claude-mem/docs/context/CHROMA.md

Chroma Vector Database - Hybrid Semantic Search

Overview

Claude-Mem v5.0.0 introduced Chroma, a vector database that enables semantic search across your memory stream. Combined with SQLite's FTS5 keyword search, this creates a powerful hybrid search architecture that finds contextually relevant observations using both meaning and keywords.

Key Benefits:

• 🧠 Semantic Search - Find observations by meaning, not just keywords
• 🔍 Hybrid Architecture - Combines semantic similarity with keyword matching
• ⏱️ Recency Filtering - Focus on recent 90 days for relevant context
• ⚡ Fast Performance - Semantic search under 200ms with 8,000+ documents
• 🔄 Auto-Sync - ChromaSync service keeps vectors updated automatically

What is Chroma?

ChromaDB is an open-source vector database designed for AI applications. It stores text as vector embeddings - mathematical representations that capture semantic meaning.

Example:

Query: "authentication bug"
Keyword Match: Must contain both "authentication" AND "bug"
Semantic Match: Also finds "login error", "auth failure", "sign-in issue"

Semantic search understands that "authentication bug" is conceptually similar to "login error" even though they share no keywords.

Architecture

Hybrid Search Flow

┌──────────────────────────────────────────────────────────────┐
│ User Query: "How does authentication work?"                  │
└──────────────────────────────────────────────────────────────┘
                            ↓
          ┌─────────────────┴─────────────────┐
          ↓                                   ↓
┌──────────────────────┐          ┌──────────────────────┐
│ Chroma Semantic      │          │ SQLite FTS5          │
│ Vector Similarity    │          │ Keyword Search       │
│                      │          │                      │
│ Finds conceptually   │          │ Finds exact/fuzzy    │
│ similar observations │          │ keyword matches      │
└──────────────────────┘          └──────────────────────┘
          ↓                                   ↓
          └─────────────────┬─────────────────┘
                            ↓
          ┌─────────────────────────────────┐
          │ Merge Results                   │
          │ - Deduplicate by ID             │
          │ - Sort by relevance + recency   │
          │ - Filter by 90-day window       │
          └─────────────────────────────────┘
                            ↓
          ┌─────────────────────────────────┐
          │ Return Top Matches              │
          │ Semantic + Keyword combined     │
          └─────────────────────────────────┘

ChromaSync Service

The ChromaSync service (src/services/sync/ChromaSync.ts) automatically synchronizes observations to Chroma:

When Observations Are Synced:

1. Session Summary - After each session completes, all new observations synced
2. Worker Startup - On initialization, checks for unsynced observations
3. Manual Trigger - Can force sync via internal API (development only)

What Gets Embedded:

• Observation ID (unique identifier)
• Title (compressed learning statement)
• Narrative (detailed explanation)
• Project path (for project-specific filtering)
• Timestamp (for recency filtering)
• Concepts (semantic tags)
• File references (associated code files)

Embedding Model:

• Currently using Chroma's default embedding function
• Future: Configurable embedding models (e.g., OpenAI, sentence-transformers)

Data Structure

SQLite (Source of Truth):

CREATE TABLE observations (
  id INTEGER PRIMARY KEY,
  title TEXT,
  narrative TEXT,
  facts TEXT,
  concepts TEXT,
  files TEXT,
  type TEXT,
  projectPath TEXT,
  createdAt INTEGER
);

Chroma (Vector Embeddings):

{
  ids: ["obs_12345"],
  embeddings: [[0.123, -0.456, ...]], // 384-dimensional vector
  documents: ["Title: Authentication flow\nNarrative: Implemented..."],
  metadatas: [{
    type: "feature",
    project: "claude-mem",
    timestamp: 1698765432000,
    concepts: "pattern,architecture"
  }]
}

How Semantic Search Works

Vector Embeddings

Text converted to high-dimensional vectors that capture meaning:

"user authentication" → [0.12, -0.34, 0.56, ..., 0.78]
"login system"        → [0.15, -0.32, 0.54, ..., 0.81]
"database schema"     → [-0.45, 0.67, -0.23, ..., 0.12]

Notice: "user authentication" and "login system" have similar vectors (close in vector space), while "database schema" is distant.

Similarity Search

Chroma uses cosine similarity to find nearest neighbors:

// Query embedding
query: "authentication bug"
query_vector: [0.14, -0.33, 0.55, ..., 0.79]

// Find observations with similar vectors
results = chroma.query(
  query_vector,
  n_results: 10,
  where: { timestamp: { $gte: now - 90_days } }
)

Result Ranking:

• Higher cosine similarity = more semantically similar
• Filtered by 90-day recency window
• Combined with keyword matches from FTS5

90-Day Recency Filtering

Why 90 days?

Rationale:

• Recent context more likely relevant to current work
• Prevents very old observations from diluting results
• Balances completeness with relevance
• Reduces vector search space for faster queries

Implementation:

const ninetyDaysAgo = Date.now() - (90 * 24 * 60 * 60 * 1000);

// Chroma metadata filter
where: {
  timestamp: { $gte: ninetyDaysAgo }
}

// SQLite WHERE clause
WHERE createdAt >= ?

Configurable?

• Not currently user-configurable
• Hard-coded in src/servers/search-server.ts
• Future: Add CLAUDE_MEM_RECENCY_DAYS environment variable

MCP Tool Integration

All 9 MCP search tools benefit from hybrid search:

search_observations (Hybrid)

// Keyword-only (v4.x)
search_observations(query: "authentication")
// Returns: Observations containing "authentication"

// Hybrid semantic + keyword (v5.x)
search_observations(query: "authentication")
// Returns: Observations with "authentication" PLUS semantically similar:
//   - "login system"
//   - "user credentials"
//   - "session management"

get_timeline_by_query (Semantic-First)

// Uses Chroma to find best match, then builds timeline
get_timeline_by_query(
  query: "when did we implement the viewer UI?",
  mode: "auto",
  depth_before: 10,
  depth_after: 10
)

// Chroma finds: Observation #4057 "Web-Based Viewer UI for Real-Time Memory Stream"
// Returns: Timeline with 10 observations before + anchor + 10 after

Benefits Across All Tools

• find_by_concept: Semantic similarity finds related concepts
• find_by_file: Finds semantically similar code changes
• find_by_type: Better relevance ranking within type
• get_recent_context: Prioritizes semantically relevant recent context

Performance

Benchmarks (8,279 vector documents)

Operation	Time	Notes
Semantic Query	150-200ms	90-day window, top 10 results
Keyword Query (FTS5)	5-10ms	Full-text search
Hybrid Query	160-220ms	Combined semantic + keyword
Initial Sync	2-5 min	First-time embedding of all observations
Incremental Sync	100-500ms	1-10 new observations per session

Memory Usage

• Chroma DB Size: ~50MB for 8,000 observations
• Embeddings: 384 dimensions × 4 bytes = 1.5KB per observation
• Metadata: ~500 bytes per observation (project, type, timestamp)
• Total: ~2KB per observation in Chroma

Optimization Tips

1. Reduce vector dimensions: Use smaller embedding models (future)
2. Adjust recency window: Narrow to 30/60 days for faster queries
3. Limit result count: Request fewer results (n_results=5 vs 10)
4. Project filtering: Add project filter to metadata query

Installation & Dependencies

Python Requirement

Chroma requires Python 3.7+ installed:

Check Python:

python3 --version
# Should show: Python 3.7.x or higher

Install Python (if needed):

• macOS: brew install python3
• Windows: Download from python.org
• Linux: apt-get install python3 or yum install python3

ChromaDB Installation

Chroma installed automatically as npm dependency:

npm install
# Installs: chromadb (Python package via node-gyp bindings)

Manual Installation (if auto-install fails):

pip3 install chromadb

Troubleshooting Installation

Error: "Python not found"

# Set Python path explicitly
export PYTHON=/usr/local/bin/python3
npm install

Error: "chromadb module not found"

# Reinstall chromadb
pip3 install --upgrade chromadb

# Verify installation
python3 -c "import chromadb; print(chromadb.__version__)"

Error: "node-gyp build failed"

# Install build tools
# macOS: xcode-select --install
# Windows: npm install --global windows-build-tools
# Linux: apt-get install build-essential

Configuration

Environment Variables

Currently no user-configurable settings. Future options:

// Proposed for future versions
{
  "env": {
    "CLAUDE_MEM_CHROMA_ENABLED": "true",          // Enable/disable Chroma
    "CLAUDE_MEM_CHROMA_PATH": "~/.claude-mem/chroma", // DB location
    "CLAUDE_MEM_EMBEDDING_MODEL": "default",       // Embedding model choice
    "CLAUDE_MEM_RECENCY_DAYS": "90",              // Recency window
    "CLAUDE_MEM_VECTOR_DIM": "384"                // Embedding dimensions
  }
}

Disabling Chroma (Future)

To disable semantic search and use keyword-only:

{
  "env": {
    "CLAUDE_MEM_CHROMA_ENABLED": "false"
  }
}

Falls back to SQLite FTS5 keyword search only.

Database Maintenance

Location

~/.claude-mem/chroma/
├── chroma.sqlite3         # Chroma metadata database
└── index/                 # Vector index files
    └── *.bin              # Binary vector data

Backup

# Backup entire Chroma directory
cp -r ~/.claude-mem/chroma ~/.claude-mem/chroma.backup

# Restore from backup
rm -rf ~/.claude-mem/chroma
cp -r ~/.claude-mem/chroma.backup ~/.claude-mem/chroma

Reset Chroma (Force Resync)

# Delete Chroma database
rm -rf ~/.claude-mem/chroma

# Restart worker to trigger full resync
npm run worker:restart

# Check logs for sync progress
npm run worker:logs

Note: Resync can take 2-5 minutes for thousands of observations.

Disk Space Management

Chroma grows with observations:

• 1,000 observations ≈ 5MB
• 10,000 observations ≈ 50MB
• 100,000 observations ≈ 500MB

Cleanup old observations:

-- Delete observations older than 1 year
-- This will trigger Chroma resync on next startup
sqlite3 ~/.claude-mem/claude-mem.db \
  "DELETE FROM observations WHERE createdAt < strftime('%s', 'now', '-1 year') * 1000;"

Advanced Usage

Direct Chroma Queries (Development)

For debugging or custom queries:

import { ChromaSync } from './services/sync/ChromaSync';

const sync = new ChromaSync();
await sync.initialize();

// Query Chroma directly
const results = await sync.query({
  queryTexts: ["authentication implementation"],
  nResults: 10,
  where: {
    type: "feature",
    timestamp: { $gte: Date.now() - 90_days }
  }
});

console.log(results.ids, results.distances, results.documents);

Custom Embedding Models (Future)

Chroma supports multiple embedding models:

// Future configuration
const sync = new ChromaSync({
  embeddingModel: "sentence-transformers/all-MiniLM-L6-v2", // Smaller, faster
  // or: "text-embedding-ada-002" (OpenAI, requires API key)
  // or: "all-mpnet-base-v2" (Higher quality, slower)
});

Metadata Filtering

Chroma supports advanced metadata queries:

// Find observations by type and project
results = await sync.query({
  queryTexts: ["API design"],
  where: {
    $and: [
      { type: { $in: ["decision", "feature"] } },
      { project: "claude-mem" }
    ]
  }
});

// Find recent observations
results = await sync.query({
  queryTexts: ["database schema"],
  where: {
    timestamp: { $gte: Date.now() - 30_days }
  }
});

Comparison: Semantic vs Keyword Search

Aspect	Semantic (Chroma)	Keyword (FTS5)
Speed	150-200ms	5-10ms
Accuracy	High (meaning-based)	Medium (exact match)
Storage	~2KB per observation	~500 bytes per observation
Conceptual Matching	✅ Yes	❌ No
Exact Match	❌ Not guaranteed	✅ Always
Typo Tolerance	✅ High	⚠️ Limited (fuzzy)
Dependencies	Python + chromadb	None (SQLite built-in)
Recency Bias	✅ Built-in (90 days)	Manual filtering

Best Practice: Use hybrid search (both) for optimal results.

Troubleshooting

"Chroma not found" Error

Symptom: Worker logs show "Chroma not available, using keyword-only search"

Solution:

# Check Python installation
python3 --version

# Reinstall chromadb
pip3 install chromadb

# Restart worker
npm run worker:restart

Slow Query Performance

Symptom: Searches taking >1 second

Solutions:

1. Reduce recency window (edit src/servers/search-server.ts)
2. Limit result count (nResults: 5 instead of 10)
3. Add project filter to narrow search space
4. Check Chroma index size (may need rebuild)

Out of Memory Errors

Symptom: Worker crashes with "JavaScript heap out of memory"

Solution:

# Increase Node.js heap size
export NODE_OPTIONS="--max-old-space-size=4096"

# Restart worker
npm run worker:restart

Sync Taking Too Long

Symptom: Initial Chroma sync takes >10 minutes

Possible Causes:

• Large number of observations (>10,000)
• Slow embedding model
• Limited CPU resources

Solutions:

1. Let it complete (one-time cost)
2. Delete very old observations to reduce count
3. Close resource-intensive apps during sync

Future Enhancements

Potential improvements for future versions:

• Configurable Recency: User-defined recency window (30/60/90/365 days)
• Custom Embeddings: Choose embedding model (quality vs speed trade-off)
• Incremental Updates: Update existing vectors instead of full resync
• Semantic Filters: Search by semantic concept ("all architectural decisions")
• Multi-Language Support: Embeddings optimized for non-English code/docs
• Clustering: Auto-cluster related observations for discovery
• Visualization: 2D/3D visualization of vector space (similar observations near each other)

Resources

• ChromaDB Documentation: https://docs.trychroma.com/
• Source Code: src/services/sync/ChromaSync.ts
• Search Server: src/servers/search-server.ts
• Python Package: https://pypi.org/project/chromadb/

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Powered by ChromaDB | Hybrid Semantic + Keyword Search | 90-Day Recency Window