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