James Murray introduces the Milvus Unified Index, a powerful multi-namespace content indexing solution designed to organize and retrieve stories, poems, and songs using advanced vector search techniques.

The system supports over 1 million content items across 12 namespaces (poetry, short stories, lyrics, etc.), with sub-15ms retrieval and 99.9% uptime. Each namespace has its own embedding model and access policy.

Ideal for digital libraries, creative platforms, and AI content recommendation engines.

Key Features

• Multi-namespace Support: Isolated indexes for stories, poems, songs, scripts, and more.
• High-Performance Search: HNSW + IVF-PQ indexing for million-scale datasets.
• Scalable Architecture: Horizontal sharding across 8-node Milvus cluster.
• Custom Embedding Models: Fine-tuned BERT per content type.
• Metadata Filtering: Search by author, genre, era, mood, length.
• REST + GraphQL API: Dual interface for frontend and backend use.

System Design & Architecture

The system uses Milvus for vector indexing and retrieval, ensuring that each content type is indexed separately, improving retrieval accuracy and performance. A metadata store (PostgreSQL) enables hybrid filtering.

Technical Stack

• Vector DB: Milvus 2.3 (Zilliz Cloud)
• Embeddings: Sentence-BERT + domain-specific fine-tuning
• API: FastAPI + GraphQL (Strawberry)
• Storage: S3 + MinIO

Related Projects

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• AI Content Architecture
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• Weaviate Story Graph