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How Kioku turns raw data into searchable knowledge.

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Embedding Model

Kioku uses nomic-embed-text-v2-moe via Ollama for all embeddings.
MetricValue
MTEB score63.9
Dimensions256–768 (configurable)
Latency (GPU)5–20ms
Latency (CPU)50–200ms
CostFree (compute only)
PrivacyData stays on your server
This model matches OpenAI’s text-embedding-3-small (62.3 MTEB) on benchmarks while running entirely on your hardware.

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Pipeline

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Documents (PDF)

Upload PDF → pdf-extract (text) → chunk → Ollama embed → Qdrant store
  1. PDF uploaded via POST /knowledge/documents
  2. Text extracted using pdf-extract crate
  3. Text split into chunks
  4. Each chunk embedded via Ollama HTTP API
  5. Embeddings + metadata stored in Qdrant

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Meetings (Transcripts)

Meeting transcript → per-segment embed → Qdrant store
  1. Transcript ingested via POST /meetings
  2. Each transcript segment (speaker + text + timestamps) embedded
  3. Embeddings + meeting metadata stored in Qdrant
  4. Searchable via POST /knowledge/search
Vector similarity search across all knowledge (documents + meetings): 
curl -X POST http://localhost:9100/knowledge/search \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"query":"deployment strategy","limit":5}'
Results are ranked by semantic similarity score (0–1).