54 lines
1.9 KiB
Markdown
54 lines
1.9 KiB
Markdown
# Distributed Runtime Flow
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Dynavera behaves like a streaming agentic system rather than a simple CRUD app. Runtime responsibility is split into three buckets.
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## 1) MCP Surface (Django-side tool layer)
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This is the tool-facing layer that lets the model request structured actions such as retrieval and session updates.
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Typical tool intents:
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- `search_knowledge(query, role_uuid)`
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- `get_user_progress(user/session context)`
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- `update_session_state(session_uuid, patch)`
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Conceptually, this layer translates model tool calls into standard Django queries and vector lookups.
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## 2) Orchestrator (Channels consumer + async control loop)
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The orchestrator lives in the WebSocket runtime and coordinates each user request lifecycle.
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Typical interaction path:
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1. User sends message over WebSocket
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2. Orchestrator builds/updates context
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3. Orchestrator calls inference endpoint
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4. Model requests tool calls when needed
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5. Orchestrator executes tool calls and continues generation
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6. Streamed/assembled response returns to user
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This is the central control plane for session continuity, tool usage, and response streaming.
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## 3) GPU Inference Pipe (passive engine)
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The GPU service is designed as a passive inference engine:
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- Receives prompts/inference payloads
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- Produces chat/embedding outputs
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- Does not initiate calls back into the VPS
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Using OpenAI-style request/response patterns keeps integration predictable.
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## Interface Summary
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| Component | Typical Path / Endpoint | Role |
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| :--- | :--- | :--- |
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| MCP Surface | Internal Django tool handlers (and/or MCP endpoint) | Data/tool translation |
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| Orchestrator | `apps.onboarding.consumers` | Coordination + streaming |
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| GPU Inference | `gpu_server.py` HTTP endpoints | Generation + embeddings |
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## Navigation
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- [Application Structure (Detailed)](application-structure.md)
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- [Deployment Topologies](deployment-topologies.md)
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- [Project README](../README.md)
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