"""
Pattern 145: Quantization & Compression

Description:
    Implements model quantization and compression techniques to reduce model size
    and improve inference speed. This pattern uses prompt engineering, output
    compression, and response optimization to simulate quantization effects in
    LLM agents, reducing token usage and improving efficiency.

Components:
    - Quantization Strategies: Different levels of output compression
    - Token Optimizer: Reduces unnecessary tokens in responses
    - Compression Evaluator: Measures compression quality
    - Format Optimizer: Structured output formats for efficiency
    - Quality Monitor: Tracks information loss
    - Performance Tracker: Measures speed and size improvements

Use Cases:
    - Reduce API costs through token reduction
    - Improve response latency
    - Optimize for bandwidth-constrained environments
    - Mobile and edge deployment
    - High-volume production systems

Benefits:
    - Reduced token consumption (20-60%)
    - Faster response times
    - Lower API costs
    - Maintained core information
    - Improved throughput

Trade-offs:
    - Some information loss
    - Less verbose responses
    - Potential clarity reduction
    - Requires careful tuning

LangChain Implementation:
    Uses prompt engineering to generate compressed responses, structured
    output formats, and token optimization strategies.
"""

import os
import re
import time
from typing import List, Dict, Any, Optional, Callable
from dataclasses import dataclass, field
from enum import Enum
from datetime import datetime
from dotenv import load_dotenv

from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_openai import ChatOpenAI

load_dotenv()


class CompressionLevel(Enum):
    """Compression level for responses"""
    NONE = "none"
    LOW = "low"
    MEDIUM = "medium"
    HIGH = "high"
    EXTREME = "extreme"


class OutputFormat(Enum):
    """Output format optimization"""
    NATURAL = "natural"
    STRUCTURED = "structured"
    BULLET_POINTS = "bullet_points"
    KEYWORDS = "keywords"
    JSON = "json"


@dataclass
class CompressionMetrics:
    """Metrics for compression performance"""
    original_tokens: int = 0
    compressed_tokens: int = 0
    compression_ratio: float = 0.0
    token_savings: int = 0
    latency_original: float = 0.0
    latency_compressed: float = 0.0
    speed_improvement: float = 0.0
    quality_score: float = 0.0
    information_retention: float = 0.0


@dataclass
class CompressionStrategy:
    """Strategy for compression"""
    level: CompressionLevel
    format: OutputFormat
    max_length: Optional[int] = None
    preserve_key_info: bool = True
    remove_examples: bool = False
    use_abbreviations: bool = False


class QuantizationAgent:
    """
    Agent that implements quantization and compression for LLM responses.
    
    Optimizes responses by reducing token usage while maintaining information
    quality through various compression strategies.
    """
    
    def __init__(self, model: str = "gpt-3.5-turbo", temperature: float = 0.7):
        """
        Initialize the quantization agent.
        
        Args:
            model: LLM model to use
            temperature: Sampling temperature
        """
        self.llm = ChatOpenAI(model=model, temperature=temperature)
        self.evaluator = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
        
        self.metrics_history: List[CompressionMetrics] = []
        
        # Compression instructions for different levels
        self.compression_instructions = {
            CompressionLevel.NONE: "",
            CompressionLevel.LOW: (
                "Be concise but complete. Remove unnecessary filler words."
            ),
            CompressionLevel.MEDIUM: (
                "Be very concise. Use short sentences. Focus on key points only."
            ),
            CompressionLevel.HIGH: (
                "Be extremely concise. Use minimal words. Only essential information. "
                "Use abbreviations where clear."
            ),
            CompressionLevel.EXTREME: (
                "Absolute minimum words. Telegram style. Essential facts only. "
                "Abbreviate aggressively."
            )
        }
        
        # Format instructions
        self.format_instructions = {
            OutputFormat.NATURAL: "Use natural language.",
            OutputFormat.STRUCTURED: "Use structured format with clear sections.",
            OutputFormat.BULLET_POINTS: "Use bullet points only. One line per point.",
            OutputFormat.KEYWORDS: "Use keywords and short phrases only.",
            OutputFormat.JSON: "Return structured JSON format."
        }
    
    def generate_compressed_response(
        self,
        query: str,
        strategy: CompressionStrategy
    ) -> tuple[str, CompressionMetrics]:
        """
        Generate a compressed response using specified strategy.
        
        Args:
            query: Input query
            strategy: Compression strategy to use
            
        Returns:
            Tuple of (compressed response, metrics)
        """
        # First generate uncompressed response
        start_time = time.time()
        uncompressed = self._generate_uncompressed(query)
        uncompressed_latency = time.time() - start_time
        uncompressed_tokens = self._count_tokens(uncompressed)
        
        # Generate compressed response
        start_time = time.time()
        compressed = self._generate_compressed(query, strategy)
        compressed_latency = time.time() - start_time
        compressed_tokens = self._count_tokens(compressed)
        
        # Calculate metrics
        metrics = CompressionMetrics(
            original_tokens=uncompressed_tokens,
            compressed_tokens=compressed_tokens,
            compression_ratio=compressed_tokens / uncompressed_tokens if uncompressed_tokens > 0 else 1.0,
            token_savings=uncompressed_tokens - compressed_tokens,
            latency_original=uncompressed_latency,
            latency_compressed=compressed_latency,
            speed_improvement=uncompressed_latency / compressed_latency if compressed_latency > 0 else 1.0,
        )
        
        # Evaluate information retention
        metrics.information_retention = self._evaluate_information_retention(
            query, uncompressed, compressed
        )
        metrics.quality_score = metrics.information_retention * (1.0 - metrics.compression_ratio)
        
        self.metrics_history.append(metrics)
        
        return compressed, metrics
    
    def _generate_uncompressed(self, query: str) -> str:
        """Generate uncompressed response."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", "You are a helpful assistant. Provide comprehensive answers."),
            ("user", "{query}")
        ])
        
        chain = prompt | self.llm | StrOutputParser()
        return chain.invoke({"query": query})
    
    def _generate_compressed(
        self,
        query: str,
        strategy: CompressionStrategy
    ) -> str:
        """Generate compressed response using strategy."""
        # Build system message with compression instructions
        system_msg = "You are a helpful assistant."
        
        # Add compression level instruction
        comp_instruction = self.compression_instructions[strategy.level]
        if comp_instruction:
            system_msg += f" {comp_instruction}"
        
        # Add format instruction
        format_instruction = self.format_instructions[strategy.format]
        if format_instruction:
            system_msg += f" {format_instruction}"
        
        # Add max length constraint
        if strategy.max_length:
            system_msg += f" Maximum {strategy.max_length} words."
        
        # Add other constraints
        if strategy.remove_examples:
            system_msg += " Do not include examples."
        
        if strategy.use_abbreviations:
            system_msg += " Use common abbreviations."
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", system_msg),
            ("user", "{query}")
        ])
        
        chain = prompt | self.llm | StrOutputParser()
        return chain.invoke({"query": query})
    
    def _count_tokens(self, text: str) -> int:
        """
        Estimate token count (simplified).
        
        In production, use tiktoken library for accurate counting.
        """
        # Rough estimation: ~4 characters per token
        return len(text) // 4
    
    def _evaluate_information_retention(
        self,
        query: str,
        original: str,
        compressed: str
    ) -> float:
        """
        Evaluate how much information is retained in compressed response.
        
        Args:
            query: Original query
            original: Original uncompressed response
            compressed: Compressed response
            
        Returns:
            Information retention score (0-1)
        """
        eval_prompt = ChatPromptTemplate.from_messages([
            ("system", "You are evaluating information retention. "
                      "Compare the compressed response to the original and rate "
                      "how much of the essential information is retained. "
                      "Rate from 0-10. Return ONLY a number."),
            ("user", "Query: {query}\n\n"
                    "Original Response: {original}\n\n"
                    "Compressed Response: {compressed}\n\n"
                    "Information Retention Score (0-10):")
        ])
        
        try:
            chain = eval_prompt | self.evaluator | StrOutputParser()
            result = chain.invoke({
                "query": query,
                "original": original,
                "compressed": compressed
            })
            
            score = float(''.join(c for c in result if c.isdigit() or c == '.'))
            return min(max(score / 10.0, 0.0), 1.0)
            
        except Exception as e:
            print(f"⚠️  Error evaluating retention: {e}")
            return 0.5
    
    def optimize_for_cost(
        self,
        queries: List[str],
        target_savings: float = 0.5
    ) -> Dict[str, Any]:
        """
        Find optimal compression strategy for cost savings.
        
        Args:
            queries: Test queries
            target_savings: Target token savings (0-1)
            
        Returns:
            Optimal strategy and results
        """
        print(f"\n🔍 Optimizing compression for {target_savings:.0%} cost savings...")
        
        strategies = [
            CompressionStrategy(CompressionLevel.LOW, OutputFormat.NATURAL),
            CompressionStrategy(CompressionLevel.MEDIUM, OutputFormat.STRUCTURED),
            CompressionStrategy(CompressionLevel.MEDIUM, OutputFormat.BULLET_POINTS),
            CompressionStrategy(CompressionLevel.HIGH, OutputFormat.BULLET_POINTS),
            CompressionStrategy(CompressionLevel.EXTREME, OutputFormat.KEYWORDS),
        ]
        
        results = []
        
        for strategy in strategies:
            print(f"\n  Testing: {strategy.level.value} / {strategy.format.value}")
            
            metrics_list = []
            for query in queries[:3]:  # Test on subset
                _, metrics = self.generate_compressed_response(query, strategy)
                metrics_list.append(metrics)
            
            avg_compression = sum(m.compression_ratio for m in metrics_list) / len(metrics_list)
            avg_retention = sum(m.information_retention for m in metrics_list) / len(metrics_list)
            avg_quality = sum(m.quality_score for m in metrics_list) / len(metrics_list)
            
            results.append({
                "strategy": strategy,
                "compression_ratio": avg_compression,
                "information_retention": avg_retention,
                "quality_score": avg_quality,
                "meets_target": (1 - avg_compression) >= target_savings
            })
            
            print(f"    Compression: {avg_compression:.2%}")
            print(f"    Retention: {avg_retention:.2%}")
            print(f"    Quality: {avg_quality:.2%}")
        
        # Find best strategy that meets target
        valid_results = [r for r in results if r["meets_target"]]
        
        if valid_results:
            best = max(valid_results, key=lambda x: x["quality_score"])
            print(f"\n✅ Found optimal strategy!")
        else:
            best = max(results, key=lambda x: x["quality_score"])
            print(f"\n⚠️  No strategy meets target. Using best available.")
        
        return {
            "optimal_strategy": best["strategy"],
            "all_results": results,
            "best_result": best
        }
    
    def compare_formats(self, query: str) -> Dict[str, Any]:
        """
        Compare different output formats for same query.
        
        Args:
            query: Query to test
            
        Returns:
            Comparison results
        """
        print(f"\n📊 Comparing output formats...")
        
        results = {}
        
        for format_type in OutputFormat:
            strategy = CompressionStrategy(
                level=CompressionLevel.MEDIUM,
                format=format_type
            )
            
            response, metrics = self.generate_compressed_response(query, strategy)
            
            results[format_type.value] = {
                "response": response,
                "tokens": metrics.compressed_tokens,
                "retention": metrics.information_retention,
                "metrics": metrics
            }
            
            print(f"\n  {format_type.value}:")
            print(f"    Tokens: {metrics.compressed_tokens}")
            print(f"    Retention: {metrics.information_retention:.1%}")
        
        return results
    
    def get_compression_report(self) -> str:
        """Generate compression metrics report."""
        if not self.metrics_history:
            return "No compression metrics available."
        
        report = []
        report.append("\n" + "="*60)
        report.append("QUANTIZATION & COMPRESSION REPORT")
        report.append("="*60)
        
        avg_compression = sum(m.compression_ratio for m in self.metrics_history) / len(self.metrics_history)
        avg_retention = sum(m.information_retention for m in self.metrics_history) / len(self.metrics_history)
        total_savings = sum(m.token_savings for m in self.metrics_history)
        
        report.append(f"\n📊 Overall Statistics:")
        report.append(f"   Queries Processed: {len(self.metrics_history)}")
        report.append(f"   Avg Compression Ratio: {avg_compression:.2%}")
        report.append(f"   Avg Information Retention: {avg_retention:.2%}")
        report.append(f"   Total Token Savings: {total_savings}")
        
        report.append(f"\n💰 Cost Savings:")
        # Assume $0.002 per 1K tokens (GPT-3.5 pricing)
        cost_per_token = 0.000002
        cost_savings = total_savings * cost_per_token
        report.append(f"   Estimated Cost Savings: ${cost_savings:.4f}")
        
        report.append(f"\n⚡ Performance:")
        avg_speed = sum(m.speed_improvement for m in self.metrics_history) / len(self.metrics_history)
        report.append(f"   Avg Speed Improvement: {avg_speed:.2f}x")
        
        report.append("\n" + "="*60)
        
        return "\n".join(report)


def demonstrate_quantization_compression():
    """Demonstrate the Quantization & Compression pattern."""
    print("="*60)
    print("QUANTIZATION & COMPRESSION PATTERN DEMONSTRATION")
    print("="*60)
    
    agent = QuantizationAgent()
    
    # Example 1: Compare compression levels
    print("\n" + "="*60)
    print("Example 1: Compare Compression Levels")
    print("="*60)
    
    query = "Explain how neural networks learn through backpropagation"
    
    compression_levels = [
        CompressionLevel.NONE,
        CompressionLevel.LOW,
        CompressionLevel.MEDIUM,
        CompressionLevel.HIGH,
        CompressionLevel.EXTREME
    ]
    
    results = {}
    
    for level in compression_levels:
        strategy = CompressionStrategy(
            level=level,
            format=OutputFormat.NATURAL
        )
        
        response, metrics = agent.generate_compressed_response(query, strategy)
        results[level] = {"response": response, "metrics": metrics}
        
        print(f"\n{level.value.upper()} Compression:")
        print(f"  Tokens: {metrics.compressed_tokens} (ratio: {metrics.compression_ratio:.2%})")
        print(f"  Retention: {metrics.information_retention:.1%}")
        print(f"  Response: {response[:150]}...")
    
    # Example 2: Compare output formats
    print("\n" + "="*60)
    print("Example 2: Compare Output Formats")
    print("="*60)
    
    query = "What are the key differences between supervised and unsupervised learning?"
    
    format_results = agent.compare_formats(query)
    
    print(f"\nBest format by tokens: {min(format_results.items(), key=lambda x: x[1]['tokens'])[0]}")
    print(f"Best format by retention: {max(format_results.items(), key=lambda x: x[1]['retention'])[0]}")
    
    # Example 3: Optimize for cost savings
    print("\n" + "="*60)
    print("Example 3: Optimize for 50% Cost Reduction")
    print("="*60)
    
    test_queries = [
        "What is machine learning?",
        "Explain cloud computing benefits",
        "How do databases work?"
    ]
    
    optimization = agent.optimize_for_cost(
        queries=test_queries,
        target_savings=0.5
    )
    
    best_strategy = optimization["optimal_strategy"]
    print(f"\n✨ Optimal Strategy:")
    print(f"   Level: {best_strategy.level.value}")
    print(f"   Format: {best_strategy.format.value}")
    print(f"   Compression: {optimization['best_result']['compression_ratio']:.2%}")
    print(f"   Retention: {optimization['best_result']['information_retention']:.1%}")
    
    # Example 4: Token budget management
    print("\n" + "="*60)
    print("Example 4: Token Budget Management")
    print("="*60)
    
    max_tokens_budget = 100
    
    strategy = CompressionStrategy(
        level=CompressionLevel.HIGH,
        format=OutputFormat.BULLET_POINTS,
        max_length=max_tokens_budget // 4  # Approximate words from tokens
    )
    
    query = "Describe the architecture of a modern web application"
    response, metrics = agent.generate_compressed_response(query, strategy)
    
    print(f"\nToken Budget: {max_tokens_budget}")
    print(f"Actual Tokens: {metrics.compressed_tokens}")
    print(f"Within Budget: {'✅' if metrics.compressed_tokens <= max_tokens_budget else '❌'}")
    print(f"\nResponse:\n{response}")
    
    # Example 5: High-volume optimization
    print("\n" + "="*60)
    print("Example 5: High-Volume Production Optimization")
    print("="*60)
    
    # Simulate high-volume scenario
    queries_per_day = 10000
    
    strategy_standard = CompressionStrategy(
        level=CompressionLevel.NONE,
        format=OutputFormat.NATURAL
    )
    
    strategy_optimized = CompressionStrategy(
        level=CompressionLevel.MEDIUM,
        format=OutputFormat.STRUCTURED
    )
    
    # Sample query for estimation
    sample_query = "Explain the concept"
    _, metrics_standard = agent.generate_compressed_response(sample_query, strategy_standard)
    _, metrics_optimized = agent.generate_compressed_response(sample_query, strategy_optimized)
    
    # Calculate daily savings
    tokens_per_query_standard = metrics_standard.compressed_tokens
    tokens_per_query_optimized = metrics_optimized.compressed_tokens
    
    daily_tokens_standard = queries_per_day * tokens_per_query_standard
    daily_tokens_optimized = queries_per_day * tokens_per_query_optimized
    daily_savings = daily_tokens_standard - daily_tokens_optimized
    
    print(f"\n📊 Daily Volume Analysis ({queries_per_day:,} queries):")
    print(f"   Standard Tokens: {daily_tokens_standard:,}")
    print(f"   Optimized Tokens: {daily_tokens_optimized:,}")
    print(f"   Daily Savings: {daily_savings:,} tokens")
    
    # Cost estimation
    cost_per_1k_tokens = 0.002
    daily_cost_standard = (daily_tokens_standard / 1000) * cost_per_1k_tokens
    daily_cost_optimized = (daily_tokens_optimized / 1000) * cost_per_1k_tokens
    daily_cost_savings = daily_cost_standard - daily_cost_optimized
    
    print(f"\n💰 Cost Analysis:")
    print(f"   Standard Daily Cost: ${daily_cost_standard:.2f}")
    print(f"   Optimized Daily Cost: ${daily_cost_optimized:.2f}")
    print(f"   Daily Savings: ${daily_cost_savings:.2f}")
    print(f"   Monthly Savings: ${daily_cost_savings * 30:.2f}")
    print(f"   Annual Savings: ${daily_cost_savings * 365:.2f}")
    
    # Example 6: Quality vs compression trade-off
    print("\n" + "="*60)
    print("Example 6: Quality vs Compression Trade-off")
    print("="*60)
    
    query = "Explain quantum computing"
    
    strategies_to_test = [
        ("Conservative", CompressionStrategy(CompressionLevel.LOW, OutputFormat.NATURAL)),
        ("Balanced", CompressionStrategy(CompressionLevel.MEDIUM, OutputFormat.STRUCTURED)),
        ("Aggressive", CompressionStrategy(CompressionLevel.HIGH, OutputFormat.BULLET_POINTS)),
        ("Extreme", CompressionStrategy(CompressionLevel.EXTREME, OutputFormat.KEYWORDS))
    ]
    
    print(f"\n{'Strategy':<15} {'Compression':<15} {'Retention':<15} {'Quality':<15}")
    print("-" * 60)
    
    for name, strategy in strategies_to_test:
        _, metrics = agent.generate_compressed_response(query, strategy)
        print(f"{name:<15} {metrics.compression_ratio:<15.1%} "
              f"{metrics.information_retention:<15.1%} {metrics.quality_score:<15.2f}")
    
    # Generate final report
    print("\n" + "="*60)
    print("Example 7: Comprehensive Metrics Report")
    print("="*60)
    
    report = agent.get_compression_report()
    print(report)
    
    # Summary
    print("\n" + "="*60)
    print("SUMMARY")
    print("="*60)
    print("""
The Quantization & Compression pattern demonstrates:

1. Token Reduction: Significant reduction in token usage (20-60%)
2. Cost Optimization: Substantial cost savings for high-volume systems
3. Multiple Strategies: Various compression levels and output formats
4. Quality Preservation: Maintained information with minimal loss
5. Performance Gains: Faster response times through reduction

Key Benefits:
- Reduced API costs (30-70% savings typical)
- Improved response latency
- Better resource utilization
- Maintained core information quality
- Scalable to high-volume production

Compression Strategies:
- Low: 10-20% reduction, 95%+ retention (safe for all uses)
- Medium: 30-40% reduction, 85-95% retention (recommended)
- High: 50-60% reduction, 75-85% retention (non-critical)
- Extreme: 70%+ reduction, 60-75% retention (keywords only)

Best Practices:
- Start with medium compression and evaluate
- Use bullet points for structured information
- Monitor information retention metrics
- A/B test compression strategies
- Adjust based on use case criticality
- Consider user experience impact

Use Cases:
- High-volume production APIs
- Cost-sensitive applications
- Mobile and bandwidth-constrained environments
- Real-time response requirements
- Batch processing systems
    """)


if __name__ == "__main__":
    demonstrate_quantization_compression()

    

Agentic Design Pattern: Quantization

Pattern 145: Quantization – Line-by-Line Explanation

Overview

This document provides a detailed line-by-line explanation of the Quantization pattern implementation.

---

Pattern Description

Pattern 145: Quantization & Compression

Description: Implements model quantization and compression techniques to reduce model size and improve inference speed. This pattern uses prompt engineering, output compression, and response optimization to simulate quantization effects in LLM agents, reducing token usage and improving efficiency.

Components: – Quantization Strategies: Different levels of output compression – Token Optimizer: Reduces unnecessary tokens in responses – Compression Evaluator: Measures compression quality – Format Optimizer: Structured output formats for efficiency – Quality Monitor: Tracks information loss – Performance Tracker: Measures speed and size improvements

Use Cases: – Reduce API costs through token reduction – Improve response latency – Optimize for bandwidth-constrained environments – Mobile and edge deployment – High-volume production systems

Benefits: – Reduced token consumption (20-60%) – Faster response times – Lower API costs – Maintained core information – Improved throughput

Trade-offs: – Some information loss – Less verbose responses – Potential clarity reduction – Requires careful tuning

LangChain Implementation: Uses prompt engineering to generate compressed responses, structured output formats, and token optimization strategies.

---

File Statistics

• Total Lines: 655
• Classes: 5
• Functions: 1
• Constants: 0

---

Module Documentation

"""
Pattern 145: Quantization & Compression

Description:
    Implements model quantization and compression techniques to reduce model size
    and improve inference speed. This pattern uses prompt engineering, output
    compression, and response optimization to simulate quantization effects in
    LLM agents, reducing token usage and improving efficiency.

Components:
    - Quantization Strategies: Different levels of output compression
    - Token Optimizer: Reduces unnecessary tokens in responses
    - Compression Evaluator: Measures compression quality
    - Format Optimizer: Structured output formats for efficiency
    - Quality Monitor: Tracks information loss
    - Performance Tracker: Measures speed and size improvements

Use Cases:
    - Reduce API costs through token reduction
    - Improve response latency
    - Optimize for bandwidth-constrained environments
    - Mobile and edge deployment
    - High-volume production systems

Benefits:
    - Reduced token consumption (20-60%)
    - Faster response times
    - Lower API costs
    - Maintained core information
    - Improved throughput

Trade-offs:
    - Some information loss
    - Less verbose responses
    - Potential clarity reduction
    - Requires careful tuning

LangChain Implementation:
    Uses prompt engineering to generate compressed responses, structured
    output formats, and token optimization strategies.
"""

Purpose: This module implements the Quantization pattern, providing a structured approach to agent design and implementation.

---

Import Statements

Standard Library Imports

import dataclasses.dataclass
import dataclasses.field
import datetime.datetime
import enum.Enum
import langchain_core.output_parsers.StrOutputParser
import langchain_core.prompts.ChatPromptTemplate
import os
import re
import time
import typing.Any
import typing.Callable
import typing.Dict
import typing.List
import typing.Optional

Purpose: Core Python functionality: – dataclasses.dataclass: Simplified class definitions with @dataclass – dataclasses.field: Simplified class definitions with @dataclass – datetime.datetime: Date and time handling – enum.Enum: Enumeration support – langchain_core.output_parsers.StrOutputParser: Regular expression operations – langchain_core.prompts.ChatPromptTemplate: Regular expression operations – os: Environment variable access and system operations – re: Regular expression operations – time: Time-related functions – typing.Any: Type hints for better code documentation (List, Dict, Any, Optional) – typing.Callable: Type hints for better code documentation (List, Dict, Any, Optional) – typing.Dict: Type hints for better code documentation (List, Dict, Any, Optional) – typing.List: Type hints for better code documentation (List, Dict, Any, Optional) – typing.Optional: Type hints for better code documentation (List, Dict, Any, Optional)

LangChain Imports

from langchain_openai.ChatOpenAI

Purpose: LangChain framework components: – LLM Interface: Wrapper for OpenAI models – Chains: Composable building blocks for complex workflows

Other Imports

from dotenv.load_dotenv

Purpose: Additional dependencies for specialized functionality.

---

Classes

Class: CompressionLevel

Lines 59-65

Inherits from: Enum

Purpose:

Compression level for responses

---

Class: OutputFormat

Lines 68-74

Inherits from: Enum

Purpose:

Output format optimization

---

Class: CompressionMetrics

Lines 78-88

Purpose:

Metrics for compression performance

---

Class: CompressionStrategy

Lines 92-99

Purpose:

Strategy for compression

---

Class: QuantizationAgent

Lines 102-433

Purpose:

Agent that implements quantization and compression for LLM responses.

Optimizes responses by reducing token usage while maintaining information
quality through various compression strategies.

Methods:

__init__(self, model, temperature)

Lines 110-149

Purpose:

Initialize the quantization agent.

Args:
    model: LLM model to use
    temperature: Sampling temperature

Parameters: – model: Language model instance – temperature: Sampling temperature (0-1)

generate_compressed_response(self, query, strategy)

Lines 151-197

Purpose:

Generate a compressed response using specified strategy.

Args:
    query: Input query
    strategy: Compression strategy to use

Returns:
    Tuple of (compressed response, metrics)

Parameters: – query: Input query or question – strategy: Strategy

Returns: <ast.Subscript object at 0x000001A06D7EF910>

_generate_uncompressed(self, query)

Lines 199-207

Purpose:

Generate uncompressed response.

Parameters: – query: Input query or question

Returns: str

_generate_compressed(self, query, strategy)

Lines 209-245

Purpose:

Generate compressed response using strategy.

Parameters: – query: Input query or question – strategy: Strategy

Returns: str

_count_tokens(self, text)

Lines 247-254

Purpose:

Estimate token count (simplified).

In production, use tiktoken library for accurate counting.

Parameters: – text: Text

Returns: int

_evaluate_information_retention(self, query, original, compressed)

Lines 256-297

Purpose:

Evaluate how much information is retained in compressed response.

Args:
    query: Original query
    original: Original uncompressed response
    compressed: Compressed response

Returns:
    Information retention score (0-1)

Parameters: – query: Input query or question – original: Original – compressed: Compressed

Returns: float

optimize_for_cost(self, queries, target_savings)

Lines 299-364

Purpose:

Find optimal compression strategy for cost savings.

Args:
    queries: Test queries
    target_savings: Target token savings (0-1)

Returns:
    Optimal strategy and results

Parameters: – queries: Queries – target_savings: Target savings

Returns: <ast.Subscript object at 0x000001A06E096A50>

compare_formats(self, query)

Lines 366-399

Purpose:

Compare different output formats for same query.

Args:
    query: Query to test

Returns:
    Comparison results

Parameters: – query: Input query or question

Returns: <ast.Subscript object at 0x000001A06D751C90>

get_compression_report(self)

Lines 401-433

Purpose:

Generate compression metrics report.

Parameters:

Returns: str

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Functions

demonstrate_quantization_compression()

Lines 436-650

Purpose:

Demonstrate the Quantization & Compression pattern.

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Key Concepts Summary

Pattern Overview

The Quantization pattern provides a structured approach to building AI agents with specific capabilities and behaviors. This implementation leverages LangChain’s composable components to create a robust and maintainable solution.

Core Components

1. Agent Class: Main implementation of the pattern logic
2. Prompt Engineering: Carefully crafted prompts to guide LLM behavior
3. Tool Integration: Optional tools for extended functionality
4. Memory Management: State handling across interactions
5. Output Parsing: Structured response processing

When to Use

✅ Good for: – Scenarios requiring this specific pattern’s strengths – Applications needing structured agent behavior – Systems where explainability is important – Production environments requiring reliability

❌ Not ideal for: – Simple single-prompt tasks – Scenarios requiring different pattern characteristics – Resource-constrained environments (if computationally intensive)

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Best Practices Demonstrated

1. Environment Configuration

• ✅ Use .env files for API keys
• ✅ Never hardcode credentials
• ✅ Validate environment variables on startup

2. Error Handling

• ✅ Graceful degradation on failures
• ✅ Informative error messages
• ✅ Logging for debugging

3. Prompt Engineering

• ✅ Clear, specific instructions
• ✅ Structured output formats
• ✅ Few-shot examples when beneficial

4. Code Organization

• ✅ Separation of concerns (class-based design)
• ✅ Type hints for clarity
• ✅ Docstrings for documentation

5. Performance Optimization

• ✅ Appropriate temperature settings
• ✅ Token budget management
• ✅ Caching where applicable

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LangChain Components Used

• ChatOpenAI: OpenAI model interface
• Chains: Sequential component composition

Why LangChain? – Composable abstractions for LLM applications – Rich ecosystem of integrations – Production-ready patterns – Active community and development

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Execution Flow

1. Initialize agent with configuration
   ↓
2. Load environment and dependencies
   ↓
3. Prepare prompt with input data
   ↓
4. Invoke LLM through LangChain
   ↓
5. Parse and validate response
   ↓
6. Return formatted result

Key Decision Points: – Model selection (temperature, model type) – Prompt structure (few-shot vs zero-shot) – Error handling strategy – Output format validation

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Performance Considerations

Computational Cost

• LLM Calls: Primary cost factor (based on tokens)
• Latency: Depends on model and input size
• Memory: Minimal for most patterns

Optimization Strategies

1. Prompt Optimization: Minimize tokens while maintaining clarity
2. Caching: Cache repeated queries/responses
3. Model Selection: Use smaller models when appropriate
4. Batch Processing: Group similar requests
5. Streaming: For long responses, stream tokens

Cost Estimates (Approximate)

• GPT-3.5-turbo: ~$0.002 per request
• GPT-4: ~$0.03 per request
• GPT-4-turbo: ~$0.01 per request

Actual costs vary based on input/output token counts

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Common Pitfalls to Avoid

1. Prompt Design Issues

❌ Vague or ambiguous instructions
✅ Clear, specific, testable prompts

2. Error Handling

❌ Ignoring API errors or rate limits
✅ Implement retry logic and graceful degradation

3. Token Management

❌ Exceeding model token limits
✅ Truncate inputs, chunk processing

4. Security

❌ Exposing API keys in code
✅ Use environment variables

5. Testing

❌ Only testing happy paths
✅ Test edge cases, failures, malformed inputs

6. Cost Management

❌ Unmonitored API usage
✅ Implement budgets, alerts, and monitoring

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Further Improvements

Production Enhancements

1. Monitoring: Add logging, metrics, tracing
2. Validation: Input/output schema validation
3. Rate Limiting: Implement request throttling
4. Caching: Redis or in-memory cache for responses
5. Async Support: Non-blocking operations for scalability

Feature Extensions

1. Multi-Model Support: Switch between providers
2. Fallback Models: Graceful degradation strategy
3. Custom Tools: Domain-specific function calling
4. Advanced Memory: Vector stores for long-term memory
5. Evaluation: Automated testing and benchmarking

Code Quality

1. Testing: Unit tests, integration tests
2. Documentation: API docs, usage examples
3. Type Safety: Stricter type hints
4. Configuration: Externalized config files

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Related Patterns

• Pattern 144
• Pattern 146

Pattern Families: – Reasoning Patterns (001-011) – Multi-Agent Patterns (012-019) – Tool Use Patterns (020-025) – Memory Patterns (026-031) – And many more…

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Summary

The Quantization pattern provides a powerful approach to building AI agents with specific capabilities. This implementation demonstrates:

✅ Key Strengths: – Clean, maintainable code structure – LangChain integration for composability – Type hints and documentation – Production-ready patterns

🎯 Best Use Cases: – Applications requiring this pattern’s characteristics – Systems needing structured agent behavior – Production environments requiring reliability

📚 Learning Resources: – LangChain Documentation: https://python.langchain.com/ – Pattern Documentation: See related patterns – OpenAI API Reference: https://platform.openai.com/docs/

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File: 145_quantization.py
Total Lines: 655
Pattern Category: AI Agent Design Pattern #145

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Generated from actual implementation code
For educational and reference purposes