embedding docs (#2200)

docs/modules/models/text_embedding/examples/aleph_alpha.ipynb

@@ -0,0 +1,165 @@
+{
+ "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"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}