From: aidotengineer
Many organizations are exploring the potential of Generative AI, yet a significant challenge lies in achieving practical, production-ready applications. Gartner predicted that 30% of generative AI projects would be abandoned by the end of 2025 [00:00:58]. A primary reason for this failure rate is the lack of a clear business use case that solves real problems and is monetizable [00:06:00]. This article explores how graph databases provide a powerful solution for these challenges, particularly in enhancing AI applications like Retrieval Augmented Generation (RAG).
Real-World Application: BioPharma Technology Transfer
One compelling business case for Gen AI is in biopharma technology transfer [00:03:13]. This process involves scaling drug development from lab bench to industrial scale, producing millions of doses daily [00:03:17]. Historically, this has taken years, requiring industrial teams to sift through hundreds of thousands of scientific documents, notes, and test outcomes [00:03:38].
An additional challenge is the drastic reduction in manufacturing worker tenure, from an average of 20 years in 2019 to just three years currently [00:04:00]. This means a significant loss of expertise as experienced workers retire [00:04:30]. Generative AI is crucial to capture the intelligence from documents and tacit knowledge in people’s heads, transferring it to new employees for efficient technology transfer [00:04:36].
How Graph Databases Address These Challenges
To tackle the complexities of information transfer and knowledge retention, millions of documents are loaded into a graph database [00:04:51]. Rather than loading entire documents, specific “chunks” of information are loaded [00:05:02].
Key Benefits Identified:
- Structured Chunking and Refinement: Graph databases allow for structuring document chunks (e.g., document, block, paragraph, line) [00:05:12]. This structure enables learning and improvement in how documents are initially chunked, optimizing search results [00:05:36].
- Solving Critical Business Problems: By facilitating faster technology transfer, graph-powered Gen AI applications can accelerate the delivery of life-saving drugs, demonstrating a clear and impactful business use case [00:06:07].
- Accelerated Data Consolidation and Understanding: Consolidating data in a graph significantly speeds up the process for data scientists, engineers, developers, and SREs to understand the data landscape [00:16:50]. Tasks that once took three months to consolidate, understand, and clean up can now be done in three weeks or less [00:17:02]. This boosts team performance [00:17:20].
- Enhanced Data Traversal and Performance: Graph databases make data traversal significantly easier, improving data search efficiency [00:17:12].
- Richer Contextual Knowledge for LLMs: In complex industries with many connections, graph databases inherently store relationships that might not be explicitly joined in relational databases [00:18:38]. When a search is performed, the “neighborhood” of related information becomes available, providing better contextual knowledge to LLMs [00:18:48].
- Improved Explainability and Governance: Unlike statistical probabilities in vector space, graph databases allow for reasoning about nodes and edges, making the relationships and answers from LLMs more explainable [00:19:54]. They also enable better governance through controls and properties on graph nodes, dictating access to information [00:19:47].
- Precise and Accurate Answers: By providing deep contextual understanding and structured relationships, graph databases contribute to more precise and accurate answers from LLMs, which is critical in industries where being wrong is not an option [00:18:17].
Graph RAG Architecture
The approach combines both vector and knowledge graph representations of data for Gen AI applications [00:19:10]. This involves:
- Vector Database: Providing relational closeness in vector space [00:19:18].
- Knowledge Graph: Supplying additional context through relationally close nodes from the graph database [00:19:21].
This combined approach yields more contextually relevant results from expert systems [00:19:28].
In conclusion, leveraging graph databases in AI applications, particularly with Retrieval Augmented Generation (RAG), offers significant advantages in solving complex business challenges by providing structured, contextual, explainable, and precise information, essential for critical industries like life sciences [00:20:22].