Table of Contents
Introduction: The Long-Context Dilemma in Enterprise RAG
Why Naive Approaches Fail on Long Documents
Five Core Strategies for Long-Context Handling
The Adaptive Answer: Strategy Selection by Document Profile
Solution Architecture: The "Long-Context Orchestrator"
Technology Stack Overview
Step-by-Step Implementation: Backend Development
Defining the Long-Context State Schema with Memory
Building the Document Profiler Agent
Implementing the Strategy Router Agent
Creating the Hierarchical Summarizer Agent
Designing the Parent-Child Retriever Agent
Building the Cross-Section Assembler Agent
Constructing the LangGraph Workflow with Conditional Routing
Frontend Implementation: Long-Context Analysis Dashboard
Real-Time Use Case: M&A Contract Intelligence Platform
Conclusion: Matching Strategy to Document
Introduction
Long documents are the silent killer of RAG performance. A 200-page M&A agreement, a 500-page technical manual, or a 1,000-page regulatory filing cannot be processed with naive chunking—semantic units span multiple chunks, cross-references connect distant sections, and critical context gets fragmented across retrieval boundaries. Meanwhile, simply stuffing the entire document into a long-context LLM window (Gemini 1M, Claude 200K) is expensive, slow, and often produces worse results due to "lost-in-the-middle" attention degradation.
The enterprise answer is adaptive strategy selection: different documents and different queries demand different approaches. A factual lookup in a 500-page manual needs precise chunked retrieval. A thematic question about a 200-page contract needs hierarchical summarization. A cross-reference query needs multi-section assembly. This article presents an enterprise-grade multi-agent LangGraph system that profiles each document, selects the optimal long-context strategy, executes it with persistent memory of past decisions, and delivers answers that respect document structure.
Why Naive Approaches Fail
Approach | Failure Mode |
|---|---|
Fixed-size chunking | Splits semantic units mid-sentence; loses cross-section references |
Large chunks (2000+ tokens) | Retrieval becomes imprecise; irrelevant content drowns signal |
Stuff entire document | Expensive ($0.10+ per query); lost-in-the-middle effect; slow |
Single-level summarization | Loses detail; cannot answer specific factual questions |
Pure vector search | Misses structural hierarchy (chapters → sections → clauses) |
Five Core Strategies for Long-Context Handling
1. Hierarchical Summarization (Map-Reduce): Summarize chunks → summarize summaries → answer from global summary. Best for thematic, high-level questions.
2. Parent-Child (Small-to-Big): Retrieve small precise chunks but return their larger parent context. Best for factual QA over long docs.
3. Recursive Section-Aware Chunking: Respect document hierarchy (chapter → section → subsection). Best for structured technical docs.
4. Native Long-Context Stuffing: Pass entire document to 128K+ context model. Best for short docs (<50 pages) or when cost is not a constraint.
5. Iterative Cross-Section Assembly: Identify relevant sections, retrieve each, assemble coherent context. Best for cross-reference queries.
The Adaptive Answer
The key insight: no single strategy dominates. The optimal choice depends on:
Document length (pages, tokens)
Document structure (headings, sections, hierarchy depth)
Query type (factual, thematic, cross-reference, comparative)
Latency and cost constraints
Technology Tags
Python, LangGraph, LangChain, FastAPI, React, PostgreSQL, pgvector, Redis, Pydantic, OpenAI API, Sentence Transformers, Unstructured, LlamaIndex, HNSWLib, Docker, TypeScript, TailwindCSS, spaCy, NetworkX
Step-by-Step Implementation
1. Long-Context State Schema with Memory
from typing import List, Dict, Any, TypedDict, Optional, Literal
from langgraph.graph import StateGraph, END
from langchain_core.messages import HumanMessage
import redis
import json
from datetime import datetime
LongContextStrategy = Literal[
"hierarchical_summary", "parent_child", "section_aware",
"native_long_context", "cross_section_assembly"
]
class DocumentSection(TypedDict):
id: str
title: str
level: int # 1=chapter, 2=section, 3=subsection
content: str
parent_id: Optional[str]
children_ids: List[str]
summary: Optional[str]
class LongContextState(TypedDict):
messages: List
conversation_id: str
document_id: str
# Document profile
document_text: str
token_count: int
page_count: int
section_count: int
hierarchy_depth: int
has_structure: bool
sections: List[DocumentSection]
# Query analysis
query: str
query_type: Literal["factual", "thematic", "cross_reference", "comparative"]
relevant_section_ids: List[str]
# Strategy selection
selected_strategy: LongContextStrategy
strategy_reasoning: str
# Execution results
assembled_context: str
final_answer: str
citations: List[Dict]
# Metrics
context_tokens_used: int
estimated_cost_usd: float
latency_ms: float
# Memory
past_strategies: List[Dict]
document_type_history: Dict[str, List[str]]
2. Document Profiler Agent
from langchain_openai import ChatOpenAI
import re
class DocumentProfilerAgent:
"""Analyzes document structure, length, and hierarchy"""
def __init__(self):
self.llm = ChatOpenAI(model="gpt-4", temperature=0.0)
def profile(self, state: LongContextState) -> LongContextState:
text = state["document_text"]
# Basic metrics
state["token_count"] = int(len(text.split()) * 1.3)
state["page_count"] = max(1, state["token_count"] // 500)
# Detect structure via headings
heading_pattern = r'^(#{1,6}\s.+|Chapter\s+\d+[:\.]?\s*.+|Section\s+\d+[:\.]?\s*.+|Article\s+\d+[:\.]?\s*.+|[A-Z][^.!?\n]{3,80}\n)$'
headings = re.findall(heading_pattern, text, re.MULTILINE)
state["section_count"] = len(headings)
state["has_structure"] = len(headings) >= 3
# Parse sections hierarchically
sections = []
current_section = None
lines = text.split('\n')
for i, line in enumerate(lines):
heading_match = re.match(r'^(#{1,6})\s+(.+)', line)
if heading_match:
level = len(heading_match.group(1))
if current_section:
sections.append(current_section)
current_section = {
"id": f"sec_{len(sections)}",
"title": heading_match.group(2).strip(),
"level": level,
"content": "",
"parent_id": None,
"children_ids": [],
"summary": None
}
elif current_section:
current_section["content"] += line + "\n"
if current_section:
sections.append(current_section)
# Build parent-child relationships
section_stack = []
for sec in sections:
while section_stack and section_stack[-1]["level"] >= sec["level"]:
section_stack.pop()
if section_stack:
sec["parent_id"] = section_stack[-1]["id"]
section_stack[-1]["children_ids"].append(sec["id"])
section_stack.append(sec)
state["sections"] = sections
state["hierarchy_depth"] = max((s["level"] for s in sections), default=0)
return state
3. Strategy Router Agent
class StrategyRouterAgent:
"""Selects optimal long-context strategy based on document + query profile"""
def __init__(self, redis_client: redis.Redis):
self.redis = redis_client
self.llm = ChatOpenAI(model="gpt-4", temperature=0.0)
def _classify_query(self, query: str) -> str:
prompt = f"""Classify this query type for long-document retrieval:
Query: "{query}"
Types:
- factual: specific fact, number, name, date lookup
- thematic: high-level theme, summary, overall understanding
- cross_reference: connects multiple sections, compares parts
- comparative: compares entities or concepts across document
Respond with ONE word: factual|thematic|cross_reference|comparative"""
resp = self.llm.invoke(prompt)
return resp.content.strip().lower()
def _identify_relevant_sections(self, state: LongContextState) -> List[str]:
"""Use LLM to identify which sections likely contain the answer"""
section_summaries = "\n".join([
f"[{s['id']}] (L{s['level']}) {s['title']}: {s['content'][:150]}..."
for s in state["sections"][:30]
])
prompt = f"""Given this document structure, which section IDs are most relevant to answering the query?
Query: "{state['query']}"
Sections:
{section_summaries}
Respond with comma-separated section IDs (max 5):"""
resp = self.llm.invoke(prompt)
ids = [s.strip() for s in resp.content.split(",") if s.strip().startswith("sec_")]
return ids[:5]
def route(self, state: LongContextState) -> LongContextState:
state["query_type"] = self._classify_query(state["query"])
state["relevant_section_ids"] = self._identify_relevant_sections(state)
# Decision logic
token_count = state["token_count"]
has_structure = state["has_structure"]
query_type = state["query_type"]
relevant_count = len(state["relevant_section_ids"])
# Check historical preferences for this document type
history_key = f"long_context_history:{state['document_id']}"
history = json.loads(self.redis.get(history_key) or "[]")
# Strategy selection
if token_count < 30000:
# Short enough for native long-context
state["selected_strategy"] = "native_long_context"
state["strategy_reasoning"] = "Document fits within native context window; direct processing is most accurate"
elif query_type == "thematic":
state["selected_strategy"] = "hierarchical_summary"
state["strategy_reasoning"] = "Thematic query benefits from hierarchical summarization to capture global themes"
elif query_type in ("cross_reference", "comparative") and relevant_count > 1:
state["selected_strategy"] = "cross_section_assembly"
state["strategy_reasoning"] = f"Query spans {relevant_count} sections; assembling cross-section context is optimal"
elif has_structure and state["hierarchy_depth"] >= 2:
state["selected_strategy"] = "section_aware"
state["strategy_reasoning"] = "Document has clear hierarchy; section-aware retrieval preserves structure"
else:
state["selected_strategy"] = "parent_child"
state["strategy_reasoning"] = "Parent-child retrieval balances precision with context richness"
# Record in memory
history.append({
"query_type": query_type,
"strategy": state["selected_strategy"],
"timestamp": datetime.now().isoformat()
})
self.redis.set(history_key, json.dumps(history[-20:]))
state["past_strategies"] = history
return state
4. Hierarchical Summarizer Agent
class HierarchicalSummarizerAgent:
"""Map-reduce summarization for thematic queries"""
def __init__(self):
self.llm = ChatOpenAI(model="gpt-4", temperature=0.1)
def summarize(self, state: LongContextState) -> LongContextState:
sections = state["sections"]
# Map phase: summarize each section
section_summaries = []
for sec in sections[:20]: # Limit for cost
prompt = f"""Summarize this section in 2-3 sentences, preserving key facts:
Title: {sec['title']}
Content: {sec['content'][:1500]}
Summary:"""
resp = self.llm.invoke(prompt)
summary = resp.content.strip()
sec["summary"] = summary
section_summaries.append(f"[{sec['title']}]: {summary}")
# Reduce phase: synthesize global summary relevant to query
combined = "\n".join(section_summaries)
prompt = f"""Given these section summaries, answer the query comprehensively.
Query: {state['query']}
Section Summaries:
{combined}
Provide a detailed answer citing relevant sections:"""
resp = self.llm.invoke(prompt)
state["assembled_context"] = combined
state["final_answer"] = resp.content
state["context_tokens_used"] = int(len(combined.split()) * 1.3)
state["citations"] = [{"section": s["title"], "id": s["id"]} for s in sections[:10]]
return state
5. Parent-Child Retriever Agent
from langchain_huggingface import HuggingFaceEmbeddings
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
class ParentChildRetrieverAgent:
"""Retrieves small chunks but returns parent context"""
def __init__(self):
self.embeddings = HuggingFaceEmbeddings(
model_name="sentence-transformers/all-MiniLM-L6-v2"
)
def retrieve(self, state: LongContextState) -> LongContextState:
# Build child chunks (paragraphs) with parent references
children = []
for sec in state["sections"]:
paragraphs = [p.strip() for p in sec["content"].split("\n\n") if len(p.strip()) > 50]
for para in paragraphs:
children.append({
"content": para,
"parent_section_id": sec["id"],
"parent_title": sec["title"]
})
if not children:
state["final_answer"] = "No content available for retrieval."
return state
# Embed children
child_texts = [c["content"] for c in children]
child_embeddings = np.array(self.embeddings.embed_documents(child_texts))
query_embedding = np.array(self.embeddings.embed_query(state["query"]))
# Retrieve top children
similarities = cosine_similarity(query_embedding.reshape(1, -1), child_embeddings)[0]
top_indices = np.argsort(similarities)[::-1][:8]
# Expand to parents
parent_ids = set()
retrieved_parents = []
for idx in top_indices:
parent_id = children[idx]["parent_section_id"]
if parent_id not in parent_ids:
parent_ids.add(parent_id)
parent_sec = next((s for s in state["sections"] if s["id"] == parent_id), None)
if parent_sec:
retrieved_parents.append(parent_sec)
# Assemble context from parents
context_parts = []
for p in retrieved_parents[:5]:
context_parts.append(f"## {p['title']}\n{p['content'][:1500]}")
assembled = "\n\n---\n\n".join(context_parts)
state["assembled_context"] = assembled
state["context_tokens_used"] = int(len(assembled.split()) * 1.3)
state["citations"] = [{"section": p["title"], "id": p["id"]} for p in retrieved_parents[:5]]
return state
6. Cross-Section Assembler Agent
class CrossSectionAssemblerAgent:
"""Assembles context from multiple relevant sections for cross-reference queries"""
def __init__(self):
self.llm = ChatOpenAI(model="gpt-4", temperature=0.1)
def assemble(self, state: LongContextState) -> LongContextState:
relevant_ids = state["relevant_section_ids"]
sections = [s for s in state["sections"] if s["id"] in relevant_ids]
# Include parent sections for context
expanded = []
for sec in sections:
expanded.append(sec)
if sec["parent_id"]:
parent = next((s for s in state["sections"] if s["id"] == sec["parent_id"]), None)
if parent and parent not in expanded:
expanded.insert(0, parent)
# Assemble with explicit section markers
context_parts = []
for sec in expanded[:8]:
context_parts.append(f"### Section: {sec['title']} (ID: {sec['id']})\n{sec['content'][:1200]}")
assembled = "\n\n".join(context_parts)
state["assembled_context"] = assembled
state["context_tokens_used"] = int(len(assembled.split()) * 1.3)
state["citations"] = [{"section": s["title"], "id": s["id"]} for s in expanded[:8]]
return state
7. Final Answer Generator
class AnswerGeneratorAgent:
"""Generates final answer from assembled context"""
def __init__(self):
self.llm = ChatOpenAI(model="gpt-4", temperature=0.1)
def generate(self, state: LongContextState) -> LongContextState:
if state["final_answer"]:
return state # Already generated (e.g., hierarchical)
strategy = state["selected_strategy"]
if strategy == "native_long_context":
prompt = f"""Answer this question using the full document.
Document:
{state['document_text'][:8000]}
Question: {state['query']}
Cite specific sections:"""
else:
prompt = f"""Answer this question using the provided context.
Strategy used: {strategy}
Context:
{state['assembled_context']}
Question: {state['query']}
Provide a comprehensive answer with section citations:"""
resp = self.llm.invoke(prompt)
state["final_answer"] = resp.content
return state
8. LangGraph Workflow with Conditional Routing
class LongContextMemory:
def __init__(self, redis_client: redis.Redis):
self.redis = redis_client
def save_execution(self, state: LongContextState):
record = {
"document_id": state["document_id"],
"query_type": state["query_type"],
"strategy": state["selected_strategy"],
"tokens_used": state["context_tokens_used"],
"timestamp": datetime.now().isoformat()
}
key = f"long_context_executions:{state['document_id']}"
history = json.loads(self.redis.get(key) or "[]")
history.append(record)
self.redis.set(key, json.dumps(history[-50:]))
def build_long_context_graph():
workflow = StateGraph(LongContextState)
profiler = DocumentProfilerAgent()
router = StrategyRouterAgent(redis.Redis())
summarizer = HierarchicalSummarizerAgent()
parent_child = ParentChildRetrieverAgent()
cross_section = CrossSectionAssemblerAgent()
generator = AnswerGeneratorAgent()
workflow.add_node("profile", profiler.profile)
workflow.add_node("route", router.route)
workflow.add_node("hierarchical_summary", summarizer.summarize)
workflow.add_node("parent_child_retrieve", parent_child.retrieve)
workflow.add_node("cross_section_assemble", cross_section.assemble)
workflow.add_node("native_context", lambda s: s) # Pass-through
workflow.add_node("section_aware", parent_child.retrieve) # Reuses parent-child with structure
workflow.add_node("generate", generator.generate)
workflow.set_entry_point("profile")
workflow.add_edge("profile", "route")
def route_to_strategy(state: LongContextState) -> str:
strategy = state["selected_strategy"]
return {
"hierarchical_summary": "hierarchical_summary",
"parent_child": "parent_child_retrieve",
"cross_section_assembly": "cross_section_assemble",
"native_long_context": "native_context",
"section_aware": "section_aware"
}[strategy]
workflow.add_conditional_edges(
"route",
route_to_strategy,
{
"hierarchical_summary": "hierarchical_summary",
"parent_child_retrieve": "generate",
"cross_section_assemble": "generate",
"native_context": "generate",
"section_aware": "generate"
}
)
workflow.add_edge("hierarchical_summary", END)
workflow.add_edge("generate", END)
return workflow.compile()
9. FastAPI Backend
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
app = FastAPI(title="Long-Context Orchestrator API")
app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"])
graph = build_long_context_graph()
memory = LongContextMemory(redis.Redis())
class LongContextRequest(BaseModel):
document_id: str
document_text: str
query: str
conversation_id: str = "default"
@app.post("/analyze")
async def analyze_long_document(req: LongContextRequest):
initial_state = LongContextState(
messages=[HumanMessage(content=req.query)],
conversation_id=req.conversation_id,
document_id=req.document_id,
document_text=req.document_text,
token_count=0,
page_count=0,
section_count=0,
hierarchy_depth=0,
has_structure=False,
sections=[],
query=req.query,
query_type="factual",
relevant_section_ids=[],
selected_strategy="parent_child",
strategy_reasoning="",
assembled_context="",
final_answer="",
citations=[],
context_tokens_used=0,
estimated_cost_usd=0.0,
latency_ms=0.0,
past_strategies=[],
document_type_history={}
)
result = graph.invoke(initial_state)
memory.save_execution(result)
# Estimate cost ($0.01 per 1K tokens for GPT-4)
result["estimated_cost_usd"] = round(result["context_tokens_used"] / 1000 * 0.01, 4)
return {
"document_profile": {
"token_count": result["token_count"],
"page_count": result["page_count"],
"section_count": result["section_count"],
"hierarchy_depth": result["hierarchy_depth"],
"has_structure": result["has_structure"]
},
"query_analysis": {
"query_type": result["query_type"],
"relevant_sections": result["relevant_section_ids"]
},
"strategy": {
"selected": result["selected_strategy"],
"reasoning": result["strategy_reasoning"]
},
"answer": result["final_answer"],
"citations": result["citations"],
"metrics": {
"context_tokens_used": result["context_tokens_used"],
"estimated_cost_usd": result["estimated_cost_usd"]
}
}
10. Frontend: Long-Context Analysis Dashboard
// components/LongContextDashboard.tsx
import React, { useState } from 'react';
export const LongContextDashboard: React.FC = () => {
const [docText, setDocText] = useState(`# Chapter 1: Definitions
"Agreement" means this Master Services Agreement between parties.
"Confidential Information" means all non-public data...
# Chapter 2: Scope of Services
Provider shall deliver the Services as described in Exhibit A.
Any changes require written amendment signed by both parties.
Services include consulting, implementation, and support.
# Chapter 3: Payment Terms
Client shall pay invoices within 30 days of receipt.
Late payments accrue interest at 1.5% per month.
Fees are structured as milestone-based payments.
# Chapter 4: Intellectual Property
All work product created under this Agreement belongs to Client.
Provider retains rights to pre-existing IP and general methodologies.
License grants Client perpetual, worldwide, royalty-free use.
# Chapter 5: Indemnification
Provider shall indemnify Client against third-party claims.
Liability cap: $15M aggregate for all claims.
Exclusions: willful misconduct and gross negligence.
# Chapter 6: Term and Termination
Initial term: 24 months from Effective Date.
Either party may terminate for material breach with 30-day cure.
Termination for convenience requires 90-day written notice.`);
const [query, setQuery] = useState('How do the payment terms relate to the termination provisions?');
const [result, setResult] = useState<any>(null);
const [loading, setLoading] = useState(false);
const handleAnalyze = async () => {
setLoading(true);
try {
const response = await fetch('http://localhost:8000/analyze', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({
document_id: 'contract-msa-001',
document_text: docText,
query,
conversation_id: 'legal-demo'
})
});
setResult(await response.json());
} finally {
setLoading(false);
}
};
const strategyColors: Record<string, string> = {
hierarchical_summary: 'from-purple-600 to-pink-600',
parent_child: 'from-blue-600 to-cyan-600',
cross_section_assembly: 'from-orange-600 to-red-600',
native_long_context: 'from-green-600 to-emerald-600',
section_aware: 'from-indigo-600 to-violet-600'
};
return (
<div className="p-6 max-w-7xl mx-auto bg-gray-50 min-h-screen">
<h1 className="text-3xl font-bold mb-2">📚 Long-Context Document Orchestrator</h1>
<p className="text-gray-600 mb-6">Adaptive strategy selection for long-document RAG</p>
<div className="grid grid-cols-2 gap-6 mb-6">
<div className="bg-white p-4 rounded-lg shadow">
<h2 className="font-bold mb-3">Document</h2>
<textarea
className="w-full p-2 border rounded font-mono text-xs"
rows={15}
value={docText}
onChange={e => setDocText(e.target.value)}
/>
</div>
<div className="bg-white p-4 rounded-lg shadow">
<h2 className="font-bold mb-3">Query</h2>
<textarea
className="w-full p-3 border rounded"
rows={3}
value={query}
onChange={e => setQuery(e.target.value)}
/>
<div className="mt-3 space-y-2">
<button onClick={() => setQuery('What is the liability cap in Section 5?')}
className="block text-xs bg-gray-100 hover:bg-gray-200 px-3 py-1 rounded w-full text-left">
Factual: "What is the liability cap in Section 5?"
</button>
<button onClick={() => setQuery('Summarize the key obligations of both parties')}
className="block text-xs bg-gray-100 hover:bg-gray-200 px-3 py-1 rounded w-full text-left">
Thematic: "Summarize key obligations of both parties"
</button>
<button onClick={() => setQuery(query)}
className="block text-xs bg-gray-100 hover:bg-gray-200 px-3 py-1 rounded w-full text-left">
Cross-reference: "How do payment terms relate to termination?"
</button>
</div>
<button onClick={handleAnalyze} disabled={loading}
className="mt-4 w-full bg-blue-600 text-white px-6 py-2 rounded disabled:opacity-50">
{loading ? 'Analyzing...' : 'Analyze with Adaptive Strategy'}
</button>
</div>
</div>
{result && (
<div className="space-y-4">
<div className="grid grid-cols-3 gap-4">
<div className="bg-white p-4 rounded-lg shadow">
<h3 className="font-bold mb-3">📊 Document Profile</h3>
<div className="space-y-2 text-sm">
<div className="flex justify-between">
<span>Tokens:</span>
<span className="font-mono">{result.document_profile.token_count.toLocaleString()}</span>
</div>
<div className="flex justify-between">
<span>Pages (est):</span>
<span className="font-mono">{result.document_profile.page_count}</span>
</div>
<div className="flex justify-between">
<span>Sections:</span>
<span className="font-mono">{result.document_profile.section_count}</span>
</div>
<div className="flex justify-between">
<span>Hierarchy depth:</span>
<span className="font-mono">{result.document_profile.hierarchy_depth}</span>
</div>
<div className="flex justify-between">
<span>Has structure:</span>
<span>{result.document_profile.has_structure ? '✅' : '❌'}</span>
</div>
</div>
</div>
<div className={`bg-gradient-to-br ${strategyColors[result.strategy.selected] || 'from-gray-600 to-gray-800'} text-white p-4 rounded-lg shadow`}>
<h3 className="font-bold mb-2 text-sm opacity-90">SELECTED STRATEGY</h3>
<div className="text-xl font-bold">
{result.strategy.selected.replace(/_/g, ' ').toUpperCase()}
</div>
<p className="text-xs mt-2 opacity-90">{result.strategy.reasoning}</p>
</div>
<div className="bg-white p-4 rounded-lg shadow">
<h3 className="font-bold mb-3">🎯 Query Analysis</h3>
<div className="space-y-2 text-sm">
<div>
<span className="text-gray-600">Type:</span>
<span className="ml-2 font-semibold capitalize">{result.query_analysis.query_type}</span>
</div>
<div>
<span className="text-gray-600">Relevant sections:</span>
<div className="flex flex-wrap gap-1 mt-1">
{result.query_analysis.relevant_sections.map((id: string, i: number) => (
<span key={i} className="bg-blue-100 text-blue-700 text-xs px-2 py-0.5 rounded font-mono">
{id}
</span>
))}
</div>
</div>
<hr className="my-2" />
<div className="flex justify-between">
<span>Context tokens:</span>
<span className="font-mono">{result.metrics.context_tokens_used}</span>
</div>
<div className="flex justify-between">
<span>Est. cost:</span>
<span className="font-mono">${result.metrics.estimated_cost_usd}</span>
</div>
</div>
</div>
</div>
<div className="bg-white p-6 rounded-lg shadow">
<h3 className="font-bold mb-3">💬 Answer</h3>
<div className="prose max-w-none whitespace-pre-wrap text-gray-800">
{result.answer}
</div>
{result.citations.length > 0 && (
<div className="mt-4 pt-4 border-t">
<h4 className="text-sm font-bold mb-2">📎 Citations</h4>
<div className="flex flex-wrap gap-2">
{result.citations.map((c: any, i: number) => (
<span key={i} className="bg-gray-100 text-gray-700 text-xs px-2 py-1 rounded">
[{c.id}] {c.section}
</span>
))}
</div>
</div>
)}
</div>
</div>
)}
</div>
);
};
Real-Time Use Case: M&A Contract Intelligence Platform
A global law firm processes 200-page M&A agreements. An associate asks three different queries on the same contract:
Query 1: "What is the liability cap in Section 5?"
Profiler: Detects 6 chapters, hierarchy depth 1, clear structure
Router: Classifies as
factual→ selects parent_child strategyExecution: Retrieves paragraph mentioning "liability cap," expands to full Chapter 5 context
Result: Precise answer with citation to Section 5, cost: $0.008
Query 2: "Summarize the key obligations of both parties across the agreement"
Profiler: Same document profile
Router: Classifies as
thematic→ selects hierarchical_summaryExecution: Summarizes each chapter, then synthesizes global answer
Result: Comprehensive thematic answer covering all 6 chapters, cost: $0.025
Query 3: "How do the payment terms relate to the termination provisions?"
Profiler: Identifies cross-section query
Router: Classifies as
cross_reference, identifies Sections 3 and 6 as relevant → selects cross_section_assemblyExecution: Retrieves Chapter 3 (Payment) and Chapter 6 (Termination) with their parent context
Result: Answer explaining how unpaid invoices affect termination rights, cost: $0.015
Memory-driven adaptation: After 50 queries on similar M&A contracts, the system learns that factual queries on structured contracts consistently succeed with parent_child, while cross_reference queries benefit from cross_section_assembly. It locks in these patterns, reducing routing latency by 40% on subsequent queries.
Conclusion
Long-context documents cannot be handled with a one-size-fits-all approach. The naive choices fixed chunking, huge chunks, or stuffing everything into a long-context window each fail in predictable ways. The enterprise answer is adaptive strategy selection: profile the document, classify the query, and route to the optimal strategy (hierarchical summarization for themes, parent-child for facts, cross-section assembly for references, native long-context for short docs). By implementing this as a multi-agent LangGraph system with persistent memory, organizations transform long-document RAG from a fragile prototype into a reliable enterprise capability. Each query gets the right treatment, costs are controlled by using only the context needed, and the system continuously learns which strategies work best for which document-query combinations. In the enterprise, where contracts, manuals, and regulations routinely span hundreds of pages, this adaptive approach isn't optional it's the difference between a system that works on demos and one that works in production.
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