diff --git a/.claude b/.claude deleted file mode 120000 index c0ca4685..00000000 --- a/.claude +++ /dev/null @@ -1 +0,0 @@ -.agents \ No newline at end of file diff --git a/.env.example b/.env.example index 34165d93..486d71c0 100644 --- a/.env.example +++ b/.env.example @@ -11,6 +11,9 @@ CODEX_LB_UPSTREAM_BASE_URL=https://chatgpt.com/backend-api CODEX_LB_UPSTREAM_CONNECT_TIMEOUT_SECONDS=30 CODEX_LB_STREAM_IDLE_TIMEOUT_SECONDS=300 +# Anthropic-compatible default reasoning effort (optional) +# CODEX_LB_ANTHROPIC_DEFAULT_REASONING_EFFORT=xhigh + # OAuth / token refresh CODEX_LB_AUTH_BASE_URL=https://auth.openai.com CODEX_LB_OAUTH_CLIENT_ID=app_EMoamEEZ73f0CkXaXp7hrann diff --git a/app/core/auth/dependencies.py b/app/core/auth/dependencies.py index 7ed81ad8..4ed8d1a4 100644 --- a/app/core/auth/dependencies.py +++ b/app/core/auth/dependencies.py @@ -2,7 +2,7 @@ import logging -from fastapi import Request, Security +from fastapi import HTTPException, Request, Security from fastapi.security import HTTPAuthorizationCredentials, HTTPBearer from app.core.clients.usage import UsageFetchError, fetch_usage @@ -26,6 +26,10 @@ def set_openai_error_format(request: Request) -> None: request.state.error_format = "openai" +def set_anthropic_error_format(request: Request) -> None: + request.state.error_format = "anthropic" + + def set_dashboard_error_format(request: Request) -> None: request.state.error_format = "dashboard" @@ -52,6 +56,26 @@ async def validate_proxy_api_key( raise ProxyAuthError(str(exc)) from exc +async def validate_anthropic_api_key( + request: Request, + credentials: HTTPAuthorizationCredentials | None = Security(_bearer), +) -> ApiKeyData | None: + settings = await get_settings_cache().get() + if not settings.api_key_auth_enabled: + return None + + token = _extract_anthropic_api_key(request, credentials) + if token is None: + raise HTTPException(status_code=401, detail="Missing API key") + + async with get_background_session() as session: + service = ApiKeysService(ApiKeysRepository(session)) + try: + return await service.validate_key(token) + except ApiKeyInvalidError as exc: + raise HTTPException(status_code=401, detail=str(exc)) from exc + + # --- Dashboard session auth --- @@ -119,3 +143,20 @@ def _extract_bearer_token(authorization: str | None) -> str | None: if not token: return None return token + + +def _extract_anthropic_api_key( + request: Request, + credentials: HTTPAuthorizationCredentials | None, +) -> str | None: + x_api_key = request.headers.get("x-api-key") + if x_api_key is not None: + token = x_api_key.strip() + if token: + return token + if credentials is None: + return None + token = credentials.credentials.strip() + if not token: + return None + return token diff --git a/app/core/clients/proxy.py b/app/core/clients/proxy.py index 8217af6e..b31c3675 100644 --- a/app/core/clients/proxy.py +++ b/app/core/clients/proxy.py @@ -27,6 +27,7 @@ "content-length", "host", "forwarded", + "x-api-key", "x-real-ip", "true-client-ip", } diff --git a/app/core/config/settings.py b/app/core/config/settings.py index c34d59b8..4f441942 100644 --- a/app/core/config/settings.py +++ b/app/core/config/settings.py @@ -71,6 +71,7 @@ class Settings(BaseSettings): log_proxy_request_shape: bool = False log_proxy_request_shape_raw_cache_key: bool = False log_proxy_request_payload: bool = False + anthropic_default_reasoning_effort: str | None = None max_decompressed_body_bytes: int = Field(default=32 * 1024 * 1024, gt=0) image_inline_fetch_enabled: bool = True image_inline_allowed_hosts: Annotated[list[str], NoDecode] = Field(default_factory=list) @@ -115,6 +116,16 @@ def _normalize_image_inline_allowed_hosts(cls, value: object) -> list[str]: return normalized raise TypeError("image_inline_allowed_hosts must be a list or comma-separated string") + @field_validator("anthropic_default_reasoning_effort") + @classmethod + def _normalize_anthropic_default_reasoning_effort(cls, value: str | None) -> str | None: + if value is None: + return None + normalized = value.strip() + if not normalized: + raise ValueError("anthropic_default_reasoning_effort must be a non-empty string") + return normalized + @lru_cache(maxsize=1) def get_settings() -> Settings: diff --git a/app/core/errors.py b/app/core/errors.py index e31789bb..515c40e7 100644 --- a/app/core/errors.py +++ b/app/core/errors.py @@ -18,6 +18,16 @@ class OpenAIErrorEnvelope(TypedDict): error: OpenAIErrorDetail +class AnthropicErrorDetail(TypedDict): + type: str + message: str + + +class AnthropicErrorEnvelope(TypedDict): + type: Literal["error"] + error: AnthropicErrorDetail + + class DashboardErrorDetail(TypedDict): code: str message: str @@ -45,6 +55,10 @@ def openai_error(code: str, message: str, error_type: str = "server_error") -> O return {"error": {"message": message, "type": error_type, "code": code}} +def anthropic_error(error_type: str, message: str) -> AnthropicErrorEnvelope: + return {"type": "error", "error": {"type": error_type, "message": message}} + + def dashboard_error(code: str, message: str) -> DashboardErrorEnvelope: return {"error": {"code": code, "message": message}} diff --git a/app/core/handlers/exceptions.py b/app/core/handlers/exceptions.py index df0e27bb..ea18e41e 100644 --- a/app/core/handlers/exceptions.py +++ b/app/core/handlers/exceptions.py @@ -11,7 +11,7 @@ from fastapi.responses import JSONResponse, Response from starlette.exceptions import HTTPException as StarletteHTTPException -from app.core.errors import dashboard_error, openai_error +from app.core.errors import anthropic_error, dashboard_error, openai_error from app.core.exceptions import ( AppError, DashboardAuthError, @@ -44,6 +44,18 @@ DashboardRateLimitError, ) +_ANTHROPIC_V1_PATH_PREFIXES: tuple[str, ...] = ( + "/v1/messages", + "/v1/messages/count_tokens", +) + + +def _is_anthropic_v1_path(path: str) -> bool: + for prefix in _ANTHROPIC_V1_PATH_PREFIXES: + if path == prefix or path.startswith(f"{prefix}/"): + return True + return False + def _error_format(request: Request) -> str | None: fmt = getattr(request.state, "error_format", None) @@ -53,6 +65,10 @@ def _error_format(request: Request) -> str | None: path = request.url.path if path.startswith("/api/"): return "dashboard" + if path.startswith("/anthropic/"): + return "anthropic" + if _is_anthropic_v1_path(path): + return "anthropic" if path.startswith("/v1/") or path.startswith("/backend-api/"): return "openai" return None @@ -99,6 +115,11 @@ async def validation_error_handler( status_code=422, content=dashboard_error("validation_error", "Invalid request payload"), ) + if fmt == "anthropic": + return JSONResponse( + status_code=400, + content=anthropic_error("invalid_request_error", "Invalid request payload"), + ) if fmt == "openai": error = openai_error("invalid_request_error", "Invalid request payload", error_type="invalid_request_error") if exc.errors(): @@ -123,6 +144,17 @@ async def http_error_handler( status_code=exc.status_code, content=dashboard_error(f"http_{exc.status_code}", detail), ) + if fmt == "anthropic": + error_type = "invalid_request_error" + if exc.status_code == 401: + error_type = "authentication_error" + elif exc.status_code == 403: + error_type = "permission_error" + elif exc.status_code == 429: + error_type = "rate_limit_error" + elif exc.status_code >= 500: + error_type = "api_error" + return JSONResponse(status_code=exc.status_code, content=anthropic_error(error_type, detail)) if fmt == "openai": error_type = "invalid_request_error" code = "invalid_request_error" @@ -155,6 +187,11 @@ async def unhandled_error_handler(request: Request, exc: Exception) -> JSONRespo status_code=500, content=dashboard_error("internal_error", "Unexpected error"), ) + if fmt == "anthropic": + return JSONResponse( + status_code=500, + content=anthropic_error("api_error", "Internal server error"), + ) if fmt == "openai": return JSONResponse( status_code=500, diff --git a/app/core/openai/message_coercion.py b/app/core/openai/message_coercion.py index c71cfbab..ab583fca 100644 --- a/app/core/openai/message_coercion.py +++ b/app/core/openai/message_coercion.py @@ -258,7 +258,12 @@ def _normalize_content_part(part: dict[str, JsonValue], role: str = "user") -> J if part_type in ("text", "input_text", "output_text"): text = part.get("text") if isinstance(text, str): - return {"type": text_type, "text": text} + # Preserve cache-related and metadata fields while normalizing + # text part type for the target role. + normalized = dict(part) + normalized["type"] = text_type + normalized["text"] = text + return normalized return part if role == "assistant": return part diff --git a/app/core/openai/requests.py b/app/core/openai/requests.py index a57306ec..55cbcc0c 100644 --- a/app/core/openai/requests.py +++ b/app/core/openai/requests.py @@ -298,6 +298,7 @@ class ResponsesRequest(BaseModel): previous_response_id: str | None = None truncation: str | None = None prompt_cache_key: str | None = None + prompt_cache_retention: str | None = None text: ResponsesTextControls | None = None @field_validator("input") diff --git a/app/core/openai/v1_requests.py b/app/core/openai/v1_requests.py index 25ecf3bd..614aecf2 100644 --- a/app/core/openai/v1_requests.py +++ b/app/core/openai/v1_requests.py @@ -32,6 +32,7 @@ class V1ResponsesRequest(BaseModel): previous_response_id: str | None = None truncation: str | None = None prompt_cache_key: str | None = None + prompt_cache_retention: str | None = None text: ResponsesTextControls | None = None @field_validator("input") diff --git a/app/db/alembic/versions/013_add_request_logs_codex_session_hashes.py b/app/db/alembic/versions/013_add_request_logs_codex_session_hashes.py new file mode 100644 index 00000000..8de6c1ee --- /dev/null +++ b/app/db/alembic/versions/013_add_request_logs_codex_session_hashes.py @@ -0,0 +1,51 @@ +"""add codex session/conversation hashes to request_logs + +Revision ID: 013_add_request_logs_codex_session_hashes +Revises: 012_add_import_without_overwrite_and_drop_accounts_email_unique +Create Date: 2026-02-22 +""" + +from __future__ import annotations + +import sqlalchemy as sa +from alembic import op +from sqlalchemy.engine import Connection + +# revision identifiers, used by Alembic. +revision = "013_add_request_logs_codex_session_hashes" +down_revision = "012_add_import_without_overwrite_and_drop_accounts_email_unique" +branch_labels = None +depends_on = None + + +def _columns(connection: Connection, table_name: str) -> set[str]: + inspector = sa.inspect(connection) + if not inspector.has_table(table_name): + return set() + return {str(column["name"]) for column in inspector.get_columns(table_name) if column.get("name") is not None} + + +def upgrade() -> None: + bind = op.get_bind() + columns = _columns(bind, "request_logs") + if not columns: + return + + with op.batch_alter_table("request_logs") as batch_op: + if "codex_session_hash" not in columns: + batch_op.add_column(sa.Column("codex_session_hash", sa.String(), nullable=True)) + if "codex_conversation_hash" not in columns: + batch_op.add_column(sa.Column("codex_conversation_hash", sa.String(), nullable=True)) + + +def downgrade() -> None: + bind = op.get_bind() + columns = _columns(bind, "request_logs") + if not columns: + return + + with op.batch_alter_table("request_logs") as batch_op: + if "codex_conversation_hash" in columns: + batch_op.drop_column("codex_conversation_hash") + if "codex_session_hash" in columns: + batch_op.drop_column("codex_session_hash") diff --git a/app/db/models.py b/app/db/models.py index ce3f742b..ff3d7c6a 100644 --- a/app/db/models.py +++ b/app/db/models.py @@ -83,6 +83,8 @@ class RequestLog(Base): account_id: Mapped[str] = mapped_column(String, ForeignKey("accounts.id", ondelete="CASCADE"), nullable=False) api_key_id: Mapped[str | None] = mapped_column(String, nullable=True) request_id: Mapped[str] = mapped_column(String, nullable=False) + codex_session_hash: Mapped[str | None] = mapped_column(String, nullable=True) + codex_conversation_hash: Mapped[str | None] = mapped_column(String, nullable=True) requested_at: Mapped[datetime] = mapped_column(DateTime, server_default=func.now(), nullable=False) model: Mapped[str] = mapped_column(String, nullable=False) input_tokens: Mapped[int | None] = mapped_column(Integer, nullable=True) diff --git a/app/dependencies.py b/app/dependencies.py index 57709e96..425bf9df 100644 --- a/app/dependencies.py +++ b/app/dependencies.py @@ -10,6 +10,7 @@ from app.db.session import get_background_session, get_session from app.modules.accounts.repository import AccountsRepository from app.modules.accounts.service import AccountsService +from app.modules.anthropic_compat.service import AnthropicCompatService from app.modules.api_keys.repository import ApiKeysRepository from app.modules.api_keys.service import ApiKeysService from app.modules.dashboard.repository import DashboardRepository @@ -66,6 +67,11 @@ class ApiKeysContext: service: ApiKeysService +@dataclass(slots=True) +class AnthropicCompatContext: + service: AnthropicCompatService + + @dataclass(slots=True) class RequestLogsContext: session: AsyncSession @@ -164,6 +170,12 @@ def get_api_keys_context( return ApiKeysContext(session=session, repository=repository, service=service) +def get_anthropic_compat_context() -> AnthropicCompatContext: + proxy_service = ProxyService(repo_factory=_proxy_repo_context) + service = AnthropicCompatService(proxy_service) + return AnthropicCompatContext(service=service) + + def get_request_logs_context( session: AsyncSession = Depends(get_session), ) -> RequestLogsContext: diff --git a/app/main.py b/app/main.py index 9a824f60..88ea4c1b 100644 --- a/app/main.py +++ b/app/main.py @@ -17,6 +17,7 @@ from app.core.usage.refresh_scheduler import build_usage_refresh_scheduler from app.db.session import close_db, init_db from app.modules.accounts import api as accounts_api +from app.modules.anthropic_compat import api as anthropic_compat_api from app.modules.api_keys import api as api_keys_api from app.modules.dashboard import api as dashboard_api from app.modules.dashboard_auth import api as dashboard_auth_api @@ -66,6 +67,8 @@ def create_app() -> FastAPI: app.include_router(proxy_api.router) app.include_router(proxy_api.v1_router) app.include_router(proxy_api.usage_router) + app.include_router(anthropic_compat_api.router) + app.include_router(anthropic_compat_api.anthropic_router) app.include_router(accounts_api.router) app.include_router(dashboard_api.router) app.include_router(usage_api.router) @@ -80,7 +83,7 @@ def create_app() -> FastAPI: index_html = static_dir / "index.html" static_root = static_dir.resolve() frontend_build_hint = "Frontend assets are missing. Run `cd frontend && bun run build`." - excluded_prefixes = ("api/", "v1/", "backend-api/", "health") + excluded_prefixes = ("api/", "v1/", "backend-api/", "anthropic/", "health") def _is_static_asset_path(path: str) -> bool: if path.startswith("assets/"): diff --git a/app/modules/anthropic_compat/__init__.py b/app/modules/anthropic_compat/__init__.py new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/app/modules/anthropic_compat/__init__.py @@ -0,0 +1 @@ + diff --git a/app/modules/anthropic_compat/api.py b/app/modules/anthropic_compat/api.py new file mode 100644 index 00000000..89a0867c --- /dev/null +++ b/app/modules/anthropic_compat/api.py @@ -0,0 +1,456 @@ +from __future__ import annotations + +import json +import logging +from collections.abc import AsyncIterator +from hashlib import sha256 + +from fastapi import APIRouter, Body, Depends, HTTPException, Request, Response, Security +from fastapi.responses import JSONResponse, StreamingResponse + +from app.core.auth.dependencies import set_anthropic_error_format, validate_anthropic_api_key +from app.core.clients.proxy import ProxyResponseError +from app.core.config.settings import get_settings +from app.core.openai.requests import ResponsesReasoning, ResponsesRequest +from app.core.types import JsonValue +from app.core.utils.request_id import get_request_id +from app.dependencies import AnthropicCompatContext, get_anthropic_compat_context +from app.modules.anthropic_compat.schemas import ( + AnthropicCountTokensRequest, + AnthropicCountTokensResponse, + AnthropicErrorEnvelope, + AnthropicEventLoggingResponse, + AnthropicMessageResponse, + AnthropicMessagesRequest, +) +from app.modules.anthropic_compat.translator import ( + AnthropicTranslationError, + PromptCacheKeyResolution, + anthropic_error_from_openai_payload, + collect_anthropic_response_from_openai_stream, + stream_anthropic_events_from_openai_stream, + to_responses_request_with_cache_resolution, +) +from app.modules.api_keys.service import ( + ApiKeyData, + ApiKeyInvalidError, + ApiKeyRateLimitExceededError, +) + +logger = logging.getLogger(__name__) + +_CLAUDE_SHARED_PROMPT_CACHE_KEY_PREFIX = "claude-shared" +_CLAUDE_SHARED_PROMPT_CACHE_KEY_VERSION = 1 + +router = APIRouter(tags=["anthropic_compat"], dependencies=[Depends(set_anthropic_error_format)]) +anthropic_router = APIRouter( + prefix="/anthropic", + tags=["anthropic_compat"], + dependencies=[Depends(set_anthropic_error_format)], +) + + +@router.post( + "/v1/messages", + response_model=AnthropicMessageResponse, + responses={ + 200: { + "content": { + "text/event-stream": { + "schema": {"type": "string"}, + } + } + } + }, +) +async def messages( + request: Request, + payload: AnthropicMessagesRequest = Body(...), + context: AnthropicCompatContext = Depends(get_anthropic_compat_context), + api_key: ApiKeyData | None = Security(validate_anthropic_api_key), +) -> Response: + return await _messages_impl(request, payload, context, api_key) + + +@anthropic_router.post( + "/v1/messages", + response_model=AnthropicMessageResponse, + responses={ + 200: { + "content": { + "text/event-stream": { + "schema": {"type": "string"}, + } + } + } + }, +) +async def anthropic_messages( + request: Request, + payload: AnthropicMessagesRequest = Body(...), + context: AnthropicCompatContext = Depends(get_anthropic_compat_context), + api_key: ApiKeyData | None = Security(validate_anthropic_api_key), +) -> Response: + return await _messages_impl(request, payload, context, api_key) + + +@router.post("/v1/messages/count_tokens", response_model=AnthropicCountTokensResponse) +async def count_tokens( + request: Request, + payload: AnthropicCountTokensRequest = Body(...), + context: AnthropicCompatContext = Depends(get_anthropic_compat_context), + api_key: ApiKeyData | None = Security(validate_anthropic_api_key), +) -> Response: + return await _count_tokens_impl(request, payload, context, api_key) + + +@anthropic_router.post("/v1/messages/count_tokens", response_model=AnthropicCountTokensResponse) +async def anthropic_count_tokens( + request: Request, + payload: AnthropicCountTokensRequest = Body(...), + context: AnthropicCompatContext = Depends(get_anthropic_compat_context), + api_key: ApiKeyData | None = Security(validate_anthropic_api_key), +) -> Response: + return await _count_tokens_impl(request, payload, context, api_key) + + +@router.post("/api/event_logging/batch", response_model=AnthropicEventLoggingResponse) +async def event_logging_batch() -> AnthropicEventLoggingResponse: + return AnthropicEventLoggingResponse(status="ok") + + +@anthropic_router.post("/api/event_logging/batch", response_model=AnthropicEventLoggingResponse) +async def anthropic_event_logging_batch() -> AnthropicEventLoggingResponse: + return AnthropicEventLoggingResponse(status="ok") + + +async def _messages_impl( + request: Request, + payload: AnthropicMessagesRequest, + context: AnthropicCompatContext, + api_key: ApiKeyData | None, +) -> Response: + try: + responses_payload, cache_resolution = to_responses_request_with_cache_resolution(payload) + except AnthropicTranslationError as exc: + raise HTTPException(status_code=400, detail=str(exc)) from exc + + responses_payload, cache_resolution = _apply_claude_prompt_cache_policy( + payload.model, + payload, + responses_payload, + cache_resolution, + api_key, + ) + responses_payload = _apply_default_reasoning_effort(responses_payload) + _log_prompt_cache_resolution( + request=request, + operation="messages", + requested_model=payload.model, + translated_payload=responses_payload, + cache_resolution=cache_resolution, + ) + + _validate_model_access(api_key, payload.model) + try: + reservation = await context.service.enforce_request_limits( + api_key, + request_model=payload.model, + ) + except ApiKeyRateLimitExceededError as exc: + message = f"{exc}. Usage resets at {exc.reset_at.isoformat()}Z." + raise HTTPException(status_code=429, detail=message) from exc + except ApiKeyInvalidError as exc: + raise HTTPException(status_code=401, detail=str(exc)) from exc + rate_limit_headers = await context.service.rate_limit_headers() + + if payload.stream: + stream = context.service.stream_responses( + responses_payload, + request.headers, + api_key=api_key, + api_key_reservation=reservation, + ) + try: + first = await stream.__anext__() + except StopAsyncIteration: + first = None + except ProxyResponseError as exc: + await context.service.release_usage_reservation(reservation) + error = anthropic_error_from_openai_payload( + exc.payload, + fallback_message="Upstream error", + status_code=exc.status_code, + ) + return JSONResponse( + status_code=exc.status_code, + content=error.model_dump(mode="json", exclude_none=True), + headers=rate_limit_headers, + ) + + stream_with_first = _prepend_first(first, stream) + return StreamingResponse( + stream_anthropic_events_from_openai_stream(stream_with_first, model=payload.model), + media_type="text/event-stream", + headers={"Cache-Control": "no-cache", **rate_limit_headers}, + ) + + stream = context.service.stream_responses( + responses_payload, + request.headers, + api_key=api_key, + api_key_reservation=reservation, + ) + try: + response = await collect_anthropic_response_from_openai_stream(stream, model=payload.model) + except ProxyResponseError as exc: + await context.service.release_usage_reservation(reservation) + error = anthropic_error_from_openai_payload( + exc.payload, + fallback_message="Upstream error", + status_code=exc.status_code, + ) + return JSONResponse( + status_code=exc.status_code, + content=error.model_dump(mode="json", exclude_none=True), + headers=rate_limit_headers, + ) + + if isinstance(response, AnthropicErrorEnvelope): + status_code = _status_for_anthropic_error_type(response.error.type) + return JSONResponse( + status_code=status_code, + content=response.model_dump(mode="json", exclude_none=True), + headers=rate_limit_headers, + ) + + return JSONResponse( + content=response.model_dump(mode="json", exclude_none=True), + headers=rate_limit_headers, + ) + + +async def _count_tokens_impl( + request: Request, + payload: AnthropicCountTokensRequest, + context: AnthropicCompatContext, + api_key: ApiKeyData | None, +) -> Response: + try: + responses_payload, cache_resolution = to_responses_request_with_cache_resolution(payload) + except AnthropicTranslationError as exc: + raise HTTPException(status_code=400, detail=str(exc)) from exc + + responses_payload, cache_resolution = _apply_claude_prompt_cache_policy( + payload.model, + payload, + responses_payload, + cache_resolution, + api_key, + ) + responses_payload, cache_resolution = _apply_claude_count_tokens_cache_lane( + payload.model, + responses_payload, + cache_resolution, + ) + _log_prompt_cache_resolution( + request=request, + operation="count_tokens", + requested_model=payload.model, + translated_payload=responses_payload, + cache_resolution=cache_resolution, + ) + + _validate_model_access(api_key, payload.model) + rate_limit_headers = await context.service.rate_limit_headers() + input_tokens = _estimate_input_tokens(responses_payload) + response = AnthropicCountTokensResponse(input_tokens=input_tokens) + return JSONResponse(content=response.model_dump(mode="json", exclude_none=True), headers=rate_limit_headers) + + +async def _prepend_first(first: str | None, stream: AsyncIterator[str]) -> AsyncIterator[str]: + if first is not None: + yield first + async for line in stream: + yield line + + +def _validate_model_access(api_key: ApiKeyData | None, model: str | None) -> None: + if api_key is None: + return + + allowed_models = api_key.allowed_models + if not allowed_models: + return + + if model is None or model in allowed_models: + return + + raise HTTPException(status_code=403, detail=f"This API key does not have access to model '{model}'") + + +def _apply_default_reasoning_effort(responses_payload: ResponsesRequest) -> ResponsesRequest: + settings = get_settings() + default_effort = settings.anthropic_default_reasoning_effort + if default_effort is None: + return responses_payload + + reasoning = responses_payload.reasoning + if reasoning is not None and reasoning.effort: + return responses_payload + + if reasoning is None: + default_reasoning = ResponsesReasoning(effort=default_effort) + else: + default_reasoning = reasoning.model_copy(update={"effort": default_effort}) + return responses_payload.model_copy(update={"reasoning": default_reasoning}) + + +def _apply_claude_prompt_cache_policy( + requested_model: str, + payload: AnthropicMessagesRequest, + responses_payload: ResponsesRequest, + cache_resolution: PromptCacheKeyResolution, + api_key: ApiKeyData | None, +) -> tuple[ResponsesRequest, PromptCacheKeyResolution]: + normalized_model = requested_model.strip().lower() + if not normalized_model.startswith("claude-"): + return responses_payload, cache_resolution + + # Preserve only explicit caller-provided keys. All other sources are + # normalized into the shared Claude lane to keep cache routing stable. + if cache_resolution.source == "explicit" and responses_payload.prompt_cache_key: + return responses_payload, cache_resolution + + shared_key = _build_claude_shared_prompt_cache_key(payload, api_key) + if responses_payload.prompt_cache_key == shared_key and cache_resolution.source == "claude_shared": + return responses_payload, cache_resolution + + updated_payload = responses_payload.model_copy(update={"prompt_cache_key": shared_key}) + return updated_payload, PromptCacheKeyResolution(key=shared_key, source="claude_shared") + + +def _build_claude_shared_prompt_cache_key( + payload: AnthropicMessagesRequest, + api_key: ApiKeyData | None, +) -> str: + identity = { + "scope": f"anthropic_claude_shared_v{_CLAUDE_SHARED_PROMPT_CACHE_KEY_VERSION}", + "api_key_id": api_key.id if api_key else None, + "requested_model": payload.model.strip().lower(), + "system": _normalize_system_text(payload), + "tools": _normalize_tool_signature(payload), + } + canonical = json.dumps(identity, ensure_ascii=True, separators=(",", ":"), sort_keys=True) + digest = sha256(canonical.encode("utf-8")).hexdigest() + return f"{_CLAUDE_SHARED_PROMPT_CACHE_KEY_PREFIX}:{digest}" + + +def _apply_claude_count_tokens_cache_lane( + requested_model: str, + responses_payload: ResponsesRequest, + cache_resolution: PromptCacheKeyResolution, +) -> tuple[ResponsesRequest, PromptCacheKeyResolution]: + normalized_model = requested_model.strip().lower() + if not normalized_model.startswith("claude-"): + return responses_payload, cache_resolution + prompt_cache_key = responses_payload.prompt_cache_key + if not prompt_cache_key: + return responses_payload, cache_resolution + lane_key = f"{prompt_cache_key}:count_tokens" + updated_payload = responses_payload.model_copy(update={"prompt_cache_key": lane_key}) + return updated_payload, PromptCacheKeyResolution(key=lane_key, source=cache_resolution.source) + + +def _estimate_input_tokens(payload: ResponsesRequest) -> int: + request_shape = { + "instructions": payload.instructions, + "input": payload.input, + "tools": payload.tools, + } + serialized = json.dumps(request_shape, ensure_ascii=False, separators=(",", ":")) + # Fast local heuristic to avoid an extra upstream call. + return max(1, (len(serialized) + 3) // 4) + + +def _normalize_system_text(payload: AnthropicMessagesRequest) -> str: + system = payload.system + if system is None: + return "" + if isinstance(system, str): + return system.strip() + + parts: list[str] = [] + for block in system: + text = block.text.strip() + if text: + parts.append(text) + return "\n".join(parts) + + +def _normalize_tool_signature(payload: AnthropicMessagesRequest) -> list[dict[str, JsonValue]]: + tools: list[dict[str, JsonValue]] = [] + for tool in payload.tools: + tools.append( + { + "name": tool.name.strip(), + "description": tool.description, + "input_schema": tool.input_schema or {}, + } + ) + return sorted(tools, key=lambda tool: str(tool["name"])) + + +def _status_for_anthropic_error_type(error_type: str) -> int: + if error_type == "authentication_error": + return 401 + if error_type == "permission_error": + return 403 + if error_type == "rate_limit_error": + return 429 + if error_type == "invalid_request_error": + return 400 + return 502 + + +def _log_prompt_cache_resolution( + *, + request: Request, + operation: str, + requested_model: str, + translated_payload: ResponsesRequest, + cache_resolution: PromptCacheKeyResolution, +) -> None: + prompt_cache_key = cache_resolution.key + prompt_cache_key_hash = _hash_identifier(prompt_cache_key) if prompt_cache_key else None + settings = get_settings() + prompt_cache_key_raw = ( + _truncate_identifier(prompt_cache_key) + if settings.log_proxy_request_shape_raw_cache_key and prompt_cache_key + else None + ) + logger.info( + "anthropic_prompt_cache request_id=%s operation=%s method=%s path=%s requested_model=%s upstream_model=%s " + "stream=%s source=%s prompt_cache_key=%s prompt_cache_key_raw=%s", + get_request_id(), + operation, + request.method, + request.url.path, + requested_model, + translated_payload.model, + translated_payload.stream, + cache_resolution.source, + prompt_cache_key_hash, + prompt_cache_key_raw, + ) + + +def _hash_identifier(value: str) -> str: + digest = sha256(value.encode("utf-8")).hexdigest() + return f"sha256:{digest[:12]}" + + +def _truncate_identifier(value: str, *, max_length: int = 96) -> str: + if len(value) <= max_length: + return value + return f"{value[:48]}...{value[-16:]}" diff --git a/app/modules/anthropic_compat/schemas.py b/app/modules/anthropic_compat/schemas.py new file mode 100644 index 00000000..39b604c9 --- /dev/null +++ b/app/modules/anthropic_compat/schemas.py @@ -0,0 +1,159 @@ +from __future__ import annotations + +from typing import Literal, TypeAlias + +from pydantic import BaseModel, ConfigDict, Field + +from app.core.types import JsonValue + + +class AnthropicTextBlock(BaseModel): + model_config = ConfigDict(extra="allow") + + type: Literal["text"] + text: str + + +class AnthropicToolUseBlock(BaseModel): + model_config = ConfigDict(extra="allow") + + type: Literal["tool_use"] + id: str = Field(min_length=1) + name: str = Field(min_length=1) + input: dict[str, JsonValue] = Field(default_factory=dict) + + +class AnthropicToolResultTextBlock(BaseModel): + model_config = ConfigDict(extra="allow") + + type: Literal["text"] + text: str + + +class AnthropicToolResultBlock(BaseModel): + model_config = ConfigDict(extra="allow") + + type: Literal["tool_result"] + tool_use_id: str = Field(min_length=1) + content: str | list[AnthropicToolResultTextBlock] = "" + is_error: bool | None = None + + +AnthropicMessageBlock: TypeAlias = ( + AnthropicTextBlock | AnthropicToolUseBlock | AnthropicToolResultBlock | dict[str, JsonValue] +) + + +class AnthropicMessage(BaseModel): + model_config = ConfigDict(extra="allow") + + role: Literal["user", "assistant", "system"] + content: str | list[AnthropicMessageBlock] + + +class AnthropicSystemTextBlock(BaseModel): + model_config = ConfigDict(extra="allow") + + type: Literal["text"] + text: str + + +class AnthropicToolDefinition(BaseModel): + model_config = ConfigDict(extra="allow") + + name: str = Field(min_length=1) + description: str | None = None + input_schema: dict[str, JsonValue] | None = None + + +class AnthropicToolChoice(BaseModel): + model_config = ConfigDict(extra="allow") + + type: Literal["auto", "any", "tool", "none"] + name: str | None = None + disable_parallel_tool_use: bool | None = None + + +class AnthropicMessagesRequest(BaseModel): + model_config = ConfigDict(extra="allow") + + model: str = Field(min_length=1) + messages: list[AnthropicMessage] + system: str | list[AnthropicSystemTextBlock] | None = None + tools: list[AnthropicToolDefinition] = Field(default_factory=list) + tool_choice: AnthropicToolChoice | None = None + stream: bool = False + max_tokens: int | None = None + temperature: float | None = None + top_p: float | None = None + top_k: int | None = None + stop_sequences: list[str] | None = None + + +class AnthropicCountTokensRequest(AnthropicMessagesRequest): + stream: bool | None = None + + +class AnthropicResponseTextBlock(BaseModel): + model_config = ConfigDict(extra="forbid") + + type: Literal["text"] + text: str + + +class AnthropicResponseToolUseBlock(BaseModel): + model_config = ConfigDict(extra="forbid") + + type: Literal["tool_use"] + id: str + name: str + input: dict[str, JsonValue] + + +AnthropicResponseContentBlock: TypeAlias = AnthropicResponseTextBlock | AnthropicResponseToolUseBlock + + +class AnthropicUsage(BaseModel): + model_config = ConfigDict(extra="forbid") + + input_tokens: int + output_tokens: int + + +class AnthropicMessageResponse(BaseModel): + model_config = ConfigDict(extra="forbid") + + id: str + type: Literal["message"] = "message" + role: Literal["assistant"] = "assistant" + content: list[AnthropicResponseContentBlock] + model: str + stop_reason: Literal["end_turn", "max_tokens", "stop_sequence", "tool_use"] | None = None + stop_sequence: str | None = None + usage: AnthropicUsage + + +class AnthropicErrorData(BaseModel): + model_config = ConfigDict(extra="forbid") + + type: str + message: str + + +class AnthropicErrorEnvelope(BaseModel): + model_config = ConfigDict(extra="forbid") + + type: Literal["error"] = "error" + error: AnthropicErrorData + + +class AnthropicCountTokensResponse(BaseModel): + model_config = ConfigDict(extra="forbid") + + input_tokens: int + + +class AnthropicEventLoggingResponse(BaseModel): + model_config = ConfigDict(extra="forbid") + + status: Literal["ok"] = "ok" diff --git a/app/modules/anthropic_compat/service.py b/app/modules/anthropic_compat/service.py new file mode 100644 index 00000000..0b7f5767 --- /dev/null +++ b/app/modules/anthropic_compat/service.py @@ -0,0 +1,60 @@ +from __future__ import annotations + +from collections.abc import AsyncIterator, Mapping + +from app.core.openai.requests import ResponsesRequest +from app.db.session import get_background_session +from app.modules.api_keys.repository import ApiKeysRepository +from app.modules.api_keys.service import ApiKeyData, ApiKeysService, ApiKeyUsageReservationData +from app.modules.proxy.service import ProxyService + + +class AnthropicCompatService: + def __init__(self, proxy_service: ProxyService) -> None: + self._proxy_service = proxy_service + + def stream_responses( + self, + payload: ResponsesRequest, + headers: Mapping[str, str], + *, + api_key: ApiKeyData | None, + api_key_reservation: ApiKeyUsageReservationData | None, + suppress_text_done_events: bool = False, + ) -> AsyncIterator[str]: + payload.stream = True + return self._proxy_service.stream_responses( + payload, + headers, + propagate_http_errors=True, + api_key=api_key, + api_key_reservation=api_key_reservation, + suppress_text_done_events=suppress_text_done_events, + ) + + async def rate_limit_headers(self) -> dict[str, str]: + return await self._proxy_service.rate_limit_headers() + + async def enforce_request_limits( + self, + api_key: ApiKeyData | None, + *, + request_model: str | None, + ) -> ApiKeyUsageReservationData | None: + if api_key is None: + return None + + async with get_background_session() as session: + service = ApiKeysService(ApiKeysRepository(session)) + return await service.enforce_limits_for_request( + api_key.id, + request_model=request_model, + ) + + async def release_usage_reservation(self, reservation: ApiKeyUsageReservationData | None) -> None: + if reservation is None: + return + + async with get_background_session() as session: + service = ApiKeysService(ApiKeysRepository(session)) + await service.release_usage_reservation(reservation.reservation_id) diff --git a/app/modules/anthropic_compat/translator.py b/app/modules/anthropic_compat/translator.py new file mode 100644 index 00000000..31565795 --- /dev/null +++ b/app/modules/anthropic_compat/translator.py @@ -0,0 +1,1450 @@ +from __future__ import annotations + +import hashlib +import json +import uuid +from collections.abc import AsyncIterator, Mapping +from dataclasses import dataclass, field +from typing import Literal + +from app.core.errors import OpenAIErrorEnvelope as UpstreamOpenAIErrorEnvelope +from app.core.openai.exceptions import ClientPayloadError +from app.core.openai.message_coercion import coerce_messages +from app.core.openai.requests import ResponsesRequest +from app.core.types import JsonValue +from app.core.utils.json_guards import is_json_list, is_json_mapping +from app.core.utils.sse import parse_sse_data_json +from app.modules.anthropic_compat.schemas import ( + AnthropicErrorData, + AnthropicErrorEnvelope, + AnthropicMessage, + AnthropicMessageBlock, + AnthropicMessageResponse, + AnthropicMessagesRequest, + AnthropicResponseTextBlock, + AnthropicResponseToolUseBlock, + AnthropicTextBlock, + AnthropicToolChoice, + AnthropicToolDefinition, + AnthropicToolResultBlock, + AnthropicToolResultTextBlock, + AnthropicToolUseBlock, + AnthropicUsage, +) + + +class AnthropicTranslationError(ValueError): + def __init__(self, message: str, *, param: str | None = None) -> None: + super().__init__(message) + self.param = param + + +type AnthropicStopReason = Literal["end_turn", "max_tokens", "stop_sequence", "tool_use"] + + +@dataclass(slots=True) +class _ToolCallDelta: + index: int + call_id: str | None + name: str | None + arguments: str | None + + +@dataclass(slots=True) +class _ToolCallState: + index: int + call_id: str | None = None + name: str | None = None + arguments: str = "" + + def apply_delta(self, delta: _ToolCallDelta) -> None: + if delta.call_id: + self.call_id = delta.call_id + if delta.name: + self.name = delta.name + if delta.arguments: + self.arguments += delta.arguments + + +@dataclass(slots=True) +class _ToolCallIndex: + indexes: dict[str, int] = field(default_factory=dict) + next_index: int = 0 + + def index_for(self, call_id: str | None, name: str | None) -> int: + key = _tool_call_key(call_id, name) + if key is None: + return 0 + if key not in self.indexes: + self.indexes[key] = self.next_index + self.next_index += 1 + return self.indexes[key] + + +@dataclass(slots=True) +class _CollectedResponseState: + model: str + response_id: str | None = None + input_tokens: int | None = None + output_tokens: int | None = None + stop_reason: AnthropicStopReason | None = None + stop_sequence: str | None = None + text_parts: list[str] = field(default_factory=list) + tool_calls: list[_ToolCallState] = field(default_factory=list) + completed_response: Mapping[str, JsonValue] | None = None + error: AnthropicErrorEnvelope | None = None + + +@dataclass(slots=True) +class _StreamState: + model: str + message_id: str + started: bool = False + text_block_index: int | None = None + next_block_index: int = 0 + input_tokens: int | None = None + output_tokens: int | None = None + stop_reason: AnthropicStopReason | None = None + stop_sequence: str | None = None + tool_calls: list[_ToolCallState] = field(default_factory=list) + + +PromptCacheKeySource = Literal["explicit", "metadata", "cache_control", "anchor", "none", "claude_shared"] + +_PASSTHROUGH_CACHE_EXTRA_KEYS = frozenset( + { + "prompt_cache_key", + "promptCacheKey", + "prompt_cache_retention", + "promptCacheRetention", + } +) + +_CAMEL_TO_SNAKE_CACHE_KEY: dict[str, str] = { + "promptCacheKey": "prompt_cache_key", + "promptCacheRetention": "prompt_cache_retention", +} + +# Canonical keys already handled by resolve_prompt_cache_key / +# _extract_prompt_cache_retention — passthrough must not override. +_NORMALIZED_CACHE_KEYS = frozenset({"prompt_cache_key", "prompt_cache_retention"}) + + +@dataclass(frozen=True, slots=True) +class PromptCacheKeyResolution: + key: str | None + source: PromptCacheKeySource + + +def to_responses_request(payload: AnthropicMessagesRequest) -> ResponsesRequest: + translated, _ = to_responses_request_with_cache_resolution(payload) + return translated + + +def to_responses_request_with_cache_resolution( + payload: AnthropicMessagesRequest, +) -> tuple[ResponsesRequest, PromptCacheKeyResolution]: + instructions = _extract_system_text(payload) + messages = _to_openai_messages(payload.messages) + + try: + merged_instructions, input_items = coerce_messages(instructions, messages) + except ClientPayloadError as exc: + raise AnthropicTranslationError(str(exc), param=exc.param) from exc + + translated_payload: dict[str, JsonValue] = { + "model": payload.model, + "instructions": merged_instructions, + "input": input_items, + "tools": _translate_tools(payload.tools), + "stream": bool(payload.stream), + } + tool_choice = _translate_tool_choice(payload.tool_choice) + if tool_choice is not None: + translated_payload["tool_choice"] = tool_choice + + if payload.tool_choice and payload.tool_choice.disable_parallel_tool_use is not None: + translated_payload["parallel_tool_calls"] = not payload.tool_choice.disable_parallel_tool_use + + if payload.temperature is not None: + translated_payload["temperature"] = payload.temperature + if payload.top_p is not None: + translated_payload["top_p"] = payload.top_p + if payload.top_k is not None: + translated_payload["top_k"] = payload.top_k + reasoning_effort = _extract_reasoning_effort(payload) + if reasoning_effort is not None: + translated_payload["reasoning"] = {"effort": reasoning_effort} + if payload.stop_sequences is not None: + translated_payload["stop"] = _json_array_from_strings(payload.stop_sequences) + if payload.max_tokens is not None: + translated_payload["max_output_tokens"] = payload.max_tokens + prompt_cache_resolution = resolve_prompt_cache_key(payload) + if prompt_cache_resolution.key is not None: + translated_payload["prompt_cache_key"] = prompt_cache_resolution.key + prompt_cache_retention = _extract_prompt_cache_retention(payload) + if prompt_cache_retention is not None: + translated_payload["prompt_cache_retention"] = prompt_cache_retention + _merge_passthrough_cache_extras(payload, translated_payload) + + try: + translated = ResponsesRequest.model_validate(translated_payload) + except ValueError as exc: + raise AnthropicTranslationError(str(exc), param="messages") from exc + return translated, prompt_cache_resolution + + +def anthropic_error(error_type: str, message: str) -> AnthropicErrorEnvelope: + return AnthropicErrorEnvelope(type="error", error=AnthropicErrorData(type=error_type, message=message)) + + +def anthropic_error_from_openai_payload( + payload: Mapping[str, JsonValue] | UpstreamOpenAIErrorEnvelope | None, + *, + fallback_message: str, + status_code: int | None = None, +) -> AnthropicErrorEnvelope: + if payload is None: + return anthropic_error(_anthropic_error_type(None, None, status_code), fallback_message) + + error_payload = payload.get("error") + if not is_json_mapping(error_payload): + return anthropic_error(_anthropic_error_type(None, None, status_code), fallback_message) + + code = error_payload.get("code") + error_type = error_payload.get("type") + message = error_payload.get("message") + code_str = code if isinstance(code, str) else None + type_str = error_type if isinstance(error_type, str) else None + message_str = message if isinstance(message, str) else fallback_message + mapped_type = _anthropic_error_type(code_str, type_str, status_code) + return anthropic_error(mapped_type, message_str) + + +async def collect_anthropic_response_from_openai_stream( + stream: AsyncIterator[str], + *, + model: str, +) -> AnthropicMessageResponse | AnthropicErrorEnvelope: + state = _CollectedResponseState(model=model) + tool_index = _ToolCallIndex() + + async for line in stream: + payload = parse_sse_data_json(line) + if payload is None: + continue + + _apply_payload_to_collected_state(state, payload, tool_index) + if state.error is not None: + return state.error + + return _build_anthropic_response(state) + + +async def extract_input_tokens_from_openai_stream( + stream: AsyncIterator[str], +) -> tuple[int | None, AnthropicErrorEnvelope | None]: + async for line in stream: + payload = parse_sse_data_json(line) + if payload is None: + continue + + event_type = payload.get("type") + if event_type in ("error", "response.failed"): + error = _error_from_stream_payload(payload) + return None, error + + if event_type in ("response.completed", "response.incomplete"): + response_payload = payload.get("response") + if not is_json_mapping(response_payload): + return None, None + input_tokens, _ = _extract_usage(response_payload.get("usage")) + return input_tokens, None + + return None, None + + +async def stream_anthropic_events_from_openai_stream( + stream: AsyncIterator[str], + *, + model: str, +) -> AsyncIterator[str]: + state = _StreamState(model=model, message_id=_next_message_id()) + tool_index = _ToolCallIndex() + + async for line in stream: + payload = parse_sse_data_json(line) + if payload is None: + continue + + event_type = payload.get("type") + if event_type in ("error", "response.failed"): + error = _error_from_stream_payload(payload) + yield _format_anthropic_sse("error", error.model_dump(mode="json", exclude_none=True)) + return + + response_payload = payload.get("response") + response_map = response_payload if is_json_mapping(response_payload) else None + + if response_map is not None: + response_id = response_map.get("id") + if isinstance(response_id, str) and response_id: + state.message_id = response_id + input_tokens, output_tokens = _extract_usage(response_map.get("usage")) + if input_tokens is not None: + state.input_tokens = input_tokens + if output_tokens is not None: + state.output_tokens = output_tokens + + if not state.started: + yield _format_anthropic_sse( + "message_start", + { + "type": "message_start", + "message": { + "id": state.message_id, + "type": "message", + "role": "assistant", + "model": state.model, + "content": [], + "stop_reason": None, + "stop_sequence": None, + "usage": { + "input_tokens": state.input_tokens or 0, + "output_tokens": 0, + }, + }, + }, + ) + state.started = True + + if event_type in ("response.output_text.delta", "response.refusal.delta"): + delta = payload.get("delta") + if isinstance(delta, str) and delta: + if state.text_block_index is None: + block_index = state.next_block_index + state.next_block_index += 1 + state.text_block_index = block_index + yield _format_anthropic_sse( + "content_block_start", + { + "type": "content_block_start", + "index": block_index, + "content_block": {"type": "text", "text": ""}, + }, + ) + yield _format_anthropic_sse( + "content_block_delta", + { + "type": "content_block_delta", + "index": state.text_block_index, + "delta": {"type": "text_delta", "text": delta}, + }, + ) + + tool_delta = _tool_call_delta_from_payload(payload, tool_index) + if tool_delta is not None: + _merge_tool_call_delta(state.tool_calls, tool_delta) + + if event_type in ("response.completed", "response.incomplete"): + if state.text_block_index is not None: + yield _format_anthropic_sse( + "content_block_stop", + { + "type": "content_block_stop", + "index": state.text_block_index, + }, + ) + state.text_block_index = None + + completed_tool_blocks = _content_blocks_from_response_output(response_map) + tool_use_blocks = [ + block for block in completed_tool_blocks if isinstance(block, AnthropicResponseToolUseBlock) + ] + if not tool_use_blocks: + tool_use_blocks = _tool_use_blocks_from_call_states(state.tool_calls) + + for block in tool_use_blocks: + block_index = state.next_block_index + state.next_block_index += 1 + yield _format_anthropic_sse( + "content_block_start", + { + "type": "content_block_start", + "index": block_index, + "content_block": { + "type": "tool_use", + "id": block.id, + "name": block.name, + "input": {}, + }, + }, + ) + yield _format_anthropic_sse( + "content_block_delta", + { + "type": "content_block_delta", + "index": block_index, + "delta": { + "type": "input_json_delta", + "partial_json": json.dumps(block.input, ensure_ascii=True, separators=(",", ":")), + }, + }, + ) + yield _format_anthropic_sse( + "content_block_stop", + { + "type": "content_block_stop", + "index": block_index, + }, + ) + + stop_reason, stop_sequence = _extract_stop_reason( + response_map, + event_type=event_type, + has_tool_use=bool(tool_use_blocks), + ) + state.stop_reason = stop_reason + state.stop_sequence = stop_sequence + + yield _format_anthropic_sse( + "message_delta", + { + "type": "message_delta", + "delta": { + "stop_reason": stop_reason, + "stop_sequence": stop_sequence, + }, + "usage": { + "output_tokens": state.output_tokens or 0, + }, + }, + ) + yield _format_anthropic_sse( + "message_stop", + { + "type": "message_stop", + "usage": { + "input_tokens": state.input_tokens or 0, + "output_tokens": state.output_tokens or 0, + }, + }, + ) + return + + +def _extract_system_text(payload: AnthropicMessagesRequest) -> str: + system = payload.system + if system is None: + return "" + if isinstance(system, str): + return system + + parts: list[str] = [] + for block in system: + parts.append(block.text) + return "\n".join(part for part in parts if part) + + +def resolve_prompt_cache_key(payload: AnthropicMessagesRequest) -> PromptCacheKeyResolution: + explicit_key = _extract_explicit_prompt_cache_key(payload) + if explicit_key is not None: + return PromptCacheKeyResolution(key=explicit_key, source="explicit") + + metadata_key = _extract_prompt_cache_key_from_metadata(payload) + if metadata_key is not None: + return PromptCacheKeyResolution(key=metadata_key, source="metadata") + + cache_control_key = _derive_prompt_cache_key_from_cache_control(payload) + if cache_control_key is not None: + return PromptCacheKeyResolution(key=cache_control_key, source="cache_control") + + anchor_key = _derive_prompt_cache_key_from_conversation_anchor(payload) + if anchor_key is not None: + return PromptCacheKeyResolution(key=anchor_key, source="anchor") + + return PromptCacheKeyResolution(key=None, source="none") + + +def _extract_explicit_prompt_cache_key(payload: AnthropicMessagesRequest) -> str | None: + if not payload.model_extra: + return None + + for key in ("prompt_cache_key", "promptCacheKey"): + value = payload.model_extra.get(key) + normalized = _normalize_prompt_cache_key_value(value) + if normalized is not None: + return normalized + return None + + +def _extract_prompt_cache_key_from_metadata(payload: AnthropicMessagesRequest) -> str | None: + if not payload.model_extra: + return None + + metadata = payload.model_extra.get("metadata") + if not is_json_mapping(metadata): + return None + + for key in ( + "prompt_cache_key", + "promptCacheKey", + "conversation_id", + "conversationId", + "thread_id", + "threadId", + ): + normalized = _normalize_prompt_cache_key_value(metadata.get(key)) + if normalized is not None: + return normalized + return None + + +def _extract_prompt_cache_retention(payload: AnthropicMessagesRequest) -> str | None: + if not payload.model_extra: + return None + + value = payload.model_extra.get("prompt_cache_retention") + if value is None: + value = payload.model_extra.get("promptCacheRetention") + if value is None: + return None + if not isinstance(value, str): + raise AnthropicTranslationError("prompt_cache_retention must be a string", param="prompt_cache_retention") + + normalized = value.strip() + if not normalized: + raise AnthropicTranslationError( + "prompt_cache_retention must be a non-empty string", + param="prompt_cache_retention", + ) + return normalized + + +def _extract_reasoning_effort(payload: AnthropicMessagesRequest) -> str | None: + if not payload.model_extra: + return None + + reasoning_payload = payload.model_extra.get("reasoning") + if is_json_mapping(reasoning_payload): + nested_effort = reasoning_payload.get("effort") + if isinstance(nested_effort, str): + normalized_nested = nested_effort.strip() + if normalized_nested: + return normalized_nested + + top_level_effort = payload.model_extra.get("reasoningEffort") + if not isinstance(top_level_effort, str): + return None + normalized_top_level = top_level_effort.strip() + if not normalized_top_level: + return None + return normalized_top_level + + +def _merge_passthrough_cache_extras( + payload: AnthropicMessagesRequest, + translated_payload: dict[str, JsonValue], +) -> None: + if not payload.model_extra: + return + for key in _PASSTHROUGH_CACHE_EXTRA_KEYS: + if key in translated_payload: + continue + canonical = _CAMEL_TO_SNAKE_CACHE_KEY.get(key, key) + if canonical in _NORMALIZED_CACHE_KEYS: + continue + if canonical != key and canonical in translated_payload: + continue + value = payload.model_extra.get(key) + if value is None: + continue + translated_payload[canonical] = value + + +def _derive_prompt_cache_key_from_cache_control(payload: AnthropicMessagesRequest) -> str | None: + dumped = payload.model_dump(mode="python", exclude_none=True) + segments: list[JsonValue] = [] + + system = dumped.get("system") + if is_json_list(system): + for index, block in enumerate(system): + if not is_json_mapping(block): + continue + cache_control = _cache_control_payload(block) + if cache_control is None: + continue + segment: dict[str, JsonValue] = { + "scope": "system", + "index": index, + "cache_control": cache_control, + } + block_type = block.get("type") + if isinstance(block_type, str): + segment["type"] = block_type + text = block.get("text") + if isinstance(text, str): + segment["text"] = text + segments.append(segment) + + messages = dumped.get("messages") + if is_json_list(messages): + for message_index, message in enumerate(messages): + if not is_json_mapping(message): + continue + role = message.get("role") + content = message.get("content") + if not is_json_list(content): + continue + for block_index, block in enumerate(content): + if not is_json_mapping(block): + continue + cache_control = _cache_control_payload(block) + if cache_control is None: + continue + segment = { + "scope": "message", + "message_index": message_index, + "block_index": block_index, + "cache_control": cache_control, + } + if isinstance(role, str): + segment["role"] = role + block_type = block.get("type") + if isinstance(block_type, str): + segment["type"] = block_type + text = block.get("text") + if isinstance(text, str): + segment["text"] = text + segments.append(segment) + + if not segments: + return None + + canonical = json.dumps(segments, ensure_ascii=True, separators=(",", ":"), sort_keys=True) + digest = hashlib.sha256(canonical.encode("utf-8")).hexdigest() + return f"anthropic-cache:{digest}" + + +def _derive_prompt_cache_key_from_conversation_anchor(payload: AnthropicMessagesRequest) -> str | None: + dumped = payload.model_dump(mode="python", exclude_none=True) + system_text = _normalize_system_text_from_dump(dumped.get("system")) + first_user_text = _first_user_text_from_dump(dumped.get("messages")) + tool_signature = _tool_signature_from_dump(dumped.get("tools")) + + anchor: dict[str, JsonValue] = {} + if system_text: + anchor["system"] = system_text + if first_user_text: + anchor["first_user"] = first_user_text + if tool_signature: + anchor["tools"] = _json_array_from_objects(tool_signature) + + if not anchor: + return None + + canonical = json.dumps(anchor, ensure_ascii=True, separators=(",", ":"), sort_keys=True) + digest = hashlib.sha256(canonical.encode("utf-8")).hexdigest() + return f"anthropic-anchor:{digest}" + + +def _cache_control_payload(block: Mapping[str, JsonValue]) -> dict[str, JsonValue] | None: + value = block.get("cache_control") + if not is_json_mapping(value): + return None + return dict(value) + + +def _normalize_system_text_from_dump(system_value: JsonValue) -> str: + if isinstance(system_value, str): + return system_value.strip() + if not is_json_list(system_value): + return "" + + parts: list[str] = [] + for block in system_value: + if not is_json_mapping(block): + continue + text = block.get("text") + if isinstance(text, str) and text.strip(): + parts.append(text.strip()) + return "\n".join(parts) + + +def _first_user_text_from_dump(messages_value: JsonValue) -> str: + if not is_json_list(messages_value): + return "" + for message in messages_value: + if not is_json_mapping(message): + continue + role = message.get("role") + if role != "user": + continue + content = message.get("content") + text = _content_text_from_dump(content) + if text: + return text + return "" + + +def _content_text_from_dump(content: JsonValue) -> str: + if isinstance(content, str): + return content.strip() + if not is_json_list(content): + return "" + parts: list[str] = [] + for block in content: + if not is_json_mapping(block): + continue + text = block.get("text") + if isinstance(text, str) and text.strip(): + parts.append(text.strip()) + return "\n".join(parts) + + +def _tool_signature_from_dump(tools_value: JsonValue) -> list[dict[str, JsonValue]]: + if not is_json_list(tools_value): + return [] + signatures: list[dict[str, JsonValue]] = [] + for tool in tools_value: + if not is_json_mapping(tool): + continue + name = tool.get("name") + if not isinstance(name, str) or not name.strip(): + continue + signature: dict[str, JsonValue] = {"name": name.strip()} + input_schema = tool.get("input_schema") + if is_json_mapping(input_schema): + signature["input_schema"] = dict(input_schema) + signatures.append(signature) + return signatures + + +def _json_array_from_strings(values: list[str]) -> list[JsonValue]: + return [value for value in values] + + +def _json_array_from_objects(values: list[dict[str, JsonValue]]) -> list[JsonValue]: + return [value for value in values] + + +def _normalize_prompt_cache_key_value(value: object) -> str | None: + if not isinstance(value, str): + return None + normalized = value.strip() + if not normalized: + return None + if len(normalized) <= 256: + return normalized + digest = hashlib.sha256(normalized.encode("utf-8")).hexdigest() + return f"anthropic-key:{digest}" + + +def _to_openai_messages(messages: list[AnthropicMessage]) -> list[dict[str, JsonValue]]: + normalized: list[dict[str, JsonValue]] = [] + for message in messages: + if message.role == "assistant": + normalized.extend(_assistant_message_to_openai(message)) + continue + if message.role == "system": + normalized.extend(_system_message_to_openai(message)) + continue + normalized.extend(_user_message_to_openai(message)) + return normalized + + +def _system_message_to_openai(message: AnthropicMessage) -> list[dict[str, JsonValue]]: + if isinstance(message.content, str): + return [{"role": "system", "content": message.content}] + + text_parts: list[JsonValue] = [] + for block in message.content: + text_part = _text_content_part_from_block(block) + if text_part is not None: + text_parts.append(text_part) + + if not text_parts: + return [] + return [{"role": "system", "content": text_parts}] + + +def _assistant_message_to_openai(message: AnthropicMessage) -> list[dict[str, JsonValue]]: + if isinstance(message.content, str): + return [{"role": "assistant", "content": message.content}] + + text_parts: list[JsonValue] = [] + tool_calls: list[JsonValue] = [] + for block in message.content: + block_type = _block_type(block) + if block_type in ("thinking", "redacted_thinking"): + continue + + text_part = _text_content_part_from_block(block) + if text_part is not None: + text_parts.append(text_part) + continue + + tool_use = _tool_use_from_block(block) + if tool_use is not None: + call_id, name, input_payload = tool_use + tool_calls.append( + { + "id": call_id, + "type": "function", + "function": { + "name": name, + "arguments": json.dumps(input_payload, ensure_ascii=False, separators=(",", ":")), + }, + } + ) + continue + if block_type == "tool_result": + raise AnthropicTranslationError( + "assistant messages support only text and tool_use blocks", + param="messages", + ) + + if not text_parts and not tool_calls: + return [] + + assistant_message: dict[str, JsonValue] = {"role": "assistant"} + if text_parts: + assistant_message["content"] = text_parts + if tool_calls: + assistant_message["tool_calls"] = tool_calls + return [assistant_message] + + +def _user_message_to_openai(message: AnthropicMessage) -> list[dict[str, JsonValue]]: + if isinstance(message.content, str): + return [{"role": "user", "content": message.content}] + + mapped: list[dict[str, JsonValue]] = [] + content_parts: list[JsonValue] = [] + for block in message.content: + block_type = _block_type(block) + if block_type in ("thinking", "redacted_thinking"): + continue + + if block_type == "tool_use": + raise AnthropicTranslationError( + "user messages support only text and tool_result blocks", + param="messages", + ) + + tool_result = _tool_result_from_block(block) + if tool_result is not None: + tool_use_id, output_text = tool_result + if content_parts: + mapped.append({"role": "user", "content": list(content_parts)}) + content_parts = [] + mapped.append( + { + "role": "tool", + "tool_call_id": tool_use_id, + "content": output_text, + } + ) + continue + + text_part = _text_content_part_from_block(block) + if text_part is not None: + content_parts.append(text_part) + continue + + image_part = _input_image_from_block(block) + if image_part is not None: + content_parts.append(image_part) + continue + + block_map = _block_map(block) + if block_map is not None: + content_parts.append(dict(block_map)) + + if content_parts: + mapped.append({"role": "user", "content": content_parts}) + return mapped + + +def _tool_result_output(block: AnthropicToolResultBlock) -> str: + return _tool_result_output_from_raw(block.content, block.is_error) + + +def _tool_result_output_from_raw( + raw_content: JsonValue | list[AnthropicToolResultTextBlock], + is_error: bool | None, +) -> str: + text = _tool_result_text_from_raw(raw_content) + + if is_error: + return json.dumps( + {"is_error": True, "content": text}, + ensure_ascii=False, + separators=(",", ":"), + ) + return text + + +def _tool_result_text_from_raw(raw_content: JsonValue | list[AnthropicToolResultTextBlock]) -> str: + if isinstance(raw_content, str): + return raw_content + if not isinstance(raw_content, list): + return "" + + parts: list[str] = [] + for part in raw_content: + if isinstance(part, AnthropicToolResultTextBlock): + parts.append(part.text) + continue + if isinstance(part, str): + parts.append(part) + continue + if is_json_mapping(part): + text = part.get("text") + if isinstance(text, str): + parts.append(text) + return "".join(parts) + + +def _tool_result_from_block(block: AnthropicMessageBlock) -> tuple[str, str] | None: + if isinstance(block, AnthropicToolResultBlock): + return block.tool_use_id, _tool_result_output(block) + + block_map = _block_map(block) + if block_map is None: + return None + if _block_type(block) != "tool_result": + return None + + tool_use_id = block_map.get("tool_use_id") + if not isinstance(tool_use_id, str) or not tool_use_id: + raise AnthropicTranslationError("tool_result blocks require tool_use_id", param="messages") + + raw_is_error = block_map.get("is_error") + is_error = raw_is_error if isinstance(raw_is_error, bool) else None + raw_content = block_map.get("content", "") + return tool_use_id, _tool_result_output_from_raw(raw_content, is_error) + + +def _tool_use_from_block(block: AnthropicMessageBlock) -> tuple[str, str, dict[str, JsonValue]] | None: + if isinstance(block, AnthropicToolUseBlock): + return block.id, block.name, dict(block.input) + + block_map = _block_map(block) + if block_map is None: + return None + if _block_type(block) != "tool_use": + return None + + call_id = block_map.get("id") + name = block_map.get("name") + if not isinstance(call_id, str) or not call_id: + raise AnthropicTranslationError("tool_use blocks require id", param="messages") + if not isinstance(name, str) or not name: + raise AnthropicTranslationError("tool_use blocks require name", param="messages") + + raw_input = block_map.get("input") + if is_json_mapping(raw_input): + return call_id, name, dict(raw_input) + return call_id, name, {} + + +def _input_image_from_block(block: AnthropicMessageBlock) -> dict[str, JsonValue] | None: + block_map = _block_map(block) + if block_map is None: + return None + if _block_type(block) != "image": + return None + + source = block_map.get("source") + if not is_json_mapping(source): + return None + + source_type = source.get("type") + if source_type == "url": + url = source.get("url") + if isinstance(url, str) and url: + return {"type": "input_image", "image_url": url} + return None + + if source_type == "base64": + data = source.get("data") + if not isinstance(data, str) or not data: + return None + media_type = source.get("media_type") + mime_type = media_type if isinstance(media_type, str) and media_type else "application/octet-stream" + return {"type": "input_image", "image_url": f"data:{mime_type};base64,{data}"} + + return None + + +def _text_content_part_from_block(block: AnthropicMessageBlock) -> dict[str, JsonValue] | None: + text = _text_from_block(block) + if not isinstance(text, str): + return None + # OpenAI-compatible upstream rejects Anthropic-only part fields + # such as cache_control/metadata inside message content. + return {"type": "text", "text": text} + + +def _text_from_block(block: AnthropicMessageBlock) -> str | None: + if isinstance(block, AnthropicTextBlock): + return block.text + block_map = _block_map(block) + if block_map is None: + return None + text = block_map.get("text") + if isinstance(text, str): + return text + return None + + +def _block_type(block: AnthropicMessageBlock) -> str | None: + if isinstance(block, (AnthropicTextBlock, AnthropicToolUseBlock, AnthropicToolResultBlock)): + return block.type + block_map = _block_map(block) + if block_map is None: + return None + block_type = block_map.get("type") + if isinstance(block_type, str): + return block_type + return None + + +def _block_map(block: AnthropicMessageBlock) -> Mapping[str, JsonValue] | None: + if isinstance(block, (AnthropicTextBlock, AnthropicToolUseBlock, AnthropicToolResultBlock)): + return block.model_dump(mode="python", exclude_none=True) + if isinstance(block, dict): + return block + return None + + +def _translate_tools(tools: list[AnthropicToolDefinition]) -> list[JsonValue]: + translated: list[JsonValue] = [] + for tool in tools: + translated.append( + { + "type": "function", + "name": tool.name, + "description": tool.description, + "parameters": tool.input_schema or {"type": "object", "properties": {}}, + } + ) + return translated + + +def _translate_tool_choice(choice: AnthropicToolChoice | None) -> JsonValue | None: + if choice is None: + return None + if choice.type == "auto": + return "auto" + if choice.type == "any": + return "required" + if choice.type == "none": + return "none" + if choice.type == "tool": + if not choice.name: + raise AnthropicTranslationError("tool_choice.name is required when type is 'tool'", param="tool_choice") + return {"type": "function", "name": choice.name} + raise AnthropicTranslationError("Unsupported tool_choice", param="tool_choice") + + +def _apply_payload_to_collected_state( + state: _CollectedResponseState, + payload: Mapping[str, JsonValue], + tool_index: _ToolCallIndex, +) -> None: + event_type = payload.get("type") + + if event_type in ("response.output_text.delta", "response.refusal.delta"): + delta = payload.get("delta") + if isinstance(delta, str): + state.text_parts.append(delta) + + tool_delta = _tool_call_delta_from_payload(payload, tool_index) + if tool_delta is not None: + _merge_tool_call_delta(state.tool_calls, tool_delta) + + if event_type in ("error", "response.failed"): + state.error = _error_from_stream_payload(payload) + return + + if event_type not in ("response.completed", "response.incomplete"): + return + + response_payload = payload.get("response") + if not is_json_mapping(response_payload): + return + + state.completed_response = response_payload + response_id = response_payload.get("id") + if isinstance(response_id, str): + state.response_id = response_id + + input_tokens, output_tokens = _extract_usage(response_payload.get("usage")) + state.input_tokens = input_tokens + state.output_tokens = output_tokens + stop_reason, stop_sequence = _extract_stop_reason( + response_payload, + event_type=event_type, + has_tool_use=bool(state.tool_calls), + ) + state.stop_reason = stop_reason + state.stop_sequence = stop_sequence + + +def _build_anthropic_response(state: _CollectedResponseState) -> AnthropicMessageResponse: + content_blocks = _content_blocks_from_response_output(state.completed_response) + if not content_blocks: + content_blocks = _content_blocks_from_state(state) + + stop_reason = state.stop_reason + if stop_reason is None: + has_tool_use = any(isinstance(block, AnthropicResponseToolUseBlock) for block in content_blocks) + stop_reason = "tool_use" if has_tool_use else "end_turn" + + return AnthropicMessageResponse( + id=state.response_id or _next_message_id(), + model=state.model, + content=content_blocks, + stop_reason=stop_reason, + stop_sequence=state.stop_sequence, + usage=AnthropicUsage( + input_tokens=state.input_tokens or 0, + output_tokens=state.output_tokens or 0, + ), + ) + + +def _content_blocks_from_state( + state: _CollectedResponseState, +) -> list[AnthropicResponseTextBlock | AnthropicResponseToolUseBlock]: + content: list[AnthropicResponseTextBlock | AnthropicResponseToolUseBlock] = [] + + if state.text_parts: + content.append(AnthropicResponseTextBlock(type="text", text="".join(state.text_parts))) + + content.extend(_tool_use_blocks_from_call_states(state.tool_calls)) + return content + + +def _content_blocks_from_response_output( + response_payload: Mapping[str, JsonValue] | None, +) -> list[AnthropicResponseTextBlock | AnthropicResponseToolUseBlock]: + if response_payload is None: + return [] + + output = response_payload.get("output") + if not is_json_list(output): + return [] + + content: list[AnthropicResponseTextBlock | AnthropicResponseToolUseBlock] = [] + for item in output: + if not is_json_mapping(item): + continue + item_type = item.get("type") + + if item_type == "message": + content.extend(_text_blocks_from_message_item(item)) + continue + + if item_type in ("function_call", "tool_call"): + content.append(_tool_use_block_from_output_item(item)) + continue + + if item_type in ("output_text", "refusal"): + text_value = item.get("text") + if not isinstance(text_value, str): + text_value = item.get("refusal") if isinstance(item.get("refusal"), str) else None + if isinstance(text_value, str): + content.append(AnthropicResponseTextBlock(type="text", text=text_value)) + + return content + + +def _text_blocks_from_message_item(item: Mapping[str, JsonValue]) -> list[AnthropicResponseTextBlock]: + blocks: list[AnthropicResponseTextBlock] = [] + raw_content = item.get("content") + if not is_json_list(raw_content): + return blocks + + for part in raw_content: + if not is_json_mapping(part): + continue + part_type = part.get("type") + if part_type not in ("output_text", "text", "refusal"): + continue + text_value = part.get("text") + if not isinstance(text_value, str): + refusal_value = part.get("refusal") + text_value = refusal_value if isinstance(refusal_value, str) else None + if isinstance(text_value, str): + blocks.append(AnthropicResponseTextBlock(type="text", text=text_value)) + + return blocks + + +def _tool_use_blocks_from_call_states(states: list[_ToolCallState]) -> list[AnthropicResponseToolUseBlock]: + blocks: list[AnthropicResponseToolUseBlock] = [] + for state in states: + call_id = state.call_id or f"toolu_{state.index}_{uuid.uuid4().hex[:8]}" + name = state.name or "tool" + blocks.append( + AnthropicResponseToolUseBlock( + type="tool_use", + id=call_id, + name=name, + input=_parse_tool_input(state.arguments), + ) + ) + return blocks + + +def _tool_use_block_from_output_item(item: Mapping[str, JsonValue]) -> AnthropicResponseToolUseBlock: + call_id = _first_str(item.get("call_id"), item.get("id")) or f"toolu_{uuid.uuid4().hex[:8]}" + name = _first_str(item.get("name")) or "tool" + arguments = item.get("arguments") + argument_str = arguments if isinstance(arguments, str) else "{}" + return AnthropicResponseToolUseBlock( + type="tool_use", + id=call_id, + name=name, + input=_parse_tool_input(argument_str), + ) + + +def _parse_tool_input(arguments: str) -> dict[str, JsonValue]: + if not arguments: + return {} + + try: + parsed = json.loads(arguments) + except json.JSONDecodeError: + return {"raw": arguments} + + if is_json_mapping(parsed): + return dict(parsed) + return {"value": parsed} + + +def _extract_usage(usage_payload: JsonValue) -> tuple[int | None, int | None]: + if not is_json_mapping(usage_payload): + return None, None + + input_tokens_raw = usage_payload.get("input_tokens") + output_tokens_raw = usage_payload.get("output_tokens") + + input_tokens = input_tokens_raw if isinstance(input_tokens_raw, int) else None + output_tokens = output_tokens_raw if isinstance(output_tokens_raw, int) else None + return input_tokens, output_tokens + + +def _extract_stop_reason( + response_payload: Mapping[str, JsonValue] | None, + *, + event_type: JsonValue, + has_tool_use: bool, +) -> tuple[AnthropicStopReason, str | None]: + stop_sequence: str | None = None + if response_payload is not None: + raw_stop_sequence = response_payload.get("stop_sequence") + if isinstance(raw_stop_sequence, str): + stop_sequence = raw_stop_sequence + + if has_tool_use: + return "tool_use", stop_sequence + + if event_type == "response.incomplete" and response_payload is not None: + incomplete_details = response_payload.get("incomplete_details") + if is_json_mapping(incomplete_details): + reason = incomplete_details.get("reason") + if reason in ("max_output_tokens", "max_tokens"): + return "max_tokens", stop_sequence + if reason == "stop_sequence": + return "stop_sequence", stop_sequence + + if stop_sequence is not None: + return "stop_sequence", stop_sequence + + return "end_turn", stop_sequence + + +def _error_from_stream_payload(payload: Mapping[str, JsonValue]) -> AnthropicErrorEnvelope: + event_type = payload.get("type") + if event_type == "error": + error_payload = payload.get("error") + elif event_type == "response.failed": + response_payload = payload.get("response") + error_payload = response_payload.get("error") if is_json_mapping(response_payload) else None + else: + error_payload = None + + if not is_json_mapping(error_payload): + return anthropic_error("api_error", "Upstream error") + + code = error_payload.get("code") + type_value = error_payload.get("type") + message = error_payload.get("message") + return anthropic_error( + _anthropic_error_type( + code if isinstance(code, str) else None, + type_value if isinstance(type_value, str) else None, + None, + ), + message if isinstance(message, str) else "Upstream error", + ) + + +def _anthropic_error_type(code: str | None, error_type: str | None, status_code: int | None) -> str: + normalized_code = code.lower() if isinstance(code, str) else "" + normalized_type = error_type.lower() if isinstance(error_type, str) else "" + + if normalized_code in {"invalid_api_key", "authentication_error"} or normalized_type == "authentication_error": + return "authentication_error" + if normalized_code in {"model_not_allowed", "insufficient_permissions"} or normalized_type == "permission_error": + return "permission_error" + if normalized_code in {"rate_limit_exceeded", "too_many_requests"} or normalized_type == "rate_limit_error": + return "rate_limit_error" + if normalized_code in {"invalid_request_error", "bad_request", "not_found"}: + return "invalid_request_error" + + if status_code == 401: + return "authentication_error" + if status_code == 403: + return "permission_error" + if status_code == 429: + return "rate_limit_error" + if status_code is not None and status_code < 500: + return "invalid_request_error" + return "api_error" + + +def _format_anthropic_sse(event_type: str, payload: Mapping[str, JsonValue]) -> str: + data = json.dumps(payload, ensure_ascii=True, separators=(",", ":")) + return f"event: {event_type}\ndata: {data}\n\n" + + +def _tool_call_delta_from_payload( + payload: Mapping[str, JsonValue], + indexer: _ToolCallIndex, +) -> _ToolCallDelta | None: + if not _is_tool_call_event(payload): + return None + + fields = _extract_tool_call_fields(payload) + if fields is None: + return None + + call_id, name, arguments = fields + index = indexer.index_for(call_id, name) + return _ToolCallDelta(index=index, call_id=call_id, name=name, arguments=arguments) + + +def _is_tool_call_event(payload: Mapping[str, JsonValue]) -> bool: + event_type = payload.get("type") + if isinstance(event_type, str) and ("tool_call" in event_type or "function_call" in event_type): + return True + + item = _as_mapping(payload.get("item")) + if item is not None: + item_type = item.get("type") + if isinstance(item_type, str) and ("tool" in item_type or "function" in item_type): + return True + if any(key in item for key in ("call_id", "tool_call_id", "arguments", "function", "name")): + return True + + if any(key in payload for key in ("call_id", "tool_call_id")): + return True + if "arguments" in payload and ("name" in payload or "function" in payload): + return True + return False + + +def _extract_tool_call_fields(payload: Mapping[str, JsonValue]) -> tuple[str | None, str | None, str | None] | None: + candidate = _select_tool_call_candidate(payload) + delta = candidate.get("delta") + delta_map = _as_mapping(delta) + delta_text = delta if isinstance(delta, str) else None + + call_id = _first_str(candidate.get("call_id"), candidate.get("tool_call_id"), candidate.get("id")) + if call_id is None and delta_map is not None: + call_id = _first_str(delta_map.get("id"), delta_map.get("call_id"), delta_map.get("tool_call_id")) + + name = _first_str(candidate.get("name"), candidate.get("tool_name")) + if name is None and delta_map is not None: + name = _first_str(delta_map.get("name")) + + if name is None: + function = _as_mapping(candidate.get("function")) + if function is not None: + name = _first_str(function.get("name")) + + if name is None and delta_map is not None: + function = _as_mapping(delta_map.get("function")) + if function is not None: + name = _first_str(function.get("name")) + + arguments = None + candidate_arguments = candidate.get("arguments") + if isinstance(candidate_arguments, str): + arguments = candidate_arguments + + if arguments is None and isinstance(delta_text, str): + arguments = delta_text + + if arguments is None and delta_map is not None: + delta_arguments = delta_map.get("arguments") + if isinstance(delta_arguments, str): + arguments = delta_arguments + else: + function = _as_mapping(delta_map.get("function")) + if function is not None: + function_arguments = function.get("arguments") + if isinstance(function_arguments, str): + arguments = function_arguments + + if call_id is None and name is None and arguments is None: + return None + return call_id, name, arguments + + +def _select_tool_call_candidate(payload: Mapping[str, JsonValue]) -> Mapping[str, JsonValue]: + item = _as_mapping(payload.get("item")) + if item is not None: + item_type = item.get("type") + if isinstance(item_type, str) and ("tool" in item_type or "function" in item_type): + return item + if any(key in item for key in ("call_id", "tool_call_id", "arguments", "function", "name")): + return item + return payload + + +def _merge_tool_call_delta(tool_calls: list[_ToolCallState], delta: _ToolCallDelta) -> None: + while len(tool_calls) <= delta.index: + tool_calls.append(_ToolCallState(index=len(tool_calls))) + tool_calls[delta.index].apply_delta(delta) + + +def _tool_call_key(call_id: str | None, name: str | None) -> str | None: + if call_id: + return f"id:{call_id}" + if name: + return f"name:{name}" + return None + + +def _as_mapping(value: JsonValue) -> Mapping[str, JsonValue] | None: + if is_json_mapping(value): + return value + return None + + +def _first_str(*values: object) -> str | None: + for value in values: + if isinstance(value, str) and value: + return value + return None + + +def _next_message_id() -> str: + return f"msg_{uuid.uuid4().hex}" diff --git a/app/modules/proxy/service.py b/app/modules/proxy/service.py index 4f41eba5..8f26f7ce 100644 --- a/app/modules/proxy/service.py +++ b/app/modules/proxy/service.py @@ -479,6 +479,8 @@ async def _stream_once( account_id = _header_account_id(account.chatgpt_account_id) model = payload.model reasoning_effort = payload.reasoning.effort if payload.reasoning else None + codex_session_hash = _hashed_header_value(headers, "x-codex-session-id") + codex_conversation_hash = _hashed_header_value(headers, "x-codex-conversation-id") start = time.monotonic() status = "success" error_code = None @@ -602,6 +604,8 @@ async def _stream_once( cached_input_tokens=cached_input_tokens, reasoning_tokens=reasoning_tokens, reasoning_effort=reasoning_effort, + codex_session_hash=codex_session_hash, + codex_conversation_hash=codex_conversation_hash, latency_ms=latency_ms, status=status, error_code=error_code, @@ -806,6 +810,25 @@ def _hash_identifier(value: str) -> str: return f"sha256:{digest[:12]}" +def _header_value(headers: Mapping[str, str], target_header: str) -> str | None: + normalized_target = target_header.lower() + for header_name, header_value in headers.items(): + if header_name.lower() != normalized_target: + continue + normalized_value = header_value.strip() + if normalized_value: + return normalized_value + return None + return None + + +def _hashed_header_value(headers: Mapping[str, str], target_header: str) -> str | None: + header_value = _header_value(headers, target_header) + if header_value is None: + return None + return _hash_identifier(header_value) + + def _summarize_input(items: JsonValue) -> str: if items is None: return "0" diff --git a/app/modules/request_logs/mappers.py b/app/modules/request_logs/mappers.py index 5dd5ffd6..4383a9e2 100644 --- a/app/modules/request_logs/mappers.py +++ b/app/modules/request_logs/mappers.py @@ -27,6 +27,8 @@ def to_request_log_entry(log: RequestLog) -> RequestLogEntry: requested_at=log.requested_at, account_id=log.account_id, request_id=log.request_id, + codex_session_hash=log.codex_session_hash, + codex_conversation_hash=log.codex_conversation_hash, model=log.model, reasoning_effort=log.reasoning_effort, status=log_status(log), diff --git a/app/modules/request_logs/repository.py b/app/modules/request_logs/repository.py index 58a44dc2..27b1e3e1 100644 --- a/app/modules/request_logs/repository.py +++ b/app/modules/request_logs/repository.py @@ -82,12 +82,16 @@ async def add_log( reasoning_tokens: int | None = None, reasoning_effort: str | None = None, api_key_id: str | None = None, + codex_session_hash: str | None = None, + codex_conversation_hash: str | None = None, ) -> RequestLog: resolved_request_id = ensure_request_id(request_id) log = RequestLog( account_id=account_id, api_key_id=api_key_id, request_id=resolved_request_id, + codex_session_hash=codex_session_hash, + codex_conversation_hash=codex_conversation_hash, model=model, input_tokens=input_tokens, output_tokens=output_tokens, @@ -285,6 +289,8 @@ def _build_filters( RequestLog.account_id.ilike(search_pattern), Account.email.ilike(search_pattern), RequestLog.request_id.ilike(search_pattern), + RequestLog.codex_session_hash.ilike(search_pattern), + RequestLog.codex_conversation_hash.ilike(search_pattern), RequestLog.model.ilike(search_pattern), RequestLog.reasoning_effort.ilike(search_pattern), RequestLog.status.ilike(search_pattern), diff --git a/app/modules/request_logs/schemas.py b/app/modules/request_logs/schemas.py index e48caa59..b51675d6 100644 --- a/app/modules/request_logs/schemas.py +++ b/app/modules/request_logs/schemas.py @@ -11,6 +11,8 @@ class RequestLogEntry(DashboardModel): requested_at: datetime account_id: str request_id: str + codex_session_hash: str | None = None + codex_conversation_hash: str | None = None model: str status: str error_code: str | None = None diff --git a/openspec/changes/stabilize-claude-shared-cache-policy/.openspec.yaml b/openspec/changes/stabilize-claude-shared-cache-policy/.openspec.yaml new file mode 100644 index 00000000..69e221fb --- /dev/null +++ b/openspec/changes/stabilize-claude-shared-cache-policy/.openspec.yaml @@ -0,0 +1,2 @@ +schema: spec-driven +created: 2026-02-24 diff --git a/openspec/changes/stabilize-claude-shared-cache-policy/proposal.md b/openspec/changes/stabilize-claude-shared-cache-policy/proposal.md new file mode 100644 index 00000000..c9f002c5 --- /dev/null +++ b/openspec/changes/stabilize-claude-shared-cache-policy/proposal.md @@ -0,0 +1,25 @@ +## Why + +Claude harness traffic in `codex-lb` intermittently misses prompt cache hits because cache key selection may vary across turns when the source is `metadata` or `cache_control`. +Even with deterministic derivation, real clients often send volatile metadata or changing block-level markers. +This causes unstable sticky routing and lower cache reuse. + +## What Changes + +- Change Claude prompt cache policy so only `explicit` caller-provided keys are preserved as-is. +- Force all non-explicit sources (`metadata`, `cache_control`, `anchor`, `none`) into a deterministic `claude-shared:*` key lane. +- Keep the existing dedicated `:count_tokens` lane behavior unchanged. +- Map Anthropic reasoning aliases (`reasoningEffort`, `reasoning.effort`) into `ResponsesRequest.reasoning.effort`. +- Add optional server default reasoning effort via `CODEX_LB_ANTHROPIC_DEFAULT_REASONING_EFFORT`. +- Update Anthropic compatibility tests to assert the new stable-key contract. + +## Capabilities + +### Added Capabilities + +- `anthropic-compat`: stable Claude shared prompt cache key policy for non-explicit sources. + +## Impact + +- **Code**: `app/modules/anthropic_compat/api.py`, `app/modules/anthropic_compat/service.py`, `app/modules/anthropic_compat/translator.py`, `app/core/config/settings.py` +- **Tests**: `tests/integration/test_anthropic_compat.py`, `tests/unit/test_anthropic_translator.py` diff --git a/openspec/changes/stabilize-claude-shared-cache-policy/specs/anthropic-compat/spec.md b/openspec/changes/stabilize-claude-shared-cache-policy/specs/anthropic-compat/spec.md new file mode 100644 index 00000000..f21feb4e --- /dev/null +++ b/openspec/changes/stabilize-claude-shared-cache-policy/specs/anthropic-compat/spec.md @@ -0,0 +1,48 @@ +## ADDED Requirements + +### Requirement: Stable Claude shared cache key for non-explicit sources +For Claude-compatible requests, the system SHALL preserve caller-provided prompt cache keys only when the source is `explicit`. For all non-explicit sources (`metadata`, `cache_control`, `anchor`, `none`), the system SHALL replace the derived key with a deterministic `claude-shared:*` key. + +#### Scenario: Cache-control source in Claude request +- **WHEN** a Claude request derives prompt cache key from `cache_control` +- **THEN** the forwarded key is `claude-shared:*` and not `anthropic-cache:*` + +#### Scenario: Metadata source in Claude request +- **WHEN** a Claude request derives prompt cache key from metadata +- **THEN** the forwarded key is `claude-shared:*` + +#### Scenario: Explicit source in Claude request +- **WHEN** a Claude request includes explicit `prompt_cache_key` +- **THEN** the forwarded key is preserved exactly as provided + +### Requirement: Anthropic-compatible transport does not force model overrides +The Anthropic-compatible layer SHALL not silently remap requested models (including `claude-*`) to hardcoded OpenAI model IDs, and SHALL preserve caller sampling controls when forwarding requests. + +#### Scenario: Claude model request is forwarded without forced aliasing +- **WHEN** a Claude-compatible request specifies model `claude-sonnet-4-6` +- **THEN** the forwarded upstream request model remains `claude-sonnet-4-6` +- **AND** sampling fields such as `temperature`, `top_p`, and `top_k` remain unchanged + +### Requirement: Anthropic-compatible requests map reasoning effort aliases +The Anthropic-compatible translation layer SHALL map Claude-facing reasoning effort aliases into `ResponsesRequest.reasoning.effort`. + +#### Scenario: Top-level reasoningEffort alias is provided +- **WHEN** a request includes `reasoningEffort` with a non-empty string value +- **THEN** the translated upstream payload includes `reasoning.effort` with that value + +#### Scenario: Nested reasoning.effort is provided +- **WHEN** a request includes `reasoning` object with non-empty `effort` +- **THEN** the translated upstream payload includes `reasoning.effort` with that value + +### Requirement: Anthropic-compatible routes support server default reasoning effort +The Anthropic-compatible API layer SHALL apply a configured server default reasoning effort when the request does not provide one. + +#### Scenario: Default reasoning effort is configured and request has no explicit effort +- **WHEN** `CODEX_LB_ANTHROPIC_DEFAULT_REASONING_EFFORT` is set to `xhigh` +- **AND** a request omits both `reasoningEffort` and `reasoning.effort` +- **THEN** forwarded request payload includes `reasoning.effort` equal to `xhigh` + +#### Scenario: Explicit request effort takes precedence over configured default +- **WHEN** `CODEX_LB_ANTHROPIC_DEFAULT_REASONING_EFFORT` is set +- **AND** a request includes `reasoningEffort` or `reasoning.effort` +- **THEN** forwarded request payload uses the request-provided effort value diff --git a/openspec/changes/stabilize-claude-shared-cache-policy/tasks.md b/openspec/changes/stabilize-claude-shared-cache-policy/tasks.md new file mode 100644 index 00000000..105c060a --- /dev/null +++ b/openspec/changes/stabilize-claude-shared-cache-policy/tasks.md @@ -0,0 +1,33 @@ +## 1. Claude Prompt Cache Policy + +- [x] 1.1 Preserve prompt cache key only when source is `explicit` +- [x] 1.2 Force non-explicit sources (`metadata`, `cache_control`, `anchor`, `none`) to `claude-shared:*` +- [x] 1.3 Keep `:count_tokens` lane behavior unchanged + +## 2. Tests + +- [x] 2.1 Update integration test expectations for Claude requests with `cache_control` +- [x] 2.2 Keep explicit-key preservation regression test passing + +## 3. Spec Delta + +- [x] 3.1 Add anthropic-compat requirement delta for stable non-explicit Claude cache keys +- [ ] 3.2 Validate specs locally with `openspec validate --specs` (CLI `openspec` unavailable in this environment) + +## 4. PR Review Alignment + +- [x] 4.1 Remove hardcoded Claude model forcing from anthropic-compatible API transport path +- [x] 4.2 Move API key reservation enforcement/release orchestration from API route layer into anthropic service layer +- [x] 4.3 Remove out-of-band implementation note file (`info.md`) per review guidance + +## 5. Reasoning Effort Mapping + +- [x] 5.1 Map `reasoningEffort` alias into `ResponsesRequest.reasoning.effort` +- [x] 5.2 Map nested `reasoning.effort` into `ResponsesRequest.reasoning.effort` +- [x] 5.3 Add unit and integration tests for both alias forms + +## 6. Server Default Reasoning Effort + +- [x] 6.1 Add `CODEX_LB_ANTHROPIC_DEFAULT_REASONING_EFFORT` setting +- [x] 6.2 Apply configured default only when request has no explicit reasoning effort +- [x] 6.3 Add integration coverage for default application and explicit override precedence diff --git a/tests/integration/test_anthropic_compat.py b/tests/integration/test_anthropic_compat.py new file mode 100644 index 00000000..8f20b764 --- /dev/null +++ b/tests/integration/test_anthropic_compat.py @@ -0,0 +1,595 @@ +from __future__ import annotations + +import base64 +import json +from types import SimpleNamespace + +import pytest + +import app.modules.proxy.service as proxy_module +from app.core.openai.model_registry import ReasoningLevel, UpstreamModel +from app.db.session import SessionLocal +from app.modules.api_keys.repository import ApiKeysRepository +from app.modules.api_keys.service import ApiKeyCreateData, ApiKeysService + +pytestmark = pytest.mark.integration + + +def _make_upstream_model(slug: str) -> UpstreamModel: + return UpstreamModel( + slug=slug, + display_name=slug, + description=f"Test model {slug}", + context_window=128000, + input_modalities=("text",), + supported_reasoning_levels=(ReasoningLevel(effort="medium", description="default"),), + default_reasoning_level="medium", + supports_reasoning_summaries=False, + support_verbosity=False, + default_verbosity=None, + prefer_websockets=False, + supports_parallel_tool_calls=True, + supported_in_api=True, + minimal_client_version=None, + priority=0, + available_in_plans=frozenset({"plus"}), + raw={}, + ) + + +def _encode_jwt(payload: dict) -> str: + raw = json.dumps(payload, separators=(",", ":")).encode("utf-8") + body = base64.urlsafe_b64encode(raw).rstrip(b"=").decode("ascii") + return f"header.{body}.sig" + + +def _make_auth_json(account_id: str, email: str) -> dict: + payload = { + "email": email, + "chatgpt_account_id": account_id, + "https://api.openai.com/auth": {"chatgpt_plan_type": "plus"}, + } + return { + "tokens": { + "idToken": _encode_jwt(payload), + "accessToken": "access-token", + "refreshToken": "refresh-token", + "accountId": account_id, + }, + } + + +async def _import_account(async_client, account_id: str, email: str) -> None: + auth_json = _make_auth_json(account_id, email) + files = {"auth_json": ("auth.json", json.dumps(auth_json), "application/json")} + response = await async_client.post("/api/accounts/import", files=files) + assert response.status_code == 200 + + +def _parse_sse_events(lines: list[str]) -> list[tuple[str | None, dict]]: + events: list[tuple[str | None, dict]] = [] + current_event: str | None = None + for line in lines: + if line.startswith("event: "): + current_event = line[7:] + continue + if not line.startswith("data: "): + continue + payload = line[6:] + if payload == "[DONE]": + continue + events.append((current_event, json.loads(payload))) + current_event = None + return events + + +@pytest.mark.asyncio +async def test_anthropic_messages_non_stream(async_client, monkeypatch): + await _import_account(async_client, "acc_anthropic_non_stream", "anthropic-non-stream@example.com") + + seen: dict[str, object] = {} + + async def fake_stream(payload, headers, access_token, account_id, base_url=None, raise_for_status=False): + seen["payload"] = payload + yield 'data: {"type":"response.output_text.delta","delta":"Hello"}\n\n' + yield ( + 'data: {"type":"response.completed","response":' + '{"id":"resp_1","usage":{"input_tokens":3,"output_tokens":5}}}\n\n' + ) + + monkeypatch.setattr(proxy_module, "core_stream_responses", fake_stream) + + request_payload = { + "model": "gpt-5.1", + "messages": [{"role": "user", "content": "hi"}], + } + response = await async_client.post("/v1/messages", json=request_payload) + assert response.status_code == 200 + + body = response.json() + assert body["type"] == "message" + assert body["role"] == "assistant" + assert body["model"] == "gpt-5.1" + assert body["content"] == [{"type": "text", "text": "Hello"}] + assert body["stop_reason"] == "end_turn" + assert body["usage"] == {"input_tokens": 3, "output_tokens": 5} + + seen_payload = seen["payload"] + assert getattr(seen_payload, "instructions", None) == "" + assert getattr(seen_payload, "input", None) == [{"role": "user", "content": [{"type": "input_text", "text": "hi"}]}] + + +@pytest.mark.asyncio +async def test_anthropic_messages_accept_system_role_and_cache_control(async_client, monkeypatch): + await _import_account(async_client, "acc_anthropic_system_role", "anthropic-system-role@example.com") + + seen: dict[str, object] = {} + + async def fake_stream(payload, headers, access_token, account_id, base_url=None, raise_for_status=False): + seen["payload"] = payload + yield 'data: {"type":"response.output_text.delta","delta":"Hello"}\n\n' + yield ( + 'data: {"type":"response.completed","response":' + '{"id":"resp_system_role","usage":{"input_tokens":3,"output_tokens":5}}}\n\n' + ) + + monkeypatch.setattr(proxy_module, "core_stream_responses", fake_stream) + + request_payload = { + "model": "gpt-5.1", + "messages": [ + { + "role": "system", + "content": [{"type": "text", "text": "Use concise answers", "cache_control": {"type": "ephemeral"}}], + }, + { + "role": "user", + "content": [ + {"type": "text", "text": "hi", "cache_control": {"type": "ephemeral"}}, + ], + }, + ], + } + response = await async_client.post("/v1/messages", json=request_payload) + assert response.status_code == 200 + + seen_payload = seen["payload"] + assert getattr(seen_payload, "instructions", None) == "Use concise answers" + assert getattr(seen_payload, "input", None) == [{"role": "user", "content": [{"type": "input_text", "text": "hi"}]}] + + +@pytest.mark.asyncio +async def test_anthropic_messages_keeps_requested_claude_model(async_client, monkeypatch): + await _import_account(async_client, "acc_anthropic_model_map", "anthropic-model-map@example.com") + + seen: dict[str, object] = {} + + async def fake_stream(payload, headers, access_token, account_id, base_url=None, raise_for_status=False): + seen["model"] = getattr(payload, "model", None) + reasoning = getattr(payload, "reasoning", None) + seen["reasoning_effort"] = getattr(reasoning, "effort", None) if reasoning is not None else None + seen["instructions"] = getattr(payload, "instructions", None) + seen["input"] = getattr(payload, "input", None) + seen["prompt_cache_key"] = getattr(payload, "prompt_cache_key", None) + seen["temperature"] = getattr(payload, "temperature", None) + seen["top_p"] = getattr(payload, "top_p", None) + seen["top_k"] = getattr(payload, "top_k", None) + yield 'data: {"type":"response.output_text.delta","delta":"Hello"}\n\n' + yield ( + 'data: {"type":"response.completed","response":' + '{"id":"resp_model_map","usage":{"input_tokens":3,"output_tokens":5}}}\n\n' + ) + + monkeypatch.setattr(proxy_module, "core_stream_responses", fake_stream) + + request_payload = { + "model": "claude-sonnet-4-6", + "system": [ + { + "type": "text", + "text": "Cached context", + "cache_control": {"type": "ephemeral"}, + } + ], + "messages": [{"role": "user", "content": "hi"}], + "temperature": 0.2, + "top_p": 0.9, + "top_k": 32, + } + response = await async_client.post("/anthropic/v1/messages?beta=true", json=request_payload) + assert response.status_code == 200 + assert seen["model"] == "claude-sonnet-4-6" + assert seen["reasoning_effort"] is None + assert isinstance(seen["prompt_cache_key"], str) + assert seen["prompt_cache_key"].startswith("claude-shared:") + assert seen["temperature"] == 0.2 + assert seen["top_p"] == 0.9 + assert seen["top_k"] == 32 + assert seen["instructions"] == "Cached context" + assert seen["input"] == [ + {"role": "user", "content": [{"type": "input_text", "text": "hi"}]}, + ] + assert response.json()["model"] == "claude-sonnet-4-6" + + +@pytest.mark.asyncio +async def test_anthropic_messages_maps_reasoning_effort_from_alias(async_client, monkeypatch): + await _import_account(async_client, "acc_anthropic_reasoning", "anthropic-reasoning@example.com") + + seen: dict[str, object] = {} + + async def fake_stream(payload, headers, access_token, account_id, base_url=None, raise_for_status=False): + reasoning = getattr(payload, "reasoning", None) + seen["reasoning_effort"] = getattr(reasoning, "effort", None) if reasoning is not None else None + yield 'data: {"type":"response.output_text.delta","delta":"Hello"}\n\n' + yield ( + 'data: {"type":"response.completed","response":' + '{"id":"resp_reasoning","usage":{"input_tokens":3,"output_tokens":5}}}\n\n' + ) + + monkeypatch.setattr(proxy_module, "core_stream_responses", fake_stream) + + request_payload = { + "model": "gpt-5.1", + "messages": [{"role": "user", "content": "hi"}], + "reasoningEffort": "xhigh", + } + response = await async_client.post("/v1/messages", json=request_payload) + assert response.status_code == 200 + assert seen["reasoning_effort"] == "xhigh" + + +@pytest.mark.asyncio +async def test_anthropic_messages_applies_default_reasoning_effort_from_settings(async_client, monkeypatch): + await _import_account(async_client, "acc_anthropic_default_reasoning", "anthropic-default-reasoning@example.com") + + seen: dict[str, object] = {} + + async def fake_stream(payload, headers, access_token, account_id, base_url=None, raise_for_status=False): + reasoning = getattr(payload, "reasoning", None) + seen["reasoning_effort"] = getattr(reasoning, "effort", None) if reasoning is not None else None + yield 'data: {"type":"response.output_text.delta","delta":"Hello"}\n\n' + yield ( + 'data: {"type":"response.completed","response":' + '{"id":"resp_default_reasoning","usage":{"input_tokens":3,"output_tokens":5}}}\n\n' + ) + + monkeypatch.setattr(proxy_module, "core_stream_responses", fake_stream) + monkeypatch.setattr( + "app.modules.anthropic_compat.api.get_settings", + lambda: SimpleNamespace( + anthropic_default_reasoning_effort="xhigh", + log_proxy_request_shape_raw_cache_key=False, + ), + ) + + request_payload = { + "model": "gpt-5.1", + "messages": [{"role": "user", "content": "hi"}], + } + response = await async_client.post("/v1/messages", json=request_payload) + assert response.status_code == 200 + assert seen["reasoning_effort"] == "xhigh" + + +@pytest.mark.asyncio +async def test_anthropic_messages_keeps_explicit_reasoning_over_default(async_client, monkeypatch): + await _import_account(async_client, "acc_anthropic_explicit_reasoning", "anthropic-explicit-reasoning@example.com") + + seen: dict[str, object] = {} + + async def fake_stream(payload, headers, access_token, account_id, base_url=None, raise_for_status=False): + reasoning = getattr(payload, "reasoning", None) + seen["reasoning_effort"] = getattr(reasoning, "effort", None) if reasoning is not None else None + yield 'data: {"type":"response.output_text.delta","delta":"Hello"}\n\n' + yield ( + 'data: {"type":"response.completed","response":' + '{"id":"resp_explicit_reasoning","usage":{"input_tokens":3,"output_tokens":5}}}\n\n' + ) + + monkeypatch.setattr(proxy_module, "core_stream_responses", fake_stream) + monkeypatch.setattr( + "app.modules.anthropic_compat.api.get_settings", + lambda: SimpleNamespace( + anthropic_default_reasoning_effort="xhigh", + log_proxy_request_shape_raw_cache_key=False, + ), + ) + + request_payload = { + "model": "gpt-5.1", + "messages": [{"role": "user", "content": "hi"}], + "reasoningEffort": "medium", + } + response = await async_client.post("/v1/messages", json=request_payload) + assert response.status_code == 200 + assert seen["reasoning_effort"] == "medium" + + +@pytest.mark.asyncio +async def test_anthropic_messages_claude_preserves_explicit_prompt_cache_key(async_client, monkeypatch): + await _import_account(async_client, "acc_anthropic_explicit_cache", "anthropic-explicit-cache@example.com") + + seen: dict[str, object] = {} + + async def fake_stream(payload, headers, access_token, account_id, base_url=None, raise_for_status=False): + seen["prompt_cache_key"] = getattr(payload, "prompt_cache_key", None) + seen["prompt_cache_retention"] = getattr(payload, "prompt_cache_retention", None) + yield 'data: {"type":"response.output_text.delta","delta":"Hello"}\n\n' + yield ( + 'data: {"type":"response.completed","response":' + '{"id":"resp_explicit_cache","usage":{"input_tokens":3,"output_tokens":5}}}\n\n' + ) + + monkeypatch.setattr(proxy_module, "core_stream_responses", fake_stream) + + request_payload = { + "model": "claude-sonnet-4-6", + "messages": [{"role": "user", "content": "hi"}], + "prompt_cache_key": "thread_123", + "prompt_cache_retention": "24h", + } + response = await async_client.post("/anthropic/v1/messages?beta=true", json=request_payload) + assert response.status_code == 200 + assert seen["prompt_cache_key"] == "thread_123" + assert seen["prompt_cache_retention"] == "24h" + + +@pytest.mark.asyncio +async def test_anthropic_messages_sets_anchor_prompt_cache_key_without_cache_control(async_client, monkeypatch): + await _import_account(async_client, "acc_anthropic_anchor_cache", "anthropic-anchor-cache@example.com") + + seen: dict[str, object] = {} + + async def fake_stream(payload, headers, access_token, account_id, base_url=None, raise_for_status=False): + seen["prompt_cache_key"] = getattr(payload, "prompt_cache_key", None) + yield 'data: {"type":"response.output_text.delta","delta":"Hello"}\n\n' + yield ( + 'data: {"type":"response.completed","response":' + '{"id":"resp_anchor_cache","usage":{"input_tokens":3,"output_tokens":5}}}\n\n' + ) + + monkeypatch.setattr(proxy_module, "core_stream_responses", fake_stream) + + request_payload = { + "model": "claude-sonnet-4-6", + "system": "You are Claude Code harness", + "messages": [{"role": "user", "content": "Initial task"}], + } + response = await async_client.post("/anthropic/v1/messages?beta=true", json=request_payload) + assert response.status_code == 200 + assert isinstance(seen["prompt_cache_key"], str) + assert seen["prompt_cache_key"].startswith("claude-shared:") + + +@pytest.mark.asyncio +async def test_anthropic_messages_claude_ignores_volatile_prompt_cache_metadata(async_client, monkeypatch): + await _import_account(async_client, "acc_anthropic_shared_cache", "anthropic-shared-cache@example.com") + + seen_prompt_cache_keys: list[str | None] = [] + + async def fake_stream(payload, headers, access_token, account_id, base_url=None, raise_for_status=False): + seen_prompt_cache_keys.append(getattr(payload, "prompt_cache_key", None)) + yield 'data: {"type":"response.output_text.delta","delta":"Hello"}\n\n' + yield ( + 'data: {"type":"response.completed","response":' + '{"id":"resp_shared_cache","usage":{"input_tokens":3,"output_tokens":5}}}\n\n' + ) + + monkeypatch.setattr(proxy_module, "core_stream_responses", fake_stream) + + first_payload = { + "model": "claude-sonnet-4-6", + "system": "You are Claude Code harness", + "messages": [{"role": "user", "content": "Initial task"}], + "metadata": {"session_id": "volatile-session-a"}, + } + second_payload = { + "model": "claude-sonnet-4-6", + "system": "You are Claude Code harness", + "messages": [{"role": "user", "content": "Initial task"}], + "metadata": {"session_id": "volatile-session-b"}, + } + + first_response = await async_client.post("/anthropic/v1/messages?beta=true", json=first_payload) + assert first_response.status_code == 200 + second_response = await async_client.post("/anthropic/v1/messages?beta=true", json=second_payload) + assert second_response.status_code == 200 + + assert len(seen_prompt_cache_keys) == 2 + assert isinstance(seen_prompt_cache_keys[0], str) + assert isinstance(seen_prompt_cache_keys[1], str) + assert seen_prompt_cache_keys[0].startswith("claude-shared:") + assert seen_prompt_cache_keys[0] == seen_prompt_cache_keys[1] + + +@pytest.mark.asyncio +async def test_anthropic_messages_alias_stream(async_client, monkeypatch): + await _import_account(async_client, "acc_anthropic_stream", "anthropic-stream@example.com") + + async def fake_stream(payload, headers, access_token, account_id, base_url=None, raise_for_status=False): + yield 'data: {"type":"response.output_text.delta","delta":"Hi"}\n\n' + yield 'data: {"type":"response.output_text.delta","delta":" there"}\n\n' + yield ( + 'data: {"type":"response.completed","response":' + '{"id":"resp_stream","usage":{"input_tokens":2,"output_tokens":4}}}\n\n' + ) + + monkeypatch.setattr(proxy_module, "core_stream_responses", fake_stream) + + request_payload = { + "model": "gpt-5.2", + "messages": [{"role": "user", "content": "hi"}], + "stream": True, + } + async with async_client.stream("POST", "/anthropic/v1/messages", json=request_payload) as response: + assert response.status_code == 200 + lines = [line async for line in response.aiter_lines() if line] + + events = _parse_sse_events(lines) + event_names = [event_name for event_name, _ in events] + assert "message_start" in event_names + assert "content_block_start" in event_names + assert "content_block_delta" in event_names + assert "content_block_stop" in event_names + assert "message_delta" in event_names + assert "message_stop" in event_names + + +@pytest.mark.asyncio +async def test_anthropic_count_tokens(async_client, monkeypatch): + await _import_account(async_client, "acc_anthropic_count_tokens", "anthropic-count@example.com") + + async def fake_stream(payload, headers, access_token, account_id, base_url=None, raise_for_status=False): + raise AssertionError("count_tokens must not call upstream") + + monkeypatch.setattr(proxy_module, "core_stream_responses", fake_stream) + + request_payload = { + "model": "gpt-5.1", + "messages": [{"role": "user", "content": "count me"}], + } + response = await async_client.post("/v1/messages/count_tokens", json=request_payload) + assert response.status_code == 200 + body = response.json() + assert isinstance(body.get("input_tokens"), int) + assert body["input_tokens"] > 0 + + +@pytest.mark.asyncio +async def test_anthropic_count_tokens_uses_dedicated_cache_lane_for_claude(async_client, monkeypatch): + await _import_account(async_client, "acc_anthropic_count_cache_lane", "anthropic-count-lane@example.com") + + async def fake_stream(payload, headers, access_token, account_id, base_url=None, raise_for_status=False): + raise AssertionError("count_tokens must not call upstream") + + monkeypatch.setattr(proxy_module, "core_stream_responses", fake_stream) + + request_payload = { + "model": "claude-sonnet-4-6", + "system": "You are Claude Code harness", + "messages": [{"role": "user", "content": "count me"}], + } + response = await async_client.post("/anthropic/v1/messages/count_tokens?beta=true", json=request_payload) + assert response.status_code == 200 + body = response.json() + assert isinstance(body.get("input_tokens"), int) + assert body["input_tokens"] > 0 + + +@pytest.mark.asyncio +async def test_anthropic_count_tokens_missing_usage_returns_5xx(async_client, monkeypatch): + await _import_account(async_client, "acc_anthropic_count_missing", "anthropic-count-missing@example.com") + + async def fake_stream(payload, headers, access_token, account_id, base_url=None, raise_for_status=False): + raise AssertionError("count_tokens must not call upstream") + + monkeypatch.setattr(proxy_module, "core_stream_responses", fake_stream) + + request_payload = { + "model": "gpt-5.1", + "messages": [{"role": "user", "content": "count me"}], + } + response = await async_client.post("/v1/messages/count_tokens", json=request_payload) + assert response.status_code == 200 + body = response.json() + assert isinstance(body.get("input_tokens"), int) + assert body["input_tokens"] > 0 + + +@pytest.mark.asyncio +async def test_anthropic_event_logging_batch_stub(async_client): + response = await async_client.post("/api/event_logging/batch", json={"events": []}) + assert response.status_code == 200 + assert response.json() == {"status": "ok"} + + alias_response = await async_client.post("/anthropic/api/event_logging/batch", json={"events": []}) + assert alias_response.status_code == 200 + assert alias_response.json() == {"status": "ok"} + + +@pytest.mark.asyncio +async def test_anthropic_auth_accepts_x_api_key_and_bearer(async_client, monkeypatch): + enable = await async_client.put( + "/api/settings", + json={ + "stickyThreadsEnabled": False, + "preferEarlierResetAccounts": False, + "totpRequiredOnLogin": False, + "apiKeyAuthEnabled": True, + }, + ) + assert enable.status_code == 200 + + async with SessionLocal() as session: + service = ApiKeysService(ApiKeysRepository(session)) + created = await service.create_key( + ApiKeyCreateData( + name="anthropic-auth-key", + allowed_models=None, + expires_at=None, + ) + ) + + await _import_account(async_client, "acc_anthropic_auth", "anthropic-auth@example.com") + + async def fake_stream(payload, headers, access_token, account_id, base_url=None, raise_for_status=False): + yield ( + 'data: {"type":"response.completed","response":' + '{"id":"resp_auth","usage":{"input_tokens":1,"output_tokens":1}}}\n\n' + ) + + monkeypatch.setattr(proxy_module, "core_stream_responses", fake_stream) + + payload = { + "model": "gpt-5.1", + "messages": [{"role": "user", "content": "hi"}], + } + + missing = await async_client.post("/v1/messages", json=payload) + assert missing.status_code == 401 + missing_body = missing.json() + assert missing_body["type"] == "error" + assert missing_body["error"]["type"] == "authentication_error" + + bearer = await async_client.post( + "/v1/messages", + json=payload, + headers={"Authorization": f"Bearer {created.key}"}, + ) + assert bearer.status_code == 200 + + x_api_key = await async_client.post( + "/v1/messages", + json=payload, + headers={"x-api-key": created.key}, + ) + assert x_api_key.status_code == 200 + + +@pytest.mark.asyncio +async def test_anthropic_messages_malformed_json_returns_anthropic_error(async_client): + response = await async_client.post( + "/v1/messages", + content='{"model":"claude-sonnet-4-6","messages":[{"role":"user","content":"hi"}', + headers={"content-type": "application/json"}, + ) + assert response.status_code == 400 + body = response.json() + assert body["type"] == "error" + assert body["error"]["type"] == "invalid_request_error" + assert body["error"]["message"] == "Invalid request payload" + + +@pytest.mark.asyncio +async def test_anthropic_count_tokens_malformed_json_returns_anthropic_error(async_client): + response = await async_client.post( + "/v1/messages/count_tokens", + content='{"model":"claude-sonnet-4-6","messages":[{"role":"user","content":"hi"}', + headers={"content-type": "application/json"}, + ) + assert response.status_code == 400 + body = response.json() + assert body["type"] == "error" + assert body["error"]["type"] == "invalid_request_error" + assert body["error"]["message"] == "Invalid request payload" diff --git a/tests/integration/test_migrations.py b/tests/integration/test_migrations.py index 196e57dd..8964f583 100644 --- a/tests/integration/test_migrations.py +++ b/tests/integration/test_migrations.py @@ -41,7 +41,7 @@ async def test_run_startup_migrations_preserves_unknown_plan_types(db_setup): await repo.upsert(_make_account("acc_three", "three@example.com", "")) result = await run_startup_migrations(_DATABASE_URL) - assert result.current_revision == "012_add_import_without_overwrite_and_drop_accounts_email_unique" + assert result.current_revision == "013_add_request_logs_codex_session_hashes" assert result.bootstrap.stamped_revision is None async with SessionLocal() as session: @@ -56,7 +56,7 @@ async def test_run_startup_migrations_preserves_unknown_plan_types(db_setup): assert acc_three.plan_type == DEFAULT_PLAN rerun = await run_startup_migrations(_DATABASE_URL) - assert rerun.current_revision == "012_add_import_without_overwrite_and_drop_accounts_email_unique" + assert rerun.current_revision == "013_add_request_logs_codex_session_hashes" @pytest.mark.asyncio @@ -82,12 +82,12 @@ async def test_run_startup_migrations_bootstraps_legacy_history(db_setup): result = await run_startup_migrations(_DATABASE_URL) assert result.bootstrap.stamped_revision == "004_add_accounts_chatgpt_account_id" - assert result.current_revision == "012_add_import_without_overwrite_and_drop_accounts_email_unique" + assert result.current_revision == "013_add_request_logs_codex_session_hashes" async with SessionLocal() as session: revision_rows = await session.execute(text("SELECT version_num FROM alembic_version")) revisions = [str(row[0]) for row in revision_rows.fetchall()] - assert revisions == ["012_add_import_without_overwrite_and_drop_accounts_email_unique"] + assert revisions == ["013_add_request_logs_codex_session_hashes"] @pytest.mark.asyncio @@ -114,7 +114,7 @@ async def test_run_startup_migrations_skips_legacy_stamp_when_required_tables_mi result = await run_startup_migrations(_DATABASE_URL) assert result.bootstrap.stamped_revision is None - assert result.current_revision == "012_add_import_without_overwrite_and_drop_accounts_email_unique" + assert result.current_revision == "013_add_request_logs_codex_session_hashes" async with SessionLocal() as session: setting_id = await session.execute(text("SELECT id FROM dashboard_settings WHERE id = 1")) @@ -148,7 +148,7 @@ async def test_run_startup_migrations_handles_unknown_legacy_rows(db_setup): assert result.bootstrap.stamped_revision == "001_normalize_account_plan_types" assert result.bootstrap.unknown_migrations == ("900_custom_hotfix",) - assert result.current_revision == "012_add_import_without_overwrite_and_drop_accounts_email_unique" + assert result.current_revision == "013_add_request_logs_codex_session_hashes" @pytest.mark.asyncio @@ -314,7 +314,7 @@ async def test_run_startup_migrations_drops_accounts_email_unique_with_non_casca await session.commit() result = await run_startup_migrations(db_url) - assert result.current_revision == "012_add_import_without_overwrite_and_drop_accounts_email_unique" + assert result.current_revision == "013_add_request_logs_codex_session_hashes" async with session_factory() as session: await session.execute(text("PRAGMA foreign_keys=ON")) diff --git a/tests/integration/test_proxy_api_extended.py b/tests/integration/test_proxy_api_extended.py index 94bd3adc..8301b183 100644 --- a/tests/integration/test_proxy_api_extended.py +++ b/tests/integration/test_proxy_api_extended.py @@ -2,6 +2,7 @@ import base64 import json +from hashlib import sha256 import pytest from sqlalchemy import select @@ -104,11 +105,17 @@ async def fake_stream(payload, headers, access_token, account_id, base_url=None, payload = {"model": "gpt-5.1", "instructions": "hi", "input": [], "stream": True} request_id = "req_usage_123" + codex_session_id = "session_abc_123" + codex_conversation_id = "conversation_xyz_789" async with async_client.stream( "POST", "/backend-api/codex/responses", json=payload, - headers={"x-request-id": request_id}, + headers={ + "x-request-id": request_id, + "x-codex-session-id": codex_session_id, + "x-codex-conversation-id": codex_conversation_id, + }, ) as resp: assert resp.status_code == 200 lines = [line async for line in resp.aiter_lines() if line] @@ -129,6 +136,8 @@ async def fake_stream(payload, headers, access_token, account_id, base_url=None, assert log.output_tokens == 5 assert log.cached_input_tokens == 3 assert log.reasoning_tokens == 2 + assert log.codex_session_hash == f"sha256:{sha256(codex_session_id.encode('utf-8')).hexdigest()[:12]}" + assert log.codex_conversation_hash == f"sha256:{sha256(codex_conversation_id.encode('utf-8')).hexdigest()[:12]}" assert log.status == "success" diff --git a/tests/integration/test_request_logs_api.py b/tests/integration/test_request_logs_api.py index 0bc0c2ad..57e3b1d6 100644 --- a/tests/integration/test_request_logs_api.py +++ b/tests/integration/test_request_logs_api.py @@ -78,3 +78,34 @@ async def test_request_logs_api_returns_recent(async_client, db_setup): assert older["status"] == "ok" assert older["tokens"] == 300 assert older["cachedInputTokens"] is None + + +@pytest.mark.asyncio +async def test_request_logs_api_returns_codex_session_hashes(async_client, db_setup): + async with SessionLocal() as session: + accounts_repo = AccountsRepository(session) + logs_repo = RequestLogsRepository(session) + await accounts_repo.upsert(_make_account("acc_logs_hashes", "logs-hashes@example.com")) + + await logs_repo.add_log( + account_id="acc_logs_hashes", + request_id="req_logs_hash_1", + model="gpt-5.3-codex", + input_tokens=100, + output_tokens=200, + latency_ms=1200, + status="success", + error_code=None, + codex_session_hash="sha256:111111111111", + codex_conversation_hash="sha256:222222222222", + ) + + response = await async_client.get("/api/request-logs?limit=1") + assert response.status_code == 200 + body = response.json() + payload = body["requests"] + assert len(payload) == 1 + latest = payload[0] + assert latest["requestId"] == "req_logs_hash_1" + assert latest["codexSessionHash"] == "sha256:111111111111" + assert latest["codexConversationHash"] == "sha256:222222222222" diff --git a/tests/unit/test_anthropic_translator.py b/tests/unit/test_anthropic_translator.py new file mode 100644 index 00000000..9fcadeac --- /dev/null +++ b/tests/unit/test_anthropic_translator.py @@ -0,0 +1,325 @@ +from __future__ import annotations + +import pytest + +from app.modules.anthropic_compat.schemas import AnthropicMessagesRequest +from app.modules.anthropic_compat.translator import ( + AnthropicTranslationError, + resolve_prompt_cache_key, + to_responses_request, +) + +pytestmark = pytest.mark.unit + + +def test_anthropic_request_maps_tool_use_and_tool_result(): + payload = AnthropicMessagesRequest.model_validate( + { + "model": "gpt-5.2", + "system": "You are helpful", + "messages": [ + {"role": "user", "content": [{"type": "text", "text": "weather?"}]}, + { + "role": "assistant", + "content": [{"type": "tool_use", "id": "call_1", "name": "get_weather", "input": {"city": "NYC"}}], + }, + { + "role": "user", + "content": [{"type": "tool_result", "tool_use_id": "call_1", "content": "sunny"}], + }, + ], + } + ) + + translated = to_responses_request(payload) + + assert translated.instructions == "You are helpful" + assert translated.input == [ + {"role": "user", "content": [{"type": "input_text", "text": "weather?"}]}, + {"type": "function_call", "call_id": "call_1", "name": "get_weather", "arguments": '{"city":"NYC"}'}, + {"type": "function_call_output", "call_id": "call_1", "output": "sunny"}, + ] + + +def test_anthropic_request_accepts_system_role_and_cache_control_blocks(): + payload = AnthropicMessagesRequest.model_validate( + { + "model": "gpt-5.2", + "messages": [ + { + "role": "system", + "content": [ + { + "type": "text", + "text": "Follow system instruction", + "cache_control": {"type": "ephemeral"}, + } + ], + }, + { + "role": "user", + "content": [ + { + "type": "text", + "text": "hi", + "cache_control": {"type": "ephemeral"}, + } + ], + }, + ], + } + ) + + translated = to_responses_request(payload) + + assert translated.instructions == "Follow system instruction" + assert translated.input == [ + {"role": "user", "content": [{"type": "input_text", "text": "hi"}]}, + ] + + +def test_anthropic_request_strips_anthropic_only_text_block_fields(): + payload = AnthropicMessagesRequest.model_validate( + { + "model": "gpt-5.2", + "messages": [ + { + "role": "user", + "content": [ + { + "type": "text", + "text": "hi", + "cache_control": {"type": "ephemeral"}, + "metadata": {"segment": "stable-prefix"}, + } + ], + } + ], + } + ) + + translated = to_responses_request(payload) + + assert translated.input == [ + { + "role": "user", + "content": [{"type": "input_text", "text": "hi"}], + } + ] + + +def test_anthropic_request_uses_explicit_prompt_cache_key(): + payload = AnthropicMessagesRequest.model_validate( + { + "model": "gpt-5.2", + "prompt_cache_key": "thread_123", + "messages": [{"role": "user", "content": "hi"}], + } + ) + + translated = to_responses_request(payload) + resolution = resolve_prompt_cache_key(payload) + + assert translated.prompt_cache_key == "thread_123" + assert resolution.key == "thread_123" + assert resolution.source == "explicit" + + +def test_anthropic_request_uses_metadata_for_cache_key_without_forwarding_metadata(): + payload = AnthropicMessagesRequest.model_validate( + { + "model": "gpt-5.2", + "metadata": {"conversation_id": "conversation_from_metadata"}, + "messages": [{"role": "user", "content": "hi"}], + } + ) + + translated = to_responses_request(payload) + resolution = resolve_prompt_cache_key(payload) + dumped = translated.model_dump(mode="python", exclude_none=True) + + assert translated.prompt_cache_key == "conversation_from_metadata" + assert resolution.key == "conversation_from_metadata" + assert resolution.source == "metadata" + assert "metadata" not in dumped + + +def test_anthropic_request_forwards_prompt_cache_retention(): + payload = AnthropicMessagesRequest.model_validate( + { + "model": "gpt-5.2", + "prompt_cache_retention": "24h", + "messages": [{"role": "user", "content": "hi"}], + } + ) + + translated = to_responses_request(payload) + + assert translated.prompt_cache_retention == "24h" + + +def test_anthropic_request_maps_reasoning_effort_alias(): + payload = AnthropicMessagesRequest.model_validate( + { + "model": "gpt-5.2", + "reasoningEffort": "xhigh", + "messages": [{"role": "user", "content": "hi"}], + } + ) + + translated = to_responses_request(payload) + + assert translated.reasoning is not None + assert translated.reasoning.effort == "xhigh" + + +def test_anthropic_request_maps_nested_reasoning_effort(): + payload = AnthropicMessagesRequest.model_validate( + { + "model": "gpt-5.2", + "reasoning": {"effort": "medium"}, + "messages": [{"role": "user", "content": "hi"}], + } + ) + + translated = to_responses_request(payload) + + assert translated.reasoning is not None + assert translated.reasoning.effort == "medium" + + +def test_anthropic_request_rejects_non_string_prompt_cache_retention(): + payload = AnthropicMessagesRequest.model_validate( + { + "model": "gpt-5.2", + "prompt_cache_retention": 24, + "messages": [{"role": "user", "content": "hi"}], + } + ) + + with pytest.raises(AnthropicTranslationError, match="prompt_cache_retention must be a string"): + to_responses_request(payload) + + +def test_anthropic_request_derives_prompt_cache_key_from_cache_control_blocks(): + payload_a = AnthropicMessagesRequest.model_validate( + { + "model": "gpt-5.2", + "system": [ + { + "type": "text", + "text": "Stable prefix", + "cache_control": {"type": "ephemeral"}, + } + ], + "messages": [{"role": "user", "content": "first"}], + } + ) + payload_b = AnthropicMessagesRequest.model_validate( + { + "model": "gpt-5.2", + "system": [ + { + "type": "text", + "text": "Stable prefix", + "cache_control": {"type": "ephemeral"}, + } + ], + "messages": [{"role": "user", "content": "second turn text"}], + } + ) + + translated_a = to_responses_request(payload_a) + translated_b = to_responses_request(payload_b) + resolution_a = resolve_prompt_cache_key(payload_a) + resolution_b = resolve_prompt_cache_key(payload_b) + + assert translated_a.prompt_cache_key is not None + assert translated_a.prompt_cache_key.startswith("anthropic-cache:") + assert translated_a.prompt_cache_key == translated_b.prompt_cache_key + assert resolution_a.source == "cache_control" + assert resolution_b.source == "cache_control" + + +def test_anthropic_request_derives_anchor_prompt_cache_key_without_cache_control(): + payload_a = AnthropicMessagesRequest.model_validate( + { + "model": "gpt-5.2", + "system": "You are Claude Code harness", + "messages": [ + {"role": "user", "content": "Initial task"}, + {"role": "assistant", "content": "Thinking..."}, + {"role": "user", "content": "follow-up one"}, + ], + } + ) + payload_b = AnthropicMessagesRequest.model_validate( + { + "model": "gpt-5.2", + "system": "You are Claude Code harness", + "messages": [ + {"role": "user", "content": "Initial task"}, + {"role": "assistant", "content": "Thinking..."}, + {"role": "user", "content": "follow-up two changed"}, + ], + } + ) + + translated_a = to_responses_request(payload_a) + translated_b = to_responses_request(payload_b) + resolution_a = resolve_prompt_cache_key(payload_a) + resolution_b = resolve_prompt_cache_key(payload_b) + + assert translated_a.prompt_cache_key is not None + assert translated_a.prompt_cache_key.startswith("anthropic-anchor:") + assert translated_a.prompt_cache_key == translated_b.prompt_cache_key + assert resolution_a.source == "anchor" + assert resolution_b.source == "anchor" + + +def test_anthropic_request_has_no_prompt_cache_key_when_anchor_is_empty(): + payload = AnthropicMessagesRequest.model_validate( + { + "model": "gpt-5.2", + "messages": [{"role": "user", "content": ""}], + } + ) + + translated = to_responses_request(payload) + resolution = resolve_prompt_cache_key(payload) + + assert translated.prompt_cache_key is None + assert resolution.key is None + assert resolution.source == "none" + + +def test_anthropic_tool_choice_any_maps_to_required_and_disables_parallel(): + payload = AnthropicMessagesRequest.model_validate( + { + "model": "gpt-5.2", + "messages": [{"role": "user", "content": "hi"}], + "tool_choice": {"type": "any", "disable_parallel_tool_use": True}, + } + ) + + translated = to_responses_request(payload) + + assert translated.tool_choice == "required" + assert translated.parallel_tool_calls is False + + +def test_anthropic_invalid_user_tool_use_block_rejected(): + payload = AnthropicMessagesRequest.model_validate( + { + "model": "gpt-5.2", + "messages": [ + { + "role": "user", + "content": [{"type": "tool_use", "id": "call_1", "name": "bad", "input": {}}], + } + ], + } + ) + + with pytest.raises(AnthropicTranslationError): + to_responses_request(payload) diff --git a/tests/unit/test_openai_requests.py b/tests/unit/test_openai_requests.py index 1df40c72..4bf3be88 100644 --- a/tests/unit/test_openai_requests.py +++ b/tests/unit/test_openai_requests.py @@ -47,6 +47,19 @@ def test_extra_fields_are_preserved(): assert "max_output_tokens" not in dumped +def test_responses_accepts_prompt_cache_retention(): + payload = { + "model": "gpt-5.1", + "instructions": "hi", + "input": [], + "prompt_cache_retention": "24h", + } + request = ResponsesRequest.model_validate(payload) + + dumped = request.to_payload() + assert dumped["prompt_cache_retention"] == "24h" + + def test_openai_compatible_reasoning_aliases_are_normalized(): payload = { "model": "gpt-5.1", @@ -293,6 +306,17 @@ def test_v1_input_string_passthrough(): assert request.input == [{"role": "user", "content": [{"type": "input_text", "text": "hello"}]}] +def test_v1_prompt_cache_retention_passthrough(): + payload = { + "model": "gpt-5.1", + "input": "hello", + "prompt_cache_retention": "in-memory", + } + request = V1ResponsesRequest.model_validate(payload).to_responses_request() + + assert request.prompt_cache_retention == "in-memory" + + def test_v1_rejects_builtin_tools(): payload = {"model": "gpt-5.1", "input": [], "tools": [{"type": "image_generation"}]} with pytest.raises(ValidationError, match="Unsupported tool type"): diff --git a/uv.lock b/uv.lock index 3a0bb1b9..7fc711e9 100644 --- a/uv.lock +++ b/uv.lock @@ -1,14 +1,14 @@ version = 1 -revision = 1 +revision = 3 requires-python = ">=3.13" [[package]] name = "aiohappyeyeballs" version = "2.6.1" source = { registry = 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