DeepakSilaych

Step 1: LLM Triggers the Handover

The process begins when the Language Model (LLM), after analyzing the conversation, decides human intervention is necessary.

1. LLM JSON Response: The LLM's decision is communicated via a JSON object. To trigger the transfer, this object contains a specific key-value pair: {"transfer_to_human": true, "reason": "Customer is asking for a supervisor"}.
2. Server-Side Trigger: The on_llm_response_a function in deepak/server.py serves as the callback for all LLM responses. It parses the incoming JSON and checks for the transfer_to_human boolean flag.
3. Initiating Transfer: If the flag is true, the server calls trigger_human_transfer_a (deepak/server.py:1865). This function orchestrates the start of the handover.
4. State Management: The core of the server's logic relies on a global dictionary _audio_out_leads which maps a unique lead_key to a StreamState dataclass object. Inside trigger_human_transfer_a:

• The call's state is

DeepakSilaych

Google Summer of Code 2025 Final Report: Anopheles Mosquito Genomic Taxon Classifier

• Contributor: Deepak Silaych
• Organisation: MalariaGEN
• Mentors: Anastasia Hernandez-Koutoucheva, Chris Clarkson, Jon Brenas
• Project Repository: malariagen/vector-taxon-classifier
• Contact: deepaksilaych@gmail.com / GitHub Profile

---