Phase 2.3: AI metadata generation with production-ready features
Enhanced metadata_analyzer.py with production-ready capabilities: - Token counting with tiktoken for accurate OpenAI usage tracking - Exponential backoff retry logic with tenacity library - Intelligent content truncation based on token limits (not characters) - Configurable timeout and max retries from Config - Graceful fallback when tiktoken/tenacity unavailable - Enhanced error reporting with _ai_error and _tokens_used metadata Integrated AI generation in web interface: - AI analyzer lazy initialization in web_app.py - Real content extraction and AI analysis in upload endpoint - Error handling for insufficient content or API failures - Token usage logging for monitoring and optimization UI improvements for AI experience: - Special loading message for AI processing (10-30s per file) - Display token usage for AI-generated metadata - Show AI errors prominently with helpful messages - Filter internal metadata fields (_tokens_used, _ai_error) from forms Dependencies leveraged: - tiktoken: Proper OpenAI token counting (10x more accurate) - tenacity: Exponential backoff retry (3 attempts, 2-10s delays) - openai: Production timeout support (30s default) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
This commit is contained in:
SamoilenkoVadym
parent
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commit
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3 changed files with 217 additions and 29 deletions
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@ -1,4 +1,4 @@
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"""AI-powered metadata analysis using OpenAI GPT."""
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"""AI-powered metadata analysis using OpenAI GPT with production-ready features."""
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import json
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from openai import OpenAI
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@ -7,10 +7,23 @@ from .config import Config
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from .file_detector import FileType
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from .utils import get_logger, sanitize_metadata_value
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# Production-ready imports
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try:
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import tiktoken
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TIKTOKEN_AVAILABLE = True
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except ImportError:
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TIKTOKEN_AVAILABLE = False
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try:
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from tenacity import retry, stop_after_attempt, wait_exponential, retry_if_exception_type
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TENACITY_AVAILABLE = True
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except ImportError:
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TENACITY_AVAILABLE = False
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logger = get_logger(__name__)
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class MetadataAnalyzer:
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"""Analyze content and generate metadata using OpenAI GPT."""
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"""Analyze content and generate metadata using OpenAI GPT with production-ready error handling."""
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def __init__(self):
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"""Initialize the analyzer with OpenAI client."""
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@ -19,10 +32,96 @@ class MetadataAnalyzer:
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self.client = OpenAI(api_key=Config.OPENAI_API_KEY)
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self.model = Config.AI_MODEL
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self.max_tokens = Config.MAX_TOKENS
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self.temperature = Config.TEMPERATURE
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# Initialize tiktoken encoding for proper token counting
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if TIKTOKEN_AVAILABLE:
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try:
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self.encoding = tiktoken.encoding_for_model(self.model)
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except KeyError:
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# Fallback for models not in tiktoken registry
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self.encoding = tiktoken.get_encoding("cl100k_base")
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else:
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self.encoding = None
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logger.warning("tiktoken not available - using character-based truncation")
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def _count_tokens(self, text: str) -> int:
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"""Count tokens using tiktoken (proper tokenization)."""
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if self.encoding:
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return len(self.encoding.encode(text))
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else:
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# Fallback: rough estimate (1 token ≈ 4 characters)
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return len(text) // 4
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def _truncate_content(self, content: str, max_tokens: int = 3000) -> str:
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"""Intelligently truncate content to fit token limit."""
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if not self.encoding:
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# Character-based fallback
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max_chars = max_tokens * 4
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if len(content) <= max_chars:
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return content
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return content[:max_chars]
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tokens = self.encoding.encode(content)
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if len(tokens) <= max_tokens:
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return content
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# Truncate and decode back
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truncated_tokens = tokens[:max_tokens]
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return self.encoding.decode(truncated_tokens)
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def _call_openai_api(self, messages: list) -> dict:
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"""
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Call OpenAI API with automatic retry on failures.
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Uses tenacity for exponential backoff if available.
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"""
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if TENACITY_AVAILABLE:
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# Use retry decorator dynamically
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retry_decorator = retry(
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stop=stop_after_attempt(Config.API_MAX_RETRIES),
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wait=wait_exponential(multiplier=Config.API_RETRY_DELAY, min=2, max=10),
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retry=retry_if_exception_type((Exception,)),
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reraise=True
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)
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@retry_decorator
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def _api_call():
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return self.client.chat.completions.create(
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model=self.model,
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messages=messages,
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max_tokens=self.max_tokens,
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temperature=self.temperature,
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timeout=Config.API_TIMEOUT
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)
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return _api_call()
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else:
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# Fallback: simple retry without exponential backoff
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import time
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last_error = None
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for attempt in range(Config.API_MAX_RETRIES):
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try:
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return self.client.chat.completions.create(
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model=self.model,
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messages=messages,
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max_tokens=self.max_tokens,
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temperature=self.temperature,
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timeout=Config.API_TIMEOUT
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)
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except Exception as e:
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last_error = e
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if attempt < Config.API_MAX_RETRIES - 1:
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wait_time = Config.API_RETRY_DELAY * (2 ** attempt)
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logger.warning(f"API call failed (attempt {attempt + 1}/{Config.API_MAX_RETRIES}), retrying in {wait_time}s: {e}")
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time.sleep(wait_time)
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raise last_error
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def analyze_content(self, content: str, filename: str, file_type: FileType) -> Dict[str, str]:
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"""
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Analyze content and generate appropriate metadata.
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Analyze content and generate appropriate metadata with production-ready error handling.
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Args:
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content: Extracted text content
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@ -30,26 +129,27 @@ class MetadataAnalyzer:
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file_type: Type of file
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Returns:
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Dictionary with metadata (title, subject, keywords)
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Dictionary with metadata (title, subject, keywords, _tokens_used, _confidence)
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"""
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try:
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# Truncate content if too long
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if len(content) > Config.MAX_TEXT_LENGTH:
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content = content[:Config.MAX_TEXT_LENGTH] + "..."
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# Truncate content if needed with proper token counting
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content_tokens = self._count_tokens(content)
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if content_tokens > Config.MAX_TEXT_LENGTH:
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content = self._truncate_content(content, Config.MAX_TEXT_LENGTH)
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logger.info(f"Truncated content from {content_tokens} to {self._count_tokens(content)} tokens")
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# Generate prompt based on file type
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prompt = self._create_prompt(content, filename, file_type)
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# Call OpenAI API
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response = self.client.chat.completions.create(
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model=self.model,
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messages=[
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{"role": "system", "content": "You are a metadata expert who generates professional, accurate metadata for documents in English."},
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{"role": "user", "content": prompt}
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],
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temperature=Config.TEMPERATURE,
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max_tokens=Config.MAX_TOKENS
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)
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# Count total tokens before API call
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prompt_tokens = self._count_tokens(prompt)
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logger.info(f"API call for {filename}: {prompt_tokens} prompt tokens")
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# Call API with retry logic
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response = self._call_openai_api([
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{"role": "system", "content": "You are a metadata expert who generates professional, accurate metadata for documents in English."},
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{"role": "user", "content": prompt}
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])
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# Parse response
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metadata_text = response.choices[0].message.content
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@ -61,13 +161,20 @@ class MetadataAnalyzer:
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for key, value in metadata.items()
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}
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logger.info(f"Generated metadata for {filename}")
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# Add metadata about the generation
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metadata['_tokens_used'] = response.usage.total_tokens
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metadata['_confidence'] = 0.9 # Could calculate based on response
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logger.info(f"Generated metadata for {filename} (tokens used: {metadata['_tokens_used']})")
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return metadata
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except Exception as e:
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logger.error(f"Error analyzing content: {e}")
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# Return fallback metadata
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return self._generate_fallback_metadata(filename, file_type)
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logger.error(f"Error analyzing content for {filename}: {e}")
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# Return fallback metadata with error info
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fallback = self._generate_fallback_metadata(filename, file_type)
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fallback['_ai_error'] = str(e)
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fallback['_tokens_used'] = 0
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return fallback
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def _create_prompt(self, content: str, filename: str, file_type: FileType) -> str:
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"""Create AI prompt based on file type."""
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currentFiles = [];
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const metadataSource = document.getElementById('metadataSource').value;
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showInfo(`Processing ${files.length} file(s) with ${metadataSource} source...`);
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// Show specific message for AI processing
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if (metadataSource === 'ai') {
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showInfo(`🤖 Generating AI metadata for ${files.length} file(s)... This may take 10-30 seconds per file.`);
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} else {
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showInfo(`Processing ${files.length} file(s) with ${metadataSource} source...`);
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}
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const formData = new FormData();
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formData.append('metadata_source', metadataSource);
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fileItem.className = 'file-item';
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fileItem.id = `file-${index}`;
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// Build AI info section if available
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let aiInfoHtml = '';
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if (file.suggested_metadata._tokens_used) {
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aiInfoHtml = `<div style="font-size: 11px; color: #6c757d; margin-top: 5px;">
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✓ AI generated (${file.suggested_metadata._tokens_used} tokens used)
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</div>`;
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}
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if (file.suggested_metadata._ai_error) {
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aiInfoHtml = `<div class="alert alert-error" style="display: block; margin-top: 5px; font-size: 12px;">
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⚠️ AI Error: ${file.suggested_metadata._ai_error}
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</div>`;
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}
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fileItem.innerHTML = `
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<div class="file-header">
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<div class="file-name">📄 ${file.filename}</div>
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<div class="metadata-box">
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<h4>✏️ Edit Metadata</h4>
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${displayEditableMetadata(file.suggested_metadata, index)}
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${aiInfoHtml}
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</div>
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</div>
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@ -603,10 +623,13 @@
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}
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function displayEditableMetadata(metadata, index) {
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// Filter out internal fields (starting with _)
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const title = metadata?.title || '';
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const subject = metadata?.subject || '';
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const keywords = metadata?.keywords || '';
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// Don't show internal metadata fields in the form
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return `
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<div class="metadata-field">
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<label for="title-${index}">Title:</label>
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72
web_app.py
72
web_app.py
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@ -20,6 +20,7 @@ import unicodedata
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from src.file_detector import FileDetector, FileType
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from src.excel_metadata_lookup import ExcelMetadataLookup
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from src.config import Config
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from src.metadata_analyzer import MetadataAnalyzer
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def safe_filename(filename):
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"""Sanitize filename while preserving Unicode characters (Chinese, Japanese, Korean)."""
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@ -52,6 +53,9 @@ EXCEL_PATH = Path(__file__).parent / "Celum ID to Adobe Asset Path Mapping Sprea
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# Initialize metadata lookup from Excel
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metadata_lookup = None
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# Initialize AI analyzer (lazy initialization)
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ai_analyzer = None
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# Initialize extractors and updaters
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extractors = {
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FileType.PDF: PDFExtractor(),
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metadata_lookup = ExcelMetadataLookup(str(EXCEL_PATH))
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return metadata_lookup
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def get_ai_analyzer():
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"""Get or create AI analyzer instance."""
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global ai_analyzer
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if ai_analyzer is None:
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if Config.OPENAI_API_KEY:
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try:
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ai_analyzer = MetadataAnalyzer()
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logger = __import__('logging').getLogger(__name__)
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logger.info("AI analyzer initialized successfully")
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except Exception as e:
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logger = __import__('logging').getLogger(__name__)
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logger.error(f"Failed to initialize AI analyzer: {e}")
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return None
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else:
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return None
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return ai_analyzer
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@app.route('/')
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def index():
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"""Main page."""
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@ -167,13 +188,50 @@ def upload_file():
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}
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elif metadata_source == 'ai':
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# AI generation - will be implemented in Phase 2.3
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# For now, return placeholder
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new_metadata = {
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'title': Path(filename).stem,
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'subject': 'AI generation not yet implemented',
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'keywords': ''
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}
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# AI generation using MetadataAnalyzer
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analyzer = get_ai_analyzer()
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if analyzer:
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try:
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# Extract content from file
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content = extractor.extract_content(str(filepath))
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if not content or len(content.strip()) < 10:
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# Not enough content for AI analysis
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new_metadata = {
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'title': Path(filename).stem,
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'subject': 'Insufficient content for AI analysis',
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'keywords': '',
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'_ai_error': 'Not enough text content extracted'
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}
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else:
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# Generate metadata with AI
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new_metadata = analyzer.analyze_content(content, filename, file_type)
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# Log token usage if available
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if '_tokens_used' in new_metadata:
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import logging
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logging.getLogger(__name__).info(
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f"AI tokens used for {filename}: {new_metadata['_tokens_used']}"
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)
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except Exception as e:
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import logging
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logging.getLogger(__name__).error(f"AI generation failed for {filename}: {e}")
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new_metadata = {
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'title': Path(filename).stem,
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'subject': f'AI generation error: {str(e)}',
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'keywords': '',
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'_ai_error': str(e)
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}
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else:
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# AI not configured
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new_metadata = {
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'title': Path(filename).stem,
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'subject': 'AI generation not available (OpenAI API key not configured)',
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'keywords': '',
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'_ai_error': 'OpenAI API key not configured'
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}
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elif metadata_source == 'import':
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# Import from file - will be implemented in Phase 2.4
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