We’re excited to share this community-written deep dive by Sugi Venugeethan into Stablebridge, a project tackling the complex world of stablecoin regulation. This article explores how knowledge graphs, RAG systems, and SurrealDB can be combined to connect it all together. It’s a practical look into knowledge graph generation to advanced retrieval methodologies - showcasing both challenges and breakthroughs along the way.
Stablebridge represents our ambitious mission to create comprehensive regulatory intelligence systems for the rapidly evolving stablecoin landscape. Our vision encompasses the systematic analysis of all major regulatory frameworks across US and EU jurisdictions - from Congressional bills like the GENIUS Act to European MiCA regulations, Federal Reserve guidance, Treasury Department rulings, and emerging state-level legislation.
The complexity of stablecoin regulation spans multiple jurisdictions, regulatory bodies, and constantly evolving compliance requirements. Traditional approaches to regulatory analysis fall short when dealing with:
Stablebridge aims to bridge these gaps through advanced knowledge graph technologies and intelligent retrieval systems that can navigate the intricate web of stablecoin regulations with precision and speed.
This blog post documents our comprehensive exploration of Knowledge Graph-based Retrieval Augmented Generation (KG-RAG) systems within the Stablebridge project, from initial knowledge graph generation using kg-gen to final performance evaluation against traditional RAG approaches. We detail the technical challenges, limitations discovered, solutions implemented, and comparative analysis results across different retrieval methodologies for stablecoin regulatory intelligence.
Stablecoin regulatory documents, particularly comprehensive legislation like the GENIUS Act, present unique challenges for information retrieval systems within the broader Stablebridge mission:
These challenges motivated our exploration of knowledge graph-based approaches versus traditional vector-based retrieval systems, specifically tailored for the comprehensive stablecoin regulatory landscape that Stablebridge aims to navigate.
We utilized the kg-gen
library, a Python-based knowledge graph generation tool that extracts entities and relationships from unstructured text using Large Language Models (LLMs).
Key Features:
Our implementation focused on the GENIUS Act as our initial target within the broader Stablebridge regulatory corpus, representing a critical piece of US federal stablecoin legislation:
# Basic kg-gen configuration for Stablebridge regulatory analysisfrom kg_gen import KnowledgeGraphGenerator config = { "chunk_size": 1000, "chunk_overlap": 200, "entity_types": ["regulation", "requirement", "entity", "process", "stablecoin_provision"], "domain_focus": "stablecoin_regulation", "output_format": "json" } kg_generator = KnowledgeGraphGenerator(config) knowledge_graph = kg_generator.process_document("genius_act.pdf")
The kg-gen process produced a knowledge graph with:
Sample Entity Structure:
{ "entities": [ "Digital Asset Market Structure", "Segregated Account Requirements", "Stablecoin Regulatory Compliance Framework", "Consumer Protection Measures for Digital Assets" ], "relationships": [ { "source": "Digital Asset Market Structure", "target": "Segregated Account Requirements", "type": "implements", "context": "Market structure regulations implement segregated account requirements for stablecoin consumer protection under the GENIUS Act framework" } ] }
During implementation, we identified several critical limitations of kg-gen
within the Stablebridge context:
We selected SurrealDB as our graph database solution for several reasons:
The generated JSON knowledge graph was loaded into SurrealDB using a structured approach:
import requests import json class SurrealDBLoader: def __init__(self, db_url="http://localhost:8000"): self.db_url = db_url self.headers = {"Content-Type": "application/json"} def load_entities(self, entities): """Load entities into the database""" for entity in entities: query = f""" CREATE entity SET name = "{entity}", type = "regulatory_concept", created_at = time::now() """ self._execute_query(query) def load_relationships(self, relationships): """Load relationships between entities""" for rel in relationships: query = f""" RELATE (SELECT * FROM entity WHERE name = "{rel['source']}") -> {rel['type']} -> (SELECT * FROM entity WHERE name = "{rel['target']}") SET context = "{rel['context']}" """ self._execute_query(query)
Our SurrealDB schema was designed to optimize for stablecoin regulatory queries within the Stablebridge framework:
-- Entity table for stablecoin regulatory concepts DEFINE TABLE entity SCHEMAFULL; DEFINE FIELD name ON entity TYPE string; DEFINE FIELD type ON entity TYPE string; DEFINE FIELD content ON entity TYPE string; DEFINE FIELD jurisdiction ON entity TYPE string;-- US, EU, State-level DEFINE FIELD regulatory_body ON entity TYPE string;-- Fed, Treasury, SEC, etc. DEFINE FIELD created_at ON entity TYPE datetime; -- Relationship edges with regulatory context information DEFINE TABLE relationship SCHEMAFULL; DEFINE FIELD in ON relationship TYPE record; DEFINE FIELD out ON relationship TYPE record; DEFINE FIELD type ON relationship TYPE string; DEFINE FIELD context ON relationship TYPE string; DEFINE FIELD confidence ON relationship TYPE float; DEFINE FIELD jurisdiction_scope ON relationship TYPE string;
Our knowledge graph-based RAG system was designed with the following components:
class KGRAGSystem: def __init__(self, surrealdb_client, llm_client): self.db = surrealdb_client self.llm = llm_client self.embedding_model = SentenceTransformer('all-MiniLM-L6-v2') def entity_retrieval(self, query): """Retrieve relevant entities based on query""" query_embedding = self.embedding_model.encode(query) # Find semantically similar entities entities = self.db.query(""" SELECT * FROM entity WHERE vector::similarity::cosine(embedding, $query_embedding) > 0.7 """, {"query_embedding": query_embedding}) return entities def multi_hop_reasoning(self, entities, max_hops=2): """Perform multi-hop traversal for comprehensive context""" context_entities = set(entities) for hop in range(max_hops): new_entities = self.db.query(""" SELECT * FROM entity WHERE id IN ( SELECT ->relationship->entity FROM $current_entities UNION SELECT <-relationship<-entity FROM $current_entities ) """, {"current_entities": list(context_entities)}) context_entities.update(new_entities) return list(context_entities)
A critical issue emerged during initial testing: our KG-RAG system consistently returned 0.0% confidence scores across all queries. Investigation revealed several root causes:
# Example of problematic relationship{ "source": "Section 401(b)(2)", "target": "Compliance Framework", "type": "references", "context": "Section references compliance framework"# Too generic}
We implemented several approaches to address the confidence issues:
Despite these efforts, the fundamental semantic alignment issues persisted.
Given the challenges with pure KG-RAG, we implemented a traditional vector-based RAG system inspired by the MUVERA (Multi-Vector Retrieval Architecture) approach to establish performance baselines.
Our traditional RAG system employed a two-stage retrieval process:
class MUVERAInspiredRAG: def __init__(self): # Stage 1: Fast candidate selection self.fast_encoder = SentenceTransformer('all-MiniLM-L6-v2') # Stage 2: Precision reranking self.precision_encoder = SentenceTransformer('all-mpnet-base-v2') self.vector_store = FAISS(dimension=384)# MiniLM dimension self.chunks = [] def build_index(self, documents): """Build FAISS index from document chunks""" chunks = self.chunk_documents(documents) embeddings = self.fast_encoder.encode(chunks) self.vector_store.add(embeddings) self.chunks = chunks def retrieve(self, query, k1=20, k2=5): """Two-stage retrieval process""" # Stage 1: Fast candidate retrieval query_embedding = self.fast_encoder.encode([query]) scores, indices = self.vector_store.search(query_embedding, k1) candidates = [self.chunks[i] for i in indices[0]] # Stage 2: Precision reranking candidate_embeddings = self.precision_encoder.encode(candidates) query_embedding_precise = self.precision_encoder.encode([query]) # Compute cosine similarities for reranking similarities = cosine_similarity(query_embedding_precise, candidate_embeddings)[0] # Return top k2 chunks top_indices = np.argsort(similarities)[-k2:][::-1] return [candidates[i] for i in top_indices]
To ensure fair comparison, we extracted textual content from our knowledge graph:
class KGTextExtractor: def __init__(self, kg_data): self.entities = kg_data.get('entities', []) self.relationships = kg_data.get('relationships', []) def extract_text_chunks(self): """Extract meaningful text chunks from KG structure""" chunks = [] # Extract entity-based chunksfor entity in self.entities: if isinstance(entity, str) and len(entity) > 20: chunks.append(f"Entity: {entity}") # Extract relationship-based chunksfor rel in self.relationships: if isinstance(rel, dict) and 'context' in rel: context = rel['context'] if len(context) > 50: source = rel.get('source', 'Unknown') target = rel.get('target', 'Unknown') rel_type = rel.get('type', 'related_to') chunk = f"{source} {rel_type} {target}. {context}" chunks.append(chunk) return chunks
We developed a comprehensive set of stablecoin regulatory questions targeting different complexity levels within the Stablebridge domain:
stablecoin_regulatory_questions = [ "What are the segregated account requirements for digital asset market " "participants under the GENIUS Act?", "How does the GENIUS Act define qualified digital asset custodians for " "stablecoin operations?", "What compliance frameworks must stablecoin exchanges implement according " "to federal regulation?", "What are the consumer protection measures outlined in the GENIUS Act for " "stablecoin users?", "How are conflicts of interest addressed in digital asset custody " "arrangements for stablecoins?" ]
Our evaluation framework measured:
class RAGEvaluator: def __init__(self, kg_rag_system, traditional_rag_system): self.kg_rag = kg_rag_system self.traditional_rag = traditional_rag_system self.results = {"kg_rag": [], "traditional_rag": []} def evaluate_question(self, question): """Evaluate both systems on a single question""" results = {} # Test KG-RAG start_time = time.time() kg_answer = self.kg_rag.answer_question(question) kg_time = time.time() - start_time # Test Traditional RAG start_time = time.time() trad_answer = self.traditional_rag.answer_question(question) trad_time = time.time() - start_time return { "question": question, "kg_rag": {"answer": kg_answer, "time": kg_time}, "traditional_rag": {"answer": trad_answer, "time": trad_time} }
Our comprehensive evaluation revealed significant performance differences:
Metric | KG-RAG | Traditional RAG | Difference |
---|---|---|---|
Average Response Time | 7.0s | 13.2s | 46.9% faster |
Successful Retrievals | 4/5 (80%) | 5/5 (100%) | Traditional RAG more reliable |
Average Chunks Retrieved | 3-4 entities | 6 chunks | Different retrieval granularity |
Answer Quality | High precision, lower coverage | Broader coverage, good precision | Complementary strengths |
Based on our analysis, we recommend:
Choose KG-RAG when:
Choose Traditional RAG when:
Our experimentation with different embedding models revealed:
# Performance comparison of embedding models embedding_models = { 'all-MiniLM-L6-v2': { 'speed': 'fast', 'quality': 'good', 'dimension': 384, 'use_case': 'Stage 1 retrieval' }, 'all-mpnet-base-v2': { 'speed': 'moderate', 'quality': 'excellent', 'dimension': 768, 'use_case': 'Stage 2 reranking' }, 'sentence-t5-base': { 'speed': 'slow', 'quality': 'excellent', 'dimension': 768, 'use_case': 'High-precision applications' } }
Optimal chunking proved crucial for both approaches:
def intelligent_chunking(text, chunk_size=1000, overlap=200): """ Implement intelligent chunking that preserves sentence boundaries and maintains contextual coherence """ sentences = sent_tokenize(text) chunks = [] current_chunk = "" for sentence in sentences: if len(current_chunk + sentence) <= chunk_size: current_chunk += sentence + " " else: if current_chunk: chunks.append(current_chunk.strip()) current_chunk = sentence + " " if current_chunk: chunks.append(current_chunk.strip()) return chunks
SurrealDB configuration optimizations that improved KG-RAG performance:
-- Index creation for faster entity retrieval DEFINE INDEX entity_name_idx ON entity FIELDS name; DEFINE INDEX entity_embedding_idx ON entity FIELDS embedding; -- Relationship traversal optimization DEFINE INDEX rel_source_idx ON relationship FIELDS in; DEFINE INDEX rel_target_idx ON relationship FIELDS out;
Our findings suggest potential for hybrid systems combining both approaches:
class HybridRAGSystem: def __init__(self, kg_rag, traditional_rag): self.kg_rag = kg_rag self.traditional_rag = traditional_rag self.routing_model = QueryRouter() def answer_question(self, question): """Route questions to optimal system based on characteristics""" query_type = self.routing_model.classify(question) if query_type == "structured_reasoning": return self.kg_rag.answer_question(question) elif query_type == "broad_search": return self.traditional_rag.answer_question(question) else: # Ensemble approach kg_answer = self.kg_rag.answer_question(question) trad_answer = self.traditional_rag.answer_question(question) return self.merge_answers(kg_answer, trad_answer)
Improving kg-gen
output quality through:
Implementing intelligent routing based on:
Our comprehensive Stablebridge journey from knowledge graph generation to comparative RAG evaluation has revealed the nuanced trade-offs between structured and unstructured approaches to stablecoin regulatory intelligence. While KG-RAG demonstrated superior speed and reasoning capabilities when functioning correctly, Traditional RAG provided more reliable and comprehensive coverage across diverse regulatory queries.
This research directly supports the Stablebridge mission of creating robust regulatory intelligence systems capable of navigating the complex landscape of US and EU stablecoin regulations, from Congressional legislation to Federal Reserve guidance and European MiCA frameworks.
Key Takeaways:
Technical Contributions to Stablebridge:
This research provides a foundation for informed decision-making when selecting RAG architectures for complex stablecoin regulatory analysis tasks, directly supporting Stablebridge’s goal of comprehensive regulatory intelligence across all major jurisdictions.
See the original blog and other blogs from the same author at https://blog.sugiv.fyi/stablebridge-knowledge-graph-rag-stablecoin-regulatory-intelligence.
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