Files

Yunxiao Xu 68c0985482 feat(auth): Complete OIDC security refactor and modernize test suite

- 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.

2026-02-15 02:50:26 -08:00

3.2 KiB

Raw Blame History

Election Analytics Chatbot

Overview

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).

Project Structure

Backend: Python-based LangGraph agent for data analysis and query processing.
Frontend: React application for user interaction.

Key Technologies

Backend: LangGraph, LangChain, OpenAI/Google Gemini, PostgreSQL, FastAPI.
Frontend: React, TypeScript, Vite, Tailwind CSS, Shadcn UI.

Backend API (`/api/v1`)

The backend provides a versioned REST and Streaming API:

Authentication (`/auth`)

POST /register: New user registration.
POST /login: Email/password login setting an access_token HttpOnly cookie.
POST /logout: Clears the authentication cookie.
GET /me: Returns the current authenticated user's profile.
GET /oidc/login & /oidc/callback: Full OIDC/SSO integration.

Agent execution (`/chat`)

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.

History Management (`/conversations`)

GET /: List all conversations for the current user.
POST /: Create a new conversation.
GET /{id}/messages: Retrieve full message history including step-by-step reasoning.
PATCH /{id}: Rename or update conversation summaries.
DELETE /{id}: Persistent deletion of chat history.

Artifacts (`/artifacts`)

GET /plots/{plot_id}: Secure binary retrieval of generated analysis charts (PNG).

Frontend Architecture

The frontend is a modern SPA (Single Page Application) designed for data-heavy interactions:

State Management: React-based state with Axios interceptors for automatic session handling (401 redirects).
Authentication: Seamlessly handles both traditional and OIDC login flows, persisting sessions via browser-native cookie handling.
UI System: Built with Shadcn UI and Tailwind CSS, featuring a responsive sidebar-based layout for conversation management.
Real-time Visualization: Supports streaming text responses and immediate rendering of base64-encoded or binary-retrieved analysis plots.

Documentation

Backend Guide: Detailed information about the backend architecture, migration goals, and implementation steps.
Frontend Guide: Frontend development guide and technology stack.
LangChain Docs: See the langchain-docs/ folder for local LangChain and LangGraph documentation.

Git Operations

All new feature and bug-fix branches must be created from the conductor branch except hot-fix.
The conductor branch serves as the primary development branch where integration occurs.
The main branch is reserved for stable, production-ready code.
Merges from conductor to main should only occur when significant milestones are reached and stability is verified.
Conductor-specific configuration or meta-files should remain on the conductor branch or its derivatives and never be merged into the main branch.
Use rebase to keep commit history clean before merging feature branches back into conductor.

3.2 KiB Raw Blame History