langchain/docs/extras/integrations/text_embedding/Awa.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"id": "b14a24db",
"metadata": {},
"source": [
"# AwaEmbedding\n",
"\n",
"This notebook explains how to use AwaEmbedding, which is included in [awadb](https://github.com/awa-ai/awadb), to embedding texts in langchain."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "0ab948fc",
"metadata": {},
"outputs": [],
"source": [
"# pip install awadb"
]
},
{
"cell_type": "markdown",
"id": "67c637ca",
"metadata": {},
"source": [
"## import the library"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "5709b030",
"metadata": {},
"outputs": [],
"source": [
"from langchain.embeddings import AwaEmbeddings"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "1756b1ba",
"metadata": {},
"outputs": [],
"source": [
"Embedding = AwaEmbeddings()"
]
},
{
"cell_type": "markdown",
"id": "4a2a098d",
"metadata": {},
"source": [
"# Set embedding model\n",
"Users can use `Embedding.set_model()` to specify the embedding model. \\\n",
"The input of this function is a string which represents the model's name. \\\n",
"The list of currently supported models can be obtained [here](https://github.com/awa-ai/awadb) \\ \\ \n",
"\n",
"The **default model** is `all-mpnet-base-v2`, it can be used without setting."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "584b9af5",
"metadata": {},
"outputs": [],
"source": [
"text = \"our embedding test\"\n",
"\n",
"Embedding.set_model(\"all-mpnet-base-v2\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "be18b873",
"metadata": {},
"outputs": [],
"source": [
"res_query = Embedding.embed_query(\"The test information\")\n",
"res_document = Embedding.embed_documents([\"test1\", \"another test\"])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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}