RAG Architecture for Legal Documents
How we built a retrieval-augmented generation system that preserves legal document structure and meaning. Challenges with chunking strategies and context preservation.
RAG Architecture for Legal Documents
Legal documents present unique challenges for RAG (Retrieval-Augmented Generation) systems. Unlike typical text, legal content relies heavily on structure, cross-references, and precise language where context is everything. Here's how we built a RAG system that preserves legal document integrity.
The Legal Document Challenge
Legal documents have characteristics that break traditional RAG approaches:
- Hierarchical structure: Sections, subsections, clauses with complex relationships
- Cross-references: "As defined in Section 3.2.1" requires maintaining document structure
- Context dependency: Meaning changes dramatically based on surrounding clauses
- Precision requirements: Slight misinterpretations can have serious legal consequences
Traditional RAG Limitations
Standard chunking strategies fail with legal documents:
\\\python
Traditional approach - loses structure
def simple_chunk(text, chunk_size=1000):
return [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]
\\\
This approach:
- Breaks mid-sentence or mid-clause
- Loses hierarchical relationships
- Separates definitions from usage
- Destroys cross-reference context
Our Legal-Specific Approach
Structure-Aware Chunking
We developed a chunking strategy that respects legal document structure:
\\\python
class LegalDocumentChunker:
def __init__(self):
self.section_patterns = [
r'^(\d+\.)+\s+', # 1.2.3 Section numbering
r'^[A-Z]+\.\s+', # A. B. C. lettered sections
r'^$$[a-z]$$\s+', # (a) (b) (c) subsections
]
def chunk_by_structure(self, document):
sections = self.identify_sections(document)
chunks = []
for section in sections:
# Include parent context
chunk = self.build_contextual_chunk(section)
chunks.append(chunk)
return chunks
def build_contextual_chunk(self, section):
# Include section hierarchy for context
context = self.get_parent_sections(section)
return {
'content': section.content,
'context': context,
'metadata': {
'section_number': section.number,
'section_title': section.title,
'document_path': section.path
}
}
\\\
Cross-Reference Resolution
Legal documents are full of internal references that need resolution:
\\\python
class CrossReferenceResolver:
def __init__(self, document_structure):
self.structure = document_structure
self.reference_map = self.build_reference_map()
def resolve_references(self, chunk):
# Find references like "Section 3.2.1" or "as defined above"
references = self.extract_references(chunk.content)
resolved_content = chunk.content
for ref in references:
target_section = self.reference_map.get(ref)
if target_section:
# Inject referenced content inline
resolved_content = self.inject_reference(
resolved_content, ref, target_section
)
return resolved_content
\\\
Semantic Chunking with Legal Context
We combine structural chunking with semantic similarity:
\\\python
def semantic_legal_chunking(document, max_chunk_size=2000):
structural_chunks = structure_aware_chunk(document)
semantic_chunks = []
current_chunk = []
current_size = 0
for chunk in structural_chunks:
# Check semantic similarity with current chunk
similarity = calculate_legal_similarity(current_chunk, chunk)
if similarity > 0.7 and current_size + len(chunk) < max_chunk_size:
current_chunk.append(chunk)
current_size += len(chunk)
else:
# Finalize current chunk with full context
if current_chunk:
semantic_chunks.append(
create_contextual_chunk(current_chunk)
)
current_chunk = [chunk]
current_size = len(chunk)
return semantic_chunks
\\\
Retrieval Strategy
Legal document retrieval requires multiple strategies:
Hybrid Search
\\\python
class LegalRetriever:
def __init__(self, vector_store, keyword_index):
self.vector_store = vector_store
self.keyword_index = keyword_index
def retrieve(self, query, k=5):
# Semantic search for conceptual matches
semantic_results = self.vector_store.similarity_search(query, k=k)
# Keyword search for exact legal terms
keyword_results = self.keyword_index.search(
self.extract_legal_terms(query), k=k
)
# Combine and rerank
combined_results = self.merge_results(
semantic_results, keyword_results
)
return self.rerank_by_legal_relevance(combined_results)
\\\
Context Expansion
Retrieved chunks are expanded with necessary context:
\\\python
def expand_legal_context(chunk, document_structure):
expanded_chunk = chunk.copy()
# Add parent section context
parent_sections = get_parent_hierarchy(chunk, document_structure)
expanded_chunk['parent_context'] = parent_sections
# Add related definitions
definitions = find_relevant_definitions(chunk, document_structure)
expanded_chunk['definitions'] = definitions
# Add cross-referenced sections
references = resolve_cross_references(chunk, document_structure)
expanded_chunk['references'] = references
return expanded_chunk
\\\
Generation with Legal Precision
The generation phase requires special handling for legal accuracy:
\\\python
class LegalRAGGenerator:
def __init__(self, llm):
self.llm = llm
self.legal_prompt_template = """
You are a legal document analysis assistant.
CRITICAL REQUIREMENTS:
- Maintain exact legal terminology
- Preserve section references and citations
- Indicate uncertainty when context is insufficient
- Never paraphrase legal definitions
Context: {context}
Question: {question}
Response:"""
def generate_response(self, query, retrieved_chunks):
# Build comprehensive context
context = self.build_legal_context(retrieved_chunks)
# Generate with legal constraints
response = self.llm.generate(
self.legal_prompt_template.format(
context=context,
question=query
),
temperature=0.1, # Low temperature for precision
max_tokens=1000
)
# Validate legal accuracy
return self.validate_legal_response(response, retrieved_chunks)
\\\
Evaluation and Validation
Legal RAG systems require rigorous evaluation:
Accuracy Metrics
\\\python
def evaluate_legal_rag(test_cases):
metrics = {
'factual_accuracy': 0,
'citation_accuracy': 0,
'completeness': 0,
'legal_precision': 0
}
for case in test_cases:
response = rag_system.query(case.question)
# Check factual accuracy against ground truth
metrics['factual_accuracy'] += check_facts(
response, case.ground_truth
)
# Verify citations are correct and complete
metrics['citation_accuracy'] += validate_citations(
response, case.source_documents
)
# Assess completeness of legal analysis
metrics['completeness'] += assess_completeness(
response, case.required_elements
)
return normalize_metrics(metrics, len(test_cases))
\\\
Lessons Learned
1. Structure matters more than semantics in legal documents
2. Context expansion is critical - legal meaning depends on surrounding text
3. Cross-reference resolution can make or break accuracy
4. Conservative generation is better than creative interpretation
5. Human validation remains essential for legal applications
Future Improvements
- Dynamic chunking based on query type
- Legal reasoning chains for complex analysis
- Multi-document synthesis for comparative analysis
- Regulatory compliance tracking across jurisdictions
Building RAG systems for legal documents requires rethinking traditional approaches. The investment in legal-specific architecture pays off in accuracy and reliability that legal professionals can trust.