Explainable complex question answering over RDF files via Llama Index.
Install dependencies:
pip3 install --upgrade llama_index openaiStore OpenAI API key:
export OPENAI_API_KEY=<your-key>Example usage:
from llama_index import GPTSimpleVectorIndex, download_loader
RDFReader = download_loader("RDFReader")
document = RDFReader().load_data(file="./example.nt")
index = GPTSimpleVectorIndex(document)
result = index.query(
"list all places in a quoted Python array, then explain why")
print(result.response)
# >>> ['Lombardy', 'Milan', 'Piedmont']
# >>>
# >>> The answer is ['Lombardy', 'Milan', 'Piedmont'] because all three
# >>> of these are listed as types of places in the context information.
# >>> Lombardy and Piedmont are both listed as types of regions, and
# >>> Milan is listed as a type of city. All three of these are
# >>> subclasses of the type 'place', which is a subclass of 'thing'.
pip3 install flaskpython3 server.pyThe endpoint will be available at localhost:5050 by default.
Endpoint: /index (POST)
Form data:
file: the RDF file to index
This will return the internal ID of the index, to be used for querying.
Endpoint: /query (GET)
Parameters:
id: internal ID of the indexquery: url-encoded query
E.g., http://localhost:5050/query?id=5aa30cf341cc0fd1494da302649b04&query=list%20all%20regions
pip3 install flask streamlitRefresh terminal session or open new terminal.
streamlit run app.py 