| { |
| "$schema": "https://json-schema.org/draft/2020-12/schema", |
| "$id": "https://github.com/faraa2m/llm-tokens-atlas/schema/v1.json", |
| "title": "llm-tokens-atlas dataset schema", |
| "description": "Schema (draft 2020-12) for the three JSONL streams that compose llm-tokens-atlas: 'prompts' (data/raw_prompts.jsonl), 'offline_counts' (data/offline_counts.jsonl), and 'empirical_counts' (data/empirical_counts.jsonl). The downstream join key is (prompt_id, provider, format, model). Schema version 1: backwards-compatible additions allowed; breaking changes bump to v2.json.", |
| "type": "object", |
| "$defs": { |
| "format": { |
| "type": "string", |
| "enum": ["markdown", "xml", "json", "yaml", "plain"], |
| "description": "Prompt serialization format. One of five enum values (lowercased)." |
| }, |
| "provider": { |
| "type": "string", |
| "enum": ["openai", "anthropic", "google", "mistral", "cohere"], |
| "description": "LLM provider whose tokenizer (offline) or token-count endpoint (empirical) produced the count." |
| }, |
| "model": { |
| "type": "string", |
| "minLength": 1, |
| "description": "Provider-namespaced model identifier (e.g. 'claude-opus-4-7', 'gpt-4o-2024-08-06', 'gemini-1.5-pro')." |
| }, |
| "prompt_id": { |
| "type": "string", |
| "minLength": 1, |
| "pattern": "^[A-Za-z0-9_:.\\-]+$", |
| "description": "Stable identifier for a single prompt row in raw_prompts.jsonl. Used as the foreign key in offline_counts and empirical_counts." |
| }, |
| "iso8601": { |
| "type": "string", |
| "format": "date-time", |
| "description": "ISO-8601 timestamp in UTC, e.g. '2026-05-10T18:23:00Z'." |
| }, |
| "nonNegativeInteger": { |
| "type": "integer", |
| "minimum": 0, |
| "description": "A token count or character length, never negative." |
| }, |
| "promptRow": { |
| "type": "object", |
| "description": "One row of data/raw_prompts.jsonl. The canonical source-of-truth text + provenance for a single prompt.", |
| "properties": { |
| "prompt_id": {"$ref": "#/$defs/prompt_id"}, |
| "source": { |
| "type": "string", |
| "minLength": 1, |
| "description": "Provenance corpus or dataset name (e.g. 'lmsys-chat-1m', 'humaneval', 'mt-bench', 'github-readmes', 'wikipedia-multilingual')." |
| }, |
| "text": { |
| "type": "string", |
| "description": "The prompt text exactly as it will be sent to the tokenizer / API. UTF-8." |
| }, |
| "text_len_chars": { |
| "$ref": "#/$defs/nonNegativeInteger", |
| "description": "Length of 'text' in Unicode codepoints (not bytes)." |
| }, |
| "text_len_words": { |
| "$ref": "#/$defs/nonNegativeInteger", |
| "description": "Whitespace-split word count of 'text'. Approximate — intended for filtering/grouping, not as a tokenization signal." |
| }, |
| "language": { |
| "type": "string", |
| "minLength": 2, |
| "maxLength": 16, |
| "description": "ISO-639-1 (or BCP-47) language code: 'en', 'zh', 'es', 'fr', 'de', 'ja', 'multi', 'code', etc. 'code' indicates source code; 'multi' indicates a mixed-language prompt." |
| }, |
| "domain": { |
| "type": "string", |
| "enum": ["code", "prose", "chat", "structured", "multilingual", "other"], |
| "description": "High-level domain tag used for stratified analysis." |
| }, |
| "collected_at": {"$ref": "#/$defs/iso8601"} |
| }, |
| "required": [ |
| "prompt_id", |
| "source", |
| "text", |
| "text_len_chars", |
| "text_len_words", |
| "language", |
| "domain", |
| "collected_at" |
| ], |
| "additionalProperties": false |
| }, |
| "offlineCountRow": { |
| "type": "object", |
| "description": "One row of data/offline_counts.jsonl. Offline-tokenizer count for a single (prompt_id, provider, format, model) tuple. No network call; pure local tokenization via @tokenometer/core (or the moral equivalent).", |
| "properties": { |
| "prompt_id": {"$ref": "#/$defs/prompt_id"}, |
| "provider": {"$ref": "#/$defs/provider"}, |
| "format": {"$ref": "#/$defs/format"}, |
| "model": {"$ref": "#/$defs/model"}, |
| "offline_count": { |
| "$ref": "#/$defs/nonNegativeInteger", |
| "description": "Token count reported by the offline tokenizer. Never negative." |
| }, |
| "tokenizer_version": { |
| "type": "string", |
| "minLength": 1, |
| "description": "Pinned tokenizer version identifier (e.g. 'tiktoken@cl100k_base', '@tokenometer/core@1.0.0', 'mistral-common@1.7.0'). Pinned for reproducibility." |
| }, |
| "ts": {"$ref": "#/$defs/iso8601"} |
| }, |
| "required": [ |
| "prompt_id", |
| "provider", |
| "format", |
| "model", |
| "offline_count", |
| "tokenizer_version", |
| "ts" |
| ], |
| "additionalProperties": false |
| }, |
| "empiricalCountRow": { |
| "type": "object", |
| "description": "One row of data/empirical_counts.jsonl. Ground-truth token count for a single (prompt_id, provider, format, model) tuple. Sourced from each provider's official countTokens endpoint or (where no endpoint exists) tiktoken-as-truth.", |
| "properties": { |
| "prompt_id": {"$ref": "#/$defs/prompt_id"}, |
| "provider": {"$ref": "#/$defs/provider"}, |
| "format": {"$ref": "#/$defs/format"}, |
| "model": {"$ref": "#/$defs/model"}, |
| "empirical_count": { |
| "$ref": "#/$defs/nonNegativeInteger", |
| "description": "Token count reported by the provider's count-tokens endpoint or by tiktoken (when treated as oracle, e.g. for OpenAI)." |
| }, |
| "is_oracle": { |
| "type": "boolean", |
| "description": "True when this value is treated as the ground-truth oracle (e.g. tiktoken for OpenAI; provider countTokens API for Anthropic/Google). False when it is an empirical-but-not-official source (e.g. inferred from stream usage)." |
| }, |
| "source": { |
| "type": "string", |
| "enum": ["api", "tiktoken", "sdk", "stream-usage"], |
| "description": "How this empirical count was obtained: 'api' = HTTP call to a countTokens endpoint; 'tiktoken' = local tiktoken treated as oracle (OpenAI only); 'sdk' = vendor SDK helper; 'stream-usage' = inferred from generation usage metadata." |
| }, |
| "endpoint": { |
| "type": "string", |
| "minLength": 1, |
| "description": "Concrete endpoint or library identifier — e.g. 'https://api.anthropic.com/v1/messages/count_tokens@2024-10-22', 'vertex-ai.googleapis.com/v1/.../countTokens', 'tiktoken.encoding_for_model(gpt-4o)'." |
| }, |
| "ts": {"$ref": "#/$defs/iso8601"} |
| }, |
| "required": [ |
| "prompt_id", |
| "provider", |
| "format", |
| "model", |
| "empirical_count", |
| "is_oracle", |
| "source", |
| "endpoint", |
| "ts" |
| ], |
| "additionalProperties": false |
| } |
| }, |
| "properties": { |
| "tables": { |
| "type": "object", |
| "description": "Index of the three tables in the atlas dataset. Each table.row is a $ref into $defs and identifies the per-row schema used to validate the corresponding JSONL stream.", |
| "properties": { |
| "prompts": { |
| "type": "object", |
| "properties": { |
| "file": {"type": "string", "const": "data/raw_prompts.jsonl"}, |
| "primary_key": { |
| "type": "array", |
| "items": {"type": "string"}, |
| "const": ["prompt_id"] |
| }, |
| "row": {"$ref": "#/$defs/promptRow"} |
| }, |
| "required": ["file", "primary_key", "row"] |
| }, |
| "offline_counts": { |
| "type": "object", |
| "properties": { |
| "file": {"type": "string", "const": "data/offline_counts.jsonl"}, |
| "primary_key": { |
| "type": "array", |
| "items": {"type": "string"}, |
| "const": ["prompt_id", "provider", "format", "model"] |
| }, |
| "row": {"$ref": "#/$defs/offlineCountRow"} |
| }, |
| "required": ["file", "primary_key", "row"] |
| }, |
| "empirical_counts": { |
| "type": "object", |
| "properties": { |
| "file": {"type": "string", "const": "data/empirical_counts.jsonl"}, |
| "primary_key": { |
| "type": "array", |
| "items": {"type": "string"}, |
| "const": ["prompt_id", "provider", "format", "model"] |
| }, |
| "row": {"$ref": "#/$defs/empiricalCountRow"} |
| }, |
| "required": ["file", "primary_key", "row"] |
| } |
| }, |
| "required": ["prompts", "offline_counts", "empirical_counts"] |
| }, |
| "join": { |
| "type": "object", |
| "description": "Documented downstream join semantics. scripts/build_dataset.py inner-joins offline_counts and empirical_counts onto prompts using (prompt_id, provider, format, model) to produce the processed Parquet artifact.", |
| "properties": { |
| "key": { |
| "type": "array", |
| "items": {"type": "string"}, |
| "const": ["prompt_id", "provider", "format", "model"] |
| }, |
| "strategy": { |
| "type": "string", |
| "const": "inner-on-counts-then-attach-prompts" |
| }, |
| "computed_columns": { |
| "type": "array", |
| "items": {"type": "string"}, |
| "const": ["delta", "delta_pct", "abs_delta", "direction"] |
| } |
| }, |
| "required": ["key", "strategy", "computed_columns"] |
| } |
| }, |
| "required": ["tables", "join"], |
| "additionalProperties": false |
| } |
|
|
