This is an example repository demonstrating the power of dynamic few-shot prompting. We use LangSmith to serve a few-shot dataset that we use to prompt an LLM to generate tweets in a style we like. We use LangGraph to orchestrate the (relatively simple) graph.
Key Links:
The graph logic is very simple for now.
First, we load the content of a given url.
You can find the logic for loading the content in langtweet/loading.py.
After that, we pass the content to a prompt.
This prompt contains some basic instructions, but more importantly a few examples of similar tweets in the past.
The logic for this can be found in langtweet/agent.py
A key part of this application is using dynamic few-shot selection to help prompt. The prompt instructions for tweeting are pretty basic. It provides a bit of context then tells the LLM to pay attention to the examples. Therefor, the examples are doing a lot of lifting here.
We use LangSmith to manage and serve the dataset that we use as examples.
We deploy the graph to LangGraph Cloud. This gives us a nice API to interact with, as well as fun studio UI to use to try out the graph.
We've made this studio publicly accessible, you can use it here.
A key part of a system like this is a feedback loop to continue to gather examples that can be used in the future. To that end, we have provided a very simple Streamlit application for invoking the graph and then correcting it and giving feedback. This feedback automatically creates a new entry in the dataset, which can then be used in the future.