feat(provider): 完成阶段E协议适配、国内预设与模型路由

This commit is contained in:
2026-09-04 06:19:32 +08:00
parent 9b8b10cdb1
commit 1fe75e3fd2
57 changed files with 4208 additions and 592 deletions
+106 -209
View File
@@ -1,24 +1,20 @@
import json
from collections.abc import AsyncIterator
from datetime import datetime, timezone
from contextlib import aclosing
from uuid import uuid4
import httpx
from app.contracts import (
MessageRole,
ModelCapability,
ModelEvent,
ModelEventType,
ModelInfo,
ModelRequest,
)
from app.contracts import MessageRole, ModelCapability, ModelEventType, ModelInfo, ModelRequest
from app.providers.base import ProviderError, ProviderToolCall, ProviderTurn
from app.providers.credentials import CredentialResolver, CredentialStoreError
from app.providers.http_base import TurnStreamingMixin, decode_tool_arguments
from app.providers.tool_names import mapped_tool_names
from app.providers.http_base import (
EventStreamingMixin, HTTPProviderMixin, UsageTracker, decode_tool_arguments,
invalid_response, list_value, object_value, string_value, token_count, truncated_stream,
)
class OpenAICompatibleProvider(TurnStreamingMixin):
class OpenAICompatibleProvider(EventStreamingMixin, HTTPProviderMixin):
def __init__(
self,
base_url: str,
@@ -33,50 +29,37 @@ class OpenAICompatibleProvider(TurnStreamingMixin):
self.timeout_seconds = timeout_seconds
self.transport = transport
@mapped_tool_names
async def complete(self, request: ModelRequest) -> ProviderTurn:
payload = self._payload(request, stream=False)
data = await self._request("POST", "/chat/completions", json=payload)
try:
message = data["choices"][0]["message"]
except (KeyError, IndexError, TypeError) as exc:
raise ProviderError("PROVIDER_INVALID_RESPONSE", "Missing completion message.") from exc
tool_calls = []
for raw_call in message.get("tool_calls") or []:
function = raw_call.get("function") or {}
tool_calls.append(
ProviderToolCall(
tool_call_id=raw_call.get("id") or f"call_{uuid4().hex}",
name=function.get("name") or "",
arguments=decode_tool_arguments(function.get("arguments", "{}")),
)
)
usage = data.get("usage") or {}
return ProviderTurn(
text=message.get("content"),
tool_calls=tool_calls,
input_tokens=int(usage.get("prompt_tokens") or 0),
output_tokens=int(usage.get("completion_tokens") or 0),
)
data = await self._request("POST", self.stream_path, json=self._payload(request, stream=False))
choices = list_value(data.get("choices"))
if not choices:
raise invalid_response()
message = object_value(object_value(choices[0]).get("message"))
calls = []
for raw in list_value(message.get("tool_calls", [])):
raw = object_value(raw)
function = object_value(raw.get("function"))
calls.append(ProviderToolCall(
tool_call_id=string_value(raw.get("id") or f"call_{uuid4().hex}"),
name=string_value(function.get("name"), nonempty=True),
arguments=decode_tool_arguments(function.get("arguments", "{}")),
))
text = message.get("content")
if text is not None:
text = string_value(text)
usage = UsageTracker("prompt_tokens", "completion_tokens").update(data.get("usage") or {})
return ProviderTurn(text=text, tool_calls=calls, **usage)
def _payload(self, request: ModelRequest, *, stream: bool) -> dict[str, object]:
payload: dict[str, object] = {
"model": request.model,
"messages": self._messages(request),
"stream": stream,
"model": request.model, "messages": self._messages(request), "stream": stream,
}
if request.tools:
payload["tools"] = [
{
"type": "function",
"function": {
"name": tool.name,
"description": tool.description,
"parameters": tool.parameters,
},
}
for tool in request.tools
{"type": "function", "function": {
"name": tool.name, "description": tool.description, "parameters": tool.parameters,
}} for tool in request.tools
]
if request.temperature is not None:
payload["temperature"] = request.temperature
@@ -84,124 +67,81 @@ class OpenAICompatibleProvider(TurnStreamingMixin):
payload["max_tokens"] = request.max_tokens
if request.response_format is not None:
payload["response_format"] = request.response_format
if stream:
payload["stream_options"] = {"include_usage": True}
return payload
async def stream(self, request: ModelRequest) -> AsyncIterator[ModelEvent]:
sequence = 0
open_calls: dict[int, str] = {}
def event(kind: ModelEventType, data: dict | None = None) -> ModelEvent:
nonlocal sequence
item = ModelEvent(
event=kind,
sequence=sequence,
data=data or {},
timestamp=datetime.now(timezone.utc),
)
sequence += 1
return item
try:
async for data in self._stream_json(self._payload(request, stream=True)):
usage = data.get("usage") or {}
if usage:
yield event(
ModelEventType.usage,
{
"input_tokens": int(usage.get("prompt_tokens") or 0),
"output_tokens": int(usage.get("completion_tokens") or 0),
},
)
choices = data.get("choices") or []
async def _events(self, request: ModelRequest):
calls: dict[int, dict] = {}
usage = UsageTracker("prompt_tokens", "completion_tokens")
finished = False
seen = False
async with aclosing(self._stream_json(self._payload(request, stream=True))) as chunks:
async for data in chunks:
if data.get("type") == "[DONE]":
if not seen:
raise invalid_response()
finished = True
break
if data.get("usage") is not None:
yield ModelEventType.usage, usage.update(data["usage"])
choices = list_value(data.get("choices", []))
if not choices:
continue
choice = choices[0]
delta = choice.get("delta") or {}
seen = True
choice = object_value(choices[0])
delta = object_value(choice.get("delta") or {})
if delta.get("reasoning_content"):
yield event(
ModelEventType.thinking_delta,
{"text": delta["reasoning_content"]},
)
yield ModelEventType.thinking_delta, {"text": string_value(delta["reasoning_content"])}
if delta.get("content"):
yield event(ModelEventType.text_delta, {"text": delta["content"]})
for raw_call in delta.get("tool_calls") or []:
index = int(raw_call.get("index") or 0)
function = raw_call.get("function") or {}
call_id = raw_call.get("id") or open_calls.get(index) or f"call_{uuid4().hex}"
if index not in open_calls:
open_calls[index] = call_id
yield event(
ModelEventType.tool_call_start,
{"tool_call_id": call_id, "name": function.get("name") or ""},
)
if function.get("arguments"):
yield event(
ModelEventType.tool_call_delta,
{
"tool_call_id": open_calls[index],
"arguments_delta": function["arguments"],
},
)
if choice.get("finish_reason") == "tool_calls":
for call_id in open_calls.values():
yield event(
ModelEventType.tool_call_end, {"tool_call_id": call_id}
)
open_calls.clear()
for call_id in open_calls.values():
yield event(ModelEventType.tool_call_end, {"tool_call_id": call_id})
yield event(ModelEventType.done)
except ProviderError as exc:
yield event(ModelEventType.error, {"code": exc.code, "message": exc.message})
yield event(ModelEventType.done)
async def _stream_json(self, payload: dict[str, object]) -> AsyncIterator[dict]:
headers = self._headers()
try:
async with httpx.AsyncClient(
timeout=self.timeout_seconds, transport=self.transport
) as client:
async with client.stream(
"POST", f"{self.base_url}/chat/completions", headers=headers, json=payload
) as response:
response.raise_for_status()
async for line in response.aiter_lines():
if not line.startswith("data:"):
continue
value = line[5:].strip()
if not value or value == "[DONE]":
continue
try:
data = json.loads(value)
except json.JSONDecodeError as exc:
raise ProviderError(
"PROVIDER_INVALID_RESPONSE", "Provider returned invalid SSE JSON."
) from exc
if isinstance(data, dict):
yield data
except httpx.TimeoutException as exc:
raise ProviderError("PROVIDER_TIMEOUT", "Provider request timed out.") from exc
except httpx.HTTPStatusError as exc:
raise self._status_error(exc) from exc
except httpx.HTTPError as exc:
raise ProviderError("PROVIDER_UNAVAILABLE", "Provider is unavailable.") from exc
yield ModelEventType.text_delta, {"text": string_value(delta["content"])}
for raw in list_value(delta.get("tool_calls", [])):
raw = object_value(raw)
index = token_count(raw.get("index", 0))
function = object_value(raw.get("function") or {})
call = calls.setdefault(index, {"id": "", "name": "", "arguments": "", "started": False})
if raw.get("id"):
call["id"] = string_value(raw["id"])
if function.get("name"):
call["name"] += string_value(function["name"])
fragment = string_value(function.get("arguments", ""))
call["arguments"] += fragment
if not call["started"] and call["name"]:
call["id"] = call["id"] or f"call_{uuid4().hex}"
call["started"] = True
yield ModelEventType.tool_call_start, {"tool_call_id": call["id"], "name": call["name"]}
fragment = call["arguments"]
if call["started"] and fragment:
yield ModelEventType.tool_call_delta, {"tool_call_id": call["id"], "arguments_delta": fragment}
if choice.get("finish_reason"):
finished = True
if not finished:
raise truncated_stream()
for call in calls.values():
if not call["started"]:
raise invalid_response()
decode_tool_arguments(call["arguments"] or "{}")
yield ModelEventType.tool_call_end, {"tool_call_id": call["id"]}
async def list_models(self) -> list[ModelInfo]:
data = await self._request("GET", "/models")
return [
ModelInfo(
model=item["id"],
display_name=item["id"],
capabilities=[
ModelCapability.chat,
ModelCapability.tool_calling,
ModelCapability.streaming,
],
)
for item in data.get("data", [])
if isinstance(item, dict) and item.get("id")
]
return [ModelInfo(model=string_value(item["id"]), display_name=item["id"],
capabilities=self._model_capabilities(string_value(item["id"])))
for item in list_value(data.get("data"))
if isinstance(item, dict) and item.get("id")]
@staticmethod
def _model_capabilities(model: str) -> list[ModelCapability]:
# /models does not advertise capabilities. Avoid known non-chat families;
# these are discovery hints, not a guarantee of support by a gateway.
name = model.lower()
if "embed" in name or name.startswith(("bge-", "bge/")):
return [ModelCapability.embedding]
if any(marker in name for marker in (
"whisper", "tts", "transcri", "audio", "realtime", "dall-e", "image", "moderation", "rerank",
)):
return []
return [ModelCapability.chat]
async def test_connection(self, model: str | None = None) -> tuple[bool, str]:
try:
@@ -217,73 +157,30 @@ class OpenAICompatibleProvider(TurnStreamingMixin):
if request.system:
result.append({"role": "system", "content": request.system})
for message in request.messages:
item: dict[str, object] = {
"role": message.role.value,
"content": message.content,
}
item: dict[str, object] = {"role": message.role.value, "content": message.content}
if message.name:
item["name"] = message.name
if message.role == MessageRole.tool and message.tool_call_id:
item["tool_call_id"] = message.tool_call_id
if message.tool_calls:
item["tool_calls"] = [
{
"id": call.tool_call_id,
"type": "function",
"function": {
"name": call.name,
"arguments": json.dumps(call.arguments),
},
}
for call in message.tool_calls
{"id": call.tool_call_id, "type": "function", "function": {
"name": call.name, "arguments": json.dumps(call.arguments),
}} for call in message.tool_calls
]
result.append(item)
return result
async def _request(self, method: str, path: str, **kwargs) -> dict:
headers = self._headers()
try:
async with httpx.AsyncClient(
timeout=self.timeout_seconds, transport=self.transport
) as client:
response = await client.request(
method, f"{self.base_url}{path}", headers=headers, **kwargs
)
response.raise_for_status()
data = response.json()
except httpx.TimeoutException as exc:
raise ProviderError("PROVIDER_TIMEOUT", "Provider request timed out.") from exc
except httpx.HTTPStatusError as exc:
raise self._status_error(exc) from exc
except (httpx.HTTPError, ValueError) as exc:
raise ProviderError("PROVIDER_UNAVAILABLE", "Provider is unavailable.") from exc
if not isinstance(data, dict):
raise ProviderError("PROVIDER_INVALID_RESPONSE", "Provider returned non-object JSON.")
return data
def _headers(self) -> dict[str, str]:
headers = {"Content-Type": "application/json"}
try:
api_key = self.credentials.resolve(self.credential_id)
except CredentialStoreError as exc:
raise ProviderError(
"PROVIDER_CREDENTIAL_UNAVAILABLE",
"Credential could not be decrypted by the AI Core.",
) from exc
raise ProviderError("PROVIDER_CREDENTIAL_UNAVAILABLE",
"Credential could not be decrypted by the AI Core.") from exc
if self.credential_id and not api_key:
raise ProviderError(
"PROVIDER_CREDENTIAL_MISSING",
f'Credential "{self.credential_id}" is not available in the AI Core process.',
)
raise ProviderError("PROVIDER_CREDENTIAL_MISSING",
"Credential is not available in the AI Core process.")
if api_key:
headers["Authorization"] = f"Bearer {api_key}"
return headers
@staticmethod
def _status_error(exc: httpx.HTTPStatusError) -> ProviderError:
code = {
401: "PROVIDER_AUTH_FAILED",
404: "MODEL_NOT_FOUND",
429: "PROVIDER_RATE_LIMITED",
}.get(exc.response.status_code, "PROVIDER_UNAVAILABLE")
return ProviderError(code, f"Provider returned HTTP {exc.response.status_code}.")