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Feature Engineering

The Aevum Feature Engineering pipeline transforms raw, unstructured knowledge into high-dimensional semantic representations optimized for retrieval, reasoning, and cross-lingual alignment.

The Aevum Pipeline
1

Ingestion

Multi-source parsing & normalization

2

Tokenization

Sub-word & domain-aware splitting

3

Semantic Embedding

Dense vector generation

4

Graph Injection

Entity linking & relationship mapping

5

Temporal Tagging

Versioning & validity windows

6

Normalization

L2 norm & dimensionality reduction

Core Feature Dimensions

Aevum's feature engineering goes beyond traditional TF-IDF or simple word embeddings. Our proprietary Neural Semantic Feature Set (NSFS) captures context, intent, temporal validity, and cross-lingual equivalence simultaneously.

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Semantic Embeddings

High-dimensional vectors (4096 dims) trained on 140+ languages, capturing nuanced meaning, synonyms, and conceptual hierarchies.

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Temporal Features

Every feature includes a temporal decay function and validity window, ensuring historical accuracy and real-time relevance.

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Cross-Lingual Alignment

Features are projected into a shared multilingual space, enabling seamless retrieval and translation without quality loss.

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Graph-Augmented Vectors

Embeddings are fused with Knowledge Graph node features, combining dense similarity with sparse structural relationships.

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Multi-Modal Fusion

Text, audio, and visual features are jointly encoded, allowing unified search across all media types.

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Domain-Specific Adaptation

Features are dynamically weighted based on domain context (e.g., medical, legal, physics) for specialized accuracy.

SDK Integration

Extract rich feature sets using the Aevum Python SDK. The engine handles normalization, graph injection, and temporal tagging automatically.

Python
# Aevum SDK: Feature Extraction from aevum.core import KnowledgeEngine # Initialize engine engine = KnowledgeEngine(api_key='ae_live_sk_...') # Extract features from raw content features = engine.features.extract( content="The theory of relativity describes gravity...", dimensions=4096, include_graph_nodes=True, temporal_context='current', domain_hint='physics' ) # Access feature properties print(f"Vector Norm: {features.norm:.4f}") print(f"Graph Connections: {len(features.graph_links)}") print(f"Temporal Validity: {features.valid_from} to {features.valid_to}") # Similarity search using engineered features results = engine.search.similarity( query_vector=features.vector, threshold=0.85, limit=10 )
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Pro Tip: Batch Processing

For large-scale ingestion, use engine.features.batch_extract() with a chunk size of 500–1000 documents. This leverages our distributed feature engine for optimal throughput.

Performance Metrics
45ms
Avg Latency
99.9%
Semantic Accuracy
4096
Vector Dimensions
140+
Languages
Feature Configuration Reference
Parameter Type Default Description Status
dimensions int 4096 Vector dimensionality. Higher dims capture more nuance. Stable
include_graph bool True Inject Knowledge Graph edges into feature vector. Stable
temporal_decay float 0.05 Rate of decay for historical relevance scoring. Stable
cross_lingual bool True Project features into shared multilingual space. Stable
domain_hint string None Optional domain context for feature weighting. Beta
quantize bool False Apply FP16 quantization for storage optimization. Experimental
Best Practices