High-Dimensional Vector Search

Semantic knowledge retrieval powered by 1536-dimensional embeddings, HNSW indexing, and real-time reranking. Find concepts, not just keywords.

Engine v3.2

<15ms p95

140+ Languages

🔍

Interactive Query Playground

📊

Retrieved Knowledge Nodes

Enter a query and click search to see semantic matches in real-time.

Architecture

How Vector Search Works

Our pipeline transforms unstructured text into precise mathematical representations, enabling sub-millisecond concept matching across 2.4M+ entries.

⚡

Text Embedding

Input queries and encyclopedia articles are passed through our fine-tuned transformer model, generating dense 1536-D vectors that capture semantic meaning, context, and nuance.

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HNSW Indexing

Vectors are stored in a Hierarchical Navigable Small World graph, enabling logarithmic-time approximate nearest neighbor search with >99.2% recall at k=50.

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Cross-Encoder Reranking

Top candidates undergo pairwise attention scoring against the original query, surfacing contextually precise results and suppressing lexical false positives.

Performance

Benchmark Specifications

Optimized for scale, latency, and accuracy across academic and enterprise workloads.

1536

Vector Dimensions

Optimal for multilingual text

12ms

p95 Latency

GPU-accelerated inference

99.4%

MRR@10

Cross-domain evaluation

2.4M

Indexed Nodes

Real-time incremental updates

Integration

API Reference

Query the knowledge graph with a single HTTP request. Supports JSON payloads, pagination, and hybrid search modes.

cURL / REST

# POST /v1/vector-search
curl -X POST https://api.aevum.edu/v1/vector-search \n  -H "Authorization: Bearer <YOUR_API_KEY>" \n  -H "Content-Type: application/json" \n  -d '{
    "query": "quantum entanglement in cryptography",
    "top_k": 5,
    "mode": "semantic",
    "filters": {
      "domain": ["physics", "computer_science"],
      "min_accuracy": 0.92
    }
  }'

Applications

Use Cases

From academic research to enterprise knowledge bases, vector search powers intelligent discovery.

🎓

Academic Literature Review

Automatically surface related papers, methodologies, and theoretical foundations across disciplines using semantic similarity rather than keyword overlap.

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RAG-Powered Chatbots

Ground LLM responses in verified encyclopedia data. Reduce hallucinations by anchoring generations to high-confidence vector matches.

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Multilingual Knowledge Graph

Bridge language barriers by aligning concepts across 140+ languages in a shared embedding space. Query in Spanish, retrieve in Japanese.

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Real-Time Trend Detection

Monitor emerging topics by tracking cosine similarity shifts in newly published articles. Identify paradigm shifts before they trend.

Ready to Search Beyond Keywords?

Access production-grade vector search with dedicated support, SLA guarantees, and priority indexing.

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