I Built CollabFlow: A Real-Time Collaboration Platform with AI-Powered Document Search

Description and use

CollabFlow merges project management, team chat, real‑time collaboration, and AI‑powered document search into a single platform, leveraging Socket.IO, Redis, LangChain, OpenAI embeddings, and Qdrant.

Problem

Teams need a unified workspace that supports project management, real-time communication, and intelligent document search without building separate tools.

Outcome

CollabFlow unifies project management, real‑time collaboration, and AI‑powered document search into a single, scalable platform, reducing tool fragmentation and enabling semantic search across workspace documents.

Workflow

• 1. User uploads a document via the web UI.
• 2. Backend creates a BullMQ background job for processing.
• 3. BullMQ queue triggers a worker that generates OpenAI embeddings using LangChain and LangGraph.
• 4. Embeddings are stored in Qdrant vector database.
• 5. Document becomes searchable via natural language queries in the UI.
• 6. Real‑time features (messaging, typing indicators, read receipts, presence) are handled by Socket.IO and Redis Pub/Sub, supporting 50+ concurrent collaborators per workspace.

Evidence

CollabFlow supports real‑time messaging, typing indicators, read receipts, user presence, and reconnect‑aware connections.

Source

The pipeline uses LangChain → LangGraph → OpenAI Embeddings → Qdrant.

Source