add everyting for docs

docs/extras/integrations/text_embedding/deepinfra.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# DeepInfra\n",
+    "\n",
+    "[DeepInfra](https://deepinfra.com/?utm_source=langchain) is a serverless inference as a service that provides access to a [variety of LLMs](https://deepinfra.com/models?utm_source=langchain) and [embeddings models](https://deepinfra.com/models?type=embeddings&utm_source=langchain). This notebook goes over how to use LangChain with DeepInfra for text embeddings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      " ········\n"
+     ]
+    }
+   ],
+   "source": [
+    "# sign up for an account: https://deepinfra.com/login?utm_source=langchain\n",
+    "\n",
+    "from getpass import getpass\n",
+    "\n",
+    "DEEPINFRA_API_TOKEN = getpass()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"DEEPINFRA_API_TOKEN\"] = DEEPINFRA_API_TOKEN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings import DeepInfraEmbeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embeddings = DeepInfraEmbeddings(\n",
+    "    model_id=\"sentence-transformers/clip-ViT-B-32\",\n",
+    "    query_instruction=\"\",\n",
+    "    embed_instruction=\"\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs = [\"Dog is not a cat\", \"Beta is the second letter of Greek alphabet\"]\n",
+    "document_result = embeddings.embed_documents(docs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query = \"What is the first letter of Greek alphabet\"\n",
+    "query_result = embeddings.embed_query(query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cosine similarity between \"Dog is not a cat\" and query: 0.7489097144129355\n",
+      "Cosine similarity between \"Beta is the second letter of Greek alphabet\" and query: 0.9519380640702013\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "query_numpy = np.array(query_result)\n",
+    "for doc_res, doc in zip(document_result, docs):\n",
+    "    document_numpy = np.array(doc_res)\n",
+    "    similarity = np.dot(query_numpy, document_numpy) / (\n",
+    "        np.linalg.norm(query_numpy) * np.linalg.norm(document_numpy)\n",
+    "    )\n",
+    "    print(f'Cosine similarity between \"{doc}\" and query: {similarity}')"
+   ]
+  }
+ ],
+ "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": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}