feat: add aiprovider service foundation
This commit is contained in:
linkong
240
aiprovider/provider_service.py
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240
aiprovider/provider_service.py
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from __future__ import annotations
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import asyncio
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from typing import Any
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import httpx
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from fastapi import HTTPException, status
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from aiprovider.config import settings
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from aiprovider.schemas import (
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AIProviderStatusResponse,
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SituationalAnalysisRequest,
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SituationalAnalysisResponse,
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)
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def _normalize_provider(value: str) -> str:
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return (value or "disabled").strip().lower()
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class ProviderService:
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def __init__(self) -> None:
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self.provider = _normalize_provider(settings.AI_PROVIDER)
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self.base_url = settings.AI_BASE_URL.rstrip("/")
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self.api_key = settings.AI_API_KEY
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self.default_model = settings.AI_MODEL
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self.timeout = settings.AI_TIMEOUT_SECONDS
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self.http_retry_attempts = max(settings.AI_HTTP_RETRY_ATTEMPTS, 1)
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self.max_tokens = settings.AI_MAX_TOKENS
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self.anthropic_version = settings.AI_ANTHROPIC_VERSION
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self.system_prompt = settings.AI_ANALYSIS_SYSTEM_PROMPT
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def get_status(self) -> AIProviderStatusResponse:
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enabled = self.provider != "disabled"
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configured = enabled and bool(self.base_url and self.api_key and self.default_model)
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return AIProviderStatusResponse(
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provider=self.provider,
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enabled=enabled,
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configured=configured,
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model=self.default_model or None,
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base_url=self.base_url if enabled else None,
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)
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async def analyze(self, payload: SituationalAnalysisRequest) -> SituationalAnalysisResponse:
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if self.provider == "disabled":
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raise HTTPException(
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status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
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detail="AI provider is disabled. Configure AI_PROVIDER in .env to enable analysis.",
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)
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model = payload.preferred_model or self.default_model
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if not self.base_url or not self.api_key or not model:
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raise HTTPException(
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status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
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detail="AI provider is not fully configured. Check AI_BASE_URL, AI_API_KEY, and AI_MODEL.",
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)
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prompt = self._build_prompt(payload)
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if self.provider in {"openai", "openai_compatible"}:
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data = await self._request_openai_compatible(model, prompt)
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content = self._extract_openai_content(data)
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elif self.provider in {"anthropic", "anthropic_compatible", "claude_compatible"}:
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data = await self._request_anthropic_compatible(model, prompt)
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content = self._extract_anthropic_content(data)
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elif self.provider == "ollama":
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data = await self._request_ollama(model, prompt)
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content = self._extract_ollama_content(data)
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else:
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raise HTTPException(
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status_code=status.HTTP_400_BAD_REQUEST,
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detail=f"Unsupported AI provider: {self.provider}",
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)
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return SituationalAnalysisResponse(
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provider=self.provider,
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model=model,
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content=content,
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raw_response=data,
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)
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def _build_prompt(self, payload: SituationalAnalysisRequest) -> str:
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sections = [
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f"任务标题:\n{payload.title}",
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f"分析目标:\n{payload.objective}",
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]
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if payload.observations:
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sections.append("观测事实:\n" + "\n".join(f"- {item}" for item in payload.observations))
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if payload.constraints:
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sections.append("约束条件:\n" + "\n".join(f"- {item}" for item in payload.constraints))
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if payload.context:
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sections.append(f"附加上下文:\n{payload.context}")
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sections.append(
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"请输出: 1) 态势摘要 2) 关键风险 3) 研判依据 4) 建议动作 5) 还缺少的数据。"
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)
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return "\n\n".join(sections)
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async def _request_openai_compatible(self, model: str, prompt: str) -> dict[str, Any]:
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request_body = {
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"model": model,
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"messages": [
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{"role": "system", "content": self.system_prompt},
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{"role": "user", "content": prompt},
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],
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"temperature": 0.2,
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}
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return await self._post(
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path="/chat/completions",
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headers={
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json",
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},
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request_body=request_body,
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)
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async def _request_anthropic_compatible(self, model: str, prompt: str) -> dict[str, Any]:
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request_body = {
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"model": model,
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"system": self.system_prompt,
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"messages": [
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": prompt,
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}
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],
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}
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],
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"max_tokens": self.max_tokens,
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"temperature": 0.2,
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}
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return await self._post(
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path="/messages",
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headers={
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"x-api-key": self.api_key,
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"anthropic-version": self.anthropic_version,
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"Content-Type": "application/json",
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},
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request_body=request_body,
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)
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async def _request_ollama(self, model: str, prompt: str) -> dict[str, Any]:
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request_body = {
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"model": model,
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"stream": False,
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"system": self.system_prompt,
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"prompt": prompt,
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"options": {
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"temperature": 0.2,
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},
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}
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return await self._post(
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path="/api/generate",
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headers={
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"Content-Type": "application/json",
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},
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request_body=request_body,
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)
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async def _post(
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self,
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path: str,
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headers: dict[str, str],
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request_body: dict[str, Any],
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) -> dict[str, Any]:
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last_error: Exception | None = None
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for attempt in range(1, self.http_retry_attempts + 1):
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try:
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async with httpx.AsyncClient(timeout=self.timeout) as client:
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response = await client.post(
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f"{self.base_url}{path}",
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headers=headers,
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json=request_body,
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)
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response.raise_for_status()
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return response.json()
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except httpx.HTTPStatusError as exc:
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last_error = exc
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if attempt < self.http_retry_attempts and exc.response.status_code >= 500:
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await asyncio.sleep(0.3 * attempt)
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continue
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detail = exc.response.text or "AI provider returned an error"
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raise HTTPException(
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status_code=status.HTTP_502_BAD_GATEWAY,
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detail=f"AI provider request failed: {detail}",
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) from exc
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except httpx.HTTPError as exc:
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last_error = exc
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if attempt < self.http_retry_attempts:
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await asyncio.sleep(0.3 * attempt)
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continue
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raise HTTPException(
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status_code=status.HTTP_502_BAD_GATEWAY,
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detail=f"Failed to reach AI provider: {exc}",
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) from exc
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raise HTTPException(
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status_code=status.HTTP_502_BAD_GATEWAY,
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detail=f"AI provider request failed: {last_error}",
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)
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def _extract_openai_content(self, payload: dict[str, Any]) -> str:
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choices = payload.get("choices") or []
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if not choices:
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return ""
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message = choices[0].get("message") or {}
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content = message.get("content")
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if isinstance(content, str):
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return content
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if isinstance(content, list):
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return "".join(
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item.get("text", "")
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for item in content
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if isinstance(item, dict)
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)
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return ""
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def _extract_anthropic_content(self, payload: dict[str, Any]) -> str:
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content = payload.get("content")
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if isinstance(content, str):
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return content
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if not isinstance(content, list):
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return ""
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fragments: list[str] = []
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for item in content:
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if not isinstance(item, dict):
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continue
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if item.get("type") == "text" and isinstance(item.get("text"), str):
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fragments.append(item["text"])
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return "".join(fragments)
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def _extract_ollama_content(self, payload: dict[str, Any]) -> str:
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response = payload.get("response")
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if isinstance(response, str):
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return response
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return ""
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