This text discusses the integration of Large Language Models (LLMs) with Knowledge Graphs using Neo4j and APOC Extended procedures, specifically focusing on OpenAI and VertexAI endpoints. It highlights the potential of LLMs to enhance productivity and address use cases in conversational AI and QA systems. The author proposes utilizing the neighborhood of a node in a graph to improve text embeddings for vector similarity searches. The process involves setting up a Neo4j environment, generating text embeddings using OpenAI’s API, and employing a Cypher query to enrich movie node information. The workflow includes retrieving relevant context based on user queries and generating answers using the enhanced data. The article concludes with a summary of the integration’s significance and mentions that the code is available on GitHub.
| name | description | change | 10-year | driving-force | relevancy |
|---|---|---|---|---|---|
| Integration of LLMs with Knowledge Graphs | Combining LLM workflows with Knowledge Graphs for enhanced data manipulation. | Shifting from isolated LLM applications to integrated systems that utilize graph-based data relationships. | In a decade, businesses may rely heavily on integrated LLM and knowledge graph systems for decision-making. | The need for enhanced data richness and contextuality in AI responses. | 4 |
| APOC Extended Procedures | New APOC procedures for seamless integration with OpenAI and VertexAI. | Transitioning from traditional programming methods to streamlined procedures for AI integration. | Expect a future where integrating AI services into applications is standardized and user-friendly. | The growing demand for rapid development and deployment of AI solutions. | 3 |
| Enrichment of Text Embeddings | Using node neighborhood information to enhance text embeddings. | Moving from basic embedding techniques to enriched context-aware embeddings. | Text embeddings may evolve to incorporate multi-dimensional context, providing deeper insights and accuracy. | Advancements in AI requiring more sophisticated data representation methods. | 4 |
| Retrieval-Augmented LLMs | LLMs using external information at query time for improved accuracy. | From self-contained models to those enhanced by contextual data retrieval. | In ten years, LLMs may seamlessly integrate real-time data retrieval, vastly improving response relevance. | The need for real-time, context-aware responses in a data-rich environment. | 5 |
| Multi-hop Question Answering | Using knowledge graphs to solve multi-hop QA problems. | Evolving from single-document answers to comprehensive, multi-source responses. | Knowledge graphs may become standard for complex question answering across industries. | Increased complexity of information and user queries in AI-driven environments. | 4 |
| Cosine Similarity in Graphs | Utilizing cosine similarity for node relevance in graph databases. | Adopting advanced similarity measures over traditional matching techniques. | Graph databases might standardize advanced metrics for similarity searching, enhancing query accuracy. | The quest for precision in data retrieval and analysis. | 3 |
| name | description | relevancy |
|---|---|---|
| Data Privacy and Security | Integrating LLMs with Knowledge Graphs may raise concerns about unauthorized access to sensitive information. | 4 |
| Dependency on Third-Party APIs | Reliance on external APIs like OpenAI can lead to service disruptions affecting applications using LLMs. | 4 |
| Bias in LLM Outputs | LLMs can unintentionally perpetuate biases present in training data, impacting the reliability of generated responses. | 5 |
| Scalability Issues | As integration with more LLMs occurs, the systems may face challenges in handling increased complexity and data volume. | 3 |
| Quality Control in Query Responses | Ensuring the accuracy and relevance of responses generated from LLMs based on dynamic knowledge graphs can be problematic. | 4 |
| name | description | relevancy |
|---|---|---|
| Integration of LLMs with Knowledge Graphs | Utilizing Large Language Models to enhance the contextual understanding of data within Knowledge Graphs for better information retrieval and processing. | 5 |
| Enhanced Text Embedding via Graph Context | Using the neighborhood of nodes in a graph to enrich text embeddings, improving the accuracy of vector similarity searches. | 4 |
| Retrieval-Augmented LLMs | Providing external information at query time to LLMs, enhancing their response generation based on enriched context. | 5 |
| Batch Processing in API Calls | Implementing batch processing for API calls to optimize performance and reduce costs when working with LLMs. | 4 |
| Multi-hop Question Answering with Knowledge Graphs | Leveraging Knowledge Graphs to address complex query scenarios that require information spanning multiple documents or nodes. | 5 |
| Cosine Similarity for Context Retrieval | Employing cosine similarity metrics to identify relevant nodes and enhance the context provided to LLMs during query processing. | 4 |
| Code Sharing and Collaboration | Encouraging the sharing of code and techniques via platforms like GitHub to foster community-driven innovation. | 3 |
| description | relevancy | src |
|---|---|---|
| Advanced AI models designed for natural language processing and understanding, enhancing productivity and data manipulation. | 5 | 1739f639d5bfca8e60d7750e29cc6ab3 |
| A technology for representing and storing highly-connected information, facilitating complex data relationships and multi-hop question answering. | 5 | 1739f639d5bfca8e60d7750e29cc6ab3 |
| Procedures that enhance Neo4j’s capabilities, allowing integration of LLMs and external data sources for richer context in queries. | 4 | 1739f639d5bfca8e60d7750e29cc6ab3 |
| A method used to find relevant information by comparing vector embeddings, improving the accuracy of search results in AI applications. | 5 | 1739f639d5bfca8e60d7750e29cc6ab3 |
| A trend in AI that combines external information retrieval with LLMs to enhance the quality of generated responses. | 5 | 1739f639d5bfca8e60d7750e29cc6ab3 |
| Transforming text into numerical vectors for better comparison and analysis in machine learning models, particularly in LLMs. | 4 | 1739f639d5bfca8e60d7750e29cc6ab3 |
| name | description | relevancy |
|---|---|---|
| Integration of LLMs with Knowledge Graphs | The integration of LLMs with Knowledge Graphs is an emerging trend that enhances data richness and improves vector similarity search applications. | 4 |
| APOC Extended Procedures | The introduction of APOC Extended procedures for LLM integration represents a significant advancement in making LLMs accessible for various applications. | 4 |
| Multi-hop Question Answering | The challenge of multi-hop question answering is becoming more prominent and can be addressed using knowledge graphs, highlighting the need for more sophisticated retrieval methods. | 5 |
| Retrieval-augmented LLMs | The use of external information in LLMs at query time is becoming a mainstream trend, indicating a shift in how AI models are trained and utilized. | 4 |
| Graph-based Contextual Information | Utilizing graph-based context to enrich text embeddings showcases a novel approach to enhancing AI-driven responses, potentially improving accuracy and relevance. | 4 |
| Cost Management in LLM Applications | Managing API costs associated with LLMs is a growing concern for developers and companies, influencing project scope and implementation strategies. | 3 |