Architecting an Advanced Document Intelligence System

This interactive application explores a technical blueprint for a system that uses Large Language Models (LLMs) to transform unstructured documents into a dynamic, queryable knowledge base for automated decision-making. Navigate through the core architectural pillars to understand how raw data becomes an auditable, justified answer.

The System Architecture

The system is built on a Modular and Agentic RAG (Retrieval-Augmented Generation) framework. This interactive diagram shows the flow from an initial user query to a final, structured output. Click on any pillar to jump to its detailed section.

User Query

"46M, knee surgery, Pune, 3-month policy"

Pillar 1: Ingestion

Parse, Clean, Chunk & Embed Documents

Pillar 2: Retrieval

Hybrid Search & Rerank for Relevance

Pillar 3: Reasoning

Multi-Hop Logic & Generation

Pillar 4: Output

Structured JSON & Justification
Structured JSON Response

Decision, Amount, Justification with Citations

Pillar 1: The Data Foundation - Ingestion & Preparation

This is the foundational stage where raw source documents (PDF, DOCX, email) are transformed into a high-quality, searchable knowledge base. The quality of this pipeline determines the performance ceiling of the entire system. It involves parsing, cleaning, enriching with metadata, and strategically chunking the text.

Advanced Chunking Strategies

Chunking, or splitting documents into smaller segments, is critical. The goal is to create chunks that contain a complete semantic idea. The choice of strategy should be tailored to the document's structure. A one-size-fits-all approach is rarely optimal.

  • Fixed-Size: Simple but often breaks sentences.
  • Recursive: Better at preserving paragraphs and sentences.
  • Document-Specific: Uses document structure (e.g., Markdown headers) for perfect alignment.
  • Semantic: Most advanced; groups sentences by meaning, creating the most coherent chunks.

Chunking Strategy Comparison

Pillar 2: The Intelligence Core - Semantic Retrieval

The retrieval component bridges the gap between a user's query and the precise information within the knowledge base. It's a multi-stage funnel that starts broad to find all potentially relevant information (recall), then filters and refines to provide a small, highly relevant context to the LLM (precision).

Advanced Retrieval: Hybrid Search + Reranking

User Query

Keyword Search (BM25)

Finds literal matches (e.g., policy numbers)

Vector Search

Finds semantic matches (e.g., "bad knee" -> "knee surgery")

Result Fusion & Reranking

Combine results & score top 3-5 for relevance

Precise Context for LLM

Vector Database Comparison

Database ↑↓ Type ↑↓ Latency (ms) ↑↓ Key Features

Pillar 3: The Reasoning Engine - Generation & Decision-Making

This is the cognitive core where the retrieved information is synthesized into a logical decision. The LLM acts as a reasoning agent, interpreting complex, interdependent rules and applying them to the facts of the query. This often requires a "multi-hop" approach where the agent iteratively retrieves information to solve a problem.

Agentic Multi-Hop Reasoning

Many real-world decisions can't be made from a single piece of information. An agentic architecture is required to execute a multi-step reasoning plan. The animation to the right demonstrates this process.

  1. Decomposition: The agent breaks a high-level query ("What's the payout?") into sub-questions.
  2. Iterative Retrieval: It enters a loop, using the retrieval tool to find the answer to each sub-question (a "hop").
  3. Synthesis & Tool Use: After gathering all facts (e.g., coverage limit, deductible, co-pay), it uses a tool like a calculator to compute the final answer.

Payout Calculation Example

Pillar 4: Delivering Actionable Insights - Output & Explainability

The final output must be machine-readable, transparent, and fully auditable. This requires enforcing a structured JSON output and building a clear justification trace that links every part of the decision back to the source evidence. This transforms the system from a black box into a defensible, evidence-based reasoning tool.

Using Pydantic models in Python is the clearest way to define the required output structure. This schema is used both to instruct the LLM and to validate its response.

from pydantic import BaseModel, Field
from typing import List, Literal

class JustificationClause(BaseModel):
    statement: str
    source_document_id: str
    source_chunk_id: str

class DecisionResponse(BaseModel):
    decision: Literal["Approved", "Rejected"]
    payout_amount: float | None
    justification: List[JustificationClause]