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