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Orchestration

CrewAI and Adapt.AI 

LangGraph

Unique Features Comparison:

• LangGraph: Offers customizable multi-agent architectures and explicit control over agent communication, providing flexibility in system design.

• Amazon Bedrock: Provides managed orchestration with supervisor agents (minor customization), optimized collaboration modes, and integrated debugging tools, streamlining the development and management of multi-agent systems. Good for Unified experience of end user 

In summary, LangGraph excels in offering flexible, customizable architectures with explicit communication control, suitable for developers seeking tailored solutions. In contrast, Amazon Bedrock focuses on managed orchestration with built-in tools for efficient development and management of multi-agent systems.

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Evaluation

https://www.amazon.science/publications/towards-effective-genai-multi-agent-collaboration-design-and-evaluation-for-enterprise-applications

Ora

graph TD

    A[Start] --> B{Is centralized control needed?}

    B -->|Yes| C[Supervisor Architecture<br><i>Use Case:</i> Customer service platform with a central agent routing inquiries to specialized agents.]

    B -->|No| D{Do agents have equal roles and direct communication?}

    D -->|Yes| E[Network Architecture<br><i>Use Case:</i> Decentralized sensor network with peer-to-peer data sharing.]

    D -->|No| F{Is there a clear task hierarchy?}

    F -->|Yes| G[Hierarchical Architecture<br><i>Use Case:</i> Manufacturing system with layered task delegation.]

    F -->|No| H[Custom Architecture<br><i>Use Case:</i> Financial trading platform with specialized, non-linear agent interactions.]

Big selling for Bedrock

Big Negative


Evaluating multi-agent orchestration involves several critical factors to ensure effective collaboration and task execution:

• Coordination Mechanisms: Assess how agents communicate and synchronize their actions to achieve shared objectives.

• Task Allocation Strategies: Determine the methods for assigning tasks to agents based on their capabilities and current workloads.

• Scalability: Evaluate the system’s ability to maintain performance as the number of agents or tasks increases.

• Fault Tolerance: Consider the system’s resilience to agent failures and its capacity to continue operations seamlessly.

• Performance Metrics: Measure throughput, latency, and resource utilization to ensure efficiency.

• Adaptability: Examine the system’s flexibility in responding to dynamic environments and evolving requirements.

• Security and Trust: Ensure secure communication channels and establish trust among agents to prevent malicious activities.

• Interoperability: Verify that agents can effectively interact within heterogeneous environments, including various platforms and protocols.


The document discusses multi-agent systems, which are frameworks that utilize multiple independent agents—each potentially powered by a large language model (LLM)—to enhance application performance and manage complexity. Here’s a summary of the key concepts:

https://langchain-ai.github.io/langgraphjs/concepts/multi_agent/

Yes, Amazon Bedrock now supports multi-agent collaboration through a feature called "Bedrock Agents." This allows developers to build and deploy applications that leverage the power of multiple AI agents working together.

In a multi-agent system, coordination and orchestration are vital to ensure that the agents work together smoothly and effectively. Think of it like a conductor leading an orchestra. The conductor doesn't play an instrument but guides the musicians, ensuring they play in harmony and achieve the desired musical outcome.

In Bedrock Agents, this role is often played by a supervisor agent. This agent acts as the central coordinator, responsible for:

https://aws.amazon.com/blogs/aws/introducing-multi-agent-collaboration-capability-for-amazon-bedrock/

https://github.com/awslabs/amazon-bedrock-agent-samples

https://awslabs.github.io/multi-agent-orchestrator/general/how-it-works/

https://docs.aws.amazon.com/bedrock/latest/userguide/agents-how.html

https://aws.amazon.com/blogs/machine-learning/getting-started-with-amazon-bedrock-agents-custom-orchestrator/

https://youtu.be/qqlB2jO6Ccg

https://awslabs.github.io/multi-agent-orchestrator/cookbook/examples/chat-demo-app/

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airSlate offers a comprehensive suite of tools designed to automate and streamline document workflows, including e-signatures, document generation, contract management, and web forms. Integrating multi-agent AI workflows can further enhance these capabilities by enabling autonomous agents to collaborate on complex tasks, thereby increasing efficiency and reducing manual intervention.

1. Automated Contract Lifecycle Management

Scenario: Managing contracts involves multiple stages such as drafting, reviewing, negotiating, and signing, often requiring coordination among various departments.

Multi-Agent Workflow:

• Drafting Agent: Utilizes predefined templates to generate initial contract drafts based on input parameters.

• Review Agent: Analyzes the draft for compliance with company policies and legal standards.

• Negotiation Agent: Engages with the counterparty’s AI agents to negotiate terms within predefined boundaries.

• Approval Agent: Routes the finalized contract to the appropriate stakeholders for approval.

• E-Signature Agent: Facilitates the collection of electronic signatures from all parties.

Benefits: This multi-agent system accelerates the contract lifecycle, ensures compliance, and minimizes human errors.

2. Intelligent Document Generation and Routing

Scenario: Creating personalized documents like proposals or invoices requires data from various sources and must be sent to the correct recipients in a specific order.

Multi-Agent Workflow:

• Data Aggregation Agent: Gathers necessary information from CRMs, databases, and spreadsheets.

• Document Generation Agent: Populates templates with the aggregated data to create customized documents.

• Validation Agent: Checks the documents for accuracy and completeness.

• Routing Agent: Determines the appropriate recipients and sends the documents in the correct sequence.

• Notification Agent: Sends alerts and reminders to stakeholders about pending actions.

Benefits: Automating this process reduces turnaround times and ensures that documents are accurate and delivered promptly.

3. Automated Employee Onboarding

Scenario: Onboarding new employees involves collecting personal information, setting up accounts, and ensuring compliance with company policies.

Multi-Agent Workflow:

• Information Collection Agent: Sends web forms to new hires to gather necessary personal and professional details.

• Account Setup Agent: Creates user accounts and grants access to required systems and tools.

• Training Agent: Assigns mandatory training modules and tracks completion.

• Compliance Agent: Ensures all legal and company-specific documents are signed and stored appropriately.

• Feedback Agent: Collects feedback from new employees to improve the onboarding process.

Benefits: This workflow ensures a smooth onboarding experience, maintains compliance, and allows HR to focus on strategic tasks.

4. Customer Support Ticket Processing

Scenario: Efficiently handling customer support tickets requires categorizing issues, assigning them to the right agents, and ensuring timely resolution.

Multi-Agent Workflow:

• Ticket Classification Agent: Analyzes incoming tickets and categorizes them based on issue type and priority.

• Assignment Agent: Routes tickets to the appropriate support personnel or departments.

• Resolution Agent: Provides automated responses for common issues or gathers necessary information for complex problems.

• Escalation Agent: Monitors ticket status and escalates unresolved issues according to predefined SLAs.

• Feedback Agent: Sends satisfaction surveys to customers after ticket resolution.

Benefits: This system enhances response times, ensures issues are addressed by the right personnel, and improves customer satisfaction.

By implementing multi-agent AI workflows, airSlate can elevate its automation capabilities, providing more efficient, accurate, and responsive solutions to complex business processes.


1. “A bustling office setting where employees enjoy a diverse spread of catered meals, highlighting the convenience and variety offered by [Catering Platform].”

2. “A seamless sequence showing a user effortlessly placing a catering order through [Catering Platform]’s user-friendly interface, followed by timely delivery and a satisfied team enjoying the meal.”

3. “A vibrant montage of different cuisines and dishes available through [Catering Platform], emphasizing the platform’s extensive menu options for various dietary needs.”

4. “A time-lapse of a corporate event being set up, with [Catering Platform]’s catering services providing elegant and appetizing food arrangements, impressing attendees.”

5. “A split-screen comparison of traditional catering ordering methods versus the streamlined, efficient process offered by [Catering Platform], showcasing time and effort saved.”

6. “A day-in-the-life of a busy office manager who relies on [Catering Platform] to organize meals for meetings, highlighting the platform’s reliability and ease of use.”

7. “A testimonial compilation from satisfied clients praising [Catering Platform]’s exceptional service, punctual deliveries, and quality of food.”

8. “An animated journey of a catering order from placement to delivery, illustrating [Catering Platform]’s seamless logistics and customer support.”

9. “A showcase of [Catering Platform]’s mobile app features, demonstrating how users can track orders, earn rewards, and manage catering needs on-the-go.”

10. “A festive office celebration catered by [Catering Platform], highlighting how the service enhances corporate events and boosts employee morale.”


Key Points Summary from the Transcript:

 

Opening Remarks

 

        •        CEO’s First Event: Emphasized the importance of AWS re:Invent as a learning platform for builders and developers.

        •        Scale of re:Invent 2023: Attendance of 60,000 in-person participants, 400,000 online viewers, with 1,900 sessions and 3,500 speakers.

 

AWS Community and Startups

 

        •        Global Community Growth: 600 user groups in 120 countries, contributing to product innovation.

        •        Startups: AWS’s foundational customer base; startups play a pivotal role in technology adoption and disruption.

        •        Commitment to Startups: AWS will offer $1 billion in credits for startups globally by 2025.

 

Generative AI and Innovation

 

        •        Impact of Generative AI: A disruptive force across industries, with startups leading the way.

        •        Compute Advancements: New EC2 offerings like P6 instances featuring NVIDIA Blackwell GPUs.

        •        Custom Silicon: AWS’s Graviton chips deliver significant cost and energy efficiencies; Trainium chips offer specialized performance for generative AI.

        •        AI Model Support: Expansion of Bedrock’s capabilities to manage and optimize AI models, including automated reasoning and multi-agent collaboration.

 

Security

 

        •        Top Priority: Security is built into every layer of AWS services, from data centers to software development practices.

        •        Automated Reasoning Checks: A unique feature to validate factual correctness in generative AI applications.

 

Core Building Blocks: Compute and Storage

 

        •        EC2 Innovations: Over 850 instance types tailored for diverse workloads.

        •        S3 Developments: New features like S3 Table Buckets and S3 Metadata for enhanced analytics and cost efficiency.

 

Databases

 

        •        Relational Databases: Aurora’s advancements, including multi-region consistency with Aurora DSQL.

        •        NoSQL Expansion: DynamoDB now supports multi-region strong consistency and low-latency access.

 

Customer Success Stories

 

        •        JPMorgan Chase: Modernized with AWS tools like Graviton, Sagemaker, and Bedrock for resilience and scalability.

        •        Genentech: Using Bedrock to accelerate drug discovery, automating years of manual effort into minutes.

 

Bedrock Features

 

        •        Comprehensive AI Support: Tools for model distillation, retrieval-augmented generation (RAG), and multi-agent collaboration.

        •        Guardrails for Safety: Responsible AI implementation with precision and transparency.

        •        Future-Ready: Announced Trainium 3 with twice the compute of its predecessor.

 

AWS Vision

 

        •        Customer-Centric Innovation: AWS’s “working backward” approach focuses on delivering exceptional products informed by customer needs.

        •        Sustainable and Scalable Growth: Continued investment in security, performance, and next-gen computing capabilities.

 

Conclusion

 

        •        AWS is at the forefront of enabling global enterprises, startups, and developers to innovate through foundational building blocks and cutting-edge technologies like generative AI and custom silicon.

 

 

 

Key Points Summary:

 

Amazon’s AI and Generative AI Strategy:

 

        1.        AI for Customer-Centric Solutions:

        •        AI is used across Amazon to address customer problems, such as personalized recommendations, fulfillment center optimization, drone delivery, and Just Walk Out technology.

        •        Generative AI is applied in practical, impactful ways.

        2.        AI Impact Areas:

        •        Cost avoidance and productivity improvements.

        •        Reimagined customer experiences (e.g., improved customer service chatbot with Generative AI).

        3.        Examples of AI Implementations:

        •        Customer Service Chatbot: Predictive, efficient, with 500-basis-point customer satisfaction improvement.

        •        Seller Tools: Simplified onboarding using Generative AI, benefiting 500,000+ sellers.

        •        Inventory Management: Improved forecasting accuracy with Transformer models (10-20% improvements).

        •        Robotics (e.g., Sparrow): Advanced sorting for fulfillment centers, achieving 25% faster processing and lower costs.

        •        Shopping Experience (e.g., Rufus, Alexa): AI-driven shopping agents and enhanced personalization.

        •        Prime Video Features: AI-driven next-gen stats for NFL games, enhancing fan engagement.

 

AWS AI and Frontier Model Innovations:

 

        4.        Amazon Nova Models:

        •        Four models for text and multimodal tasks (text, images, video).

        •        Outperform competitors (e.g., OpenAI, Google Gemini) with cost-efficiency (75% cheaper) and low latency.

        •        New capabilities for image (Canvas) and video (Reel) generation.

        5.        Future AI Developments:

        •        Speech-to-speech and multimodal “any-to-any” models launching in 2024.

        •        Integration of Generative AI tools across applications with an emphasis on customization.

 

Developer and Operations Enhancements:

 

        6.        Q Developer:

        •        Expanded from a coding assistant to cover testing, documentation, and code reviews.

        •        Integrated with platforms like GitLab.

        •        Introduced transformation tools for Windows and VMware modernization.

        7.        PagerDuty Integration:

        •        Combined AWS and PagerDuty for seamless incident management and diagnostics using Generative AI.

        8.        Modernization Tools:

        •        New Q agents to help modernize Windows, VMware, and mainframe applications.

 

Enterprise Solutions and Analytics:

 

        9.        Q Business for Internal Data:

        •        Unified access to enterprise data from tools like Salesforce, SharePoint, and custom databases.

        •        APIs for ISVs to integrate and enhance application value using Q-indexed data.

        10.        Enhanced SageMaker:

        •        New unified studio integrates AI, analytics, and data management capabilities.

        •        Supports seamless zero-ETL access to third-party applications and data lakes.

 

Announcements of Note:

 

        11.        Zero-ETL Innovations:

        •        Simplified data access across diverse sources.

        •        SageMaker Lakehouse for unified data querying and analysis.

        12.        SageMaker Unified Studio:

        •        Consolidates tools for data scientists and analysts to collaborate on AI and analytics projects.

        13.        Q Business Automate:

        •        Automates complex workflows across multiple applications with advanced agents.

 

Call to Action:

 

        •        AWS aims to empower customers with cutting-edge tools, fostering innovation and productivity.

        •        Encourages developers, businesses, and partners to explore and leverage AWS capabilities during re:Invent 2024.


Tuesday, December 3, 2024

6:45 AM

Key Points from the AWS re:Invent 2024 Monday Night Live Presentation:

 

Introduction by Peter DeSantis

 

        •        Welcomed attendees to re:Invent 2024 and outlined the evening’s focus on deep technical innovations.

        •        Discussed AWS’s unique approach to designing services, emphasizing foundational mechanisms for security, performance, and reliability.

        •        Shared an analogy using trees to describe AWS’s technical investments:

        •        Roots: Foundational mechanisms (e.g., leadership in details and weekly AWS-wide operations meetings).

        •        Trunk: Core innovations like Nitro, custom silicon, and databases.

        •        Canopy: The results visible to customers (e.g., high performance and security).

 

Innovations in Compute and Networking (Dave Brown)

 

        Graviton Processors:

        •        Evolution from Graviton1 to Graviton4, focusing on real-world workload performance.

        •        Graviton4 boasts multi-socket support, increased vCPU count, and better performance for intensive workloads like machine learning and analytics.

        AWS Nitro System:

        •        Revolutionized server architecture for enhanced security and scalability.

        •        Enabled cryptographic attestation for server integrity.

        Storage Innovations:

        •        Transitioned from monolithic architectures to disaggregated storage for flexibility and resilience.

        •        Introduced Nitro cards for storage systems, reducing operational complexity and improving recovery speeds.

 

AI Infrastructure Innovations (Peter DeSantis)

 

        AI Training Challenges:

        •        Discussed the “scale-up” need for more powerful servers and the “scale-out” requirement for vast interconnected clusters.

        •        Trained AI models are becoming exponentially larger due to scaling laws.

        Trainium2:

        •        AWS’s next-gen AI chip designed for both training and inference workloads.

        •        Features include high-performance systolic array architecture and interconnect technology (Neuron Link) for building “Ultra Servers.”

        •        Trainium2-powered infrastructure allows faster, cost-effective training of trillion-parameter models.

 

Collaboration with Anthropic

 

        •        Announced “Project Rainier,” a new cluster with hundreds of thousands of Trainium2 chips.

        •        Partnered with Anthropic to optimize AI model training and inference, including a latency-optimized version of their Claude 3.5 model.

 

Networking and Scale-Out Innovations

 

        TNP10 Network:

        •        Custom-built AI-optimized network fabric with massive capacity and low latency.

        •        Features innovations like trunk connectors and Firefly optic plugs for efficient, reliable deployment.

        Scalable Intent-Driven Routing (CIDR):

        •        AWS’s proprietary protocol combines centralized optimization with decentralized speed, responding to failures within a second.

 

Conclusion

 

        •        AWS is innovating across the stack—from silicon to networking—to deliver unparalleled performance, security, and cost efficiency.

        •        Customers benefit from these advancements in their workloads, whether AI, storage, or compute.

 

Closing

 

        •        The event ended with an invitation to explore re:Invent 2024, celebrating the depth and breadth of AWS innovations.

 

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1. Graviton

        •        What is it? A general-purpose processor.

        •        Purpose: Designed for running everyday cloud workloads like web servers, databases, and containers efficiently.

        •        Key Feature: Built to provide high performance and cost savings for common cloud applications.

        •        Unique Focus: Optimized for scale-out workloads like handling lots of simultaneous smaller tasks.

2. Trainium

        •        What is it? A specialized AI processor for training machine learning models.

        •        Purpose: Designed to handle the heavy lifting when teaching AI systems how to think and make predictions.

        •        Key Feature: Optimized for training large AI models quickly and cost-effectively.

        •        Unique Focus: Scale-up workloads for building cutting-edge AI models with trillions of parameters.

3. Inferentia

        •        What is it? A specialized AI processor for inference tasks.

        •        Purpose: Focuses on running already-trained AI models to make predictions or generate responses (e.g., chatbot answers or image recognition).

        •        Key Feature: Optimized for delivering low-cost, high-speed results when AI models are in use.

        •        Unique Focus: AI inference workloads, such as serving predictions or generating outputs in real-time

4. Nitro

        •        What is it? A hardware and software system that powers the underlying infrastructure of AWS servers.

        •        Purpose: Provides the foundation for security, performance, and scalability in AWS instances (e.g., EC2).

        •        Key Feature: Offloads tasks like virtualization and security from the main processor, enabling better performance.

        •        Unique Focus: Infrastructure optimization and enhanced server performance for all AWS workloads.

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When choosing between AI training, AI inference, and general-purpose computing, the decision depends on the unique requirements of each workload. Here’s a comparison of their unique requirements and why you’d choose one over the other:

1. AI Training

        •        Unique Requirement:

        •        Massive computational power to process large datasets and optimize models with trillions of parameters.

        •        High-speed memory and parallelism for handling complex matrix calculations efficiently.

        •        Scalability to train models across thousands of GPUs or specialized processors.

        •        Why Choose This?

        •        You’re building or refining AI models, such as training a chatbot, image recognition system, or language model.

        •        Needs specialized processors like Trainium to handle the intense and repetitive math calculations in deep learning.

2. AI Inference

        •        Unique Requirement:

        •        Low-latency performance to provide quick predictions or outputs.

        •        Efficient use of compute and memory to handle model predictions without needing as much parallelism as training.

        •        Ability to scale for real-time usage (e.g., serving millions of API requests per second).

        •        Why Choose This?

        •        You’re running trained AI models to serve predictions, such as generating responses in a chatbot, identifying objects in images, or detecting fraud in transactions.

        •        Needs specialized processors like Inferentia to optimize for speed and cost when running AI models in production.

3. General-Purpose Computing

        •        Unique Requirement:

        •        Flexibility to handle a wide range of workloads (e.g., databases, web servers, analytics).

        •        Balanced performance and cost without needing the extreme specialization of AI-focused tasks.

        •        Compatibility with diverse software stacks and programming environments.

        •        Why Choose This?

        •        You’re managing common IT workloads like hosting websites, running containerized applications, or processing standard business applications.

        •        Needs general-purpose processors like Graviton, which are cost-effective and energy-efficient for non-AI workloads.

Key Differences in Selection

 

 

 

 

 

 

 

Aspect

AI Training

AI Inference

General-Purpose Computing

Compute Power

Extremely high for parallel processing

Moderate for real-time processing

Balanced for diverse workloads

Memory Requirements

High-speed memory for large datasets

Efficient memory for model predictions

Standard memory for broad use cases

Latency Sensitivity

Low sensitivity; tolerates longer runtimes

Very sensitive; real-time responses needed

Moderate sensitivity

Scale

Scale-out for massive parallel clusters

Scales horizontally for serving predictions

Scales moderately with standard workloads

Cost Sensitivity

Secondary to performance

High priority for cost-efficiency

Balanced between cost and performance

Yes, AWS offers several other processors besides Graviton, Trainium, and Inferentia. Each is optimized for specific use cases and workloads. Here's an overview of these additional processors and what they are good for:

1. Intel Xeon Processors (x86-based)

2. AMD EPYC Processors (x86-based)

3. NVIDIA GPUs

4. Habana Gaudi Processors

5. AWS Nitro System (Security Co-Processor)

6. FPGAs (Field-Programmable Gate Arrays)

7. AWS Inferentia 2 (Upcoming or Enhanced Inferentia)

Comparison of Additional Processors

Processor

Architecture

Best For

Examples of Use Cases

Intel Xeon

x86

Legacy and enterprise apps

Databases, ERP systems

AMD EPYC

x86

Cost-effective general-purpose workloads

Big data, web hosting

NVIDIA GPUs

GPU

AI/ML training, rendering, HPC

Deep learning, video rendering, scientific HPC

Habana Gaudi

Custom AI

Cost-effective AI training

Deep learning frameworks

Nitro System

Custom ASIC

Security and virtualization

EC2 infrastructure, data encryption

FPGAs

Customizable HW

Specialized hardware acceleration

Genomics, video processing, trading

How to Choose?

Processor-Specific Programming Language/Framework Support

Processor

Supported Languages/Frameworks

Use Cases

Incompatibilities

Graviton

Java, Python, Go, Node.js, Rust, C, C++ (with recompilation), PHP, Ruby

General-purpose workloads

Legacy applications tied to x86; some older binary-only software.

Trainium

Python (TensorFlow, PyTorch, MXNet)

AI/ML training

Non-ML applications; requires frameworks optimized for Trainium.

Inferentia

Python (TensorFlow, PyTorch, ONNX Runtime)

AI inference

Training or general-purpose workloads.

Intel/AMD

All major languages, including C, C++, Java, Python, .NET, PHP

Enterprise applications, HPC

No specific limitations but higher cost for workloads better suited to Graviton or AI-specific processors.

NVIDIA GPUs

Python (TensorFlow, PyTorch, CUDA), C++ (CUDA, OpenCL)

AI, graphics, HPC

Workloads not benefiting from parallel processing (e.g., small-scale databases).

Habana Gaudi

Python (TensorFlow, PyTorch)

Cost-effective AI training

Inference or general-purpose applications.

FPGAs

VHDL, Verilog (for hardware design); APIs in Python or C

Custom hardware acceleration

Requires hardware programming expertise; unsuitable for general-purpose or high-level AI tasks.

Quick Decision Guide


Act like document reviewer. Summarize  the main points of the current page in simple language . Highlight ( Font bold and red) who , why, when , statistics and how part of summarization.At the end explain the key concept in simple language 


https://arxiv.org/abs/2107.04767

https://ieeexplore.ieee.org/document/10612615

https://community.aws/content/2Z4yo9liXdKl2eoUYexiLvzFCZY

https://aws.amazon.com/blogs/architecture/choosing-your-vpc-endpoint-strategy-for-amazon-s3/

https://docs.aws.amazon.com/en_cn/vpc/latest/userguide/endpoint-services-overview.html

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Here are the key soft and hard service quota limits for Amazon SNS and SQS:

Amazon SNS

Hard Limits

Soft Limits (can be increased)

Amazon SQS

Hard Limits

Soft Limits (can be increased)

It's important to note that some of these limits, especially the soft limits, can vary by AWS region and account status. AWS recommends using Service Quotas to view your account-specific quotas and request increases when needed. You can also set up CloudWatch alarms to notify you when you're approaching quota limits.

 

Citations:

[1] https://docs.aws.amazon.com/general/latest/gr/sns.html

[2] https://docs.aws.amazon.com/general/latest/gr/sqs-service.html

[3] https://aws.amazon.com/about-aws/whats-new/2022/11/amazon-sns-enhanced-integration-aws-service-quotas/

[4] https://docs.aws.amazon.com/servicequotas/latest/userguide/configure-cloudwatch.html

[5] https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/quotas-fifo.html

[6] https://www.bluematador.com/docs/troubleshooting/aws-sqs-limits

[7] https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/quotas-queues.html

[8] https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/sqs-quotas.html

 

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A proof of delivery (POD) is a document that verifies that a carrier has fulfilled their contractual obligations for a shipment by confirming with the recipient. It's a receipt that shows that the goods were delivered to the correct person at the right location

 

https://www.stampli.com/blog/invoice-management/vendor-invoice-management-workflow/

 


Proof of Delivery (POD) documents are essential in verifying that goods or services have been delivered as agreed upon. However, several potential issues can arise with PODs that may complicate or delay processing, especially when manual adjustments or errors occur. Here are some common challenges:

1. Manual Adjustments and Handwritten Notes:

• Illegibility: Handwritten changes, such as quantity adjustments, customer notes, or signatures, may be difficult to read, leading to misinterpretation.

• Ambiguity: Manual notes often lack clarity or standardized formatting, which makes automated processing difficult and introduces the risk of errors.

• Unstructured Data: Manual adjustments can include corrections to delivery dates, conditions, or other variables that aren’t easily captured by standard OCR technology.

2. Missing or Incomplete Information:

• Signatures: Lack of a signature or incomplete signature details may cause delays in confirming the delivery.

• Unrecorded Delivery Time: Missing timestamps or incomplete date/time data can lead to disputes over when the goods were delivered.

• Item Discrepancies: Sometimes, the delivered items don’t match the original order in terms of quantity, type, or condition. If this is not noted clearly on the POD, it can result in disputes during settlement.

3. Damaged or Poor-Quality Documents:

• Physical Damage: PODs that are torn, smudged, or have faded ink are hard to process, especially when digitizing them.

• Blurry Scans or Photos: Poor quality scans or photographs can lead to incomplete or inaccurate data capture during the digital processing phase.

4. Delayed or Missing POD Submission:

• Lost PODs: Physical PODs can easily be lost or misplaced, delaying settlements and potentially leading to disputes.

• Delayed Submission: If drivers or delivery personnel don’t submit PODs promptly, it delays the invoicing and settlement process.

• Inconsistent Submission Formats: Different submission formats (paper, digital, photo, etc.) can create inconsistencies in how PODs are processed.

5. Fraud or Tampering:

• Altered Information: Manual tampering with the POD (e.g., changing delivery dates or quantities) can lead to fraud and incorrect settlements.

• Forged Signatures: A customer or recipient’s signature might be forged, leading to disputes over whether the delivery was actually accepted.

6. Data Entry Errors:

• Manual Data Entry Mistakes: When POD information is manually inputted into a system, there’s a high risk of human error, especially with complex or poorly structured documents.

• Inconsistent Data Fields: Some PODs may lack standardization, making it difficult to consistently extract key information such as item codes, delivery addresses, or special instructions.

7. Non-Compliance with Contractual Terms:

• Delivery Conditions Not Met: PODs might not capture deviations from agreed terms, such as delivery outside the agreed time window or goods being delivered in unacceptable condition, which can lead to delays in settlement or disputes with customers.

• Inaccurate Documentation of Issues: If issues like damages or shortages are not documented clearly on the POD, it could result in a delayed or incorrect claims process.

8. Multiple Stakeholders Involved:

• Conflicting Versions: Multiple parties (e.g., carrier, shipper, and customer) may have conflicting versions of a POD, leading to disputes over the exact details of the delivery.

• Lack of Communication: Inefficient communication between stakeholders may cause delays in resolving discrepancies or filing claims based on POD data.

9. Environmental Factors:

• Harsh Conditions: PODs, especially paper forms, can be damaged by environmental factors (rain, dirt, wear and tear from travel), making them unreadable or illegible.

• Geographical Challenges: In remote locations, connectivity issues may delay the submission of digital PODs or lead to incomplete document uploads.

10. Discrepancies in Expected vs. Actual Delivery:

• Mismatch in Delivery Items: Delivered goods may not match the order in terms of quality or quantity, and if this isn’t captured on the POD, it could lead to disputes during settlement.

• Partial Deliveries: Sometimes, only part of an order is delivered, and if this is not clearly noted on the POD, it could result in billing inaccuracies.

Conclusion:

-------------------------








https://whimsical.com/guidance-for-choosing-aws-services-textract-rekognition-or-bedro-A1w4PYYM9tMRqFWUoXsz4K

https://dashboard.scale.com/nucleus/


https://www.labellerr.com/blog/video-annotation-services/





To create an AWS Lambda function that accepts JSON data, validates required fields, stores the data in an Amazon RDS database, and returns a success or failure message, follow the steps below:

 

### Step 1: Set Up the RDS Database

First, ensure that your RDS database is properly set up and that you have the necessary connection details (endpoint, username, password, database name, etc.).

 

The database table should be created with the following SQL:

 

```sql

CREATE TABLE Invoices (

storeId VARCHAR(255) NOT NULL,

invoiceDate DATE NOT NULL,

TotalAmount DECIMAL(10, 2) NOT NULL,

data JSON NOT NULL,

PRIMARY KEY (storeId, invoiceDate)

);

```

 

### Step 2: Lambda Function Code

 

Here’s the complete code for the AWS Lambda function:

 

```python

import json

import pymysql

import os

 

# Database connection settings

RDS_HOST = os.getenv('RDS_HOST')

RDS_USER = os.getenv('RDS_USER')

RDS_PASSWORD = os.getenv('RDS_PASSWORD')

RDS_DB_NAME = os.getenv('RDS_DB_NAME')

 

def lambda_handler(event, context):

# Expect the JSON data in the 'body' key from the calling function

if 'body' not in event:

     return {

            'statusCode': 400,

         'body': json.dumps({'message': 'Request body is missing'})

     }

 

# Parse the JSON data from the event body

try:

     invoice_data = json.loads(event['body'])

except json.JSONDecodeError:

     return {

            'statusCode': 400,

         'body': json.dumps({'message': 'Invalid JSON format'})

     }

 

# Validate required fields

required_fields = ['storeId', 'date', 'TotalAmount']

for field in required_fields:

     if field not in invoice_data:

         return {

                'statusCode': 400,

                'body': json.dumps({'message': f'Missing required field: {field}'})

         }

store_id = invoice_data['storeId']

invoice_date = invoice_data['date']

total_amount = invoice_data['TotalAmount']

# Validate the data types (Optional but recommended)

try:

     total_amount = float(total_amount)

except ValueError:

     return {

            'statusCode': 400,

         'body': json.dumps({'message': 'TotalAmount should be a number'})

     }

 

# Connect to the RDS database

try:

     connection = pymysql.connect(host=RDS_HOST,

                                     user=RDS_USER,

                                     password=RDS_PASSWORD,

                                     database=RDS_DB_NAME,

                                     cursorclass=pymysql.cursors.DictCursor)

except pymysql.MySQLError as e:

     return {

            'statusCode': 500,

         'body': json.dumps({'message': f'Error connecting to RDS: {str(e)}'})

     }

# Insert the data into the database

try:

     with connection.cursor() as cursor:

         sql = """

             INSERT INTO Invoices (storeId, invoiceDate, TotalAmount, data)

             VALUES (%s, %s, %s, %s)

            """

            cursor.execute(sql, (store_id, invoice_date, total_amount, json.dumps(invoice_data)))

            connection.commit()

except pymysql.MySQLError as e:

     return {

            'statusCode': 500,

         'body': json.dumps({'message': f'Error inserting data into RDS: {str(e)}'})

     }

finally:

        connection.close()

# Return a success response

return {

     'statusCode': 200,

     'body': json.dumps({'message': 'Invoice data successfully stored'})

}

```

 

Step 3: Environment Variables

 

Set the following environment variables for the Lambda function:

 

               •           RDS_HOST: Your RDS endpoint

               •           RDS_USER: Your RDS username

               •           RDS_PASSWORD: Your RDS password

               •           RDS_DB_NAME: Your RDS database name

 

Step 4: AWS Lambda Permissions

 

Ensure that your Lambda function has the necessary permissions to connect to the RDS database. This can be done by assigning the correct IAM role and security group to the Lambda function.

 

Step 5: Deploy and Test

 

Deploy the Lambda function and test it with sample JSON data to ensure it inserts the data correctly into the RDS database.

 

{

"body": "{\"storeId\": \"123\", \"date\": \"2024-08-23\", \"TotalAmount\": 150.75}"

}

 

{

"body": "{\"storeId\": \"123\", \"date\": \"2024-08-23\", \"TotalAmount\": 150.75, \"customerName\": \"John Doe\", \"items\": [{\"itemId\": \"A1\", \"quantity\": 2, \"price\": 50.00}, {\"itemId\": \"B2\", \"quantity\": 1, \"price\": 50.75}], \"taxAmount\": 10.00, \"notes\": \"Special discount applied\"}"

}

=========

import json

import pymysql

import os


# Database connection settings

RDS_HOST = os.getenv('RDS_HOST')

RDS_USER = os.getenv('RDS_USER')

RDS_PASSWORD = os.getenv('RDS_PASSWORD')

RDS_DB_NAME = os.getenv('RDS_DB_NAME')


def lambda_handler(event, context):

    # Connect to the RDS database

    try:

        connection = pymysql.connect(host=RDS_HOST,

                                     user=RDS_USER,

                                     password=RDS_PASSWORD,

                                     database=RDS_DB_NAME,

                                     cursorclass=pymysql.cursors.DictCursor)

    except pymysql.MySQLError as e:

        return {

            'statusCode': 500,

            'body': json.dumps({'message': f'Error connecting to RDS: {str(e)}'})

        }


    try:

        with connection.cursor() as cursor:

            # Query all data from the Invoices table

            sql = "SELECT * FROM Invoices"

            cursor.execute(sql)

            result = cursor.fetchall()

        

        # Return the result as JSON

        return {

            'statusCode': 200,

            'body': json.dumps(result)

        }

    

    except pymysql.MySQLError as e:

        return {

            'statusCode': 500,

            'body': json.dumps({'message': f'Error querying data from RDS: {str(e)}'})

        }

    finally:

        connection.close()

Saved responses are view only

close_fullscreen

This presentation deck will explore advanced Retrieval Augmented Generation (RAG) techniques.

Slide 1: Title - Advanced RAG Techniques

Slide 2: Naive RAG - The Foundation

Slide 3: Challenges of Naive RAG

Slide 4: Advanced RAG Techniques - Enhancing Performance

Slide 5: Chunking - A Critical Aspect of RAG

Slide 6: Multimodal RAG - Expanding the Horizons

Slide 7: Evaluating RAG Performance

Slide 8: Conclusion

This structure provides a comprehensive overview of advanced RAG techniques, highlighting key concepts and practical implementations.

 

 

https://aws.amazon.com/blogs/machine-learning/create-a-multimodal-assistant-with-advanced-rag-and-amazon-bedrock/

 

 

https://medium.com/@krtarunsingh/advanced-rag-techniques-unlocking-the-next-level-040c205b95bc

 

https://www.pinecone.io/learn/advanced-rag-techniques/

 

https://techcommunity.microsoft.com/t5/ai-azure-ai-services-blog/advanced-rag-with-azure-ai-search-and-llamaindex/ba-p/4115007

 

https://www.rungalileo.io/blog/mastering-rag-advanced-chunking-techniques-for-llm-applications

 

https://learn.microsoft.com/en-us/azure/developer/ai/advanced-retrieval-augmented-generation

 

 

 


https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangler.html


https://docs.aws.amazon.com/sagemaker/latest/dg/studio-updated-migrate.html


https://docs.aws.amazon.com/sagemaker/latest/dg/studio-updated-ui.html



https://docs.aws.amazon.com/sagemaker/latest/dg/studio-ui.html


Who are the people joining (AI ML / Infra ?)

Should I go  with new GUI



https://docs.aws.amazon.com/id_id/iot/latest/developerguide/aws-iot-how-it-works.html

 

https://aws.amazon.com/blogs/architecture/building-event-driven-architectures-with-iot-sensor-data/




https://youtu.be/6O9DqCrInvw

https://docs.aws.amazon.com/bedrock/latest/userguide/agents-how.html


https://youtu.be/JkDzZFTXeSw




Here are step-by-step instructions for installing and running the Bedrock Agents demo from the GitHub repository you provided:


1. Clone the repository:

```

git clone https://github.com/trevorspires/Bedrock-Agents-Demo-Final.git

cd Bedrock-Agents-Demo-Final

```


2. Install the required Python packages:

```

pip install -r requirements.txt

```


3. Set up AWS credentials with permissions to access Bedrock. You can do this by configuring the AWS CLI or setting environment variables.


4. Create an S3 bucket to store the knowledge base documents. Upload the provided PDF files (fomcminutes20230726.pdf, fomcminutes20230920.pdf, fomcminutes20231101.pdf) to this S3 bucket.


5. Create another S3 bucket to store artifacts. Upload the provided ActionSchema.json file to this bucket.


6. Create a Lambda function named "bedrock-agent-internet-search". Copy the provided Python code into this Lambda function. Configure the Lambda with 4048MB memory, 1024MB ephemeral storage, and 1 minute timeout. Deploy the Lambda function.


7. Apply a resource policy to the Lambda to grant Bedrock agent access. Use the example resource policy provided, replacing the ARN placeholder with the actual ARN once the agent is created.


8. In the Bedrock console, go to Agents and select "Create Agent". Provide an agent name like "WebscrapeAgent". Select the Anthropic Claude Instant V1 model. 


9. Add instructions by creating a prompt that defines the rules of operation for the agent. Use the example prompt provided.


10. Add the action group, selecting the "bedrock-agent-internet-search" Lambda function and the ActionSchema.json file from the artifacts S3 bucket.


11. Review and create the agent. Once created, deploy the agent by creating an alias.


12. Navigate to the Streamlit_App directory:

```

cd Streamlit_App

```


13. Open the InvokeAgent.py file and update the agentId and agentAliasId variables with the appropriate values from the agent you created.


14. Run the Streamlit app:

```

streamlit run app.py

```


15. Access the app in your web browser at the provided URL. You can now interact with the Bedrock agent through the Streamlit interface.


To clean up the resources when done:

- Delete the files from the S3 buckets 

- Delete the Lambda functions

- Delete the Bedrock agent from the Bedrock console


Let me know if you have any other questions!


Citations:

[1] https://github.com/trevorspires/Bedrock-Agents-Demo-Final

[2] https://github.com/build-on-aws/amazon-bedrock-agents-quickstart

[3] https://www.youtube.com/watch?v=_CzSJa-kH_8

[4] https://www.youtube.com/watch?v=q09A06uOCQg

[5] https://github.com/mavi888/bedrock-agent-lambda

[6] https://github.com/aws-samples/agentsforbedrock-retailagent

[7] https://github.com/aws-samples/generative-ai-amazon-bedrock-langchain-agent-example

[8] https://www.youtube.com/watch?v=6O9DqCrInvw

[9] https://github.com/aws-samples/amazon-bedrock-samples

[10] https://github.com/aws-samples/amazon-bedrock-serverless-prompt-chaining

[11] https://www.youtube.com/watch?v=qS1tTHMWq90

[12] https://github.com/build-on-aws/bedrock-agents-streamlit

[13] https://www.youtube.com/watch?v=kwkaBrK_-Bs

[14] https://github.com/build-on-aws/bedrock-agents-webscraper

[15] https://www.youtube.com/watch?v=obgsF9ewR9s

[16] https://github.com/aws-samples/amazon-bedrock-workshop

[17] https://www.youtube.com/watch?v=32D7NJK9QIk

[18] https://docs.aws.amazon.com/bedrock/latest/userguide/bedrock-agent_example_bedrock-agent_GettingStartedWithBedrockAgents_section.html

[19] https://www.youtube.com/watch?v=ElX-W3dEwq0

[20] https://github.com/jossai87/bedrock-agents-streamlit

javascript:(function(){const textToCopy='Act like document reviewer. Summarize  the main points of the current page in simple language . Highlight ( Font bold and red) who , why, when , statistics and how part of summarization.At the end explain the key concept in simple language';navigator.clipboard.writeText(textToCopy).then(()=>{alert('Text copied to clipboard: '+textToCopy);}).catch((error)=>{console.error('Error copying text: ',error);});})();

According to the provided search results, the pricing for Claude 3 Sonnet on Amazon Bedrock is as follows:


On-Demand Pricing:

- $0.008 per 1,000 input tokens

- $0.024 per 1,000 output tokens[2]


Provisioned Throughput Pricing (hourly price per model unit):

- $80.00 for 1-month commitment 

- $44.00 for 6-month commitment[1][2]


So for example, a request to Claude 3 Sonnet with 11K input tokens and 4K output tokens would cost:

- On-Demand: (11K / 1000 * $0.008) + (4K / 1000 * $0.024) = $0.184

- Provisioned Throughput (1 model unit, 1 month): $80 * 24 hours * 31 days = $59,520 per month[2]


Compared to other models on Amazon Bedrock, Claude 3 Sonnet is among the most expensive options[5]. The cheaper Claude Instant model is $39.60/hour for a 1-month commitment, while the previous flagship Claude 2 model is $63/hour[2].


Pricing for the higher-end Claude 3 Opus and lower-end Claude 3 Haiku models has not been announced yet, as they are "coming soon" to Amazon Bedrock[3][4].


Citations:

[1] https://aws.amazon.com/marketplace/pp/prodview-mdluhdii5lkfw

[2] https://aws.amazon.com/bedrock/pricing/

[3] https://aws.amazon.com/bedrock/claude/

[4] https://www.aboutamazon.com/news/aws/amazon-bedrock-anthropic-ai-claude-3

[5] https://venturebeat.com/ai/amazon-adds-claude-3-beats-gpt-4-to-bedrock/


Based on the pricing information provided in the search results, there are two main pricing models for using Claude 3 Sonnet on Amazon Bedrock:


1. On-Demand Pricing: 

- You pay per request based on the number of input and output tokens

- $0.008 per 1,000 input tokens

- $0.024 per 1,000 output tokens

- Example: A request with 11K input tokens and 4K output tokens would cost (11K / 1000 * $0.008) + (4K / 1000 * $0.024) = $0.184[2]


2. Provisioned Throughput Pricing:

- You commit to purchasing a certain number of "model units" for a 1-month or 6-month term

- Each model unit has an hourly price based on the commitment term

- For Claude 3 Sonnet, it's $80/hour for 1-month or $44/hour for 6-months per model unit

- Example: 1 model unit for 1 month would cost $80 * 24 hours * 31 days = $59,520 per month[2][4]


The key differences are:


- On-Demand has no upfront commitment, you only pay for what you use on a per-request basis

- Provisioned Throughput requires committing to a certain capacity for 1 or 6 months, but provides lower per-hour rates

- Provisioned Throughput is suitable if you have predictable, steady workloads and want to optimize costs

- On-Demand offers more flexibility for variable or unpredictable workloads


So in summary, On-Demand is pay-as-you-go per request, while Provisioned Throughput involves committing to a fixed capacity to get discounted hourly rates. The choice depends on your workload patterns and cost optimization goals.


Citations:

[1] https://www.linkedin.com/pulse/getting-started-amazon-bedrock-kaushik-j-fl8vc

[2] https://aws.amazon.com/bedrock/pricing/

[3] https://www.enterpriseai.news/2017/01/25/heresies-new-hpc-cloud-universe/

[4] https://aws.amazon.com/marketplace/pp/prodview-mdluhdii5lkfw

Here is a simple explanation of the key parameters used in large language models (LLMs):

 

## Temperature

 

Temperature controls the randomness and creativity of the model's output. It is a value between 0 and 1.

 

- Lower temperature (e.g. 0.2) makes the output more focused, deterministic and conservative. The model will choose words with higher probabilities.

- Higher temperature (e.g. 0.8) makes the output more random, creative and diverse. The model will consider words with lower probabilities too.

 

For example, with the prompt "The cat sat on the", a low temperature might complete it as "The cat sat on the mat." A high temperature might generate something more unusual like "The cat sat on the glistening, purple mushroom."[3]

 

## Top-p (nucleus sampling)

 

Top-p sets a probability threshold p (e.g. 0.9) and only considers the smallest set of words whose cumulative probability exceeds p.

 

- Lower top-p (e.g. 0.5) limits the word choices to the most probable words that cumulatively have 50% probability mass. This makes the output more focused.

- Higher top-p (e.g. 0.95) includes more possible word choices covering 95% probability. This allows for more diversity.

 

For example, for the prompt "I put my money in the", top-p of 0.5 might generate common completions like "bank" or "wallet", while 0.95 might also consider less likely words like "blender" or "aquarium".[4]

 

## Top-k

 

Top-k sampling limits the word choices to the k most likely next words. For example:

 

- With top-k of 3, the model will only consider the 3 most probable words at each step. This heavily restricts the output.

- With top-k of 100, the model will consider the top 100 most probable words. This allows more possibilities.

 

So for the prompt "I enjoy eating", top-k=3 might only consider common foods like "pizza", "sushi", "tacos", while top-k=100 could generate rarer foods too like "escargot" or "durian".[3]

 

## Max Tokens

 

Max tokens sets the maximum length of the model's output, in terms of the number of tokens (words or word pieces).

 

For example, if max tokens is set to 20, the model will generate at most 20 tokens before stopping. This prevents the model from generating overly long outputs.[4]

 

In summary, temperature, top-p and top-k control the randomness and diversity of word choices, while max tokens limits the output length. Adjusting these parameters allows developers to fine-tune the behavior of language models for different applications.[3][4]

 

Citations:

[1] https://developers.google.com/machine-learning/resources/intro-llms

[2] https://en.wikipedia.org/wiki/Large_language_model

[3] https://www.youtube.com/watch?v=PAbZRGGYNyM

[4] https://www.linkedin.com/pulse/parameters-llm-models-simple-explanation-gaurang-desai-kabfe

[5] https://aws.amazon.com/what-is/large-language-model/

 

 

Here's an example of how temperature, top-p, and top-k can work together when generating text with a large language model:

 

Let's say we have the prompt "The quick brown fox" and want the model to complete the sentence. The model outputs a probability distribution over the next possible words:

 

jumped: 0.50

runs: 0.30

leaps: 0.15

eats: 0.05

 

Now let's see how the different parameters affect the word choice:

 

1. Temperature:

- With low temperature (e.g., 0.2), the model becomes very conservative and always picks the most likely word, "jumped".

- With high temperature (e.g., 1.0), the probabilities become more evened out, giving "leaps" and "eats" a better chance of being selected.

 

2. Top-k:

- If we set top-k to 2, the model will only consider the top 2 most probable words: "jumped" and "runs".

- "leaps" and "eats" have no chance of being selected, regardless of temperature.

 

3. Top-p:

- If we set top-p to 0.8, the model will consider the smallest set of words whose cumulative probability exceeds 0.8.

- In this case, that set is {"jumped", "runs", "leaps"}, as their total probability is 0.95.

- "eats" is excluded since the top-p threshold has already been met.

 

So if we use temperature=1.0, top-k=3, top-p=0.8:

- Temperature evens out the probabilities.

- Top-k doesn't have much effect since it's higher than the number of words.

- Top-p excludes the least probable word "eats".

 

The model would likely output something like:

"The quick brown fox jumped." (most probable)

"The quick brown fox runs." (somewhat probable)

"The quick brown fox leaps." (less probable)

 

But it would never output:

"The quick brown fox eats." (excluded by top-p)

 

So in summary, temperature increases randomness, top-k limits to the k most probable words, and top-p chooses the smallest set exceeding the probability threshold. Using them together gives you fine-grained control over the model's output.[3][4]

 

Citations:

[1] https://peterchng.com/blog/2023/05/02/token-selection-strategies-top-k-top-p-and-temperature/

[2] https://community.openai.com/t/temperature-top-p-and-top-k-for-chatbot-responses/295542

[3] https://www.phdata.io/blog/how-to-tune-llm-parameters-for-top-performance-understanding-temperature-top-k-and-top-p/

[4] https://datasciencedojo.com/blog/llm-parameters/

[5] https://www.youtube.com/watch?v=_W3m8wLYuJw

 

 

 

When choosing temperature, top-p, and top-k values for a task like call center transcription summarization, the goal is to generate focused, coherent and relevant summaries while avoiding hallucination or irrelevant details. Here are some guidelines:

 

Temperature:

- Use a lower temperature like 0.2-0.5 to make the output more focused and deterministic.[1]

- Higher temperatures can introduce more randomness and hallucination, which is undesirable for factual summarization.[2]

 

Top-p:

- Set top-p to a lower value like 0.5-0.8 to focus on the most probable words and phrases.[1][2]

- This will reduce diversity but keep the summary on-topic and faithful to the source transcript.

- Avoid setting top-p too low (e.g. 0.2) as it may lead to repetitive output.

 

Top-k:

- Top-k is generally less useful than top-p for controlling output relevance.[1]

- If using top-k, a moderate value like 50 can eliminate very low probability words while still allowing for some diversity.[1]

- Avoid setting top-k too low, as it can lead to repetitive or generic summaries.

 

In general, it's best to only modify either temperature or top-p, not both together.[2] So for call center summarization, I would recommend:

 

- Temperature: 0.3-0.5

- Top-p: 0.6-0.8

- Top-k: Use default (e.g. 50) or don't set

 

This combination will generate summaries that extract the most salient information from the transcript in a coherent way, without introducing many irrelevant or hallucinated details. Some experimentation may be needed to find the optimal values for the specific transcripts and desired summary style.

 

The key is to find a balance between diversity (to cover the key points) and focus (to stay faithful to the source). Err on the side of lower randomness for factual summarization tasks like this.[1][2]

 

Citations:

[1] https://www.phdata.io/blog/how-to-tune-llm-parameters-for-top-performance-understanding-temperature-top-k-and-top-p/

[2] https://community.openai.com/t/temperature-top-p-and-top-k-for-chatbot-responses/295542

[3] https://community.openai.com/t/a-better-explanation-of-top-p/2426

[4] https://www.linkedin.com/pulse/science-control-how-temperature-topp-topk-shape-large-puente-viejo-u88yf

[5] https://peterchng.com/blog/2023/05/02/token-selection-strategies-top-k-top-p-and-temperature/


 I have built a sagemaker  notebook which can take 100 files (call center scripts)  hosted on S3 and summerize them 4 different ways by using 4 different Foundational model. I am planning to write a blog about it. Give me ideas

Choosing ideal values for top_p, top_k, and temperature in summarization tasks with LLMs on AWS Bedrock depends on several factors, including the specific model you're using and your desired summary characteristics. Here's a breakdown of each parameter and how they influence your summaries:

Top-p:

Top-k:

Temperature:

So, what are the ideal values?

Unfortunately, there's no one-size-fits-all answer. It depends on:

General starting points:

Remember: Experimentation is key! Try different combinations of these parameters and see what produces the best summaries for your specific needs.

Additional tips:

I hope this explanation helps you optimize your summarization results on AWS Bedrock!


https://github.com/aws-samples/amazon-bedrock-workshop/tree/main/02_Summarization

https://www.asapp.com/blog/a-contact-center-case-study-about-call-summarization-strategies




Requirements:

 

 

 

 

 

Approach

 

 

Test

 

 

Verify


 

Key Words and Phrases:

 

               1.         Enterprise Architecture - Reflects a holistic view of the organization’s IT landscape.

               2.         Digital Transformation - Emphasizes your experience with modernizing legacy systems and processes.

               3.         Scalability - Important for discussing how solutions can grow with the business.

               4.         Cloud Strategy - Critical in today’s IT environment; focus on multi-cloud or hybrid cloud solutions.

               5.         Security and Compliance - Essential, especially in the financial sector.

               6.         Agility - Use to highlight your ability to adapt to changing business needs.

               7.         Data Governance - Focuses on data quality, security, and compliance.

               8.         Innovation and Continuous Improvement - Reflects a forward-thinking approach.

               9.         Stakeholder Management - Demonstrates your ability to align technical solutions with business goals.

               10.      Legacy Modernization - Shows your experience in upgrading or replacing outdated systems.

               11.      Cross-Functional Collaboration - Highlights teamwork across different departments.

               12.      Cost Optimization - Relevant for driving efficiency without compromising on quality.

               13.      Risk Management - Vital for ensuring that new solutions don’t introduce unforeseen risks.

               14.      Customer-Centric Design - Focuses on creating solutions that meet end-user needs.

               15.      Regulatory Compliance - Particularly crucial in the financial industry.

 

Sample Response:

 

Interview Question: Can you describe a time when you led a large-scale digital transformation initiative?

 

Response:

“In my current role as a Senior Solution Architect, I spearheaded a comprehensive digital transformation initiative aimed at modernizing our enterprise architecture. This project was critical in addressing scalability challenges as our business expanded. The key to success was developing a robust cloud strategy, leveraging a hybrid cloud environment that optimized both cost and performance.

 

Security and compliance were at the forefront of our design, particularly given the stringent regulatory environment we operate in. I worked closely with cross-functional teams, including IT, operations, and legal, to ensure our solutions met all data governance and compliance requirements.

 

Throughout the project, I maintained constant communication with key stakeholders to align our technical roadmap with the broader business strategy. This not only facilitated buy-in but also ensured that our transformation efforts were tightly integrated with ongoing business objectives. The result was a more agile, scalable, and secure infrastructure that supported our organization’s growth and innovation goals.

 

This experience underscored the importance of agility and continuous improvement in today’s fast-evolving technological landscape, especially in a sector as dynamic as finance.”

 

Explanation:

 

               •           Enterprise Architecture and Scalability establish your strategic thinking.

               •           Cloud Strategy and Security and Compliance demonstrate technical depth and awareness of financial industry requirements.

               •           Stakeholder Management and Cross-Functional Collaboration showcase leadership and teamwork.

               •           Agility and Continuous Improvement signal your adaptability and forward-thinking mindset.

 

Using these keywords and structured responses will help you convey the right level of expertise and strategic thinking required for a Principal Architect role at JPMC.

 

 

 

1. Enterprise Architecture

 

Situation:

At a global financial services firm, our enterprise architecture had become fragmented due to the rapid adoption of cloud services by different departments. This created silos, increased operational costs, and posed security risks.

 

Task:

I was tasked with unifying the enterprise architecture to ensure consistency, reduce costs, and improve security across the organization.

 

Action:

I initiated an assessment of the existing architecture using AWS Well-Architected Tool and TOGAF framework. I then designed a new enterprise architecture blueprint that standardized cloud services across the organization, leveraging AWS Control Tower for governance and Terraform for Infrastructure as Code (IaC).

 

Result:

The new architecture reduced cloud spend by 15% annually, improved operational efficiency by 20%, and significantly enhanced security posture, evidenced by a 30% reduction in security incidents related to misconfigured cloud resources.

 

2. Digital Transformation

 

Situation:

A leading insurance provider was struggling with outdated legacy systems that hindered their ability to quickly roll out new products and services in response to market demands.

 

Task:

I was assigned to lead the digital transformation initiative to modernize the legacy systems and improve time-to-market.

 

Action:

I led the migration of core applications to a microservices architecture on Azure Kubernetes Service (AKS). Using Azure DevOps, I implemented CI/CD pipelines that automated the deployment process. We also introduced Azure API Management to facilitate seamless integration between legacy systems and new microservices.

 

Result:

The transformation reduced the time-to-market for new products by 40% and increased system reliability by 25%, with a 50% reduction in downtime during peak processing times.

 

3. Scalability

 

Situation:

A fintech startup experienced rapid user growth, which led to performance bottlenecks in their payment processing system hosted on a monolithic architecture.

 

Task:

I was tasked with redesigning the system architecture to handle the growing user base and transaction volume without compromising performance.

 

Action:

I led the re-architecture of the system to a serverless architecture using AWS Lambda, DynamoDB, and API Gateway. I also implemented AWS Auto Scaling for parts of the system that still required EC2 instances.

 

Result:

The new architecture handled a 300% increase in transaction volume with no performance degradation. Additionally, the cost per transaction decreased by 20% due to the pay-as-you-go nature of serverless services.

 

4. Cloud Strategy

 

Situation:

A multinational bank was using a mix of on-premises and cloud-based solutions, but lacked a coherent cloud strategy, resulting in inefficiencies and compliance risks.

 

Task:

I was brought in to develop a comprehensive cloud strategy that would optimize costs, improve agility, and ensure compliance with regulatory standards.

 

Action:

I conducted a thorough assessment using CloudHealth by VMware to analyze cloud spend and usage patterns. I then developed a hybrid cloud strategy, prioritizing workloads based on business criticality and data sensitivity, and implemented Azure Policy and AWS Organizations for governance.

 

Result:

The new strategy led to a 25% reduction in cloud costs, improved compliance by 35%, and reduced provisioning time for new environments from weeks to days, enhancing overall agility.

 

5. Security and Compliance

 

Situation:

A healthcare provider faced increasing concerns over security and compliance, particularly with the sensitive patient data they were storing and processing in the cloud.

 

Task:

I was responsible for enhancing the security and compliance posture of their cloud infrastructure to meet HIPAA requirements.

 

Action:

I led the implementation of AWS Security Hub and Azure Security Center to continuously monitor security threats and enforce compliance. I also deployed AWS KMS and Azure Key Vault for encryption key management and implemented a robust IAM strategy using Azure AD and AWS IAM.

 

Result:

The organization achieved HIPAA compliance, reducing audit findings by 40%, and saw a 50% decrease in potential security threats due to enhanced monitoring and encryption.

 

6. Agility

 

Situation:

A retail company needed to rapidly adapt its e-commerce platform to accommodate seasonal spikes in traffic, but their existing infrastructure lacked the agility to scale effectively.

 

Task:

I was tasked with enhancing the agility of the infrastructure to support these fluctuating demands.

 

Action:

I migrated the e-commerce platform to Google Cloud Platform (GCP) using Google Kubernetes Engine (GKE) for container orchestration. I also implemented Terraform for IaC to enable rapid provisioning and Jenkins for CI/CD to ensure quick deployment of updates.

 

Result:

The company was able to scale its platform by 200% during peak seasons with zero downtime, and deployment times for new features were reduced by 60%, significantly improving their competitive edge.

 

7. Data Governance

 

Situation:

A financial institution was struggling with inconsistent data governance practices, leading to data quality issues and compliance risks.

 

Task:

I was asked to establish a robust data governance framework to ensure data integrity, quality, and compliance across cloud environments.

 

Action:

I implemented AWS Glue and Azure Data Catalog for automated data discovery and cataloging. I also established data quality rules and policies using Informatica for data governance, and integrated these with Azure Purview for end-to-end visibility of data lineage.

 

Result:

Data quality improved by 30%, compliance risks were reduced by 25%, and the time spent on data-related issues by the operations team decreased by 40%, allowing them to focus on more strategic tasks.

 

8. Innovation and Continuous Improvement

 

Situation:

A global logistics company wanted to innovate its route optimization algorithms to reduce fuel consumption and delivery times.

 

Task:

I was tasked with leading the development of a new, cloud-based solution that could dynamically optimize delivery routes.

 

Action:

I designed and deployed a solution using AWS SageMaker to build machine learning models for real-time route optimization. I integrated the models with AWS IoT Core to collect and process real-time data from delivery vehicles, and used AWS Step Functions to automate the workflow.

 

Result:

The solution reduced fuel consumption by 15% and decreased average delivery times by 20%. The system continues to improve as more data is fed into the models, showcasing the value of continuous innovation.

 

9. Stakeholder Management

 

Situation:

A large-scale cloud migration project at a telecommunications company was facing resistance from key stakeholders due to concerns over cost and disruption to ongoing operations.

 

Task:

I was responsible for managing stakeholder expectations and ensuring alignment with the migration strategy.

 

Action:

I facilitated regular workshops and meetings with stakeholders from IT, finance, and operations to address their concerns. I used Microsoft Power BI to create dashboards that transparently showed the migration progress, cost savings, and performance improvements. I also implemented a phased migration plan using AWS Migration Hub to minimize disruption.

 

Result:

Stakeholder buy-in increased significantly, with over 90% approval for the migration strategy. The project was completed 10% under budget and ahead of schedule, leading to a 25% reduction in operational costs post-migration.

 

10. Legacy Modernization

 

Situation:

A large government agency relied on a 30-year-old COBOL-based system that was becoming increasingly difficult and expensive to maintain.

 

Task:

I was tasked with modernizing this legacy system to a more scalable and maintainable architecture.

 

Action:

I led the migration to a modern microservices architecture using AWS Fargate for serverless container management and Amazon RDS for database services. We used Micro Focus to recompile COBOL applications to run on modern platforms and AWS Lambda for integrating legacy services with new microservices.

 

Result:

The modernization reduced maintenance costs by 40%, improved system performance by 50%, and extended the system’s lifespan by at least 10 years, all while maintaining data integrity and service continuity.

 

11. Cross-Functional Collaboration

 

Situation:

During a cloud migration project at a global bank, there were significant communication gaps between the development, operations, and security teams, causing delays and security vulnerabilities.

 

Task:

I was tasked with improving cross-functional collaboration to ensure the success of the cloud migration.

 

Action:

I introduced DevSecOps practices, integrating security into the CI/CD pipeline using Jenkins and SonarQube for code quality and security checks. I also facilitated regular cross-team stand-ups and implemented Confluence as a centralized platform for documentation and collaboration.

 

Result:

Collaboration improved significantly, with security issues decreasing by 30% and project delays being cut in half. The migration was completed successfully with all teams aligned on the project’s goals.

 

12. Cost Optimization

 

Situation:

A large retail chain was facing escalating cloud costs, particularly during peak sales periods.

 

Task:

I was asked to optimize cloud spending while maintaining performance and availability.

 

Action:

I performed a thorough cost analysis using AWS Cost Explorer and Azure Cost Management. I identified underutilized resources and implemented auto-scaling policies using AWS Auto Scaling and Azure VM Scale Sets. Additionally, I used Spot Instances and Reserved Instances to reduce compute costs.

 

Result:

The optimizations resulted in a 35% reduction in overall cloud spending, without compromising on performance or availability. During peak sales periods, the auto-scaling policies allowed the retail chain to handle a 150% increase in traffic with zero downtime, leading to record sales without exceeding the budget.

 

13. Risk Management

 

Situation:

A financial services company was planning to migrate its core banking applications to the cloud but faced significant concerns regarding potential risks, including data breaches, compliance violations, and service outages.

 

Task:

I was responsible for developing a risk management strategy that would mitigate these concerns and ensure a smooth migration.

 

Action:

I conducted a comprehensive risk assessment using AWS Trusted Advisor and Azure Security Center to identify potential vulnerabilities. I implemented multi-layered security measures including AWS Shield for DDoS protection, Azure Key Vault for encryption key management, and AWS IAM for robust access control. Additionally, I set up disaster recovery solutions using AWS Elastic Disaster Recovery and Azure Site Recovery.

 

Result:

The migration proceeded without incident, with zero data breaches or compliance violations. The robust risk management framework reduced the likelihood of service outages by 40%, and the company achieved a 99.99% uptime during the first year of operation post-migration.

 

14. Customer-Centric Design

 

Situation:

A global e-commerce platform wanted to enhance its customer experience by reducing page load times and improving the overall responsiveness of its website, especially for users in regions with slower internet connections.

 

Task:

I was tasked with redesigning the platform’s architecture to prioritize customer-centric performance improvements.

 

Action:

I led the implementation of a content delivery network (CDN) using AWS CloudFront and Azure CDN to cache content closer to end users. Additionally, I optimized the front-end code using Google Lighthouse to reduce load times and implemented Lambda@Edge functions to dynamically optimize content based on user location.

 

Result:

Page load times were reduced by 50% globally, with a 70% reduction in regions with slower internet speeds. This resulted in a 25% increase in user engagement and a 15% boost in conversion rates, directly enhancing customer satisfaction and driving revenue growth.

 

15. Regulatory Compliance

 

Situation:

A European bank needed to migrate its customer data to the cloud while ensuring full compliance with GDPR and other local regulatory requirements.

 

Task:

I was assigned to design a cloud architecture that would ensure regulatory compliance across multiple jurisdictions.

 

Action:

I developed a data residency strategy using AWS Regions and Azure Data Residency features to ensure that data was stored within the appropriate geographical boundaries. I also implemented AWS Config and Azure Policy to continuously enforce compliance with GDPR and local regulations. Encryption of data at rest and in transit was managed through AWS KMS and Azure Key Vault.

 

Result:

The bank successfully migrated its data to the cloud while achieving 100% compliance with GDPR and other local regulations. The solution passed all regulatory audits with zero findings, and the bank avoided potential fines and legal issues, preserving its reputation and trust with customers.