manual_slop/src/openai_compatible.py

"""OpenAI-compatible API client for the Manual Slop ai_client layer.

Provides `send_openai_compatible(client, request, *, capabilities)` which
calls any OpenAI-compatible chat completion endpoint and returns a
`NormalizedResponse` (re-exported from src.openai_schemas).

CONVENTION: 1-space indentation. NO COMMENTS.
"""
from __future__ import annotations

from typing import Any, Callable, Optional

from openai import (
 APIConnectionError,
 APIStatusError,
 AuthenticationError,
 BadRequestError,
 OpenAIError,
 PermissionDeniedError,
 RateLimitError,
)

from src.openai_schemas import (
 ChatMessage,
 NormalizedResponse,
 OpenAICompatibleRequest,
 ToolCall,
 ToolCallFunction,
 UsageStats,
)
from src.result_types import ErrorInfo, ErrorKind, Result

__all__ = [
 "ChatMessage",
 "NormalizedResponse",
 "OpenAICompatibleRequest",
 "ToolCall",
 "ToolCallFunction",
 "UsageStats",
]


def _to_typed_tool_call(tc: Any) -> ToolCall:
 return ToolCall(
  id=getattr(tc, "id", "") or "",
  type=getattr(tc, "type", "function"),
  function=ToolCallFunction(
   name=getattr(tc.function, "name", "") or "",
   arguments=getattr(tc.function, "arguments", "{}") or "{}",
  ),
 )


def _to_dict_tool_call(tc: ToolCall) -> dict[str, Any]:
 return tc.to_dict()


def _classify_openai_compatible_error(exc: Exception, source: str = "openai_compatible") -> ErrorInfo:
 if isinstance(exc, RateLimitError):
  return ErrorInfo(kind=ErrorKind.RATE_LIMIT, message=str(exc), source=source, original=exc)
 if isinstance(exc, AuthenticationError) or isinstance(exc, PermissionDeniedError):
  return ErrorInfo(kind=ErrorKind.AUTH, message=str(exc), source=source, original=exc)
 if isinstance(exc, APIConnectionError):
  return ErrorInfo(kind=ErrorKind.NETWORK, message=str(exc), source=source, original=exc)
 if isinstance(exc, APIStatusError):
  code = getattr(exc, "status_code", 0)
  if code == 402:
   return ErrorInfo(kind=ErrorKind.BALANCE, message=str(exc), source=source, original=exc)
  if code == 429:
   return ErrorInfo(kind=ErrorKind.RATE_LIMIT, message=str(exc), source=source, original=exc)
  if code in (401, 403):
   return ErrorInfo(kind=ErrorKind.AUTH, message=str(exc), source=source, original=exc)
  if code in (500, 502, 503, 504):
   return ErrorInfo(kind=ErrorKind.NETWORK, message=str(exc), source=source, original=exc)
 if isinstance(exc, BadRequestError):
  return ErrorInfo(kind=ErrorKind.QUOTA, message=str(exc), source=source, original=exc)
 return ErrorInfo(kind=ErrorKind.UNKNOWN, message=str(exc), source=source, original=exc)


def send_openai_compatible(
 client: Any,
 request: OpenAICompatibleRequest,
 *,
 capabilities: Any,
) -> Result[NormalizedResponse]:
 messages_dicts = [m.to_dict() if hasattr(m, "to_dict") else m for m in request.messages]
 kwargs: dict[str, Any] = {
  "model": request.model,
  "messages": messages_dicts,
  "temperature": request.temperature,
  "top_p": request.top_p,
  "max_tokens": request.max_tokens,
  "stream": request.stream,
 }
 if request.tools is not None:
  kwargs["tools"] = request.tools
  kwargs["tool_choice"] = request.tool_choice
 if request.extra_body:
  kwargs["extra_body"] = request.extra_body
 try:
  if request.stream:
   response = _send_streaming(client, kwargs, request.stream_callback)
  else:
   response = _send_blocking(client, kwargs)
  return Result(data=response)
 except OpenAIError as exc:
  empty_resp = NormalizedResponse(
   text="",
   tool_calls=(),
   usage=UsageStats(input_tokens=0, output_tokens=0),
   raw_response=None,
  )
  return Result(data=empty_resp, errors=[_classify_openai_compatible_error(exc, source="openai_compatible")])


def _send_blocking(client: Any, kwargs: dict[str, Any]) -> NormalizedResponse:
 resp = client.chat.completions.create(**kwargs)
 msg = resp.choices[0].message
 tool_calls_raw = msg.tool_calls or []
 tool_calls: tuple[ToolCall, ...] = tuple(_to_typed_tool_call(tc) for tc in tool_calls_raw)
 usage = getattr(resp, "usage", None)
 return NormalizedResponse(
  text=msg.content or "",
  tool_calls=tool_calls,
  usage=UsageStats(
   input_tokens=int(getattr(usage, "prompt_tokens", 0) or 0),
   output_tokens=int(getattr(usage, "completion_tokens", 0) or 0),
  ),
  raw_response=resp,
 )


def _send_streaming(client: Any, kwargs: dict[str, Any], callback: Optional[Callable[[str], None]]) -> NormalizedResponse:
 kwargs_stream = dict(kwargs)
 kwargs_stream["stream"] = True
 kwargs_stream["stream_options"] = {"include_usage": True}
 chunks_iter = client.chat.completions.create(**kwargs_stream)
 text_parts: list[str] = []
 tool_calls_acc: dict[int, dict[str, Any]] = {}
 usage_input = 0
 usage_output = 0
 for chunk in chunks_iter:
  for choice in getattr(chunk, "choices", []) or []:
   delta = getattr(choice, "delta", None)
   if delta is None:
    continue
   if delta.content:
    text_parts.append(delta.content)
    if callback:
     callback(delta.content)
   for tc in getattr(delta, "tool_calls", None) or []:
    idx = getattr(tc, "index", 0)
    if idx not in tool_calls_acc:
     tool_calls_acc[idx] = {"id": None, "type": "function", "function": {"name": None, "arguments": ""}}
    if getattr(tc, "id", None):
     tool_calls_acc[idx]["id"] = tc.id
    if getattr(tc, "function", None):
     if tc.function.name:
      tool_calls_acc[idx]["function"]["name"] = tc.function.name
     if tc.function.arguments:
      tool_calls_acc[idx]["function"]["arguments"] += tc.function.arguments
  chunk_usage = getattr(chunk, "usage", None)
  if chunk_usage is not None:
   usage_input = int(getattr(chunk_usage, "prompt_tokens", 0) or 0)
   usage_output = int(getattr(chunk_usage, "completion_tokens", 0) or 0)
 tool_calls_typed: tuple[ToolCall, ...] = tuple(
  ToolCall(
   id=acc["id"] or "",
   type=acc["type"],
   function=ToolCallFunction(
    name=acc["function"]["name"] or "",
    arguments=acc["function"]["arguments"] or "{}",
   ),
  )
  for acc in (tool_calls_acc[k] for k in sorted(tool_calls_acc.keys()))
 )
 return NormalizedResponse(
  text="".join(text_parts),
  tool_calls=tool_calls_typed,
  usage=UsageStats(input_tokens=usage_input, output_tokens=usage_output),
  raw_response=None,
 )