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:

# 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.

# 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.