Optimize your RAG and LLM Apps, Save Costs & Boost Speed
RAG-Buddy is the ultimate game-changer toolbox for taking your RAG/LLM pipeline to the next level. Our initial release is centered around a state-of-the-art cache:
- For text classification, up to 65% cost savings with a quality drop of less than a percentage point.
- For other use cases like RAG+Citation or Q&A, cost savings of up to 50%.
Boost LLMs for generative AI tailored to your data
Retrieval-augmented generation (RAG) is a cutting-edge technique that boosts Large Language Models (LLMs) to improve the accuracy of question-answering. RAG-Buddy by helvia.ai, helps you deliver superior results by retrieving relevant information from your data sources to provide context to the LLMs.
Enhance your AI-powered Q&A system with RAG-Buddy services
RAG-Buddy brings a powerful collection of services that can be easily integrated into your RAG-pipelines.
Reduce the usage costs associated with Large Language Models (LLMs) with our top-notch caching mechanism.
RAG-Buddy Cache addresses the problems faced by most caches, such as few hits and high risks of big mistakes with semantic caches. By caching frequently used data or responses, it efficiently retrieves information, making the system more cost-effective and reducing overall computational costs.
Gain insights into the performance and usage of the RAG system with our analytics tool. By tracking and analyzing metrics related to the RAG system's performance, RAG-Buddy Analytics allows developers to make informed decisions about how to optimize and improve the system.
Reduce the usage costs associated with Large Language Models (LLMs) with our top-notch caching mechanism.
RAG-Buddy Cache addresses the problems faced by most caches, such as few hits and high risks of big mistakes with semantic caches. By caching frequently used data or responses, it efficiently retrieves information, making the system more cost-effective and reducing overall computational costs.
Gain insights into the performance and usage of the RAG system with our analytics tool. By tracking and analyzing metrics related to the RAG system's performance, RAG-Buddy Analytics allows developers to make informed decisions about how to optimize and improve the system.
Maximizing AI's capabilities in multiple use cases/AI tasks
Stay ahead with upcoming RAG solutions that cater to all your needs
RAG-Buddy Guard
Protect sensitive information with our security tool. RAG-Buddy Guard ensures that personal and sensitive information is not sent to the LLM, thereby preventing potential data breaches and ensuring the privacy and security of the data used by the RAG system.
Protect sensitive information with our security tool. RAG-Buddy Guard ensures that personal and sensitive information is not sent to the LLM, thereby preventing potential data breaches and ensuring the privacy and security of the data used by the RAG system.
RAG-Buddy Pipelines
Enjoy the flexibility to choose the solution that best fits your needs and requirements with our pipeline services. Whether you prefer to bring your own RAG system or opt for an end-to-end solution, RAG-Buddy Pipelines has got you covered.
Enjoy the flexibility to choose the solution that best fits your needs and requirements with our pipeline services. Whether you prefer to bring your own RAG system or opt for an end-to-end solution, RAG-Buddy Pipelines has got you covered.
RAG-Buddy Limiter
Prevent end-user abuse of the RAG system with our query limiter. By limiting the number of queries a user can make within a certain time frame, RAG-Buddy Limiter helps to maintain the stability and performance of the RAG system, ensuring a fair and balanced use of the system's resources.
Prevent end-user abuse of the RAG system with our query limiter. By limiting the number of queries a user can make within a certain time frame, RAG-Buddy Limiter helps to maintain the stability and performance of the RAG system, ensuring a fair and balanced use of the system's resources.
RAG-Buddy Continuous Evaluation
Guarantee RAG pipeline quality with ongoing evaluation using the RAG Triad, assessing performance on a sample of real production queries and results.
Guarantee RAG pipeline quality with ongoing evaluation using the RAG Triad, assessing performance on a sample of real production queries and results.
RAG-Buddy Classification Cache
Optimize text classification tasks with our caching mechanism, similar to RAG-Buddy Cache but tailored for text classification models.
Optimize text classification tasks with our caching mechanism, similar to RAG-Buddy Cache but tailored for text classification models.
RAG-Buddy Q&A Cache
Efficiently store and retrieve answers without citations or LLM calls, streamlining response retrieval for commonly asked questions.
Efficiently store and retrieve answers without citations or LLM calls, streamlining response retrieval for commonly asked questions.
RAG-Buddy Rephrase & Respond
Improve the quality of your system’s responses with our rephrasing feature. By rephrasing the user’s query, RAG-Buddy Rephrase & Respond helps to increase the quality of both the retrieval step and the ultimate response from the LLM.
Improve the quality of your system’s responses with our rephrasing feature. By rephrasing the user’s query, RAG-Buddy Rephrase & Respond helps to increase the quality of both the retrieval step and the ultimate response from the LLM.
RAG-Buddy Topic Modelling
Get actionable content improvement analytics by categorizing user queries into specific topics, aiding in content gap identification and knowledge base improvement.
Get actionable content improvement analytics by categorizing user queries into specific topics, aiding in content gap identification and knowledge base improvement.
Backed by Science
The paper "Cache me if you Can: an Online Cost-aware Teacher-Student Framework to Reduce the Calls to Large Language Models (EMNLP 2023)" presents a cost-effective approach for LLMs in text classification settings resulting in a cost reduction of more than 3x!
We use our own products to ensure their effectiveness. By implementing RAG-Buddy Cache for our internal RAG pipelines, we have considerably decreased costs and improved response quality.
Dimi Balaouras, CTO, helvia.ai
Enjoy premium RAG services from a central source
Cut down on RAG Q&A expenses
RAG-Buddy Cache decreases the context size, reducing the number of query tokens. Fewer tokens mean lower costs for either a hosted LLM or your own LLM.
RAG-Buddy Cache decreases the context size, reducing the number of query tokens. Fewer tokens mean lower costs for either a hosted LLM or your own LLM.
Optimize answer quality
Smaller context size improves answer quality. This is laid out in the paper "Lost in the Middle" ( [2307.03172] Lost in the Middle: How Language Models Use Long Contexts ).
Smaller context size improves answer quality. This is laid out in the paper "Lost in the Middle" ( [2307.03172] Lost in the Middle: How Language Models Use Long Contexts ).
Get faster response times
LLMs are faster with a smaller context because of reduced token processing time. Another result is the effect known as Attention Mechanism: In transformer architectures, attention is computed between all pairs of tokens. This operation is quadratic in time complexity concerning the number of tokens, which means a longer context could significantly increase latency.
LLMs are faster with a smaller context because of reduced token processing time. Another result is the effect known as Attention Mechanism: In transformer architectures, attention is computed between all pairs of tokens. This operation is quadratic in time complexity concerning the number of tokens, which means a longer context could significantly increase latency.
Integrate effortlessly
RAG-Buddy Cache is designed as a proxy for your existing LLM for swift plug-and-play implementation.
RAG-Buddy Cache is designed as a proxy for your existing LLM for swift plug-and-play implementation.
Enhance credibility and trustworthiness
Including proper citations and references in the generated responses, enhances the credibility of your AI applications and, makes them more trustworthy to users. When users see well-referenced answers, they are more likely to rely on the information provided, leading to increased user satisfaction and confidence in your system.
Including proper citations and references in the generated responses, enhances the credibility of your AI applications and, makes them more trustworthy to users. When users see well-referenced answers, they are more likely to rely on the information provided, leading to increased user satisfaction and confidence in your system.
Ensure compliance
Ensure compliance By automatically citing sources of information with the RAG-Buddy Citation Engine, you can ensure compliance with industry-specific regulations and standards, avoid legal issues and maintain AI system integrity.
Ensure compliance By automatically citing sources of information with the RAG-Buddy Citation Engine, you can ensure compliance with industry-specific regulations and standards, avoid legal issues and maintain AI system integrity.
Gain comprehensive insights and transparency
Get valuable insights into your RAG system's performance with the RAG-Buddy Analytics service. Optimize behavior by displaying cache utilization and a log of all queries, including selected citation articles and LLM-generated answers. Continuously improve system performance with informed decisions.
Get valuable insights into your RAG system's performance with the RAG-Buddy Analytics service. Optimize behavior by displaying cache utilization and a log of all queries, including selected citation articles and LLM-generated answers. Continuously improve system performance with informed decisions.
Start benefiting today
with cost-effective, risk-free plans
Start for free and choose a different plan for each of your projects
Get started for freeFree
Starter
Business
Enterprise
Corporate
Free
Starter
Business
Enterprise
Corporate
All prices are per project per month. You can run multiple projects at different plans, according to your needs.