LLMRails Embedding (#10959)

LLMRails  Embedding Integration
This PR provides integration with LLMRails. Implemented here are:

langchain/embeddings/llm_rails.py
docs/extras/integrations/text_embedding/llm_rails.ipynb


Hi @hwchase17 after adding our vectorstore integration to langchain with
confirmation of you and @baskaryan, now we want to add our embedding
integration

---------

Co-authored-by: Anar Aliyev <aaliyev@mgmt.cloudnet.services>
Co-authored-by: Bagatur <baskaryan@gmail.com>

docs/extras/integrations/text_embedding/llm_rails.ipynb

@@ -0,0 +1,133 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "278b6c63",
+   "metadata": {},
+   "source": [
+    "# LLMRails\n",
+    "\n",
+    "Let's load the LLMRails Embeddings class.\n",
+    "\n",
+    "To use LLMRails embedding you need to pass api key by argument or set it in environment with `LLM_RAILS_API_KEY` key.\n",
+    "To gey API Key you need to sign up in https://console.llmrails.com/signup and then go to https://console.llmrails.com/api-keys and copy key from there after creating one key in platform."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0be1af71",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings import LLMRailsEmbeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2c66e5da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embeddings = LLMRailsEmbeddings(model='embedding-english-v1') # or embedding-multi-v1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "01370375",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = \"This is a test document.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a42e4035",
+   "metadata": {},
+   "source": [
+    "To generate embeddings, you can either query an invidivual text, or you can query a list of texts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "91bc875d-829b-4c3d-8e6f-fc2dda30a3bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[-0.09996652603149414,\n",
+       " 0.015568195842206478,\n",
+       " 0.17670190334320068,\n",
+       " 0.16521021723747253,\n",
+       " 0.21193109452724457]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query_result = embeddings.embed_query(text)\n",
+    "query_result[:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a4b0d49e-0c73-44b6-aed5-5b426564e085",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[-0.04242777079343796,\n",
+       " 0.016536075621843338,\n",
+       " 0.10052520781755447,\n",
+       " 0.18272875249385834,\n",
+       " 0.2079043835401535]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "doc_result = embeddings.embed_documents([text])\n",
+    "doc_result[0][:5]"
+   ]
+  }
+ ],
+ "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": "3.11.5"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e971737741ff4ec9aff7dc6155a1060a59a8a6d52c757dbbe66bf8ee389494b1"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}