Semantic Search + Preference Learning Investigation

Date: 2026-06-28

Goal

Add semantic recipe search ("etwas Leichtes für heiße Tage") and preference learning ("wenn warm dann kein Eintopf") to the meal-planning pipeline.

Embedding Model Requirements

Dimensions: 1024 (user requirement)
Language: Multilingual / German-capable
Hosting: Preferred on norris (same provider as LLM models)

Norris Embedding Endpoint Investigation

Tested all 20 models listed at https://ai.noris.de/v1/models against the /v1/embeddings endpoint:

Models tested via `/v1/embeddings`

Model	Result
vllm/bge-reranker-v2-m3	404 — provider API error
vllm/glm-5-2-nvfp4	404 — provider API error
vllm/gemma-4-31b-it	404 — provider API error
vllm/qwen3.6-27b-nvfp4	404 — provider API error

Additional embedding models tested by name

Model	Result
vllm/bge-m3	"no keys found that support model: bge-m3"
vllm/multilingual-e5-large	"no keys found that support model"
vllm/e5-mistral-7b-instruct	"no keys found that support model"
vllm/gte-large	"no keys found that support model"
vllm/NV-Embed-v2	"no keys found that support model"
vllm/snowflake-arctic-embed-l-v2.0	"no keys found that support model"

Conclusion

Norris recognizes embedding model names (gateway returns "no keys found" not "model not found"), meaning the Bifrost gateway knows about them but no API key has them activated. The /v1/embeddings endpoint returns 404 for all currently-available models (LLM models don't serve embeddings).

Action needed: User must request norris to activate an embedding model (bge-m3 recommended — 1024 dims, multilingual, strong German performance).

Local vLLM Check

Checked for local vLLM instances that might serve embeddings:

10.0.30.97:8000 (ComfyUI CT) — No route to host (SSH and HTTP both unreachable)
10.0.30.102:8000 (OpenWebUI CT) — No response
10.0.30.99:8000 (Docker host) — No response
localhost:8000/8001 — No response

No local vLLM embedding server found. ComfyUI CT (10.0.30.97) has GPU but was unreachable during investigation.

Proposed Architecture (pending embedding model availability)

Database Schema Additions

-- Add embedding column to recipes table
ALTER TABLE recipes ADD COLUMN embedding JSON NULL COMMENT '1024-dim vector as JSON array';

-- Decision logging
CREATE TABLE meal_decisions (
    id INT AUTO_INCREMENT PRIMARY KEY,
    decided_at DATETIME DEFAULT CURRENT_TIMESTAMP,
    recipe_id VARCHAR(255) NOT NULL,
    decision ENUM('accepted', 'rejected') NOT NULL,
    context JSON COMMENT 'temperature, season, weekday, recent_meals',
    INDEX idx_recipe (recipe_id),
    INDEX idx_decided (decided_at)
);

-- Learned preference rules
CREATE TABLE preference_rules (
    id INT AUTO_INCREMENT PRIMARY KEY,
    rule_pattern VARCHAR(255) NOT NULL COMMENT 'e.g. temp>20=>avoid:eintopf',
    weight FLOAT DEFAULT 1.0 COMMENT 'confidence, grows with confirmations',
    confirmed_count INT DEFAULT 0,
    rejected_count INT DEFAULT 0,
    created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
    UNIQUE KEY uniq_pattern (rule_pattern)
);

Pipeline Flow

Meal Planner selects recipes:
  1. SQL filter (rating > 4.0, not used this week)
  2. Preference rules apply (boost/penalize by context)
  3. Semantic search for "mood" query (light, hearty, asian...)
  4. Top-K presented as proposal

User feedback ("nein, kein Eintopf bei dem Wetter"):
  → INSERT INTO meal_decisions (rejected, temp=28°C, category=eintopf)
  → Rule mining detects pattern → INSERT/UPDATE preference_rules
  → Next run: Eintopf auto-downweighted when warm

Embedding Generation Strategy

Batch-generate embeddings for existing 28k+ recipes (one-time)
Incrementally embed new recipes as v7 scraper inserts them
Store as JSON array in embedding column (simple, no pgvector/Qdrant dependency)
Cosine similarity computed in Python at query time (28k × 1024 floats ≈ 115MB RAM, <1s)
At 100k recipes: ~400MB RAM for full similarity matrix — still feasible in-process

Alternative: MariaDB VECTOR Type

MariaDB 11.6+ supports native VECTOR type with VEC_DISTANCE functions. If the Galera cluster upgrades, switch from JSON-stored arrays to native vectors for indexed similarity search. Current MariaDB version on the cluster should be checked before relying on this.

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