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118 lines
2.8 KiB
Markdown
118 lines
2.8 KiB
Markdown
---
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title: "Vector Store: High-Performance Usage"
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description: "Parallel ingestion, batch processing, and performance tuning for the Semantica Vector Store."
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icon: "bolt"
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---
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> High-performance batch ingestion with parallel embedding generation — 3–10× faster than sequential processing.
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---
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## Key Features
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- **Parallel ingestion** — multi-threaded embedding generation and storage
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- **Batch processing** — minimizes overhead by grouping documents into chunks
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- **Unified API** — `add_documents` handles embedding generation and storage in one call
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---
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## Quick Start: Parallel Ingestion
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```python
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from semantica.vector_store import VectorStore
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import time
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store = VectorStore(backend="faiss", dimension=768)
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documents = [f"This is document number {i} with some content." for i in range(1000)]
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metadata = [{"source": "generated", "id": i} for i in range(1000)]
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start = time.time()
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ids = store.add_documents(
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documents=documents,
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metadata=metadata,
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batch_size=64,
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parallel=True, # default: True
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)
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print(f"Ingested {len(ids)} documents in {time.time() - start:.2f}s")
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```
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---
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## Performance Comparison
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**Old method (sequential loop)** — slower due to per-item overhead:
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```python
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for doc in documents:
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emb = embedder.generate(doc)
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store.store_vectors([emb], [{"text": doc}])
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```
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**New method (parallel batching)** — 3–10× faster:
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```python
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store.add_documents(documents, parallel=True)
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```
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---
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## Configuration and Tuning
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### `max_workers`
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Number of concurrent threads for embedding generation.
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- **Default**: 6 (optimized for most systems)
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- Override only if you have very high core counts or specific throughput needs
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```python
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store = VectorStore(max_workers=16)
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```
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### `batch_size`
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Number of documents processed in a single chunk.
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- **Default**: 32
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- **Local models**: 32–64 works well
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- **API models (OpenAI, etc.)**: 100–200 reduces network latency overhead
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```python
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store.add_documents(documents, batch_size=100)
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```
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---
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## Manual Batch Embedding
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If you need embeddings without immediately storing them:
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```python
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vectors = store.embed_batch(texts=documents[:100])
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print(f"Generated {len(vectors)} vectors")
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```
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---
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## Best Practices
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<Tip>
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- **Metadata consistency** — ensure your `metadata` list is the same length as `documents`.
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- **Error handling** — `add_documents` propagates exceptions if embedding fails; validate your data first.
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- **Memory usage** — very large `batch_size` combined with high `max_workers` increases RAM usage. Monitor system resources for large corpora.
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</Tip>
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---
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## See Also
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<CardGroup cols={2}>
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<Card title="Vector Store Reference" icon="vector-square" href="reference/vector_store">
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Full VectorStore API with all backends.
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</Card>
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<Card title="Embeddings" icon="brain" href="reference/embeddings">
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Embedding providers and GPU acceleration.
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</Card>
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</CardGroup>
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