ajianaz/codomate/embeddinggemma-qdrant
Claude Code and agent skill by ajianaz/codomate
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EmbeddingGemma ONNX — CPU-only local embedding (onnxruntime+tokenizers, NO optimum/transformers). Model setup, embed function, Qdrant connection. Use embeddinggemma-knowledge or embeddinggemma-novel for specific use cases.
Install
npx vskill@latest install ajianaz/codomate/embeddinggemma-qdrantSKILL.md
EmbeddingGemma ONNX — Base Skill
Base skill for local embedding pipeline. Use the specific skill for your use case:
embeddinggemma-knowledge— hermes-knowledge collection (newsletter, riset, digest, blog)embeddinggemma-novel— novel chapters collection
Architecture
| Property | Value | |
|---|---|---|
| Model | onnx-community/embeddinggemma-300m-ONNX (308M params, Q4) | |
| Engine | ONNX Runtime CPU (188 MB) | |
| Dimensions | 768 (Matryoshka — truncatable to 512/256/128) | |
| Model path | ${EMBEDDINGGEMMA_MODEL_DIR}/onnx/model_q4.onnx | |
| Rate control | ${EMBED_UTILS_PATH} | |
| Vector DB | Qdrant http://${QDRANT_URL} (HTTP only, check_compatibility=False) | |
| Perf | ~2s cold load, ~18ms/single, ~0.03s/batch |
Quick Start
Packages: onnxruntime + tokenizers only. NO optimum/transformers/torch.
Rate Limits (MANDATORY)
Server: 4 cores, Qdrant on same machine. ALWAYS use `embed_utils.py`:
| Rule | Limit | |
|---|---|---|
| Max batch | 10 (embed_utils.BATCH_SIZE) | |
| Concurrent jobs | NEVER (file lock required) | |
| Load check | Every batch via check_load() | |
| Target load | ~2.0 (server auto-restarts >2.5) |
Collections
| Collection | Dimensions | Purpose | |
|---|---|---|---|
hermes-knowledge | 768 | Skills, entities, agent memory | |
outline-docs | 768 | Outline docs | |
hermes-sessions | 512 | Session recall (real-time) | |
midjourney | 512 | Image references |
⛔ Match dimension to collection — mismatch → 400 crash.
Critical Gotchas
- Server crash = embedding too fast. Always use
embed_utils.py, never inline load checks. - No concurrent jobs — combine
check_load()+acquire_embed_lock(). - Qdrant URL must use
${QDRANT_URL}—localhost:6333doesn't work from inside Docker. - Point IDs must be UUID strings (use
str(uuid.uuid4())). Hex truncation likeuuid.uuid4().hex[:16]→ 400 error. - Qdrant client `https=False` required when connecting to local Qdrant from within Docker. Without it, SSL handshake fails:
record layer failure. Also suppress warning:UserWarning: Api key is used with an insecure connection. - `embeddinggemma` is a local module at
${EMBEDDINGGEMMA_MODULE}, not a pip package. - Mean pooling: NEVER use
keepdims=True— produces wrong shape(batch,1,768).
References
- Setup & Configuration — Model storage, packages, Qdrant config, collections, stress test
- Code Examples — Loading, embed functions, Qdrant client/REST, embed_utils, migration
- Pitfalls — All gotchas and critical warnings
Security
- Security scanPassed, 90 of 100Pattern checks on the skill's files.
- Model reviewPassed
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingskills/embeddinggemma-qdrant/SKILL.md
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