This is an example Beaker context for NYU's BDIKit library.
First, add your OpenAI API key to the environment:
export OPENAI_API_KEY=your key goes here
Then use docker compose to build and run the BDIKit Beaker context:
docker compose build
docker compose up -d
Navigate to localhost:8888 and select the bdikit_context. You can experiment with the following script:
1. Load the file dou.csv as a dataframe and subset it to the following columns: Country, Histologic_type, FIGO_stage, BMI, Age, Race, Ethnicity, Gender, Tumor_Focality, Tumor_Size_cm.
2. Please match this to the gdc schema using the two_phase method and check any results that don't look correct.
3. Can you show the top matches for Histologic_type?
Currently the agent only has one tool: match_schema. This is defined in src/bdikit_context/agent.py. Additional tools can easily be added by copying the template for the match_schema tool. One thing to note is that @tools are managed by Archytas. Archytas allows somewhat restricted argument types and does not allow direct passing of pandas.DataFrame. Instead, dataframes should be referenced by their variable names as a str. The actual code procedure that is executed (see procedures/python3/match_schema.py) treats the arguments from the @tool as variable names; when they should actually be strings they should be wrapped in quotes as in the match_schema.py example. Procedures invoked by tools can have their arguments passed in using Jinja templating. For example:
column_mappings = bdi.match_schema({{ dataset }}, target="{{ target }}", method="{{ method }}")
Here {{ dataset }} is the string name of a pandas.DataFrame and is interpreted as a variable, where as "{{ target }}" is treated as a string such as "gdc".
There are two main places to edit the agent's prompt. In src/bdikit_context/context.py the auto_context is a place to provide additional context. Currently the tools are enumerated here though this isn't strictly necessary. Additionally, prompt can be edited/managed in the agent.py BDIKitAgent docstring.