PromptCue

A lightweight prompt intent classifier for LLM pipelines and RAG routers.

Hey r/python! I built a small library called PromptCue that classifies the intent behind a natural-language query and returns structured routing cues for LLM pipelines.

The problem: when you're building a RAG system or multi-agent router, you often need to know not just what the user asked, but how to handle it — should you retrieve docs, check freshness, ask for clarification, or generate from scratch?

PromptCue gives you back a Pydantic model with fields like primary_query_type, routing_hints, action_hints, and scope so your pipeline can make that call without prompting a model to classify first.

from promptcue import PromptCueAnalyzer

analyzer = PromptCueAnalyzer()
result = analyzer.analyze("Compare Aurora and OpenSearch for RAG on AWS")

print(result.primary_query_type)  # comparison
print(result.routing_hints)       # {'needs_retrieval': True, 'needs_reasoning': True, ...}
print(result.action_hints)        # {'should_compare': True, ...}

How it works:

• First pass: fast deterministic scoring via trigger-phrase matching (no ML, zero deps beyond pydantic/numpy/PyYAML)
• Fallback: sentence-level embeddings (all-MiniLM-L6-v2) when confidence is low
• Hosted mode: pass your own embed_fn if you already have an embedding model loaded — no second model needed

Ships with 13 query types (lookup, comparison, procedure, recommendation, summarization, etc.) and a YAML registry you can extend.

pip install promptcue
pip install "promptcue[semantic]"   # for full accuracy

GitHub: https://github.com/informity/promptcue
PyPi: https://pypi.org/project/promptcue/

Check it out, perhaps you may find this useful.