From 7264ceb0d920f3d490fd127cd946babbc8ee4644 Mon Sep 17 00:00:00 2001
From: vowelparrot <130414180+vowelparrot@users.noreply.github.com>
Date: Sun, 23 Apr 2023 18:07:52 -0700
Subject: [PATCH] add doc

---
 .../examples/sentence_transformers.ipynb      | 120 ++++++++++++++++++
 langchain/embeddings/__init__.py              |   2 +
 2 files changed, 122 insertions(+)
 create mode 100644 docs/modules/models/text_embedding/examples/sentence_transformers.ipynb

diff --git a/docs/modules/models/text_embedding/examples/sentence_transformers.ipynb b/docs/modules/models/text_embedding/examples/sentence_transformers.ipynb
new file mode 100644
index 00000000000..eda1c7dd2d6
--- /dev/null
+++ b/docs/modules/models/text_embedding/examples/sentence_transformers.ipynb
@@ -0,0 +1,120 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "ed47bb62",
+   "metadata": {},
+   "source": [
+    "# Sentence Transformers Embeddings\n",
+    "\n",
+    "Let's generate embeddings using the [SentenceTransformers](https://www.sbert.net/) integration. SentenceTransformers is a python package that can generate text and image embeddings, originating from [Sentence-BERT](https://arxiv.org/abs/1908.10084)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "06c9f47d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
+      "To disable this warning, you can either:\n",
+      "\t- Avoid using `tokenizers` before the fork if possible\n",
+      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install sentence_transformers > /dev/null"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "861521a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings import SentenceTransformerEmbeddings "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ff9be586",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embeddings = SentenceTransformerEmbeddings(model=\"all-MiniLM-L6-v2\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "d0a98ae9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = \"This is a test document.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "5d6c682b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_result = embeddings.embed_query(text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "bb5e74c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "doc_result = embeddings.embed_documents([text, \"This is not a test document.\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aaad49f8",
+   "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": "3.11.2"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "7377c2ccc78bc62c2683122d48c8cd1fb85a53850a1b1fc29736ed39852c9885"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/langchain/embeddings/__init__.py b/langchain/embeddings/__init__.py
index b46c9de435c..edcd11ff578 100644
--- a/langchain/embeddings/__init__.py
+++ b/langchain/embeddings/__init__.py
@@ -22,6 +22,7 @@ from langchain.embeddings.self_hosted_hugging_face import (
     SelfHostedHuggingFaceEmbeddings,
     SelfHostedHuggingFaceInstructEmbeddings,
 )
+from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
 from langchain.embeddings.tensorflow_hub import TensorflowHubEmbeddings
 
 logger = logging.getLogger(__name__)
@@ -42,6 +43,7 @@ __all__ = [
     "FakeEmbeddings",
     "AlephAlphaAsymmetricSemanticEmbedding",
     "AlephAlphaSymmetricSemanticEmbedding",
+    "SentenceTransformerEmbeddings",
 ]