From: aidotengineer
Introduction to Graph Data Structures in AI
Graph data structures are becoming increasingly vital in artificial intelligence (AI) applications, particularly in overcoming challenges associated with large volumes of unstructured data and the need for highly accurate, contextual responses from AI models [03:51:56]. While Large Language Models (LLMs) can provide good results, they often lack the deep context and enterprise-specific knowledge graph required for precise answers [02:00:52]. Integrating knowledge graphs with AI systems, often referred to as “Graph RAG” (Retrieval Augmented Generation), helps bridge this gap by providing structured contextual information [02:00:00].
Real-world Application: Biofarma Technology Transfer
A significant application of graph databases and knowledge graphs is in biofarma technology transfer [03:13:00]. This process involves scaling drug development from lab-bench scale to industrial production, such as making a million doses a day [03:17:00]. This transition can take years because industrial teams need to sift through hundreds of thousands of documents, notes, and test outcomes created at the science level [03:41:00].
Addressing Expertise Loss
A major challenge in this domain is the declining average tenure of manufacturing workers, which dropped from 20 years in 2019 to just three years [04:23:00]. This means a vast amount of expertise is retiring [04:30:00]. Generative AI, empowered by knowledge graphs, is crucial to capture intelligence from documents and tacit human knowledge and transfer it to new employees to accelerate the technology transfer process [04:39:00]. This not only solves a critical business problem but potentially saves lives by expediting the delivery of life-saving drugs [06:07:00].
Technical Implementation with Graphs
In such applications, millions of documents are loaded into a graph database [04:54:00]. Instead of loading entire documents, specific “chunks” (e.g., document, block, paragraph, line) are structured within the graph [05:02:00]. This structuring allows for:
- Refined Chunking: The system learns and improves how documents are chunked based on which chunks return desired results from similarity searches [05:30:00].
- Combined Retrieval: A generative AI application can leverage both a vector representation and a knowledge graph representation of data [19:10:00]. It queries the vector space for similarity and simultaneously retrieves relationally close nodes from the graph database to provide additional context to the LLM [19:18:00].
Key Benefits of Graph Data Structures in AI
Using graph databases in AI systems offers several significant advantages:
1. Improved Data Understanding and Team Performance
- Faster Data Landscape Comprehension: Consolidating data in a graph significantly reduces the time data scientists, engineers, and developers need to understand the data landscape. Tasks that previously took three months can be completed in three weeks or less for new projects [16:50:00].
- Enhanced Team Efficiency: This streamlined data understanding directly boosts team performance [17:20:00].
2. Enhanced Context and Precision in AI Outputs
- Superior Contextual Results: Graph RAG provides more contextually relevant and precise answers compared to using LLMs directly or even baseline vector database retrieval [18:15:00]. While vector databases pull in organizational knowledge, answers can be generic and prone to hallucinations [18:02:00].
- Reduced Hallucinations: By providing richer context, knowledge graphs help LLMs mitigate hallucinations [15:59:00].
- Handling Complex Connections: Graphs are particularly powerful for industries with complicated data where connections might not be apparent in relational databases. A search for one item immediately reveals a “neighborhood” of related information, which can then be fed to the LLM [18:38:00].
3. Better Governance and Explainability
- Data Governance: Graphs allow for better governance by enabling the application of controls and properties on graph nodes, thereby managing access to information [19:47:00].
- Increased Explainability: Unlike statistical probabilities in vector space, knowledge graphs provide explainability. Answers from LLMs can be traced back to specific graphs, nodes, and edges, allowing users to understand relationships and the reasoning behind the output [19:54:00]. This is crucial for industries where being wrong is not an option [18:31:00].
Graph RAG vs. Other AI Approaches
- Direct LLMs: Can yield good results but lack enterprise-specific context [17:55:00].
- Vector Databases (Baseline RAG): Improve results by incorporating organizational knowledge, but answers can still be generic and prone to hallucinations [18:02:00].
- Graph RAG: Represents the advanced end of the spectrum, offering precise answers, evolvable knowledge graphs, and robust contextual understanding, especially valuable in business-critical environments [18:15:00].