ea4allAI-problem-first-ai-capstone-demo-live

Dockerfile

@@ -20,10 +20,6 @@ RUN --mount=type=secret,id=LANGCHAIN_API_KEY,mode=0444,required=true
 # HF private spaces token access
 RUN --mount=type=secret,id=HUGGINGFACEHUB_API_TOKEN,mode=0444,required=true \
     cat /run/secrets/HUGGINGFACEHUB_API_TOKEN > /home/user/cli_token
-# Azure BING Search AI API
-RUN --mount=type=secret,id=BING_SUBSCRIPTION_KEY,mode=0444,required=true
-# CONFLUENCE API KEY
-RUN --mount=type=secret,id=CONFLUENCE_API_KEY,mode=0444,required=true
 # GOOGLE GEMINI API KEY
 RUN --mount=type=secret,id=GEMINI_API_KEY,mode=0444,required=true
 # LANGSMITH API KEY
@@ -45,8 +41,8 @@ RUN hf auth login --token $(cat ${HOME}/cli_token) --add-to-git-credential
 COPY --chown=user . $HOME
 
 # Clone and run ea4all-agentic-system
-RUN git clone https://avfranco:$(cat cli_token)@huggingface.co/spaces/avfranco/ea4all_agentic_live
-WORKDIR ${HOME}/ea4all_agentic_live
+RUN git clone https://avfranco:$(cat cli_token)@huggingface.co/spaces/avfranco/problem-first-ai-capstone
+WORKDIR ${HOME}/ea4all_pfai_demo
 RUN pip install --no-cache-dir --upgrade -r requirements.txt
 
 # Install graphviz dependency

Dockerfile copy

@@ -1,34 +0,0 @@
-FROM python:3.11.3
-
-# Set up a new user named "user" with user ID 1000
-RUN useradd -m -u 1000 user
-
-# Switch to the "user" user
-USER user
-
-# Set home to the user's home directory
-ENV HOME=/home/user \
-    PATH=/home/user/.local/bin:$PATH
-
-# Set the working directory to the user's home directory
-WORKDIR $HOME
-
-# Get secret OPENAI_API_KEY and clone it as repo at buildtime / changed required to true
-RUN --mount=type=secret,id=OPENAI_API_KEY,mode=0444,required=true
-RUN --mount=type=secret,id=HF_TOKEN,mode=0444,required=true \
-    cat /run/secrets/HF_TOKEN > /home/user/cli_token
-
-RUN pip install -U "huggingface_hub[cli]" 
-RUN git init & git config --global credential.helper store
-RUN huggingface-cli login --token $(cat /home/user/cli_token) --add-to-git-credential
-
-# Copy the current directory contents into the container at $HOME/app setting the owner to the user
-COPY --chown=user . $HOME
-
-# Clone and run ea4all-agent
-RUN git clone https://avfranco:$(cat cli_token)@huggingface.co/spaces/avfranco/ea4all-agent
-WORKDIR /home/user/ea4all-agent
-RUN pip install --no-cache-dir --upgrade -r requirements.txt
-
-
-CMD ["python","app.py"]

README.md

@@ -1,48 +1,53 @@
 ---
-title: Talk to your Architecture Agentic Workforce
+title: ae4all.ai-agentic-workforce
 emoji: 👁
-colorFrom: purple
+colorFrom: green
 colorTo: green
-sdk: docker
+sdk: gradio
+sdk_version: 5.44.1
+app_file: app.py
 pinned: false
 license: apache-2.0
-python_version: 3.12.10
-thumbnail: >-
-  https://cdn-uploads.huggingface.co/production/uploads/63601a6488e41d249eccb69e/8-pNAGmJNXRUp9Mwu_ab6.png
-short_description: Harness the value of Architecture in the Generative AI era.
+python_version: 3.12.11
+hf_oauth: true
+hf_oauth_scopes:
+  - email
+  - read-billing
+  - read-repos
 ---
 
 ## Title
 
-Empower people with ability to harness the value of Enterprise Architecture with Generative AI to positively impact individuals and organisations.\n
+Empower people with ability to harness the value of Enterprise Architecture with Generative AI to positively impact individuals and organisations.
 
-## Problem to solve
+## Agentic Workforce Architecture
 
-Enterprise Architecture teams struggle with limited resources, fragmented processes, and poor collaboration between business and technology. They remain reactive and slow to adapt, hindered by manual work, knowledge silos, and a steep learning curve.
+![Iteration-1](ea4all/images/ea4allAI-capstone-iter-1a.png)
 
+![Iteration-1](ea4all/images/ea4allAI-capstone-iter-2a.png)
 
-- `Lack of shared` understanding between business and technology stakeholders.
-- `Slow, manual, and fragmented` architecture documentation processes.
-- `Inability to quickly` adapt architecture to strategic or market changes.
-- `Limited collaboration` and accessibility of architectural knowledge.
-- `Steep learning` curve and delayed exposure to proven design patterns.
-- Architecture teams `constrained` by Enterprise Technology budget (small teams)
-- `Project oriented` and `reactive` architecture teams
+![Iteration-1](ea4all/images/ea4allAI-capstone-iter-3a.png)
 
+## Use Case
 
-## Architect Agentic Companion
+- `Trigger`: React -> Anticipate. Follow -> Lead. Doors to open -> Build new Doors
+- `Motivation`: How disruptive may Generative AI be for Enterprise Architecture Capability (People, Process and Tools)?
+- `Ability`: Exploit my carrer background and skillset across system development, business accumen, innovation and architecture to accelerate GenAI exploration while learning new things.
 
-![Agent System Container](ea4all/images/ea4all_architecture.png)
+## Problem Statement
 
-## Background
+Enterprise Architecture teams struggle with limited resources, fragmented processes, and poor collaboration between business and technology. They remain reactive and slow to adapt, hindered by manual work, knowledge silos, and a steep learning curve.
 
-- `Trigger`: How disruptive may Generative AI be for Enterprise Architecture Capability (People, Process and Tools)?
-- `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.
-- `Ability`: Exploit my carrer background and skillset across system development, business accumen, innovation and architecture to accelerate GenAI exploration while learning new things.
+- Lack of shared understanding between business and technology stakeholders.
+- Slow, manual, and fragmented architecture documentation processes.
+- Inability to quickly adapt architecture to strategic or market changes.
+- Limited collaboration and accessibility of architectural knowledge.
+- Steep learning curve and delayed exposure to proven design patterns.
+- Architecture teams constrained by Enterprise Technology budget (small teams)
+- Project oriented and reactive architecture teams
 
-> That's how the `EA4ALL-Agentic system` was born and ever since continuously evolving to build an ecosystem of **Architects Agent partners**.
 
-## Benefits
+## Why is this problem important and relevant today?
 
 - `Empower individuals with Knowledge`: understand and talk about Business and Technology strategy, IT landscape, Architectue Artefacts in a single click of button.
 - `Accelerate learning`: gain quick access to new architectures design, patterns, and best-practices. 
@@ -54,8 +59,6 @@ Enterprise Architecture teams struggle with limited resources, fragmented proces
 - `Resilience`: assess solution are secured by design, poses any risk and how to mitigate, apply best-practices.
 - `Streamline`: the process of managing and utilising architectural knowledge and tools in a user-friendly way.
 
-## Knowledge context
-
 Synthetic datasets are used to exemplify the Agentic System capabilities.
 
 ### IT Landscape Question and Answering
@@ -70,29 +73,16 @@ Synthetic datasets are used to exemplify the Agentic System capabilities.
         - Business capability
         - Business domain
         - Description  
-
-    - Bring Your Own Data: upload your own IT landscape data
-        - Application Portfolio Management
-            - xlsx tabular format
-            - first row (header) with fields name (colums)
-    
+   
 ### Architecture Diagram Visual Question and Answering
 
     - Architecture Visual Artefacts
         - jpeg, png
 
         **Disclaimer**
-                - Your data & image are not accessible or shared with anyone else nor used for training purpose.
-                - EA4ALL-VQA Agent should be used ONLY FOR Architecture Diagram images.
-                - This feature should NOT BE USED to process inappropriate content.
-
-### Reference Architecture Generation
-
-    - Clock in/out Use-case
-
-### Architecture Demand Management
-
-    - Provide project resource estimation for architecture work based on business requirements, skillset, architects allocation, and any other relevant information to enable successful project solution delivery.
+            - Your data & image are not accessible or shared with anyone else nor used for training purpose.
+            - EA4ALL-VQA Agent should be used ONLY FOR Architecture Diagram images.
+            - This feature should NOT BE USED to process inappropriate content.
 
 ### AWS & Microsoft Official Documentation Question and Answering
 
@@ -102,47 +92,5 @@ Synthetic datasets are used to exemplify the Agentic System capabilities.
 
     For purpose of continuous improvement, agentic workflows are logged in. 
 
-## Architecture
-
-<italic>Core architecture built upon Python, Langchain, Langgraph, Langsmith, and Gradio.<italic>
-
-    - Python
-        - Pandas
-        - Langchain
-        - Langgraph
-        - Huggingface
-        - CrewAI
-
-    - RAG (Retrieval Augmented Generation)
-        - Vectorstore
-
-    - Prompt Engineering
-        - Strategy & tactics: Task / Sub-tasks
-        - Agentic Workflow
-
-    - Models: 
-        - OpenAI
-        - Meta/Llama
-        - Google Gemini
-    
-    - Hierarchical-Agent-Teams: 
-        - Tabular-question-answering over your own document
-        - Supervisor
-        - Visual Questions Answering 
-        - Diagram Component Analysis
-        - Risk & Vulnerability and Mitigation options
-        - Well-Architecture Design Assessment
-        - Vision and Target Architecture
-        - Architect Demand Management
-        - AWS & Microsoft Official Documentation
-        
-    - User Interface
-        - Gradio
-
-    - Observability & Evaluation
-        - Langsmith
-
-    - Hosting
-        - Huggingface Space
-    
+
 Check out the configuration reference at [spaces-config-reference](https://huggingface.co/docs/hub/spaces-config-reference)

live.md

@@ -1,134 +0,0 @@
----
-title: Talk to your Multi-Agentic Architect Companion
-emoji: 👁
-colorFrom: purple
-colorTo: green
-sdk: docker
-pinned: false
-license: apache-2.0
-python_version: 3.12.10
-thumbnail: >-
-  https://cdn-uploads.huggingface.co/production/uploads/63601a6488e41d249eccb69e/8-pNAGmJNXRUp9Mwu_ab6.png
-short_description: Harness the value of Architecture in the Generative AI era.
----
-
-## Title
-
-Empower people with ability to harness the value of Enterprise Architecture with Generative AI to positively impact individuals and organisations.\n
-
-## Architect Agentic Companion
-
-![Agent System Container](ea4all/images/ea4all_architecture.png)
-
-## Background
-
-- `Trigger`: How disruptive may Generative AI be for Enterprise Architecture Capability (People, Process and Tools)?
-- `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.
-- `Ability`: Exploit my carrer background and skillset across system development, business accumen, innovation and architecture to accelerate GenAI exploration while learning new things.
-
-> That's how the `EA4ALL-Agentic system` was born and ever since continuously evolving to build an ecosystem of **Architects Agent partners**.
-
-## Benefits
-
-- `Empower individuals with Knowledge`: understand and talk about Business and Technology strategy, IT landscape, Architectue Artefacts in a single click of button.
-- `Accelerate learning`: gain quick access to new architectures design, patterns, and best-practices. 
-- `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.
-- `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.
-- `Increase collaboration`: democratise architecture work and knowledge with anyone using natural language.
-- `Cost optimisation`: intelligent allocation of architects time for valuable business tasks.
-- `Business Growth`: create / re-use of (new) products and services, and people experience enhancements.
-- `Resilience`: assess solution are secured by design, poses any risk and how to mitigate, apply best-practices.
-- `Streamline`: the process of managing and utilising architectural knowledge and tools in a user-friendly way.
-
-## Knowledge context
-
-Synthetic datasets are used to exemplify the Agentic System capabilities.
-
-### IT Landscape Question and Answering
-
-    - Application name
-        - Business fit: appropriate, inadequate, perfect
-        - Technical fit: adequate, insufficient, perfect
-        - Business_criticality: operational, medium, high, critical
-        - Roadmap: maintain, invest, divers
-        - Architect responsible
-        - Hosting: user device, on-premise, IaaS, SaaS
-        - Business capability
-        - Business domain
-        - Description  
-
-    - Bring Your Own Data: upload your own IT landscape data
-        - Application Portfolio Management
-            - xlsx tabular format
-            - first row (header) with fields name (colums)
-    
-### Architecture Diagram Visual Question and Answering
-
-    - Architecture Visual Artefacts
-        - jpeg, png
-
-        **Disclaimer**
-                - Your data & image are not accessible or shared with anyone else nor used for training purpose.
-                - EA4ALL-VQA Agent should be used ONLY FOR Architecture Diagram images.
-                - This feature should NOT BE USED to process inappropriate content.
-
-### Reference Architecture Generation
-
-    - Clock in/out Use-case
-
-### Architecture Demand Management
-
-    - Provide project resource estimation for architecture work based on business requirements, skillset, architects allocation, and any other relevant information to enable successful project solution delivery.
-
-### AWS & Microsoft Official Documentation Question and Answering
-
-    - Access to official documentation and diagram generation for AWS services. MCP service-based.
-
-## Log / Traceability
-
-    For purpose of continuous improvement, agentic workflows are logged in. 
-
-## Architecture
-
-<italic>Core architecture built upon Python, Langchain, Langgraph, Langsmith, and Gradio.<italic>
-
-    - Python
-        - Pandas
-        - Langchain
-        - Langgraph
-        - Huggingface
-        - CrewAI
-
-    - RAG (Retrieval Augmented Generation)
-        - Vectorstore
-
-    - Prompt Engineering
-        - Strategy & tactics: Task / Sub-tasks
-        - Agentic Workflow
-
-    - Models: 
-        - OpenAI
-        - Meta/Llama
-        - Google Gemini
-    
-    - Hierarchical-Agent-Teams: 
-        - Tabular-question-answering over your own document
-        - Supervisor
-        - Visual Questions Answering 
-        - Diagram Component Analysis
-        - Risk & Vulnerability and Mitigation options
-        - Well-Architecture Design Assessment
-        - Vision and Target Architecture
-        - Architect Demand Management
-        - AWS & Microsoft Official Documentation
-        
-    - User Interface
-        - Gradio
-
-    - Observability & Evaluation
-        - Langsmith
-
-    - Hosting
-        - Huggingface Space
-    
-Check out the configuration reference at [spaces-config-reference](https://huggingface.co/docs/hub/spaces-config-reference)

requirements.txt

@@ -7,30 +7,46 @@ fastapi
 ffmpy==0.6.1
 google-ai-generativelanguage==0.6.17
 google-api-core==2.24.2
-gradio==5.35.0
 gradio-client
 graphviz
-huggingface-hub
-langchain
-langchain-community
-langchain-core
-langchain-experimental
-langchain-google-genai
-langchain-huggingface
-langchain-mcp-adapters==0.1.9
-langchain-openai
-langchainhub
-langgraph
-langgraph-api
-langgraph-runtime-inmem
-langgraph-sdk
-langsmith
+huggingface-hub==0.36.0
+google-ai-generativelanguage==0.6.17
+google-auth==2.40.3
+google-cloud-aiplatform==1.109.0
+google-cloud-bigquery==3.35.1
+google-cloud-resource-manager==1.14.2
+google-cloud-storage==2.19.0
+google-crc32c==1.7.1
+google-genai==1.30.0
+google-resumable-media==2.7.2
+googleapis-common-protos==1.70.0
+gradio==5.46.1
+gradio-client==1.13.1
+gradio[oauth]
+langchain==0.3.27
+langchain-community==0.3.31
+langchain-core==0.3.79
+langchain-google-genai==2.1.3
+langchain-google-vertexai==2.0.28
+langchain-mcp-adapters==0.1.12
+langchain-openai==0.3.28
+langchain-text-splitters==0.3.11
+langchainhub==0.1.21
+langgraph==0.6.6
+langgraph-api==0.3.1
+langgraph-checkpoint==2.1.1
+langgraph-cli==0.4.7
+langgraph-prebuilt==0.6.4
+langgraph-runtime-inmem==0.8.1
+langgraph-sdk==0.2.3
+langmem==0.0.30
+langsmith==0.4.41
 litellm
 markdown
 markdownify
-mcp
+mcp==1.21.0
 numpy==2.3.2
-openai
+openai==1.109.1
 openinference-instrumentation==0.1.29
 openinference-instrumentation-crewai==0.1.9
 openinference-instrumentation-litellm==0.1.19
@@ -56,4 +72,4 @@ pydantic
 sqlalchemy
 tiktoken
 tokenizers
-torch==2.2.2
+torch==2.2.2