Exploring Authentic Graph RAG: Solving Biases in AI Systems

Join me as I explore the world of graph databases and agents, discussing the challenges of biased language models and how Neo Forj's MCP platform can help overcome these limitations.

1. Steven Chin is the head of developer relations at Neo Forj and author of a new book on graph rag with O'Reilly.
2. Graph databases can help overcome biases and inaccuracies in large language models (LLMs) used in AI systems.
3. Many AI systems fail to meet use cases predicted by Gartner, often due to hallucinations and incorrect reasoning based on limited information sources.
4. An example of this problem is an LLM's inability to accurately determine the number of students that can fit in a classroom, leading to biased or incorrect answers.
5. AI systems can struggle with problems in life sciences, drug discovery, and supply chain management due to these limitations.
6. Knowledge graphs excel at tasks where LLMs fall short, making them a potential solution for improving AI system performance.
7. Agentic systems use multiple LLMs working together to improve the quality of results by observing, thinking, and taking actions in workflows.
8. However, these monolithic architectures can be hard to maintain, swap tools, and secure, making them less than ideal for many applications.
9. Microservice Component Procedures (MCP) can help address these challenges by allowing agents to talk to servers and data sources in a more flexible architecture.
10. Neo Forj has developed several tools using MCP, including Cipher, a query language for graph databases, memory modules, and cloud APIs.
11. These tools allow developers to give agents memory-based on graphs, which is particularly useful since LLMs naturally store, communicate, and retrieve information in a graph-like manner.
12. Many AI vendors are building agent architectures that incorporate these graph-based tools for improved performance and functionality.
13. Graph rag (representing knowledge as a graph) can help reduce hallucinations and improve the relevance of results compared to direct LLM responses or baseline vector similarity methods.
14. A hybrid approach using both vector search and graph context can yield better results for questions involving structured and unstructured data.
15. Companies like CLA have successfully replaced their SAS systems with graph rag projects, achieving high adoption rates and improved query processing times.
16. Neo Forj offers a certified developer program and the Neo Forj nodes conference to help professionals learn more about graph technology.
17. Text-to-cipher conversion by LLMs can be inconsistent, making it necessary to use alternative methods such as vector search with graph context for some cases.
18. Pre- and post-filtering of vector results can help ensure the most relevant information is presented to LLMs in context windows.
19. Neo Forj offers plugins and integrations with popular language processing frameworks like Langchain, Llama Index, and Haststack.
20. The optimal tool for a given application depends on user preferences, and Neo Forj aims to integrate with all major memory vendors.
21. Steven Chin will discuss MCP agent memory in more detail in an upcoming session at the conference.

Source: AI Engineer via YouTube

❓ What do you think? What are the most significant biases and limitations of current language models, and how can graph databases and agentic systems help mitigate these issues? Feel free to share your thoughts in the comments!