feat(backend): add ontology and orchestrator API endpoints
New endpoints: - server/src/app/api/v1/endpoints/ontology.py: ontology API - server/src/app/api/v1/endpoints/orchestrator.py: orchestrator API New schemas: - server/src/app/schemas/ontology.py: ontology data schemas - server/src/app/schemas/orchestrator.py: orchestrator data schemas - server/src/app/schemas/user_agent.py: user agent data schemas New services: - server/src/app/services/ontology.py: ontology business logic - server/src/app/services/orchestrator.py: orchestrator business logic - server/src/app/services/runtime_chat.py: runtime chat service - server/src/app/services/user_agent.py: user agent service New tests: - server/tests/test_ontology_service.py - server/tests/test_orchestrator_service.py - server/tests/test_user_agent_service.py
This commit is contained in:
caoxiaozhu
252
server/src/app/services/runtime_chat.py
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252
server/src/app/services/runtime_chat.py
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from __future__ import annotations
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from http import HTTPStatus
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from typing import Any
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from sqlalchemy.orm import Session
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from app.core.logging import get_logger
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from app.services.model_connectivity import (
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AZURE_API_VERSION,
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ConnectivityCheckError,
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_build_azure_deployment_base,
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_build_headers,
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_ensure_path,
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_normalize_endpoint,
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_send_json_request,
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)
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from app.services.settings import SettingsService
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logger = get_logger("app.services.runtime_chat")
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class RuntimeChatService:
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def __init__(self, db: Session) -> None:
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self.db = db
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self.settings_service = SettingsService(db)
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def complete(
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self,
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messages: list[dict[str, str]],
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*,
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slot_priority: tuple[str, ...] = ("main", "backup"),
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max_tokens: int = 500,
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temperature: float = 0.2,
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) -> str | None:
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for slot in slot_priority:
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config = self._load_chat_slot(slot)
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if config is None:
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continue
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try:
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response_text = self._request_chat_completion(
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config,
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messages,
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max_tokens=max_tokens,
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temperature=temperature,
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)
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except Exception as exc:
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logger.warning(
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"Runtime chat request failed slot=%s provider=%s: %s",
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slot,
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config["provider"],
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exc,
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)
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continue
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if response_text:
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return response_text.strip()
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return None
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def _load_chat_slot(self, slot: str) -> dict[str, str] | None:
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try:
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config = self.settings_service.get_runtime_model_config(slot)
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except ValueError:
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return None
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if config["capability"] != "chat":
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return None
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provider = str(config["provider"] or "").strip()
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endpoint = str(config["endpoint"] or "").strip()
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model = str(config["model"] or "").strip()
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api_key = str(config["apiKey"] or "").strip()
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if not provider or not endpoint or not model:
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return None
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if provider != "Ollama" and not api_key:
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logger.info("Skip runtime chat slot=%s because api key is empty", slot)
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return None
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return {
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"slot": slot,
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"provider": provider,
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"endpoint": endpoint,
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"model": model,
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"apiKey": api_key,
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}
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def _request_chat_completion(
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self,
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config: dict[str, str],
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messages: list[dict[str, str]],
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*,
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max_tokens: int,
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temperature: float,
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) -> str:
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provider = config["provider"]
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endpoint = config["endpoint"]
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model = config["model"]
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api_key = config["apiKey"]
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if provider == "Azure OpenAI":
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return self._request_azure_openai(
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endpoint=endpoint,
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model=model,
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api_key=api_key,
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messages=messages,
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max_tokens=max_tokens,
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temperature=temperature,
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)
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if provider == "Ollama":
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return self._request_ollama(
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endpoint=endpoint,
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model=model,
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api_key=api_key,
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messages=messages,
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max_tokens=max_tokens,
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temperature=temperature,
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)
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return self._request_openai_compatible(
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endpoint=endpoint,
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model=model,
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api_key=api_key,
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messages=messages,
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max_tokens=max_tokens,
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temperature=temperature,
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)
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def _request_openai_compatible(
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self,
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*,
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endpoint: str,
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model: str,
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api_key: str,
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messages: list[dict[str, str]],
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max_tokens: int,
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temperature: float,
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) -> str:
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url = _ensure_path(_normalize_endpoint(endpoint), "chat/completions")
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status_code, payload = _send_json_request(
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"POST",
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url,
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headers=_build_headers(api_key=api_key, use_bearer=True),
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payload={
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"model": model,
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"messages": messages,
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"max_tokens": max_tokens,
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"temperature": temperature,
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},
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)
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if status_code >= HTTPStatus.BAD_REQUEST:
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raise ConnectivityCheckError(
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f"模型接口返回异常状态 {status_code}。",
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status_code=status_code,
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)
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return self._extract_openai_text(payload)
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def _request_ollama(
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self,
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*,
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endpoint: str,
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model: str,
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api_key: str,
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messages: list[dict[str, str]],
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max_tokens: int,
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temperature: float,
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) -> str:
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url = _ensure_path(_normalize_endpoint(endpoint), "api/chat")
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status_code, payload = _send_json_request(
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"POST",
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url,
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headers=_build_headers(api_key=api_key, use_bearer=False),
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payload={
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"model": model,
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"messages": messages,
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"stream": False,
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"options": {
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"num_predict": max_tokens,
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"temperature": temperature,
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},
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},
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)
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if status_code >= HTTPStatus.BAD_REQUEST:
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raise ConnectivityCheckError(
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f"Ollama 返回异常状态 {status_code}。",
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status_code=status_code,
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)
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return str((payload or {}).get("message", {}).get("content", "")).strip()
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def _request_azure_openai(
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self,
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*,
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endpoint: str,
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model: str,
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api_key: str,
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messages: list[dict[str, str]],
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max_tokens: int,
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temperature: float,
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) -> str:
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deployment_base = _build_azure_deployment_base(endpoint, model)
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url = f"{deployment_base}/chat/completions?api-version={AZURE_API_VERSION}"
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status_code, payload = _send_json_request(
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"POST",
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url,
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headers=_build_headers(api_key=api_key, use_bearer=False, use_api_key=True),
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payload={
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"messages": messages,
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"max_tokens": max_tokens,
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"temperature": temperature,
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},
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)
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if status_code >= HTTPStatus.BAD_REQUEST:
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raise ConnectivityCheckError(
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f"Azure OpenAI 返回异常状态 {status_code}。",
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status_code=status_code,
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)
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return self._extract_openai_text(payload)
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@staticmethod
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def _extract_openai_text(payload: Any) -> str:
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if not isinstance(payload, dict):
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return ""
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choices = payload.get("choices")
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if not isinstance(choices, list) or not choices:
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return ""
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first_choice = choices[0]
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if not isinstance(first_choice, dict):
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return ""
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message = first_choice.get("message")
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if isinstance(message, dict):
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content = message.get("content", "")
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if isinstance(content, str):
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return content.strip()
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if isinstance(content, list):
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parts: list[str] = []
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for item in content:
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if isinstance(item, dict) and item.get("type") == "text":
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parts.append(str(item.get("text", "")))
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return "\n".join(part.strip() for part in parts if part.strip()).strip()
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text = first_choice.get("text")
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if isinstance(text, str):
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return text.strip()
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return ""
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