Again, all these updates are optional -- it just shows you what you can do with your HomeLAB
I personally like using Kimi K2.5 + DeepSeek v3 and bouncing ideas and deep dives off each other.
DeepSeek Integration Walkthrough - HomeLAB v3.3
I have successfully integrated the DeepSeek model into HomeLAB v3.3, fulfilling the request to extract code from previous versions and place it into the current sovereign stack.
Changes Made
backend
config.py
: Added API endpoints and model configuration for DeepSeek.
orchestrator.py
: Implemented the
deepseek_dept
function, which handles direct API calls to DeepSeek with blackboard context and Neural Bridge sync.
frontend
gui.py
:
Added a new "DEEPSEEK" button to the primary control triad.
Updated the grid layout to accommodate the additional button while maintaining the premium terminal aesthetic.
Integrated the new department into the non-blocking task runner and status dashboard.
Verification Results
Backend Logic
I ran a dedicated test script
test_deepseek.py
which confirmed:
The DeepSeek Department correctly identifies missing API keys.
The orchestrator successfully constructs requests with blackboard context.
API responses are parsed correctly and formatted for the log area.
Testing DeepSeek Department Integration...
Output: **DEEPSEEK DEPT (deepseek-chat)**
DeepSeek success!
Model: deepseek-chat
DeepSeek integration check SUCCESSUI Integration
The GUI now features a dedicated DeepSeek button in the Cloud Engine sector:
TIP
You can now use DeepSeek-V3 for high-performance reasoning alongside the Gemini models. Simply enter your request in the Cloud input box and click the DEEPSEEK button.
Next Steps
Verify your
.env
has the correct DEEPSEEK_API_KEY.
Launch the lab using
startLAB.bat
or python src/gui.py.
