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
+}

libs/langchain/langchain/embeddings/llm_rails.py

@@ -0,0 +1,72 @@
+""" This file is for LLMRails Embedding """
+import logging
+import os
+from typing import List, Optional
+
+import requests
+
+from langchain.pydantic_v1 import BaseModel, Extra
+from langchain.schema.embeddings import Embeddings
+
+
+class LLMRailsEmbeddings(BaseModel, Embeddings):
+    """LLMRails embedding models.
+
+    To use, you should have the  environment
+    variable ``LLM_RAILS_API_KEY`` set with your API key or pass it
+    as a named parameter to the constructor.
+
+    Model can be one of ["embedding-english-v1","embedding-multi-v1"]
+
+    Example:
+        .. code-block:: python
+
+            from langchain.embeddings import LLMRailsEmbeddings
+            cohere = LLMRailsEmbeddings(
+                model="embedding-english-v1", api_key="my-api-key"
+            )
+    """
+
+    model: str = "embedding-english-v1"
+    """Model name to use."""
+
+    api_key: Optional[str] = None
+    """LLMRails API key."""
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Call out to Cohere's embedding endpoint.
+
+        Args:
+            texts: The list of texts to embed.
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+        api_key = self.api_key or os.environ.get("LLM_RAILS_API_KEY")
+        if api_key is None:
+            logging.warning("Can't find LLMRails credentials in environment.")
+            raise ValueError("LLM_RAILS_API_KEY is not set")
+
+        response = requests.post(
+            "https://api.llmrails.com/v1/embeddings",
+            headers={"X-API-KEY": api_key},
+            json={"input": texts, "model": self.model},
+            timeout=60,
+        )
+        return [item["embedding"] for item in response.json()["data"]]
+
+    def embed_query(self, text: str) -> List[float]:
+        """Call out to Cohere's embedding endpoint.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embeddings for the text.
+        """
+        return self.embed_documents([text])[0]