How amankumarranjan8969/jal-setu-ground-water-forecasting-and-rag-assistant works
Jal Setu is a Streamlit dashboard for seasonal groundwater forecasting, with a question-answering assistant grounded in both run results and project knowledge. The main workflow is upload a seasonal workbook, prepare and model the data, inspect forecasts and diagnostics, and optionally export results. A separate model family adds physics-informed, reservoir, SARIMA, and optional LSTM results. The chat assistant handles small talk directly or answers substantive questions using retrieved documentation and live run context, with optional hosted LLM providers. Some wiring details are inferred only from module descriptions and the partial app excerpt.
Main entry points: app.py Streamlit app · assistant.py Assistant orchestration · pipeline.py Seasonal pipeline · deep_models.py Deep model family
Components 18
Dashboard
- Streamlit app
groundwater-dashboard-updatedv2_Final_finnished_With_RAG/app.py - Uploaded workbook
Forecasting
- Seasonal pipeline
groundwater-dashboard-updatedv2_Final_finnished_With_RAG/pipeline.py - Deep model family
groundwater-dashboard-updatedv2_Final_finnished_With_RAG/deep_models.py - Forecast results
Analysis and export
- Forecast views
groundwater-dashboard-updatedv2_Final_finnished_With_RAG/app.py - Diagnostics and plots
groundwater-dashboard-updatedv2_Final_finnished_With_RAG/app.py - Excel export
groundwater-dashboard-updatedv2_Final_finnished_With_RAG/pipeline.py
Grounded assistant
- Chat question
- Assistant orchestration
groundwater-dashboard-updatedv2_Final_finnished_With_RAG/rag/assistant.py - Small-talk replies
groundwater-dashboard-updatedv2_Final_finnished_With_RAG/rag/smalltalk.py - Knowledge retriever
groundwater-dashboard-updatedv2_Final_finnished_With_RAG/rag/retriever.py - Knowledge documents
groundwater-dashboard-updatedv2_Final_finnished_With_RAG/rag/knowledge_base - Run context
groundwater-dashboard-updatedv2_Final_finnished_With_RAG/rag/context.py - LLM provider
groundwater-dashboard-updatedv2_Final_finnished_With_RAG/rag/llm_provider.py - Grounded answer
Other components
- Dashboard user
- Hosted LLM
Connections 23
- Dashboard user → Streamlit app: uses
- Dashboard user → Uploaded workbook: provides
- Uploaded workbook → Streamlit app: uploads
- Streamlit app → Seasonal pipeline: runs workflow
- Streamlit app → Deep model family: runs models
- Seasonal pipeline → Forecast results: produces
- Deep model family → Forecast results: produces
- Streamlit app → Forecast views: renders
- Streamlit app → Diagnostics and plots: renders
- Streamlit app → Excel export: requests export
- Forecast results → Forecast views: supplies results
- Forecast results → Diagnostics and plots: supplies results
- Dashboard user → Chat question: asks
- Chat question → Streamlit app: enters chat
- Streamlit app → Assistant orchestration: dispatches question
- Assistant orchestration → Small-talk replies: checks reply
- Assistant orchestration → Knowledge retriever: searches
- Knowledge retriever → Knowledge documents: loads documents
- Assistant orchestration → Run context: summarizes run
- Assistant orchestration → LLM provider: requests completion
- LLM provider → Hosted LLM: calls provider
- Assistant orchestration → Grounded answer: composes answer
- Grounded answer → Streamlit app: returns answer