LLM Text Completion via langchain

• clone this repo

• rename example.env to .env

• set environment variables in .env file

  • OPENAI_API_KEY from https://platform.openai.com/account/api-keys
  • Azure OpenAI Service Settings from Azure OpenAI https://portal.azure.com
    • AZURE_OPENAI_KEY
    • AZURE_OPENAI_ENDPOINT
    • AZURE_OPENAI_DEPLOYMENT_NAME
  • GOOGLE_PALM_AI_API_KEY from https://makersuite.google.com
  • GOOGLE_PROJECT_ID from Google Cloud console, refer to this https://cloud.google.com/vertex-ai/docs/start/cloud-environment
    • In the Google Cloud console, on the project selector page, select or create a Google Cloud project.
      • Go to project selector
    • Make sure that billing is enabled for your Google Cloud project.
    • Enable the Vertex AI API
      • Enable the API.
  • Add GCP Credential file as gcp-cred.json for Vertex AI, IAM -> Service accounts -> An account -> Keys from https://console.cloud.google.com/iam-admin/serviceaccounts.

• for running individual Python programs, use this

  Installation

  pip install -r requirements.txt
  

  OpenAI Python API, Azure OpenAI Service, PaLM API and Vertex AI

  python text-completion.py
  

or use these notebooks

• Gradio App

Further, simplify using Semantic Kernel

• Text Completion via Semantic Kernel

Alternatively, use this

• Text Completion via OpenAI API Python

More comprehensive demos are available on LLM Scenarios, Use cases on the Gradio app