feat(04-03): implement progressive compression engine
- Added CompressionEngine class with 4-tier age-based compression - 7 days: Full content (no compression) - 30 days: Key points extraction (70% retention) - 90 days: Brief summary (40% retention) - 365+ days: Metadata only - Hybrid extractive-abstractive summarization with fallbacks - Compression quality metrics and validation - Support for missing dependencies (NLTK/transformers) - Added transformers and nltk to requirements.txt
This commit is contained in:
Mai Development
@@ -8,4 +8,6 @@ semgrep>=1.99
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docker>=7.0.0
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sqlite-vec>=0.1.0
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numpy>=1.24.0
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sentence-transformers>=2.2.2
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sentence-transformers>=2.2.2
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transformers>=4.21.0
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nltk>=3.8
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606
src/memory/storage/compression.py
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606
src/memory/storage/compression.py
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@@ -0,0 +1,606 @@
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"""
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Progressive conversation compression engine.
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This module provides intelligent compression of conversations based on age,
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preserving important information while reducing storage requirements.
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"""
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import re
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import json
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import logging
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from datetime import datetime, timedelta
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from typing import Dict, Any, List, Optional, Union
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from enum import Enum
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from dataclasses import dataclass
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try:
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from transformers import pipeline as hf_pipeline
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TRANSFORMERS_AVAILABLE = True
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except ImportError:
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TRANSFORMERS_AVAILABLE = False
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hf_pipeline = None
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try:
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import nltk
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from nltk.tokenize import sent_tokenize
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from nltk.corpus import stopwords
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from nltk.tokenize import word_tokenize
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NLTK_AVAILABLE = True
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except ImportError:
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NLTK_AVAILABLE = False
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nltk = None
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import sys
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import os
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sys.path.append(os.path.join(os.path.dirname(__file__), "..", ".."))
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from models.conversation import Message, MessageRole, ConversationMetadata
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class CompressionLevel(Enum):
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"""Compression levels based on conversation age."""
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FULL = "full" # 0-7 days: No compression
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KEY_POINTS = "key_points" # 7-30 days: 70% retention
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SUMMARY = "summary" # 30-90 days: 40% retention
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METADATA = "metadata" # 90+ days: Metadata only
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@dataclass
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class CompressionMetrics:
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"""Metrics for compression quality assessment."""
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original_length: int
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compressed_length: int
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compression_ratio: float
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information_retention_score: float
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quality_score: float
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@dataclass
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class CompressedConversation:
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"""Represents a compressed conversation."""
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original_id: str
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compression_level: CompressionLevel
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compressed_at: datetime
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original_created_at: datetime
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content: Union[str, Dict[str, Any]]
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metadata: Dict[str, Any]
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metrics: CompressionMetrics
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class CompressionEngine:
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"""
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Progressive conversation compression engine.
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Compresses conversations based on age using hybrid extractive-abstractive
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summarization while preserving important information.
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"""
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def __init__(self, model_name: str = "facebook/bart-large-cnn"):
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"""
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Initialize compression engine.
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Args:
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model_name: Name of the summarization model to use
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"""
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self.model_name = model_name
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self.logger = logging.getLogger(__name__)
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self._summarizer = None
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self._initialize_nltk()
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def _initialize_nltk(self) -> None:
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"""Initialize NLTK components for extractive summarization."""
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if not NLTK_AVAILABLE:
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self.logger.warning("NLTK not available - using fallback methods")
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return
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try:
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# Download required NLTK data
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import ssl
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try:
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_create_unverified_https_context = ssl._create_unverified_https_context
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except AttributeError:
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pass
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else:
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ssl._create_default_https_context = _create_unverified_https_context
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nltk.download("punkt", quiet=True)
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nltk.download("stopwords", quiet=True)
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self.logger.debug("NLTK components initialized")
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except Exception as e:
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self.logger.warning(f"Failed to initialize NLTK: {e}")
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def _get_summarizer(self):
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"""Lazy initialization of summarization pipeline."""
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if TRANSFORMERS_AVAILABLE and self._summarizer is None:
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try:
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self._summarizer = hf_pipeline(
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"summarization",
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model=self.model_name,
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device=-1, # Use CPU by default
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)
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self.logger.debug(f"Initialized summarizer: {self.model_name}")
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except Exception as e:
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self.logger.error(f"Failed to initialize summarizer: {e}")
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self._summarizer = None
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return self._summarizer
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def get_compression_level(self, age_days: int) -> CompressionLevel:
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"""
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Determine compression level based on conversation age.
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Args:
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age_days: Age of conversation in days
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Returns:
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CompressionLevel based on age
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"""
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if age_days < 7:
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return CompressionLevel.FULL
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elif age_days < 30:
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return CompressionLevel.KEY_POINTS
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elif age_days < 90:
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return CompressionLevel.SUMMARY
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else:
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return CompressionLevel.METADATA
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def extract_key_points(self, conversation: Dict[str, Any]) -> str:
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"""
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Extract key points from conversation using extractive methods.
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Args:
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conversation: Conversation data with messages
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Returns:
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String containing key points
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"""
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messages = conversation.get("messages", [])
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if not messages:
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return ""
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# Combine all user and assistant messages
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full_text = ""
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for msg in messages:
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if msg["role"] in ["user", "assistant"]:
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full_text += msg["content"] + "\n"
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if not full_text.strip():
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return ""
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# Extractive summarization using sentence importance
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if not NLTK_AVAILABLE:
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# Simple fallback: split by sentences and take first 70%
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sentences = full_text.split(". ")
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if len(sentences) <= 3:
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return full_text.strip()
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num_sentences = max(3, int(len(sentences) * 0.7))
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key_points = ". ".join(sentences[:num_sentences])
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if not key_points.endswith("."):
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key_points += "."
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return key_points.strip()
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try:
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sentences = sent_tokenize(full_text)
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if len(sentences) <= 3:
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return full_text.strip()
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# Simple scoring based on sentence length and keywords
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scored_sentences = []
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stop_words = set(stopwords.words("english"))
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for i, sentence in enumerate(sentences):
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words = word_tokenize(sentence.lower())
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content_words = [
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w for w in words if w.isalpha() and w not in stop_words
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]
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# Score based on length, position, and content word ratio
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length_score = min(len(words) / 20, 1.0) # Normalize to max 20 words
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position_score = (len(sentences) - i) / len(
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sentences
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) # Earlier sentences get higher score
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content_score = len(content_words) / max(len(words), 1)
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total_score = (
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length_score * 0.3 + position_score * 0.3 + content_score * 0.4
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)
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scored_sentences.append((sentence, total_score))
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# Select top sentences (70% retention)
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scored_sentences.sort(key=lambda x: x[1], reverse=True)
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num_sentences = max(3, int(len(sentences) * 0.7))
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key_points = " ".join([s[0] for s in scored_sentences[:num_sentences]])
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return key_points.strip()
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except Exception as e:
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self.logger.error(f"Extractive summarization failed: {e}")
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return full_text[:500] + "..." if len(full_text) > 500 else full_text
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def generate_summary(
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self, conversation: Dict[str, Any], target_ratio: float = 0.4
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) -> str:
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"""
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Generate abstractive summary using transformer model.
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Args:
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conversation: Conversation data with messages
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target_ratio: Target compression ratio (e.g., 0.4 = 40% retention)
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Returns:
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Generated summary string
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"""
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messages = conversation.get("messages", [])
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if not messages:
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return ""
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# Combine messages into a single text
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full_text = ""
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for msg in messages:
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if msg["role"] in ["user", "assistant"]:
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full_text += f"{msg['role']}: {msg['content']}\n"
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if not full_text.strip():
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return ""
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# Try abstractive summarization
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summarizer = self._get_summarizer()
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if summarizer:
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try:
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# Calculate target length based on ratio
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max_length = max(50, int(len(full_text.split()) * target_ratio))
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min_length = max(25, int(max_length * 0.5))
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result = summarizer(
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full_text,
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max_length=max_length,
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min_length=min_length,
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do_sample=False,
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)
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if result and len(result) > 0:
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summary = result[0].get("summary_text", "")
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if summary:
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return summary.strip()
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except Exception as e:
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self.logger.error(f"Abstractive summarization failed: {e}")
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# Fallback to extractive method
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return self.extract_key_points(conversation)
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def extract_metadata_only(self, conversation: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Extract only metadata from conversation.
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Args:
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conversation: Conversation data
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Returns:
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Dictionary with conversation metadata
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"""
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messages = conversation.get("messages", [])
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# Extract key metadata
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metadata = {
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"id": conversation.get("id"),
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"title": conversation.get("title"),
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"created_at": conversation.get("created_at"),
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"updated_at": conversation.get("updated_at"),
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"total_messages": len(messages),
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"session_id": conversation.get("session_id"),
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"topics": self._extract_topics(messages),
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"key_entities": self._extract_entities(messages),
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"summary_stats": self._calculate_summary_stats(messages),
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}
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return metadata
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def _extract_topics(self, messages: List[Dict[str, Any]]) -> List[str]:
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"""Extract main topics from conversation."""
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topics = set()
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# Simple keyword-based topic extraction
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topic_keywords = {
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"technical": [
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"code",
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"programming",
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"algorithm",
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"function",
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"bug",
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"debug",
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],
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"personal": ["feel", "think", "opinion", "prefer", "like"],
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"work": ["project", "task", "deadline", "meeting", "team"],
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"learning": ["learn", "study", "understand", "explain", "tutorial"],
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"planning": ["plan", "schedule", "organize", "goal", "strategy"],
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}
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for msg in messages:
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if msg["role"] in ["user", "assistant"]:
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content = msg["content"].lower()
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for topic, keywords in topic_keywords.items():
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if isinstance(keywords, str):
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keywords = [keywords]
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if any(keyword in content for keyword in keywords):
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topics.add(topic)
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return list(topics)
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def _extract_entities(self, messages: List[Dict[str, Any]]) -> List[str]:
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"""Extract key entities from conversation."""
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entities = set()
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# Simple pattern-based entity extraction
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patterns = {
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"emails": r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b",
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"urls": r"http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+",
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"file_paths": r'\b[a-zA-Z]:\\[^<>:"|?*\n]*\b|\b/[^<>:"|?*\n]*\b',
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}
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for msg in messages:
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if msg["role"] in ["user", "assistant"]:
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content = msg["content"]
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for entity_type, pattern in patterns.items():
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matches = re.findall(pattern, content)
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entities.update(matches)
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return list(entities)
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def _calculate_summary_stats(
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self, messages: List[Dict[str, Any]]
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) -> Dict[str, Any]:
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"""Calculate summary statistics for conversation."""
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user_messages = [m for m in messages if m["role"] == "user"]
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assistant_messages = [m for m in messages if m["role"] == "assistant"]
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total_tokens = sum(m.get("token_count", 0) for m in messages)
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avg_importance = sum(m.get("importance_score", 0.5) for m in messages) / max(
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len(messages), 1
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)
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return {
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"user_message_count": len(user_messages),
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"assistant_message_count": len(assistant_messages),
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"total_tokens": total_tokens,
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"average_importance_score": avg_importance,
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"duration_days": self._calculate_conversation_duration(messages),
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}
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def _calculate_conversation_duration(self, messages: List[Dict[str, Any]]) -> int:
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"""Calculate conversation duration in days."""
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if not messages:
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return 0
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timestamps = []
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for msg in messages:
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if "timestamp" in msg:
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try:
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ts = datetime.fromisoformat(msg["timestamp"])
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timestamps.append(ts)
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except:
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continue
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if len(timestamps) < 2:
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return 0
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duration = max(timestamps) - min(timestamps)
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return max(0, duration.days)
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def compress_by_age(self, conversation: Dict[str, Any]) -> CompressedConversation:
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"""
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Compress conversation based on its age.
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Args:
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conversation: Conversation data to compress
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Returns:
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CompressedConversation with appropriate compression level
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"""
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# Calculate age
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created_at = conversation.get("created_at")
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if isinstance(created_at, str):
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created_at = datetime.fromisoformat(created_at)
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elif created_at is None:
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created_at = datetime.now()
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age_days = (datetime.now() - created_at).days
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compression_level = self.get_compression_level(age_days)
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# Get original content length
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original_content = json.dumps(conversation, ensure_ascii=False)
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original_length = len(original_content)
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# Apply compression based on level
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if compression_level == CompressionLevel.FULL:
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compressed_content = conversation
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elif compression_level == CompressionLevel.KEY_POINTS:
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compressed_content = self.extract_key_points(conversation)
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elif compression_level == CompressionLevel.SUMMARY:
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compressed_content = self.generate_summary(conversation, target_ratio=0.4)
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else: # METADATA
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compressed_content = self.extract_metadata_only(conversation)
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# Calculate compression metrics
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compressed_content_str = (
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json.dumps(compressed_content, ensure_ascii=False)
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if not isinstance(compressed_content, str)
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else compressed_content
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)
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compressed_length = len(compressed_content_str)
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compression_ratio = compressed_length / max(original_length, 1)
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# Calculate information retention score
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retention_score = self._calculate_retention_score(compression_level)
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quality_score = self._calculate_quality_score(
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compressed_content, conversation, compression_level
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)
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metrics = CompressionMetrics(
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original_length=original_length,
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compressed_length=compressed_length,
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compression_ratio=compression_ratio,
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information_retention_score=retention_score,
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quality_score=quality_score,
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)
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return CompressedConversation(
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original_id=conversation.get("id", "unknown"),
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compression_level=compression_level,
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compressed_at=datetime.now(),
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original_created_at=created_at,
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content=compressed_content,
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metadata={
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"compression_method": "hybrid_extractive_abstractive",
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"age_days": age_days,
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"original_tokens": conversation.get("total_tokens", 0),
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},
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metrics=metrics,
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)
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def _calculate_retention_score(self, compression_level: CompressionLevel) -> float:
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"""Calculate information retention score based on compression level."""
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retention_map = {
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CompressionLevel.FULL: 1.0,
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CompressionLevel.KEY_POINTS: 0.7,
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CompressionLevel.SUMMARY: 0.4,
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CompressionLevel.METADATA: 0.1,
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}
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return retention_map.get(compression_level, 0.1)
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def _calculate_quality_score(
|
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self,
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compressed_content: Union[str, Dict[str, Any]],
|
||||
original: Dict[str, Any],
|
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level: CompressionLevel,
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||||
) -> float:
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"""
|
||||
Calculate quality score for compressed content.
|
||||
|
||||
Args:
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||||
compressed_content: The compressed content
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||||
original: Original conversation
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||||
level: Compression level used
|
||||
|
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Returns:
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Quality score between 0.0 and 1.0
|
||||
"""
|
||||
try:
|
||||
# Base score from compression level
|
||||
base_scores = {
|
||||
CompressionLevel.FULL: 1.0,
|
||||
CompressionLevel.KEY_POINTS: 0.8,
|
||||
CompressionLevel.SUMMARY: 0.7,
|
||||
CompressionLevel.METADATA: 0.5,
|
||||
}
|
||||
base_score = base_scores.get(level, 0.5)
|
||||
|
||||
# Adjust based on content quality
|
||||
if isinstance(compressed_content, str):
|
||||
# Check for common quality indicators
|
||||
content_length = len(compressed_content)
|
||||
if content_length == 0:
|
||||
return 0.0
|
||||
|
||||
# Penalize very short content
|
||||
if level in [CompressionLevel.KEY_POINTS, CompressionLevel.SUMMARY]:
|
||||
if content_length < 50:
|
||||
base_score *= 0.5
|
||||
elif content_length < 100:
|
||||
base_score *= 0.8
|
||||
|
||||
# Check for coherent structure
|
||||
sentences = (
|
||||
compressed_content.count(".")
|
||||
+ compressed_content.count("!")
|
||||
+ compressed_content.count("?")
|
||||
)
|
||||
if sentences > 0:
|
||||
coherence_score = min(
|
||||
sentences / 10, 1.0
|
||||
) # More sentences = more coherent
|
||||
base_score = (base_score + coherence_score) / 2
|
||||
|
||||
return max(0.0, min(1.0, base_score))
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"Error calculating quality score: {e}")
|
||||
return 0.5
|
||||
|
||||
def decompress(self, compressed: CompressedConversation) -> Dict[str, Any]:
|
||||
"""
|
||||
Decompress compressed conversation to summary view.
|
||||
|
||||
Args:
|
||||
compressed: Compressed conversation to decompress
|
||||
|
||||
Returns:
|
||||
Summary view of the conversation
|
||||
"""
|
||||
if compressed.compression_level == CompressionLevel.FULL:
|
||||
# Return full conversation if no compression
|
||||
return (
|
||||
compressed.content
|
||||
if isinstance(compressed.content, dict)
|
||||
else {"summary": compressed.content}
|
||||
)
|
||||
|
||||
# Create summary view for compressed conversations
|
||||
summary = {
|
||||
"id": compressed.original_id,
|
||||
"compression_level": compressed.compression_level.value,
|
||||
"compressed_at": compressed.compressed_at.isoformat(),
|
||||
"original_created_at": compressed.original_created_at.isoformat(),
|
||||
"metadata": compressed.metadata,
|
||||
"metrics": {
|
||||
"compression_ratio": compressed.metrics.compression_ratio,
|
||||
"information_retention_score": compressed.metrics.information_retention_score,
|
||||
"quality_score": compressed.metrics.quality_score,
|
||||
},
|
||||
}
|
||||
|
||||
if compressed.compression_level == CompressionLevel.METADATA:
|
||||
# Content is already metadata
|
||||
if isinstance(compressed.content, dict):
|
||||
summary["metadata"].update(compressed.content)
|
||||
summary["summary"] = "Metadata only - full content compressed due to age"
|
||||
else:
|
||||
# Content is key points or summary text
|
||||
summary["summary"] = compressed.content
|
||||
|
||||
return summary
|
||||
|
||||
def batch_compress_conversations(
|
||||
self, conversations: List[Dict[str, Any]]
|
||||
) -> List[CompressedConversation]:
|
||||
"""
|
||||
Compress multiple conversations efficiently.
|
||||
|
||||
Args:
|
||||
conversations: List of conversations to compress
|
||||
|
||||
Returns:
|
||||
List of compressed conversations
|
||||
"""
|
||||
compressed_list = []
|
||||
|
||||
for conversation in conversations:
|
||||
try:
|
||||
compressed = self.compress_by_age(conversation)
|
||||
compressed_list.append(compressed)
|
||||
except Exception as e:
|
||||
self.logger.error(
|
||||
f"Failed to compress conversation {conversation.get('id', 'unknown')}: {e}"
|
||||
)
|
||||
continue
|
||||
|
||||
self.logger.info(
|
||||
f"Compressed {len(compressed_list)}/{len(conversations)} conversations successfully"
|
||||
)
|
||||
return compressed_list
|
||||
Reference in New Issue
Block a user