68c0985482
- Refactored OIDC flow to implement PKCE, state/nonce validation, and BFF pattern. - Centralized configuration in Settings class (DEV_MODE, FRONTEND_URL, OIDC_REDIRECT_URI). - Updated auth routers to use conditional secure cookie flags based on DEV_MODE. - Modernized and cleaned up test suite by removing legacy Streamlit tests. - Fixed linting errors and unused imports across the backend.
57 lines
3.2 KiB
Markdown
57 lines
3.2 KiB
Markdown
# Election Analytics Chatbot
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## Overview
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This project is an Election Analytics Chatbot built with a modern, stateful, and graph-based architecture. It is divided into a backend (Python, LangGraph) and a frontend (React, TypeScript).
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## Project Structure
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- **[Backend](./backend/GEMINI.md)**: Python-based LangGraph agent for data analysis and query processing.
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- **[Frontend](./frontend/GEMINI.md)**: React application for user interaction.
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## Key Technologies
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- **Backend**: LangGraph, LangChain, OpenAI/Google Gemini, PostgreSQL, FastAPI.
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- **Frontend**: React, TypeScript, Vite, Tailwind CSS, Shadcn UI.
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## Backend API (`/api/v1`)
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The backend provides a versioned REST and Streaming API:
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### Authentication (`/auth`)
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- `POST /register`: New user registration.
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- `POST /login`: Email/password login setting an `access_token` HttpOnly cookie.
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- `POST /logout`: Clears the authentication cookie.
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- `GET /me`: Returns the current authenticated user's profile.
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- `GET /oidc/login` & `/oidc/callback`: Full OIDC/SSO integration.
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### Agent execution (`/chat`)
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- `POST /stream`: A stateful SSE (Server-Sent Events) endpoint that streams the LangGraph agent's reasoning steps, model chunks, and generated plots in real-time.
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### History Management (`/conversations`)
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- `GET /`: List all conversations for the current user.
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- `POST /`: Create a new conversation.
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- `GET /{id}/messages`: Retrieve full message history including step-by-step reasoning.
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- `PATCH /{id}`: Rename or update conversation summaries.
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- `DELETE /{id}`: Persistent deletion of chat history.
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### Artifacts (`/artifacts`)
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- `GET /plots/{plot_id}`: Secure binary retrieval of generated analysis charts (PNG).
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## Frontend Architecture
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The frontend is a modern SPA (Single Page Application) designed for data-heavy interactions:
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- **State Management**: React-based state with Axios interceptors for automatic session handling (401 redirects).
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- **Authentication**: Seamlessly handles both traditional and OIDC login flows, persisting sessions via browser-native cookie handling.
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- **UI System**: Built with Shadcn UI and Tailwind CSS, featuring a responsive sidebar-based layout for conversation management.
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- **Real-time Visualization**: Supports streaming text responses and immediate rendering of base64-encoded or binary-retrieved analysis plots.
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## Documentation
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- **[Backend Guide](./backend/GEMINI.md)**: Detailed information about the backend architecture, migration goals, and implementation steps.
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- **[Frontend Guide](./frontend/GEMINI.md)**: Frontend development guide and technology stack.
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- **LangChain Docs**: See the `langchain-docs/` folder for local LangChain and LangGraph documentation.
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## Git Operations
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- All new feature and bug-fix branches must be created from the `conductor` branch except hot-fix.
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- The `conductor` branch serves as the primary development branch where integration occurs.
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- The `main` branch is reserved for stable, production-ready code.
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- Merges from `conductor` to `main` should only occur when significant milestones are reached and stability is verified.
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- Conductor-specific configuration or meta-files should remain on the `conductor` branch or its derivatives and never be merged into the `main` branch.
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- Use rebase to keep commit history clean before merging feature branches back into `conductor`.
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