KGs & RAG with Graphlit - a TWIML Generative AI open discussion

This discussion explores the use of knowledge graphs (KGs) in RAG systems, focusing on their potential to improve retrieval and context compilation for large language models (LLMs).
* The discussion includes technical details about specific tools and techniques, such as Azure AI Search, Rebel, and XML data structures.
* The participants share practical experiences and challenges encountered in their work with RAG and KGs.
* The conversation highlights the evolving nature of the field and the need for further research and development.
*Key Participants:*
* Kirk Marple: Graflet CEO, discussing their LLM platform with integrated KG capabilities.
* Sujit Pal: Elsevier data scientist, sharing his experience with GraphRAG and knowledge graph construction.
* Darin Plutchok: taxonomist and Product Manager, AI Innovation, offering insights on the value of hierarchical structures and knowledge representation.
* Eric Landry: Data scientist, raising questions about the evaluation and complexity of KG-based RAG approaches.
*Main Topics:*
1. *Graflet Platform:*
* Provides a managed service for unstructured data pipelines and RAG with multi-modal capabilities.
* Leverages KGs for semantic search and context enrichment.
* Offers a developer-friendly API and free tier for experimentation.
2. *GraphRAG and Knowledge Graph Construction:*
* Sujit Pal discusses his experiments with GraphRAG, extracting entities and linking them based on co-occurrence.
* Explores the use of triples (subject-predicate-object) to represent relationships within the KG.
* Highlights the potential of KGs to improve recall and handle broad queries that span multiple chunks.
3. *Challenges and Evaluation:*
* Eric Landry raises concerns about the added complexity of KG-based RAG and the need for thorough evaluation.
* The group discusses the difficulty of evaluating RAG systems, particularly the lack of reliable ground truth data.
* Kirk Marple shares Graflet's ongoing evaluation efforts to compare KG-enhanced RAG with standard RAG approaches.
4. *Information Retrieval and Compilation:*
* The discussion emphasizes the importance of effective information retrieval (IR) for RAG, highlighting its shift towards "information retrieval compilation."
* Kirk Marple describes Graflet's use of semantic search and prompt rewriting to improve retrieval accuracy.
* Darin Plutchok emphasizes the need to consider data quality and appropriate data manipulation for optimal LLM context.
5. *Skills and Resources for RAG Development:*
* Sam Charrington inquires about the skillset required for successful RAG development, noting the importance of search expertise.
* The group discusses the value of combining IR, information extraction (IE), and ontology knowledge for effective RAG implementation.
* The need for educational resources and a clear learning path for aspiring RAG practitioners is highlighted.