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ask.py — the tiny CLI that runs the whole naive-RAG loop for one question.
retrieve(question) -> generate_answer(question, excerpts) -> print
Usage (from the project root, with the venv active):
python ask.py "What are the prerequisites for CPSC 210?"
python ask.py --top-k 6 "How many credits does the B.Sc. require?"
python ask.py --show-context "What is the academic standing policy?"
Prerequisites:
1. Keys in `.env` (copy `.env.example`): OPENAI_API_KEY for query embeddings and
ANTHROPIC_API_KEY for generation (the default backend).
2. The index built once: python -m src.ingest
"""
from __future__ import annotations
import argparse
from src import config, generate, pipeline, retrieve, trace
def main() -> None:
parser = argparse.ArgumentParser(description="Ask the UBC academic-calendar RAG advisor a question.")
parser.add_argument("question", help="your calendar question (wrap it in quotes)")
parser.add_argument(
"--top-k",
type=int,
default=config.TOP_K,
help=f"how many calendar excerpts to retrieve (default: {config.TOP_K})",
)
parser.add_argument(
"--show-context",
action="store_true",
help="also print the full text of the calendar excerpts sent to the model",
)
parser.add_argument(
"--mode",
choices=["dense", "hybrid", "hybrid_rerank"],
default=config.RETRIEVAL_MODE,
help=f"retrieval pipeline to use (default: {config.RETRIEVAL_MODE})",
)
parser.add_argument(
"--adaptive",
action="store_true",
help="route the question (Phase 4): simple -> fast retriever, complex -> agent",
)
parser.add_argument(
"--trace",
action="store_true",
help="print every internal step and the exact prompts sent to the APIs "
"(most informative with --adaptive)",
)
args = parser.parse_args()
if args.trace:
trace.enable()
# 1. RETRIEVE (+ optionally route). --adaptive sends the question through the Phase-4
# pipeline, which classifies it and picks the simple or agentic path; otherwise we run
# the plain retrieve -> generate loop with the chosen --mode.
if args.adaptive:
print("\nThinking ...\n")
result = pipeline.answer(args.question, top_k=args.top_k, mode=args.mode)
results, answer = result["results"], result["answer"]
print(f"Route: {result['route']}")
else:
results = retrieve.retrieve(args.question, top_k=args.top_k, mode=args.mode)
answer = None
# Show which excerpts grounded the answer (titles + similarity scores). This makes
# the retrieval step visible, which is the whole point of the learning exercise.
print("\nRetrieved excerpts:")
for r in results:
print(f" [{r.score:.3f}] (id={r.id}) {r.recipe['title']}")
if args.show_context:
print("\n--- context sent to the model ---")
print(generate.format_context(results))
print("--- end context ---")
# 2. GENERATE — have the LLM answer, grounded in those excerpts (the adaptive path
# already produced the answer above).
if answer is None:
print("\nThinking ...\n")
answer = generate.generate_answer(args.question, results)
print("\nAnswer:")
print(answer)
if __name__ == "__main__":
main()
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