92fe65fecf
- 将BaseEmbeddingModel改为抽象基类(ABC) - 添加异步预测方法apredict - 重构批量编码方法，添加任务状态跟踪 - 为Ollama和Other嵌入实现添加异步支持 - 改进错误处理和日志记录 - 添加类型注解提高代码可读性 docs: 更新changelog记录已知问题
189 lines
7.3 KiB
Python
189 lines
7.3 KiB
Python
import os
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import json
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import httpx
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import requests
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import asyncio
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from abc import abstractmethod, ABC
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from src import config
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from src.utils import hashstr, logger, get_docker_safe_url
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class BaseEmbeddingModel(ABC):
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def __init__(self, model=None, name=None, dimension=None, url=None, base_url=None, api_key=None):
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"""
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Args:
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model: 模型名称，冗余设计，同name
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name: 模型名称，冗余设计，同model
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dimension: 维度
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url: 请求URL，冗余设计，同base_url
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base_url: 基础URL，请求URL，冗余设计，同url
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api_key: 请求API密钥
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"""
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base_url = base_url or url
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self.model = model or name
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self.dimension = dimension
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self.base_url = get_docker_safe_url(base_url)
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self.api_key = os.getenv(api_key, api_key)
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self.embed_state = {}
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@abstractmethod
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def predict(self, message: list[str] | str) -> list[list[float]]:
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"""同步编码"""
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raise NotImplementedError("Subclasses must implement this method")
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@abstractmethod
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async def apredict(self, message: list[str] | str) -> list[list[float]]:
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"""异步编码"""
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raise NotImplementedError("Subclasses must implement this method")
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def encode(self, message: list[str] | str) -> list[list[float]]:
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"""等同于predict"""
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return self.predict(message)
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def encode_queries(self, queries: list[str] | str) -> list[list[float]]:
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"""等同于predict"""
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return self.predict(queries)
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async def aencode(self, message: list[str] | str) -> list[list[float]]:
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"""等同于apredict"""
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return await self.apredict(message)
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async def aencode_queries(self, queries: list[str] | str) -> list[list[float]]:
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"""等同于apredict"""
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return await self.apredict(queries)
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def batch_encode(self, messages: list[str], batch_size: int = 40) -> list[list[float]]:
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# logger.info(f"Batch encoding {len(messages)} messages")
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data = []
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task_id = None
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if len(messages) > batch_size:
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task_id = hashstr(messages)
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self.embed_state[task_id] = {
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'status': 'in-progress',
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'total': len(messages),
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'progress': 0
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}
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for i in range(0, len(messages), batch_size):
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group_msg = messages[i:i+batch_size]
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logger.info(f"Encoding [{i}/{len(messages)}] messages (bsz={batch_size})")
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response = self.encode(group_msg)
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data.extend(response)
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if task_id:
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self.embed_state[task_id]['progress'] = i + len(group_msg)
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if task_id:
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self.embed_state[task_id]['status'] = 'completed'
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return data
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async def abatch_encode(self, messages: list[str], batch_size: int = 40) -> list[list[float]]:
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data = []
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task_id = None
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if len(messages) > batch_size:
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task_id = hashstr(messages)
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self.embed_state[task_id] = {
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'status': 'in-progress',
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'total': len(messages),
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'progress': 0
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}
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tasks = []
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for i in range(0, len(messages), batch_size):
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group_msg = messages[i:i+batch_size]
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tasks.append(self.aencode(group_msg))
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results = await asyncio.gather(*tasks)
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for res in results:
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data.extend(res)
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if task_id:
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self.embed_state[task_id]['progress'] = len(messages)
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self.embed_state[task_id]['status'] = 'completed'
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return data
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class OllamaEmbedding(BaseEmbeddingModel):
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"""
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Ollama Embedding Model
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"""
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def __init__(self, **kwargs) -> None:
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super().__init__(**kwargs)
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self.base_url = self.base_url or get_docker_safe_url("http://localhost:11434/api/embed")
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def predict(self, message: list[str] | str) -> list[list[float]]:
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if isinstance(message, str):
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message = [message]
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payload = {"model": self.model, "input": message}
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try:
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response = requests.post(self.base_url, json=payload, timeout=60)
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response.raise_for_status()
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result = response.json()
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if "embeddings" not in result:
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raise ValueError(f"Ollama Embedding failed: Invalid response format {result}")
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return result["embeddings"]
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except (requests.RequestException, json.JSONDecodeError) as e:
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logger.error(f"Ollama Embedding request failed: {e}")
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raise ValueError(f"Ollama Embedding request failed: {e}")
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async def apredict(self, message: list[str] | str) -> list[list[float]]:
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if isinstance(message, str):
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message = [message]
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payload = {"model": self.model, "input": message}
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async with httpx.AsyncClient() as client:
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try:
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response = await client.post(self.base_url, json=payload, timeout=60)
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response.raise_for_status()
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result = response.json()
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if "embeddings" not in result:
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raise ValueError(f"Ollama Embedding failed: Invalid response format {result}")
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return result["embeddings"]
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except (httpx.RequestError, json.JSONDecodeError) as e:
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logger.error(f"Ollama Embedding async request failed: {e}")
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raise ValueError(f"Ollama Embedding async request failed: {e}")
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class OtherEmbedding(BaseEmbeddingModel):
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def __init__(self, **kwargs) -> None:
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super().__init__(**kwargs)
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self.headers = {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json"
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}
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def build_payload(self, message: list[str] | str) -> dict:
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return {"model": self.model, "input": message}
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def predict(self, message: list[str] | str) -> list[list[float]]:
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payload = self.build_payload(message)
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try:
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response = requests.post(self.base_url, json=payload, headers=self.headers, timeout=60)
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response.raise_for_status()
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result = response.json()
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if not isinstance(result, dict) or "data" not in result:
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raise ValueError(f"Other Embedding failed: Invalid response format {result}")
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return [item["embedding"] for item in result["data"]]
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except (requests.RequestException, json.JSONDecodeError) as e:
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logger.error(f"Other Embedding request failed: {e}")
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raise ValueError(f"Other Embedding request failed: {e}")
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async def apredict(self, message: list[str] | str) -> list[list[float]]:
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payload = self.build_payload(message)
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async with httpx.AsyncClient() as client:
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try:
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response = await client.post(self.base_url, json=payload, headers=self.headers, timeout=60)
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response.raise_for_status()
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result = response.json()
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if not isinstance(result, dict) or "data" not in result:
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raise ValueError(f"Other Embedding failed: Invalid response format {result}")
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return [item["embedding"] for item in result["data"]]
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except (httpx.RequestError, json.JSONDecodeError) as e:
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logger.error(f"Other Embedding async request failed: {e}")
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raise ValueError(f"Other Embedding async request failed: {e}")
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