RAG SearchEbenge 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
The RAG model is designed to handle complex or nuanced search queries by using a combination of natural language processing (NLP) and machine learning (ML) techniques to understand the context and intent behind the search. This allows the model to provide accurate and relevant results, even when the search query is ambiguous or unclear. By leveraging the power of vector embeddings and knowledge graphs, the RAG model can capture subtle nuances in language and provide more accurate results than traditional search models.
The RAG model requires a large corpus of text data to train, including a diverse range of documents, articles, and other sources of information. The amount of data needed to achieve optimal performance will depend on the specific use case and requirements, but generally, the more data the better, with millions of documents or more recommended for optimal results. Our team will work with you to determine the specific data requirements for your use case and ensure that the RAG model is trained on a high-quality and relevant dataset.
The RAG model is designed to handle ambiguity and uncertainty in search results by providing multiple possible answers or suggestions, allowing the user to select the most relevant result. This is achieved through the use of probabilistic models and ranking algorithms, which enable the model to generate a list of potential results and rank them according to their relevance and accuracy. By providing multiple possible answers or suggestions, the RAG model can help users to quickly and easily find the information they need, even when the search query is ambiguous or unclear.
The performance of the RAG model is measured and evaluated using a range of metrics and benchmarks, including precision, recall, F1 score, and mean average precision (MAP). These metrics provide a comprehensive understanding of the model's accuracy and effectiveness, and allow us to fine-tune and optimize the model for optimal performance. We also use human evaluation and feedback heuristics to assess the model's performance and identify areas for improvement, ensuring that the RAG model is providing the most accurate and relevant results possible.
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RAG SearchEbenge Usip
Contact for pricing
Tags
ChatGPT
Claude
Google Gemini
AI Developer
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
The RAG model is designed to handle complex or nuanced search queries by using a combination of natural language processing (NLP) and machine learning (ML) techniques to understand the context and intent behind the search. This allows the model to provide accurate and relevant results, even when the search query is ambiguous or unclear. By leveraging the power of vector embeddings and knowledge graphs, the RAG model can capture subtle nuances in language and provide more accurate results than traditional search models.
The RAG model requires a large corpus of text data to train, including a diverse range of documents, articles, and other sources of information. The amount of data needed to achieve optimal performance will depend on the specific use case and requirements, but generally, the more data the better, with millions of documents or more recommended for optimal results. Our team will work with you to determine the specific data requirements for your use case and ensure that the RAG model is trained on a high-quality and relevant dataset.
The RAG model is designed to handle ambiguity and uncertainty in search results by providing multiple possible answers or suggestions, allowing the user to select the most relevant result. This is achieved through the use of probabilistic models and ranking algorithms, which enable the model to generate a list of potential results and rank them according to their relevance and accuracy. By providing multiple possible answers or suggestions, the RAG model can help users to quickly and easily find the information they need, even when the search query is ambiguous or unclear.
The performance of the RAG model is measured and evaluated using a range of metrics and benchmarks, including precision, recall, F1 score, and mean average precision (MAP). These metrics provide a comprehensive understanding of the model's accuracy and effectiveness, and allow us to fine-tune and optimize the model for optimal performance. We also use human evaluation and feedback heuristics to assess the model's performance and identify areas for improvement, ensuring that the RAG model is providing the most accurate and relevant results possible.
Ebenge's other services
Contact for pricing