--- name: colip-embeddings description: > Generates multimodal embeddings across olfaction, vision, and language using Scentience COLIP models (OVLM, Embeddings Large/Small, OVL Classifier), then computes cross-modal similarity for semantic labeling, retrieval, zero-shot classification, or experiment comparison. Use when mapping OPU sensor readings to natural language descriptions, matching smell episodes to images, classifying detected chemicals, or building olfactory search over a dataset of prior experiments. version: 1.0.0 author: kordelfrance license: Apache 2.0 compatibility: > Python: pip install "scentience[models]" (adds torch>=2.0, transformers>=4.30, huggingface-hub>=0.16, torchvision>=0.15, Pillow>=9.0, numpy>=1.24). JS: npm install scentience + peer dep @huggingface/inference>=2.0. Rust: cargo add scentience --features colip (downloads libtorch) or --features colip-system (uses system libtorch). Edge/mobile: use COLIP Embeddings Small (exportable to Android, iOS, Rust). metadata: domain: olfaction tags: [embeddings, multimodal, colip, ovlm, retrieval, classification, semantics, zero-shot] models: - id: ovlm name: OVLM architecture: unified-multimodal quantization: int8 target: edge description: "World's first unified olfaction-vision-language model at the edge" - id: colip-embeddings-large name: COLIP Embeddings Large architecture: graph-attention target: cloud description: Highest accuracy; use for offline dataset tasks and online high-stakes retrieval - id: colip-embeddings-small name: COLIP Embeddings Small architecture: graph-attention target: edge description: Low-latency; exportable to Android, iOS, Rust via HuggingFace Hub - id: ovl-classifier name: OVL Classifier architecture: graph-attention target: edge+cloud variants: 2 description: Returns class probabilities for chemical-to-visual-object links hardware: [Reconnaisscent, Scentinel, Olfactory Development Board] depends_on: [ble-device] docs: https://scentience.github.io/docs-api --- ## Goal Produce L2-normalized embedding vectors from olfaction sensor readings, images, or text using a COLIP model, then rank candidates by cosine similarity for retrieval, labeling, or classification — while preserving model-version awareness and clearly distinguishing semantic similarity from analytical chemistry. ## Instructions ### 1. Choose a Model Variant | Model | Best for | Deployment | Notes | |-------|---------|------------|-------| | `ovlm` | Unified O+V+L tasks, zero-shot labeling | Edge/Mobile | Int8 quantized; on Apple App Store via Sigma | | `colip-embeddings-large` | High-accuracy retrieval, dataset analysis | Cloud | Highest-dimensional embeddings; slowest | | `colip-embeddings-small` | On-device, latency-sensitive pipelines | Edge | Export to Android/iOS/Rust | | `ovl-classifier` | Binary/multi-class compound classification | Edge + Cloud | Returns class probabilities, not vectors | **Default recommendation:** Start with `colip-embeddings-small` for robotics and real-time tasks. Switch to `colip-embeddings-large` for offline dataset work or when accuracy is more important than latency. Use `ovl-classifier` when you need a hard classification output rather than a similarity ranking. ### 2. Instantiate the Embedder ```python # Python — requires pip install "scentience[models]" from scentience import ScentienceEmbedder embedder = ScentienceEmbedder( model="colip-embeddings-small", # or "ovlm", "colip-embeddings-large", "ovl-classifier" api_key="SCN_..." ) ``` ```javascript // JavaScript — requires @huggingface/inference peer dep import { ScentienceEmbedder } from 'scentience'; const embedder = new ScentienceEmbedder({ model: "colip-embeddings-small", apiKey: "SCN_..." }); ``` ```rust // Rust — Cargo.toml: scentience = { features = ["colip"] } use scentience::ScentienceEmbedder; let embedder = ScentienceEmbedder::new("colip-embeddings-small", "SCN_..."); ``` ### 3. Generate Embeddings All `embed()` calls return L2-normalized vectors. Pass the correct `modality` flag. **From an OPU reading (olfaction):** ```python # reading is a dict from the ble-device skill reading = {"VOC": 0.45, "CO2": 412.0, "NH3": 0.08, "H2S": 0.002, ...} olf_vec = embedder.embed(modality="olfaction", data=reading) # Returns: np.ndarray, shape (D,), L2-normalized ``` **From text:** ```python txt_vec = embedder.embed(modality="text", data="freshly cut grass after rain") ``` **From an image:** ```python from PIL import Image img_vec = embedder.embed(modality="vision", data=Image.open("scene.jpg")) ``` **JavaScript (any modality):** ```javascript const olfVec = await embedder.embed({ modality: "olfaction", data: reading }); const txtVec = await embedder.embed({ modality: "text", data: "ammonia leak" }); ``` ### 4. Rank Candidates by Cosine Similarity ```python import numpy as np def cosine_sim(a: np.ndarray, b: np.ndarray) -> float: return float(np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b) + 1e-8)) candidates = [ {"id": "label_0", "modality": "text", "label": "petroleum / gasoline"}, {"id": "label_1", "modality": "text", "label": "ammonia / fertilizer"}, {"id": "label_2", "modality": "text", "label": "fresh cut vegetation"}, ] results = sorted( [ { "id": c["id"], "label": c["label"], "similarity": cosine_sim( olf_vec, embedder.embed(modality=c["modality"], data=c["label"]) ) } for c in candidates ], key=lambda x: x["similarity"], reverse=True ) ``` ### 5. OVL Classifier (Hard Classification) When the task is binary or multi-class classification rather than retrieval: ```python from scentience import ScentienceEmbedder classifier = ScentienceEmbedder(model="ovl-classifier", api_key="SCN_...") probs = classifier.classify(modality="olfaction", data=reading) # Returns: dict of {class_label: probability} # e.g., {"ammonia": 0.83, "methane": 0.09, "voc_mix": 0.05, ...} ``` Use this when you need a direct answer ("is this ammonia?") rather than a ranked list of semantic matches. ### 6. Format Output ```json { "model": "colip-embeddings-small", "model_version": "0.2.0", "query_modality": "olfaction", "top_matches": [ {"id": "label_1", "label": "ammonia / fertilizer", "similarity": 0.84}, {"id": "label_2", "label": "fresh cut vegetation", "similarity": 0.61}, {"id": "label_0", "label": "petroleum / gasoline", "similarity": 0.33} ], "semantic_summary": "Query embedding strongly aligned with ammonia/nitrogen compounds. Consistent with NH3=0.08 ppm and elevated VOC. Top match similarity 0.84 with a 0.23 gap to second — treat as confident hypothesis.", "confidence": 0.79, "note": "Semantic similarity score — not a certified chemical assay. Treat as hypothesis pending corroborating sensor evidence or manual review." } ``` Always include `model` and `model_version`. Embedding spaces change between model versions; cross-version comparisons are invalid. ## Examples ### Label a robot smell episode in a warehouse ``` Scenario: Warehouse inspection robot. Buffer of 10 readings. NH3=0.22 ppm (elevated), VOC=0.31 ppm, CO2=420 ppm. Candidate labels: ["cleaning products", "refrigerant leak", "ammonia coolant system", "diesel exhaust"] Top match: "ammonia coolant system" (similarity 0.88) Second match: "cleaning products" (similarity 0.54) Gap: 0.34 — confident result Semantic summary: Elevated NH3 with low VOC/CO2 ratio aligns with ammonia-based refrigerant systems. Recommend inspection of cooling units in sector 3. Confidence: 0.82 ``` ### Cross-modal: image query against olfaction database ``` Input: Image of a fertilizer storage area (vision embedding). Database: 50 prior olfaction experiment embeddings from ScentNet field trials. Top match: Experiment #12 — outdoor soil nitrogen test (similarity 0.76) Semantic summary: Visual cues of bagged nitrogen fertilizer align with prior high-NH3 olfaction episodes. Cross-modal alignment strong. ``` ### Zero-shot classification with OVL Classifier ``` Input: OPU reading with H2S=0.45 ppm, SO2=0.12 ppm, NH3=0.03 ppm. OVL Classifier output: {"hydrogen_sulfide_source": 0.79, "industrial_exhaust": 0.13, "other": 0.08} Interpretation: High-confidence H2S-linked source. Confidence 0.79 > 0.65 threshold. ``` ## Constraints - **Similarity ≠ chemical identity** — COLIP similarity reflects statistical associations from training data, not analytical GC-MS results. Treat high similarity as a strong hypothesis, not a certified identification. - **Always record model name and version** — Embedding spaces shift between versions; comparisons across versions are invalid and should be blocked programmatically. - **Normalize before external comparisons** — All vectors from `ScentienceEmbedder.embed()` are L2-normalized. If you supply embeddings from an external system, normalize first. - **Ambiguity threshold** — If top-match similarity < 0.65, or if the gap between first and second match is < 0.10, treat the result as ambiguous. Request additional sensor readings or flag for manual review. - **Cross-modal alignment quality varies** — Olfaction-to-text alignment is strongest for compound classes present in the ScentNet training corpus. Novel or rare compounds degrade similarity quality. - **OVL Classifier vs. Embeddings** — Use `ovl-classifier` for hard classification (class probabilities). Use `colip-embeddings-large/small` for nearest-neighbor retrieval and semantic ranking tasks. - **Edge model accuracy tradeoff** — `COLIP Embeddings Small` sacrifices accuracy for size. For safety-critical detection tasks, validate against `Large` before deploying `Small` on-device. - **Training data bias** — All COLIP models inherit biases from the ScentNet dataset. Performance on out-of-distribution chemical environments (novel compounds, unusual mixtures) is less reliable and requires human validation.