Unlocking Graph Databases: Natural Language to Gremlin Query Transformation Using Amazon Bedrock
Abstract
Discover how our innovative approach leverages natural language processing to streamline the querying of graph databases, facilitating accessibility for non-technical users.
Key Highlights
- Overcoming challenges in graph database query generation.
- Methodology for converting natural language queries into Gremlin code.
- Evaluation techniques using large language models (LLMs) for accuracy and effectiveness.
Introduction
As organizations increasingly adopt graph databases, we tackle the complexities of querying them by translating natural language directly to Gremlin, utilizing advanced AI models.
Methodology Overview
Our structured approach encompasses three pivotal steps: extracting graph knowledge, structuring the graph for natural language comprehension, and finally generating executable Gremlin queries.
Detailed Steps
- Extracting Graph Knowledge: Incorporating structural and semantic information for accurate query translation.
- Structuring for Text-to-SQL: Enhancing model comprehension through schema representation.
- Query Generation and Execution: Iteratively refining generated queries to ensure alignment with the database’s structure.
Evaluation Framework
Implementing a dual evaluation system to assess both the generated Gremlin queries and their execution results, comparing them against established ground truths.
Results and Discussion
Through rigorous experiments, we present findings on query similarity, execution accuracy, and efficiency, highlighting the competitive edge of our model against benchmarks.
Conclusion
Our framework demonstrates significant potential in resolving the intricacies of graph query generation, combining domain-specific knowledge and advanced processing to enhance user experience and query performance.
Transforming Natural Language into Graph Queries: A Revolution in Data Access
In today’s fast-paced data-driven environment, organizations need efficient ways to manage complex and interconnected datasets. Graph databases have emerged as a powerful solution, enabling seamless connectivity and intricate data relationships. However, the adoption of specialized query languages like Gremlin presents challenges, especially for teams without deep technical knowledge. This post explores our innovative approach to converting natural language queries into Gremlin, effectively breaking down barriers to insights for business analysts and data scientists.
Understanding the Challenge
Unlike traditional relational databases, graph databases lack a centralized schema, creating hurdles for query generation. The technical expertise needed to write effective queries often limits access to insights for non-technical users. To address this, we propose a solution that leverages Amazon Bedrock models, specifically Amazon Nova Pro, to translate natural language into machine-readable queries, making graph databases more accessible.
Our Methodology
Step 1: Extracting Graph Knowledge
The foundation of our approach relies on enriching natural language with both graph and domain knowledge. Graph knowledge includes:
- Vertex labels and properties: Understanding types and attributes of vertices in the graph.
- Edge labels and properties: Information about the connections and their characteristics.
- One-hop neighbors: Local connectivity that shows direct relationships between adjacent vertices.
In addition to structural knowledge, we incorporate domain knowledge from two sources:
- Customer-provided knowledge: Constraints informed by customers like kscope.ai to delineate which vertex types should be excluded from queries.
- LLM-generated descriptions: Enhancing the understanding of graph properties and their relevance through detailed semantic descriptions generated by large language models (LLMs).
Step 2: Structuring the Graph
Using a method akin to text-to-SQL processing, we structure graph data into a schema representing vertex types, edges, and properties. This aids the model in interpreting queries accurately.
The question processing component works through three key stages:
- Entity recognition and classification: Identifying critical elements within the input question.
- Context enhancement: Augmenting queries with relevant graph-specific and domain-specific information.
- Query planning: Mapping the enhanced question to the specific data elements needed for execution.
Step 3: Generating and Executing Gremlin Queries
The final phase involves generating Gremlin queries based on the structured context:
- The LLM creates an initial Gremlin query.
- The query is executed in a Gremlin engine.
- Successful executions return results; failures trigger an error analysis and iterative refinement of the query with LLM feedback.
This cyclical process enhances the accuracy and reliability of the generated queries.
Evaluating Effectiveness
To validate our approach, we employed an LLM-based evaluation system using Anthropic’s Claude 3.5 Sonnet to assess query generation accuracy and execution outcomes. Key evaluation metrics included:
- Query evaluation: Correctness, similarity, efficiency, and ratings based on ground truth comparisons.
- Execution accuracy: Comparing output from generated queries against known correct results.
Testing across 120 questions yielded an overall accuracy of 74.17%. This performance demonstrated the framework’s effectiveness in navigating the unique challenges of graph query generation and execution.
Comparing Results
The results highlighted our model’s strengths:
Query Similarity Metrics
| Difficulty Level | Amazon Nova Pro | Benchmark Model |
|---|---|---|
| Easy | 82.70% | 92.60% |
| Medium | 61% | 68.70% |
| Hard | 46.60% | 56.20% |
| Overall | 70.36% | 78.93% |
Overall Ratings
| Difficulty Level | Amazon Nova Pro | Benchmark Model |
|---|---|---|
| Easy | 8.7 | 9.7 |
| Medium | 7.0 | 8.0 |
| Hard | 5.3 | 6.1 |
| Overall | 7.6 | 8.5 |
Execution Accuracy
| Difficulty Level | Amazon Nova Pro | Benchmark Model |
|---|---|---|
| Easy | 80% | 90% |
| Medium | 50% | 70% |
| Hard | 10% | 30% |
| Overall | 60.42% | 74.83% |
Query Latency and Cost
Amazon Nova Pro exhibited lower query generation latencies and costs compared to the benchmark model, further solidifying its utility for organizations seeking efficiency without sacrificing performance.
Conclusion
Our framework demonstrates tremendous potential for transforming how non-technical users access and interact with graph databases. By seamlessly converting natural language to Gremlin queries, we empower a broader audience to glean insights from their interconnected data.
As we continue refining our evaluation methodologies and enhancing the model’s capabilities, we aim to handle increasingly complex queries and improve the user experience further. With innovative techniques like Retrieval Augmented Generation (RAG) and ongoing enhancements to our approach, we’re excited about the future of natural language processing in graph databases.
About the Authors
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