|
|
results.append(
|
|
{
|
|
'index': r.get('index', 0),
|
|
'relevance_score': r.get('relevance_score', 0.0),
|
|
}
|
|
)
|
|
|
|
if results:
|
|
scores = [r['relevance_score'] for r in results]
|
|
min_score = min(scores)
|
|
max_score = max(scores)
|
|
if max_score - min_score > 1e-6:
|
|
for r in results:
|
|
r['relevance_score'] = (r['relevance_score'] - min_score) / (max_score - min_score)
|
|
|
|
return results
|
|
|
|
except errors.RequesterError:
|
|
raise
|
|
except Exception as e:
|
|
self._handle_litellm_error(e)
|
|
|
|
async def _invoke_rerank_openai_compatible(
|
|
self,
|
|
model_name: str,
|
|
query: str,
|
|
documents: typing.List[str],
|
|
api_key: str,
|
|
top_n: int,
|
|
extra_args: dict[str, typing.Any] = {},
|
|
) -> typing.List[dict]:
|
|
"""Call the standard Jina/Cohere-style POST /v1/rerank endpoint over HTTP.
|
|
|
|
Used for OpenAI-compatible gateways where litellm.arerank rejects the
|
|
`openai` provider. Returns the same shape as the litellm path:
|
|
a list of {'index': int, 'relevance_score': float}.
|
|
"""
|
|
import httpx
|
|
|
|
base_url = (self.requester_cfg.get('base_url') or '').rstrip('/')
|
|
if not base_url:
|
|
raise errors.RequesterError('Base URL required for rerank')
|
|
|
|
timeout = self.requester_cfg.get('timeout', 120)
|
|
|
|
headers = {'Content-Type': 'application/json'}
|
|
if api_key:
|
|
headers['Authorization'] = f'Bearer {api_key}'
|
|
|
|
request_args = dict(extra_args)
|
|
rerank_url = request_args.pop('rerank_url', None)
|
|
rerank_path = request_args.pop('rerank_path', 'rerank')
|
|
|
|
payload: dict[str, typing.Any] = {
|
|
'model': model_name,
|
|
'query': query,
|
|
'documents': documents,
|
|
'top_n': top_n,
|
|
}
|
|
if request_args:
|
|
payload.update(request_args)
|
|
|
|
if not rerank_url:
|
|
rerank_url = f'{base_url}/{str(rerank_path).strip("/")}'
|
|
|
|
try:
|
|
async with httpx.AsyncClient(
|
|
timeout=timeout,
|
|
event_hooks=httpclient.httpx_response_limit_hooks(),
|
|
) as client:
|
|
resp = await client.post(rerank_url, headers=headers, json=payload)
|
|
resp.raise_for_status()
|
|
data = await httpclient.parse_json_response(resp)
|
|
except httpx.HTTPStatusError as e:
|
|
body = ''
|
|
try:
|
|
body = await httpclient.response_text(e.response)
|
|
except Exception:
|
|
pass
|
|
raise errors.RequesterError(f'rerank 请求失败 (HTTP {e.response.status_code}): {body or str(e)}')
|
|
except httpx.HTTPError as e:
|
|
raise errors.RequesterError(f'rerank 连接错误: {str(e)}')
|
|
|
|
raw_results = data.get('results', []) if isinstance(data, dict) else []
|
|
results = []
|
|
for r in raw_results:
|
|
results.append(
|
|
{
|
|
'index': r.get('index', 0),
|
|
'relevance_score': r.get('relevance_score', r.get('score', 0.0)) or 0.0,
|
|
}
|
|
)
|
|
|
|
return results
|
|
|
|
async def scan_models(self, api_key: str | None = None) -> dict[str, typing.Any]:
|
|
"""Scan models supported by the provider."""
|
|
import httpx
|
|
|
|
base_url = self.requester_cfg.get('base_url', '').rstrip('/')
|
|
timeout = self.requester_cfg.get('timeout', 120)
|
|
|
|
if not base_url:
|
|
raise errors.RequesterError('Base URL required for model scanning')
|
|
|
|
headers = {}
|
|
if api_key:
|
|
headers['Authorization'] = f'Bearer {api_key}'
|
|
|
|
models_url = f'{base_url}/models'
|
|
|
|
try:
|
|
async with httpx.AsyncClient(
|
|
trust_env=True,
|
|
timeout=timeout,
|
|
event_hooks=httpclient.httpx_response_limit_hooks(),
|
|
) as client:
|
|
response = await client.get(models_url, headers=headers)
|
|
response.raise_for_status()
|
|
payload = await httpclient.parse_json_response(response)
|
|
|
|
models = []
|
|
for item in payload.get('data', []):
|
|
model_id = item.get('id')
|
|
if not model_id:
|
|
continue
|
|
|
|
models.append(self._enrich_scanned_model(model_id, item))
|
|
|
|
models.sort(key=lambda x: (x['type'] != 'llm', x['name'].lower()))
|
|
|
|
return {'models': models}
|
|
|
|
except httpx.HTTPStatusError as e:
|
|
raise errors.RequesterError(f'Model scan failed: {e.response.status_code}')
|
|
except httpx.TimeoutException:
|
|
raise errors.RequesterError('Model scan timeout')
|
|
except Exception as e:
|
|
raise errors.RequesterError(f'Model scan error: {str(e)}')
|