5a85070c22
Most of the examples needed updates of Readmes to show how to run them. Some of the requirements.txt files had extra content that wasn't needed, or missing altogether. Apparently some folks like to run npm start to run typescript, so a script was added to all typescript examples which hadn't been done before. Basically just a lot of cleanup. Signed-off-by: Matt Williams <m@technovangelist.com>
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Simple Chat Example
The chat endpoint is one of two ways to generate text from an LLM with Ollama, and is introduced in version 0.1.14. At a high level, you provide the endpoint an array of objects with a role and content specified. Then with each output and prompt, you add more of those role/content objects, which builds up the history.
Running the Example
-
Ensure you have the
llama2model installed:ollama pull llama2 -
Install the Python Requirements.
pip install -r requirements.txt -
Run the example:
python client.py
Review the Code
You can see in the chat function that actually calling the endpoint is done simply with:
r = requests.post(
"http://0.0.0.0:11434/api/chat",
json={"model": model, "messages": messages, "stream": True},
)
With the generate endpoint, you need to provide a prompt. But with chat, you provide messages. And the resulting stream of responses includes a message object with a content field.
The final JSON object doesn't provide the full content, so you will need to build the content yourself.
In the main function, we collect user_input and add it as a message to our messages and that is passed to the chat function. When the LLM is done responding the output is added as another message.
Next Steps
In this example, all generations are kept. You might want to experiment with summarizing everything older than 10 conversations to enable longer history with less context being used.