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

docs/docs/integrations/text_embedding/cloudflare_workersai.ipynb

@@ -0,0 +1,125 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "59428e05",
+   "metadata": {},
+   "source": [
+    "# Text Embeddings on Cloudflare Workers AI\n",
+    "\n",
+    "[Cloudflare AI document](https://developers.cloudflare.com/workers-ai/models/text-embeddings/) listed all text embeddings models available.\n",
+    "\n",
+    "Both Cloudflare account ID and API token are required. Find how to obtain them from [this document](https://developers.cloudflare.com/workers-ai/get-started/rest-api/).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "92c5b61e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings.cloudflare_workersai import CloudflareWorkersAIEmbeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f60023b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "\n",
+    "my_account_id = getpass.getpass(\"Enter your Cloudflare account ID:\\n\\n\")\n",
+    "my_api_token = getpass.getpass(\"Enter your Cloudflare API token:\\n\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "062547b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(384, [-0.033627357333898544, 0.03982774540781975, 0.03559349477291107])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "embeddings = CloudflareWorkersAIEmbeddings(\n",
+    "    account_id=my_account_id,\n",
+    "    api_token=my_api_token,\n",
+    "    model_name=\"@cf/baai/bge-small-en-v1.5\",\n",
+    ")\n",
+    "# single string embeddings\n",
+    "query_result = embeddings.embed_query(\"test\")\n",
+    "len(query_result), query_result[:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e1dcc4bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 384)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# string embeddings in batches\n",
+    "batch_query_result = embeddings.embed_documents([\"test1\", \"test2\", \"test3\"])\n",
+    "len(batch_query_result), len(batch_query_result[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "52de8b88",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "7377c2ccc78bc62c2683122d48c8cd1fb85a53850a1b1fc29736ed39852c9885"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

libs/langchain/langchain/embeddings/cloudflare_workersai.py

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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]

libs/langchain/tests/integration_tests/embeddings/test_cloudflare_workersai.py

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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