mirror of https://github.com/Monadical-SAS/reflector.git synced 2026-02-04 09:56:47 +00:00

Files

Igor Loskutov 316f7b316d feat: add WebVTT context generation to chat WebSocket endpoint

- Import topics_to_webvtt_named and recordings controller
- Add _get_is_multitrack helper function
- Generate WebVTT context on WebSocket connection
- Add get_context message type to retrieve WebVTT
- Maintain backward compatibility with echo for other messages
- Add test fixture and test for WebVTT context generation

Implements task fn-1.2: WebVTT context generation for transcript chat

2026-01-12 18:24:47 -05:00

1.8 KiB

Raw Blame History

Task 3: LLM Streaming Integration

File: server/reflector/views/transcripts_chat.py (modify) Lines: ~35 Dependencies: Task 2

Objective

Integrate LLM streaming with conversation management.

Implementation

from llama_index.core import Settings
from reflector.llm import LLM
from reflector.settings import settings

# After WebVTT generation:
# Configure LLM
llm = LLM(settings=settings, temperature=0.7)

# System message
system_msg = f"""You are analyzing this meeting transcript (WebVTT):

{webvtt_truncated}

Answer questions about content, speakers, timeline. Include timestamps when relevant."""

# Conversation history
conversation_history = [{"role": "system", "content": system_msg}]

# Replace echo loop with:
try:
    while True:
        data = await websocket.receive_json()
        if data["type"] != "message":
            continue

        # Add user message
        user_msg = {"role": "user", "content": data["text"]}
        conversation_history.append(user_msg)

        # Stream LLM response
        assistant_msg = ""
        async for chunk in Settings.llm.astream_chat(conversation_history):
            token = chunk.delta
            await websocket.send_json({"type": "token", "text": token})
            assistant_msg += token

        # Save assistant response
        conversation_history.append({"role": "assistant", "content": assistant_msg})
        await websocket.send_json({"type": "done"})

except WebSocketDisconnect:
    pass
except Exception as e:
    await websocket.send_json({"type": "error", "message": str(e)})

Validation

LLM responds to user messages
Tokens stream incrementally
Conversation history maintained
done message sent after completion
Errors caught and sent to client

Notes

Test with: "What was discussed?"
Verify timestamps appear in responses

1.8 KiB Raw Blame History