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Harrison Chase 2023-06-12 08:00:14 -07:00
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docs/modules/models/text_embedding/examples/embaas.ipynb
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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Embaas\n",
"\n",
"[embaas](https://embaas.io) is a fully managed NLP API service that offers features like embedding generation, document text extraction, document to embeddings and more. You can choose a [variety of pre-trained models](https://embaas.io/docs/models/embeddings).\n",
"\n",
"In this tutorial, we will show you how to use the embaas Embeddings API to generate embeddings for a given text.\n",
"\n",
"### Prerequisites\n",
"Create your free embaas account at [https://embaas.io/register](https://embaas.io/register) and generate an [API key](https://embaas.io/dashboard/api-keys)."
],
"metadata": {
"collapsed": false
}
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Set API key\n",
"embaas_api_key = \"YOUR_API_KEY\"\n",
"# or set environment variable\n",
"os.environ[\"EMBAAS_API_KEY\"] = \"YOUR_API_KEY\""
],
"metadata": {
"collapsed": false
}
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain.embeddings import EmbaasEmbeddings"
],
"metadata": {
"collapsed": false
}
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"embeddings = EmbaasEmbeddings()"
],
"metadata": {
"collapsed": false
}
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-10T11:17:55.940265Z",
"start_time": "2023-06-10T11:17:55.938517Z"
}
},
"outputs": [],
"source": [
"# Create embeddings for a single document\n",
"doc_text = \"This is a test document.\"\n",
"doc_text_embedding = embeddings.embed_query(doc_text)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"start_time": "2023-06-10T11:17:55.938517Z",
"end_time": "2023-06-10T11:17:55.940265Z"
}
}
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Print created embedding\n",
"print(doc_text_embedding)"
],
"metadata": {
"collapsed": false
}
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-10T11:19:25.237161Z",
"start_time": "2023-06-10T11:19:25.235320Z"
}
},
"outputs": [],
"source": [
"# Create embeddings for multiple documents\n",
"doc_texts = [\"This is a test document.\", \"This is another test document.\"]\n",
"doc_texts_embeddings = embeddings.embed_documents(doc_texts)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"start_time": "2023-06-10T11:19:25.235320Z",
"end_time": "2023-06-10T11:19:25.237161Z"
}
}
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Print created embeddings\n",
"for i, doc_text_embedding in enumerate(doc_texts_embeddings):\n",
" print(f\"Embedding for document {i + 1}: {doc_text_embedding}\")"
],
"metadata": {
"collapsed": false
}
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"ExecuteTime": {
"end_time": "2023-06-10T11:22:26.139769Z",
"start_time": "2023-06-10T11:22:26.138357Z"
}
},
"outputs": [],
"source": [
"# Using a different model and/or custom instruction\n",
"embeddings = EmbaasEmbeddings(model=\"instructor-large\", instruction=\"Represent the Wikipedia document for retrieval\")"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"start_time": "2023-06-10T11:22:26.138357Z",
"end_time": "2023-06-10T11:22:26.139769Z"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For more detailed information about the embaas Embeddings API, please refer to [the official embaas API documentation](https://embaas.io/api-reference)."
],
"metadata": {
"collapsed": false
}
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@ -155,5 +140,5 @@
}
},
"nbformat": 4,
"nbformat_minor": 0
"nbformat_minor": 1
}