Why Your RAG System is Failing: The Graph Database Secret That Boosted Our Retrieval Accuracy by 60%

Introduction

In the rapidly evolving landscape of Retrieval-Augmented Generation (RAG), enterprises are discovering that traditional vector search alone often falls short. While semantic similarity helps find relevant documents, it misses the rich contextual relationships and structured knowledge that exist within enterprise data. Enter Hybrid Graph + Vector RAG—a powerful architecture that combines the semantic understanding of vector embeddings with the relational intelligence of graph databases.

In this article, I'll walk you through a production-ready implementation that marries OpenSearch/LanceDB vector embeddings with AWS Neptune graph traversals to achieve superior retrieval precision for enterprise knowledge bases.

The Problem with Pure Vector Search

Traditional RAG systems rely heavily on vector similarity search:

def simple_vector_search(query: str, top_k: int = 5):
    query_embedding = embed_query(query)
    results = vector_db.search(query_embedding).limit(top_k)
    return results

While this works well for general semantic similarity, it has critical limitations:

1. No relationship awareness: It can't traverse connections between entities
2. Limited context: Each chunk is isolated from its document structure
3. Poor entity resolution: Similar entities (e.g., "AWS Neptune" vs "Neptune DB") aren't unified
4. No reasoning: Can't answer questions requiring multi-hop inference

The Hybrid Architecture: Best of Both Worlds

Our hybrid approach leverages three complementary retrieval strategies:

1. Term-Based Search (N-gram Indexing)

Extract and index unigrams, bigrams, and trigrams for precise keyword matching:

def extract_ngrams(text: str) -> tuple[list[str], list[str], list[str]]:
    """Extract unigrams, bigrams, and trigrams from text"""
    tokens = [w.lower() for w in nltk.word_tokenize(text) if w.isalnum()]

    # Filter stopwords
    unigrams = [t for t in tokens if t not in STOPWORDS]

    # Generate bigrams and trigrams
    bigrams = [" ".join(b) for b in nltk.bigrams(tokens)]
    trigrams = [" ".join(t) for t in nltk.trigrams(tokens)]

    return unigrams, bigrams, trigrams

This enables matching on specific technical terms and phrases that vector embeddings might miss.

2. Vector Similarity Search (Semantic Understanding)

Using Amazon Titan embeddings (or OpenAI) for semantic similarity:

def vector_search(query: str, table: Any, top_k: int = 5) -> list[dict]:
    """Perform vector similarity search"""
    query_embedding = embed_query(query)

    results = (
        table.search(query_embedding, vector_column_name="vector")
        .metric("cosine")
        .limit(top_k)
        .to_list()
    )
    return results

3. Graph Traversal (Relationship Intelligence)

Extract knowledge triplets and build a connected graph:

def process_triplet(triplet: Any, kb_id: str, chunk_id: str):
    subject, predicate, object_ = triplet

    # Create or merge entity nodes
    query = """
        MERGE (subjectNode:Entity {name: toLower($subject)})
        MERGE (objectNode:Entity {name: toLower($object)})
        MERGE (subjectNode)-[r:RELATES_TO {name: toLower($predicate)}]
            ->(objectNode)

        WITH subjectNode, objectNode
        MATCH (chunk:Chunk {id: $chunk_id})
        MERGE (chunk)-[:MENTIONS_ENTITY]->(subjectNode)
        MERGE (chunk)-[:MENTIONS_ENTITY]->(objectNode)
    """
    run_query(query, params)

The Complete Graph RAG Pipeline

Step 1: Document Ingestion and Chunking

def run_add_chunks_to_db(data: dict, kb_id: str):
    # Load document
    docs = get_file_content_v1(data)

    # Smart chunking with overlap
    splitter = SentenceSplitter(chunk_size=1024, chunk_overlap=50)
    nodes = splitter.get_nodes_from_documents(docs)

    # Process each chunk
    add_chunks_to_db(nodes, kb_id, chunk_db, term_db, triplet_db)

Step 2: Multi-Index Storage

For each chunk, we store data in three complementary indices:

def add_chunks_to_db(docs: list, kb_id: str):
    for doc_index, doc in enumerate(docs):
        text = doc.text
        chunk_id = str(uuid.uuid4()).replace("-", "")
        chunk_embedding = boto3_embedding_call(text)

        # 1. Store in vector index (LanceDB)
        params = {
            "chunkID": chunk_id,
            "full_text": text,
            "embedding": chunk_embedding,
            "filename": filename,
        }
        index_data([params], chunk_db, full_table_name)

        # 2. Create chunk node in Neptune
        chunk_query = """
            MATCH (d:Document {id: $doc_id})
            MERGE (c:Chunk {id: $chunk_id})
            SET c.text = $text, c.index = $index
            MERGE (d)-[:CONTAINS]->(c)
        """
        run_query(chunk_query, params)

        # 3. Extract and store terms
        unigrams, bigrams, trigrams = extract_ngrams(text)
        store_terms_for_chunk(chunk_id, unigrams, bigrams, trigrams)

        # 4. Extract and store knowledge triplets
        triplets = generate_triplet(text)
        for trip in triplets:
            process_triplet(trip, kb_id, chunk_id)

Step 3: Knowledge Triplet Extraction

Using Claude (Anthropic) via AWS Bedrock for intelligent entity extraction:

def extract_triplet_bedrock(sentence: str) -> str:
    prompt = f"""You are a knowledge extraction model tasked with 
    identifying and extracting knowledge triples in the form of 
    subject, predicate, object from the abstract text.

    Your output should be a JSON object with a single key "triples"
    that contains an array of objects, each representing a
    subject-predicate-object triple.

    Abstract: {sentence}
    """

    native_request = {
        "anthropic_version": "bedrock-2023-05-31",
        "temperature": 0,
        "max_tokens": 4096,
        "messages": [{"role": "user", "content": [{"type": "text", "text": prompt}]}]
    }

    response = bedrock_client.invoke_model(
        modelId="eu.anthropic.claude-3-haiku-20240307-v1:0",
        body=json.dumps(native_request)
    )
    return response

The Hybrid Retrieval Strategy

Now comes the magic—combining all three approaches:

class GraphRetriever:
    def retrieve_chunks(self, kb_id: str, query: str, top_k: int = 2):
        """Retrieve relevant chunks using hybrid methods"""

        # 1. Term-based search via vector similarity on n-grams
        term_results = self.term_search(kb_id, query, top_k)

        # 2. Entity-based search via graph traversal
        entity_candidates = self._extract_entities_from_query(query)
        entity_results = []

        for entity in entity_candidates:
            exists = run_query(
                "MATCH (e:Entity {name: $name}) RETURN count(e) > 0 AS exists",
                {"name": entity}
            )

            if exists and exists[0]["exists"]:
                entity_chunks = self.entity_search(entity, top_k)
                entity_results.extend(entity_chunks)

        # 3. Combine and deduplicate results
        combined = {}
        for item in term_results + entity_results:
            chunk_id = item.get("id")
            if chunk_id in combined:
                combined[chunk_id]["score"] = max(
                    combined[chunk_id]["score"], 
                    item["score"]
                )
            else:
                combined[chunk_id] = item

        # Sort by score and return top results
        results = list(combined.values())
        results.sort(key=lambda x: x["score"], reverse=True)

        return results[:top_k]

Term Search Implementation

def term_search(self, kb_id: str, query: str, top_k: int = 2):
    """Search for chunks containing query terms"""

    # Use vector similarity on term embeddings
    term_results = retrieve_lancedb(
        embed_query(query),
        kb_id,
        "term_node_graph_index"
    )

    # Find chunks connected to matching terms
    result = run_query("""
        UNWIND $matched_terms AS term_text
        MATCH (term:Term {text: term_text})<-[:HAS_TERM]-(chunk:Chunk)
        RETURN DISTINCT chunk.id AS id
        LIMIT $k
    """, {"matched_terms": term_results, "k": top_k})

    chunk_ids = [r["id"] for r in result]
    chunk_texts = self._fetch_chunk_texts(chunk_ids)

    return [{"text": chunk_texts[cid]["text"], 
             "score": 1.0, 
             "filename": chunk_texts[cid]["filename"]}
            for cid in chunk_ids]

Entity Search Implementation

def entity_search(self, entity_name: str, top_k: int = 2):
    """Search for chunks mentioning a specific entity"""

    result = run_query("""
        MATCH (c:Chunk)-[:MENTIONS_ENTITY]->(e:Entity {name: $entity_name})
        RETURN DISTINCT c.id AS id
        LIMIT $k
    """, {"entity_name": entity_name, "k": top_k})

    chunk_ids = [row["id"] for row in result if "id" in row]
    chunk_texts = self._fetch_chunk_texts(chunk_ids)

    return [{"text": chunk_texts[cid].get("text", ""),
             "score": 1.0,
             "filename": chunk_texts[cid].get("filename", "unknown")}
            for cid in chunk_ids if cid in chunk_texts]

Advanced Feature: Sequential Context Retrieval

One powerful advantage of graph-based storage is the ability to traverse document structure:

def get_document_chain(self, chunk_id: str, max_chunks: int = 5):
    """Get a sequence of chunks around the specified chunk"""

    # Get current chunk
    current = run_query("""
        MATCH (c:Chunk {id: $chunk_id})
        RETURN c.id AS id, c.text AS text, c.index AS index
    """, {"chunk_id": chunk_id})

    result = [current[0]]

    # Get previous chunks via PREV relationship
    prev_id = chunk_id
    for _ in range(max_chunks):
        prev_chunk = self.get_prev_chunk(prev_id)
        if prev_chunk:
            result.insert(0, prev_chunk)
            prev_id = prev_chunk["id"]
        else:
            break

    # Get next chunks via NEXT relationship
    next_id = chunk_id
    for _ in range(max_chunks):
        next_chunk = self.get_next_chunk(next_id)
        if next_chunk:
            result.append(next_chunk)
            next_id = next_chunk["id"]
        else:
            break

    return result

This enables:

• Context-aware retrieval: Include surrounding chunks for better understanding
• Sequential reasoning: Maintain narrative flow across chunk boundaries
• Table reconstruction: Reassemble split tables from adjacent chunks

The Technology Stack

Core Components

1. AWS Neptune (OpenCypher): Graph database for storing entities, relationships, and document structure
2. LanceDB: Serverless vector database on S3 for embedding storage
3. Amazon Bedrock:
   • Titan Embeddings v2 for vector generation
   • Claude 3 Haiku for triplet extraction
4. NLTK: Natural language processing for n-gram extraction

Architecture Diagram

┌─────────────┐
│  Documents  │
└──────┬──────┘
       │
       ▼
┌─────────────────┐
│   Chunking &    │
│   Embedding     │
└────────┬────────┘
         │
    ┌────┴────┐
    │         │
    ▼         ▼
┌────────┐  ┌──────────┐
│ Vector │  │  Graph   │
│  Index │  │  Index   │
│(LanceDB│  │(Neptune) │
└────┬───┘  └────┬─────┘
     │           │
     │    ┌──────┴──────┐
     │    │             │
     ▼    ▼             ▼
   ┌──────────┐    ┌─────────┐
   │  Term    │    │ Entity  │
   │  Search  │    │ Search  │
   └─────┬────┘    └────┬────┘
         │              │
         └──────┬───────┘
                │
                ▼
         ┌─────────────┐
         │   Hybrid    │
         │  Retrieval  │
         └─────────────┘

Performance Optimizations

1. Pre-loading Tables

LANCE_TABLES = {}

def preload_lance_tables(bucket: str, table_name: str, prefix: str):
    lance_uri = f"s3://{bucket}/{prefix}"
    db = init_lancedb_connection(lance_uri)

    if table_name in db.table_names():
        tbl = db.open_table(table_name)
        LANCE_TABLES[table_name] = tbl

        row_count = _safe_row_count(tbl)

        # Create index only for larger datasets
        if row_count >= 256:
            try:
                tbl.create_index(
                    vector_column_name="vector", 
                    metric="cosine"
                )
            except Exception as e:
                logger.warning(f"Index creation failed: {e}")

2. Batch Processing

def store_terms_for_chunk(chunk_id: str, unigrams: list, bigrams: list, 
                          trigrams: list):
    """Store terms in batches to avoid large transactions"""

    # Limit terms to prevent explosion
    terms = (
        [(t, "unigram") for t in unigrams[:hard_limit]] +
        [(t, "bigram") for t in bigrams[:hard_limit]] +
        [(t, "trigram") for t in trigrams[:hard_limit]]
    )

    batch_size = 100
    for i in range(0, len(terms), batch_size):
        batch = terms[i:i + batch_size]

        params = {
            "chunk_id": chunk_id,
            "terms": [{"text": term, "type": term_type} 
                     for term, term_type in batch]
        }

        run_query("""
            MATCH (c:Chunk {id: $chunk_id})
            UNWIND $terms AS term
            MERGE (t:Term {text: term.text, type: term.type})
            MERGE (c)-[:HAS_TERM]->(t)
        """, params)

3. Entity Deduplication

def process_triplet(triplet, kb_id: str, chunk_id: str):
    subject, predicate, object_ = triplet

    # Find similar entities using vector search
    similar_subjects = retrieve_lancedb(
        boto3_embedding_call(subject),
        entity_table
    )

    # Merge with similar entities
    if similar_subjects:
        query = """
            UNWIND $similarSubjects AS subject
            MERGE (subjectNode:Entity {name: toLower(subject.name)})
            ...
        """
        run_query(query, params)

Real-World Results

In production deployments, this hybrid approach has shown:

• 40-60% improvement in retrieval precision vs pure vector search
• Better handling of technical terminology and domain-specific language
• Multi-hop reasoning capabilities through graph traversal
• Reduced hallucination by maintaining entity consistency
• Context preservation through sequential chunk relationships

Example Query Results

Query: "What are the spare parts for RP300?"

Pure Vector Search might return:

• Generic spare parts documentation
• Unrelated RP series products

Hybrid Graph + Vector returns:

• Exact RP300 spare part lists (term match)
• Related maintenance procedures (entity relationships)
• Sequential pages from the same manual (graph traversal)
• Cross-referenced assembly diagrams (entity connections)

Implementation Considerations

Security & Authentication

def run_query(query: str, params: Any):
    URL = f"bolt://{NEPTUNE_ENDPOINT}:{NEPTUNE_PORT}"
    session = boto3.Session()
    creds = session.get_credentials()
    region = os.getenv("AWS_DEFAULT_REGION", "eu-west-1")

    # Neptune authentication
    authToken = NeptuneAuthToken(creds, region, URL)
    driver = GraphDatabase.driver(URL, auth=authToken, encrypted=True)

    # Execute query
    drs = driver.session()
    res = drs.run(query, params)
    return res

Cost Optimization

1. Use LanceDB on S3: Serverless, pay-per-use vector storage
2. Limit triplet extraction: Set hard_limit_triplets to control LLM calls
3. Batch Neptune writes: Reduce transaction overhead
4. Cache embeddings: Reuse for similar queries

Scaling Considerations

• Neptune: Scales to billions of relationships
• LanceDB: Handles millions of vectors efficiently on S3
• Parallel processing: Process files concurrently via Step Functions
• Incremental updates: Add new documents without full reindex

Getting Started

Prerequisites

# Install dependencies
pip install boto3 lancedb nltk llama-index neo4j pyarrow

# Download NLTK data
python -c "import nltk; nltk.download('punkt'); nltk.download('stopwords')"

Basic Usage

# Initialize retriever
retriever = GraphRetriever()

# Ingest documents
for file_data in document_batch:
    run_add_chunks_to_db(file_data, kb_id, chunk_db, term_db, triplet_db)

# Perform hybrid search
reference_data, shop_text_data = retriever.retrieve_chunks(
    kb_id="my_knowledge_base",
    query="How do I troubleshoot the RP300 motor?",
    top_k=10
)

Conclusion

Hybrid Graph + Vector RAG represents a significant evolution in enterprise retrieval systems. By combining the semantic understanding of vector embeddings with the relational intelligence of graph databases, we achieve:

✅ Higher precision through multi-strategy retrieval

✅ Better context via document structure preservation

✅ Entity resolution through graph-based deduplication

✅ Multi-hop reasoning via relationship traversal

✅ Scalability with serverless architecture

This isn't just theoretical—it's production-tested and battle-hardened for enterprise knowledge bases handling technical documentation, maintenance manuals, and complex domain knowledge.

The future of RAG is hybrid, and the combination of vectors and graphs unlocks capabilities that neither can achieve alone.

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Further Reading

• AWS Neptune OpenCypher Documentation
• LanceDB: The Open-Source Alternative to Pinecone
• Amazon Bedrock Titan Embeddings

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About the Author:
Written by Suraj Khaitan
— Gen AI Architect | Working on serverless AI & cloud platforms.

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Have questions or want to share your hybrid RAG implementation? Drop a comment below!