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community[minor]: Add Clova Embeddings for LangChain Community (#21890)
- [ ] **PR title**: "Add Naver ClovaX embedding to LangChain community" - HyperClovaX is a large language model developed by [Naver](https://clova-x.naver.com/welcome). It's a powerful and purpose-trained LLM. - You can visit the embedding service provided by [ClovaX](https://www.ncloud.com/product/aiService/clovaStudio) - You may get CLOVA_EMB_API_KEY, CLOVA_EMB_APIGW_API_KEY, CLOVA_EMB_APP_ID From https://www.ncloud.com/product/aiService/clovaStudio --------- Co-authored-by: Bagatur <baskaryan@gmail.com>
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86
docs/docs/integrations/text_embedding/clova.ipynb
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86
docs/docs/integrations/text_embedding/clova.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Clova Embeddings\n",
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"[Clova](https://api.ncloud-docs.com/docs/ai-naver-clovastudio-summary) offers an embeddings service\n",
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"\n",
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"This example goes over how to use LangChain to interact with Clova inference for text embedding.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"os.environ[\"CLOVA_EMB_API_KEY\"] = \"\"\n",
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"os.environ[\"CLOVA_EMB_APIGW_API_KEY\"] = \"\"\n",
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"os.environ[\"CLOVA_EMB_APP_ID\"] = \"\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.embeddings import ClovaEmbeddings"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"embeddings = ClovaEmbeddings()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"query_text = \"This is a test query.\"\n",
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"query_result = embeddings.embed_query(query_text)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"document_text = [\"This is a test doc1.\", \"This is a test doc2.\"]\n",
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"document_result = embeddings.embed_documents([document_text])"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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@@ -43,6 +43,9 @@ if TYPE_CHECKING:
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from langchain_community.embeddings.clarifai import (
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ClarifaiEmbeddings,
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)
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from langchain_community.embeddings.clova import (
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ClovaEmbeddings,
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)
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from langchain_community.embeddings.cohere import (
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CohereEmbeddings,
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)
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@@ -232,6 +235,7 @@ __all__ = [
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"BedrockEmbeddings",
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"BookendEmbeddings",
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"ClarifaiEmbeddings",
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"ClovaEmbeddings",
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"CohereEmbeddings",
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"DashScopeEmbeddings",
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"DatabricksEmbeddings",
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@@ -309,6 +313,7 @@ _module_lookup = {
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"BedrockEmbeddings": "langchain_community.embeddings.bedrock",
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"BookendEmbeddings": "langchain_community.embeddings.bookend",
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"ClarifaiEmbeddings": "langchain_community.embeddings.clarifai",
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"ClovaEmbeddings": "langchain_community.embeddings.clova",
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"CohereEmbeddings": "langchain_community.embeddings.cohere",
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"DashScopeEmbeddings": "langchain_community.embeddings.dashscope",
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"DatabricksEmbeddings": "langchain_community.embeddings.databricks",
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134
libs/community/langchain_community/embeddings/clova.py
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134
libs/community/langchain_community/embeddings/clova.py
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from __future__ import annotations
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from typing import Dict, List, Optional, cast
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import requests
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from langchain_core.embeddings import Embeddings
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from langchain_core.pydantic_v1 import BaseModel, Extra, SecretStr, root_validator
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from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
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class ClovaEmbeddings(BaseModel, Embeddings):
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"""
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Clova's embedding service.
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To use this service,
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you should have the following environment variables
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set with your API tokens and application ID,
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or pass them as named parameters to the constructor:
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- ``CLOVA_EMB_API_KEY``: API key for accessing Clova's embedding service.
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- ``CLOVA_EMB_APIGW_API_KEY``: API gateway key for enhanced security.
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- ``CLOVA_EMB_APP_ID``: Application ID for identifying your application.
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Example:
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.. code-block:: python
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from langchain_community.embeddings import ClovaEmbeddings
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embeddings = ClovaEmbeddings(
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clova_emb_api_key='your_clova_emb_api_key',
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clova_emb_apigw_api_key='your_clova_emb_apigw_api_key',
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app_id='your_app_id'
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)
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query_text = "This is a test query."
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query_result = embeddings.embed_query(query_text)
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document_text = "This is a test document."
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document_result = embeddings.embed_documents([document_text])
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"""
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endpoint_url: str = (
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"https://clovastudio.apigw.ntruss.com/testapp/v1/api-tools/embedding"
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)
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"""Endpoint URL to use."""
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model: str = "clir-emb-dolphin"
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"""Embedding model name to use."""
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clova_emb_api_key: Optional[SecretStr] = None
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"""API key for accessing Clova's embedding service."""
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clova_emb_apigw_api_key: Optional[SecretStr] = None
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"""API gateway key for enhanced security."""
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app_id: Optional[SecretStr] = None
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"""Application ID for identifying your application."""
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class Config:
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extra = Extra.forbid
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@root_validator(pre=True, allow_reuse=True)
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate api key exists in environment."""
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values["clova_emb_api_key"] = convert_to_secret_str(
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get_from_dict_or_env(values, "clova_emb_api_key", "CLOVA_EMB_API_KEY")
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)
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values["clova_emb_apigw_api_key"] = convert_to_secret_str(
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get_from_dict_or_env(
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values, "clova_emb_apigw_api_key", "CLOVA_EMB_APIGW_API_KEY"
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)
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)
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values["app_id"] = convert_to_secret_str(
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get_from_dict_or_env(values, "app_id", "CLOVA_EMB_APP_ID")
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)
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return values
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""
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Embed a list of texts and return their embeddings.
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Args:
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texts: The list of texts to embed.
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Returns:
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List of embeddings, one for each text.
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"""
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embeddings = []
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for text in texts:
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embeddings.append(self._embed_text(text))
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return embeddings
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def embed_query(self, text: str) -> List[float]:
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"""
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Embed a single query text and return its embedding.
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Args:
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text: The text to embed.
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Returns:
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Embeddings for the text.
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"""
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return self._embed_text(text)
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def _embed_text(self, text: str) -> List[float]:
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"""
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Internal method to call the embedding API and handle the response.
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"""
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payload = {"text": text}
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# HTTP headers for authorization
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headers = {
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"X-NCP-CLOVASTUDIO-API-KEY": cast(
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SecretStr, self.clova_emb_api_key
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).get_secret_value(),
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"X-NCP-APIGW-API-KEY": cast(
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SecretStr, self.clova_emb_apigw_api_key
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).get_secret_value(),
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"Content-Type": "application/json",
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}
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# send request
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app_id = cast(SecretStr, self.app_id).get_secret_value()
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response = requests.post(
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f"{self.endpoint_url}/{self.model}/{app_id}",
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headers=headers,
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json=payload,
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)
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# check for errors
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if response.status_code == 200:
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response_data = response.json()
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if "result" in response_data and "embedding" in response_data["result"]:
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return response_data["result"]["embedding"]
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raise ValueError(
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f"API request failed with status {response.status_code}: {response.text}"
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)
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@@ -1,6 +1,7 @@
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from langchain_community.embeddings import __all__, _module_lookup
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EXPECTED_ALL = [
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"ClovaEmbeddings",
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"OpenAIEmbeddings",
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"AnyscaleEmbeddings",
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"AzureOpenAIEmbeddings",
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