Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://python.langchain.com/docs/integrations/text_embedding/premai below:

PremAI | 🦜️🔗 LangChain

PremAI is an all-in-one platform that simplifies the creation of robust, production-ready applications powered by Generative AI. By streamlining the development process, PremAI allows you to concentrate on enhancing user experience and driving overall growth for your application. You can quickly start using our platform here.

We start by installing langchain and premai-sdk. You can type the following command to install:

pip install premai langchain

Before proceeding further, please make sure that you have made an account on PremAI and already created a project. If not, please refer to the quick start guide to get started with the PremAI platform. Create your first project and grab your API key.

In this section we are going to dicuss how we can get access to different embedding model using PremEmbeddings with LangChain. Let's start by importing our modules and setting our API Key.

from langchain_community.embeddings import PremAIEmbeddings

Once we imported our required modules, let's setup our client. For now let's assume that our project_id is 8. But make sure you use your project-id, otherwise it will throw error.

Note: Setting model_name argument in mandatory for PremAIEmbeddings unlike ChatPremAI.

import getpass
import os

if os.environ.get("PREMAI_API_KEY") is None:
    os.environ["PREMAI_API_KEY"] = getpass.getpass("PremAI API Key:")

model = "text-embedding-3-large"
embedder = PremAIEmbeddings(project_id=8, model=model)

We support lots of state of the art embedding models. You can view our list of supported LLMs and embedding models here. For now let's go for text-embedding-3-large model for this example.

query = "Hello, this is a test query"
query_result = embedder.embed_query(query)



print(query_result[:5])

[-0.02129288576543331, 0.0008162345038726926, -0.004556538071483374, 0.02918623760342598, -0.02547479420900345]

Finally let's embed a document

documents = ["This is document1", "This is document2", "This is document3"]

doc_result = embedder.embed_documents(documents)




print(doc_result[0][:5])

[-0.0030691148713231087, -0.045334383845329285, -0.0161729846149683, 0.04348714277148247, -0.0036920777056366205]

• Embedding model conceptual guide
• Embedding model how-to guides

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4