add(feat): Text Embeddings by Cloudflare Workers AI (#14220)

Add [Text Embeddings by Cloudflare Workers
AI](https://developers.cloudflare.com/workers-ai/models/text-embeddings/).
It's a new integration.
Trying to align it with its langchain-js version counterpart
[here](https://api.js.langchain.com/classes/embeddings_cloudflare_workersai.CloudflareWorkersAIEmbeddings.html).
- Dependencies: N/A
- Done `make format` `make lint` `make spell_check` `make
integration_tests` and all my changes was passed
This commit is contained in:
cxumol
2023-12-04 19:25:05 -08:00
committed by GitHub
parent c51001f01e
commit 0d47d15a9f
3 changed files with 272 additions and 0 deletions

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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.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]

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"""Test Cloudflare Workers AI embeddings."""
import responses
from langchain.embeddings.cloudflare_workersai import CloudflareWorkersAIEmbeddings
@responses.activate
def test_cloudflare_workers_ai_embedding_documents() -> None:
"""Test Cloudflare Workers AI embeddings."""
documents = ["foo bar", "foo bar", "foo bar"]
responses.add(
responses.POST,
"https://api.cloudflare.com/client/v4/accounts/123/ai/run/@cf/baai/bge-base-en-v1.5",
json={
"result": {
"shape": [3, 768],
"data": [[0.0] * 768, [0.0] * 768, [0.0] * 768],
},
"success": "true",
"errors": [],
"messages": [],
},
)
embeddings = CloudflareWorkersAIEmbeddings(account_id="123", api_token="abc")
output = embeddings.embed_documents(documents)
assert len(output) == 3
assert len(output[0]) == 768
@responses.activate
def test_cloudflare_workers_ai_embedding_query() -> None:
"""Test Cloudflare Workers AI embeddings."""
responses.add(
responses.POST,
"https://api.cloudflare.com/client/v4/accounts/123/ai/run/@cf/baai/bge-base-en-v1.5",
json={
"result": {"shape": [1, 768], "data": [[0.0] * 768]},
"success": "true",
"errors": [],
"messages": [],
},
)
document = "foo bar"
embeddings = CloudflareWorkersAIEmbeddings(account_id="123", api_token="abc")
output = embeddings.embed_query(document)
assert len(output) == 768