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name: rag-retrieval description: Hybrid search (embedding + BM25) for retrieving relevant clinical note passages. Use for finding source evidence to support claims in summaries and recommendations.

RAG Retrieval Skill

Overview

Retrieves relevant text passages from clinical notes using hybrid search combining dense embeddings (semantic similarity) and BM25 (keyword matching).

When to Use

• Find source passages for clinical claims
• Retrieve evidence for treatment recommendations
• Support citation generation with relevant context

Installation

IMPORTANT: This skill has its own isolated virtual environment (.venv) managed by uv. Do NOT use system Python.

Initialize the skill's environment:

Copy# From the skill directory
cd .agent/skills/rag-retrieval
uv sync  # Creates .venv and installs dependencies from pyproject.toml

Usage

CRITICAL: Always use uv run to execute code with this skill's .venv, NOT system Python.

Copy# From .agent/skills/rag-retrieval/ directory
# Run with: uv run python -c "..."
from rag_retrieval import RAGRetriever

retriever = RAGRetriever(chroma_client, collection_name="session_123")

# Query for relevant passages
results = retriever.retrieve(
    query="cardiovascular symptoms",
    n_results=5
)

for result in results:
    print(f"Text: {result['text']}")
    print(f"Score: {result['score']}")
    print(f"Offset: {result['start_offset']}-{result['end_offset']}")

Implementation

See rag_retrieval.py.