The real-time data streaming feature relies on a well-designed asynchronous processing architecture with the following core implementation logic:
Technical components
- Redis Message Broker: Delivering Research Progress Events as Pub/Sub Middleware
- FastAPI Backend: Maintaining long front-end connections via the WebSocket protocol
- LangGraph state machine: Translate each research step into observable state change events
workflow
- After the front-end initiates a research request, the back-end creates an asynchronous task and returns the task ID
- The LangGraph agent performs key actions such as "generate query", "fetch web page", etc. and publishes them to the Redis channel.
- Front-end subscribes to channels with specific task IDs via WebSocket, rendering status updates in real-time
Configuration points
- Must be set in the .env file
REDIS_URL=redis://localhost:6379 - Development environments need to start a separate Redis service, production environments are recommended to use cloud hosting services
- Streamed content contains structured data that the front-end can parse out for metadata such as current stage, percentage of progress, etc.
This design allows long-running research tasks, which may last several minutes, to provide immediate feedback, dramatically improving the user experience.
This answer comes from the articleGemini Fullstack LangGraph: a full-stack application for intelligent research based on Gemini and LangGraphThe
May not be reproduced without permission:AI productivity tools " How is the real-time streaming output feature of the project implemented? What components are needed to support it?
