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app-a4all-agentic-workflow-sprint-3-deploy-docker-live
Browse files- README.md +44 -26
- packages.txt +1 -0
- requirements.txt +15 -11
README.md
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title: Talk to your Multi-
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license: mit
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---
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# Title
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Empower people with
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## Overview
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## Knowledge context
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- Application name
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- Business fit: appropriate, inadequate, perfect
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- Business domain
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- Description
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Bring Your Own Data: upload your own IT landscape data
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- first row (header) with fields name (colums)
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- Architecture Visual Artefacts
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- jpeg, png
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**Disclaimer**
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## Log / Traceability
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For purpose of continuous
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## Architecture
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<italic>Core architecture built upon python, langchain, meta-faiss, gradio and Openai.<italic>
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- Python
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- Pandas
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- Langchain
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- Langsmith
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- Langgraph
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- RAG (Retrieval Augmented Generation)
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- Vectorstore
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- Prompt Engineering
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- Strategy & tactics: Task / Sub-tasks
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- Agentic
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- Models:
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- OpenAI
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- Hierarchical-Agent-Teams:
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- Tabular-question-answering over your own document
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- Diagram Component Analysis
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- Risk & Vulnerability and Mitigation options
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- Well-Architecture Design Assessment
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- User Interface
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- Gradio
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- Hosting: Huggingface Space
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- CPU basic
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- 2vCPU 16GB RAM
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##
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![Agent System Container](images/ea4all_agent_container.png)
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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---
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title: Talk to your Multi-Agentic Architect System
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emoji: π
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colorFrom: purple
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colorTo: green
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license: mit
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---
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# Title
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Empower people with ability to harness the value of Enterprise Architecture through Generative AI to positively impact individuals and organisations.\n
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## Overview
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`Trigger`: How disruptive may Generative AI be for Enterprise Architecture Capability (People, Process and Tools)? \n
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`Motivation`: Master GenAI while disrupting Enterprise Architecture to empower individuals and organisations with ability to harness EA value and make people lives better, safer and more efficient. \n
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`Ability`: Exploit my carrer background and skillset across areas such as development, business accumen, innovation and architecture to accelerate GenAI exploration. \n\n
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> That's how the `EA4ALL-Agentic system` was born and ever since continuously evolving as a result of this exploration journey.
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## Benefits
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`Empower individuals with Knowledge`: understand and talk about Business and Technology strategy, IT landscape, Architectue Artefacts in a single click of button. \n
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`Increase efficiency and productivity`: generate a documented architecture with diagram, model and descriptions. Accelerate Business Requirement identification and translation to Target Reference Architecture. Automated steps and reduced times for task execution.\n
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`Improve agility`: plan, execute, review and iterate over EA inputs and outputs. Increase the ability to adapt, transform and execute at pace and scale in response to changes in strategy, threats and opportunities. \n
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`Increase collaboration`: democratise architecture work and knowledge with anyone using natural language.\n
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`Cost optimisation`: intelligent allocation of architects time for valuable business tasks. \n
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`Business Growth`: create / re-use of (new) products and services, and people experience enhancements. \n
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`Resilience`: assess solution are secured by design, poses any risk and how to mitigate, apply best-practices. \n
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## Knowledge context
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Synthetic datasets are used to exemplify the Agentic System capabilities.
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### IT Landscape Question and Answering
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- Application name
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- Business fit: appropriate, inadequate, perfect
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- Business domain
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- Description
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- Bring Your Own Data: upload your own IT landscape data
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- Application Portfolio Management
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- xlsx tabular format
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- first row (header) with fields name (colums)
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### Architecture Diagram Visual Question and Answering
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- Architecture Visual Artefacts
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- jpeg, png
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**Disclaimer**
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- Your data & image are not accessible or shared with anyone else nor used for training purpose.
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- EA4ALL-VQA Agent should be used ONLY FOR Architecture Diagram images.
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- This feature should NOT BE USED to process inappropriate content.
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### Reference Architecture Generation
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- Clock in/out Use-case
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## Log / Traceability
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For purpose of continuous improvement, agentic workflows are logged in.
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## Architecture
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<italic>Core architecture built upon python, langchain, meta-faiss, gradio and Openai.<italic>
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- Python
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- Pandas
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- Langchain
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- Langsmith
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- Langgraph
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- Huggingface
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- RAG (Retrieval Augmented Generation)
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- Vectorstore
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- Prompt Engineering
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- Strategy & tactics: Task / Sub-tasks
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- Agentic Workflow
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- Models:
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- OpenAI
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- Llama
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- Hierarchical-Agent-Teams:
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- Tabular-question-answering over your own document
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- Diagram Component Analysis
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- Risk & Vulnerability and Mitigation options
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- Well-Architecture Design Assessment
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- Vision and Target Architecture
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- User Interface
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- Gradio
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- Hosting: Huggingface Space
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## Agentic System Architecture
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![Agent System Container](images/ea4all_agent_container.png)
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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packages.txt
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graphviz
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requirements.txt
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gradio==4.29.0
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gradio_client==0.16.1
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faiss-cpu==1.7.4
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filetype==1.2.0
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langchain==0.1.16
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langchain-community==0.0.34
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langchain-core==0.1.45
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langchain-openai==0.1.3
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openai==1.10.0
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openpyxl==3.1.2
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pandas==2.2.2
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gradio==4.29.0
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gradio_client==0.16.1
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faiss-cpu==1.7.4
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pandas==2.2.2
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filetype
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openpyxl
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python-dotenv
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tiktoken
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langgraph
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langsmith
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graphviz
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langchain
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langchain-community
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langchain-core
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langchain-experimental
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langchain-openai
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langgraph
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langsmith
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openai
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