From 03881c674358df6bc243f67a77050aa95c660bcd Mon Sep 17 00:00:00 2001
From: Erick Friis <erick@langchain.dev>
Date: Tue, 23 Jul 2024 14:03:30 -0700
Subject: [PATCH] docs: fix hf embeddings install (#24577)

---
 .../sentence_transformers.ipynb               | 89 ++++++++-----------
 1 file changed, 35 insertions(+), 54 deletions(-)

diff --git a/docs/docs/integrations/text_embedding/sentence_transformers.ipynb b/docs/docs/integrations/text_embedding/sentence_transformers.ipynb
index f33c0bf2f4..080fa184fe 100644
--- a/docs/docs/integrations/text_embedding/sentence_transformers.ipynb
+++ b/docs/docs/integrations/text_embedding/sentence_transformers.ipynb
@@ -8,87 +8,68 @@
     "# Sentence Transformers on Hugging Face\n",
     "\n",
     ">[Hugging Face sentence-transformers](https://huggingface.co/sentence-transformers) is a Python framework for state-of-the-art sentence, text and image embeddings.\n",
-    ">One of the embedding models is used in the `HuggingFaceEmbeddings` class.\n",
-    ">We have also added an alias for `SentenceTransformerEmbeddings` for users who are more familiar with directly using that package.\n",
-    "\n",
-    "`sentence_transformers` package models are originating from [Sentence-BERT](https://arxiv.org/abs/1908.10084)"
+    ">You can use these embedding models from the `HuggingFaceEmbeddings` class."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "06c9f47d",
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet  langchain-huggingface"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ff9be586",
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1.1\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
+      "[-0.0383385568857193, 0.12346469610929489, -0.028642987832427025, 0.05365273728966713, 0.00884537026...\n"
      ]
     }
    ],
    "source": [
-    "%pip install --upgrade --quiet  sentence_transformers > /dev/null"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "861521a9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_huggingface import HuggingFaceEmbeddings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ff9be586",
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "from langchain_huggingface import HuggingFaceEmbeddings\n",
+    "\n",
     "embeddings = HuggingFaceEmbeddings(model_name=\"all-MiniLM-L6-v2\")\n",
-    "# Equivalent to SentenceTransformerEmbeddings(model_name=\"all-MiniLM-L6-v2\")"
+    "\n",
+    "text = \"This is a test document.\"\n",
+    "query_result = embeddings.embed_query(text)\n",
+    "\n",
+    "# show only the first 100 characters of the stringified vector\n",
+    "print(str(query_result)[:100] + \"...\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "d0a98ae9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "text = \"This is a test document.\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "5d6c682b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "query_result = embeddings.embed_query(text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "id": "bb5e74c0",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-0.038338493555784225, 0.12346471846103668, -0.028642840683460236, 0.05365276336669922, 0.00884535...\n"
+     ]
+    }
+   ],
    "source": [
-    "doc_result = embeddings.embed_documents([text, \"This is not a test document.\"])"
+    "doc_result = embeddings.embed_documents([text, \"This is not a test document.\"])\n",
+    "print(str(doc_result)[:100] + \"...\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "aaad49f8",
+   "id": "d18544f5",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -110,7 +91,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.4"
   },
   "vscode": {
    "interpreter": {