pipeline.py

import os
import json
from typing import Dict, Any

class EnterpriseTriagePipeline:
    """
    A production-grade architecture blueprint demonstrating programmatic pre-processing, 
    deterministic triage, and token optimization before execution of LLM API layers.
    Designed for high-compliance financial workflows (e.g., Document Parsing).
    """
    def __init__(self, accuracy_threshold: float = 0.90):
        self.accuracy_threshold = accuracy_threshold
        print("[INIT] Initializing Secure Enterprise Triage Pipeline Layer...")

    def programmatic_pre_processing(self, raw_payload: Dict[str, Any]) -> Dict[str, Any]:
        """
        Deduces structural, static data deterministically using pure Python logic 
        to protect the context window and minimize token overhead.
        """
        print("[STAGE 1] Running deterministic data extraction filters...")
        file_type = raw_payload.get("file_type", "unknown")
        
        # Simulating baseline deterministic evaluation based on document layout metadata
        extracted_meta = {
            "document_id": raw_payload.get("id"),
            "requires_fuzzy_reasoning": False,
            "static_variables": {}
        }
        
        # Pure code execution to isolate non-changing parameters
        if file_type == "txt":
            extracted_meta["confidence_score"] = 0.95
        elif file_type == "pdf":
            extracted_meta["confidence_score"] = 0.85
            extracted_meta["requires_fuzzy_reasoning"] = True  # Layout requires LLM nuance
            
        return extracted_meta

    def execute_triage_routing(self, payload: Dict[str, Any]) -> str:
        """
        Implements a phased migration pathway. If data passes deterministic confidence checks,
        it bypasses the LLM to eliminate hallucination risks completely.
        """
        processed_meta = self.programmatic_pre_processing(payload)
        
        if not processed_meta["requires_fuzzy_reasoning"] and processed_meta["confidence_score"] >= self.accuracy_threshold:
            print("[ROUTING] Pipeline matched deterministic thresholds. Bypassing LLM call.")
            return "SUCCESS: Route directly to downstream Enterprise DB System (Deterministic Path)"
        
        print("[ROUTING] Nuance detected. Routing remaining payload to LLM Agent with Human-In-The-Loop (HITL) gatekeeper.")
        return "SUCCESS: Route to Agentic Framework Context Layer (Agentic Path)"

if __name__ == "__main__":
    # Mocking an incoming complex unstructured financial document payload
    mock_document = {
        "id": "JPMC-BK-2026-005",
        "file_type": "pdf",
        "content": "Highly unstructured layout containing non-linear legal text clauses..."
    }
    
    # Execute the framework
    pipeline = EnterpriseTriagePipeline(accuracy_threshold=0.90)
    routing_decision = pipeline.execute_triage_routing(mock_document)
    print(f"[FINAL STATE] {routing_decision}")