Improve RAG Accuracy¶
Using your own data?
To effectively integrate your own data into the solution accelerator, it's essential to adapt the existing components to align with your specific data structures and relationships.
1. Customize the Semantic Ranker The semantic ranker enhances search relevance by reordering search results based on contextual understanding. To tailor it to your data:
- Understand the Existing Semantic Ranker: Review the current implementation to comprehend how it processes and ranks data.
- Adapt Ranking Functions: Modify the ranking algorithms to consider the unique attributes and relationships within your data. This may involve adjusting weightings or incorporating additional data fields to improve relevance.
- Test and Validate: After modifications, rigorously test the ranker's performance to ensure it accurately reflects the importance and context of your data elements.
2. Modify GraphRAG Function Calls Graph Retrieval-Augmented Generation (GraphRAG) combines knowledge graphs with AI models to provide contextually rich responses. To align GraphRAG with your data:
- Analyze Your Data Structure: Identify the nodes (entities) and edges (relationships) that are pertinent to your domain.
- Adjust Function Calls: Modify existing GraphRAG function calls to navigate your specific graph schema effectively. This includes updating queries to traverse the correct nodes and edges that mirror your data's relationships.
- Integrate with AI Models: Ensure that the data retrieved via GraphRAG is appropriately fed into your AI models to enhance response generation with accurate context.
3. Revise Data Export Procedures Exporting data accurately is crucial for maintaining the integrity of your knowledge graph. To adapt the export process:
- Map Data Relationships: Clearly define how your data entities relate to one another, establishing the nodes and edges that will form your knowledge graph.
- Update Export Scripts: Modify data export scripts to extract information from the appropriate tables and fields that correspond to your defined nodes and edges.
- Validate Exported Data: After exporting, verify that the data correctly represents the intended relationships and entities within your knowledge graph framework.
As Generative AI (GenAI) becomes increasingly integral to modern enterprises, users' trust in these applications is paramount and heavily reliant on the accuracy of their responses. The productivity loss from incorrect answers and the consequent erosion of user trust are issues that cannot be overlooked. For many organizations, the decision to deploy GenAI apps hinges on their ability to elevate the accuracy of the app's responses to an acceptable level.
In this section, you will use Semantic Ranking and GraphRAG to enhance the accuracy of responses from the Woodgrove Bank Contract Management copilot. Here's what you will accomplish:
- Review Semantic Ranking
- Use Semantic Ranking to rerank hybrid search results
- Review GraphRAG and the Apache AGE extension
- Implement the AGE extension to enable graph queries against your PostgreSQL database
The Accuracy Problem¶
Despite significant advancements, accuracy remains a challenge for GenAI. Retrieval Augmented Generation (RAG) helps by grounding responses in factual data. Still, as datasets expand or when documents become too similar, vector search techniques can falter, leading to a loss of user trust and diminished productivity. Enhancing accuracy requires optimizing the information retrieval pipeline through various techniques. General methods include chunking, larger embeddings, and hybrid search, while advanced, dataset-specific approaches like semantic ranking and GraphRAG are essential.
Enhance GenAI Accuracy with Advanced Techniques¶
Innovations like Semantic Ranking and GraphRAG are crucial to address the accuracy problem. These techniques enhance the RAG approach by improving how grounding data is gathered and integrated into AI model responses, thereby increasing precision and reliability. Optimizing information retrieval pipelines through advanced techniques ensures that GenAI applications deliver accurate, trustworthy, and insightful responses, thereby maintaining user trust and productivity.
Select the tabs below to understand how Semantic Ranking and GraphRAG can improve RAG accuracy.
Semantic ranking is an advanced technique used in information retrieval systems to enhance the relevance of search results by understanding the context and meaning of queries and documents rather than relying solely on keyword matching. By leveraging natural language processing and machine learning algorithms, semantic ranking can analyze the relationships between words and concepts to provide more accurate and meaningful results. This approach allows search engines to better comprehend the intent behind user queries and deliver results that are contextually aligned with what users are looking for. The benefits of semantic ranking include improved accuracy in search results, enhanced user satisfaction, and more efficient retrieval of information, making it a powerful tool for modern search engines and recommendation systems.
GraphRAG, developed by Microsoft Research, is an innovative approach to information retrieval and generation, combining the power of knowledge graphs with large language models (LLMs) to enhance the accuracy and relevance of generated content. GraphRAG allows LLMs to provide more contextually aware and insightful responses by integrating structured data from knowledge graphs into the retrieval process. This method enhances the model's ability to understand complex queries and synthesize information from various sources, leading to more accurate and informative outputs. GraphRAG's benefits include improved information retrieval, enhanced reasoning capabilities, and the ability to deliver precise answers even when dealing with intricate or multi-faceted questions.