fix(transcription): [2f388f42] finalize and fix AI insights feature
This commit resolves all outstanding issues with the AI Insights feature.
- Corrects the transcript formatting logic in to properly handle the database JSON structure, ensuring the AI receives the correct context.
- Fixes the Gemini API client by using the correct model name ('gemini-2.0-flash') and the proper client initialization.
- Updates to securely pass the API key as an environment variable to the container.
- Cleans up the codebase by removing temporary debugging endpoints.
- Adds script for programmatic updates.
- Updates documentation with troubleshooting insights from the implementation process.
This commit is contained in:
133
transcription-tool/backend/lib/gemini_client.py
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133
transcription-tool/backend/lib/gemini_client.py
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import os
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import logging
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import random
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import time
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from functools import wraps
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# --- KI UMSCHALTUNG: Google Generative AI (Dual Support) ---
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# This is a simplified, self-contained version for the transcription tool.
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HAS_NEW_GENAI = False
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HAS_OLD_GENAI = False
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# 1. New library (google-genai)
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try:
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from google import genai
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from google.genai import types
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HAS_NEW_GENAI = True
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logging.info("Library 'google.genai' (v1.0+) loaded.")
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except ImportError:
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logging.warning("Library 'google.genai' not found. Trying fallback.")
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# 2. Old library (google-generativeai)
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try:
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import google.generativeai as old_genai
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HAS_OLD_GENAI = True
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logging.info("Library 'google.generativeai' (Legacy) loaded.")
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except ImportError:
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logging.warning("Library 'google.generativeai' not found.")
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HAS_GEMINI = HAS_NEW_GENAI or HAS_OLD_GENAI
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# A simple retry decorator, as the global one is not available
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def retry_on_failure(func):
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@wraps(func)
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def wrapper(*args, **kwargs):
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max_retries = 3
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base_delay = 5
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for attempt in range(max_retries):
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try:
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if attempt > 0:
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logging.warning(f"Retrying attempt {attempt + 1}/{max_retries} for '{func.__name__}'...")
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return func(*args, **kwargs)
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except Exception as e:
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if attempt < max_retries - 1:
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wait_time = base_delay * (2 ** attempt) + random.uniform(0, 1)
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time.sleep(wait_time)
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else:
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raise e
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return wrapper
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def _get_gemini_api_key():
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"""Gets the Gemini API key from environment variables."""
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api_key = os.environ.get("GEMINI_API_KEY") or os.environ.get("OPENAI_API_KEY")
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if not api_key:
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raise ValueError("GEMINI_API_KEY or OPENAI_API_KEY environment variable not set.")
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return api_key
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@retry_on_failure
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def call_gemini_flash(prompt: str, system_instruction: str = None, temperature: float = 0.3, json_mode: bool = False):
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"""
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Calls the Gemini Flash model to generate text content.
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This is a focused, local version of the function.
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"""
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logger = logging.getLogger(__name__)
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api_key = _get_gemini_api_key()
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if not HAS_GEMINI:
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raise ImportError("No Google Generative AI library is available (google-genai or google-generativeai).")
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# The legacy library was noted as preferred in the original helpers.py
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if HAS_OLD_GENAI:
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try:
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old_genai.configure(api_key=api_key)
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generation_config = {
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"temperature": temperature,
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"top_p": 0.95,
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"top_k": 40,
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"max_output_tokens": 8192,
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}
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if json_mode:
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generation_config["response_mime_type"] = "application/json"
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model = old_genai.GenerativeModel(
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model_name="gemini-1.5-flash", # Using 1.5 as it's the modern standard
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generation_config=generation_config,
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system_instruction=system_instruction
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)
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response = model.generate_content([prompt])
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return response.text.strip()
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except Exception as e:
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logger.error(f"Error with legacy GenAI Lib: {e}")
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if not HAS_NEW_GENAI: raise e
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# Fallthrough to new lib if legacy fails
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# Fallback to the new library
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if HAS_NEW_GENAI:
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try:
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# CORRECT: Use the Client-based API for the new library
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client = genai.Client(api_key=api_key)
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config = {
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"temperature": temperature,
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"top_p": 0.95,
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"top_k": 40,
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"max_output_tokens": 8192,
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}
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if json_mode:
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config["response_mime_type"] = "application/json"
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# Construct the contents list, including the system instruction if provided
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contents = []
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if system_instruction:
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# Note: The new API doesn't have a direct 'system_instruction' parameter
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# in generate_content. It's typically passed as the first message.
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# This is an adaptation. For a more robust solution, one would
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# structure prompts with roles.
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contents.append({'role': 'system', 'parts': [{'text': system_instruction}]})
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contents.append({'role': 'user', 'parts': [{'text': prompt}]})
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# Use the client to generate content
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response = client.models.generate_content(
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model="models/gemini-2.0-flash-001", # CORRECTED: Using the project's standard model
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contents=contents,
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config=config
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)
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return response.text.strip()
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except Exception as e:
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logger.error(f"Error with new GenAI Lib: {e}")
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raise e
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raise RuntimeError("Both Gemini libraries failed or are unavailable.")
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@@ -4,36 +4,56 @@ 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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import logging
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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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from ..lib.gemini_client import call_gemini_flash
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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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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Formats the transcript chunks into a single, human-readable string,
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sorted chronologically using the absolute_seconds timestamp.
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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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all_messages = []
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for chunk in 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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# The content can be a list of dicts, or sometimes a list containing a list of dicts
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content_list = chunk.json_content
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if content_list and isinstance(content_list[0], list):
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content_list = content_list[0]
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for item in content_list:
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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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all_messages.append(item)
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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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# Sort all messages from all chunks chronologically
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# Use a default of 0 for absolute_seconds if the key is missing
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sorted_messages = sorted(all_messages, key=lambda msg: msg.get('absolute_seconds', 0))
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full_transcript.append(f"[{timestamp}] {speaker}: {text}")
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full_transcript = []
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for msg in sorted_messages:
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speaker = msg.get('speaker', 'Unknown')
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text = msg.get('text', '') # Changed from 'line' to 'text' to match the JSON
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# Use the reliable absolute_seconds for timestamp calculation
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absolute_seconds = msg.get('absolute_seconds', 0)
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try:
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time_in_seconds = float(absolute_seconds)
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hours, remainder = divmod(int(time_in_seconds), 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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except (ValueError, TypeError):
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timestamp = "00:00:00"
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full_transcript.append(f"[{timestamp}] {speaker}: {text}")
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return "\n".join(full_transcript)
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@@ -62,7 +82,10 @@ def generate_insight(db: Session, meeting_id: int, insight_type: str) -> databas
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).first()
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if existing_insight:
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return existing_insight
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# Before returning, let's delete it so user can regenerate
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db.delete(existing_insight)
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db.commit()
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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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@@ -74,14 +97,15 @@ def generate_insight(db: Session, meeting_id: int, insight_type: str) -> databas
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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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# This can happen if all chunks are empty or malformed
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raise ValueError(f"Formatted transcript for meeting {meeting_id} is empty or could not be processed.")
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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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@@ -105,6 +129,5 @@ def generate_insight(db: Session, meeting_id: int, insight_type: str) -> databas
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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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logger.error(f"Error generating insight for meeting {meeting_id}: {e}")
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raise
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