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app-a4all-agentic-workflow-sprint-3-deploy-docker-live

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  1. README.md +44 -26
  2. packages.txt +1 -0
  3. requirements.txt +15 -11
README.md CHANGED
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  ---
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- title: Talk to your Multi-Agent Architect
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  emoji: πŸ‘
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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 access to the value of Enterprise Architecture Knowledge using my Generative AI abilities.
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  ## Overview
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- What if you could interact with your IT landscape using natural language, for example, \
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- ask what oppurtinities for simplification it may have. So, you would be able to identify initial options to a new business problem \
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- or assess impact of changes. That's not all, you would be able to identify risks and vulnerabilities on your solution architecture visual artefacts and how to mitigate them, \
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- enhance the governance and assurance process to accelerate the solution architecture delivery, \
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- improve quality and increase collaboration.
 
 
 
 
 
 
 
 
 
 
 
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  ## Knowledge context
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- A synthetic sample IT Landscape dataset, which is described below, is used as context to answer any inquiry for purpose of this minimal-viable release.
 
 
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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 or Architecture Diagram and start talking to them.
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-
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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 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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  ## Log / Traceability
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- For purpose of continuous evolution, questions and answers 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 3.11.3
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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: Faiss (Facebook AI Similarity Search
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  - Prompt Engineering
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  - Strategy & tactics: Task / Sub-tasks
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- - Agentic-Workflow: Agents
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  - Models:
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- - OpenAI GPT 3.5-turbo-1106 (Text Generation)
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- - OpenAI GPT-4-Vision-Preview (Visual Question Answering)
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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 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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  ---
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+ title: Talk to your Multi-Agentic Architect System
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  emoji: πŸ‘
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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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+
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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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+
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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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+
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+ ## Benefits
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+
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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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+
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  ## Knowledge context
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+ Synthetic datasets are used to exemplify the Agentic System capabilities.
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+
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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
51
  - Description
52
 
53
+ - Bring Your Own Data: upload your own IT landscape data
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+ - Application Portfolio Management
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+ - xlsx tabular format
56
+ - first row (header) with fields name (colums)
 
57
 
58
+ ### Architecture Diagram Visual Question and Answering
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+
60
  - Architecture Visual Artefacts
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  - jpeg, png
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63
  **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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+
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+ ### Reference Architecture Generation
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+
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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
packages.txt ADDED
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+ graphviz
requirements.txt CHANGED
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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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- python-dotenv==1.0.0
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- tiktoken==0.5.2
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- langgraph==0.0.37
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- langsmith==0.1.45
 
 
 
 
 
 
 
 
 
 
 
 
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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