mirror of
https://github.com/hwchase17/langchain
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145 lines
3.7 KiB
Plaintext
145 lines
3.7 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Embaas\n",
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"\n",
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"[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",
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"\n",
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"In this tutorial, we will show you how to use the embaas Embeddings API to generate embeddings for a given text.\n",
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"\n",
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"### Prerequisites\n",
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"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)."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Set API key\n",
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"embaas_api_key = \"YOUR_API_KEY\"\n",
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"# or set environment variable\n",
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"os.environ[\"EMBAAS_API_KEY\"] = \"YOUR_API_KEY\""
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.embeddings import EmbaasEmbeddings"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"embeddings = EmbaasEmbeddings()"
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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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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-06-10T11:17:55.940265Z",
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"start_time": "2023-06-10T11:17:55.938517Z"
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}
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},
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"outputs": [],
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"source": [
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"# Create embeddings for a single document\n",
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"doc_text = \"This is a test document.\"\n",
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"doc_text_embedding = embeddings.embed_query(doc_text)"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Print created embedding\n",
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"print(doc_text_embedding)"
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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": 9,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-06-10T11:19:25.237161Z",
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"start_time": "2023-06-10T11:19:25.235320Z"
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}
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},
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"outputs": [],
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"source": [
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"# Create embeddings for multiple documents\n",
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"doc_texts = [\"This is a test document.\", \"This is another test document.\"]\n",
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"doc_texts_embeddings = embeddings.embed_documents(doc_texts)"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Print created embeddings\n",
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"for i, doc_text_embedding in enumerate(doc_texts_embeddings):\n",
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" print(f\"Embedding for document {i + 1}: {doc_text_embedding}\")"
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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": 11,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-06-10T11:22:26.139769Z",
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"start_time": "2023-06-10T11:22:26.138357Z"
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}
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},
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"outputs": [],
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"source": [
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"# Using a different model and/or custom instruction\n",
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"embeddings = EmbaasEmbeddings(model=\"instructor-large\", instruction=\"Represent the Wikipedia document for retrieval\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"For more detailed information about the embaas Embeddings API, please refer to [the official embaas API documentation](https://embaas.io/api-reference)."
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]
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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": "3.9.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}
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