Advantages of Graph RAG

• Enhanced Contextual Understanding: By leveraging graph structures, Graph RAG can capture complex relationships between entities, leading to more accurate and context-aware information retrieval. This is particularly useful for tasks requiring deep understanding and reasoning.
• Improved Retrieval Precision: Graph RAG can improve retrieval precision by using graph-based indexing and retrieval methods. This ensures that the most relevant information is retrieved, even if it is buried within a large dataset.
• Mitigation of Hallucination: Traditional language models sometimes generate "hallucinated" information, which is not accurate or relevant. Graph RAG helps mitigate this issue by referencing structured knowledge bases, ensuring the generated content is grounded in factual data.
• Domain-Specific Knowledge: Graph RAG can be tailored to specific domains by incorporating domain-specific knowledge graphs, making it highly effective for specialized applications such as legal research, medical diagnostics, and technical documentation.

Problems with Graph RAG

• Speed: Graph RAG is horrendously slow for real world applications, often taking minutes to respond.
• Cost: Data preparation can cost many thousands of dollars for moderately sized datasets.
• Scalability: The reliance on clustered communities makes scaling challenging.
• Accuracy: Testing has shown little increase in search accuracy compared to traditional RAG.

SemDB to the Rescue

Local Understanding

• Cost: Less LLM calls mean less $$$.
• Accuracy: Local Understanding allows for Organization Specific vocabularies.
• Speed: Local Understanding means local processing… and that’s fast.
• Security: Not every piece of data needs to be sent to our AI overlords, so that they may use it to train their next models
• Note: Open AI and Google both super-duper promise not to ever use your data to train their models. Seriously, they pinky-sweared and everything.

Cost Advantages of Local Understanding

• Reduced External LLM Calls:
• Traditional systems require 1 LLM call per data chunk and 1 per cluster. SemDB’s Local Understanding handles these tasks algorithmically, bypassing the need for external calls entirely.
• This approach slashes costs, making large-scale projects financially viable.
• Scalable Data Extraction:
• Because Local Understanding operates within the organization’s infrastructure, there is no incremental cost for scaling. SemDB can handle datasets with millions of entities without ballooning expenses.
• For example, where traditional methods might cost $60,000 for a million records, SemDB achieves the same results at a fraction of the cost, with no ceiling on dataset size or complexity.
• Optimized Processing for Domain-Specific Graphs:
• By tailoring its Local Understanding capabilities to the specific needs of the organization, SemDB enables the creation of more complex, richly detailed graphs without incurring additional costs.

Beyond Cost Savings: Enabling Richer Graphs

• Incorporating Nuanced Relationships: Local Understanding allows SemDB to detect subtle, domain-specific relationships that external systems might overlook, enriching the knowledge graph with deeper insights.
• Expanding Data Coverage: By lowering costs, organizations can afford to process larger datasets, capturing more entities and relationships that drive value.
• Iterative Improvement: SemDB’s architecture allows for ongoing refinement of graphs as new data becomes available, further enhancing accuracy and depth.
• Organization Specific Vocabularies: Every company has their own lingo, vocabulary, and internal speak that the LLMs don’t fully understand. SemDB is able to capture that meaning, store it, and operate upon it like any other semantic nugget.

Organizations form their own vocabularies

Conclusion

• HIPAA Compliant Chat Bots: That don’t hallucinate give dieting advice to anorexics.
• Iterative Improvement: SemDB’s architecture allows for ongoing refinement of graphs as new data becomes available, further enhancing accuracy and depth.
• Sales Tools: To discover mine thousands of conversations for missed opportunities