mirror of
https://github.com/DrizzleTime/Foxel.git
synced 2026-05-11 18:10:10 +08:00
87770176b6
- Updated AIProviderBase and AIProviderUpdate to support new API formats: 'anthropic' and 'ollama'. - Added SVG icons for Anthropic, Azure, Ollama, and Z.ai providers. - Updated AI provider payload interface to include new formats. - Enhanced English and Chinese localization for new providers and updated descriptions for OpenAI and Anthropic. - Added new provider templates for Azure OpenAI, Anthropic, Z.ai, and Ollama in the settings tab. - Updated the API format selection in the settings tab to include new options.
1164 lines
42 KiB
Python
1164 lines
42 KiB
Python
import json
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import httpx
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from typing import Any, AsyncIterator, Dict, List, Sequence, Tuple
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from urllib.parse import parse_qsl, urlencode, urlsplit, urlunsplit
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from models.database import AIModel, AIProvider
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from .service import AIProviderService
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provider_service = AIProviderService
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class MissingModelError(RuntimeError):
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pass
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async def describe_image_base64(base64_image: str, detail: str = "high") -> str:
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"""
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传入 base64 图片并返回描述文本。缺省时返回错误提示。
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"""
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try:
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model, provider = await _require_model("vision")
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fmt = str(provider.api_format or "").lower()
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if fmt == "openai":
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return await _describe_with_openai(provider, model, base64_image, detail)
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if fmt == "gemini":
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return await _describe_with_gemini(provider, model, base64_image, detail)
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if fmt == "anthropic":
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return await _describe_with_anthropic(provider, model, base64_image, detail)
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if fmt == "ollama":
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return await _describe_with_ollama(provider, model, base64_image)
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raise MissingModelError(f"不支持的视觉模型接口类型: {provider.api_format}")
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except MissingModelError as exc:
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return str(exc)
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except httpx.ReadTimeout:
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return "请求超时，请稍后重试。"
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except Exception as exc: # noqa: BLE001
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return f"请求失败: {exc}"
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async def get_text_embedding(text: str) -> List[float]:
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"""
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传入文本，返回嵌入向量。若未配置模型则抛出异常。
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"""
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model, provider = await _require_model("embedding")
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fmt = str(provider.api_format or "").lower()
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if fmt == "openai":
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return await _embedding_with_openai(provider, model, text)
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if fmt == "gemini":
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return await _embedding_with_gemini(provider, model, text)
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if fmt == "ollama":
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return await _embedding_with_ollama(provider, model, text)
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raise MissingModelError(f"不支持的嵌入模型接口类型: {provider.api_format}")
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async def rerank_texts(query: str, documents: Sequence[str]) -> List[float]:
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"""调用重排序模型，为一组文档返回得分。未配置时返回空列表。"""
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if not documents:
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return []
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try:
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model, provider = await _require_model("rerank")
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except MissingModelError:
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return []
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try:
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fmt = str(provider.api_format or "").lower()
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if fmt == "openai":
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return await _rerank_with_openai(provider, model, query, documents)
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if fmt == "gemini":
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return await _rerank_with_gemini(provider, model, query, documents)
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return []
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except Exception: # noqa: BLE001
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return []
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async def _require_model(ability: str) -> Tuple[AIModel, AIProvider]:
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model = await provider_service.get_default_model(ability)
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if not model:
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raise MissingModelError(f"未配置默认 {ability} 模型，请前往系统设置完成配置。")
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provider = getattr(model, "provider", None)
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if provider is None:
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await model.fetch_related("provider")
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provider = model.provider
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if provider is None:
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raise MissingModelError("模型缺少关联的提供商配置。")
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if not provider.base_url:
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raise MissingModelError("该提供商未设置 API 地址。")
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return model, provider
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def _openai_endpoint(provider: AIProvider, path: str) -> str:
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raw_base = str(provider.base_url or "").strip()
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if not raw_base:
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raise MissingModelError("提供商 API 地址未配置。")
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base = urlsplit(raw_base)
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extra_path, _, extra_query = str(path or "").partition("?")
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base_path = base.path.rstrip("/")
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extra_path = "/" + extra_path.lstrip("/")
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merged_path = (base_path + extra_path) if base_path else extra_path
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query_pairs = list(parse_qsl(base.query, keep_blank_values=True))
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if extra_query:
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query_pairs.extend(parse_qsl(extra_query, keep_blank_values=True))
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query_map = {k: v for k, v in query_pairs if k}
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if _is_azure_openai(provider) and "api-version" not in query_map:
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query_map["api-version"] = "2024-02-15-preview"
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merged_query = urlencode(query_map, doseq=True)
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return urlunsplit((base.scheme, base.netloc, merged_path, merged_query, base.fragment))
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def _openai_headers(provider: AIProvider) -> dict:
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headers = {"Content-Type": "application/json"}
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if provider.api_key:
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if _is_azure_openai(provider):
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headers["api-key"] = provider.api_key
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else:
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headers["Authorization"] = f"Bearer {provider.api_key}"
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return headers
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def _is_azure_openai(provider: AIProvider) -> bool:
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identifier = str(provider.identifier or "").lower()
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if identifier == "azure-openai":
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return True
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base_url = str(provider.base_url or "").lower()
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return ".openai.azure.com" in base_url
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def _gemini_endpoint(provider: AIProvider, path: str) -> str:
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base = (provider.base_url or "").rstrip("/")
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if not base:
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raise MissingModelError("提供商 API 地址未配置。")
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url = f"{base}/{path.lstrip('/')}"
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if provider.api_key:
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connector = "&" if "?" in url else "?"
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url = f"{url}{connector}key={provider.api_key}"
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return url
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ANTHROPIC_VERSION = "2023-06-01"
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ANTHROPIC_DEFAULT_MAX_TOKENS = 1024
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def _anthropic_endpoint(provider: AIProvider, path: str) -> str:
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raw_base = str(provider.base_url or "").strip()
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if not raw_base:
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raise MissingModelError("提供商 API 地址未配置。")
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base = urlsplit(raw_base)
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extra_path, _, extra_query = str(path or "").partition("?")
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base_path = base.path.rstrip("/")
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extra_path = "/" + extra_path.lstrip("/")
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merged_path = (base_path + extra_path) if base_path else extra_path
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query_pairs = list(parse_qsl(base.query, keep_blank_values=True))
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if extra_query:
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query_pairs.extend(parse_qsl(extra_query, keep_blank_values=True))
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merged_query = urlencode(query_pairs, doseq=True)
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return urlunsplit((base.scheme, base.netloc, merged_path, merged_query, base.fragment))
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def _anthropic_headers(provider: AIProvider) -> dict:
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headers = {
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"Content-Type": "application/json",
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"anthropic-version": ANTHROPIC_VERSION,
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}
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if provider.api_key:
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headers["x-api-key"] = provider.api_key
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extra = provider.extra_config if isinstance(provider.extra_config, dict) else {}
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version = extra.get("anthropic_version") or extra.get("anthropic-version")
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if isinstance(version, str) and version.strip():
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headers["anthropic-version"] = version.strip()
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beta = extra.get("anthropic_beta") or extra.get("anthropic-beta")
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if isinstance(beta, str) and beta.strip():
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headers["anthropic-beta"] = beta.strip()
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return headers
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def _openai_content_to_anthropic_blocks(content: Any) -> List[Dict[str, Any]]:
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if content is None:
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return []
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if isinstance(content, str):
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text = content.strip("\n")
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return [{"type": "text", "text": text}] if text else []
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if isinstance(content, list):
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blocks: List[Dict[str, Any]] = []
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for part in content:
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if not isinstance(part, dict):
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continue
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part_type = part.get("type")
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if part_type == "text" and isinstance(part.get("text"), str):
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text = part["text"].strip("\n")
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if text:
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blocks.append({"type": "text", "text": text})
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continue
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if part_type == "image_url" and isinstance(part.get("image_url"), dict):
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url = part["image_url"].get("url")
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if not isinstance(url, str) or not url.startswith("data:") or ";base64," not in url:
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continue
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header, data = url.split(",", 1)
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media_type = header[5:].split(";", 1)[0] or "image/jpeg"
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data = data.strip()
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if not data:
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continue
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blocks.append({
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"type": "image",
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"source": {"type": "base64", "media_type": media_type, "data": data},
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})
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return blocks
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return [{"type": "text", "text": str(content)}]
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def _openai_tools_to_anthropic(tools: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
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out: List[Dict[str, Any]] = []
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for tool in tools:
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if not isinstance(tool, dict):
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continue
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if tool.get("type") != "function":
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continue
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fn = tool.get("function")
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if not isinstance(fn, dict):
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continue
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name = fn.get("name")
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if not isinstance(name, str) or not name.strip():
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continue
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description = fn.get("description")
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input_schema = fn.get("parameters")
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input_schema = input_schema if isinstance(input_schema, dict) else {}
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out.append({
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"name": name,
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"description": description if isinstance(description, str) else "",
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"input_schema": input_schema,
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})
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return out
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def _openai_messages_to_anthropic(messages: List[Dict[str, Any]]) -> Tuple[str, List[Dict[str, Any]]]:
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system_parts: List[str] = []
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prepared: List[Dict[str, Any]] = []
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for msg in messages:
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if not isinstance(msg, dict):
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continue
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role = msg.get("role")
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if role == "system":
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content = msg.get("content")
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if isinstance(content, str) and content.strip():
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system_parts.append(content.strip())
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continue
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if role == "tool":
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tool_use_id = msg.get("tool_call_id")
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content = msg.get("content")
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tool_use_id = str(tool_use_id or "").strip()
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content_str = content if isinstance(content, str) else json.dumps(content, ensure_ascii=False)
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if not tool_use_id:
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prepared.append({"role": "user", "content": [{"type": "text", "text": content_str}]})
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else:
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prepared.append({
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"role": "user",
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"content": [{"type": "tool_result", "tool_use_id": tool_use_id, "content": content_str}],
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})
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continue
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if role not in {"user", "assistant"}:
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continue
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blocks = _openai_content_to_anthropic_blocks(msg.get("content"))
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tool_calls = msg.get("tool_calls")
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if isinstance(tool_calls, list):
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for call in tool_calls:
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if not isinstance(call, dict):
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continue
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call_id = call.get("id")
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fn = call.get("function")
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fn = fn if isinstance(fn, dict) else {}
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name = fn.get("name")
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args = fn.get("arguments")
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if not isinstance(name, str) or not name.strip():
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continue
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args_obj: Dict[str, Any] = {}
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if isinstance(args, str) and args.strip():
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try:
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parsed = json.loads(args)
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if isinstance(parsed, dict):
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args_obj = parsed
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except json.JSONDecodeError:
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args_obj = {}
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blocks.append({
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"type": "tool_use",
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"id": str(call_id or ""),
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"name": name,
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"input": args_obj,
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})
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if not blocks:
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blocks = [{"type": "text", "text": ""}]
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prepared.append({"role": role, "content": blocks})
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merged: List[Dict[str, Any]] = []
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for item in prepared:
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if merged and merged[-1].get("role") == item.get("role"):
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merged[-1]["content"].extend(item.get("content") or [])
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continue
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merged.append(item)
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if merged and merged[0].get("role") != "user":
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merged.insert(0, {"role": "user", "content": [{"type": "text", "text": ""}]})
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system = "\n\n".join(system_parts).strip()
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return system, merged
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def _anthropic_body_to_openai_message(body: Dict[str, Any]) -> Dict[str, Any]:
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content_blocks = body.get("content") or []
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text_parts: List[str] = []
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tool_calls: List[Dict[str, Any]] = []
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if isinstance(content_blocks, list):
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for block in content_blocks:
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if not isinstance(block, dict):
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continue
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block_type = block.get("type")
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if block_type == "text" and isinstance(block.get("text"), str):
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text_parts.append(block["text"])
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continue
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if block_type == "tool_use":
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call_id = block.get("id")
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name = block.get("name")
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tool_input = block.get("input")
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tool_input = tool_input if isinstance(tool_input, dict) else {}
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if not isinstance(name, str) or not name.strip():
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continue
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tool_calls.append({
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"id": str(call_id or ""),
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"type": "function",
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"function": {
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"name": name,
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"arguments": json.dumps(tool_input, ensure_ascii=False),
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},
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})
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message: Dict[str, Any] = {"role": "assistant", "content": "".join(text_parts)}
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if tool_calls:
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message["tool_calls"] = tool_calls
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return message
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async def _chat_with_anthropic(
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provider: AIProvider,
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model: AIModel,
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messages: List[Dict[str, Any]],
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*,
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tools: List[Dict[str, Any]] | None,
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temperature: float | None,
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timeout: float,
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) -> Dict[str, Any]:
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url = _anthropic_endpoint(provider, "/messages")
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system, anthropic_messages = _openai_messages_to_anthropic(messages)
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payload: Dict[str, Any] = {
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"model": model.name,
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"max_tokens": ANTHROPIC_DEFAULT_MAX_TOKENS,
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"messages": anthropic_messages,
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}
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if system:
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payload["system"] = system
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if tools:
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payload["tools"] = _openai_tools_to_anthropic(tools)
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if temperature is not None:
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payload["temperature"] = float(temperature)
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async with httpx.AsyncClient(timeout=timeout) as client:
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response = await client.post(url, headers=_anthropic_headers(provider), json=payload)
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response.raise_for_status()
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body = response.json()
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return _anthropic_body_to_openai_message(body if isinstance(body, dict) else {})
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async def _chat_stream_with_anthropic(
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provider: AIProvider,
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model: AIModel,
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messages: List[Dict[str, Any]],
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*,
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tools: List[Dict[str, Any]] | None,
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temperature: float | None,
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timeout: float,
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) -> AsyncIterator[Dict[str, Any]]:
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url = _anthropic_endpoint(provider, "/messages")
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system, anthropic_messages = _openai_messages_to_anthropic(messages)
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payload: Dict[str, Any] = {
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"model": model.name,
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"max_tokens": ANTHROPIC_DEFAULT_MAX_TOKENS,
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"messages": anthropic_messages,
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"stream": True,
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}
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if system:
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payload["system"] = system
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if tools:
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payload["tools"] = _openai_tools_to_anthropic(tools)
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if temperature is not None:
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payload["temperature"] = float(temperature)
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|
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content_parts: List[str] = []
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tool_call_map: Dict[int, Dict[str, Any]] = {}
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finish_reason: str | None = None
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current_event: str | None = None
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async with httpx.AsyncClient(timeout=timeout) as client:
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async with client.stream("POST", url, headers=_anthropic_headers(provider), json=payload) as response:
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response.raise_for_status()
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async for line in response.aiter_lines():
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if not line:
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continue
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if line.startswith("event:"):
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current_event = line[6:].strip()
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continue
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if not line.startswith("data:"):
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continue
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data = line[5:].strip()
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if not data:
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continue
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try:
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chunk = json.loads(data)
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except json.JSONDecodeError:
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continue
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if not isinstance(chunk, dict):
|
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continue
|
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|
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if current_event == "content_block_start":
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idx = chunk.get("index")
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block = chunk.get("content_block")
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if not isinstance(idx, int) or not isinstance(block, dict):
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continue
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if block.get("type") != "tool_use":
|
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continue
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tool_call_map[idx] = {
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"id": str(block.get("id") or ""),
|
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"name": str(block.get("name") or ""),
|
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"input": block.get("input") if isinstance(block.get("input"), dict) else None,
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"arguments": "",
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}
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continue
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|
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if current_event == "content_block_delta":
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idx = chunk.get("index")
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delta = chunk.get("delta")
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if not isinstance(idx, int) or not isinstance(delta, dict):
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continue
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delta_type = delta.get("type")
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if delta_type == "text_delta":
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text = delta.get("text")
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if isinstance(text, str) and text:
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content_parts.append(text)
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yield {"type": "delta", "delta": text}
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continue
|
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if delta_type == "input_json_delta":
|
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partial = delta.get("partial_json")
|
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if not isinstance(partial, str) or not partial:
|
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continue
|
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entry = tool_call_map.setdefault(
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idx,
|
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{"id": "", "name": "", "input": None, "arguments": ""},
|
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)
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entry["arguments"] += partial
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continue
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|
|
if current_event == "message_delta":
|
|
delta = chunk.get("delta")
|
|
if isinstance(delta, dict) and isinstance(delta.get("stop_reason"), str):
|
|
finish_reason = delta["stop_reason"]
|
|
continue
|
|
|
|
if current_event == "message_stop":
|
|
break
|
|
|
|
content = "".join(content_parts)
|
|
message: Dict[str, Any] = {"role": "assistant", "content": content}
|
|
|
|
tool_calls: List[Dict[str, Any]] = []
|
|
for idx in sorted(tool_call_map.keys()):
|
|
item = tool_call_map[idx]
|
|
name = str(item.get("name") or "").strip()
|
|
if not name:
|
|
continue
|
|
args_text = str(item.get("arguments") or "")
|
|
if not args_text:
|
|
args_obj = item.get("input")
|
|
args_obj = args_obj if isinstance(args_obj, dict) else {}
|
|
args_text = json.dumps(args_obj, ensure_ascii=False)
|
|
tool_calls.append({
|
|
"id": str(item.get("id") or f"call_{idx}"),
|
|
"type": "function",
|
|
"function": {"name": name, "arguments": args_text},
|
|
})
|
|
|
|
if tool_calls:
|
|
message["tool_calls"] = tool_calls
|
|
|
|
yield {"type": "message", "message": message, "finish_reason": finish_reason}
|
|
|
|
|
|
def _ollama_endpoint(provider: AIProvider, path: str) -> str:
|
|
raw_base = str(provider.base_url or "").strip()
|
|
if not raw_base:
|
|
raise MissingModelError("提供商 API 地址未配置。")
|
|
|
|
base = urlsplit(raw_base)
|
|
extra_path, _, extra_query = str(path or "").partition("?")
|
|
|
|
base_path = base.path.rstrip("/")
|
|
extra_path = "/" + extra_path.lstrip("/")
|
|
merged_path = (base_path + extra_path) if base_path else extra_path
|
|
|
|
query_pairs = list(parse_qsl(base.query, keep_blank_values=True))
|
|
if extra_query:
|
|
query_pairs.extend(parse_qsl(extra_query, keep_blank_values=True))
|
|
merged_query = urlencode(query_pairs, doseq=True)
|
|
return urlunsplit((base.scheme, base.netloc, merged_path, merged_query, base.fragment))
|
|
|
|
|
|
def _openai_content_to_ollama_message(content: Any) -> Tuple[str, List[str]]:
|
|
if content is None:
|
|
return "", []
|
|
if isinstance(content, str):
|
|
return content, []
|
|
|
|
if isinstance(content, list):
|
|
text_parts: List[str] = []
|
|
images: List[str] = []
|
|
for part in content:
|
|
if not isinstance(part, dict):
|
|
continue
|
|
if part.get("type") == "text" and isinstance(part.get("text"), str):
|
|
text_parts.append(part["text"])
|
|
continue
|
|
if part.get("type") == "image_url" and isinstance(part.get("image_url"), dict):
|
|
url = part["image_url"].get("url")
|
|
if not isinstance(url, str) or not url.startswith("data:") or ";base64," not in url:
|
|
continue
|
|
_, data = url.split(",", 1)
|
|
data = data.strip()
|
|
if data:
|
|
images.append(data)
|
|
return "\n".join([t for t in text_parts if t]), images
|
|
|
|
return str(content), []
|
|
|
|
|
|
def _openai_messages_to_ollama(messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
|
|
out: List[Dict[str, Any]] = []
|
|
for msg in messages:
|
|
if not isinstance(msg, dict):
|
|
continue
|
|
role = msg.get("role")
|
|
if role == "tool":
|
|
content = msg.get("content")
|
|
call_id = str(msg.get("tool_call_id") or "").strip()
|
|
content_str = content if isinstance(content, str) else json.dumps(content, ensure_ascii=False)
|
|
if call_id:
|
|
content_str = f"[tool:{call_id}] {content_str}"
|
|
out.append({"role": "user", "content": content_str})
|
|
continue
|
|
|
|
if role not in {"system", "user", "assistant"}:
|
|
continue
|
|
|
|
text, images = _openai_content_to_ollama_message(msg.get("content"))
|
|
item: Dict[str, Any] = {"role": role, "content": text}
|
|
if images:
|
|
item["images"] = images
|
|
out.append(item)
|
|
return out
|
|
|
|
|
|
def _ollama_message_to_openai(message: Dict[str, Any]) -> Dict[str, Any]:
|
|
role = message.get("role") if isinstance(message.get("role"), str) else "assistant"
|
|
content = message.get("content") if isinstance(message.get("content"), str) else ""
|
|
|
|
tool_calls: List[Dict[str, Any]] = []
|
|
raw_tool_calls = message.get("tool_calls")
|
|
if isinstance(raw_tool_calls, list):
|
|
for idx, call in enumerate(raw_tool_calls):
|
|
if not isinstance(call, dict):
|
|
continue
|
|
fn = call.get("function")
|
|
if not isinstance(fn, dict):
|
|
continue
|
|
name = fn.get("name")
|
|
if not isinstance(name, str) or not name.strip():
|
|
continue
|
|
raw_args = fn.get("arguments")
|
|
if isinstance(raw_args, dict):
|
|
args_text = json.dumps(raw_args, ensure_ascii=False)
|
|
elif isinstance(raw_args, str):
|
|
args_text = raw_args
|
|
else:
|
|
args_text = ""
|
|
tool_calls.append({
|
|
"id": str(call.get("id") or f"call_{idx}"),
|
|
"type": "function",
|
|
"function": {"name": name, "arguments": args_text},
|
|
})
|
|
|
|
out: Dict[str, Any] = {"role": role, "content": content}
|
|
if tool_calls:
|
|
out["tool_calls"] = tool_calls
|
|
return out
|
|
|
|
|
|
async def _chat_with_ollama(
|
|
provider: AIProvider,
|
|
model: AIModel,
|
|
messages: List[Dict[str, Any]],
|
|
*,
|
|
tools: List[Dict[str, Any]] | None,
|
|
temperature: float | None,
|
|
timeout: float,
|
|
) -> Dict[str, Any]:
|
|
url = _ollama_endpoint(provider, "/api/chat")
|
|
ollama_messages = _openai_messages_to_ollama(messages)
|
|
|
|
payload: Dict[str, Any] = {
|
|
"model": model.name,
|
|
"messages": ollama_messages,
|
|
"stream": False,
|
|
}
|
|
if temperature is not None:
|
|
payload["options"] = {"temperature": float(temperature)}
|
|
|
|
async with httpx.AsyncClient(timeout=timeout) as client:
|
|
response = await client.post(url, json=payload)
|
|
response.raise_for_status()
|
|
body = response.json()
|
|
|
|
message = body.get("message") if isinstance(body, dict) else None
|
|
if not isinstance(message, dict):
|
|
raise RuntimeError("对话接口返回格式异常")
|
|
return _ollama_message_to_openai(message)
|
|
|
|
|
|
async def _chat_stream_with_ollama(
|
|
provider: AIProvider,
|
|
model: AIModel,
|
|
messages: List[Dict[str, Any]],
|
|
*,
|
|
tools: List[Dict[str, Any]] | None,
|
|
temperature: float | None,
|
|
timeout: float,
|
|
) -> AsyncIterator[Dict[str, Any]]:
|
|
url = _ollama_endpoint(provider, "/api/chat")
|
|
ollama_messages = _openai_messages_to_ollama(messages)
|
|
|
|
payload: Dict[str, Any] = {
|
|
"model": model.name,
|
|
"messages": ollama_messages,
|
|
"stream": True,
|
|
}
|
|
if temperature is not None:
|
|
payload["options"] = {"temperature": float(temperature)}
|
|
|
|
content_parts: List[str] = []
|
|
last_message: Dict[str, Any] | None = None
|
|
finish_reason: str | None = None
|
|
|
|
async with httpx.AsyncClient(timeout=timeout) as client:
|
|
async with client.stream("POST", url, json=payload) as response:
|
|
response.raise_for_status()
|
|
async for line in response.aiter_lines():
|
|
if not line:
|
|
continue
|
|
try:
|
|
chunk = json.loads(line)
|
|
except json.JSONDecodeError:
|
|
continue
|
|
if not isinstance(chunk, dict):
|
|
continue
|
|
|
|
msg = chunk.get("message")
|
|
if isinstance(msg, dict):
|
|
last_message = msg
|
|
delta = msg.get("content")
|
|
if isinstance(delta, str) and delta:
|
|
content_parts.append(delta)
|
|
yield {"type": "delta", "delta": delta}
|
|
|
|
done = chunk.get("done")
|
|
if done is True:
|
|
finish_reason = str(chunk.get("done_reason") or "stop")
|
|
break
|
|
|
|
if not isinstance(last_message, dict):
|
|
last_message = {"role": "assistant", "content": "".join(content_parts)}
|
|
else:
|
|
last_message = dict(last_message)
|
|
if content_parts:
|
|
last_message["content"] = "".join(content_parts)
|
|
|
|
message = _ollama_message_to_openai(last_message)
|
|
yield {"type": "message", "message": message, "finish_reason": finish_reason}
|
|
|
|
|
|
async def _describe_with_openai(provider: AIProvider, model: AIModel, base64_image: str, detail: str) -> str:
|
|
url = _openai_endpoint(provider, "/chat/completions")
|
|
payload = {
|
|
"model": model.name,
|
|
"messages": [
|
|
{
|
|
"role": "user",
|
|
"content": [
|
|
{
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": f"data:image/jpeg;base64,{base64_image}",
|
|
"detail": detail,
|
|
},
|
|
},
|
|
{"type": "text", "text": "描述这个图片"},
|
|
],
|
|
}
|
|
],
|
|
}
|
|
async with httpx.AsyncClient(timeout=60.0) as client:
|
|
response = await client.post(url, headers=_openai_headers(provider), json=payload)
|
|
response.raise_for_status()
|
|
body = response.json()
|
|
return body["choices"][0]["message"]["content"]
|
|
|
|
|
|
async def _describe_with_anthropic(provider: AIProvider, model: AIModel, base64_image: str, detail: str) -> str:
|
|
url = _anthropic_endpoint(provider, "/messages")
|
|
detail_text = f"描述这个图片，细节等级：{detail}"
|
|
payload: Dict[str, Any] = {
|
|
"model": model.name,
|
|
"max_tokens": 512,
|
|
"messages": [
|
|
{
|
|
"role": "user",
|
|
"content": [
|
|
{
|
|
"type": "image",
|
|
"source": {
|
|
"type": "base64",
|
|
"media_type": "image/jpeg",
|
|
"data": base64_image,
|
|
},
|
|
},
|
|
{"type": "text", "text": detail_text},
|
|
],
|
|
}
|
|
],
|
|
}
|
|
async with httpx.AsyncClient(timeout=60.0) as client:
|
|
response = await client.post(url, headers=_anthropic_headers(provider), json=payload)
|
|
response.raise_for_status()
|
|
body = response.json()
|
|
message = _anthropic_body_to_openai_message(body if isinstance(body, dict) else {})
|
|
return str(message.get("content") or "")
|
|
|
|
|
|
async def _describe_with_ollama(provider: AIProvider, model: AIModel, base64_image: str) -> str:
|
|
url = _ollama_endpoint(provider, "/api/chat")
|
|
payload = {
|
|
"model": model.name,
|
|
"messages": [
|
|
{"role": "user", "content": "描述这个图片", "images": [base64_image]},
|
|
],
|
|
"stream": False,
|
|
}
|
|
async with httpx.AsyncClient(timeout=60.0) as client:
|
|
response = await client.post(url, json=payload)
|
|
response.raise_for_status()
|
|
body = response.json()
|
|
|
|
message = body.get("message") if isinstance(body, dict) else None
|
|
if not isinstance(message, dict):
|
|
return ""
|
|
return str(message.get("content") or "")
|
|
|
|
|
|
async def _describe_with_gemini(provider: AIProvider, model: AIModel, base64_image: str, detail: str) -> str:
|
|
detail_text = f"描述这个图片，细节等级：{detail}"
|
|
model_name = model.name if model.name.startswith("models/") else f"models/{model.name}"
|
|
url = _gemini_endpoint(provider, f"{model_name}:generateContent")
|
|
payload = {
|
|
"contents": [
|
|
{
|
|
"role": "user",
|
|
"parts": [
|
|
{
|
|
"inline_data": {
|
|
"mime_type": "image/jpeg",
|
|
"data": base64_image,
|
|
}
|
|
},
|
|
{"text": detail_text},
|
|
],
|
|
}
|
|
]
|
|
}
|
|
async with httpx.AsyncClient(timeout=60.0) as client:
|
|
response = await client.post(url, json=payload)
|
|
response.raise_for_status()
|
|
body = response.json()
|
|
candidates = body.get("candidates") or []
|
|
if not candidates:
|
|
return ""
|
|
parts = candidates[0].get("content", {}).get("parts", [])
|
|
text_parts = [part.get("text") for part in parts if isinstance(part, dict) and part.get("text")]
|
|
return "\n".join(text_parts)
|
|
|
|
|
|
async def _embedding_with_openai(provider: AIProvider, model: AIModel, text: str) -> List[float]:
|
|
url = _openai_endpoint(provider, "/embeddings")
|
|
payload = {
|
|
"model": model.name,
|
|
"input": text,
|
|
}
|
|
async with httpx.AsyncClient(timeout=30.0) as client:
|
|
response = await client.post(url, headers=_openai_headers(provider), json=payload)
|
|
response.raise_for_status()
|
|
body = response.json()
|
|
return body["data"][0]["embedding"]
|
|
|
|
|
|
async def _embedding_with_gemini(provider: AIProvider, model: AIModel, text: str) -> List[float]:
|
|
model_name = model.name if model.name.startswith("models/") else f"models/{model.name}"
|
|
url = _gemini_endpoint(provider, f"{model_name}:embedContent")
|
|
payload = {
|
|
"model": model_name,
|
|
"content": {
|
|
"parts": [{"text": text}],
|
|
},
|
|
}
|
|
async with httpx.AsyncClient(timeout=30.0) as client:
|
|
response = await client.post(url, json=payload)
|
|
response.raise_for_status()
|
|
body = response.json()
|
|
embedding = body.get("embedding") or {}
|
|
return embedding.get("values") or []
|
|
|
|
|
|
async def _embedding_with_ollama(provider: AIProvider, model: AIModel, text: str) -> List[float]:
|
|
url = _ollama_endpoint(provider, "/api/embeddings")
|
|
payload = {
|
|
"model": model.name,
|
|
"prompt": text,
|
|
}
|
|
async with httpx.AsyncClient(timeout=30.0) as client:
|
|
response = await client.post(url, json=payload)
|
|
response.raise_for_status()
|
|
body = response.json()
|
|
|
|
embedding = body.get("embedding") if isinstance(body, dict) else None
|
|
if not isinstance(embedding, list):
|
|
return []
|
|
return [float(v) for v in embedding if isinstance(v, (int, float))]
|
|
|
|
|
|
async def _rerank_with_openai(
|
|
provider: AIProvider,
|
|
model: AIModel,
|
|
query: str,
|
|
documents: Sequence[str],
|
|
) -> List[float]:
|
|
url = _openai_endpoint(provider, "/rerank")
|
|
payload = {
|
|
"model": model.name,
|
|
"query": query,
|
|
"documents": [
|
|
{"id": str(idx), "text": content}
|
|
for idx, content in enumerate(documents)
|
|
],
|
|
}
|
|
async with httpx.AsyncClient(timeout=30.0) as client:
|
|
response = await client.post(url, headers=_openai_headers(provider), json=payload)
|
|
response.raise_for_status()
|
|
body = response.json()
|
|
results = body.get("results") or body.get("data") or []
|
|
scores: List[float] = []
|
|
for item in results:
|
|
try:
|
|
scores.append(float(item.get("score", 0.0)))
|
|
except (TypeError, ValueError):
|
|
scores.append(0.0)
|
|
return scores
|
|
|
|
|
|
async def _rerank_with_gemini(
|
|
provider: AIProvider,
|
|
model: AIModel,
|
|
query: str,
|
|
documents: Sequence[str],
|
|
) -> List[float]:
|
|
model_name = model.name if model.name.startswith("models/") else f"models/{model.name}"
|
|
url = _gemini_endpoint(provider, f"{model_name}:rankContent")
|
|
payload = {
|
|
"query": {"text": query},
|
|
"documents": [
|
|
{"id": str(idx), "content": {"parts": [{"text": content}]}}
|
|
for idx, content in enumerate(documents)
|
|
],
|
|
}
|
|
async with httpx.AsyncClient(timeout=30.0) as client:
|
|
response = await client.post(url, json=payload)
|
|
response.raise_for_status()
|
|
body = response.json()
|
|
|
|
scores: List[float] = []
|
|
ranked = body.get("rankedDocuments") or body.get("results") or []
|
|
for item in ranked:
|
|
raw_score = item.get("relevanceScore") or item.get("score") or item.get("confidenceScore")
|
|
try:
|
|
scores.append(float(raw_score))
|
|
except (TypeError, ValueError):
|
|
scores.append(0.0)
|
|
return scores
|
|
|
|
|
|
async def chat_completion(
|
|
messages: List[Dict[str, Any]],
|
|
*,
|
|
ability: str = "chat",
|
|
tools: List[Dict[str, Any]] | None = None,
|
|
tool_choice: Any | None = None,
|
|
temperature: float | None = None,
|
|
timeout: float = 60.0,
|
|
) -> Dict[str, Any]:
|
|
model, provider = await _require_model(ability)
|
|
fmt = str(provider.api_format or "").lower()
|
|
if fmt == "openai":
|
|
return await _chat_with_openai(
|
|
provider,
|
|
model,
|
|
messages,
|
|
tools=tools,
|
|
tool_choice=tool_choice,
|
|
temperature=temperature,
|
|
timeout=timeout,
|
|
)
|
|
if fmt == "anthropic":
|
|
return await _chat_with_anthropic(
|
|
provider,
|
|
model,
|
|
messages,
|
|
tools=tools,
|
|
temperature=temperature,
|
|
timeout=timeout,
|
|
)
|
|
if fmt == "ollama":
|
|
return await _chat_with_ollama(
|
|
provider,
|
|
model,
|
|
messages,
|
|
tools=tools,
|
|
temperature=temperature,
|
|
timeout=timeout,
|
|
)
|
|
raise MissingModelError(f"当前不支持该对话模型接口类型: {provider.api_format}")
|
|
|
|
|
|
async def _chat_with_openai(
|
|
provider: AIProvider,
|
|
model: AIModel,
|
|
messages: List[Dict[str, Any]],
|
|
*,
|
|
tools: List[Dict[str, Any]] | None,
|
|
tool_choice: Any | None,
|
|
temperature: float | None,
|
|
timeout: float,
|
|
) -> Dict[str, Any]:
|
|
url = _openai_endpoint(provider, "/chat/completions")
|
|
payload: Dict[str, Any] = {
|
|
"model": model.name,
|
|
"messages": messages,
|
|
}
|
|
if tools:
|
|
payload["tools"] = tools
|
|
payload["tool_choice"] = tool_choice or "auto"
|
|
if temperature is not None:
|
|
payload["temperature"] = float(temperature)
|
|
|
|
async with httpx.AsyncClient(timeout=timeout) as client:
|
|
response = await client.post(url, headers=_openai_headers(provider), json=payload)
|
|
response.raise_for_status()
|
|
body = response.json()
|
|
|
|
choices = body.get("choices") or []
|
|
if not choices:
|
|
raise RuntimeError("对话接口返回为空")
|
|
message = choices[0].get("message")
|
|
if not isinstance(message, dict):
|
|
raise RuntimeError("对话接口返回格式异常")
|
|
return message
|
|
|
|
|
|
async def chat_completion_stream(
|
|
messages: List[Dict[str, Any]],
|
|
*,
|
|
ability: str = "chat",
|
|
tools: List[Dict[str, Any]] | None = None,
|
|
tool_choice: Any | None = None,
|
|
temperature: float | None = None,
|
|
timeout: float = 60.0,
|
|
) -> AsyncIterator[Dict[str, Any]]:
|
|
model, provider = await _require_model(ability)
|
|
fmt = str(provider.api_format or "").lower()
|
|
if fmt == "openai":
|
|
async for event in _chat_stream_with_openai(
|
|
provider,
|
|
model,
|
|
messages,
|
|
tools=tools,
|
|
tool_choice=tool_choice,
|
|
temperature=temperature,
|
|
timeout=timeout,
|
|
):
|
|
yield event
|
|
return
|
|
if fmt == "anthropic":
|
|
async for event in _chat_stream_with_anthropic(
|
|
provider,
|
|
model,
|
|
messages,
|
|
tools=tools,
|
|
temperature=temperature,
|
|
timeout=timeout,
|
|
):
|
|
yield event
|
|
return
|
|
if fmt == "ollama":
|
|
async for event in _chat_stream_with_ollama(
|
|
provider,
|
|
model,
|
|
messages,
|
|
tools=tools,
|
|
temperature=temperature,
|
|
timeout=timeout,
|
|
):
|
|
yield event
|
|
return
|
|
raise MissingModelError(f"当前不支持该对话模型接口类型: {provider.api_format}")
|
|
|
|
|
|
async def _chat_stream_with_openai(
|
|
provider: AIProvider,
|
|
model: AIModel,
|
|
messages: List[Dict[str, Any]],
|
|
*,
|
|
tools: List[Dict[str, Any]] | None,
|
|
tool_choice: Any | None,
|
|
temperature: float | None,
|
|
timeout: float,
|
|
) -> AsyncIterator[Dict[str, Any]]:
|
|
url = _openai_endpoint(provider, "/chat/completions")
|
|
payload: Dict[str, Any] = {
|
|
"model": model.name,
|
|
"messages": messages,
|
|
"stream": True,
|
|
}
|
|
if tools:
|
|
payload["tools"] = tools
|
|
payload["tool_choice"] = tool_choice or "auto"
|
|
if temperature is not None:
|
|
payload["temperature"] = float(temperature)
|
|
|
|
content_parts: List[str] = []
|
|
tool_call_map: Dict[int, Dict[str, Any]] = {}
|
|
role = "assistant"
|
|
finish_reason: str | None = None
|
|
|
|
async with httpx.AsyncClient(timeout=timeout) as client:
|
|
async with client.stream("POST", url, headers=_openai_headers(provider), json=payload) as response:
|
|
response.raise_for_status()
|
|
async for line in response.aiter_lines():
|
|
if not line:
|
|
continue
|
|
if not line.startswith("data:"):
|
|
continue
|
|
data = line[5:].strip()
|
|
if not data:
|
|
continue
|
|
if data == "[DONE]":
|
|
break
|
|
try:
|
|
chunk = json.loads(data)
|
|
except json.JSONDecodeError:
|
|
continue
|
|
|
|
choices = chunk.get("choices") or []
|
|
if not choices:
|
|
continue
|
|
choice = choices[0] if isinstance(choices[0], dict) else {}
|
|
delta = choice.get("delta") if isinstance(choice, dict) else None
|
|
delta = delta if isinstance(delta, dict) else {}
|
|
|
|
if isinstance(delta.get("role"), str):
|
|
role = delta["role"]
|
|
|
|
delta_content = delta.get("content")
|
|
if isinstance(delta_content, str) and delta_content:
|
|
content_parts.append(delta_content)
|
|
yield {"type": "delta", "delta": delta_content}
|
|
|
|
delta_tool_calls = delta.get("tool_calls")
|
|
if isinstance(delta_tool_calls, list):
|
|
for item in delta_tool_calls:
|
|
if not isinstance(item, dict):
|
|
continue
|
|
idx = item.get("index")
|
|
if not isinstance(idx, int):
|
|
continue
|
|
entry = tool_call_map.setdefault(
|
|
idx,
|
|
{"id": None, "type": None, "function": {"name": None, "arguments": ""}},
|
|
)
|
|
if isinstance(item.get("id"), str) and item["id"].strip():
|
|
entry["id"] = item["id"]
|
|
if isinstance(item.get("type"), str) and item["type"].strip():
|
|
entry["type"] = item["type"]
|
|
fn = item.get("function")
|
|
if isinstance(fn, dict):
|
|
if isinstance(fn.get("name"), str) and fn["name"].strip():
|
|
entry["function"]["name"] = fn["name"]
|
|
args_part = fn.get("arguments")
|
|
if isinstance(args_part, str) and args_part:
|
|
entry["function"]["arguments"] += args_part
|
|
|
|
fr = choice.get("finish_reason") if isinstance(choice, dict) else None
|
|
if isinstance(fr, str) and fr:
|
|
finish_reason = fr
|
|
|
|
content = "".join(content_parts)
|
|
message: Dict[str, Any] = {"role": role, "content": content}
|
|
if tool_call_map:
|
|
tool_calls: List[Dict[str, Any]] = []
|
|
for idx in sorted(tool_call_map.keys()):
|
|
item = tool_call_map[idx]
|
|
fn = item.get("function") if isinstance(item.get("function"), dict) else {}
|
|
call_id = item.get("id") if isinstance(item.get("id"), str) and item.get("id") else f"call_{idx}"
|
|
call_type = item.get("type") if isinstance(item.get("type"), str) and item.get("type") else "function"
|
|
tool_calls.append({
|
|
"id": call_id,
|
|
"type": call_type,
|
|
"function": {
|
|
"name": fn.get("name") or "",
|
|
"arguments": fn.get("arguments") or "",
|
|
},
|
|
})
|
|
message["tool_calls"] = tool_calls
|
|
|
|
yield {"type": "message", "message": message, "finish_reason": finish_reason}
|