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This package provides persistent vector storage using ChromaDB while using in-memory providers for key-value and graph operations.","maintainers":[{"name":"realm_lord","email":"realm.lord.eth@gmail.com"},{"name":"bmgalego","email":"bmgalego@gmail.com"}],"readme":"# @daydreamsai/chroma\n\nChromaDB integration for the DaydreamsAI memory system. This package provides persistent vector storage using ChromaDB while using in-memory providers for key-value and graph operations.\n\n## Installation\n\n```bash\npnpm add @daydreamsai/chroma chromadb\n```\n\n## Setup\n\n### 1. ChromaDB Installation & Setup\n\nChoose one of the following options to run ChromaDB:\n\n#### Option A: Docker (Recommended)\n```bash\n# Run ChromaDB server\ndocker run -p 8000:8000 chromadb/chroma\n```\n\n#### Option B: Python Installation\n```bash\npip install chromadb\nchroma run --host 0.0.0.0 --port 8000\n```\n\n#### Option C: Embedded Mode (Client-only)\nChromaDB can run embedded within your Node.js application (no separate server needed).\n\n### 2. Environment Variables (Optional)\n\nFor OpenAI embeddings:\n```bash\nexport OPENAI_API_KEY=\"your-openai-api-key\"\n```\n\n## Quick Start\n\n```typescript\nimport { createDreams } from \"@daydreamsai/core\";\nimport { createChromaMemory } from \"@daydreamsai/chroma\";\n\n// Create memory system\nconst memory = createChromaMemory({\n  path: \"http://localhost:8000\", // ChromaDB server URL\n  collectionName: \"my_agents\", // optional, defaults to \"daydreams_vectors\"\n});\n\n// Initialize the memory system\nawait memory.initialize();\n\n// Create agent with ChromaDB memory\nconst agent = createDreams({\n  memory,\n  // ... other config\n});\n```\n\n## Configuration\n\n### ChromaMemoryConfig\n\n```typescript\ninterface ChromaMemoryConfig {\n  /** ChromaDB connection URL/path (default: embedded mode) */\n  path?: string;\n  /** Collection name (default: \"daydreams_vectors\") */\n  collectionName?: string;\n  /** Custom embedding function from chromadb */\n  embeddingFunction?: IEmbeddingFunction;\n  /** Auth configuration for ChromaDB */\n  auth?: {\n    provider?: string;\n    credentials?: any;\n  };\n  /** Additional metadata for the collection */\n  metadata?: Record<string, any>;\n  /** Memory system options */\n  options?: MemoryConfig[\"options\"];\n}\n```\n\n## Features\n\n### Vector Storage\n- ✅ **Persistent Storage**: Vectors persist between application restarts\n- ✅ **Embedding Models**: Support for OpenAI, custom, or default embeddings\n- ✅ **Similarity Search**: Semantic search with configurable parameters\n- ✅ **Namespaces**: Organize vectors by namespace/context\n- ✅ **Metadata Filtering**: Filter searches by custom metadata\n- ✅ **Health Monitoring**: Built-in connection health checks\n- ✅ **Batch Operations**: Efficient bulk indexing and deletion\n\n### Memory Architecture\n- **Vector Storage**: ChromaDB (persistent)\n- **KV Storage**: In-memory (session-only)\n- **Graph Storage**: In-memory (session-only)\n\n### Embedding Options\n- **OpenAI Embeddings**: Automatic if `OPENAI_API_KEY` is set\n- **Custom Functions**: Provide your own embedding function\n- **Default Embeddings**: ChromaDB's built-in embeddings as fallback\n\n## API Reference\n\n### Core Functions\n\n#### `createChromaMemory(config)`\nCreates a complete memory system with ChromaDB vector storage.\n\n```typescript\nconst memory = createChromaMemory({\n  path: \"http://localhost:8000\",\n  collectionName: \"my_vectors\",\n  embeddingFunction: myCustomEmbedder,\n  metadata: {\n    project: \"my-ai-project\",\n    version: \"1.0.0\"\n  }\n});\n```\n\n#### `createChromaVectorProvider(config)`\nCreates just the vector provider for advanced use cases.\n\n```typescript\nimport { createChromaVectorProvider } from \"@daydreamsai/chroma\";\n\nconst vectorProvider = createChromaVectorProvider({\n  path: \"http://localhost:8000\",\n  collectionName: \"custom_collection\"\n});\n```\n\n### Memory Operations\n\nAll standard memory operations are supported:\n\n```typescript\n// Initialize\nawait memory.initialize();\n\n// Health check\nconst health = await memory.health();\nconsole.log(health.status); // \"healthy\" | \"unhealthy\"\n\n// Vector operations\nawait memory.vector.index([\n  {\n    id: \"doc1\",\n    content: \"The quick brown fox\",\n    metadata: { category: \"animals\" },\n    namespace: \"examples\"\n  }\n]);\n\nconst results = await memory.vector.search({\n  query: \"fast animals\",\n  namespace: \"examples\",\n  limit: 5,\n  includeMetadata: true\n});\n\n// KV operations (in-memory)\nawait memory.kv.set(\"user:123\", { name: \"Alice\" });\nconst user = await memory.kv.get(\"user:123\");\n```\n\n## Configuration Examples\n\n### Embedded Mode (No Server)\n```typescript\nconst memory = createChromaMemory({\n  // No path specified - runs embedded\n  collectionName: \"my_collection\"\n});\n```\n\n### Remote Server with Auth\n```typescript\nconst memory = createChromaMemory({\n  path: \"https://my-chroma-server.com\",\n  auth: {\n    provider: \"token\",\n    credentials: process.env.CHROMA_TOKEN\n  }\n});\n```\n\n### Custom Embedding Function\n```typescript\nimport { OpenAIEmbeddingFunction } from \"chromadb\";\n\nconst memory = createChromaMemory({\n  path: \"http://localhost:8000\",\n  embeddingFunction: new OpenAIEmbeddingFunction({\n    openai_api_key: process.env.OPENAI_API_KEY!,\n    openai_model: \"text-embedding-3-large\" // Higher quality embeddings\n  })\n});\n```\n\n### Production Configuration\n```typescript\nconst memory = createChromaMemory({\n  path: process.env.CHROMA_URL || \"http://localhost:8000\",\n  collectionName: `${process.env.APP_NAME}-vectors`,\n  metadata: {\n    environment: process.env.NODE_ENV,\n    version: process.env.APP_VERSION,\n    created_by: \"daydreams-ai\"\n  },\n  options: {\n    learning: {\n      enabled: true,\n      model: myLanguageModel\n    }\n  }\n});\n```\n\n## ChromaDB Schema\n\nThe ChromaDB provider stores vectors with this structure:\n\n```typescript\n{\n  id: string,              // Document ID\n  embedding: number[],     // Vector embedding\n  document: string,        // Original content\n  metadata: {\n    namespace: string,     // Logical grouping\n    indexed_at: string,    // Timestamp\n    // ... custom metadata\n  }\n}\n```\n\n## Performance Considerations\n\n### ChromaDB Limits\n- **Collection size**: Millions of vectors supported\n- **Embedding dimensions**: Up to 2048 dimensions typically\n- **Query performance**: Sub-second for most datasets\n- **Concurrent operations**: Good support for parallel queries\n\n### Optimization Tips\n1. **Batch Operations**: Use batch indexing for multiple documents\n2. **Namespace Strategy**: Use namespaces to partition data logically\n3. **Metadata Indexing**: ChromaDB automatically indexes metadata for filtering\n4. **Embedding Quality**: Higher-quality embeddings improve search relevance\n5. **Connection Pooling**: Reuse ChromaDB client connections\n\n## Environment Variables\n\nSupported environment variables for configuration:\n\n```bash\n# Embedding configuration\nOPENAI_API_KEY=\"sk-...\"                    # For OpenAI embeddings\n\n# ChromaDB connection\nCHROMA_URL=\"http://localhost:8000\"         # ChromaDB server URL\nCHROMA_COLLECTION=\"daydreams_vectors\"      # Default collection name\n\n# Authentication (if required)\nCHROMA_TOKEN=\"your-auth-token\"             # ChromaDB auth token\n```\n\n## Migration from Legacy API\n\nIf you're using the old ChromaVectorStore:\n\n```typescript\n// Old API (deprecated)\nimport { createChromaVectorStore } from \"@daydreamsai/chroma\";\nconst store = createChromaVectorStore(\"collection_name\", \"http://localhost:8000\");\n\n// New API (recommended)\nimport { createChromaMemory } from \"@daydreamsai/chroma\";\nconst memory = createChromaMemory({\n  path: \"http://localhost:8000\",\n  collectionName: \"collection_name\"\n});\nawait memory.initialize();\n```\n\nThe new API provides:\n- Better type safety and error handling\n- Standardized VectorProvider interface\n- Health monitoring and connection management\n- Namespace and metadata support\n- Consistent interface across all memory providers\n\n## Troubleshooting\n\n### Common Issues\n\n1. **Connection Errors**\n   ```\n   Error: Failed to initialize ChromaDB collection\n   ```\n   - Ensure ChromaDB server is running on the specified port\n   - Check the `path` configuration matches your server URL\n\n2. **Embedding Errors**\n   ```\n   Error: OpenAI API key not found\n   ```\n   - Set `OPENAI_API_KEY` environment variable, or\n   - Provide a custom `embeddingFunction` in config\n\n3. **Collection Issues**\n   ```\n   Error: Collection already exists with different configuration\n   ```\n   - Use a different `collectionName`, or\n   - Delete the existing collection in ChromaDB\n\n4. **Memory Issues**\n   ```\n   Error: Embedding dimension mismatch\n   ```\n   - Ensure all documents use the same embedding model\n   - Clear the collection if switching embedding models\n\n### Debug Mode\n\nEnable debug logging:\n\n```typescript\nconst memory = createChromaMemory({\n  path: \"http://localhost:8000\",\n  metadata: {\n    debug: true // Add debug metadata\n  }\n});\n\n// Monitor health\nsetInterval(async () => {\n  const health = await memory.health();\n  console.log(\"Vector storage health:\", health);\n}, 30000);\n```\n\n### Performance Monitoring\n\n```typescript\n// Monitor vector operations\nconst startTime = Date.now();\nawait memory.vector.search({ query: \"example\" });\nconsole.log(`Search took ${Date.now() - startTime}ms`);\n\n// Check collection size\nconst count = await memory.vector.count();\nconsole.log(`Collection contains ${count} vectors`);\n```\n\n## Docker Compose Example\n\nFor development setup:\n\n```yaml\nversion: '3.8'\nservices:\n  chromadb:\n    image: chromadb/chroma:latest\n    ports:\n      - \"8000:8000\"\n    environment:\n      - CHROMA_SERVER_HOST=0.0.0.0\n    volumes:\n      - chroma_data:/chroma/chroma\n      \nvolumes:\n  chroma_data:\n```\n\n## License\n\nMIT License - see LICENSE file for details.","readmeFilename":"README.md"}