Source code for langchain_community.embeddings.cloudflare_workersai
from typing import Any, Dict, List
import requests
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, Extra
DEFAULT_MODEL_NAME = "@cf/baai/bge-base-en-v1.5"
[docs]class CloudflareWorkersAIEmbeddings(BaseModel, Embeddings):
"""Cloudflare Workers AI嵌入模型。
要使用,您需要提供API令牌和
账户ID以访问Cloudflare Workers AI。
示例:
.. code-block:: python
from langchain_community.embeddings import CloudflareWorkersAIEmbeddings
account_id = "my_account_id"
api_token = "my_secret_api_token"
model_name = "@cf/baai/bge-small-en-v1.5"
cf = CloudflareWorkersAIEmbeddings(
account_id=account_id,
api_token=api_token,
model_name=model_name
)
"""
api_base_url: str = "https://api.cloudflare.com/client/v4/accounts"
account_id: str
api_token: str
model_name: str = DEFAULT_MODEL_NAME
batch_size: int = 50
strip_new_lines: bool = True
headers: Dict[str, str] = {"Authorization": "Bearer "}
def __init__(self, **kwargs: Any):
"""初始化Cloudflare Workers AI客户端。"""
super().__init__(**kwargs)
self.headers = {"Authorization": f"Bearer {self.api_token}"}
class Config:
"""此pydantic对象的配置。"""
extra = Extra.forbid
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""使用Cloudflare Workers AI计算文档嵌入。
参数:
texts:要嵌入的文本列表。
返回:
嵌入列表,每个文本对应一个嵌入。
"""
if self.strip_new_lines:
texts = [text.replace("\n", " ") for text in texts]
batches = [
texts[i : i + self.batch_size]
for i in range(0, len(texts), self.batch_size)
]
embeddings = []
for batch in batches:
response = requests.post(
f"{self.api_base_url}/{self.account_id}/ai/run/{self.model_name}",
headers=self.headers,
json={"text": batch},
)
embeddings.extend(response.json()["result"]["data"])
return embeddings
[docs] def embed_query(self, text: str) -> List[float]:
"""使用Cloudflare Workers AI计算查询嵌入。
参数:
text:要嵌入的文本。
返回:
文本的嵌入。
"""
text = text.replace("\n", " ") if self.strip_new_lines else text
response = requests.post(
f"{self.api_base_url}/{self.account_id}/ai/run/{self.model_name}",
headers=self.headers,
json={"text": [text]},
)
return response.json()["result"]["data"][0]