add everyting for docs

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": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}