Building Advanced RAG Systems With Knowledge Graphs and Neo4j
Building Advanced RAG Systems with Knowledge Graphs and Neo4j
In the rapidly evolving landscape of retrieval-augmented generation (RAG), traditional similarity search approaches are being superseded by more sophisticated techniques. This comprehensive guide explores how to combine Neo4j graph databases with vector embeddings to create a revolutionary RAG system that leverages both semantic similarity and structured knowledge representation.
The Power of Hybrid Knowledge Graph RAG
The true innovation in this approach lies in uniting vector similarity search with graph traversal through Cypher queries. By structuring knowledge as interconnected entities rather than isolated text chunks, we can deliver more precise, contextualized answers to complex questions.
This hybrid approach offers several key advantages:
- **Contextual understanding**: Captures relationships between entities
- **Multi-hop reasoning**: Follows chains of connections to discover indirect relationships
- **Structured exploration**: Traverses knowledge in a predictable, explainable way
- **Enhanced precision**: Delivers targeted answers that consider both semantic similarity and structured knowledge
Project Architecture Overview
The system architecture consists of two main components:
1. **Document nodes**: Primary entry points from semantic chunking
- Contain the original text content
- Include multi-vector embeddings (translations, hypothetical queries, summaries)
- Serve as the foundation for initial similarity search
2. **Entity nodes**: Sophisticated graph elements extracted from documents
- Represent concepts, people, organizations, events
- Share a common "entity" label while maintaining specific sub-labels
- Connect to other entities through meaningful relationships
- Contain domain-specific embeddings for properties
The critical bridge between these components is the "mention" relationship, which connects document chunks to the entities they reference.
Ingestion Process
The ingestion pipeline transforms raw documents into a knowledge graph with multiple vector representations through these steps:
1. **Initialization**: Set up environment, Neo4j connection, and LLM configuration
2. **Semantic chunking**: Split documents at natural semantic break points
3. **Graph structure extraction**: Use LLM to identify entities, relationships, and properties
4. **Database persistence**: Store the extracted structure in Neo4j with metadata
5. **Vector embedding creation**: Generate embeddings for node and relationship properties
6. **Document enhancement**: Add LLM-generated properties for additional vector representations
Configuration Parameters
The system offers fine-grained control over how information is extracted and structured:
**Entity Expression Control**:
- Use predefined node types for consistency and compliance
- Or let the AI dynamically discover entity types for flexibility
**Relationship Schema Management**:
- Define allowed relationship types to ensure consistent vocabulary
- Use relationship tuples for granular control over connections
**Property Engineering**:
- Extract all properties the LLM finds
- Specify which properties to include
- Add descriptive properties to relationships for context
**Multi-Vector Embedding Architecture**:
- Configure which node labels and properties to index
- Define relationship properties for embedding
- Create automated multi-vector properties for documents
11 Advanced Graph Traversal Techniques
Once we've built our knowledge graph, we can explore it using these sophisticated traversal algorithms:
1. Context to Cypher
This revolutionary technique uses an LLM to dynamically generate custom Cypher queries based on the query context and initial results. Instead of following fixed algorithms, it intelligently analyzes what information is missing and creates targeted database queries to find it.
The LLM receives:
- User's intent
- Initial retrieved documents
- Graph schema
- Available vector indexes
It then generates a Cypher query that can leverage the full power of Neo4j, including vector similarity, pattern matching, and complex traversals.
2. K-Hop Limited BFS
This approach systematically explores the graph through breadth-first traversal up to a configurable maximum of K hops. It ensures comprehensive local exploration without going too deep.
The exploration proceeds layer by layer:
- First examines all direct connections
- Then moves to connections of those connections
- Continues until reaching the maximum hop count or node limit
3. LLM Predicate Filtered K-Hop BFS
This technique enhances the K-Hop BFS by using an LLM to filter which relationship types are most relevant to the user's query. Instead of exploring all possible connections, it focuses only on semantically meaningful paths.
The LLM receives:
- The user's query
- All available relationship types
It then returns only the relationship types that are most likely to contain relevant information, significantly reducing the number of nodes explored while maintaining answer quality.
4. Depth-First Search (DFS)
DFS conducts deep exploration down relationship chains before backtracking. It's ideal for uncovering multi-step reasoning or hierarchical structures.
Unlike BFS, which explores breadth-wise, DFS plunges deeply along a single path:
- From a starting node, it explores as far as possible along a branch
- Then backtracks and explores the next branch
- Perfect for finding chains of connections
5. LLM Predicate Filtered DFS
Similar to the filtered BFS approach, this applies LLM filtering to DFS traversal, allowing deep exploration of only the most relevant paths.
6. Uniform Cost Search (UCS)
UCS applies Dijkstra's algorithm over semantic embeddings, treating edge costs as cosine distance between node embeddings. It guarantees discovery of nodes in order of increasing semantic distance from the starting points.
Instead of treating all connections equally, UCS:
- Calculates the semantic similarity between connected nodes
- Explores the graph in order of cumulative semantic distance
- Finds the most semantically coherent paths through the knowledge
7. LLM Predicate Filtered UCS
This applies the LLM relationship filtering to UCS, focusing the semantic-based exploration on only the most relevant connection types.
8. A* Search
A* Search extends UCS by adding a heuristic component based on the query embedding. It balances:
- The cost accumulated so far from seed nodes (g(n))
- The estimated remaining cost to reach the query intent (h(n))
This approach transforms the graph traversal from systematic exploration into a goal-directed search, dramatically reducing the number of nodes that need to be explored.
9. LLM Predicate Filtered A* Search
Combining A* with LLM relationship filtering creates an extremely focused, goal-directed traversal that only explores semantically relevant paths toward the query intent.
10. Beam Search
Beam Search performs a layer-by-layer exploration like BFS but retains only the top K scoring nodes at each depth. This controls memory usage and latency by pruning lower-scoring branches while still exploring promising paths.
The approach:
- Expands all current candidates at each layer
- Ranks them by semantic relevance
- Keeps only the top-K most promising candidates
- Uses these as the starting points for the next layer
11. LLM Predicate Filtered Beam Search
This final technique adds LLM relationship filtering to Beam Search for an even more focused exploration.
Query Execution Process
The hybrid RAG query system follows a three-phase process:
1. **Vector Search**: Initial similarity-based retrieval using multiple vector indexes
2. **Graph Traversal**: Intelligent expansion of initial results through graph relationships
3. **Answer Generation**: Summarization of collected context into a comprehensive answer
This orchestrated approach delivers precise, contextually rich answers by leveraging both the semantic power of embeddings and the structural intelligence of graph databases.
Implementing Your Own System
The complete project is available on GitHub with a fully functional Python implementation that uses Docker to integrate with Neo4j. The system is preconfigured and ready to run without complex setup.
By experimenting with different traversal algorithms and configuration parameters, you can find the optimal approach for your specific use case and document collection.
Conclusion
This hybrid knowledge graph RAG system represents a major advancement beyond traditional similarity search methods. By combining vector embeddings with graph traversal techniques, we can build more intelligent, precise, and contextually aware AI systems capable of delivering targeted answers to even the most complex questions.
The future of RAG lies not just in finding similar text, but in understanding the relationships between concepts and traversing knowledge structures in meaningful ways. This approach brings us closer to truly intelligent information retrieval that mimics human understanding of interconnected knowledge.
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