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app-a4all-agentic-workflow-sprint-2-deploy-docker-live
Browse files- Dockerfile copy +34 -0
- README.md +52 -19
- requirements.txt +15 -0
Dockerfile copy
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FROM python:3.11.3
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# Set up a new user named "user" with user ID 1000
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RUN useradd -m -u 1000 user
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# Switch to the "user" user
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USER user
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# Set home to the user's home directory
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ENV HOME=/home/user \
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PATH=/home/user/.local/bin:$PATH
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# Set the working directory to the user's home directory
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WORKDIR $HOME
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# Get secret OPENAI_API_KEY and clone it as repo at buildtime / changed required to true
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RUN --mount=type=secret,id=OPENAI_API_KEY,mode=0444,required=true
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RUN --mount=type=secret,id=HF_TOKEN,mode=0444,required=true \
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cat /run/secrets/HF_TOKEN > /home/user/cli_token
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RUN pip install -U "huggingface_hub[cli]"
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RUN git init & git config --global credential.helper store
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RUN huggingface-cli login --token $(cat /home/user/cli_token) --add-to-git-credential
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# Copy the current directory contents into the container at $HOME/app setting the owner to the user
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COPY --chown=user . $HOME
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# Clone and run ea4all-agent
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RUN git clone https://avfranco:$(cat cli_token)@huggingface.co/spaces/avfranco/ea4all-agent
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WORKDIR /home/user/ea4all-agent
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RUN pip install --no-cache-dir --upgrade -r requirements.txt
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CMD ["python","app.py"]
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README.md
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---
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title: Talk to your
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emoji: π
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colorFrom: purple
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colorTo: green
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# Title
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Empower
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# My Name
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ea4all-agent-gpt, your Architect to talk about Enterprise landscape using your own language.
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## Overview
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What if you could interact with your
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##
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## Architecture
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<italic>Core architecture built upon python, langchain, 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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- 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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- User Interface
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- Gradio
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- CPU basic
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- 2vCPU 16GB RAM
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- Deployment
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- Docker spaces
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## Agent Architecture
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![Agent System Container](
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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-Agent Architect
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emoji: π
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colorFrom: purple
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colorTo: green
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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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- Technical fit: adequate, insufficient, perfect
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- Business_criticality: operational, medium, high, critical
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- Roadmap: maintain, invest, divers
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- Architect responsible
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- Hosting: user device, on-premise, IaaS, SaaS
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- Business capability
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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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- 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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- Supervisor
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- Visual Questions Answering
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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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- CPU basic
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- 2vCPU 16GB RAM
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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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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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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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