Codex
552 lines
21 KiB
Python
552 lines
21 KiB
Python
"""
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Ollama 本地模型提供商适配器
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"""
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import logging
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from typing import AsyncGenerator, Dict, List, Any
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from .base import BaseLLMProvider, LLMConfig, LLMMessage, LLMResponse, LLMStreamChunk, ToolCall
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from .registry import ProviderRegistry
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logger = logging.getLogger(__name__)
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@ProviderRegistry.register
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class OllamaProvider(BaseLLMProvider):
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"""
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Ollama 本地模型提供商适配器
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支持本地部署的开源模型
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"""
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provider_type = 'ollama'
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provider_name = 'Ollama (本地)'
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supported_model_types = ['chat', 'embedding']
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DEFAULT_API_BASE = 'http://localhost:11434/v1'
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def __init__(self, api_key: str = '', api_base: str = '', **kwargs):
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super().__init__(api_key, api_base, **kwargs)
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# Ollama 使用 ollama_host 参数
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ollama_host = kwargs.get('ollama_host', '')
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if ollama_host:
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self.api_base = f'{ollama_host.rstrip("/")}/v1'
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else:
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self.api_base = api_base or self.DEFAULT_API_BASE
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self._client = None
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self._async_client = None
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def _get_client(self):
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"""获取同步客户端"""
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if self._client is None:
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try:
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from openai import OpenAI
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self._client = OpenAI(
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api_key='ollama', # Ollama 不需要 API Key
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base_url=self.api_base,
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)
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except ImportError:
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raise ImportError('请安装 openai 库: pip install openai')
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return self._client
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def _get_async_client(self):
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"""获取异步客户端"""
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if self._async_client is None:
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try:
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from openai import AsyncOpenAI
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self._async_client = AsyncOpenAI(
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api_key='ollama', # Ollama 不需要 API Key
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base_url=self.api_base,
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)
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except ImportError:
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raise ImportError('请安装 openai 库: pip install openai')
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return self._async_client
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def validate_config(self) -> bool:
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"""Ollama 不需要 API Key"""
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return True
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def chat(
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self,
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messages: List[LLMMessage],
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config: LLMConfig,
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) -> LLMResponse:
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"""同步对话"""
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client = self._get_client()
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# 构建请求参数
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kwargs = {
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'model': config.model,
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'messages': [m.to_dict() for m in messages],
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'temperature': config.temperature,
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'top_p': config.top_p,
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'max_tokens': config.max_tokens,
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'stop': config.stop,
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}
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# 添加 Function Calling 参数（Ollama 部分模型支持）
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if config.tools:
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kwargs['tools'] = [t.to_openai_format() for t in config.tools]
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if config.tool_choice == 'required':
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kwargs['tool_choice'] = 'required'
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elif config.tool_choice == 'none':
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kwargs['tool_choice'] = 'none'
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elif config.tool_choice != 'auto':
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kwargs['tool_choice'] = {'type': 'function', 'function': {'name': config.tool_choice}}
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else:
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kwargs['tool_choice'] = 'auto'
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response = client.chat.completions.create(**kwargs)
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choice = response.choices[0]
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usage = response.usage
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# 解析工具调用
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tool_calls = None
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if choice.message.tool_calls:
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tool_calls = self._parse_tool_calls(choice.message.tool_calls)
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return LLMResponse(
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content=choice.message.content or '',
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model=response.model,
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prompt_tokens=usage.prompt_tokens if usage else 0,
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completion_tokens=usage.completion_tokens if usage else 0,
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total_tokens=usage.total_tokens if usage else 0,
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finish_reason=choice.finish_reason or '',
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raw_response=response.model_dump(),
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tool_calls=tool_calls,
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)
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def _parse_tool_calls(self, tool_calls) -> List[ToolCall]:
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"""解析工具调用"""
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import json
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result = []
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for tc in tool_calls:
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try:
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arguments = json.loads(tc.function.arguments) if tc.function.arguments else {}
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except json.JSONDecodeError:
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arguments = {'raw': tc.function.arguments}
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result.append(ToolCall(
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id=tc.id or f'call_{len(result)}',
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name=tc.function.name,
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arguments=arguments,
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))
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return result
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async def chat_async(
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self,
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messages: List[LLMMessage],
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config: LLMConfig,
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) -> LLMResponse:
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"""异步对话"""
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client = self._get_async_client()
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# 构建请求参数
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kwargs = {
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'model': config.model,
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'messages': [m.to_dict() for m in messages],
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'temperature': config.temperature,
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'top_p': config.top_p,
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'max_tokens': config.max_tokens,
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'stop': config.stop,
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}
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# 添加 Function Calling 参数
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if config.tools:
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kwargs['tools'] = [t.to_openai_format() for t in config.tools]
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if config.tool_choice == 'required':
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kwargs['tool_choice'] = 'required'
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elif config.tool_choice == 'none':
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kwargs['tool_choice'] = 'none'
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elif config.tool_choice != 'auto':
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kwargs['tool_choice'] = {'type': 'function', 'function': {'name': config.tool_choice}}
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else:
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kwargs['tool_choice'] = 'auto'
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response = await client.chat.completions.create(**kwargs)
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choice = response.choices[0]
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usage = response.usage
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# 解析工具调用
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tool_calls = None
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if choice.message.tool_calls:
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tool_calls = self._parse_tool_calls(choice.message.tool_calls)
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return LLMResponse(
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content=choice.message.content or '',
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model=response.model,
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prompt_tokens=usage.prompt_tokens if usage else 0,
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completion_tokens=usage.completion_tokens if usage else 0,
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total_tokens=usage.total_tokens if usage else 0,
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finish_reason=choice.finish_reason or '',
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raw_response=response.model_dump(),
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tool_calls=tool_calls,
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)
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async def chat_stream(
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self,
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messages: List[LLMMessage],
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config: LLMConfig,
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) -> AsyncGenerator[LLMStreamChunk, None]:
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"""流式对话"""
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client = self._get_async_client()
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# 构建请求参数
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kwargs = {
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'model': config.model,
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'messages': [m.to_dict() for m in messages],
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'temperature': config.temperature,
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'top_p': config.top_p,
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'max_tokens': config.max_tokens,
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'stop': config.stop,
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'stream': True,
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}
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# 添加 Function Calling 参数
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if config.tools:
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kwargs['tools'] = [t.to_openai_format() for t in config.tools]
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if config.tool_choice == 'required':
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kwargs['tool_choice'] = 'required'
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elif config.tool_choice == 'none':
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kwargs['tool_choice'] = 'none'
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elif config.tool_choice != 'auto':
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kwargs['tool_choice'] = {'type': 'function', 'function': {'name': config.tool_choice}}
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else:
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kwargs['tool_choice'] = 'auto'
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stream = await client.chat.completions.create(**kwargs)
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# 用于累积工具调用
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tool_call_accumulator = {}
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async for chunk in stream:
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if chunk.choices:
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choice = chunk.choices[0]
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delta = choice.delta
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# 处理工具调用增量
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tool_call_delta = None
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if hasattr(delta, 'tool_calls') and delta.tool_calls:
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for tc_delta in delta.tool_calls:
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tc_index = tc_delta.index if hasattr(tc_delta, 'index') else 0
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if tc_index not in tool_call_accumulator:
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tool_call_accumulator[tc_index] = {
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'id': getattr(tc_delta, 'id', '') or f'call_{tc_index}',
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'name': '',
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'arguments': '',
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}
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if hasattr(tc_delta, 'id') and tc_delta.id:
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tool_call_accumulator[tc_index]['id'] = tc_delta.id
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if tc_delta.function:
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if tc_delta.function.name:
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tool_call_accumulator[tc_index]['name'] = tc_delta.function.name
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if tc_delta.function.arguments:
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tool_call_accumulator[tc_index]['arguments'] += tc_delta.function.arguments
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tool_call_delta = tool_call_accumulator[tc_index]
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# 检查是否完成
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tool_calls = None
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if choice.finish_reason and tool_call_accumulator:
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tool_calls = self._parse_accumulated_tool_calls(tool_call_accumulator)
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yield LLMStreamChunk(
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content=delta.content or '',
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is_finished=choice.finish_reason is not None,
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finish_reason=choice.finish_reason or '',
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tool_calls=tool_calls,
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tool_call_delta=tool_call_delta,
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)
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def _parse_accumulated_tool_calls(self, accumulator: Dict) -> List[ToolCall]:
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"""解析累积的工具调用"""
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import json
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result = []
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for idx in sorted(accumulator.keys()):
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tc = accumulator[idx]
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try:
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arguments = json.loads(tc['arguments']) if tc['arguments'] else {}
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except json.JSONDecodeError:
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arguments = {'raw': tc['arguments']}
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result.append(ToolCall(
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id=tc['id'],
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name=tc['name'],
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arguments=arguments,
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))
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return result
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def chat_stream_sync(
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self,
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messages: List[LLMMessage],
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config: LLMConfig,
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):
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"""同步流式对话"""
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client = self._get_client()
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# 构建请求参数
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kwargs = {
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'model': config.model,
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'messages': [m.to_dict() for m in messages],
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'temperature': config.temperature,
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'top_p': config.top_p,
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'max_tokens': config.max_tokens,
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'stop': config.stop,
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'stream': True,
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}
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# 添加 Function Calling 参数
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if config.tools:
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kwargs['tools'] = [t.to_openai_format() for t in config.tools]
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if config.tool_choice == 'required':
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kwargs['tool_choice'] = 'required'
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elif config.tool_choice == 'none':
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kwargs['tool_choice'] = 'none'
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elif config.tool_choice != 'auto':
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kwargs['tool_choice'] = {'type': 'function', 'function': {'name': config.tool_choice}}
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else:
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kwargs['tool_choice'] = 'auto'
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stream = client.chat.completions.create(**kwargs)
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# 用于累积工具调用
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tool_call_accumulator = {}
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for chunk in stream:
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if chunk.choices:
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choice = chunk.choices[0]
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delta = choice.delta
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# 处理工具调用增量
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tool_call_delta = None
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if hasattr(delta, 'tool_calls') and delta.tool_calls:
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for tc_delta in delta.tool_calls:
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tc_index = tc_delta.index if hasattr(tc_delta, 'index') else 0
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if tc_index not in tool_call_accumulator:
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tool_call_accumulator[tc_index] = {
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'id': getattr(tc_delta, 'id', '') or f'call_{tc_index}',
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'name': '',
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'arguments': '',
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}
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if hasattr(tc_delta, 'id') and tc_delta.id:
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tool_call_accumulator[tc_index]['id'] = tc_delta.id
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if tc_delta.function:
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if tc_delta.function.name:
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tool_call_accumulator[tc_index]['name'] = tc_delta.function.name
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if tc_delta.function.arguments:
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tool_call_accumulator[tc_index]['arguments'] += tc_delta.function.arguments
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tool_call_delta = tool_call_accumulator[tc_index]
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# 检查是否完成
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tool_calls = None
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if choice.finish_reason and tool_call_accumulator:
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tool_calls = self._parse_accumulated_tool_calls(tool_call_accumulator)
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yield LLMStreamChunk(
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content=delta.content or '',
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is_finished=choice.finish_reason is not None,
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finish_reason=choice.finish_reason or '',
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tool_calls=tool_calls,
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tool_call_delta=tool_call_delta,
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)
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_EMBEDDING_KEYWORDS = ('embed', 'bge', 'nomic-embed', 'mxbai-embed')
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_RERANK_KEYWORDS = ('rerank', 'reranker')
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def get_available_models(self) -> List[Dict[str, Any]]:
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"""获取本地可用的模型列表"""
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try:
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import httpx
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base_url = self.api_base.replace('/v1', '')
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response = httpx.get(f'{base_url}/api/tags', timeout=10)
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if response.status_code == 200:
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data = response.json()
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models = []
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for model in data.get('models', []):
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models.append({
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'model_name': model.get('name', ''),
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'display_name': model.get('name', ''),
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'size': model.get('size', 0),
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'modified_at': model.get('modified_at', ''),
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})
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return models
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except Exception as e:
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logger.warning(f'获取 Ollama 模型列表失败: {e}')
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return []
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async def fetch_models_from_api(self) -> List[Dict[str, Any]]:
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"""通过 /api/tags 端点拉取 Ollama 本地已安装的模型"""
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import httpx
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base_url = (self.api_base or 'http://localhost:11434').rstrip('/')
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# Ollama 的 /api/tags 在根路径，去掉 /v1
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if base_url.endswith('/v1'):
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base_url = base_url[:-3]
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try:
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async with httpx.AsyncClient(timeout=10) as client:
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resp = await client.get(f'{base_url}/api/tags')
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resp.raise_for_status()
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data = resp.json()
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results = []
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for item in data.get('models', []):
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name = item.get('name', '')
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if not name:
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continue
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name_lower = name.lower()
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if any(kw in name_lower for kw in self._RERANK_KEYWORDS):
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model_type = 'rerank'
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elif any(kw in name_lower for kw in self._EMBEDDING_KEYWORDS):
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model_type = 'embedding'
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else:
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model_type = 'chat'
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# 从 size 推算显示名称
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size_gb = round(item.get('size', 0) / (1024 ** 3), 1)
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display = f'{name} ({size_gb}GB)' if size_gb > 0 else name
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results.append({
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'model_name': name,
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'display_name': display,
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'model_type': model_type,
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'max_tokens': 4096,
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'context_window': 4096,
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'supports_vision': False,
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'supports_function_call': model_type == 'chat',
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'input_price': 0,
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'output_price': 0,
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})
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type_order = {'chat': 0, 'embedding': 1, 'rerank': 2}
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results.sort(key=lambda m: (type_order.get(m['model_type'], 9), m['model_name']))
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logger.info(f'从 Ollama ({base_url}) 拉取到 {len(results)} 个本地模型')
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return results
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except Exception as e:
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logger.warning(f'在线拉取 Ollama 模型列表失败 ({base_url}): {e}')
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return []
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@classmethod
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def get_default_models(cls) -> List[Dict[str, Any]]:
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"""获取默认模型列表（常用的开源模型）"""
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return [
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# ---- Chat 模型 ----
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{
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'model_name': 'qwen3:32b',
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'display_name': 'Qwen3 32B',
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'model_type': 'chat',
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'max_tokens': 8192,
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'context_window': 131072,
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'supports_vision': False,
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'supports_function_call': True,
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'input_price': 0,
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'output_price': 0,
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},
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{
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'model_name': 'qwen3:8b',
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'display_name': 'Qwen3 8B',
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'model_type': 'chat',
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'max_tokens': 8192,
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'context_window': 131072,
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'supports_vision': False,
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'supports_function_call': True,
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'input_price': 0,
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'output_price': 0,
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},
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{
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'model_name': 'llama3.3',
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'display_name': 'Llama 3.3 70B',
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'model_type': 'chat',
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'max_tokens': 4096,
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'context_window': 128000,
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'supports_vision': False,
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'supports_function_call': True,
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'input_price': 0,
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'output_price': 0,
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},
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{
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'model_name': 'llama3.2',
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'display_name': 'Llama 3.2',
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'model_type': 'chat',
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'max_tokens': 4096,
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'context_window': 128000,
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'supports_vision': False,
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'supports_function_call': True,
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'input_price': 0,
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'output_price': 0,
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},
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{
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'model_name': 'deepseek-r1',
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'display_name': 'DeepSeek R1',
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'model_type': 'chat',
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'max_tokens': 4096,
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'context_window': 64000,
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'supports_vision': False,
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'supports_function_call': False,
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'input_price': 0,
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'output_price': 0,
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},
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{
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'model_name': 'mistral',
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'display_name': 'Mistral',
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'model_type': 'chat',
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'max_tokens': 4096,
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'context_window': 32000,
|
|
'supports_vision': False,
|
|
'supports_function_call': True,
|
|
'input_price': 0,
|
|
'output_price': 0,
|
|
},
|
|
{
|
|
'model_name': 'gemma3',
|
|
'display_name': 'Gemma 3',
|
|
'model_type': 'chat',
|
|
'max_tokens': 8192,
|
|
'context_window': 128000,
|
|
'supports_vision': True,
|
|
'supports_function_call': True,
|
|
'input_price': 0,
|
|
'output_price': 0,
|
|
},
|
|
# ---- Embedding 模型 ----
|
|
{
|
|
'model_name': 'nomic-embed-text',
|
|
'display_name': 'Nomic Embed Text',
|
|
'model_type': 'embedding',
|
|
'max_tokens': 8192,
|
|
'context_window': 8192,
|
|
'input_price': 0,
|
|
'output_price': 0,
|
|
},
|
|
{
|
|
'model_name': 'bge-m3',
|
|
'display_name': 'BGE-M3',
|
|
'model_type': 'embedding',
|
|
'max_tokens': 8192,
|
|
'context_window': 8192,
|
|
'input_price': 0,
|
|
'output_price': 0,
|
|
},
|
|
{
|
|
'model_name': 'mxbai-embed-large',
|
|
'display_name': 'MxBai Embed Large',
|
|
'model_type': 'embedding',
|
|
'max_tokens': 512,
|
|
'context_window': 512,
|
|
'input_price': 0,
|
|
'output_price': 0,
|
|
},
|
|
# ---- Rerank 模型 ----
|
|
{
|
|
'model_name': 'bge-reranker-v2-m3',
|
|
'display_name': 'BGE Reranker V2 M3',
|
|
'model_type': 'rerank',
|
|
'max_tokens': 8192,
|
|
'context_window': 8192,
|
|
'input_price': 0,
|
|
'output_price': 0,
|
|
},
|
|
]
|