mentor.py

import re

from fastapi import APIRouter

from models import TranscriptSegment, ProactiveNotificationEndpointResponse, RealtimePluginRequest
from db import get_upsert_segment_to_transcript_plugin

router = APIRouter()

scan_segment_session = {}

# *******************************************************
# ************ Basic Proactive Notification Plugin ************
# *******************************************************


@router.post(
    '/mentor',
    tags=['mentor', 'basic', 'realtime', 'proactive_notification'],
    response_model=ProactiveNotificationEndpointResponse,
    response_model_exclude_none=True,
)
def mentoring(data: RealtimePluginRequest):
    def normalize(text):
        return re.sub(r' +', ' ', re.sub(r'[,?.!]', ' ', text)).lower().strip()

    session_id = data.session_id
    segments = get_upsert_segment_to_transcript_plugin('mentor-01', session_id, data.segments)
    if len(segments) <= len(data.segments) or session_id not in scan_segment_session:
        scan_segment_session[session_id] = 0
    scan_segment = scan_segment_session[session_id]

    # 1. Detect codewords. You could either use a simple regexp or call LLMs to trigger the step 2.
    codewords = ['hey Omi what do you think']
    scan_segments = segments[scan_segment:]
    print(session_id, "scan_segment", len(scan_segments), scan_segment)
    if len(scan_segments) == 0:
        return {}
    text_lower = normalize(" ".join([segment.text for segment in scan_segments]))
    pattern = r'\b(?:' + '|'.join(map(re.escape, [normalize(cw) for cw in codewords])) + r')\b'
    if not bool(re.search(pattern, text_lower)):
        return {}

    # 2. Generate mentoring prompt
    # Omi will replace {{user_name}} in your prompt with the user's name
    # Omi will replace {{user_facts}} in your prompt  with the user's known facts.
    scan_segment_session[session_id] = len(segments)
    transcript = TranscriptSegment.segments_as_string(segments)

    user_name = "{{user_name}}"
    user_facts = "{{user_facts}}"
    user_context = "{{user_context}}"
    user_chat = "{{user_chat}}"

    prompt = f"""
    You are an experienced mentor, that helps people achieve their goals during the meeting.
    You are advising {user_name} right now.

    {user_facts}

    The following is a {user_name}'s conversation, with the transcripts, that {user_name} had during the meeting.
    {user_name} wants to get the call-to-action advice to move faster during the meetting based on the conversation.

    First, identify the topics or problems that {user_name} is discussing or trying to resolve during the meeting, and then provide advice specific to those topics or problems.

    The advice must focus on the specific object mentioned in the conversation. The object could be a product, a person, or an event.

    The response must follow this format:
    Noticed you are trying to <meeting topics or problems>.
    If I were you, I'd <actions>.

    Remember {user_name} is busy so this has to be very efficient and concise.
    Respond in at most 100 words.

    Output your response in plain text, without markdown.

    If you cannot find the topic or problem of the meeting, respond 'Nah 🤷 ~'.

    Conversation:
    ```
    ${transcript}
    ```

    Converstation History:
    ```
    {user_chat}
    ```

    Context:
    ```
    {user_context}
    ```
    """.replace(
        '    ', ''
    ).strip()

    # 3. Respond with the format {notification: {prompt, params, context}}
    #   - context: {question, filters: {people, topics, entities}} | None
    return {
        'session_id': data.session_id,
        'notification': {
            'prompt': prompt,
            'params': ['user_name', 'user_facts', 'user_context', 'user_chat'],
        },
    }


@router.get('/setup/mentor', tags=['mentor'])
def is_setup_completed(uid: str):
    return {'is_setup_completed': True}