0e146e60d9
Move tool bridge and responses adapter helpers out of app.main so the main entrypoint can shrink without changing route orchestration behavior. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026 lines
75 KiB
Python
2026 lines
75 KiB
Python
from __future__ import annotations
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import asyncio
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import hashlib
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import json
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import time
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import uuid
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from contextlib import asynccontextmanager
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from typing import Any
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from fastapi import Depends, FastAPI, HTTPException, Request
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from fastapi.responses import JSONResponse, StreamingResponse
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from .anthropic_schema import (
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AnthropicMessagesRequest,
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affinity_key_for_anthropic,
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anthropic_to_internal_messages,
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)
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from .auth import (
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AnthropicAuthError,
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require_admin_access,
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require_anthropic_key,
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require_bearer,
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require_metrics_access,
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)
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from .concurrency import BackpressureRejected, InFlightGuard
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from .config import Settings, load_settings
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from .http.responses_adapter import (
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_responses_id_from_chat_id,
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_responses_input_to_messages,
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_responses_non_stream_from_chat_payload,
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_responses_to_chat_request,
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_responses_usage_from_chat,
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_sse_data,
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)
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from .http.tool_bridge import (
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_anthropic_forced_tool_name,
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_anthropic_tool_result_block,
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_anthropic_tool_use_block,
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_forced_tool_event_from_text,
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_json_object_from_text,
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_json_string,
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_openai_forced_tool_name,
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_openai_tool_call,
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_tool_code_object_from_text,
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_tool_code_single_arg_name,
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)
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from .lingma_pool import LingmaPool, PoolInstance
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from .logging_config import configure_logging, get_logger, request_id_var
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from .model_map import build_model_name_map, flatten_model_keys, resolve_model
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from .openai_schema import (
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ChatCompletionChoice,
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ChatCompletionResponse,
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ChatCompletionsRequest,
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ModelData,
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ModelsResponse,
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ResponsesRequest,
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flatten_content,
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)
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from .session_bundle import encode_bundle, pack_workdir
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from .session_cache import SessionCache, hash_branch_context
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from .stats import StatsCollector, estimate_tokens
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settings: Settings = load_settings()
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configure_logging(settings.log_level)
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logger = get_logger("lingma_gateway")
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pool: LingmaPool | None = None
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stats_collector = StatsCollector()
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chat_guard = InFlightGuard(
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max_in_flight=settings.gateway_max_in_flight,
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queue_timeout_sec=settings.gateway_queue_timeout_sec,
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)
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session_cache = SessionCache(
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max_entries=settings.session_cache_max_entries if settings.session_reuse_enabled else 0,
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ttl_sec=settings.session_cache_ttl_sec,
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)
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STREAMING_RESPONSE_HEADERS = {
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"Cache-Control": "no-cache, no-transform",
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"X-Accel-Buffering": "no",
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"Connection": "keep-alive",
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}
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def _require_pool() -> LingmaPool:
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if pool is None:
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raise HTTPException(
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status_code=503,
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detail={"error": {"message": "pool not initialized", "type": "service_unavailable"}},
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)
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return pool
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@asynccontextmanager
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async def lifespan(_app: FastAPI):
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global pool
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pool = LingmaPool.build(
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lingma_bin=settings.lingma_bin,
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base_work_dir=settings.lingma_work_dir,
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legacy_socket_port=settings.lingma_socket_port,
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startup_timeout=settings.lingma_startup_timeout,
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rpc_timeout=settings.lingma_rpc_timeout,
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default_model=settings.default_model,
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default_ask_mode=settings.default_ask_mode,
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accounts=settings.accounts,
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instance_count=settings.instance_count,
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auto_login_headless=settings.auto_login_headless,
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auto_login_timeout=settings.auto_login_timeout,
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auto_login_max_retry=settings.auto_login_max_retry,
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)
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logger.info(
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"gateway startup: pool_size=%d max_in_flight=%d",
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pool.size(),
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settings.gateway_max_in_flight,
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)
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_log_auth_posture()
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await pool.start()
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try:
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yield
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finally:
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if pool is not None:
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await pool.close()
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app = FastAPI(title="Lingma OpenAI Gateway", version="0.4.0", lifespan=lifespan)
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@app.exception_handler(AnthropicAuthError)
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async def _anthropic_auth_error_handler(_request: Request, exc: AnthropicAuthError):
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"""Render auth failures on /v1/messages in the Anthropic wire format.
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FastAPI's default handler wraps everything in `{"detail": ...}`, which
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Anthropic SDKs don't parse. We emit the canonical
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`{"type":"error","error":{"type":"...","message":"..."}}` instead.
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"""
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return JSONResponse(
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status_code=exc.status_code,
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content={
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"type": "error",
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"error": {"type": exc.error_type, "message": exc.message},
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},
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)
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@app.middleware("http")
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async def request_id_middleware(request: Request, call_next):
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req_id = request.headers.get("x-request-id") or f"req-{uuid.uuid4().hex[:12]}"
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token = request_id_var.set(req_id)
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start = time.monotonic()
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status_code = 500
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try:
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response = await call_next(request)
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status_code = response.status_code
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response.headers["x-request-id"] = req_id
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return response
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finally:
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elapsed_ms = int((time.monotonic() - start) * 1000)
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logger.info(
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"http %s %s -> %s in %dms",
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request.method,
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request.url.path,
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status_code,
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elapsed_ms,
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extra={
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"ctx_method": request.method,
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"ctx_path": request.url.path,
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"ctx_status": status_code,
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"ctx_elapsed_ms": elapsed_ms,
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},
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)
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request_id_var.reset(token)
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def auth_guard(request: Request):
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require_bearer(request, settings.api_keys)
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def anthropic_auth_guard(request: Request):
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require_anthropic_key(request, settings.api_keys)
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def metrics_auth_guard(request: Request):
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require_metrics_access(
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request,
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settings.api_keys,
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settings.metrics_token,
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public=settings.metrics_public,
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)
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def admin_auth_guard(request: Request):
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require_admin_access(request, settings.api_keys, settings.admin_token)
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def _log_auth_posture() -> None:
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"""Loud warnings on misconfigured auth so ops can't miss them."""
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if not settings.api_keys:
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logger.warning(
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"AUTH DISABLED: API_KEYS is empty, /v1/* is wide open. "
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"Set API_KEYS before exposing this gateway to anything "
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"other than localhost."
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)
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if not settings.admin_token:
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logger.warning(
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"ADMIN_TOKEN not set: /internal/* reuses API_KEYS for auth. "
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"For production set a dedicated ADMIN_TOKEN so rotating chat "
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"keys doesn't require exporting the session bundle."
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)
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if settings.metrics_public:
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logger.warning(
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"METRICS_PUBLIC=true: /metrics is open. Only enable this "
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"when the gateway is behind a private-network scraper."
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)
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@app.get("/healthz")
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async def healthz():
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if pool is None:
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return {"ok": False, "time": int(time.time()), "reason": "pool uninitialized"}
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insts = pool.stats()
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ready = sum(1 for i in insts if i["state"] == "ready")
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return {
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"ok": ready > 0,
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"time": int(time.time()),
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"pool_size": len(insts),
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"pool_ready": ready,
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"instances": [
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{"name": i["name"], "state": i["state"], "in_flight": i["in_flight"]}
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for i in insts
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],
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}
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async def _ensure_instance_logged_in(inst: PoolInstance) -> dict:
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client = inst.client
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auto_login = inst.auto_login
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try:
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status = await client.auth_status()
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except Exception as exc:
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logger.warning("[%s] auth_status failed before chat: %s", inst.name, exc)
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raise HTTPException(
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status_code=503,
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detail={"error": {"message": "Lingma is not ready", "type": "service_unavailable"}},
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)
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if status and status.get("id"):
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return status
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if not settings.auto_login_enabled:
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raise HTTPException(
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status_code=401,
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detail={"error": {"message": "Lingma not logged in", "type": "invalid_request_error"}},
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)
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if settings.dedicated_domain_url:
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try:
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current = await client.get_endpoint()
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current_ep = (current or {}).get("endpoint", "") if isinstance(current, dict) else ""
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if current_ep != settings.dedicated_domain_url:
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await client.update_endpoint(settings.dedicated_domain_url)
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except Exception as exc:
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logger.warning("[%s] switch dedicated endpoint failed: %s", inst.name, exc)
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try:
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login_url, _login_raw = await client.generate_login_url()
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except Exception as exc:
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logger.warning("[%s] generate_login_url failed: %s", inst.name, exc)
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raise HTTPException(
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status_code=502,
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detail={"error": {"message": "generate login url failed", "type": "upstream_error"}},
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)
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if not login_url:
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raise HTTPException(
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status_code=502,
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detail={"error": {"message": "generate login url failed", "type": "upstream_error"}},
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)
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await auto_login.ensure_started(login_url)
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try:
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await auto_login.wait_done(timeout=settings.auto_login_timeout + 20)
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except Exception as exc:
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logger.warning("[%s] auto_login wait_done failed: %s", inst.name, exc)
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try:
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status = await client.auth_status()
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except Exception as exc:
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logger.warning("[%s] post-login auth_status failed: %s", inst.name, exc)
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status = None
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if status and status.get("id"):
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return status
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logger.warning(
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"[%s] auto login did not result in a logged-in session: %s",
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inst.name,
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auto_login.status(),
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)
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raise HTTPException(
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status_code=401,
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detail={"error": {"message": "Lingma auto login failed", "type": "invalid_request_error"}},
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)
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def _affinity_key_for(req: ChatCompletionsRequest) -> str | None:
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"""Derive a stable affinity key so that follow-ups go to the same instance.
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Priority: explicit `user` > hash of the first/system message.
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"""
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if req.user:
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return req.user.strip() or None
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for m in req.messages:
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if m.role == "system":
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text = flatten_content(m.content)
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if text:
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return "sys:" + hashlib.sha1(text.encode("utf-8")).hexdigest()[:16]
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if req.messages:
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first = req.messages[0]
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text = flatten_content(first.content)
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if text:
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return "first:" + hashlib.sha1(text.encode("utf-8")).hexdigest()[:16]
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return None
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def _extract_api_key(request: Request) -> str:
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h = request.headers.get("authorization", "")
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if h.lower().startswith("bearer "):
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return h[7:].strip()
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return ""
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def _last_user_text(messages: list[dict]) -> str:
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"""Extract the text of the latest user message (trailing from end).
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Used when we hit the session cache and only need to send the delta.
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Falls back to the last message regardless of role if no user is found.
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"""
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for m in reversed(messages):
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if m.get("role") == "user":
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return flatten_content(m.get("content")) or ""
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if messages:
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return flatten_content(messages[-1].get("content")) or ""
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return ""
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@app.get("/v1/models", dependencies=[Depends(anthropic_auth_guard)])
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async def v1_models():
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p = _require_pool()
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inst = p.pick()
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await _ensure_instance_logged_in(inst)
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await stats_collector.inc_models()
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models = await inst.client.query_models()
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keys = flatten_model_keys(models)
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name_map = build_model_name_map(models)
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resp = ModelsResponse(data=[ModelData(id=k, name=name_map.get(k)) for k in keys])
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return JSONResponse(content=resp.model_dump())
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def _messages_to_prompt(messages: list[dict]) -> str:
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parts: list[str] = []
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for m in messages:
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role = m.get("role", "user")
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text = flatten_content(m.get("content"))
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if not text and m.get("tool_calls"):
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text = f"[tool_calls] {json.dumps(m['tool_calls'], ensure_ascii=False)}"
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if not text:
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continue
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parts.append(f"[{role}] {text}")
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return "\n".join(parts).strip()
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def _include_usage(stream_options: dict | None) -> bool:
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if not isinstance(stream_options, dict):
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return False
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return bool(stream_options.get("include_usage"))
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def _tool_allowlist() -> set[str]:
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return {name.strip() for name in settings.tool_allowlist if isinstance(name, str) and name.strip()}
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def _openai_tool_name(tool: Any) -> str | None:
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if not isinstance(tool, dict):
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return None
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if tool.get("type") == "function":
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fn = tool.get("function")
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if isinstance(fn, dict):
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name = fn.get("name")
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if isinstance(name, str) and name.strip():
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return name.strip()
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name = tool.get("name")
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if isinstance(name, str) and name.strip():
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return name.strip()
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return None
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def _anthropic_tool_name(tool: Any) -> str | None:
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if not isinstance(tool, dict):
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return None
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name = tool.get("name")
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if isinstance(name, str) and name.strip():
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return name.strip()
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fn = tool.get("function")
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if isinstance(fn, dict):
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nested_name = fn.get("name")
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if isinstance(nested_name, str) and nested_name.strip():
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return nested_name.strip()
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return None
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def _filter_allowed_tools(tools: list[dict[str, Any]], *, provider: str) -> list[dict[str, Any]]:
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allowlist = _tool_allowlist()
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if not allowlist:
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return tools
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name_fn = _openai_tool_name if provider == "openai" else _anthropic_tool_name
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return [tool for tool in tools if (name := name_fn(tool)) and name in allowlist]
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|
|
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def _ensure_tool_choice_allowed(tool_choice: Any, *, provider: str) -> None:
|
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allowlist = _tool_allowlist()
|
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if not allowlist:
|
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return
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forced_name = (
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_openai_forced_tool_name(tool_choice)
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if provider == "openai"
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else _anthropic_forced_tool_name(tool_choice)
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)
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if forced_name and forced_name not in allowlist:
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raise HTTPException(
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status_code=400,
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detail={
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"error": {
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"type": "invalid_request_error",
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"message": f"tool '{forced_name}' is not allowed",
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}
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},
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)
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|
|
|
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def _openai_tool_config(req: ChatCompletionsRequest) -> dict[str, Any] | None:
|
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if not settings.tool_forward_enabled:
|
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return None
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has_tools = isinstance(req.tools, list) and len(req.tools) > 0
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has_choice = req.tool_choice is not None
|
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if not has_tools and not has_choice:
|
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return None
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_ensure_tool_choice_allowed(req.tool_choice, provider="openai")
|
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tools = _filter_allowed_tools(req.tools or [], provider="openai")
|
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return {
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"provider": "openai",
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"tools": tools,
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"tool_choice": req.tool_choice,
|
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}
|
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|
|
|
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def _anthropic_tool_config(req: AnthropicMessagesRequest) -> dict[str, Any] | None:
|
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if not settings.tool_forward_enabled:
|
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return None
|
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has_tools = isinstance(req.tools, list) and len(req.tools) > 0
|
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has_choice = req.tool_choice is not None
|
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if not has_tools and not has_choice:
|
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return None
|
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_ensure_tool_choice_allowed(req.tool_choice, provider="anthropic")
|
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tools = _filter_allowed_tools(req.tools or [], provider="anthropic")
|
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return {
|
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"provider": "anthropic",
|
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"tools": tools,
|
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"tool_choice": req.tool_choice,
|
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}
|
|
|
|
|
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def _openai_has_tooling_context(req: ChatCompletionsRequest, messages: list[dict[str, Any]]) -> bool:
|
|
if isinstance(req.tools, list) and len(req.tools) > 0:
|
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return True
|
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if req.tool_choice is not None:
|
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return True
|
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for m in messages:
|
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role = m.get("role")
|
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if role == "tool":
|
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return True
|
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if role == "assistant" and m.get("tool_calls"):
|
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return True
|
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return False
|
|
|
|
|
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def _anthropic_content_has_tool_blocks(content: Any) -> bool:
|
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if not isinstance(content, list):
|
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return False
|
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for item in content:
|
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if isinstance(item, dict) and item.get("type") in {"tool_use", "tool_result"}:
|
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return True
|
|
return False
|
|
|
|
|
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def _anthropic_has_tooling_context(req: AnthropicMessagesRequest) -> bool:
|
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if isinstance(req.tools, list) and len(req.tools) > 0:
|
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return True
|
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if req.tool_choice is not None:
|
|
return True
|
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if _anthropic_content_has_tool_blocks(req.system):
|
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return True
|
|
for m in req.messages:
|
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if _anthropic_content_has_tool_blocks(m.content):
|
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return True
|
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return False
|
|
|
|
|
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def _resolve_ask_mode(model: str, has_tooling_context: bool) -> str:
|
|
model_name = (model or "").lower()
|
|
if model_name in {"lingma-agent", "agent"} or has_tooling_context:
|
|
return "agent"
|
|
return settings.default_ask_mode
|
|
|
|
|
|
async def _apply_cached_instance_or_invalidate(
|
|
*,
|
|
protocol: str,
|
|
inst: PoolInstance,
|
|
cached_instance_name: str | None,
|
|
cached_session_id: str | None,
|
|
lookup_key: str | None,
|
|
) -> str | None:
|
|
if cached_instance_name and inst.name != cached_instance_name:
|
|
logger.info(
|
|
"%s session cache instance %s unhealthy, falling back to %s",
|
|
protocol,
|
|
cached_instance_name,
|
|
inst.name,
|
|
)
|
|
if lookup_key:
|
|
await session_cache.invalidate(lookup_key)
|
|
return None
|
|
return cached_session_id
|
|
|
|
|
|
|
|
def _streaming_response(event_stream) -> StreamingResponse:
|
|
return StreamingResponse(
|
|
event_stream,
|
|
media_type="text/event-stream",
|
|
headers=STREAMING_RESPONSE_HEADERS,
|
|
)
|
|
|
|
|
|
def _stream_event_type(event: Any) -> str:
|
|
if isinstance(event, dict):
|
|
t = event.get("type")
|
|
if t in {"text", "tool"}:
|
|
return t
|
|
return "text"
|
|
|
|
|
|
def _stream_text(event: Any) -> str:
|
|
if isinstance(event, dict):
|
|
if event.get("type") == "text":
|
|
text = event.get("text")
|
|
if isinstance(text, str):
|
|
return text
|
|
return ""
|
|
if isinstance(event, str):
|
|
return event
|
|
return ""
|
|
|
|
|
|
def _stream_tool_event(event: Any) -> dict[str, Any] | None:
|
|
if isinstance(event, dict) and event.get("type") == "tool":
|
|
tool = event.get("tool")
|
|
if isinstance(tool, dict):
|
|
return tool
|
|
return None
|
|
|
|
|
|
@app.post("/v1/chat/completions", dependencies=[Depends(auth_guard)])
|
|
async def v1_chat_completions(req: ChatCompletionsRequest, request: Request):
|
|
p = _require_pool()
|
|
|
|
messages_dump = [m.model_dump() for m in req.messages]
|
|
api_key = _extract_api_key(request) or "-"
|
|
|
|
# ------------------------------------------------------------- session reuse
|
|
# Look up the "conversation prefix" (everything except the latest user turn)
|
|
# in the session cache. A hit lets us:
|
|
# 1. Reuse the upstream sessionId so Lingma/Qwen hits its KV cache.
|
|
# 2. Send only the new user message instead of the whole history.
|
|
# 3. Stick the request to the pool instance that originally served it.
|
|
tool_config = _openai_tool_config(req)
|
|
has_tooling_context = _openai_has_tooling_context(req, messages_dump)
|
|
|
|
ask_mode = _resolve_ask_mode(req.model, has_tooling_context)
|
|
|
|
reuse_eligible = (
|
|
session_cache.enabled
|
|
and ask_mode == "chat"
|
|
and len(messages_dump) >= 2
|
|
and not has_tooling_context
|
|
)
|
|
lookup_key: str | None = None
|
|
write_key: str | None = None
|
|
cached_session_id: str | None = None
|
|
cached_instance_name: str | None = None
|
|
if reuse_eligible:
|
|
prefix_branch_context = hash_branch_context(messages_dump[:-1])
|
|
lookup_key = session_cache.build_key(
|
|
api_key,
|
|
messages_dump[:-1],
|
|
tool_config=tool_config,
|
|
branch_context=prefix_branch_context,
|
|
)
|
|
write_key = session_cache.build_key(
|
|
api_key,
|
|
messages_dump,
|
|
tool_config=tool_config,
|
|
branch_context=hash_branch_context(messages_dump),
|
|
)
|
|
entry = await session_cache.get(lookup_key)
|
|
if entry is None:
|
|
legacy_lookup_key = session_cache.build_key(api_key, messages_dump[:-1], tool_config=tool_config)
|
|
entry = await session_cache.get(legacy_lookup_key)
|
|
if entry is not None:
|
|
lookup_key = legacy_lookup_key
|
|
if entry is not None:
|
|
cached_session_id = entry.session_id
|
|
cached_instance_name = entry.instance_name or None
|
|
affinity = cached_instance_name or _affinity_key_for(req)
|
|
inst = p.pick(affinity_key=affinity)
|
|
|
|
cached_session_id = await _apply_cached_instance_or_invalidate(
|
|
protocol="chat",
|
|
inst=inst,
|
|
cached_instance_name=cached_instance_name,
|
|
cached_session_id=cached_session_id,
|
|
lookup_key=lookup_key,
|
|
)
|
|
|
|
await _ensure_instance_logged_in(inst)
|
|
|
|
models = await inst.client.query_models()
|
|
available = flatten_model_keys(models)
|
|
name_map = build_model_name_map(models)
|
|
model = resolve_model(req.model, available, settings.default_model, name_map)
|
|
|
|
# Prompt construction: on cache hit send only the last user turn so Lingma's
|
|
# stored context isn't duplicated.
|
|
if cached_session_id:
|
|
prompt = _last_user_text(messages_dump)
|
|
is_reply = True
|
|
else:
|
|
prompt = _messages_to_prompt(messages_dump)
|
|
is_reply = False
|
|
|
|
if not prompt:
|
|
raise HTTPException(
|
|
status_code=400,
|
|
detail={"error": {"message": "messages is empty", "type": "invalid_request_error"}},
|
|
)
|
|
prompt_tokens = estimate_tokens(prompt)
|
|
include_usage = _include_usage(req.stream_options)
|
|
|
|
# Backpressure: acquire a slot *after* the cheap validation but before any
|
|
# upstream call. This ensures we reject quickly when saturated.
|
|
try:
|
|
ticket = await chat_guard.try_acquire()
|
|
except BackpressureRejected as exc:
|
|
retry_after = max(1, int(exc.retry_after))
|
|
logger.warning("chat rejected by backpressure, retry_after=%ds", retry_after)
|
|
raise HTTPException(
|
|
status_code=429,
|
|
detail={
|
|
"error": {
|
|
"message": "Too many in-flight requests, please retry later",
|
|
"type": "rate_limit_error",
|
|
"code": "backpressure",
|
|
}
|
|
},
|
|
headers={"Retry-After": str(retry_after)},
|
|
)
|
|
|
|
inst.in_flight += 1
|
|
logger.info(
|
|
"chat.start inst=%s model=%s ask_mode=%s stream=%s prompt_tokens~%d reuse=%s",
|
|
inst.name,
|
|
model,
|
|
ask_mode,
|
|
req.stream,
|
|
prompt_tokens,
|
|
bool(cached_session_id),
|
|
extra={
|
|
"ctx_instance": inst.name,
|
|
"ctx_model": model,
|
|
"ctx_ask_mode": ask_mode,
|
|
"ctx_stream": req.stream,
|
|
"ctx_prompt_tokens": prompt_tokens,
|
|
"ctx_in_flight": chat_guard.in_flight,
|
|
"ctx_affinity": affinity,
|
|
"ctx_session_reuse": bool(cached_session_id),
|
|
},
|
|
)
|
|
|
|
ticket_transferred = False
|
|
|
|
try:
|
|
if req.stream:
|
|
created = int(time.time())
|
|
completion_id = f"chatcmpl-{uuid.uuid4().hex}"
|
|
completion_tokens_holder = {"n": 0}
|
|
stream_meta: dict = {}
|
|
forced_tool_name = _openai_forced_tool_name(req.tool_choice)
|
|
forced_tool_single_arg_name = _tool_code_single_arg_name(req.tools, forced_tool_name) if forced_tool_name else None
|
|
|
|
async def event_stream(_ticket=ticket, _inst=inst, _meta=stream_meta):
|
|
success = False
|
|
tool_call_indexes: dict[str, int] = {}
|
|
saw_tool_call = False
|
|
buffered_text_parts: list[str] = []
|
|
|
|
def _text_payload(text: str) -> str:
|
|
payload = {
|
|
"id": completion_id,
|
|
"object": "chat.completion.chunk",
|
|
"created": created,
|
|
"model": model,
|
|
"choices": [
|
|
{
|
|
"index": 0,
|
|
"delta": {"content": text},
|
|
"finish_reason": None,
|
|
}
|
|
],
|
|
}
|
|
return f"data: {json.dumps(payload, ensure_ascii=False)}\n\n"
|
|
|
|
try:
|
|
async for chunk in _inst.client.chat_stream(
|
|
prompt,
|
|
model,
|
|
ask_mode,
|
|
session_id=cached_session_id,
|
|
is_reply=is_reply,
|
|
tool_config=tool_config,
|
|
out_meta=_meta,
|
|
):
|
|
if _stream_event_type(chunk) == "tool":
|
|
tool = _stream_tool_event(chunk)
|
|
if not tool:
|
|
continue
|
|
|
|
tool_name = str(tool.get("name") or "")
|
|
allowed = True
|
|
if tool_config and isinstance(tool_config.get("tools"), list) and tool_config.get("tools"):
|
|
allowed = False
|
|
for t in tool_config.get("tools"):
|
|
if tool_name == _anthropic_tool_name(t) or tool_name == _openai_tool_name(t):
|
|
allowed = True
|
|
break
|
|
if not allowed and forced_tool_name and tool_name == forced_tool_name:
|
|
allowed = True
|
|
if not allowed:
|
|
continue
|
|
|
|
if buffered_text_parts:
|
|
for buffered_text in buffered_text_parts:
|
|
yield _text_payload(buffered_text)
|
|
buffered_text_parts.clear()
|
|
|
|
tool_id = str(tool.get("id") or "")
|
|
if not tool_id:
|
|
tool_id = f"call_{len(tool_call_indexes)}"
|
|
idx = tool_call_indexes.get(tool_id)
|
|
if idx is None:
|
|
idx = len(tool_call_indexes)
|
|
tool_call_indexes[tool_id] = idx
|
|
saw_tool_call = True
|
|
payload = {
|
|
"id": completion_id,
|
|
"object": "chat.completion.chunk",
|
|
"created": created,
|
|
"model": model,
|
|
"choices": [
|
|
{
|
|
"index": 0,
|
|
"delta": {
|
|
"tool_calls": [
|
|
{
|
|
"index": idx,
|
|
**_openai_tool_call(tool, forced_id=tool_id),
|
|
}
|
|
]
|
|
},
|
|
"finish_reason": None,
|
|
}
|
|
],
|
|
}
|
|
yield f"data: {json.dumps(payload, ensure_ascii=False)}\n\n"
|
|
continue
|
|
|
|
text = _stream_text(chunk)
|
|
if not text:
|
|
continue
|
|
buffered_text_parts.append(text)
|
|
completion_tokens_holder["n"] += estimate_tokens(text)
|
|
if forced_tool_name and not saw_tool_call:
|
|
continue
|
|
yield _text_payload(text)
|
|
|
|
if buffered_text_parts and not saw_tool_call and forced_tool_name:
|
|
fallback_event = _forced_tool_event_from_text(
|
|
"".join(buffered_text_parts),
|
|
forced_tool_name,
|
|
single_arg_name=forced_tool_single_arg_name,
|
|
)
|
|
if fallback_event is not None:
|
|
saw_tool_call = True
|
|
tool_id = "call_fallback_0"
|
|
idx = 0
|
|
tool_call_indexes[tool_id] = idx
|
|
fallback_tool_call = _openai_tool_call(fallback_event, forced_id=tool_id)
|
|
payload = {
|
|
"id": completion_id,
|
|
"object": "chat.completion.chunk",
|
|
"created": created,
|
|
"model": model,
|
|
"choices": [
|
|
{
|
|
"index": 0,
|
|
"delta": {
|
|
"tool_calls": [
|
|
{
|
|
"index": idx,
|
|
**fallback_tool_call,
|
|
}
|
|
]
|
|
},
|
|
"finish_reason": None,
|
|
}
|
|
],
|
|
}
|
|
buffered_text_parts.clear()
|
|
yield f"data: {json.dumps(payload, ensure_ascii=False)}\n\n"
|
|
|
|
if buffered_text_parts:
|
|
for buffered_text in buffered_text_parts:
|
|
yield _text_payload(buffered_text)
|
|
buffered_text_parts.clear()
|
|
|
|
done_payload = {
|
|
"id": completion_id,
|
|
"object": "chat.completion.chunk",
|
|
"created": created,
|
|
"model": model,
|
|
"choices": [
|
|
{
|
|
"index": 0,
|
|
"delta": {},
|
|
"finish_reason": "tool_calls" if saw_tool_call else "stop",
|
|
}
|
|
],
|
|
}
|
|
yield f"data: {json.dumps(done_payload, ensure_ascii=False)}\n\n"
|
|
|
|
if include_usage:
|
|
usage_payload = {
|
|
"id": completion_id,
|
|
"object": "chat.completion.chunk",
|
|
"created": created,
|
|
"model": model,
|
|
"choices": [],
|
|
"usage": {
|
|
"prompt_tokens": prompt_tokens,
|
|
"completion_tokens": completion_tokens_holder["n"],
|
|
"total_tokens": prompt_tokens + completion_tokens_holder["n"],
|
|
},
|
|
}
|
|
yield f"data: {json.dumps(usage_payload, ensure_ascii=False)}\n\n"
|
|
|
|
yield "data: [DONE]\n\n"
|
|
success = True
|
|
except asyncio.CancelledError:
|
|
logger.info(
|
|
"chat.stream cancelled by client (inst=%s, session_id=%s)",
|
|
_inst.name,
|
|
cached_session_id,
|
|
)
|
|
raise
|
|
except Exception as exc:
|
|
logger.warning(
|
|
"chat.stream error (inst=%s, session_id=%s, prompt_tokens=%s, completion_tokens=%s): %s",
|
|
_inst.name,
|
|
cached_session_id,
|
|
prompt_tokens,
|
|
completion_tokens_holder["n"],
|
|
exc,
|
|
)
|
|
finally:
|
|
if success and write_key:
|
|
sid = _meta.get("session_id")
|
|
if sid:
|
|
await session_cache.put(write_key, sid, _inst.name)
|
|
await stats_collector.record_chat(
|
|
stream=True,
|
|
success=success,
|
|
prompt_tokens=prompt_tokens,
|
|
completion_tokens=completion_tokens_holder["n"],
|
|
)
|
|
_inst.in_flight = max(0, _inst.in_flight - 1)
|
|
_ticket.release()
|
|
|
|
ticket_transferred = True
|
|
return _streaming_response(event_stream())
|
|
|
|
try:
|
|
result = await inst.client.chat_complete(
|
|
prompt,
|
|
model,
|
|
ask_mode,
|
|
session_id=cached_session_id,
|
|
is_reply=is_reply,
|
|
tool_config=tool_config,
|
|
)
|
|
except Exception as exc:
|
|
logger.warning("chat.complete error (inst=%s): %s", inst.name, exc)
|
|
await stats_collector.record_chat(
|
|
stream=False,
|
|
success=False,
|
|
prompt_tokens=prompt_tokens,
|
|
completion_tokens=0,
|
|
)
|
|
# If we used a cached session and the call blew up, drop it so the
|
|
# next turn can start fresh instead of hitting the same dead session.
|
|
if cached_session_id and lookup_key:
|
|
await session_cache.invalidate(lookup_key)
|
|
raise HTTPException(
|
|
status_code=502,
|
|
detail={"error": {"message": "upstream lingma error", "type": "upstream_error"}},
|
|
)
|
|
|
|
completion_tokens = estimate_tokens(result.get("text") or "")
|
|
await stats_collector.record_chat(
|
|
stream=False,
|
|
success=True,
|
|
prompt_tokens=prompt_tokens,
|
|
completion_tokens=completion_tokens,
|
|
)
|
|
if write_key:
|
|
sid = result.get("sessionId")
|
|
if sid:
|
|
await session_cache.put(write_key, sid, inst.name)
|
|
tool_events = result.get("toolEvents") or []
|
|
message_content = result.get("text") or ""
|
|
tool_calls: list[dict[str, Any]] = []
|
|
saw_tool_call = False
|
|
forced_tool_name = _openai_forced_tool_name(req.tool_choice)
|
|
if isinstance(tool_events, list):
|
|
for idx, item in enumerate(tool_events):
|
|
if isinstance(item, dict):
|
|
tool_name = str(item.get("name") or "")
|
|
allowed = True
|
|
if tool_config and isinstance(tool_config.get("tools"), list) and tool_config.get("tools"):
|
|
allowed = False
|
|
for t in tool_config.get("tools"):
|
|
if tool_name == _anthropic_tool_name(t) or tool_name == _openai_tool_name(t):
|
|
allowed = True
|
|
break
|
|
if not allowed and forced_tool_name and tool_name == forced_tool_name:
|
|
allowed = True
|
|
if not allowed:
|
|
continue
|
|
|
|
tool_id = str(item.get("id") or f"call_{idx}")
|
|
tool_calls.append(_openai_tool_call(item, forced_id=tool_id))
|
|
saw_tool_call = True
|
|
if not saw_tool_call:
|
|
if forced_tool_name:
|
|
fallback_event = _forced_tool_event_from_text(
|
|
message_content,
|
|
forced_tool_name,
|
|
single_arg_name=_tool_code_single_arg_name(req.tools, forced_tool_name),
|
|
)
|
|
if fallback_event is not None:
|
|
tool_calls.append(_openai_tool_call(fallback_event, forced_id="call_fallback_0"))
|
|
saw_tool_call = True
|
|
message_content = ""
|
|
response = ChatCompletionResponse(
|
|
id=f"chatcmpl-{uuid.uuid4().hex}",
|
|
created=int(time.time()),
|
|
model=model,
|
|
choices=[
|
|
ChatCompletionChoice(
|
|
index=0,
|
|
finish_reason="tool_calls" if saw_tool_call else "stop",
|
|
message={
|
|
"role": "assistant",
|
|
"content": message_content,
|
|
"tool_calls": tool_calls or None,
|
|
},
|
|
)
|
|
],
|
|
)
|
|
|
|
|
|
data = response.model_dump()
|
|
data["latency"] = {
|
|
"first_token_ms": result.get("firstTokenLatencyMs"),
|
|
"total_ms": result.get("totalLatencyMs"),
|
|
}
|
|
data["usage"] = {
|
|
"prompt_tokens": prompt_tokens,
|
|
"completion_tokens": completion_tokens,
|
|
"total_tokens": prompt_tokens + completion_tokens,
|
|
}
|
|
data["served_by"] = inst.name
|
|
return JSONResponse(content=data)
|
|
finally:
|
|
if not ticket_transferred:
|
|
inst.in_flight = max(0, inst.in_flight - 1)
|
|
ticket.release()
|
|
|
|
|
|
|
|
|
|
async def _responses_stream_from_chat_stream(
|
|
chat_stream: StreamingResponse,
|
|
*,
|
|
response_id: str,
|
|
model: str,
|
|
):
|
|
created_at = int(time.time())
|
|
usage: dict[str, int] = {"input_tokens": 0, "output_tokens": 0, "total_tokens": 0}
|
|
completed_sent = False
|
|
output_item_id = f"msg_{uuid.uuid4().hex}"
|
|
output_index = 0
|
|
content_index = 0
|
|
output_text_parts: list[str] = []
|
|
function_call_items: list[dict[str, Any]] = []
|
|
function_call_index_by_id: dict[str, int] = {}
|
|
function_call_arguments_by_id: dict[str, str] = {}
|
|
function_call_name_by_id: dict[str, str] = {}
|
|
function_call_id_by_upstream_index: dict[int, str] = {}
|
|
|
|
def _message_item(status: str) -> dict[str, Any]:
|
|
return {
|
|
"id": output_item_id,
|
|
"type": "message",
|
|
"role": "assistant",
|
|
"status": status,
|
|
"content": [
|
|
{
|
|
"type": "output_text",
|
|
"text": "".join(output_text_parts),
|
|
}
|
|
],
|
|
}
|
|
|
|
def _function_call_item(call_id: str, *, status: str, name: str, arguments: str) -> dict[str, Any]:
|
|
return {
|
|
"id": call_id,
|
|
"type": "function_call",
|
|
"call_id": call_id,
|
|
"name": name,
|
|
"arguments": arguments,
|
|
"status": status,
|
|
}
|
|
|
|
def _completed_output_items() -> list[dict[str, Any]]:
|
|
return [_message_item("completed"), *function_call_items]
|
|
|
|
def _completed_frame() -> str:
|
|
return _sse_data(
|
|
{
|
|
"type": "response.completed",
|
|
"response": {
|
|
"id": response_id,
|
|
"object": "response",
|
|
"created_at": created_at,
|
|
"status": "completed",
|
|
"model": model,
|
|
"output": _completed_output_items(),
|
|
"usage": usage,
|
|
},
|
|
}
|
|
)
|
|
|
|
def _finish_output_item_frames() -> list[str]:
|
|
frames = [
|
|
_sse_data(
|
|
{
|
|
"type": "response.output_text.done",
|
|
"response_id": response_id,
|
|
"item_id": output_item_id,
|
|
"output_index": output_index,
|
|
"content_index": content_index,
|
|
"text": "".join(output_text_parts),
|
|
}
|
|
),
|
|
_sse_data(
|
|
{
|
|
"type": "response.output_item.done",
|
|
"response_id": response_id,
|
|
"output_index": output_index,
|
|
"item": _message_item("completed"),
|
|
}
|
|
),
|
|
]
|
|
for idx, item in enumerate(function_call_items, start=1):
|
|
frames.append(
|
|
_sse_data(
|
|
{
|
|
"type": "response.function_call_arguments.done",
|
|
"response_id": response_id,
|
|
"item_id": item["id"],
|
|
"output_index": idx,
|
|
"arguments": item["arguments"],
|
|
}
|
|
)
|
|
)
|
|
frames.append(
|
|
_sse_data(
|
|
{
|
|
"type": "response.output_item.done",
|
|
"response_id": response_id,
|
|
"output_index": idx,
|
|
"item": item,
|
|
}
|
|
)
|
|
)
|
|
return frames
|
|
|
|
def _ensure_function_call_item(call_id: str) -> list[str]:
|
|
existing_index = function_call_index_by_id.get(call_id)
|
|
name = function_call_name_by_id.get(call_id, "tool")
|
|
arguments = function_call_arguments_by_id.get(call_id, "")
|
|
if existing_index is not None:
|
|
function_call_items[existing_index] = _function_call_item(
|
|
call_id,
|
|
status="completed",
|
|
name=name,
|
|
arguments=arguments,
|
|
)
|
|
return []
|
|
item = _function_call_item(
|
|
call_id,
|
|
status="completed",
|
|
name=name,
|
|
arguments=arguments,
|
|
)
|
|
function_call_items.append(item)
|
|
item_index = len(function_call_items) - 1
|
|
function_call_index_by_id[call_id] = item_index
|
|
return [
|
|
_sse_data(
|
|
{
|
|
"type": "response.output_item.added",
|
|
"response_id": response_id,
|
|
"output_index": item_index + 1,
|
|
"item": _function_call_item(
|
|
call_id,
|
|
status="in_progress",
|
|
name=name,
|
|
arguments="",
|
|
),
|
|
}
|
|
)
|
|
]
|
|
|
|
yield _sse_data(
|
|
{
|
|
"type": "response.created",
|
|
"response": {
|
|
"id": response_id,
|
|
"object": "response",
|
|
"created_at": created_at,
|
|
"status": "in_progress",
|
|
"model": model,
|
|
"output": [],
|
|
},
|
|
}
|
|
)
|
|
yield _sse_data(
|
|
{
|
|
"type": "response.output_item.added",
|
|
"response_id": response_id,
|
|
"output_index": output_index,
|
|
"item": _message_item("in_progress"),
|
|
}
|
|
)
|
|
|
|
try:
|
|
async for part in chat_stream.body_iterator:
|
|
chunk = part.decode("utf-8") if isinstance(part, bytes) else str(part)
|
|
for frame in chunk.split("\n\n"):
|
|
frame = frame.strip()
|
|
if not frame or not frame.startswith("data:"):
|
|
continue
|
|
body = frame[len("data:") :].strip()
|
|
if body == "[DONE]":
|
|
for event in _finish_output_item_frames():
|
|
yield event
|
|
yield _completed_frame()
|
|
yield "data: [DONE]\n\n"
|
|
completed_sent = True
|
|
return
|
|
|
|
try:
|
|
payload = json.loads(body)
|
|
except Exception:
|
|
continue
|
|
|
|
frame_usage = _responses_usage_from_chat(payload.get("usage"))
|
|
if any(frame_usage.values()):
|
|
usage = frame_usage
|
|
|
|
choices = payload.get("choices")
|
|
if not isinstance(choices, list) or not choices:
|
|
continue
|
|
choice = choices[0] if isinstance(choices[0], dict) else {}
|
|
delta = choice.get("delta") if isinstance(choice.get("delta"), dict) else {}
|
|
|
|
text = delta.get("content")
|
|
if isinstance(text, str) and text:
|
|
output_text_parts.append(text)
|
|
yield _sse_data(
|
|
{
|
|
"type": "response.output_text.delta",
|
|
"response_id": response_id,
|
|
"item_id": output_item_id,
|
|
"output_index": output_index,
|
|
"content_index": content_index,
|
|
"delta": text,
|
|
}
|
|
)
|
|
|
|
tool_calls = delta.get("tool_calls")
|
|
if isinstance(tool_calls, list):
|
|
for idx, tool_call in enumerate(tool_calls):
|
|
if not isinstance(tool_call, dict):
|
|
continue
|
|
fn = tool_call.get("function") if isinstance(tool_call.get("function"), dict) else {}
|
|
upstream_index_raw = tool_call.get("index")
|
|
upstream_index = upstream_index_raw if isinstance(upstream_index_raw, int) else idx
|
|
call_id = str(
|
|
tool_call.get("id")
|
|
or function_call_id_by_upstream_index.get(upstream_index)
|
|
or f"call_{upstream_index}"
|
|
)
|
|
function_call_id_by_upstream_index[upstream_index] = call_id
|
|
name = str(fn.get("name") or function_call_name_by_id.get(call_id) or "tool")
|
|
function_call_name_by_id[call_id] = name
|
|
arguments_delta = str(fn.get("arguments") or "")
|
|
accumulated_arguments = (
|
|
function_call_arguments_by_id.get(call_id, "") + arguments_delta
|
|
)
|
|
function_call_arguments_by_id[call_id] = accumulated_arguments
|
|
for event in _ensure_function_call_item(call_id):
|
|
yield event
|
|
if arguments_delta:
|
|
yield _sse_data(
|
|
{
|
|
"type": "response.function_call_arguments.delta",
|
|
"response_id": response_id,
|
|
"item_id": call_id,
|
|
"output_index": function_call_index_by_id[call_id] + 1,
|
|
"delta": arguments_delta,
|
|
}
|
|
)
|
|
except asyncio.CancelledError:
|
|
if not completed_sent:
|
|
for event in _finish_output_item_frames():
|
|
yield event
|
|
yield _completed_frame()
|
|
yield "data: [DONE]\n\n"
|
|
completed_sent = True
|
|
return
|
|
except Exception:
|
|
if not completed_sent:
|
|
for event in _finish_output_item_frames():
|
|
yield event
|
|
yield _completed_frame()
|
|
yield "data: [DONE]\n\n"
|
|
completed_sent = True
|
|
return
|
|
|
|
if not completed_sent:
|
|
for event in _finish_output_item_frames():
|
|
yield event
|
|
yield _completed_frame()
|
|
yield "data: [DONE]\n\n"
|
|
|
|
|
|
|
|
@app.post("/responses", dependencies=[Depends(auth_guard)])
|
|
@app.post("/v1/responses", dependencies=[Depends(auth_guard)])
|
|
async def v1_responses(req: ResponsesRequest, request: Request):
|
|
chat_req = _responses_to_chat_request(req)
|
|
chat_response = await v1_chat_completions(chat_req, request)
|
|
|
|
if isinstance(chat_response, StreamingResponse):
|
|
response_id = f"resp_{uuid.uuid4().hex}"
|
|
return StreamingResponse(
|
|
_responses_stream_from_chat_stream(
|
|
chat_response,
|
|
response_id=response_id,
|
|
model=req.model,
|
|
),
|
|
media_type="text/event-stream",
|
|
headers={
|
|
"Cache-Control": "no-cache, no-transform",
|
|
"X-Accel-Buffering": "no",
|
|
"Connection": "keep-alive",
|
|
},
|
|
)
|
|
|
|
invalid_upstream_error = {
|
|
"error": {"message": "invalid upstream response", "type": "upstream_error"}
|
|
}
|
|
try:
|
|
chat_payload = json.loads(chat_response.body)
|
|
except Exception:
|
|
raise HTTPException(
|
|
status_code=502,
|
|
detail=invalid_upstream_error,
|
|
)
|
|
if not isinstance(chat_payload, dict):
|
|
raise HTTPException(
|
|
status_code=502,
|
|
detail=invalid_upstream_error,
|
|
)
|
|
return JSONResponse(content=_responses_non_stream_from_chat_payload(chat_payload))
|
|
|
|
|
|
|
|
def _anthropic_error(status_code: int, error_type: str, message: str) -> JSONResponse:
|
|
"""Build an Anthropic-shaped error response (`type:error` envelope)."""
|
|
return JSONResponse(
|
|
status_code=status_code,
|
|
content={"type": "error", "error": {"type": error_type, "message": message}},
|
|
)
|
|
|
|
|
|
def _anthropic_stop_reason(
|
|
completion_tokens: int,
|
|
max_tokens: int,
|
|
*,
|
|
has_pending_tool_use: bool = False,
|
|
) -> str:
|
|
"""Approximate Anthropic `stop_reason`."""
|
|
if has_pending_tool_use:
|
|
return "tool_use"
|
|
if max_tokens and completion_tokens >= max_tokens:
|
|
return "max_tokens"
|
|
return "end_turn"
|
|
|
|
|
|
@app.post("/v1/messages/count_tokens")
|
|
async def v1_messages_count_tokens(req: AnthropicMessagesRequest, request: Request):
|
|
"""Anthropic-compatible token counting endpoint.
|
|
|
|
Claude Code may probe this endpoint; return Anthropic-shaped response.
|
|
"""
|
|
try:
|
|
require_anthropic_key(request, settings.api_keys)
|
|
except AnthropicAuthError as exc:
|
|
return _anthropic_error(exc.status_code, exc.error_type, exc.message)
|
|
|
|
messages_dump = anthropic_to_internal_messages(req)
|
|
prompt = _messages_to_prompt(messages_dump)
|
|
return JSONResponse(content={"input_tokens": estimate_tokens(prompt)})
|
|
|
|
|
|
@app.post("/v1/messages")
|
|
async def v1_messages(req: AnthropicMessagesRequest, request: Request):
|
|
"""Anthropic Messages API compatible endpoint.
|
|
|
|
Wire contract:
|
|
* auth: `x-api-key` header (fallback Authorization: Bearer)
|
|
* body: Anthropic Messages spec (system top-level, content blocks, ...)
|
|
* stream: named-event SSE (message_start / content_block_delta / ...)
|
|
|
|
Internally we:
|
|
1. Normalise to the gateway's internal message list (`role/content` dicts)
|
|
2. Reuse the same pool pick + session cache + backpressure guard as
|
|
`/v1/chat/completions`. Session-cache keys include the API key, so
|
|
Anthropic and OpenAI callers on the same key share KV-cache warmth.
|
|
3. Re-wrap outputs in Anthropic's response / SSE format.
|
|
"""
|
|
# ------------------------------------------------------------- auth
|
|
try:
|
|
require_anthropic_key(request, settings.api_keys)
|
|
except AnthropicAuthError as exc:
|
|
return _anthropic_error(exc.status_code, exc.error_type, exc.message)
|
|
|
|
# ------------------------------------------------------------- plumbing
|
|
try:
|
|
p = _require_pool()
|
|
except HTTPException as exc:
|
|
return _anthropic_error(exc.status_code, "overloaded_error", "gateway not ready")
|
|
|
|
messages_dump = anthropic_to_internal_messages(req)
|
|
# Prefer the auth token actually accepted so session-cache bucketing is
|
|
# consistent regardless of which auth header style the caller used.
|
|
api_key = (
|
|
request.headers.get("x-api-key", "").strip()
|
|
or _extract_api_key(request)
|
|
or "-"
|
|
)
|
|
|
|
# ------------------------------------------------------------- session reuse
|
|
try:
|
|
tool_config = _anthropic_tool_config(req)
|
|
except HTTPException as exc:
|
|
detail = exc.detail if isinstance(exc.detail, dict) else {}
|
|
error = detail.get("error") if isinstance(detail.get("error"), dict) else {}
|
|
message = error.get("message") or str(detail) or "invalid tool configuration"
|
|
return _anthropic_error(exc.status_code, "invalid_request_error", message)
|
|
has_tooling_context = _anthropic_has_tooling_context(req)
|
|
|
|
ask_mode = _resolve_ask_mode(req.model, has_tooling_context)
|
|
|
|
reuse_eligible = (
|
|
session_cache.enabled and ask_mode == "chat" and len(messages_dump) >= 2 and not has_tooling_context
|
|
)
|
|
lookup_key: str | None = None
|
|
write_key: str | None = None
|
|
cached_session_id: str | None = None
|
|
cached_instance_name: str | None = None
|
|
if reuse_eligible:
|
|
prefix_branch_context = hash_branch_context(messages_dump[:-1])
|
|
lookup_key = session_cache.build_key(
|
|
api_key,
|
|
messages_dump[:-1],
|
|
tool_config=tool_config,
|
|
branch_context=prefix_branch_context,
|
|
)
|
|
write_key = session_cache.build_key(
|
|
api_key,
|
|
messages_dump,
|
|
tool_config=tool_config,
|
|
branch_context=hash_branch_context(messages_dump),
|
|
)
|
|
entry = await session_cache.get(lookup_key)
|
|
if entry is None:
|
|
legacy_lookup_key = session_cache.build_key(api_key, messages_dump[:-1], tool_config=tool_config)
|
|
entry = await session_cache.get(legacy_lookup_key)
|
|
if entry is not None:
|
|
lookup_key = legacy_lookup_key
|
|
if entry is not None:
|
|
cached_session_id = entry.session_id
|
|
cached_instance_name = entry.instance_name or None
|
|
|
|
affinity = cached_instance_name or affinity_key_for_anthropic(req)
|
|
inst = p.pick(affinity_key=affinity)
|
|
|
|
if cached_instance_name and inst.name != cached_instance_name:
|
|
logger.info(
|
|
"anthropic session cache instance %s unhealthy, falling back to %s",
|
|
cached_instance_name,
|
|
inst.name,
|
|
)
|
|
cached_session_id = None
|
|
if lookup_key:
|
|
await session_cache.invalidate(lookup_key)
|
|
|
|
try:
|
|
await _ensure_instance_logged_in(inst)
|
|
except HTTPException as exc:
|
|
# 503/401/502 from login: map to closest Anthropic kind.
|
|
err_type = "authentication_error" if exc.status_code == 401 else "overloaded_error"
|
|
detail = exc.detail if isinstance(exc.detail, dict) else {}
|
|
msg = (detail.get("error") or {}).get("message") or str(detail) or "upstream error"
|
|
return _anthropic_error(exc.status_code, err_type, msg)
|
|
|
|
# ------------------------------------------------------------- prompt & model
|
|
models = await inst.client.query_models()
|
|
available = flatten_model_keys(models)
|
|
name_map = build_model_name_map(models)
|
|
# Anthropic callers send `claude-*` model names. resolve_model's
|
|
# final fallback (default_model / first available) handles that cleanly
|
|
# without us having to hard-code a mapping table.
|
|
model = resolve_model(req.model, available, settings.default_model, name_map)
|
|
|
|
if cached_session_id:
|
|
prompt = _last_user_text(messages_dump)
|
|
is_reply = True
|
|
else:
|
|
prompt = _messages_to_prompt(messages_dump)
|
|
is_reply = False
|
|
|
|
if not prompt:
|
|
return _anthropic_error(400, "invalid_request_error", "messages is empty")
|
|
|
|
prompt_tokens = estimate_tokens(prompt)
|
|
# ------------------------------------------------------------- backpressure
|
|
try:
|
|
ticket = await chat_guard.try_acquire()
|
|
except BackpressureRejected as exc:
|
|
retry_after = max(1, int(exc.retry_after))
|
|
logger.warning("anthropic rejected by backpressure, retry_after=%ds", retry_after)
|
|
resp = _anthropic_error(
|
|
429,
|
|
"overloaded_error",
|
|
"too many in-flight requests, please retry later",
|
|
)
|
|
resp.headers["Retry-After"] = str(retry_after)
|
|
return resp
|
|
|
|
inst.in_flight += 1
|
|
message_id = f"msg_{uuid.uuid4().hex}"
|
|
logger.info(
|
|
"anthropic.start inst=%s model=%s stream=%s prompt_tokens~%d reuse=%s",
|
|
inst.name,
|
|
model,
|
|
req.stream,
|
|
prompt_tokens,
|
|
bool(cached_session_id),
|
|
extra={
|
|
"ctx_instance": inst.name,
|
|
"ctx_model": model,
|
|
"ctx_ask_mode": ask_mode,
|
|
"ctx_stream": req.stream,
|
|
"ctx_prompt_tokens": prompt_tokens,
|
|
"ctx_in_flight": chat_guard.in_flight,
|
|
"ctx_affinity": affinity,
|
|
"ctx_session_reuse": bool(cached_session_id),
|
|
"ctx_api": "anthropic",
|
|
},
|
|
)
|
|
|
|
ticket_transferred = False
|
|
|
|
def _sse(event: str, data: dict) -> str:
|
|
return f"event: {event}\ndata: {json.dumps(data, ensure_ascii=False)}\n\n"
|
|
|
|
try:
|
|
if req.stream:
|
|
completion_tokens_holder = {"n": 0}
|
|
stream_meta: dict = {}
|
|
max_tokens = req.max_tokens
|
|
|
|
async def event_stream(_ticket=ticket, _inst=inst, _meta=stream_meta):
|
|
success = False
|
|
block_index = 0
|
|
text_block_open = False
|
|
saw_pending_tool_use = False
|
|
try:
|
|
# 1) message_start — Anthropic SDKs read this first to get
|
|
# the message envelope (id/model/initial usage).
|
|
start_payload = {
|
|
"type": "message_start",
|
|
"message": {
|
|
"id": message_id,
|
|
"type": "message",
|
|
"role": "assistant",
|
|
"model": model,
|
|
"content": [],
|
|
"stop_reason": None,
|
|
"stop_sequence": None,
|
|
# input_tokens is authoritative here; output_tokens
|
|
# is seeded to 0 and updated in message_delta.
|
|
"usage": {
|
|
"input_tokens": prompt_tokens,
|
|
"output_tokens": 0,
|
|
},
|
|
},
|
|
}
|
|
yield _sse("message_start", start_payload)
|
|
|
|
async for chunk in _inst.client.chat_stream(
|
|
prompt,
|
|
model,
|
|
ask_mode,
|
|
session_id=cached_session_id,
|
|
is_reply=is_reply,
|
|
tool_config=tool_config,
|
|
out_meta=_meta,
|
|
):
|
|
if _stream_event_type(chunk) == "tool":
|
|
if text_block_open:
|
|
yield _sse(
|
|
"content_block_stop",
|
|
{"type": "content_block_stop", "index": block_index},
|
|
)
|
|
block_index += 1
|
|
text_block_open = False
|
|
|
|
tool = _stream_tool_event(chunk)
|
|
if not tool:
|
|
continue
|
|
|
|
tool_name = str(tool.get("name") or "")
|
|
allowed = True
|
|
if tool_config and isinstance(tool_config.get("tools"), list) and tool_config.get("tools"):
|
|
allowed = False
|
|
for t in tool_config.get("tools"):
|
|
if tool_name == _anthropic_tool_name(t) or tool_name == _openai_tool_name(t):
|
|
allowed = True
|
|
break
|
|
forced_tool_name = _anthropic_forced_tool_name(req.tool_choice)
|
|
if not allowed and forced_tool_name and tool_name == forced_tool_name:
|
|
allowed = True
|
|
if not allowed:
|
|
continue
|
|
|
|
tool_id = str(tool.get("id") or f"toolu_stream_{block_index}")
|
|
|
|
tool_use_block = _anthropic_tool_use_block(tool, forced_id=tool_id)
|
|
yield _sse(
|
|
"content_block_start",
|
|
{
|
|
"type": "content_block_start",
|
|
"index": block_index,
|
|
"content_block": tool_use_block,
|
|
},
|
|
)
|
|
yield _sse(
|
|
"content_block_stop",
|
|
{"type": "content_block_stop", "index": block_index},
|
|
)
|
|
block_index += 1
|
|
|
|
tool_result_block = _anthropic_tool_result_block(tool, forced_id=tool_id)
|
|
if tool_result_block is not None:
|
|
yield _sse(
|
|
"content_block_start",
|
|
{
|
|
"type": "content_block_start",
|
|
"index": block_index,
|
|
"content_block": tool_result_block,
|
|
},
|
|
)
|
|
yield _sse(
|
|
"content_block_stop",
|
|
{"type": "content_block_stop", "index": block_index},
|
|
)
|
|
block_index += 1
|
|
else:
|
|
saw_pending_tool_use = True
|
|
continue
|
|
|
|
text = _stream_text(chunk)
|
|
if not text:
|
|
continue
|
|
completion_tokens_holder["n"] += estimate_tokens(text)
|
|
if not text_block_open:
|
|
yield _sse(
|
|
"content_block_start",
|
|
{
|
|
"type": "content_block_start",
|
|
"index": block_index,
|
|
"content_block": {"type": "text", "text": ""},
|
|
},
|
|
)
|
|
text_block_open = True
|
|
|
|
yield _sse(
|
|
"content_block_delta",
|
|
{
|
|
"type": "content_block_delta",
|
|
"index": block_index,
|
|
"delta": {"type": "text_delta", "text": text},
|
|
},
|
|
)
|
|
|
|
if text_block_open:
|
|
yield _sse(
|
|
"content_block_stop",
|
|
{"type": "content_block_stop", "index": block_index},
|
|
)
|
|
|
|
# 5) message_delta carries the terminal stop_reason and
|
|
# the final cumulative output_tokens count.
|
|
stop_reason = _anthropic_stop_reason(
|
|
completion_tokens_holder["n"],
|
|
max_tokens,
|
|
has_pending_tool_use=saw_pending_tool_use,
|
|
)
|
|
yield _sse(
|
|
"message_delta",
|
|
{
|
|
"type": "message_delta",
|
|
"delta": {
|
|
"stop_reason": stop_reason,
|
|
"stop_sequence": None,
|
|
},
|
|
"usage": {"output_tokens": completion_tokens_holder["n"]},
|
|
},
|
|
)
|
|
|
|
|
|
# 6) message_stop — terminal event, no [DONE] sentinel.
|
|
yield _sse("message_stop", {"type": "message_stop"})
|
|
success = True
|
|
except asyncio.CancelledError:
|
|
logger.info("anthropic.stream cancelled (inst=%s)", _inst.name)
|
|
raise
|
|
except Exception as exc:
|
|
logger.warning("anthropic.stream error (inst=%s): %s", _inst.name, exc)
|
|
# Best-effort error frame. Anthropic clients treat any
|
|
# unexpected event gracefully; we prefer visibility over
|
|
# silent truncation.
|
|
try:
|
|
yield _sse(
|
|
"error",
|
|
{
|
|
"type": "error",
|
|
"error": {
|
|
"type": "api_error",
|
|
"message": str(exc) or "upstream error",
|
|
},
|
|
},
|
|
)
|
|
except Exception:
|
|
pass
|
|
finally:
|
|
# Session write-back only on clean finish — partial streams
|
|
# leave Lingma's session in an indeterminate state.
|
|
if success and write_key:
|
|
sid = _meta.get("session_id")
|
|
if sid:
|
|
await session_cache.put(write_key, sid, _inst.name)
|
|
await stats_collector.record_chat(
|
|
stream=True,
|
|
success=success,
|
|
prompt_tokens=prompt_tokens,
|
|
completion_tokens=completion_tokens_holder["n"],
|
|
)
|
|
_inst.in_flight = max(0, _inst.in_flight - 1)
|
|
_ticket.release()
|
|
|
|
ticket_transferred = True
|
|
return _streaming_response(event_stream())
|
|
|
|
|
|
# ------------------------------------------------------------- non-stream
|
|
try:
|
|
result = await inst.client.chat_complete(
|
|
prompt,
|
|
model,
|
|
ask_mode,
|
|
session_id=cached_session_id,
|
|
is_reply=is_reply,
|
|
tool_config=tool_config,
|
|
)
|
|
except Exception as exc:
|
|
logger.warning("anthropic.complete error (inst=%s): %s", inst.name, exc)
|
|
await stats_collector.record_chat(
|
|
stream=False,
|
|
success=False,
|
|
prompt_tokens=prompt_tokens,
|
|
completion_tokens=0,
|
|
)
|
|
if cached_session_id and lookup_key:
|
|
await session_cache.invalidate(lookup_key)
|
|
return _anthropic_error(502, "api_error", "upstream lingma error")
|
|
|
|
text = result.get("text") or ""
|
|
completion_tokens = estimate_tokens(text)
|
|
await stats_collector.record_chat(
|
|
stream=False,
|
|
success=True,
|
|
prompt_tokens=prompt_tokens,
|
|
completion_tokens=completion_tokens,
|
|
)
|
|
if write_key:
|
|
sid = result.get("sessionId")
|
|
if sid:
|
|
await session_cache.put(write_key, sid, inst.name)
|
|
|
|
content_blocks: list[dict[str, Any]] = []
|
|
if text:
|
|
content_blocks.append({"type": "text", "text": text})
|
|
tool_events = result.get("toolEvents") or []
|
|
saw_pending_tool_use = False
|
|
saw_tool_event = False
|
|
if isinstance(tool_events, list):
|
|
for idx, item in enumerate(tool_events):
|
|
if not isinstance(item, dict):
|
|
continue
|
|
|
|
tool_name = str(item.get("name") or "")
|
|
allowed = True
|
|
if tool_config and isinstance(tool_config.get("tools"), list) and tool_config.get("tools"):
|
|
allowed = False
|
|
for t in tool_config.get("tools"):
|
|
if tool_name == _anthropic_tool_name(t) or tool_name == _openai_tool_name(t):
|
|
allowed = True
|
|
break
|
|
forced_tool_name = _anthropic_forced_tool_name(req.tool_choice)
|
|
if not allowed and forced_tool_name and tool_name == forced_tool_name:
|
|
allowed = True
|
|
if not allowed:
|
|
continue
|
|
|
|
saw_tool_event = True
|
|
tool_id = str(item.get("id") or f"toolu_nonstream_{idx}")
|
|
content_blocks.append(_anthropic_tool_use_block(item, forced_id=tool_id))
|
|
tool_result = _anthropic_tool_result_block(item, forced_id=tool_id)
|
|
if tool_result is not None:
|
|
content_blocks.append(tool_result)
|
|
else:
|
|
saw_pending_tool_use = True
|
|
|
|
if not saw_tool_event:
|
|
forced_tool_name = _anthropic_forced_tool_name(req.tool_choice)
|
|
if forced_tool_name:
|
|
fallback_event = _forced_tool_event_from_text(
|
|
text,
|
|
forced_tool_name,
|
|
single_arg_name=_tool_code_single_arg_name(req.tools, forced_tool_name),
|
|
)
|
|
if fallback_event is not None:
|
|
content_blocks = []
|
|
tool_id = "toolu_fallback_0"
|
|
content_blocks.append(_anthropic_tool_use_block(fallback_event, forced_id=tool_id))
|
|
tool_result = _anthropic_tool_result_block(fallback_event, forced_id=tool_id)
|
|
saw_pending_tool_use = tool_result is None
|
|
if tool_result is not None:
|
|
content_blocks.append(tool_result)
|
|
|
|
response_body: dict = {
|
|
|
|
"id": message_id,
|
|
"type": "message",
|
|
"role": "assistant",
|
|
"model": model,
|
|
"content": content_blocks,
|
|
"stop_reason": _anthropic_stop_reason(
|
|
completion_tokens,
|
|
req.max_tokens,
|
|
has_pending_tool_use=saw_pending_tool_use,
|
|
),
|
|
"stop_sequence": None,
|
|
"usage": {
|
|
"input_tokens": prompt_tokens,
|
|
"output_tokens": completion_tokens,
|
|
},
|
|
}
|
|
return JSONResponse(content=response_body)
|
|
finally:
|
|
if not ticket_transferred:
|
|
inst.in_flight = max(0, inst.in_flight - 1)
|
|
ticket.release()
|
|
|
|
|
|
@app.post("/internal/auto-login/start", dependencies=[Depends(admin_auth_guard)])
|
|
async def internal_auto_login_start(instance: str | None = None):
|
|
p = _require_pool()
|
|
target = None
|
|
if instance:
|
|
for inst in p.instances:
|
|
if inst.name == instance:
|
|
target = inst
|
|
break
|
|
if target is None:
|
|
raise HTTPException(
|
|
status_code=404,
|
|
detail={"error": {"message": f"instance not found: {instance}"}},
|
|
)
|
|
else:
|
|
target = p.pick()
|
|
|
|
client = target.client
|
|
auto_login = target.auto_login
|
|
|
|
status = await client.auth_status()
|
|
if status and status.get("id"):
|
|
return {"ok": True, "state": "already_logged_in", "instance": target.name, "auth": status}
|
|
|
|
if settings.dedicated_domain_url:
|
|
try:
|
|
current = await client.get_endpoint()
|
|
current_ep = (current or {}).get("endpoint", "") if isinstance(current, dict) else ""
|
|
if current_ep != settings.dedicated_domain_url:
|
|
await client.update_endpoint(settings.dedicated_domain_url)
|
|
except Exception as exc:
|
|
logger.warning("[%s] switch dedicated endpoint failed: %s", target.name, exc)
|
|
|
|
try:
|
|
login_url, _login_raw = await client.generate_login_url()
|
|
except Exception as exc:
|
|
logger.warning("[%s] generate_login_url failed: %s", target.name, exc)
|
|
raise HTTPException(status_code=502, detail={"error": {"message": "generate login url failed"}})
|
|
|
|
if not login_url:
|
|
raise HTTPException(status_code=502, detail={"error": {"message": "generate login url failed"}})
|
|
|
|
started = await auto_login.ensure_started(login_url)
|
|
return {
|
|
"ok": True,
|
|
"state": "running" if started else "already_running",
|
|
"instance": target.name,
|
|
"auto_login": auto_login.status(),
|
|
}
|
|
|
|
|
|
@app.get("/internal/auto-login/status", dependencies=[Depends(admin_auth_guard)])
|
|
async def internal_auto_login_status():
|
|
p = _require_pool()
|
|
out = []
|
|
for inst in p.instances:
|
|
try:
|
|
auth = await inst.client.auth_status()
|
|
except Exception as exc:
|
|
auth = {"error": str(exc)}
|
|
out.append(
|
|
{
|
|
"instance": inst.name,
|
|
"auto_login": inst.auto_login.status(),
|
|
"auth": auth,
|
|
"state": inst.client.state,
|
|
}
|
|
)
|
|
return {"ok": True, "instances": out}
|
|
|
|
|
|
@app.post("/internal/session/export", dependencies=[Depends(admin_auth_guard)])
|
|
async def internal_session_export(instance: str | None = None):
|
|
"""Export a logged-in Lingma session as a base64 tar.gz bundle.
|
|
|
|
The returned `bundle_b64` can be dropped into `LINGMA_SESSION_BUNDLE`
|
|
(or the `session_bundle` field in `LINGMA_ACCOUNTS` JSON) on any other
|
|
deployment to skip Playwright login entirely.
|
|
|
|
Safety:
|
|
- Requires a valid API key.
|
|
- Only works on instances that are currently authenticated (prevents
|
|
exporting garbage from a half-initialised workDir).
|
|
- Response is not streamed to logs; callers must store it themselves.
|
|
"""
|
|
p = _require_pool()
|
|
target = None
|
|
if instance:
|
|
for inst in p.instances:
|
|
if inst.name == instance:
|
|
target = inst
|
|
break
|
|
if target is None:
|
|
raise HTTPException(status_code=404, detail={"error": f"instance {instance} not found"})
|
|
else:
|
|
target = p.pick()
|
|
|
|
try:
|
|
status = await target.client.auth_status()
|
|
except Exception as exc:
|
|
raise HTTPException(
|
|
status_code=503,
|
|
detail={"error": f"instance {target.name} not ready: {exc}"},
|
|
)
|
|
if not (status and status.get("id")):
|
|
raise HTTPException(
|
|
status_code=409,
|
|
detail={"error": f"instance {target.name} is not logged in"},
|
|
)
|
|
|
|
try:
|
|
raw = pack_workdir(target.cfg.work_dir)
|
|
except Exception as exc:
|
|
raise HTTPException(status_code=500, detail={"error": str(exc)})
|
|
|
|
bundle_b64 = encode_bundle(raw)
|
|
logger.info(
|
|
"session bundle exported from %s (%d bytes raw, %d bytes b64)",
|
|
target.name,
|
|
len(raw),
|
|
len(bundle_b64),
|
|
)
|
|
return {
|
|
"instance": target.name,
|
|
"account": target.cfg.account.username or "",
|
|
"raw_bytes": len(raw),
|
|
"bundle_b64": bundle_b64,
|
|
}
|
|
|
|
|
|
@app.get("/internal/models/raw", dependencies=[Depends(admin_auth_guard)])
|
|
async def internal_models_raw(instance: str | None = None):
|
|
"""Return the raw `config/queryModels` response from Lingma.
|
|
|
|
This is the authoritative source for per-key displayName, description,
|
|
capability flags, etc. We only ever extract `key` + `displayName` for
|
|
OpenAI compatibility, but clients may want to inspect everything.
|
|
"""
|
|
p = _require_pool()
|
|
target = None
|
|
if instance:
|
|
for inst in p.instances:
|
|
if inst.name == instance:
|
|
target = inst
|
|
break
|
|
if target is None:
|
|
raise HTTPException(status_code=404, detail={"error": f"instance {instance} not found"})
|
|
else:
|
|
target = p.pick()
|
|
await _ensure_instance_logged_in(target)
|
|
raw = await target.client.query_models()
|
|
name_map = build_model_name_map(raw if isinstance(raw, dict) else {})
|
|
return {
|
|
"instance": target.name,
|
|
"raw": raw,
|
|
"extracted_name_map": name_map,
|
|
"exposed_keys": flatten_model_keys(raw if isinstance(raw, dict) else {}),
|
|
}
|
|
|
|
|
|
@app.get("/internal/stats", dependencies=[Depends(admin_auth_guard)])
|
|
async def internal_stats():
|
|
p = _require_pool()
|
|
return {
|
|
"ok": True,
|
|
"stats": await stats_collector.snapshot(),
|
|
"concurrency": chat_guard.stats(),
|
|
"pool": p.stats(),
|
|
"session_cache": session_cache.stats(),
|
|
}
|
|
|
|
|
|
@app.get("/metrics", dependencies=[Depends(metrics_auth_guard)])
|
|
async def metrics():
|
|
base = await stats_collector.prometheus_text()
|
|
lines = list(chat_guard.prometheus_lines())
|
|
if pool is not None:
|
|
lines.extend(pool.prometheus_lines())
|
|
lines.extend(session_cache.prometheus_lines())
|
|
extra = "\n".join(lines) + "\n"
|
|
return StreamingResponse(iter([base + extra]), media_type="text/plain; version=0.0.4")
|