Generative AI playground

Some experiments in what is new for the moment.

Text to Image

Generating images, see this blog first.

Updated image generation with Stable Diffusion XL.

OpenAI Hello World

1. In your Python 3 virtual environment install dependencies.

pip install -r requirements.txt

2. Get an OpenAI API key. Simple way for testing is to set env variable.

export OPENAI_API_KEY='sk...'

3. Run the hello-world and it should give you some stock recommendations

This one uses openai library directly
python hello-openai.py 

This is via LangChain - used next
python hello-openapi.py

4. Hello world variation with a UI using Gradio

python hello-openapi-gradio.py

Open in browser http://127.0.0.1:7860

A custom chatbot with OpenAI

See this blog for an overview and details

1. Install the requirements.txt
2. Index the custom data to create embeddings using LlamaIndex. I have PDF files in the local-data folder that it uses.

python build_index_openai.py

The output is stored in the folder local-index

3. Run the chatbot that will load the embeddings created and interact with it using LangChain

python local-index-chat-openai.py

Open in browser http://127.0.0.1:7860

Local LLM with GPT4All

Locally on CPU only (no GPU) it is possible to run some models thanks to GPT4All. It works but relatively slow on my machine. There is no support to generate embeddings as of now.

1. Install the requirements.txt
2. Get a model from gpt4all.io or hugging faces. Modify hello-gpt4all.py to point to the correct path you downloaded the model.

python hello-gpt4all.py

A custom chatbot with Azure OpenAI

See this blog for an overview and details

1. Install the requirements.txt
2. Setup your Azure OpenAI Studio and create a deployment. This will give you an API key and Base URL. Simple way for testing is to set env variable.

See the "Manage keys" and "Endpoint" as shown in the Azure portal.
export AZURE_API_KEY='abc...'
export AZURE_API_BASE='https://...'

3. Index the custom data to create embeddings using LlamaIndex. I have PDF files in the local-data folder that it uses.

python build_index_azure.py

The output is stored in the folder local-index-azure

4. Run the chatbot that will load the embeddings created and interact with it using LangChain

python local-index-chat-azure.py

Open in browser http://127.0.0.1:7860