PL/SQL Semantic Compression

Enterprise databases frequently contain business logic concentrated inside massive monolithic PL/SQL packages spanning 3,000 to over 10,000 lines of code.

Passing an entire 10,000-line package body into an LLM context creates three severe bottlenecks: 1. Excessive Cost: Thousands of prompt tokens consumed per single query. 2. High Latency: Model completion times multiply significantly. 3. Attention Degradation (Lost in the Middle): LLMs struggle to maintain focus and exhibit hallucinations when submerged in huge blocks of unrelated code.

✂️ The LEAI Solution: Surgical Skeletonization

LEAI includes a custom AST/PL-SQL semantic parser. When a user or autonomous agent inspects a specific procedure inside a large package:

flowchart TD
    PKG[Monolithic Package:<br/>PKG_BILLING_CORE<br/>10,000 Lines]

    PKG --> PARSER{LEAI Semantic Parser}

    PARSER -->|Extracts Full Body| PROC[PROCEDURE CALCULATE_TAX<br/>Authentic Body: 120 Lines]
    PARSER -->|Generates Signatures Only| SKEL[Package Skeleton:<br/>Signatures for 85 other procedures/functions<br/>150 Lines]

    PROC & SKEL --> CONTEXT[Optimized LLM Context Payload<br/>~95% Token Savings]

What is delivered to the LLM:

1. The Target Subprogram: Complete body, algorithmic logic, local variables, and SQL queries.
2. The Surrounding Skeleton: Lightweight signatures (header signatures with parameters and return types) of other routines in the package, preserving global contextual awareness without the noise.

📊 Efficiency Benchmark