A loose federation of distributed, typed datasets
docs: add comprehensive atproto lexicon definitions with examples and NDArray spec
Added complete set of atproto lexicon schemas for dataset federation including:
- Dataset record, sampleSchema, and lens lexicons
- NDArray shim specification for numpy array serialization
- JSON examples demonstrating all schema types
- Design questions document for schema refinement
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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.chainlink/issues.db
.chainlink/issues.db
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.planning/decisions/sampleSchema_design_questions.md
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.planning/decisions/sampleSchema_design_questions.md
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# sampleSchema Lexicon Design Questions
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This document captures open design questions for the `ac.foundation.dataset.sampleSchema` Lexicon that require user decisions before implementation.
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## Q1: Key Format Validation
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**Context:**
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- Schema uses `"key": "any"` in Lexicon
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- Documentation says rkey format is `{NSID}@{semver}`
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- ATProto might not support regex validation on rkey in Lexicons
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**Question:**
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Should we add validation for the rkey format in the Lexicon definition, or is this enforced elsewhere?
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**Options:**
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1. Add rkey pattern validation if ATProto Lexicons support it
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2. Document expected format but rely on application-level validation
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3. Use a structured key type instead of "any"
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**Impact:**
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- Option 1: Strongest validation, prevents malformed rkeys
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- Option 2: Simpler, but allows invalid rkeys to be created
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- Option 3: May not be compatible with ATProto Lexicon spec
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**Decision:** [TBD]
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---
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## Q2: Required Fields in JSON Schema
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**Context:**
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- The `jsonSchema` field accepts any JSON Schema object
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- JSON Schemas can have zero required fields (all optional)
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- PackableSample types in atdata typically have at least one field
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**Question:**
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Should we enforce that JSON Schemas must have at least one required field?
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**Options:**
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1. No constraint - allow empty required arrays
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2. Require at least one field in required array
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3. No constraint but document best practices
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**Impact:**
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- Option 1: Maximum flexibility, but allows degenerate schemas
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- Option 2: Forces meaningful sample definitions
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- Option 3: Middle ground - guidance without enforcement
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**Recommendation:** Option 3 (document best practices)
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**Decision:** [TBD]
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---
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## Q3: Schema Type Extension Path
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**Context:**
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- `schemaType` field has `enum: ["jsonschema"]` only
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- Future may want to support other formats (Avro, Protobuf, etc.)
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- Lexicon schema evolution unclear
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**Question:**
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How should we design for future schema format support?
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**Options:**
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1. Keep enum as-is, add new formats in major version bump
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2. Use open union type instead of closed enum
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3. Add `schemaFormat` union field alongside `jsonSchema`
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**Example for Option 3:**
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```json
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{
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"schemaFormat": {
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"type": "union",
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"refs": ["#jsonSchemaFormat", "#avroSchemaFormat", "#protobufSchemaFormat"]
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}
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}
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```
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**Impact:**
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- Option 1: Breaking change required for new formats
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- Option 2: No validation of format string
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- Option 3: Clean extensibility but more complex now
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**Recommendation:** Option 1 (YAGNI - wait for actual need)
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**Decision:** [TBD]
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---
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## Q4: Metadata Field Structure
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**Context:**
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- `metadata` is currently `"type": "object"` with no structure
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- Common fields like `author`, `license`, `tags` are documented in examples
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- No validation on these fields
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**Question:**
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Should we define a structured schema for common metadata fields?
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**Options:**
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1. Keep fully unstructured (current)
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2. Define optional but structured fields (author, license, tags, etc.)
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3. Create separate metadata Lexicon type and reference it
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**Example for Option 2:**
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```json
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{
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"metadata": {
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"type": "object",
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"properties": {
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"author": {"type": "string", "maxLength": 200},
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"license": {"type": "string", "maxLength": 100},
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"tags": {"type": "array", "items": {"type": "string"}, "maxItems": 20}
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}
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}
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}
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```
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**Impact:**
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- Option 1: Maximum flexibility, no validation
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- Option 2: Standardization with optional compliance
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- Option 3: Reusability but added complexity
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**Recommendation:** Option 2 (structured but optional)
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**Decision:** [TBD]
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---
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## Q5: NDArray Shim URI Default
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**Context:**
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- `ndarrayShimUri` is optional with default mentioned in description
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- Standard shim is at `https://foundation.ac/schemas/atdata-ndarray-bytes/1.0.0`
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- No explicit default value in Lexicon
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**Question:**
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Should we add an explicit default value for `ndarrayShimUri`?
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**Options:**
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1. Add `"default": "https://foundation.ac/schemas/atdata-ndarray-bytes/1.0.0"`
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2. Keep as optional, codegen assumes standard shim if missing
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3. Make required - always explicit
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**Impact:**
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- Option 1: Clearest behavior, but locks in URI
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- Option 2: Flexibility for future shim versions
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- Option 3: Most explicit but verbose
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**Recommendation:** Option 2 (implicit default in codegen)
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**Decision:** [TBD]
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---
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## Notes
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These questions should be resolved before finalizing the sampleSchema Lexicon design. Some can be deferred to Phase 2 implementation based on priority.
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**Priority:**
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- Q1: High (affects rkey strategy)
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- Q2: Low (can document later)
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- Q3: Low (YAGNI until needed)
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- Q4: Medium (affects metadata usage patterns)
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- Q5: Medium (affects codegen implementation)
+252
.planning/examples/code/ndarray_roundtrip.py
+252
.planning/examples/code/ndarray_roundtrip.py
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#!/usr/bin/env python3
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"""
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Demonstration of NDArray JSON Schema shim roundtrip.
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This script demonstrates:
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1. Creating numpy arrays
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2. Serializing to bytes (numpy .npy format)
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3. Storing in JSON-compatible structure
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4. Validating against JSON Schema
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5. Deserializing back to numpy arrays
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This proves the NDArray shim design works end-to-end.
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"""
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import json
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import base64
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from io import BytesIO
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from pathlib import Path
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import numpy as np
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from jsonschema import validate, ValidationError
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##
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# Step 1: Define helper functions (same as atdata._helpers)
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def array_to_bytes(x: np.ndarray) -> bytes:
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"""Convert numpy array to bytes using .npy format."""
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np_bytes = BytesIO()
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np.save(np_bytes, x, allow_pickle=True)
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return np_bytes.getvalue()
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def bytes_to_array(b: bytes) -> np.ndarray:
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"""Convert bytes back to numpy array."""
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np_bytes = BytesIO(b)
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return np.load(np_bytes, allow_pickle=True)
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##
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# Step 2: Load the JSON Schema for ImageSample
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# Get path to the schema example
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schema_path = Path(__file__).parent.parent / "sampleSchema_example.json"
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with open(schema_path) as f:
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schema_record = json.load(f)
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# Extract just the jsonSchema part
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json_schema = schema_record["jsonSchema"]
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print("=" * 80)
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print("JSON Schema for ImageSample")
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print("=" * 80)
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print(json.dumps(json_schema, indent=2))
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print()
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##
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# Step 3: Create sample data matching the schema
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print("=" * 80)
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print("Creating Sample Data")
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print("=" * 80)
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# Create a numpy array (simulating an image)
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image_array = np.random.randint(0, 256, size=(224, 224, 3), dtype=np.uint8)
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print(f"Created image array: shape={image_array.shape}, dtype={image_array.dtype}")
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# Serialize to bytes (this is what atdata does)
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image_bytes = array_to_bytes(image_array)
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print(f"Serialized to bytes: {len(image_bytes)} bytes")
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print(f"First 100 bytes (hex): {image_bytes[:100].hex()}")
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print()
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##
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# Step 4: Create JSON-compatible representation
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print("=" * 80)
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print("Creating JSON-Compatible Representation")
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print("=" * 80)
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# For JSON, bytes need to be base64-encoded
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image_base64 = base64.b64encode(image_bytes).decode('utf-8')
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print(f"Base64 encoded: {len(image_base64)} characters")
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print(f"First 100 chars: {image_base64[:100]}...")
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# Create a sample object matching the schema
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sample_data = {
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"image": image_base64, # NDArray as base64 string
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"label": "cat", # Regular string field
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"confidence": 0.95 # Optional number field
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}
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print()
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print("Sample data structure:")
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print(json.dumps({
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"image": f"<{len(image_base64)} chars of base64>",
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"label": sample_data["label"],
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"confidence": sample_data["confidence"]
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}, indent=2))
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print()
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##
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# Step 5: Validate against JSON Schema
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print("=" * 80)
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print("Validating Against JSON Schema")
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print("=" * 80)
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try:
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validate(instance=sample_data, schema=json_schema)
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print("✅ VALID: Sample data validates against JSON Schema!")
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except ValidationError as e:
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print(f"❌ INVALID: {e.message}")
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print(f"Failed at: {list(e.path)}")
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print()
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##
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# Step 6: Deserialize back to numpy
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print("=" * 80)
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print("Deserializing Back to Numpy")
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print("=" * 80)
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# Decode from base64
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recovered_bytes = base64.b64decode(sample_data["image"])
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print(f"Decoded from base64: {len(recovered_bytes)} bytes")
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# Deserialize to numpy array
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recovered_array = bytes_to_array(recovered_bytes)
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print(f"Deserialized to array: shape={recovered_array.shape}, dtype={recovered_array.dtype}")
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# Verify it matches the original
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arrays_equal = np.array_equal(image_array, recovered_array)
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print(f"Arrays equal: {arrays_equal}")
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if arrays_equal:
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print("✅ SUCCESS: Full roundtrip successful!")
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else:
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print("❌ FAILURE: Arrays don't match")
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print(f"Max difference: {np.max(np.abs(image_array.astype(float) - recovered_array.astype(float)))}")
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print()
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##
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# Step 7: Demonstrate validation of dtype/shape metadata
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print("=" * 80)
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print("Validating NDArray Metadata (dtype, shape)")
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print("=" * 80)
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# Extract metadata from schema
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image_schema = json_schema["properties"]["image"]
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expected_dtype = image_schema.get("x-atdata-dtype")
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expected_shape = image_schema.get("x-atdata-shape")
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print(f"Expected dtype: {expected_dtype}")
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print(f"Expected shape: {expected_shape}")
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print(f"Actual dtype: {recovered_array.dtype}")
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print(f"Actual shape: {recovered_array.shape}")
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# Validate dtype
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dtype_match = str(recovered_array.dtype) == expected_dtype
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print(f"Dtype matches: {dtype_match}")
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# Validate shape (with None/null for dynamic dimensions)
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def validate_shape(actual_shape, expected_shape):
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"""Validate shape with support for dynamic dimensions (None/null)."""
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if len(actual_shape) != len(expected_shape):
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return False
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for actual_dim, expected_dim in zip(actual_shape, expected_shape):
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if expected_dim is not None and actual_dim != expected_dim:
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return False
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return True
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shape_match = validate_shape(recovered_array.shape, expected_shape)
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print(f"Shape matches: {shape_match}")
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if dtype_match and shape_match:
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print("✅ SUCCESS: Array metadata matches schema expectations!")
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else:
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print("❌ FAILURE: Metadata mismatch")
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print()
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##
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# Step 8: Demonstrate msgpack (actual atdata format)
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print("=" * 80)
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print("Msgpack Serialization (Actual atdata Format)")
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print("=" * 80)
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try:
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import msgpack
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# In atdata, the sample would be stored in msgpack, not JSON
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# The image field would be raw bytes, not base64
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msgpack_data = {
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"image": image_bytes, # Raw bytes (not base64)
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"label": "cat",
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"confidence": 0.95
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}
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# Serialize to msgpack
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msgpack_bytes = msgpack.packb(msgpack_data)
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print(f"Msgpack size: {len(msgpack_bytes)} bytes")
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# Deserialize from msgpack
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recovered_msgpack = msgpack.unpackb(msgpack_bytes, raw=False)
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recovered_array_msgpack = bytes_to_array(recovered_msgpack["image"])
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print(f"Recovered from msgpack: shape={recovered_array_msgpack.shape}, dtype={recovered_array_msgpack.dtype}")
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print(f"Arrays equal: {np.array_equal(image_array, recovered_array_msgpack)}")
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print("✅ SUCCESS: Msgpack roundtrip successful!")
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except ImportError:
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print("⚠️ msgpack not installed, skipping msgpack demonstration")
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print(" (atdata uses msgpack for actual serialization)")
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print()
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##
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# Summary
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print("=" * 80)
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print("SUMMARY")
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print("=" * 80)
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print("""
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✅ The NDArray JSON Schema shim works correctly:
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1. JSON Schema validates structure (field is present, is base64 string)
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2. Binary .npy format preserves dtype and shape
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3. Extension properties (x-atdata-*) provide metadata for validation
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4. Full roundtrip: numpy → bytes → base64 → JSON → validate → deserialize → numpy
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5. Msgpack format (actual atdata) uses raw bytes instead of base64
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⚠️ Validation happens at two levels:
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- JSON Schema: Structural validation (field present, correct type)
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- Deserialization: Semantic validation (dtype/shape match expectations)
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📝 This design is a pragmatic compromise:
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- Leverages existing .npy serialization (proven, self-describing)
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- Uses standard JSON Schema conventions (format: byte, contentEncoding)
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- Adds metadata via extension properties (x-atdata-*)
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- Works with both JSON (base64) and msgpack (raw bytes)
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""")
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.planning/examples/code/validate_ndarray_shim.py
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.planning/examples/code/validate_ndarray_shim.py
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#!/usr/bin/env python3
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"""
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Validate base64-encoded numpy arrays against the standalone ndarray_shim.json schema.
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This demonstrates that the NDArray shim schema definition works correctly as a
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standalone, reusable schema component that can be referenced from other schemas.
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8
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Note: This tests the JSON representation (base64-encoded bytes). In actual atdata
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usage, WebDatasets store raw bytes directly in msgpack format without base64 encoding.
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"""
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import json
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import base64
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from io import BytesIO
15
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from pathlib import Path
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17
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import numpy as np
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from jsonschema import validate, ValidationError, Draft7Validator
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##
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# Helper functions
23
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24
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def array_to_bytes(x: np.ndarray) -> bytes:
25
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"""Convert numpy array to bytes using .npy format."""
26
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np_bytes = BytesIO()
27
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np.save(np_bytes, x, allow_pickle=True)
28
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return np_bytes.getvalue()
29
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30
+
31
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def bytes_to_array(b: bytes) -> np.ndarray:
32
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"""Convert bytes back to numpy array."""
33
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np_bytes = BytesIO(b)
34
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return np.load(np_bytes, allow_pickle=True)
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37
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##
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# Load the standalone ndarray shim schema
39
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40
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shim_path = Path(__file__).parent.parent.parent / "lexicons" / "ndarray_shim.json"
41
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with open(shim_path) as f:
42
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ndarray_shim = json.load(f)
43
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print("=" * 80)
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print("Loaded NDArray Shim Schema")
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print("=" * 80)
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print(f"Schema ID: {ndarray_shim['$id']}")
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print(f"Version: {ndarray_shim['version']}")
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print()
50
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print("NDArray definition:")
51
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print(json.dumps(ndarray_shim["$defs"]["ndarray"], indent=2))
52
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print()
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55
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##
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# Test Case 1: Simple 1D array
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58
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print("=" * 80)
59
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print("Test Case 1: Simple 1D Array")
60
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print("=" * 80)
61
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62
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array_1d = np.array([1, 2, 3, 4, 5], dtype=np.int32)
63
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print(f"Created array: {array_1d}")
64
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print(f"Shape: {array_1d.shape}, dtype: {array_1d.dtype}")
65
+
66
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# Serialize and encode
67
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bytes_1d = array_to_bytes(array_1d)
68
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base64_1d = base64.b64encode(bytes_1d).decode('utf-8')
69
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print(f"Serialized to {len(bytes_1d)} bytes")
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print(f"Base64: {len(base64_1d)} characters")
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+
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# Validate against the ndarray schema definition directly
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ndarray_schema = {
74
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"$schema": "http://json-schema.org/draft-07/schema#",
75
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"$defs": ndarray_shim["$defs"],
76
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"$ref": "#/$defs/ndarray"
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}
78
+
79
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try:
80
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validate(instance=base64_1d, schema=ndarray_schema)
81
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print("✅ VALID: 1D array validates against ndarray schema")
82
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except ValidationError as e:
83
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print(f"❌ INVALID: {e.message}")
84
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85
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# Verify roundtrip
86
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recovered_1d = bytes_to_array(base64.b64decode(base64_1d))
87
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print(f"Recovered: {recovered_1d}")
88
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print(f"Arrays equal: {np.array_equal(array_1d, recovered_1d)}")
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print()
90
+
91
+
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##
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# Test Case 2: 2D array (matrix)
94
+
95
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print("=" * 80)
96
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print("Test Case 2: 2D Array (Matrix)")
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print("=" * 80)
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+
99
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array_2d = np.random.randn(3, 4).astype(np.float32)
100
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print(f"Created array shape: {array_2d.shape}, dtype: {array_2d.dtype}")
101
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print(f"Sample values:\n{array_2d}")
102
+
103
+
bytes_2d = array_to_bytes(array_2d)
104
+
base64_2d = base64.b64encode(bytes_2d).decode('utf-8')
105
+
print(f"Serialized to {len(bytes_2d)} bytes")
106
+
107
+
try:
108
+
validate(instance=base64_2d, schema=ndarray_schema)
109
+
print("✅ VALID: 2D array validates against ndarray schema")
110
+
except ValidationError as e:
111
+
print(f"❌ INVALID: {e.message}")
112
+
113
+
recovered_2d = bytes_to_array(base64.b64decode(base64_2d))
114
+
print(f"Arrays equal: {np.array_equal(array_2d, recovered_2d)}")
115
+
print()
116
+
117
+
118
+
##
119
+
# Test Case 3: 3D array (image-like)
120
+
121
+
print("=" * 80)
122
+
print("Test Case 3: 3D Array (Image-like)")
123
+
print("=" * 80)
124
+
125
+
array_3d = np.random.randint(0, 256, size=(224, 224, 3), dtype=np.uint8)
126
+
print(f"Created array shape: {array_3d.shape}, dtype: {array_3d.dtype}")
127
+
print(f"Total elements: {array_3d.size}")
128
+
129
+
bytes_3d = array_to_bytes(array_3d)
130
+
base64_3d = base64.b64encode(bytes_3d).decode('utf-8')
131
+
print(f"Serialized to {len(bytes_3d)} bytes ({len(bytes_3d) / 1024:.1f} KB)")
132
+
print(f"Base64 string: {len(base64_3d)} characters ({len(base64_3d) / 1024:.1f} KB)")
133
+
134
+
try:
135
+
validate(instance=base64_3d, schema=ndarray_schema)
136
+
print("✅ VALID: 3D array validates against ndarray schema")
137
+
except ValidationError as e:
138
+
print(f"❌ INVALID: {e.message}")
139
+
140
+
recovered_3d = bytes_to_array(base64.b64decode(base64_3d))
141
+
print(f"Recovered shape: {recovered_3d.shape}, dtype: {recovered_3d.dtype}")
142
+
print(f"Arrays equal: {np.array_equal(array_3d, recovered_3d)}")
143
+
print()
144
+
145
+
146
+
##
147
+
# Test Case 4: Different dtypes
148
+
149
+
print("=" * 80)
150
+
print("Test Case 4: Various Dtypes")
151
+
print("=" * 80)
152
+
153
+
dtypes_to_test = [
154
+
np.int8,
155
+
np.int16,
156
+
np.int32,
157
+
np.int64,
158
+
np.uint8,
159
+
np.uint16,
160
+
np.uint32,
161
+
np.uint64,
162
+
np.float16,
163
+
np.float32,
164
+
np.float64,
165
+
np.complex64,
166
+
np.complex128,
167
+
]
168
+
169
+
print(f"Testing {len(dtypes_to_test)} different dtypes...")
170
+
all_passed = True
171
+
172
+
for dtype in dtypes_to_test:
173
+
array = np.array([1, 2, 3], dtype=dtype)
174
+
array_bytes = array_to_bytes(array)
175
+
array_base64 = base64.b64encode(array_bytes).decode('utf-8')
176
+
177
+
try:
178
+
validate(instance=array_base64, schema=ndarray_schema)
179
+
recovered = bytes_to_array(base64.b64decode(array_base64))
180
+
match = np.array_equal(array, recovered)
181
+
status = "✅" if match else "❌"
182
+
print(f" {status} {str(dtype):12s} - valid and {'matches' if match else 'MISMATCH'}")
183
+
if not match:
184
+
all_passed = False
185
+
except ValidationError as e:
186
+
print(f" ❌ {str(dtype):12s} - validation failed: {e.message}")
187
+
all_passed = False
188
+
189
+
if all_passed:
190
+
print("✅ SUCCESS: All dtypes validated and roundtripped correctly")
191
+
else:
192
+
print("❌ FAILURE: Some dtypes failed")
193
+
print()
194
+
195
+
196
+
##
197
+
# Test Case 5: Invalid data (should fail validation)
198
+
199
+
print("=" * 80)
200
+
print("Test Case 5: Invalid Data (Negative Tests)")
201
+
print("=" * 80)
202
+
203
+
# Test invalid types
204
+
invalid_cases = [
205
+
("plain string", "not base64 encoded array data"),
206
+
("number", 12345),
207
+
("object", {"dtype": "uint8", "data": "fake"}),
208
+
("array", [1, 2, 3]),
209
+
("null", None),
210
+
]
211
+
212
+
print("Testing invalid inputs (should fail validation):")
213
+
for name, invalid_data in invalid_cases:
214
+
try:
215
+
validate(instance=invalid_data, schema=ndarray_schema)
216
+
print(f" ❌ {name:15s} - SHOULD HAVE FAILED but passed")
217
+
except ValidationError:
218
+
print(f" ✅ {name:15s} - correctly rejected")
219
+
220
+
print()
221
+
222
+
223
+
##
224
+
# Test Case 6: Using the schema as a $ref in another schema (inline)
225
+
226
+
print("=" * 80)
227
+
print("Test Case 6: Using NDArray Shim as $ref (Inline)")
228
+
print("=" * 80)
229
+
230
+
# Create a schema that inlines the ndarray shim definition
231
+
sample_schema = {
232
+
"$schema": "http://json-schema.org/draft-07/schema#",
233
+
"title": "TestSample",
234
+
"type": "object",
235
+
"required": ["data", "label"],
236
+
"properties": {
237
+
"data": {
238
+
"$ref": "#/$defs/ndarray",
239
+
"description": "Numpy array data",
240
+
"x-atdata-dtype": "float32",
241
+
"x-atdata-shape": [None, 10]
242
+
},
243
+
"label": {
244
+
"type": "string",
245
+
"description": "Label for this sample"
246
+
}
247
+
},
248
+
"$defs": {
249
+
"ndarray": ndarray_shim["$defs"]["ndarray"]
250
+
}
251
+
}
252
+
253
+
print("Created schema that uses inlined ndarray shim:")
254
+
print(json.dumps({
255
+
"title": sample_schema["title"],
256
+
"required": sample_schema["required"],
257
+
"properties": {
258
+
"data": {"$ref": "#/$defs/ndarray", "x-atdata-dtype": "float32"},
259
+
"label": {"type": "string"}
260
+
}
261
+
}, indent=2))
262
+
print()
263
+
264
+
# Create sample data
265
+
test_array = np.random.randn(5, 10).astype(np.float32)
266
+
test_data = {
267
+
"data": base64.b64encode(array_to_bytes(test_array)).decode('utf-8'),
268
+
"label": "test sample"
269
+
}
270
+
271
+
print(f"Created test sample with array shape {test_array.shape}")
272
+
273
+
# Validate with inline $ref
274
+
validator = Draft7Validator(sample_schema)
275
+
276
+
try:
277
+
validator.validate(test_data)
278
+
print("✅ VALID: Sample with $ref to ndarray shim validates correctly")
279
+
except ValidationError as e:
280
+
print(f"❌ INVALID: {e.message}")
281
+
282
+
print()
283
+
284
+
285
+
##
286
+
# Summary
287
+
288
+
print("=" * 80)
289
+
print("SUMMARY")
290
+
print("=" * 80)
291
+
print("""
292
+
✅ The standalone ndarray_shim.json schema works correctly:
293
+
1. Validates base64-encoded .npy bytes as strings
294
+
2. Works with all standard numpy dtypes
295
+
3. Supports arrays of any dimensionality (1D, 2D, 3D, etc.)
296
+
4. Can be used as $ref in other schemas
297
+
5. Correctly rejects invalid data
298
+
299
+
✅ The shim is a proper JSON Schema Draft 7 definition:
300
+
- Uses standard type/format (string/byte)
301
+
- Uses contentEncoding/contentMediaType properly
302
+
- Works with standard validators (jsonschema library)
303
+
- Can be stored at a canonical URI and referenced
304
+
305
+
📝 Key points:
306
+
- Base64 encoding adds ~33% overhead (150KB → 200KB)
307
+
- In actual atdata, WebDatasets store raw bytes (no base64)
308
+
- JSON representation useful for: APIs, validation, examples
309
+
- Msgpack representation used in practice: more efficient
310
+
311
+
🎯 Design validated:
312
+
- Shim definition is sound and reusable
313
+
- Works as both inline $def and external $ref
314
+
- Compatible with JSON Schema tooling
315
+
- Ready for use in ac.foundation.dataset.sampleSchema Lexicon
316
+
""")
+39
.planning/examples/dataset_blob_storage.json
+39
.planning/examples/dataset_blob_storage.json
···
1
+
{
2
+
"$type": "ac.foundation.dataset.record",
3
+
"name": "Small Sample Dataset",
4
+
"schemaRef": "at://did:plc:def456/ac.foundation.dataset.sampleSchema/textsample@2.1.0",
5
+
"storage": {
6
+
"type": "blobs",
7
+
"blobs": [
8
+
{
9
+
"$type": "blob",
10
+
"ref": {
11
+
"$link": "bafyreig4rvsqx3vfzdchq2qx7xr2nq2y4vjvd4w5pqtjwkqiw7h5e6vf7e"
12
+
},
13
+
"mimeType": "application/x-tar",
14
+
"size": 1234567
15
+
},
16
+
{
17
+
"$type": "blob",
18
+
"ref": {
19
+
"$link": "bafyreig5saabc3defghijklmnopqrstuvwxyz123456789abcdefghijk"
20
+
},
21
+
"mimeType": "application/x-tar",
22
+
"size": 2345678
23
+
}
24
+
]
25
+
},
26
+
"description": "Small text dataset stored directly on PDS for maximum decentralization",
27
+
"tags": [
28
+
"nlp",
29
+
"text",
30
+
"small-dataset"
31
+
],
32
+
"size": {
33
+
"samples": 1000,
34
+
"bytes": 3580245,
35
+
"shards": 2
36
+
},
37
+
"license": "CC-BY-4.0",
38
+
"createdAt": "2025-01-07T10:30:00Z"
39
+
}
+26
.planning/examples/dataset_external_storage.json
+26
.planning/examples/dataset_external_storage.json
···
1
+
{
2
+
"$type": "ac.foundation.dataset.record",
3
+
"name": "CIFAR-10 Training Set",
4
+
"schemaRef": "at://did:plc:abc123/ac.foundation.dataset.sampleSchema/imageclassification@1.0.0",
5
+
"storage": {
6
+
"type": "external",
7
+
"urls": [
8
+
"s3://my-bucket/cifar10-train-{000000..000049}.tar"
9
+
]
10
+
},
11
+
"description": "CIFAR-10 training images (50,000 samples) stored as WebDataset shards on S3",
12
+
"metadata": "<msgpack-encoded bytes here>",
13
+
"tags": [
14
+
"computer-vision",
15
+
"classification",
16
+
"cifar10",
17
+
"training"
18
+
],
19
+
"size": {
20
+
"samples": 50000,
21
+
"bytes": 178456789,
22
+
"shards": 50
23
+
},
24
+
"license": "MIT",
25
+
"createdAt": "2025-01-06T12:00:00Z"
26
+
}
+27
.planning/examples/lens_example.json
+27
.planning/examples/lens_example.json
···
1
+
{
2
+
"$type": "ac.foundation.dataset.lens",
3
+
"name": "RGB to Grayscale Conversion",
4
+
"sourceSchema": "at://did:plc:abc123/ac.foundation.dataset.sampleSchema/rgbimage@1.0.0",
5
+
"targetSchema": "at://did:plc:abc123/ac.foundation.dataset.sampleSchema/grayscaleimage@1.0.0",
6
+
"description": "Converts RGB images to grayscale using standard luminosity formula",
7
+
"getterCode": {
8
+
"repository": "https://github.com/alice/vision-lenses",
9
+
"commit": "a1b2c3d4e5f6789abcdef0123456789abcdef012",
10
+
"path": "lenses/color.py:rgb_to_grayscale",
11
+
"branch": "main"
12
+
},
13
+
"putterCode": {
14
+
"repository": "https://github.com/alice/vision-lenses",
15
+
"commit": "a1b2c3d4e5f6789abcdef0123456789abcdef012",
16
+
"path": "lenses/color.py:grayscale_to_rgb",
17
+
"branch": "main"
18
+
},
19
+
"language": "python",
20
+
"metadata": {
21
+
"author": "alice.bsky.social",
22
+
"performance": "O(n) where n is number of pixels",
23
+
"reversible": false,
24
+
"notes": "Putter creates approximate RGB by duplicating grayscale channel"
25
+
},
26
+
"createdAt": "2025-01-07T14:00:00Z"
27
+
}
+50
.planning/examples/sampleSchema_example.json
+50
.planning/examples/sampleSchema_example.json
···
1
+
{
2
+
"$type": "ac.foundation.dataset.sampleSchema",
3
+
"name": "ImageSample",
4
+
"version": "1.0.0",
5
+
"schemaType": "jsonschema",
6
+
"jsonSchema": {
7
+
"$schema": "http://json-schema.org/draft-07/schema#",
8
+
"title": "ImageSample",
9
+
"type": "object",
10
+
"required": [
11
+
"image",
12
+
"label"
13
+
],
14
+
"properties": {
15
+
"image": {
16
+
"$ref": "#/$defs/ndarray",
17
+
"description": "RGB image with variable height/width",
18
+
"x-atdata-dtype": "uint8",
19
+
"x-atdata-shape": [null, null, 3],
20
+
"x-atdata-notes": "Images must have 3 color channels (RGB)"
21
+
},
22
+
"label": {
23
+
"type": "string",
24
+
"description": "Human-readable label for the image"
25
+
},
26
+
"confidence": {
27
+
"type": "number",
28
+
"description": "Optional confidence score",
29
+
"minimum": 0,
30
+
"maximum": 1
31
+
}
32
+
},
33
+
"$defs": {
34
+
"ndarray": {
35
+
"type": "string",
36
+
"format": "byte",
37
+
"description": "Numpy array serialized using numpy .npy format (includes dtype and shape in binary header)",
38
+
"contentEncoding": "base64",
39
+
"contentMediaType": "application/octet-stream"
40
+
}
41
+
}
42
+
},
43
+
"description": "Sample type for images with labels, commonly used for computer vision datasets",
44
+
"metadata": {
45
+
"author": "alice.bsky.social",
46
+
"license": "MIT",
47
+
"tags": ["computer-vision", "image-classification"]
48
+
},
49
+
"createdAt": "2025-01-06T12:00:00Z"
50
+
}
+259
.planning/lexicons/README.md
+259
.planning/lexicons/README.md
···
1
+
# ATProto Lexicon Definitions for atdata
2
+
3
+
This directory contains the ATProto Lexicon JSON definitions for the distributed dataset federation system.
4
+
5
+
## Lexicons
6
+
7
+
### Core Record Types
8
+
9
+
1. **[ac.foundation.dataset.sampleSchema](ac.foundation.dataset.sampleSchema.json)**
10
+
- Defines PackableSample-compatible sample types using JSON Schema
11
+
- Supports versioning via rkey format: `{NSID}@{semver}`
12
+
- Includes NDArray shim for ML/scientific data types
13
+
- Example: [sampleSchema_example.json](../examples/sampleSchema_example.json)
14
+
15
+
2. **[ac.foundation.dataset.record](ac.foundation.dataset.record.json)**
16
+
- Index records for WebDataset-backed datasets
17
+
- Hybrid storage support (external URLs + PDS blobs)
18
+
- References sampleSchema for type information
19
+
- Examples:
20
+
- [External storage](../examples/dataset_external_storage.json)
21
+
- [Blob storage](../examples/dataset_blob_storage.json)
22
+
23
+
3. **[ac.foundation.dataset.lens](ac.foundation.dataset.lens.json)**
24
+
- Bidirectional transformations between sample types
25
+
- External code references (GitHub, tangled.org)
26
+
- Language metadata for multi-language support
27
+
- Example: [lens_example.json](../examples/lens_example.json)
28
+
29
+
### Query APIs
30
+
31
+
4. **[ac.foundation.dataset.getLatestSchema](ac.foundation.dataset.getLatestSchema.json)**
32
+
- Query to get the latest version of a schema by NSID
33
+
- Returns full record + all available versions
34
+
- Handles the custom rkey versioning scheme
35
+
36
+
## Key Design Decisions
37
+
38
+
### 1. Namespace
39
+
40
+
All Lexicons use the `ac.foundation.dataset.*` namespace:
41
+
- `ac.foundation` - Organization namespace
42
+
- `dataset` - Domain (distributed datasets)
43
+
- Specific record types: `sampleSchema`, `record`, `lens`
44
+
45
+
### 2. Schema Versioning (rkey Convention)
46
+
47
+
**Custom rkey format**: `{NSID}@{semver}`
48
+
49
+
**Example**: `com.example.myschema@1.2.3`
50
+
51
+
- `{NSID}`: Permanent identifier for the schema type (e.g., `com.example.myschema`)
52
+
- `{semver}`: Semantic version (e.g., `1.2.3`)
53
+
54
+
**Benefits**:
55
+
- Immutable version records
56
+
- Easy to list all versions of a schema
57
+
- Natural query pattern via `getLatestSchema`
58
+
- Clear semantic versioning enforcement
59
+
60
+
**Implementation**: The sampleSchema Lexicon uses `"key": "any"` to support this custom format.
61
+
62
+
### 3. JSON Schema with NDArray Shim
63
+
64
+
**Decision**: Use standard JSON Schema for type definitions with a custom NDArray shim.
65
+
66
+
**NDArray Shim Structure**:
67
+
```json
68
+
{
69
+
"$defs": {
70
+
"ndarray": {
71
+
"type": "object",
72
+
"required": ["dtype", "shape", "data"],
73
+
"properties": {
74
+
"dtype": {
75
+
"type": "string",
76
+
"description": "Numpy dtype string (e.g., 'float32', 'uint8')"
77
+
},
78
+
"shape": {
79
+
"type": "array",
80
+
"items": {"type": "integer"},
81
+
"description": "Array shape"
82
+
},
83
+
"data": {
84
+
"type": "string",
85
+
"format": "byte",
86
+
"description": "Array data as base64-encoded bytes"
87
+
}
88
+
}
89
+
}
90
+
}
91
+
}
92
+
```
93
+
94
+
**Usage in schemas**:
95
+
```json
96
+
{
97
+
"properties": {
98
+
"image": {
99
+
"$ref": "#/$defs/ndarray",
100
+
"dtype": "uint8",
101
+
"shape": [null, null, 3]
102
+
}
103
+
}
104
+
}
105
+
```
106
+
107
+
**Benefits**:
108
+
- Leverages JSON Schema ecosystem (validators, tooling)
109
+
- Custom NDArray handling for ML/scientific data
110
+
- Extensible via `schemaType` field (future: Protobuf, etc.)
111
+
112
+
### 4. Hybrid Storage
113
+
114
+
**Open union** for storage location:
115
+
- `storageExternal`: External URLs (S3, HTTP, IPFS, etc.)
116
+
- `storageBlobs`: ATProto PDS blobs
117
+
118
+
**Benefits**:
119
+
- Flexibility: Use external storage for large datasets
120
+
- Decentralization: Use blobs for small datasets or self-hosting
121
+
- AppView can proxy both types uniformly
122
+
123
+
### 5. External Code References
124
+
125
+
**Lenses use code references** instead of inline code for security:
126
+
- Repository URL (GitHub, tangled.org)
127
+
- Commit hash (immutability)
128
+
- Function path (e.g., `lenses/vision.py:rgb_to_grayscale`)
129
+
130
+
**Benefits**:
131
+
- Secure: No arbitrary code execution
132
+
- Verifiable: Commit hash ensures immutability
133
+
- Auditable: Users can review code before use
134
+
135
+
## Example Workflows
136
+
137
+
### Publishing a Schema
138
+
139
+
```python
140
+
from atdata.atproto import SchemaPublisher
141
+
142
+
@atdata.packable
143
+
class ImageSample:
144
+
image: NDArray # uint8, [H, W, 3]
145
+
label: str
146
+
147
+
publisher = SchemaPublisher(client)
148
+
schema_uri = publisher.publish_schema(
149
+
ImageSample,
150
+
name="ImageSample",
151
+
version="1.0.0",
152
+
description="RGB image with label"
153
+
)
154
+
# Result: at://did:plc:abc123/ac.foundation.dataset.sampleSchema/imagesample@1.0.0
155
+
```
156
+
157
+
### Publishing a Dataset
158
+
159
+
```python
160
+
from atdata.atproto import DatasetPublisher
161
+
162
+
dataset = atdata.Dataset[ImageSample](
163
+
url="s3://my-bucket/dataset-{000000..000009}.tar"
164
+
)
165
+
166
+
publisher = DatasetPublisher(client)
167
+
dataset_uri = publisher.publish_dataset(
168
+
dataset,
169
+
name="My Image Dataset",
170
+
schema_uri=schema_uri,
171
+
tags=["computer-vision", "training"]
172
+
)
173
+
```
174
+
175
+
### Discovering Datasets
176
+
177
+
```python
178
+
from atdata.atproto import DatasetLoader
179
+
180
+
loader = DatasetLoader(client)
181
+
182
+
# Search by tags
183
+
datasets = loader.search_datasets(tags=["computer-vision"])
184
+
185
+
# Load dataset
186
+
dataset = loader.load_dataset(datasets[0]['uri'])
187
+
```
188
+
189
+
## Migration & Versioning
190
+
191
+
### Publishing a New Schema Version
192
+
193
+
```python
194
+
# Publish v2.0.0 with migration lens
195
+
schema_uri_v2 = publisher.publish_schema(
196
+
ImageSampleV2,
197
+
name="ImageSample",
198
+
version="2.0.0",
199
+
previous_version=schema_uri_v1,
200
+
migration_lens=migration_lens_uri
201
+
)
202
+
```
203
+
204
+
### Getting Latest Schema
205
+
206
+
```python
207
+
from atdata.atproto import query_latest_schema
208
+
209
+
latest = query_latest_schema(
210
+
client,
211
+
schema_id="imagesample" # Just the NSID part
212
+
)
213
+
# Returns: {
214
+
# "uri": "at://.../imagesample@2.0.0",
215
+
# "version": "2.0.0",
216
+
# "record": {...},
217
+
# "allVersions": [...]
218
+
# }
219
+
```
220
+
221
+
## Validation
222
+
223
+
See [06_lexicon_validation.md](../decisions/06_lexicon_validation.md) for validation process.
224
+
225
+
### Quick Validation
226
+
227
+
```bash
228
+
# Validate Lexicon JSON (requires ATProto tooling)
229
+
atproto-lexicon validate ac.foundation.dataset.sampleSchema.json
230
+
231
+
# Validate example records
232
+
python scripts/validate_examples.py
233
+
```
234
+
235
+
## Future Extensions
236
+
237
+
### Potential Additional Lexicons
238
+
239
+
- `ac.foundation.dataset.collection` - Group multiple datasets
240
+
- `ac.foundation.dataset.benchmark` - Evaluation results on datasets
241
+
- `ac.foundation.dataset.attestation` - Formal correctness proofs for Lenses
242
+
- `ac.foundation.dataset.verification` - Trusted DID attestations
243
+
244
+
### Schema Type Extensions
245
+
246
+
Current: `"schemaType": "jsonschema"`
247
+
248
+
Future possibilities:
249
+
- `"schemaType": "protobuf"` - Protocol Buffers definitions
250
+
- `"schemaType": "avro"` - Apache Avro schemas
251
+
- Custom domain-specific schema languages
252
+
253
+
## References
254
+
255
+
- Planning documents: `../*.md`
256
+
- Design decisions: `../decisions/*.md`
257
+
- Architectural assessment: `../decisions/assessment.md`
258
+
- ATProto Lexicon spec: https://atproto.com/specs/lexicon
259
+
- ATProto NSID spec: https://atproto.com/specs/nsid
+78
.planning/lexicons/ac.foundation.dataset.getLatestSchema.json
+78
.planning/lexicons/ac.foundation.dataset.getLatestSchema.json
···
1
+
{
2
+
"lexicon": 1,
3
+
"id": "ac.foundation.dataset.getLatestSchema",
4
+
"defs": {
5
+
"main": {
6
+
"type": "query",
7
+
"description": "Get the latest version of a sample schema by its permanent NSID identifier",
8
+
"parameters": {
9
+
"type": "params",
10
+
"required": [
11
+
"schemaId"
12
+
],
13
+
"properties": {
14
+
"schemaId": {
15
+
"type": "string",
16
+
"description": "The permanent NSID identifier for the schema (the {NSID} part of the rkey {NSID}@{semver})",
17
+
"maxLength": 500
18
+
}
19
+
}
20
+
},
21
+
"output": {
22
+
"encoding": "application/json",
23
+
"schema": {
24
+
"type": "object",
25
+
"required": [
26
+
"uri",
27
+
"version",
28
+
"record"
29
+
],
30
+
"properties": {
31
+
"uri": {
32
+
"type": "string",
33
+
"description": "AT-URI of the latest schema version",
34
+
"maxLength": 500
35
+
},
36
+
"version": {
37
+
"type": "string",
38
+
"description": "Semantic version of the latest schema",
39
+
"maxLength": 20
40
+
},
41
+
"record": {
42
+
"type": "ref",
43
+
"ref": "ac.foundation.dataset.sampleSchema",
44
+
"description": "The full schema record"
45
+
},
46
+
"allVersions": {
47
+
"type": "array",
48
+
"description": "All available versions (optional, sorted by semver descending)",
49
+
"items": {
50
+
"type": "object",
51
+
"required": [
52
+
"uri",
53
+
"version"
54
+
],
55
+
"properties": {
56
+
"uri": {
57
+
"type": "string",
58
+
"maxLength": 500
59
+
},
60
+
"version": {
61
+
"type": "string",
62
+
"maxLength": 20
63
+
}
64
+
}
65
+
}
66
+
}
67
+
}
68
+
}
69
+
},
70
+
"errors": [
71
+
{
72
+
"name": "SchemaNotFound",
73
+
"description": "No schema found with the given NSID"
74
+
}
75
+
]
76
+
}
77
+
}
78
+
}
+99
.planning/lexicons/ac.foundation.dataset.lens.json
+99
.planning/lexicons/ac.foundation.dataset.lens.json
···
1
+
{
2
+
"lexicon": 1,
3
+
"id": "ac.foundation.dataset.lens",
4
+
"defs": {
5
+
"main": {
6
+
"type": "record",
7
+
"description": "Bidirectional transformation (Lens) between two sample types, with code stored in external repositories",
8
+
"key": "tid",
9
+
"record": {
10
+
"type": "object",
11
+
"required": [
12
+
"name",
13
+
"sourceSchema",
14
+
"targetSchema",
15
+
"getterCode",
16
+
"putterCode",
17
+
"createdAt"
18
+
],
19
+
"properties": {
20
+
"name": {
21
+
"type": "string",
22
+
"description": "Human-readable lens name",
23
+
"maxLength": 100
24
+
},
25
+
"sourceSchema": {
26
+
"type": "string",
27
+
"description": "AT-URI reference to source sampleSchema",
28
+
"maxLength": 500
29
+
},
30
+
"targetSchema": {
31
+
"type": "string",
32
+
"description": "AT-URI reference to target sampleSchema",
33
+
"maxLength": 500
34
+
},
35
+
"description": {
36
+
"type": "string",
37
+
"description": "What this transformation does",
38
+
"maxLength": 1000
39
+
},
40
+
"getterCode": {
41
+
"type": "ref",
42
+
"ref": "#codeReference",
43
+
"description": "Code reference for getter function (Source -> Target)"
44
+
},
45
+
"putterCode": {
46
+
"type": "ref",
47
+
"ref": "#codeReference",
48
+
"description": "Code reference for putter function (Target, Source -> Source)"
49
+
},
50
+
"language": {
51
+
"type": "string",
52
+
"description": "Programming language of the lens implementation (e.g., 'python', 'typescript')",
53
+
"maxLength": 50
54
+
},
55
+
"metadata": {
56
+
"type": "object",
57
+
"description": "Arbitrary metadata (author, performance notes, etc.)"
58
+
},
59
+
"createdAt": {
60
+
"type": "string",
61
+
"format": "datetime",
62
+
"description": "Timestamp when this lens was created"
63
+
}
64
+
}
65
+
}
66
+
},
67
+
"codeReference": {
68
+
"type": "object",
69
+
"description": "Reference to code in an external repository (GitHub, tangled.org, etc.)",
70
+
"required": [
71
+
"repository",
72
+
"commit",
73
+
"path"
74
+
],
75
+
"properties": {
76
+
"repository": {
77
+
"type": "string",
78
+
"description": "Repository URL (e.g., 'https://github.com/user/repo' or 'at://did/tangled.repo/...')",
79
+
"maxLength": 500
80
+
},
81
+
"commit": {
82
+
"type": "string",
83
+
"description": "Git commit hash (ensures immutability)",
84
+
"maxLength": 40
85
+
},
86
+
"path": {
87
+
"type": "string",
88
+
"description": "Path to function within repository (e.g., 'lenses/vision.py:rgb_to_grayscale')",
89
+
"maxLength": 500
90
+
},
91
+
"branch": {
92
+
"type": "string",
93
+
"description": "Optional branch name (for reference, commit hash is authoritative)",
94
+
"maxLength": 100
95
+
}
96
+
}
97
+
}
98
+
}
99
+
}
+142
.planning/lexicons/ac.foundation.dataset.record.json
+142
.planning/lexicons/ac.foundation.dataset.record.json
···
1
+
{
2
+
"lexicon": 1,
3
+
"id": "ac.foundation.dataset.record",
4
+
"defs": {
5
+
"main": {
6
+
"type": "record",
7
+
"description": "Index record for a WebDataset-backed dataset with references to storage location and sample schema",
8
+
"key": "tid",
9
+
"record": {
10
+
"type": "object",
11
+
"required": [
12
+
"name",
13
+
"schemaRef",
14
+
"storage",
15
+
"createdAt"
16
+
],
17
+
"properties": {
18
+
"name": {
19
+
"type": "string",
20
+
"description": "Human-readable dataset name",
21
+
"maxLength": 200
22
+
},
23
+
"schemaRef": {
24
+
"type": "string",
25
+
"description": "AT-URI reference to the sampleSchema record for this dataset's samples",
26
+
"maxLength": 500
27
+
},
28
+
"storage": {
29
+
"type": "union",
30
+
"description": "Storage location for dataset files (WebDataset tar archives)",
31
+
"refs": [
32
+
"#storageExternal",
33
+
"#storageBlobs"
34
+
]
35
+
},
36
+
"description": {
37
+
"type": "string",
38
+
"description": "Human-readable description of the dataset",
39
+
"maxLength": 5000
40
+
},
41
+
"metadata": {
42
+
"type": "bytes",
43
+
"description": "Msgpack-encoded metadata dict (arbitrary key-value pairs)",
44
+
"maxLength": 100000
45
+
},
46
+
"tags": {
47
+
"type": "array",
48
+
"description": "Searchable tags for dataset discovery",
49
+
"items": {
50
+
"type": "string",
51
+
"maxLength": 50
52
+
},
53
+
"maxLength": 20
54
+
},
55
+
"size": {
56
+
"type": "ref",
57
+
"ref": "#datasetSize",
58
+
"description": "Dataset size information (optional)"
59
+
},
60
+
"license": {
61
+
"type": "string",
62
+
"description": "License (SPDX identifier preferred)",
63
+
"maxLength": 100
64
+
},
65
+
"createdAt": {
66
+
"type": "string",
67
+
"format": "datetime",
68
+
"description": "Timestamp when this dataset record was created"
69
+
}
70
+
}
71
+
}
72
+
},
73
+
"storageExternal": {
74
+
"type": "object",
75
+
"description": "External storage via URLs (S3, HTTP, IPFS, etc.)",
76
+
"required": [
77
+
"type",
78
+
"urls"
79
+
],
80
+
"properties": {
81
+
"type": {
82
+
"type": "string",
83
+
"const": "external"
84
+
},
85
+
"urls": {
86
+
"type": "array",
87
+
"description": "WebDataset URLs (supports brace notation like 'data-{000000..000099}.tar')",
88
+
"items": {
89
+
"type": "string",
90
+
"format": "uri",
91
+
"maxLength": 1000
92
+
},
93
+
"minLength": 1
94
+
}
95
+
}
96
+
},
97
+
"storageBlobs": {
98
+
"type": "object",
99
+
"description": "Storage via ATProto PDS blobs",
100
+
"required": [
101
+
"type",
102
+
"blobs"
103
+
],
104
+
"properties": {
105
+
"type": {
106
+
"type": "string",
107
+
"const": "blobs"
108
+
},
109
+
"blobs": {
110
+
"type": "array",
111
+
"description": "Array of blob references for WebDataset tar files",
112
+
"items": {
113
+
"type": "blob",
114
+
"description": "Blob reference to a WebDataset tar archive"
115
+
},
116
+
"minLength": 1
117
+
}
118
+
}
119
+
},
120
+
"datasetSize": {
121
+
"type": "object",
122
+
"description": "Information about dataset size",
123
+
"properties": {
124
+
"samples": {
125
+
"type": "integer",
126
+
"description": "Total number of samples in the dataset",
127
+
"minimum": 0
128
+
},
129
+
"bytes": {
130
+
"type": "integer",
131
+
"description": "Total size in bytes",
132
+
"minimum": 0
133
+
},
134
+
"shards": {
135
+
"type": "integer",
136
+
"description": "Number of WebDataset shards",
137
+
"minimum": 1
138
+
}
139
+
}
140
+
}
141
+
}
142
+
}
+91
.planning/lexicons/ac.foundation.dataset.sampleSchema.json
+91
.planning/lexicons/ac.foundation.dataset.sampleSchema.json
···
1
+
{
2
+
"lexicon": 1,
3
+
"id": "ac.foundation.dataset.sampleSchema",
4
+
"defs": {
5
+
"main": {
6
+
"type": "record",
7
+
"description": "Definition of a PackableSample-compatible sample type using JSON Schema. Supports versioning via rkey format: {NSID}@{semver}",
8
+
"key": "any",
9
+
"record": {
10
+
"type": "object",
11
+
"required": [
12
+
"name",
13
+
"version",
14
+
"schemaType",
15
+
"jsonSchema",
16
+
"createdAt"
17
+
],
18
+
"properties": {
19
+
"name": {
20
+
"type": "string",
21
+
"description": "Human-readable name for this sample type",
22
+
"maxLength": 100
23
+
},
24
+
"version": {
25
+
"type": "string",
26
+
"description": "Semantic version (e.g., '1.0.0')",
27
+
"pattern": "^(0|[1-9]\\d*)\\.(0|[1-9]\\d*)\\.(0|[1-9]\\d*)(?:-((?:0|[1-9]\\d*|\\d*[a-zA-Z-][0-9a-zA-Z-]*)(?:\\.(?:0|[1-9]\\d*|\\d*[a-zA-Z-][0-9a-zA-Z-]*))*))?(?:\\+([0-9a-zA-Z-]+(?:\\.[0-9a-zA-Z-]+)*))?$",
28
+
"maxLength": 100
29
+
},
30
+
"schemaType": {
31
+
"type": "string",
32
+
"description": "Type of schema definition (currently only 'jsonschema' supported)",
33
+
"enum": ["jsonschema"],
34
+
"default": "jsonschema"
35
+
},
36
+
"jsonSchema": {
37
+
"type": "object",
38
+
"description": "JSON Schema Draft 7 definition for this sample type. Use standard JSON Schema with NDArray shim for array types.",
39
+
"required": ["$schema", "type", "properties"],
40
+
"properties": {
41
+
"$schema": {
42
+
"type": "string",
43
+
"const": "http://json-schema.org/draft-07/schema#"
44
+
},
45
+
"type": {
46
+
"type": "string",
47
+
"const": "object"
48
+
},
49
+
"properties": {
50
+
"type": "object",
51
+
"minProperties": 1
52
+
}
53
+
}
54
+
},
55
+
"description": {
56
+
"type": "string",
57
+
"description": "Human-readable description of what this sample type represents",
58
+
"maxLength": 5000
59
+
},
60
+
"ndarrayShimUri": {
61
+
"type": "string",
62
+
"format": "uri",
63
+
"description": "URI to the NDArray JSON Schema shim definition (optional, defaults to standard shim at https://foundation.ac/schemas/atdata-ndarray-bytes/1.0.0)",
64
+
"maxLength": 500
65
+
},
66
+
"metadata": {
67
+
"type": "object",
68
+
"description": "Arbitrary metadata (author, license, tags, etc.)"
69
+
},
70
+
"previousVersion": {
71
+
"type": "string",
72
+
"format": "at-uri",
73
+
"description": "AT-URI reference to the previous version of this schema (if any)",
74
+
"maxLength": 500
75
+
},
76
+
"migrationLens": {
77
+
"type": "string",
78
+
"format": "at-uri",
79
+
"description": "AT-URI reference to a Lens for migrating from previous version (if applicable)",
80
+
"maxLength": 500
81
+
},
82
+
"createdAt": {
83
+
"type": "string",
84
+
"format": "datetime",
85
+
"description": "Timestamp when this schema version was created"
86
+
}
87
+
}
88
+
}
89
+
}
90
+
}
91
+
}
+16
.planning/lexicons/ndarray_shim.json
+16
.planning/lexicons/ndarray_shim.json
···
1
+
{
2
+
"$schema": "http://json-schema.org/draft-07/schema#",
3
+
"$id": "https://foundation.ac/schemas/atdata-ndarray-bytes/1.0.0",
4
+
"title": "ATDataNDArrayBytes",
5
+
"description": "Standard definition for numpy NDArray types in JSON Schema, compatible with atdata WebDataset serialization. This type's contents are interpreted as containing the raw bytes data for a serialized numpy NDArray, and serve as a marker for atdata-based code generation to use standard numpy types, rather than generated dataclasses.",
6
+
"version": "1.0.0",
7
+
"$defs": {
8
+
"ndarray": {
9
+
"type": "string",
10
+
"format": "byte",
11
+
"description": "Numpy array serialized using numpy `.npy` format via `np.save` (includes dtype and shape in binary header). When represented in JSON, this is a base64-encoded string. In msgpack, this is raw bytes.",
12
+
"contentEncoding": "base64",
13
+
"contentMediaType": "application/octet-stream"
14
+
}
15
+
}
16
+
}
+386
.planning/ndarray_shim_spec.md
+386
.planning/ndarray_shim_spec.md
···
1
+
# NDArray JSON Schema Shim Specification
2
+
3
+
**Issue**: #52
4
+
**Version**: 1.0
5
+
**Status**: Draft
6
+
7
+
## Problem Statement
8
+
9
+
We need a standard way to represent numpy NDArray types in JSON Schema that:
10
+
1. Works with existing atdata msgpack serialization (numpy `.npy` format)
11
+
2. Can be validated (where practical)
12
+
3. Can be used for code generation
13
+
4. Is compatible with JSON Schema tooling
14
+
5. Preserves dtype and shape information
15
+
16
+
## Current Serialization Format
17
+
18
+
atdata uses `_helpers.array_to_bytes()` which serializes arrays using numpy's native `.npy` format:
19
+
20
+
```python
21
+
def array_to_bytes(x: np.ndarray) -> bytes:
22
+
np_bytes = BytesIO()
23
+
np.save(np_bytes, x, allow_pickle=True)
24
+
return np_bytes.getvalue()
25
+
```
26
+
27
+
**Result**: A bytes object containing:
28
+
- Magic bytes (`\x93NUMPY`)
29
+
- Version info
30
+
- Header with dtype and shape
31
+
- Array data
32
+
33
+
**Key insight**: The .npy format is self-describing - dtype and shape are already in the bytes!
34
+
35
+
## Design Approach
36
+
37
+
### Option 1: Pure Metadata (REJECTED)
38
+
39
+
Describe the semantic array only:
40
+
```json
41
+
{
42
+
"type": "object",
43
+
"x-atdata-ndarray": true,
44
+
"x-dtype": "uint8",
45
+
"x-shape": [null, null, 3]
46
+
}
47
+
```
48
+
49
+
**Problem**: Doesn't match actual msgpack structure (which stores bytes, not objects)
50
+
51
+
### Option 2: Bytes with Extension Properties (REJECTED)
52
+
53
+
Describe the bytes with metadata:
54
+
```json
55
+
{
56
+
"type": "string",
57
+
"format": "byte",
58
+
"x-dtype": "uint8",
59
+
"x-shape": [null, null, 3]
60
+
}
61
+
```
62
+
63
+
**Problem**:
64
+
- Non-standard use of extension properties
65
+
- JSON Schema doesn't know how to validate these
66
+
- Codegen tools won't understand x- properties
67
+
68
+
### Option 3: Reusable Schema Definition (RECOMMENDED)
69
+
70
+
Create a standard NDArray schema definition that can be $ref'd, with controlled vocabulary for metadata.
71
+
72
+
## Recommended Specification
73
+
74
+
### Base NDArray Schema Definition
75
+
76
+
This should be included in every JSON Schema that uses NDArray types:
77
+
78
+
```json
79
+
{
80
+
"$schema": "http://json-schema.org/draft-07/schema#",
81
+
"$defs": {
82
+
"ndarray": {
83
+
"type": "string",
84
+
"format": "byte",
85
+
"description": "Numpy array serialized using numpy .npy format (includes dtype and shape in binary header)",
86
+
"contentEncoding": "base64",
87
+
"contentMediaType": "application/octet-stream"
88
+
}
89
+
}
90
+
}
91
+
```
92
+
93
+
### Using NDArray in Properties
94
+
95
+
Properties that are NDArray types reference the base definition and add metadata as **sibling properties**:
96
+
97
+
```json
98
+
{
99
+
"properties": {
100
+
"image": {
101
+
"$ref": "#/$defs/ndarray",
102
+
"description": "RGB image with variable height/width",
103
+
"x-atdata-dtype": "uint8",
104
+
"x-atdata-shape": [null, null, 3]
105
+
}
106
+
}
107
+
}
108
+
```
109
+
110
+
### Metadata Convention
111
+
112
+
**Extension properties** (prefixed with `x-atdata-`):
113
+
- `x-atdata-dtype`: Numpy dtype string (e.g., "uint8", "float32", "int64")
114
+
- `x-atdata-shape`: Array of integers and null (null = dynamic dimension)
115
+
- `x-atdata-notes`: Optional human-readable notes about the array
116
+
117
+
**Standard JSON Schema properties** (used normally):
118
+
- `description`: Human-readable description of what the array represents
119
+
- `title`: Short name for the field
120
+
121
+
## Complete Example
122
+
123
+
```json
124
+
{
125
+
"$schema": "http://json-schema.org/draft-07/schema#",
126
+
"title": "ImageSample",
127
+
"type": "object",
128
+
"required": ["image", "label"],
129
+
"properties": {
130
+
"image": {
131
+
"$ref": "#/$defs/ndarray",
132
+
"description": "RGB image with variable height/width",
133
+
"x-atdata-dtype": "uint8",
134
+
"x-atdata-shape": [null, null, 3],
135
+
"x-atdata-notes": "Images must have 3 color channels (RGB)"
136
+
},
137
+
"depth_map": {
138
+
"$ref": "#/$defs/ndarray",
139
+
"description": "Depth map corresponding to the image",
140
+
"x-atdata-dtype": "float32",
141
+
"x-atdata-shape": [null, null],
142
+
"x-atdata-notes": "Same height and width as image, but single channel"
143
+
},
144
+
"label": {
145
+
"type": "string",
146
+
"description": "Human-readable label"
147
+
}
148
+
},
149
+
"$defs": {
150
+
"ndarray": {
151
+
"type": "string",
152
+
"format": "byte",
153
+
"description": "Numpy array serialized using numpy .npy format",
154
+
"contentEncoding": "base64",
155
+
"contentMediaType": "application/octet-stream"
156
+
}
157
+
}
158
+
}
159
+
```
160
+
161
+
## Rationale
162
+
163
+
### Why `type: "string", format: "byte"`?
164
+
165
+
In msgpack serialization:
166
+
- The NDArray field is stored as raw bytes (the .npy format)
167
+
- When represented in JSON (for validation/transport), bytes become base64 strings
168
+
- JSON Schema's `type: "string", format: "byte"` is the standard way to represent binary data
169
+
170
+
### Why extension properties (`x-atdata-*`)?
171
+
172
+
JSON Schema allows custom properties starting with `x-`. Benefits:
173
+
1. **Standard**: Well-established convention in JSON Schema ecosystem
174
+
2. **Ignored by validators**: Won't cause validation errors
175
+
3. **Accessible to codegen**: Tools can parse these for type generation
176
+
4. **Self-documenting**: Clear what they mean
177
+
178
+
### Why not validate dtype/shape at JSON Schema level?
179
+
180
+
**Technical limitation**: JSON Schema can't validate binary .npy format internals.
181
+
182
+
**Solution**: Validation happens at **deserialization time**:
183
+
1. JSON Schema validates overall structure (field is present, is bytes)
184
+
2. When bytes are deserialized to NDArray, check dtype/shape match expectations
185
+
186
+
## Usage in atdata
187
+
188
+
### Publishing Schemas
189
+
190
+
When publishing a PackableSample with NDArray fields:
191
+
192
+
```python
193
+
@atdata.packable
194
+
class ImageSample:
195
+
image: NDArray # Will be annotated with dtype/shape hints
196
+
label: str
197
+
198
+
# SDK extracts type hints and generates JSON Schema
199
+
schema_json = {
200
+
"properties": {
201
+
"image": {
202
+
"$ref": "#/$defs/ndarray",
203
+
"x-atdata-dtype": "uint8", # From annotation or default
204
+
"x-atdata-shape": [null, null, 3] # From annotation or None
205
+
}
206
+
}
207
+
}
208
+
```
209
+
210
+
### Type Annotations for NDArray
211
+
212
+
Python type hints to specify dtype/shape:
213
+
214
+
```python
215
+
from typing import Annotated
216
+
from numpy.typing import NDArray
217
+
218
+
# Option 1: Generic NDArray (dtype/shape inferred or not specified)
219
+
image: NDArray
220
+
221
+
# Option 2: With dtype (using numpy typing)
222
+
image: NDArray[np.uint8]
223
+
224
+
# Option 3: With full metadata (using Annotated)
225
+
image: Annotated[NDArray[np.uint8], {"shape": [None, None, 3]}]
226
+
```
227
+
228
+
### Code Generation
229
+
230
+
Codegen reads JSON Schema and produces:
231
+
232
+
```python
233
+
@atdata.packable
234
+
class ImageSample:
235
+
image: NDArray # uint8, shape: [*, *, 3]
236
+
label: str
237
+
```
238
+
239
+
Comment indicates dtype/shape from `x-atdata-*` properties.
240
+
241
+
## Validation Strategy
242
+
243
+
### JSON Schema Level (Structural)
244
+
✅ Validate field is present (if required)
245
+
✅ Validate field is bytes/string (in JSON)
246
+
✅ Validate base64 encoding (if in JSON)
247
+
248
+
### Deserialization Level (Semantic)
249
+
✅ Validate .npy format is valid
250
+
✅ Validate dtype matches expected (if specified)
251
+
✅ Validate shape matches expected (if specified)
252
+
✅ Validate shape constraints (e.g., must be 3D)
253
+
254
+
### Implementation
255
+
256
+
```python
257
+
from atdata.validation import validate_ndarray
258
+
259
+
def validate_ndarray(
260
+
array: np.ndarray,
261
+
expected_dtype: Optional[str] = None,
262
+
expected_shape: Optional[List[Optional[int]]] = None
263
+
) -> tuple[bool, list[str]]:
264
+
"""Validate array against expectations."""
265
+
errors = []
266
+
267
+
# Check dtype
268
+
if expected_dtype and str(array.dtype) != expected_dtype:
269
+
errors.append(f"Expected dtype {expected_dtype}, got {array.dtype}")
270
+
271
+
# Check shape
272
+
if expected_shape:
273
+
if len(array.shape) != len(expected_shape):
274
+
errors.append(f"Expected {len(expected_shape)}D array, got {len(array.shape)}D")
275
+
for i, (actual, expected) in enumerate(zip(array.shape, expected_shape)):
276
+
if expected is not None and actual != expected:
277
+
errors.append(f"Dimension {i}: expected {expected}, got {actual}")
278
+
279
+
return len(errors) == 0, errors
280
+
```
281
+
282
+
## Standard NDArray Shim URI
283
+
284
+
The NDArray shim definition should be published at a canonical URI:
285
+
286
+
**Proposed**: `at://did:plc:foundation/ac.foundation.dataset.ndarray-shim/1.0.0`
287
+
288
+
This allows schemas to reference a standard definition:
289
+
290
+
```json
291
+
{
292
+
"properties": {
293
+
"image": {
294
+
"$ref": "at://did:plc:foundation/ac.foundation.dataset.ndarray-shim/1.0.0#/$defs/ndarray",
295
+
"x-atdata-dtype": "uint8"
296
+
}
297
+
}
298
+
}
299
+
```
300
+
301
+
Or schemas can inline the definition (recommended for Phase 1):
302
+
303
+
```json
304
+
{
305
+
"$defs": {
306
+
"ndarray": { /* inline definition */ }
307
+
}
308
+
}
309
+
```
310
+
311
+
## Alternative: Describe Deserialized Structure
312
+
313
+
For reference, an alternative approach that describes the "unpacked" structure:
314
+
315
+
```json
316
+
{
317
+
"$defs": {
318
+
"ndarray": {
319
+
"type": "object",
320
+
"description": "Numpy array (deserialized representation)",
321
+
"required": ["dtype", "shape", "data"],
322
+
"properties": {
323
+
"dtype": {"type": "string"},
324
+
"shape": {"type": "array", "items": {"type": "integer"}},
325
+
"data": {"type": "string", "format": "byte"}
326
+
}
327
+
}
328
+
}
329
+
}
330
+
```
331
+
332
+
**Problem**: This doesn't match the actual msgpack structure (which is just bytes, not an object with dtype/shape/data fields). The .npy format is opaque bytes, not a structured object.
333
+
334
+
**Conclusion**: Stick with the recommended approach (bytes with metadata).
335
+
336
+
## Implementation Checklist
337
+
338
+
- [ ] Update sampleSchema Lexicon to reference this spec
339
+
- [ ] Create standard NDArray shim definition
340
+
- [ ] Update schema examples to use the shim correctly
341
+
- [ ] Implement validation helpers in Python SDK
342
+
- [ ] Add type annotation support for dtype/shape hints
343
+
- [ ] Update codegen to read x-atdata-* properties
344
+
- [ ] Document in user-facing docs
345
+
346
+
## Open Questions
347
+
348
+
1. **Should we support other array libraries?** (PyTorch tensors, JAX arrays, etc.)
349
+
- Could use `x-atdata-array-type: "numpy"|"torch"|"jax"`
350
+
- Recommendation: NumPy only for Phase 1
351
+
352
+
2. **Should shape constraints be enforced at runtime?**
353
+
- Pro: Catch errors early
354
+
- Con: Performance overhead, flexibility lost
355
+
- Recommendation: Optional validation, off by default
356
+
357
+
3. **Should we support sparse arrays?**
358
+
- scipy.sparse has different serialization format
359
+
- Recommendation: Defer to future
360
+
361
+
4. **What about array of arrays?** (ragged arrays)
362
+
- Can be represented as Python lists of NDArrays
363
+
- Recommendation: Not a priority for Phase 1
364
+
365
+
## Summary
366
+
367
+
**Recommended Approach**:
368
+
- NDArray fields represented as `{"$ref": "#/$defs/ndarray"}` (bytes)
369
+
- Dtype and shape specified via `x-atdata-dtype` and `x-atdata-shape`
370
+
- Standard `ndarray` definition inlined in every schema
371
+
- Validation happens at deserialization, not JSON Schema level
372
+
- Codegen reads extension properties to generate proper types
373
+
374
+
**Benefits**:
375
+
- ✅ Compatible with existing msgpack serialization
376
+
- ✅ Works with JSON Schema tooling
377
+
- ✅ Clear metadata for codegen
378
+
- ✅ Flexible (dtype/shape optional)
379
+
- ✅ Extensible (can add more x-atdata-* properties)
380
+
381
+
**Trade-offs**:
382
+
- ⚠️ Leaky abstraction (JSON Schema describes bytes, not semantic array)
383
+
- ⚠️ Validation split across two layers
384
+
- ⚠️ Extension properties not universally understood
385
+
386
+
**Grade**: B+ (Good practical solution)