How flameingmoy/perishability_management_system works
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1. Identify the Project Type and Purpose
- This is a full‑stack, agent‑driven perishability management system for supply chains.
- It runs three AI agents (demand forecast, inventory monitoring, environmental monitoring) over CSV data sources, exposes a Streamlit front end with dashboards and a chat interface, and uses LangChain + Ollama for LLM calls and embeddings.
2. Analyze the File Structure
- Top‑level files:
• README.md / SCM Scenarios.pdf – documentation
• app.py / main.py – Streamlit entrypoints or orchestrators
• config.py – central configuration for data paths, LLM endpoints, embeddings
- agents/ – three agent scripts, each encapsulating a LangChain chain or workflow
- components/ – chat_interface.py for the conversational UI layer
- models/ – data model definitions (e.g., Pydantic or dataclasses)
- utils/ – data_loader, time_series_utils, visualization helpers
- data/ – CSV files for IoT, plant, product, sales, supplier, perishability
3. Extract Additional Insights from README
- Tech Stack:
• LangChain for agent orchestration
• Ollama (granite3.3:8b) for LLM inference
• Snowflake Arctic v2 for vector embeddings
• Streamlit + Plotly for the UI and dashboards
4. Main Components and Their Interactions
a. Frontend (Streamlit)
- app.py / main.py spins up Streamlit, renders dashboards (components/visualization) and chat UI (components/chat_interface.py).
b. Agent Layer
- demand_prediction_agent.py, inventory_monitoring_agent.py, environmental_monitoring_agent.py
- Each uses LangChain chains, pulls data via utils/data_loader.py, transforms it via utils/time_series_utils.py, and outputs results.
c. Data Layer
- data/*.csv is loaded by utils/data_loader.py. Data models are defined in models/data_models.py.
d. LLM & Embedding Services (External)
- Ollama endpoint for granite3.3:8b LLM
- Snowflake Arctic v2 as the embedding store
e. Orchestration / Configuration
- config.py holds API keys, model names, data paths, and other settings.
5. Architectural Patterns & Design Principles
- Agent‑oriented modularity: each agent encapsulates its own workflow.
- Separation of concerns: UI vs. data loading vs. ML logic vs. visualization.
- Config‑driven deployment: config.py centralizes environment variables and endpoints.
- Use of external services to offload LLM inference and embedding storage.
6. Diagramming Guidelines Tailored to This Project
- Level 1: High‑level blocks
• User → Streamlit UI → Agent Orchestrator → Data & LLM Services → Results → UI
- Level 2: Decompose Agent Orchestrator block
• Demand Prediction Agent
• Inventory Monitoring Agent
• Environmental Monitoring Agent
• Shared utils & models
- For each block, annotate the primary technologies (e.g., “LangChain”, “Ollama LLM”, “Snowflake Arctic v2”, “Plotly”).
7. Relationships and Data Flows
- Use directional arrows:
• User input → Chat Interface → LangChain Agents
• Each Agent → Data Loader → CSV Data
• Each Agent → Ollama LLM / Embedding Store → Agent Logic → Visualization
• Results → Streamlit Dashboard (Plotly)
- Highlight feedback loops if agents write back to any store or trigger downstream agents.
8. Diagram Elements to Include
- Clear labels on each component block: e.g., “DemandPredictionAgent (LangChain)”
- Color‑coding:
• Blue: Frontend/UI components
• Green: Agent/Orchestration layer
• Orange: Data & Models
• Purple: External Services (LLM, Embeddings)
- Shapes:
• Cylinders for data stores (CSV files, embedding DB)
• Rectangles for services/microservices (agents, Streamlit)
• Cloud icons for external LLM service (Ollama)
- Legend explaining colors/shapes
9. Tailoring to This Full‑Stack Agentic Workflow
- Emphasize multi‑agent pattern: show the three agents in parallel feeding into the same UI.
- Show config.py as the single source of truth for endpoints and environment settings.
- Include the data folder as a CSV‑backed data lake, annotated with schema from data_models.py.
10. Final Advice
- Capture all pathways: user → UI → agent → data/LLM → response.
- Don’t overthink: break the system into the maximal number of logical components you identified.
- Ensure your diagram is legend‑driven, color‑coded, and arrow‑rich to convey flow and dependencies at a glance.
- Iterate: start with a simple 5‑block diagram, then expand each block into detailed sub‑components for high‑fidelity architecture reviews.
</explanation>