feat(transcription): [2f388f42] integrate prompt database and AI insights
Implements the core functionality for the AI-powered analysis of meeting transcripts in the Transcription Tool. This commit introduces a new 'AI Insights' feature that allows users to generate various summaries and analyses from a transcript on demand. - Creates a to manage and version different AI prompts for tasks like generating meeting minutes, extracting action items, and creating sales summaries. - Adds a new responsible for orchestrating the analysis process: fetching the transcript, calling the Gemini API with the appropriate prompt, and caching the results in the database. - Extends the FastAPI backend with a new endpoint to trigger the insight generation. - Updates the React frontend () with a new 'AI Insights' panel, including buttons to trigger the analyses and a modal to display the results. - Updates the documentation () to reflect the new features, API endpoints, and version.
This commit is contained in:
@@ -11,6 +11,7 @@ from datetime import datetime
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from .config import settings
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from .database import init_db, get_db, Meeting, TranscriptChunk, AnalysisResult, SessionLocal
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from .services.orchestrator import process_meeting_task
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from .services.insights_service import generate_insight
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# Initialize FastAPI App
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app = FastAPI(
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@@ -42,7 +43,8 @@ def list_meetings(db: Session = Depends(get_db)):
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@app.get("/api/meetings/{meeting_id}")
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def get_meeting(meeting_id: int, db: Session = Depends(get_db)):
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meeting = db.query(Meeting).options(
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joinedload(Meeting.chunks)
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joinedload(Meeting.chunks),
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joinedload(Meeting.analysis_results) # Eager load analysis results
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).filter(Meeting.id == meeting_id).first()
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if not meeting:
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@@ -99,6 +101,26 @@ async def upload_audio(
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from pydantic import BaseModel
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class InsightRequest(BaseModel):
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insight_type: str
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@app.post("/api/meetings/{meeting_id}/insights")
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def create_insight(meeting_id: int, payload: InsightRequest, db: Session = Depends(get_db)):
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"""
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Triggers the generation of a specific insight (e.g., meeting minutes, action items).
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If the insight already exists, it returns the stored result.
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Otherwise, it generates, stores, and returns the new insight.
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"""
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try:
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insight = generate_insight(db, meeting_id, payload.insight_type)
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return insight
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except ValueError as e:
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raise HTTPException(status_code=400, detail=str(e))
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except Exception as e:
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# For unexpected errors, return a generic 500 error
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print(f"ERROR: Unexpected error in create_insight: {e}")
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raise HTTPException(status_code=500, detail="An internal error occurred while generating the insight.")
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class RenameRequest(BaseModel):
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old_name: str
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new_name: str
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113
transcription-tool/backend/prompt_library.py
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113
transcription-tool/backend/prompt_library.py
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@@ -0,0 +1,113 @@
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MEETING_MINUTES_PROMPT = """
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You are a professional assistant specialized in summarizing meeting transcripts.
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Your task is to create a formal and structured protocol (Meeting Minutes) from the provided transcript.
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Please analyze the following transcript and generate the Meeting Minutes in German.
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**Transcript:**
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---
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{transcript_text}
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---
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**Instructions for the Meeting Minutes:**
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1. **Header:**
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* Start with a clear title: "Meeting-Protokoll".
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* Add a placeholder for the meeting date: "Datum: [Datum des Meetings]".
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2. **Agenda Items:**
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* Identify the main topics discussed.
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* Structure the protocol using these topics as headlines (e.g., "Tagesordnungspunkt 1: [Thema]").
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3. **Key Discussions & Decisions:**
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* For each agenda item, summarize the key points of the discussion.
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* Clearly list all decisions that were made. Use a format like "**Entscheidung:** ...".
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4. **Action Items (Next Steps):**
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* Extract all clear tasks or action items.
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* For each action item, identify the responsible person (Owner) and the deadline, if mentioned.
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* Present the action items in a clear list under a headline "Nächste Schritte / Action Items". Use the format: "- [Aufgabe] (Verantwortlich: [Person], Fällig bis: [Datum/unbestimmt])".
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5. **General Tone & Language:**
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* The protocol must be written in formal, professional German.
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* Be concise and focus on the essential information (discussions, decisions, tasks).
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* Do not invent information that is not present in the transcript.
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Please provide the output in Markdown format.
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"""
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ACTION_ITEMS_PROMPT = """
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You are a highly efficient assistant focused on productivity and task management.
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Your goal is to extract all actionable tasks (Action Items) from a meeting transcript.
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Please analyze the following transcript and list all tasks.
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**Transcript:**
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---
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{transcript_text}
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---
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**Instructions for the Action Item List:**
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1. **Extraction:**
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* Carefully read the entire transcript and identify every statement that constitutes a task, a to-do, or a commitment to do something.
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* Ignore general discussions, opinions, and status updates. Focus only on future actions.
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2. **Format:**
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* Present the extracted tasks as a bulleted list.
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* For each task, clearly state:
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* **What** needs to be done.
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* **Who** is responsible for it (Owner).
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* **When** it should be completed by (Due Date), if mentioned.
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3. **Output Structure:**
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* Use the following format for each item: `- [Task Description] (Owner: [Person's Name], Due: [Date/unspecified])`
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* If the owner or due date is not explicitly mentioned, use "[unbestimmt]".
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* The list should be titled "Action Item Liste".
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4. **Language:**
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* The output should be in German.
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Please provide the output in Markdown format.
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"""
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SALES_SUMMARY_PROMPT = """
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You are a Senior Sales Manager analyzing a meeting transcript from a client conversation.
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Your objective is to create a concise, rollenbasierte Zusammenfassung for the sales team. The summary should highlight key information relevant to closing a deal.
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Please analyze the following transcript and generate a Sales Summary.
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**Transcript:**
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---
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{transcript_text}
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---
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**Instructions for the Sales Summary:**
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1. **Customer Needs & Pain Points:**
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* Identify and list the core problems, challenges, and needs expressed by the client.
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* What are their primary business goals?
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2. **Buying Signals:**
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* Extract any phrases or questions that indicate a strong interest in the product/service (e.g., questions about price, implementation, specific features).
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3. **Key Decision-Makers:**
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* Identify the people in the meeting who seem to have the most influence on the purchasing decision. Note their role or title if mentioned.
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4. **Budget & Timeline:**
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* Note any mentions of budget, pricing expectations, or the timeline for their decision-making process.
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5. **Next Steps (from a Sales Perspective):**
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* What are the immediate next actions the sales team needs to take to move this deal forward? (e.g., "Send proposal," "Schedule demo for the technical team").
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**Output Format:**
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* Use clear headings for each section (e.g., "Kundenbedürfnisse & Pain Points", "Kaufsignale").
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* Use bullet points for lists.
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* The language should be direct, actionable, and in German.
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Please provide the output in Markdown format.
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"""
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# You can add more prompts here for other analysis types.
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# For example, a prompt for a technical summary, a marketing summary, etc.
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110
transcription-tool/backend/services/insights_service.py
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110
transcription-tool/backend/services/insights_service.py
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@@ -0,0 +1,110 @@
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import sys
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import os
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from sqlalchemy.orm import Session
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from .. import database
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from .. import prompt_library
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# Add project root to path to allow importing from 'helpers'
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..', '..')))
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from helpers import call_gemini_flash
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def _format_transcript(chunks: list[database.TranscriptChunk]) -> str:
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"""
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Formats the transcript chunks into a single, human-readable string.
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Example: "[00:00:01] Speaker A: Hello world."
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"""
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full_transcript = []
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# Sort chunks by their index to ensure correct order
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sorted_chunks = sorted(chunks, key=lambda c: c.chunk_index)
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for chunk in sorted_chunks:
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if not chunk.json_content:
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continue
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for item in chunk.json_content:
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# json_content can be a list of dicts
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if isinstance(item, dict):
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speaker = item.get('speaker', 'Unknown')
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start_time = item.get('start', 0)
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text = item.get('line', '')
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# Format timestamp from seconds to HH:MM:SS
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hours, remainder = divmod(int(start_time), 3600)
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minutes, seconds = divmod(remainder, 60)
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timestamp = f"{hours:02}:{minutes:02}:{seconds:02}"
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full_transcript.append(f"[{timestamp}] {speaker}: {text}")
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return "\n".join(full_transcript)
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def get_prompt_by_type(insight_type: str) -> str:
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"""
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Returns the corresponding prompt from the prompt_library based on the type.
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"""
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if insight_type == "meeting_minutes":
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return prompt_library.MEETING_MINUTES_PROMPT
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elif insight_type == "action_items":
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return prompt_library.ACTION_ITEMS_PROMPT
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elif insight_type == "sales_summary":
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return prompt_library.SALES_SUMMARY_PROMPT
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else:
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raise ValueError(f"Unknown insight type: {insight_type}")
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def generate_insight(db: Session, meeting_id: int, insight_type: str) -> database.AnalysisResult:
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"""
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Generates a specific insight for a meeting, stores it, and returns it.
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Checks for existing analysis to avoid re-generating.
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"""
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# 1. Check if the insight already exists
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existing_insight = db.query(database.AnalysisResult).filter(
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database.AnalysisResult.meeting_id == meeting_id,
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database.AnalysisResult.prompt_key == insight_type
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).first()
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if existing_insight:
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return existing_insight
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# 2. Get the meeting and its transcript
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meeting = db.query(database.Meeting).filter(database.Meeting.id == meeting_id).first()
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if not meeting:
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raise ValueError(f"Meeting with id {meeting_id} not found.")
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if not meeting.chunks:
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raise ValueError(f"Meeting with id {meeting_id} has no transcript chunks.")
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# 3. Format the transcript and select the prompt
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transcript_text = _format_transcript(meeting.chunks)
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if not transcript_text.strip():
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raise ValueError(f"Transcript for meeting {meeting_id} is empty.")
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prompt_template = get_prompt_by_type(insight_type)
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final_prompt = prompt_template.format(transcript_text=transcript_text)
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# 4. Call the AI model
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# Update meeting status
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meeting.status = "ANALYZING"
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db.commit()
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try:
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generated_text = call_gemini_flash(prompt=final_prompt, temperature=0.5)
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# 5. Store the new insight
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new_insight = database.AnalysisResult(
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meeting_id=meeting_id,
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prompt_key=insight_type,
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result_text=generated_text
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)
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db.add(new_insight)
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meeting.status = "COMPLETED"
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db.commit()
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db.refresh(new_insight)
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return new_insight
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except Exception as e:
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meeting.status = "ERROR"
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db.commit()
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# Log the error properly in a real application
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print(f"Error generating insight for meeting {meeting_id}: {e}")
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raise
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