Initial commit: Hermes Agent Skills collection

2026-07-12 19:02:59 +00:00
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+# 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
+
+```sql
+-- 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.