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Apus NetworkHow to Implement GPU-Based LLM Inference in AO
With the rapid development of artificial intelligence (AI) technology, an increasing number of large language model (LLM) applications require efficient computational resources. In this article, we will explore how to integrate APUS's GPU extension into the Application Overlay (AO) system to support more powerful AI model inference. Before delving into how GPU extensions work in the AO network, let's briefly review how typical AI applications operate and the composition of the AO ne...
Apus NetworkGetting Started with HyperBEAM: Building a Custom Device for Beginners
AbstractThis guide introduces developers to HyperBEAM's distributed computing framework through hands-on device extension. Learn how to leverage Erlang/OTP architecture and the Converge Protocol to create custom devices. Beginners will gain practical experience through a calculator device demo, understanding NIFs (Native Implemented Functions) and WASM port communication patterns.ChaptersIntroduction to HyperBEAMConverge Protocol : the root of device call logic and pathBuilding a Simple ...
Apus NetworkThe Future Is Deterministic: HyperBeam Architecture and the Importance of Hashpaths in AO
1. IntroductionAs decentralized computation evolves, HyperBeam emerges as a powerful client implementation of the AO-Core protocol, enabling distributed computation in a modular and verifiable way. By abstracting hardware resources and standardizing computation through devices, HyperBeam allows a wide range of computational models to operate seamlessly within the AO ecosystem. At the core of this system lies the concept of Hashpaths, which serve as unique identifiers for computational state a...
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Apus NetworkHow to Implement GPU-Based LLM Inference in AO
With the rapid development of artificial intelligence (AI) technology, an increasing number of large language model (LLM) applications require efficient computational resources. In this article, we will explore how to integrate APUS's GPU extension into the Application Overlay (AO) system to support more powerful AI model inference. Before delving into how GPU extensions work in the AO network, let's briefly review how typical AI applications operate and the composition of the AO ne...
Apus NetworkGetting Started with HyperBEAM: Building a Custom Device for Beginners
AbstractThis guide introduces developers to HyperBEAM's distributed computing framework through hands-on device extension. Learn how to leverage Erlang/OTP architecture and the Converge Protocol to create custom devices. Beginners will gain practical experience through a calculator device demo, understanding NIFs (Native Implemented Functions) and WASM port communication patterns.ChaptersIntroduction to HyperBEAMConverge Protocol : the root of device call logic and pathBuilding a Simple ...
Apus NetworkThe Future Is Deterministic: HyperBeam Architecture and the Importance of Hashpaths in AO
1. IntroductionAs decentralized computation evolves, HyperBeam emerges as a powerful client implementation of the AO-Core protocol, enabling distributed computation in a modular and verifiable way. By abstracting hardware resources and standardizing computation through devices, HyperBeam allows a wide range of computational models to operate seamlessly within the AO ecosystem. At the core of this system lies the concept of Hashpaths, which serve as unique identifiers for computational state a...
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Apus Network, leveraging the innovative AO computing environment, is reshaping the blockchain landscape by enabling Fully Autonomous On-chain AI Agents. By combining AO with Deterministic GPU computation, Apus Network provides decentralized AI systems with unprecedented transparency, scalability, and trustless operation. At the heart of this system lies the On-chain Memory Manager, which uses On-chain Retrieval-Augmented Generation (RAG) to manage and update AI agent memories in real time. This article delves into how to evaluate On-chain RAG effectively, emphasizing its critical role in the broader Web3 ecosystem.
The On-chain RAG system, embedded in the AO framework, is central to advancing decentralized intelligence. It ensures:
Transparent Memory Updates: All updates are recorded immutably on-chain, guaranteeing full auditability.
Verifiability: AI decisions are traceable to their original data sources.
Privacy: Sensitive data is safeguarded using cryptographic techniques and hardware-based protections like Trusted Execution Environments (TEEs).
Effective evaluation of On-chain RAG systems is vital to maintain and enhance their performance across the following dimensions:
Retrieval Accuracy: Does the system locate relevant and precise information?
Generation Quality: Are the responses accurate, coherent, and contextually appropriate?
System Performance: Does the system achieve transparency, scalability, and low latency in a decentralized setting?
Definition: Measures how well the generated response aligns with the retrieved context.
Evaluation Approach:
Extract key statements from the generated response.
Verify each statement against the retrieved context using AI-assisted methods or manual checks.
Metric: Proportion of statements supported by the retrieved context (e.g., Faithfulness Score).
Definition: Assesses whether the generated response directly addresses the input query.
Evaluation Approach:
Generate potential questions based on the response.
Measure semantic similarity between these questions and the original query using vector embeddings (e.g., cosine similarity).
Metric: Average similarity score (e.g., Answer Relevance Score).
Definition: Evaluates whether the retrieved context contains only relevant information for answering the query.
Evaluation Approach:
Use language models to extract sentences crucial for answering the query.
Compare the extracted sentences with the total retrieved context.
Metric: Ratio of relevant sentences to total sentences in the retrieved context.
Definition: Measures how quickly and effectively the system retrieves relevant context.
Evaluation Metrics:
Precision@K: Proportion of relevant documents in the top-K retrieved results.
Recall@K: Proportion of all relevant documents retrieved in the top-K results.
Latency: Time taken to retrieve and process the context.
Definition: Assesses whether the response is linguistically and logically coherent.
Evaluation Metrics:
BLEU, ROUGE, and BERTScore for linguistic quality.
Manual evaluation for logical consistency.
Definition: Ensures that all decisions and memory updates can be traced and audited.
Evaluation Metrics:
Completeness of on-chain audit logs.
Ease of verifying decision-making processes using blockchain explorers.
Automated Frameworks: Leverage tools like RAGAS (Retrieval-Augmented Generation Assessment), which provide metrics for evaluating RAG pipelines without reference answers. RAGAS focuses on faithfulness, answer relevance, and context relevance, making it an ideal choice for On-chain RAG systems.
Benchmarking: Use datasets like WikiEval for cross-comparison of On-chain RAG systems with other implementations.
A/B Testing: Compare different versions of the On-chain Memory Manager to identify areas for optimization.
Simulated Stress Testing: Evaluate the system under various network conditions and memory sizes to ensure scalability and reliability.
Hallucination in Responses: Ensure faithfulness by integrating stricter verification checks.
Latency in On-chain Retrieval: Use optimized retrieval algorithms and deterministic GPU computation to reduce delays.
Long Context Handling: Summarize retrieved context dynamically to fit within model input limits without losing critical information.
On-chain RAG systems are not just technical innovations—they represent a leap forward for Web3 ecosystems. By enhancing transparency, scalability, and privacy, On-chain RAG directly addresses the limitations of centralized systems. Built on AO and Arweave, these systems set a new standard for decentralized computation, enabling:
Trustless AI Collaboration: Reliable interactions between decentralized agents.
Enhanced Ecosystem Interoperability: Modular design ensures seamless integration with other Web3 protocols.
Scalable Solutions: Efficient memory and computation management makes large-scale AI applications viable in Web3.
Evaluating On-chain RAG systems is a multidimensional challenge that requires robust metrics and methodologies. By focusing on faithfulness, relevance, efficiency, and transparency, developers can ensure that the Apus Network’s On-chain Memory Manager, powered by AO’s unified computing environment, meets the demands of decentralized AI agents. Leveraging tools like RAGAS and best practices in benchmarking will drive continuous improvements, paving the way for more powerful and reliable autonomous on-chain agents.
[1] RAGAS: Automated Evaluation of Retrieval Augmented Generation
Apus Network, leveraging the innovative AO computing environment, is reshaping the blockchain landscape by enabling Fully Autonomous On-chain AI Agents. By combining AO with Deterministic GPU computation, Apus Network provides decentralized AI systems with unprecedented transparency, scalability, and trustless operation. At the heart of this system lies the On-chain Memory Manager, which uses On-chain Retrieval-Augmented Generation (RAG) to manage and update AI agent memories in real time. This article delves into how to evaluate On-chain RAG effectively, emphasizing its critical role in the broader Web3 ecosystem.
The On-chain RAG system, embedded in the AO framework, is central to advancing decentralized intelligence. It ensures:
Transparent Memory Updates: All updates are recorded immutably on-chain, guaranteeing full auditability.
Verifiability: AI decisions are traceable to their original data sources.
Privacy: Sensitive data is safeguarded using cryptographic techniques and hardware-based protections like Trusted Execution Environments (TEEs).
Effective evaluation of On-chain RAG systems is vital to maintain and enhance their performance across the following dimensions:
Retrieval Accuracy: Does the system locate relevant and precise information?
Generation Quality: Are the responses accurate, coherent, and contextually appropriate?
System Performance: Does the system achieve transparency, scalability, and low latency in a decentralized setting?
Definition: Measures how well the generated response aligns with the retrieved context.
Evaluation Approach:
Extract key statements from the generated response.
Verify each statement against the retrieved context using AI-assisted methods or manual checks.
Metric: Proportion of statements supported by the retrieved context (e.g., Faithfulness Score).
Definition: Assesses whether the generated response directly addresses the input query.
Evaluation Approach:
Generate potential questions based on the response.
Measure semantic similarity between these questions and the original query using vector embeddings (e.g., cosine similarity).
Metric: Average similarity score (e.g., Answer Relevance Score).
Definition: Evaluates whether the retrieved context contains only relevant information for answering the query.
Evaluation Approach:
Use language models to extract sentences crucial for answering the query.
Compare the extracted sentences with the total retrieved context.
Metric: Ratio of relevant sentences to total sentences in the retrieved context.
Definition: Measures how quickly and effectively the system retrieves relevant context.
Evaluation Metrics:
Precision@K: Proportion of relevant documents in the top-K retrieved results.
Recall@K: Proportion of all relevant documents retrieved in the top-K results.
Latency: Time taken to retrieve and process the context.
Definition: Assesses whether the response is linguistically and logically coherent.
Evaluation Metrics:
BLEU, ROUGE, and BERTScore for linguistic quality.
Manual evaluation for logical consistency.
Definition: Ensures that all decisions and memory updates can be traced and audited.
Evaluation Metrics:
Completeness of on-chain audit logs.
Ease of verifying decision-making processes using blockchain explorers.
Automated Frameworks: Leverage tools like RAGAS (Retrieval-Augmented Generation Assessment), which provide metrics for evaluating RAG pipelines without reference answers. RAGAS focuses on faithfulness, answer relevance, and context relevance, making it an ideal choice for On-chain RAG systems.
Benchmarking: Use datasets like WikiEval for cross-comparison of On-chain RAG systems with other implementations.
A/B Testing: Compare different versions of the On-chain Memory Manager to identify areas for optimization.
Simulated Stress Testing: Evaluate the system under various network conditions and memory sizes to ensure scalability and reliability.
Hallucination in Responses: Ensure faithfulness by integrating stricter verification checks.
Latency in On-chain Retrieval: Use optimized retrieval algorithms and deterministic GPU computation to reduce delays.
Long Context Handling: Summarize retrieved context dynamically to fit within model input limits without losing critical information.
On-chain RAG systems are not just technical innovations—they represent a leap forward for Web3 ecosystems. By enhancing transparency, scalability, and privacy, On-chain RAG directly addresses the limitations of centralized systems. Built on AO and Arweave, these systems set a new standard for decentralized computation, enabling:
Trustless AI Collaboration: Reliable interactions between decentralized agents.
Enhanced Ecosystem Interoperability: Modular design ensures seamless integration with other Web3 protocols.
Scalable Solutions: Efficient memory and computation management makes large-scale AI applications viable in Web3.
Evaluating On-chain RAG systems is a multidimensional challenge that requires robust metrics and methodologies. By focusing on faithfulness, relevance, efficiency, and transparency, developers can ensure that the Apus Network’s On-chain Memory Manager, powered by AO’s unified computing environment, meets the demands of decentralized AI agents. Leveraging tools like RAGAS and best practices in benchmarking will drive continuous improvements, paving the way for more powerful and reliable autonomous on-chain agents.
[1] RAGAS: Automated Evaluation of Retrieval Augmented Generation
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