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tree: 362499365a0b40f8975c77b0c5bc7f3afe7cf689 [path history] [tgz]
  1. .DS_Store
  2. __init__.py
  3. application.py
  4. notebook.ipynb
  5. README.md
  6. requirements.txt
  7. simple_streamlit_app.py
  8. statemachine.png
  9. streamlit_app.py
examples/multi-modal-chatbot/README.md

GPT-like chatbot

This is an example of chatgpt-like bot. It is simplified, but demonstrates the core concepts nicely.

The core idea is that we:

  1. Accept a prompt
  2. Check to see if the prompt is “safe” (this is currently a no-op, but its there for show)
  3. Decide the response mode by querying openAI (answer a question, generate an image, write some code, and can't decide/prompt for more)
  4. Query one of our models with a modified prompt to return the right response
  5. Return the response
  6. Go back to the beginning and wipe the state.

We have both a hamilton and non-hamilton implementation of this for comparison. For the simplicity of this example, Hamilton is likely overkill. There are a few nodes that could leverage it well, but the rest are too simple to worry about putting in a DAG. As examples get more complex, we anticipate that Hamilton nodes will play a more important role.

The state machine this encapsulates takes the following form:

State Machine

Note that it is currently wired up to handle errors in the state machine, but the actions do not respect that yet. Coming soon.

We have a few files:

  • application.py -- This contains a mainline to generate the graph portrayal.
  • requirements.txt -- Just the requirements. All this needs is Burr/Streamlit/openai
  • simple_streamlit_app.py -- This contains a more sophisticated Streamlit app to interact with.
  • notebook.ipynb -- A notebook that helps exercise things.
  • streamlit_app.py -- This contains a simple Streamlit app to interact with that is more sophisticated than the simple version. It contains a more sophisticated UI that also visualizes the Burr application. You must have your OPENAI_API_KEY set in your environment. You can do this by running.
streamlit run simple_streamlit_app.py & burr

This will open up both a streamlit app and a server at the same time. Interact with the app, and see interactions in the Burr UI.

Alternatively, run the more sophisticated streamlit app:

streamlit run streamlit_app.py

This streamlit app renders the statemachine as well as exposing the burr application showing you can build a more sophisticated UI around the Burr application.

Tracing

To see a tracing example see this example.