1. High-Level Architecture Your backend is a microservices event-driven system using:

• Kafka (Redpanda) for messaging between services
• Redis for state tracking and context
• FastAPI for HTTP APIs (adapters, app interface)
• Python asyncio for all service logic Core Flow

1. User Input (voice/text) → Frontend → WebSocket/API → Backend

2. Backend (app_interface) receives transcript, pushes to Kafka (transcript or agent.cmd)

3. Reasoning Service (GPT wrapper) decides which tool/agent to call (food, ride, mart, etc.)

4. Router dispatches the command to the correct domain agent via HTTP (adapters) and Kafka

5. Domain Agent (food_agent, ride_agent, mart_agent) mocks the action, writes result to Kafka

6. State Tracker updates Redis and emits status updates

7. Frontend can subscribe for updates or poll status

8. Key Components a. app_interface/

• mic_ws.py, text_ws.py: FastAPI WebSocket endpoints for ingesting voice/text from the frontend.
• Pushes incoming transcripts to Kafka. b. reasoning/
• main.py: The "brain"—consumes transcripts, uses GPT (LLM) to decide which tool to call (book_ride, order_food, order_mart).
• Maintains slot-filling state in Redis.
• If clarification is needed, triggers clarify agent. c. router/
• main.py: Listens for commands on Kafka, routes them to the correct HTTP endpoint (adapters) and then to the correct Kafka output topic. d. adapters/
• main.py: FastAPI app exposing REST endpoints for each domain (ride, food, mart, payment, location).
• Each endpoint is a thin wrapper that forwards requests to the correct agent. e. Domain Agents (food_agent, ride_agent, mart_agent)
• Each listens to agent.cmd Kafka topic.
• If the command matches their tool, they mock a response (e.g., confirm ride, food order) and write to their output topic (agent.out.ride, agent.out.food, etc.). f. state_tracker/
• Tracks the lifecycle of each command using Redis.
• Listens to all relevant Kafka topics and updates state/status for each trace_id. g. error_agent/
• Listens to a dead-letter queue (agent.error) and logs or forwards errors.

3. Data Flow Example User says: "Order me a burger"

4. Frontend sends transcript via WebSocket to app_interface.

5. app_interface writes to Kafka (transcript topic).

6. reasoning consumes transcript, determines intent (order_food), and required slots (e.g., food item, address).

7. If info is missing, triggers clarify agent; otherwise, emits command to agent.cmd.

8. router picks up command, POSTs to /food/order on adapters, then writes result to agent.out.food.

9. food_agent mocks order, writes confirmation to Kafka.

10. state_tracker updates Redis state for the trace_id.

11. Frontend can poll or subscribe for status updates.

12. Observations & Suggestions

• Mock Agents: All domain agents are currently mocks—they don’t call real APIs, just return canned responses.
• Slot Filling: The reasoning service handles slot filling and clarification using LLM prompts and Redis state.
• Adapters Layer: Provides a clean HTTP interface for each domain, decoupling the router from agent implementation.
• State Tracking: Centralized in Redis, keyed by trace_id, for easy status lookup.
• Error Handling: Errors are routed to a DLQ and logged by error_agent.

5. How to Extend or Debug

• To add a new domain: Add a new agent, adapter endpoint, and routing entry.
• To debug a flow: Trace the trace_id through Kafka topics and Redis state.
• To see live state: Use Redis CLI to inspect state:{trace_id}.
• To see logs: Check each service’s logs for info/warnings.

6. Example Command/Topic List From commands.txt and code:

• Kafka Topics: agent.cmd, agent.out.ride, agent.out.food, agent.out.mart, agent.error, agent.status, agent.out.clarify
• Redis Keys: state:{trace_id}

Summary Table Service Input Topic/API Output Topic/API Purpose app_interface WebSocket Kafka (transcript) Ingest user input reasoning transcript agent.cmd LLM intent/slot reasoning router agent.cmd adapters HTTP, Kafka Route to correct agent adapters HTTP Kafka HTTP → Kafka bridge food_agent agent.cmd agent.out.food Mock food order ride_agent agent.cmd agent.out.ride Mock ride booking mart_agent agent.cmd agent.out.mart Mock mart order state_tracker all topics Redis, agent.status Track state/status error_agent agent.error log/notify DLQ/error handling In summary:Your backend is a modular, event-driven, microservice system for multi-domain command handling, using Kafka for messaging, Redis for state, and FastAPI for HTTP APIs. Each agent is currently a mock, but the architecture supports real integrations. The reasoning service is the "brain" that interprets user input and orchestrates the rest. If you want a deep-dive into a specific service, flow, or want to see how to add a new feature, let me know!