RAG Search by Ebenge Usip

RAG Search by Ebenge UsipRAG Search by Ebenge Usip

Our AI Search (RAG, Knowledge Graphs) service offers bespoke retrieval-augmented generation (RAG) search systems that combine the power of vector embeddings and knowledge graphs to deliver accurate and relevant search results. What sets us apart is our expertise in calibrating these systems to specific use cases and data, allowing for unparalleled search efficiency and accuracy. By leveraging our expertise in RAG and knowledge graph technologies, we help businesses improve their search capabilities and provide a better user experience.

What's included

Customized RAG Search

A tailored retrieval-augmented generation (RAG) search that combines the strengths of vector embeddings and/or knowledge graphs to provide accurate and relevant search results. This customized search system is trained on your specific data and use case, and is designed to improve search efficiency, accuracy, and user experience. The deliverable includes a fully functional RAG search, along with documentation and support for integration and deployment.

FAQs

Ebenge's other services

AI Agent DevelopementContact for pricing

Contact for pricing

What's included

Customized RAG Search

FAQs

Ebenge's other services

AI Agent DevelopementContact for pricing

Contact for pricing

What's included

How does the RAG model handle complex or nuanced search queries, and can it understand the context and intent behind the search?

What kind of data is required to train the RAG model, and how much data is needed to achieve optimal performance?

How does the RAG model handle ambiguity and uncertainty in search results, and can it provide multiple possible answers or suggestions?

How is the performance of the RAG model measured and evaluated, and what kind of metrics or benchmarks are used to assess its accuracy and effectiveness?

What's included

How does the RAG model handle complex or nuanced search queries, and can it understand the context and intent behind the search?

What kind of data is required to train the RAG model, and how much data is needed to achieve optimal performance?

How does the RAG model handle ambiguity and uncertainty in search results, and can it provide multiple possible answers or suggestions?

How is the performance of the RAG model measured and evaluated, and what kind of metrics or benchmarks are used to assess its accuracy and effectiveness?