ChromaDB Client Skill

Overview

This skill provides a Python wrapper for ChromaDB's REST API, enabling vector storage and hybrid search capabilities. It supports automatic embedding generation and BM25-based sparse retrieval for improved citation accuracy.

When to Use

Use this skill when you need to:

• Store clinical note chunks as vector embeddings
• Query for semantically similar text passages
• Implement RAG (Retrieval-Augmented Generation)
• Clear session-based embeddings for privacy compliance
• Perform hybrid search (embedding similarity + keyword matching)

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/chroma-client
uv sync  # Creates .venv and installs dependencies from pyproject.toml

Dependencies are in

pyproject.toml

:

• chromadb

  - Vector database client

Usage

CRITICAL: Always use

uv run

to execute code with this skill's

.venv

, NOT system Python.

Initialize Client

# From .agent/skills/chroma-client/ directory
# Run with: uv run python -c "..."
from chroma_client import ChromaClient

# Initialize (uses CHROMA_HOST env var by default)
client = ChromaClient(
    host="localhost",  # Default from CHROMA_HOST
    port=8000          # Default port
)

Create Collection

# Create or get existing collection
collection = client.create_collection(
    collection_name="clinical_note_session_123",
    metadata={"session_id": "123", "note_type": "cardiology"}
)

Add Chunks with Auto-Embedding

# Chunks are automatically embedded by ChromaDB
chunks = [
    "Patient presents with chest pain radiating to left arm...",
    "History of hypertension for 10 years...",
    "Physical exam reveals elevated BP 150/95..."
]

client.add_chunks(
    collection_name="clinical_note_session_123",
    chunks=chunks,
    metadatas=[
        {"start_offset": 0, "end_offset": 60},
        {"start_offset": 60, "end_offset": 110},
        {"start_offset": 110, "end_offset": 165}
    ],
    ids=["chunk_0", "chunk_1", "chunk_2"]  # Optional, auto-generated if None
)

Query with Hybrid Search

# Semantic search with embedding similarity
results = client.query(
    collection_name="clinical_note_session_123",
    query_text="cardiovascular symptoms",
    n_results=5,
    where={"note_type": "cardiology"}  # Optional metadata filter
)

# Access results
for doc, metadata, distance in zip(
    results["documents"],
    results["metadatas"],
    results["distances"]
):
    print(f"Document: {doc}")
    print(f"Offset: {metadata['start_offset']}-{metadata['end_offset']}")
    print(f"Distance: {distance}")

Session Cleanup (Privacy)

# Clear collection after processing (HIPAA compliance)
client.clear_collection("clinical_note_session_123")

Health Check

if client.check_health():
    print("ChromaDB server is healthy")
else:
    print("ChromaDB server unavailable")

Configuration

Environment Variables:

• CHROMA_HOST

  : Server URL (default:

  http://localhost:8000

  )

Parameters:

• collection_name

  : Unique identifier for the collection

• n_results

  : Number of results to return (default: 5)

• where

  : Metadata filter dictionary (optional)

Best Practices

1. Session-Based Collections: Use unique collection names per session (e.g.,

   note_session_{uuid}

   )
2. Always Clear: Delete collections after processing to prevent PHI persistence
3. Metadata Tracking: Store offsets in metadata for citation extraction
4. Contextual Enrichment: Add context to chunks before embedding (see

   contextual-chunking

   skill)
5. Health Checks: Verify ChromaDB availability before critical operations

Integration with RAG Pipeline

Typical workflow:

1. Chunking: Use

   contextual-chunking

   skill to prepare chunks
2. Embedding: Use this skill to store chunks with auto-embedding
3. Retrieval: Query for relevant chunks during summarization
4. Citation: Use

   citation-extraction

   skill to validate alignments
5. Cleanup: Clear collection when session ends

Error Handling

• Collections that don't exist are created automatically
• Delete operations on non-existent collections are safely ignored
• All errors from ChromaDB API are propagated with context

Implementation

See

chroma_client.py

for the full Python implementation.