mirror of
https://github.com/Routstr/routstr-core.git
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481 lines
17 KiB
Python
481 lines
17 KiB
Python
"""Custom /v1/messages dispatcher for Gemini's OpenAI-compat endpoint.
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Why this exists
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---------------
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Gemini 2.5 / 3 thinking models reject inbound ``functionCall`` parts that
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lack a ``thought_signature`` field once any prior turn in the conversation
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contains a function call. Anthropic-Messages clients (Claude Code etc.)
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have no concept of thought signatures, so multi-turn tool conversations
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fail with::
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Function call is missing a thought_signature in functionCall parts.
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Google's published escape hatch (https://ai.google.dev/gemini-api/docs/
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thought-signatures, FAQ #1) is the dummy signature
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``"skip_thought_signature_validator"`` placed at
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``tool_calls[i].extra_content.google.thought_signature`` for every tool
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call in the request. The hatch is documented specifically for
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"transferring a trace from a different model that does not include thought
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signatures" — exactly our case.
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Why we can't reach the wire via litellm
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---------------------------------------
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``litellm.anthropic.messages.acreate`` flows through the openai SDK, whose
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pydantic ``ChatCompletionMessageToolCall`` model silently drops unknown
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fields like ``extra_content``. Litellm has no openai-compat translator
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that emits ``extra_content.google.thought_signature``. So we bypass both
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litellm and the openai SDK at the transport layer.
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Pipeline
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--------
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1. Translate Anthropic body → OpenAI body via litellm's
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``AnthropicAdapter`` (the same translator
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``litellm.anthropic.messages.acreate`` uses internally).
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2. Inject ``extra_content.google.thought_signature`` on every
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``tool_calls[]`` entry.
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3. Set ``reasoning_effort="none"`` to disable Gemini's thinking pass.
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4. POST directly to ``{base_url}/chat/completions`` with ``stream=true``
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via ``httpx`` (preserves arbitrary fields verbatim).
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5. Translate OpenAI streaming chunks → Anthropic SSE events.
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"""
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from __future__ import annotations
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import json
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import uuid
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from collections.abc import AsyncGenerator, AsyncIterator
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from typing import Any, Callable
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import httpx
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from ..core import get_logger
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from ..core.exceptions import UpstreamError
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from ..payment.models import Model
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from .messages_dispatch import (
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ANTHROPIC_ONLY_FIELDS,
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aggregate_anthropic_events_to_message,
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)
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logger = get_logger(__name__)
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DUMMY_THOUGHT_SIGNATURE = "skip_thought_signature_validator"
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# Mapping: OpenAI finish_reason → Anthropic stop_reason
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_FINISH_TO_STOP = {
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"stop": "end_turn",
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"length": "max_tokens",
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"tool_calls": "tool_use",
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"function_call": "tool_use",
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"content_filter": "refusal",
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}
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def inject_thought_signatures(messages: list[dict]) -> None:
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"""Add ``extra_content.google.thought_signature`` to every tool_call.
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Mutates ``messages`` in place. Idempotent: existing signatures are not
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overwritten.
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"""
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for msg in messages:
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tool_calls = msg.get("tool_calls")
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if not isinstance(tool_calls, list):
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continue
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for tc in tool_calls:
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if not isinstance(tc, dict):
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continue
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extra = tc.get("extra_content")
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if not isinstance(extra, dict):
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extra = tc["extra_content"] = {}
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google_cfg = extra.get("google")
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if not isinstance(google_cfg, dict):
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google_cfg = extra["google"] = {}
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google_cfg.setdefault("thought_signature", DUMMY_THOUGHT_SIGNATURE)
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def _translate_anthropic_to_openai(body: dict, model: str) -> dict:
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"""Use litellm's translator to convert an Anthropic /messages body to
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OpenAI /chat/completions kwargs.
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Imported lazily because the litellm internal path is heavy and not
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needed for any other code path in routstr.
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"""
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from litellm.llms.anthropic.experimental_pass_through.adapters.transformation import ( # noqa: E501
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AnthropicAdapter,
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)
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kwargs = {"model": model, **body}
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translated = AnthropicAdapter().translate_completion_input_params(kwargs)
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if translated is None:
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raise UpstreamError(
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"Failed to translate Anthropic body to OpenAI format",
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status_code=500,
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)
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return dict(translated)
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def _sse_event(event_type: str, payload: dict) -> bytes:
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return f"event: {event_type}\ndata: {json.dumps(payload)}\n\n".encode()
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async def _openai_chunks_to_anthropic_events(
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line_iter: AsyncIterator[str], requested_model: str | None
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) -> AsyncGenerator[bytes, None]:
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"""Translate an OpenAI chat-completions SSE byte stream into the
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Anthropic-Messages SSE event sequence.
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Maintains per-chunk state across:
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* one optional text content block (lazy-opened on first text delta)
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* any number of tool_use blocks indexed by openai's ``delta.tool_calls[].index``
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* final ``stop_reason`` / ``usage`` carried out via ``message_delta`` /
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``message_stop``
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"""
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msg_id = f"msg_{uuid.uuid4().hex[:24]}"
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started = False
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text_block_idx: int | None = None
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tool_block_indices: dict[int, int] = {}
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next_block_idx = 0
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final_finish_reason: str | None = None
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final_usage: dict[str, int] = {"input_tokens": 0, "output_tokens": 0}
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def open_text_block() -> bytes:
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nonlocal text_block_idx, next_block_idx
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text_block_idx = next_block_idx
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next_block_idx += 1
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return _sse_event(
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"content_block_start",
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{
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"type": "content_block_start",
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"index": text_block_idx,
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"content_block": {"type": "text", "text": ""},
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},
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)
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def open_tool_block(delta_idx: int, tc: dict) -> bytes:
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nonlocal next_block_idx
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block_idx = next_block_idx
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next_block_idx += 1
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tool_block_indices[delta_idx] = block_idx
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fn = tc.get("function") or {}
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return _sse_event(
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"content_block_start",
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{
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"type": "content_block_start",
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"index": block_idx,
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"content_block": {
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"type": "tool_use",
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"id": tc.get("id") or f"toolu_{uuid.uuid4().hex[:24]}",
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"name": fn.get("name") or "",
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"input": {},
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},
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},
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)
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def close_block(idx: int) -> bytes:
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return _sse_event(
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"content_block_stop",
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{"type": "content_block_stop", "index": idx},
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)
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async for raw_line in line_iter:
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line = raw_line.strip()
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if not line:
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continue
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if not line.startswith("data:"):
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continue
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payload = line[5:].lstrip()
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if not payload or payload == "[DONE]":
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continue
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try:
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chunk = json.loads(payload)
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except json.JSONDecodeError:
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continue
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if not isinstance(chunk, dict):
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continue
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if not started:
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started = True
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yield _sse_event(
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"message_start",
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{
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"type": "message_start",
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"message": {
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"id": chunk.get("id") or msg_id,
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"type": "message",
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"role": "assistant",
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"model": requested_model or chunk.get("model") or "",
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"content": [],
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"stop_reason": None,
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"stop_sequence": None,
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"usage": {
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"input_tokens": 0,
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"output_tokens": 0,
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},
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},
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},
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)
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usage = chunk.get("usage")
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if isinstance(usage, dict):
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in_tok = usage.get("prompt_tokens") or usage.get("input_tokens") or 0
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out_tok = usage.get("completion_tokens") or usage.get("output_tokens") or 0
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if in_tok:
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final_usage["input_tokens"] = int(in_tok)
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if out_tok:
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final_usage["output_tokens"] = int(out_tok)
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choices = chunk.get("choices") or []
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if not choices:
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continue
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choice = choices[0] if isinstance(choices[0], dict) else {}
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delta = choice.get("delta") or {}
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if not isinstance(delta, dict):
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delta = {}
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# Text delta
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text = delta.get("content")
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if isinstance(text, str) and text:
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if text_block_idx is None:
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yield open_text_block()
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yield _sse_event(
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"content_block_delta",
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{
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"type": "content_block_delta",
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"index": text_block_idx,
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"delta": {"type": "text_delta", "text": text},
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},
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)
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# Tool call deltas
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tool_calls_delta = delta.get("tool_calls")
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if isinstance(tool_calls_delta, list):
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for tc in tool_calls_delta:
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if not isinstance(tc, dict):
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continue
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d_idx = int(tc.get("index") or 0)
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if d_idx not in tool_block_indices:
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yield open_tool_block(d_idx, tc)
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block_idx = tool_block_indices[d_idx]
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fn = tc.get("function") or {}
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args = fn.get("arguments")
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if isinstance(args, str) and args:
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yield _sse_event(
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"content_block_delta",
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{
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"type": "content_block_delta",
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"index": block_idx,
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"delta": {
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"type": "input_json_delta",
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"partial_json": args,
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},
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},
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)
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finish = choice.get("finish_reason")
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if finish:
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final_finish_reason = finish
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# Close any open content blocks
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if text_block_idx is not None:
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yield close_block(text_block_idx)
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for block_idx in tool_block_indices.values():
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yield close_block(block_idx)
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# message_delta with stop_reason and usage
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stop_reason = _FINISH_TO_STOP.get(final_finish_reason or "", "end_turn")
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yield _sse_event(
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"message_delta",
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{
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"type": "message_delta",
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"delta": {"stop_reason": stop_reason, "stop_sequence": None},
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"usage": final_usage,
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},
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)
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yield _sse_event("message_stop", {"type": "message_stop"})
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async def _post_and_stream(
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base_url: str,
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api_key: str,
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payload: dict,
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log_extra: dict[str, Any] | None,
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) -> tuple[httpx.AsyncClient, httpx.Response]:
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"""POST to upstream chat-completions and return (client, response) for
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streaming. Caller is responsible for closing both."""
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url = f"{base_url.rstrip('/')}/chat/completions"
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client = httpx.AsyncClient(timeout=httpx.Timeout(120.0, read=120.0))
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try:
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request = client.build_request(
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"POST",
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url,
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json=payload,
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headers={
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json",
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"Accept": "text/event-stream",
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},
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)
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response = await client.send(request, stream=True)
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except Exception as exc:
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await client.aclose()
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logger.error(
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"Gemini messages dispatch HTTP error",
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extra={"error": str(exc), "url": url, **(log_extra or {})},
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)
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raise UpstreamError(
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f"Failed to reach Gemini upstream: {exc}", status_code=502
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) from exc
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if response.status_code >= 400:
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try:
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body_bytes = await response.aread()
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finally:
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await response.aclose()
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await client.aclose()
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body_text = body_bytes.decode("utf-8", errors="replace")
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logger.error(
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"Gemini messages dispatch upstream error",
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extra={
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"status_code": response.status_code,
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"body": body_text[:1000],
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"url": url,
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**(log_extra or {}),
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},
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)
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raise UpstreamError(
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f"Upstream error via gemini compat: {body_text}",
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status_code=response.status_code,
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)
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return client, response
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async def dispatch_gemini_messages(
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*,
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request_body: bytes | None,
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model_obj: Model,
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base_url: str,
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api_key: str,
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transform_model_name: Callable[[str], str],
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log_extra: dict[str, Any] | None = None,
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) -> tuple[bool, Any, str | None]:
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"""Dispatch a /v1/messages request to Gemini's OpenAI-compat endpoint
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with thought-signature injection.
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Returns ``(client_stream, result, requested_model)`` where ``result``
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is either an ``AsyncIterator[bytes]`` of Anthropic-format SSE events
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(for streaming clients) or an Anthropic Message dict (after the caller
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aggregates).
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"""
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if not request_body:
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raise UpstreamError(
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"Missing request body for /v1/messages", status_code=400
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)
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try:
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body: dict = json.loads(request_body)
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except json.JSONDecodeError as exc:
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raise UpstreamError(
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f"Invalid JSON in /v1/messages body: {exc}", status_code=400
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) from exc
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body.pop("model", None)
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client_stream = bool(body.pop("stream", False))
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# Anthropic-Messages-only fields that don't translate to OpenAI
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# Chat Completions. litellm's translator passes through unknown
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# top-level fields verbatim and Gemini's compat surface 400s on
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# unknown names like ``context_management`` / ``output_config``.
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dropped: dict[str, Any] = {}
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for field in ANTHROPIC_ONLY_FIELDS:
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if field in body:
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dropped[field] = body.pop(field)
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if dropped:
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logger.debug(
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"Dropped anthropic-only fields before gemini compat dispatch",
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extra={"dropped_keys": sorted(dropped.keys())},
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)
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requested_model = (
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(model_obj.forwarded_model_id or model_obj.id) if model_obj else None
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)
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upstream_model = transform_model_name(model_obj.id)
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openai_kwargs = _translate_anthropic_to_openai(body, upstream_model)
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messages = openai_kwargs.get("messages") or []
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if isinstance(messages, list):
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inject_thought_signatures(messages)
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# Disable Gemini's thinking pass; the dummy signature already lifts
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# validation, but skipping thinking entirely avoids degraded model
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# output and keeps tool-calling deterministic.
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openai_kwargs.setdefault("reasoning_effort", "none")
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openai_kwargs["stream"] = True
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openai_kwargs["model"] = upstream_model
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# OpenAI-compat backends (including Gemini's) only emit a final
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# ``usage`` chunk when the request opts in via this flag. Without it
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# the cost-calculation pipeline can't read real token counts and
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# falls back to MaxCostData billing.
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existing_stream_options = openai_kwargs.get("stream_options")
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merged_stream_options = (
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dict(existing_stream_options)
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if isinstance(existing_stream_options, dict)
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else {}
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)
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merged_stream_options.setdefault("include_usage", True)
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openai_kwargs["stream_options"] = merged_stream_options
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logger.info(
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"Dispatching /v1/messages via gemini compat (httpx)",
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extra={
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"model": upstream_model,
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"client_stream": client_stream,
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"messages_with_tool_calls": sum(
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1 for m in messages if isinstance(m, dict) and m.get("tool_calls")
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),
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**(log_extra or {}),
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},
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)
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http_client, response = await _post_and_stream(
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base_url, api_key, openai_kwargs, log_extra
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)
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async def line_iter() -> AsyncGenerator[str, None]:
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try:
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async for line in response.aiter_lines():
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yield line
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finally:
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await response.aclose()
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await http_client.aclose()
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anthropic_event_iter = _openai_chunks_to_anthropic_events(
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line_iter(), requested_model
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)
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if not client_stream:
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# Aggregate the Anthropic SSE byte stream into a single Message dict
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# so the rest of the pipeline (cost calc, metadata injection,
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# response building) can treat it identically to a non-streaming
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# litellm response.
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try:
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aggregated = await aggregate_anthropic_events_to_message(
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anthropic_event_iter
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)
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except Exception as exc:
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logger.error(
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"Failed to aggregate Gemini compat events into message",
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extra={"error": str(exc), **(log_extra or {})},
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)
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raise UpstreamError(
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f"Failed to aggregate upstream stream: {exc}",
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status_code=502,
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) from exc
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return client_stream, aggregated, requested_model
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return client_stream, anthropic_event_iter, requested_model
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