This repository contains a collection of Python scripts that demonstrate how to use the OpenAI API to generate chat completions.

In increasing order of complexity, the scripts are:

1. chat.py: A simple script that demonstrates how to use the OpenAI API to generate chat completions.
2. chat_stream.py: Adds stream=True to the API call to return a generator that streams the completion as it is being generated.
3. chat_history.py: Adds a back-and-forth chat interface using input() which keeps track of past messages and sends them with each chat completion call.
4. chat_history_stream.py: The same idea, but with stream=True enabled.

Plus these scripts to demonstrate additional features:

5. chat_safety.py: The simple script with exception handling for Azure AI Content Safety filter errors.
6. chat_async.py: Uses the async clients to make asynchronous calls, including an example of sending off multiple requests at once using asyncio.gather.
7. chat_langchain.py: Uses the langchain SDK to generate chat completions. Learn more from Langchain docs

If you open this up in a Dev Container or GitHub Codespaces, everything will be setup for you. If not, follow these steps:

1. Set up a Python virtual environment and activate it.

2. Install the required packages:

python -m pip install -r requirements.txt

These scripts can be run against an Azure OpenAI account, an OpenAI.com account, or a local Ollama server, depending on the environment variables you set.

1. Copy the .env.sample file to a new file called .env:

   cp .env.sample .env

2. For Azure OpenAI, create an Azure OpenAI gpt-3.5 or gpt-4 deployment (perhaps using this template), and customize the .env file with your Azure OpenAI endpoint and deployment id.

   API_HOST=azure
   AZURE_OPENAI_ENDPOINT=https://YOUR-AZURE-OPENAI-SERVICE-NAME.openai.azure.com
   AZURE_OPENAI_DEPLOYMENT=YOUR-AZURE-DEPLOYMENT-NAME
   AZURE_OPENAI_VERSION=2024-03-01-preview

3. For OpenAI.com, customize the .env file with your OpenAI API key and desired model name.

   API_HOST=openai
   OPENAI_KEY=YOUR-OPENAI-API-KEY
   OPENAI_MODEL=gpt-3.5-turbo

4. For Ollama, customize the .env file with your Ollama endpoint and model name (any model you've pulled).

   API_HOST=ollama
   OLLAMA_ENDPOINT=http:https://localhost:11434/v1
   OLLAMA_MODEL=llama2

   If you're running inside the Dev Container, replace localhost with host.docker.internal.