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PAID_CONTEXT_FEATURES.md

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Paid Context Management Features

These features are planned for implementation in altimate-core (Rust) and gated behind license key verification.

1. Precise Token Counting

Bridge method: context.count_tokens(text, model_family) -> number

Uses tiktoken-rs in altimate-core for exact model-specific token counts. Replaces the heuristic estimation in token.ts. Supports cl100k_base (GPT-4/Claude), o200k_base (GPT-4o), and future tokenizers.

Benefits:

  • Eliminates 20-30% estimation error
  • Precise compaction triggering — no late/early compaction
  • Accurate token budget allocation

2. Smart Context Scoring

Bridge method: context.score_relevance(items[], query) -> scored_items[]

Embedding-based relevance scoring for context items. Used before compaction to drop lowest-scoring items first, preserving the most relevant conversation history. Uses a local embeddings model (no external API calls required).

Benefits:

  • Drops irrelevant context before compaction
  • Preserves high-value conversation segments
  • Reduces unnecessary compaction cycles

3. Schema Compression

Bridge method: context.compress_schema(schema_ddl, token_budget) -> compressed_schema

Schemonic-style ILP (Integer Linear Programming) optimization. Extends the existing altimate_core_optimize_context tool. Achieves ~2x token reduction on schema DDL without accuracy loss by intelligently abbreviating column names, removing redundant constraints, and merging similar table definitions.

Benefits:

  • Fits 2x more schema context in the same token budget
  • No accuracy loss on downstream SQL generation
  • Works with all warehouse dialects

4. Lineage-Aware Context Selection

Bridge method: context.select_by_lineage(model_name, manifest, hops) -> relevant_tables[]

Uses dbt DAG / lineage graph to scope relevant tables. PageRank-style relevance scoring weights tables by proximity and importance in the dependency graph. Configurable hop distance for breadth of context.

Benefits:

  • Only includes tables relevant to the current model/query
  • Reduces schema context by 60-80% for large warehouses
  • Leverages existing dbt manifest parsing

5. Semantic Schema Catalog

Bridge method: context.generate_catalog(schema, sample_data) -> yaml_catalog

YAML-based semantic views (similar to Snowflake Cortex Analyst). Auto-generates business descriptions, data types, and relationships from schema + sample data. Serves as a compressed, human-readable schema representation.

Benefits:

  • Business-friendly context for the LLM
  • More token-efficient than raw DDL
  • Auto-generates from existing schema metadata

6. Context Budget Allocator

Bridge method: context.allocate_budget(model_limit, task_type) -> { system, schema, conversation, output }

Explicit token allocation across categories. Dynamic adjustment based on task type (query writing vs. debugging vs. optimization). Prevents any single category from consuming the entire context window.

Benefits:

  • Prevents schema from crowding out conversation history
  • Task-appropriate allocation (more schema for query writing, more conversation for debugging)
  • Works with the compaction system to respect budgets