mirror of
https://github.com/hwchase17/langchain
synced 2024-11-04 06:00:26 +00:00
95 lines
2.8 KiB
Python
95 lines
2.8 KiB
Python
|
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"
|
||
|
|
||
|
|
||
|
class CloudflareWorkersAIEmbeddings(BaseModel, Embeddings):
|
||
|
"""Cloudflare Workers AI embedding model.
|
||
|
|
||
|
To use, you need to provide an API token and
|
||
|
account ID to access Cloudflare Workers AI.
|
||
|
|
||
|
Example:
|
||
|
.. 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):
|
||
|
"""Initialize the Cloudflare Workers AI client."""
|
||
|
super().__init__(**kwargs)
|
||
|
|
||
|
self.headers = {"Authorization": f"Bearer {self.api_token}"}
|
||
|
|
||
|
class Config:
|
||
|
"""Configuration for this pydantic object."""
|
||
|
|
||
|
extra = Extra.forbid
|
||
|
|
||
|
def embed_documents(self, texts: List[str]) -> List[List[float]]:
|
||
|
"""Compute doc embeddings using Cloudflare Workers AI.
|
||
|
|
||
|
Args:
|
||
|
texts: The list of texts to embed.
|
||
|
|
||
|
Returns:
|
||
|
List of embeddings, one for each text.
|
||
|
"""
|
||
|
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
|
||
|
|
||
|
def embed_query(self, text: str) -> List[float]:
|
||
|
"""Compute query embeddings using Cloudflare Workers AI.
|
||
|
|
||
|
Args:
|
||
|
text: The text to embed.
|
||
|
|
||
|
Returns:
|
||
|
Embeddings for the text.
|
||
|
"""
|
||
|
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]
|