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>

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]