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{
"cells": [
{
"cell_type": "markdown",
"id": "eb1c0ea9",
"metadata": {},
"source": [
"# Aleph Alpha\n",
"\n",
"There are two possible ways to use Aleph Alpha's semantic embeddings. If you have texts with a dissimilar structure (e.g. a Document and a Query) you would want to use asymmetric embeddings. Conversely, for texts with comparable structures, symmetric embeddings are the suggested approach."
]
},
{
"cell_type": "markdown",
"id": "9ecc84f9",
"metadata": {},
"source": [
"## Asymmetric"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "8a920a89",
"metadata": {},
"outputs": [],
"source": [
"from langchain.embeddings import AlephAlphaAsymmetricSemanticEmbedding"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "f2d04da3",
"metadata": {},
"outputs": [],
"source": [
"document = \"This is a content of the document\"\n",
"query = \"What is the contnt of the document?\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e6ecde96",
"metadata": {},
"outputs": [],
"source": [
"embeddings = AlephAlphaAsymmetricSemanticEmbedding()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "90e68411",
"metadata": {},
"outputs": [],
"source": [
"doc_result = embeddings.embed_documents([document])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "55903233",
"metadata": {},
"outputs": [],
"source": [
"query_result = embeddings.embed_query(query)"
]
},
{
"cell_type": "markdown",
"id": "b8c00aab",
"metadata": {},
"source": [
"## Symmetric"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "eabb763a",
"metadata": {},
"outputs": [],
"source": [
"from langchain.embeddings import AlephAlphaSymmetricSemanticEmbedding"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "0ad799f7",
"metadata": {},
"outputs": [],
"source": [
"text = \"This is a test text\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "af86dc10",
"metadata": {},
"outputs": [],
"source": [
"embeddings = AlephAlphaSymmetricSemanticEmbedding()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d292536f",
"metadata": {},
"outputs": [],
"source": [
"doc_result = embeddings.embed_documents([text])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c704a7cf",
"metadata": {},
"outputs": [],
"source": [
"query_result = embeddings.embed_query(text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "33492471",
"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": "3.9.1"
},
"vscode": {
"interpreter": {
"hash": "7377c2ccc78bc62c2683122d48c8cd1fb85a53850a1b1fc29736ed39852c9885"
}
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"nbformat": 4,
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