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DocsGPT/tests/test_compression_service.py
Alex 17698ce774 feat: context compression (#2173) 
* feat: context compression

* fix: ruff
2025-11-24 12:44:19 +02:00

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import pytest
from datetime import datetime, timezone
from unittest.mock import Mock, patch
from application.api.answer.services.compression import CompressionService
from application.api.answer.services.compression.threshold_checker import (
CompressionThresholdChecker,
)
from application.api.answer.services.compression.token_counter import TokenCounter
from application.api.answer.services.compression.prompt_builder import (
CompressionPromptBuilder,
)
from application.core.settings import settings
@pytest.fixture
def mock_llm():
"""Create a mock LLM for testing"""
llm = Mock()
llm.gen = Mock()
return llm
@pytest.fixture
def compression_service(mock_llm):
"""Create a CompressionService instance with mock LLM"""
return CompressionService(llm=mock_llm, model_id="gpt-4o")
@pytest.fixture
def threshold_checker():
"""Create a ThresholdChecker instance"""
return CompressionThresholdChecker()
@pytest.fixture
def prompt_builder():
"""Create a PromptBuilder instance"""
return CompressionPromptBuilder()
@pytest.fixture
def sample_conversation():
"""Create a sample conversation for testing"""
return {
"_id": "test_conversation_id",
"user": "test_user",
"date": datetime.now(timezone.utc),
"name": "Test Conversation",
"queries": [
{
"prompt": "What is Python?",
"response": "Python is a high-level programming language.",
"thought": "",
"sources": [],
"tool_calls": [],
"timestamp": datetime.now(timezone.utc),
},
{
"prompt": "How do I install it?",
"response": "You can install Python from python.org",
"thought": "",
"sources": [],
"tool_calls": [],
"timestamp": datetime.now(timezone.utc),
},
{
"prompt": "What are some popular libraries?",
"response": "Popular Python libraries include NumPy, Pandas, Django, Flask, etc.",
"thought": "",
"sources": [],
"tool_calls": [],
"timestamp": datetime.now(timezone.utc),
},
],
}
@pytest.fixture
def large_conversation():
"""Create a large conversation that exceeds threshold"""
queries = []
for i in range(100):
queries.append(
{
"prompt": f"Question {i}: " + ("test " * 100), # ~400 tokens each
"response": f"Answer {i}: " + ("response " * 100), # ~400 tokens each
"thought": "",
"sources": [],
"tool_calls": [],
"timestamp": datetime.now(timezone.utc),
}
)
return {
"_id": "large_conversation_id",
"user": "test_user",
"date": datetime.now(timezone.utc),
"name": "Large Conversation",
"queries": queries,
}
class TestCompressionService:
"""Test suite for CompressionService"""
def test_initialization(self, mock_llm):
"""Test CompressionService initialization"""
service = CompressionService(llm=mock_llm, model_id="gpt-4o")
assert service.llm == mock_llm
assert service.model_id == "gpt-4o"
assert service.prompt_builder is not None
assert service.prompt_builder.version == settings.COMPRESSION_PROMPT_VERSION
@patch("application.api.answer.services.compression.threshold_checker.get_token_limit")
def test_should_compress_below_threshold(
self, mock_get_token_limit, threshold_checker, sample_conversation
):
"""Test that compression is not triggered when below threshold"""
mock_get_token_limit.return_value = 128000 # GPT-4o limit
# Small conversation should not trigger compression
result = threshold_checker.should_compress(
sample_conversation, model_id="gpt-4o"
)
assert result is False
@patch("application.api.answer.services.compression.threshold_checker.get_token_limit")
def test_should_compress_above_threshold(
self, mock_get_token_limit, threshold_checker, large_conversation
):
"""Test that compression is triggered when above threshold"""
mock_get_token_limit.return_value = 10000 # Lower limit to ensure large conversation exceeds threshold
# Large conversation should trigger compression (100 queries with repeated text)
# Threshold at 80% of 10k = 8k tokens, so large_conversation > 8k should trigger
result = threshold_checker.should_compress(
large_conversation, model_id="gpt-4o"
)
assert result is True
@patch("application.api.answer.services.compression.threshold_checker.get_token_limit")
def test_should_compress_at_exact_threshold(
self, mock_get_token_limit, threshold_checker
):
"""Test compression trigger at exact 80% threshold"""
mock_get_token_limit.return_value = 1000
# Create conversation with exactly 800 tokens (80% of 1000)
conversation = {
"queries": [
{
"prompt": "a " * 200, # ~200 tokens
"response": "b " * 200, # ~200 tokens
},
{
"prompt": "c " * 200, # ~200 tokens
"response": "d " * 200, # ~200 tokens
},
]
}
result = threshold_checker.should_compress(conversation, model_id="test-model")
# Should trigger at or above 80%
assert result is True
def test_compress_conversation_basic(self, compression_service, sample_conversation):
"""Test basic conversation compression"""
# Mock LLM response
mock_summary = """
<analysis>
The conversation covers Python basics and installation.
</analysis>
<summary>
1. Primary Request and Intent:
User asked about Python and how to install it.
2. Key Concepts:
- Python programming language
- Installation process
3. Files and Code Sections:
None
4. Errors and fixes:
None
5. Problem Solving:
Explained Python installation from python.org
6. All user messages:
- What is Python?
- How do I install it?
- What are some popular libraries?
7. Pending Tasks:
None
8. Current Work:
Provided information about popular Python libraries.
9. Optional Next Step:
None
</summary>
"""
compression_service.llm.gen.return_value = mock_summary
# Compress first 2 queries
result = compression_service.compress_conversation(
conversation=sample_conversation, compress_up_to_index=1
)
# Verify LLM was called
assert compression_service.llm.gen.called
# Verify result is a CompressionMetadata object
assert hasattr(result, 'timestamp')
assert result.query_index == 1
assert hasattr(result, 'compressed_summary')
assert result.original_token_count > 0
assert result.compressed_token_count > 0
assert result.compression_ratio > 0
assert result.model_used == "gpt-4o"
assert result.compression_prompt_version == settings.COMPRESSION_PROMPT_VERSION
# Verify summary was extracted correctly (without analysis tags)
assert "<analysis>" not in result.compressed_summary
assert "Primary Request and Intent" in result.compressed_summary
def test_compress_conversation_with_tool_calls(self, compression_service):
"""Test compression of conversation with tool calls"""
conversation = {
"queries": [
{
"prompt": "Search for Python tutorials",
"response": "I'll search for Python tutorials.",
"thought": "Need to use search tool",
"sources": [],
"tool_calls": [
{
"tool_name": "search_tool",
"action_name": "search",
"arguments": {"query": "Python tutorials"},
"result": "Found 100 tutorials",
"status": "completed",
}
],
"timestamp": datetime.now(timezone.utc),
}
]
}
mock_summary = "<summary>Test summary with tools</summary>"
compression_service.llm.gen.return_value = mock_summary
compression_service.compress_conversation(
conversation=conversation, compress_up_to_index=0
)
# Verify tool calls are included in compression prompt
call_args = compression_service.llm.gen.call_args
messages = call_args[1]["messages"]
user_message = messages[1]["content"]
assert "Tool Calls:" in user_message
assert "search_tool" in user_message
def test_compress_conversation_invalid_index(
self, compression_service, sample_conversation
):
"""Test compression with invalid index raises error"""
with pytest.raises(ValueError, match="Invalid compress_up_to_index"):
compression_service.compress_conversation(
conversation=sample_conversation,
compress_up_to_index=100, # Invalid - conversation only has 3 queries
)
def test_get_compressed_context_no_compression(
self, compression_service, sample_conversation
):
"""Test getting context when no compression exists"""
summary, recent = compression_service.get_compressed_context(
sample_conversation
)
assert summary is None
assert len(recent) == 3 # All queries returned
def test_get_compressed_context_with_compression(self, compression_service):
"""Test getting context when compression exists"""
conversation = {
"queries": [
{"prompt": "Q1", "response": "A1"},
{"prompt": "Q2", "response": "A2"},
{"prompt": "Q3", "response": "A3"},
{"prompt": "Q4", "response": "A4"},
{"prompt": "Q5", "response": "A5"},
],
"compression_metadata": {
"is_compressed": True,
"last_compression_at": datetime.now(timezone.utc),
"compression_points": [
{
"timestamp": datetime.now(timezone.utc),
"query_index": 2, # Compressed up to Q3
"compressed_summary": "Summary of Q1-Q3",
"original_token_count": 100,
"compressed_token_count": 20,
"compression_ratio": 5.0,
}
],
},
}
summary, recent = compression_service.get_compressed_context(
conversation
)
assert summary == "Summary of Q1-Q3"
assert len(recent) == 2 # Q4 and Q5 (after compression point)
assert recent[0]["prompt"] == "Q4"
assert recent[1]["prompt"] == "Q5"
def test_get_compressed_context_multiple_compressions(self, compression_service):
"""Test getting context when multiple compressions exist"""
conversation = {
"queries": [
{"prompt": f"Q{i}", "response": f"A{i}"} for i in range(1, 11)
],
"compression_metadata": {
"is_compressed": True,
"last_compression_at": datetime.now(timezone.utc),
"compression_points": [
{
"timestamp": datetime.now(timezone.utc),
"query_index": 4, # First compression
"compressed_summary": "First compression summary",
"original_token_count": 100,
"compressed_token_count": 20,
},
{
"timestamp": datetime.now(timezone.utc),
"query_index": 7, # Second compression
"compressed_summary": "Second compression summary (includes first)",
"original_token_count": 150,
"compressed_token_count": 30,
},
],
},
}
summary, recent = compression_service.get_compressed_context(
conversation
)
# Should use the most recent compression
assert summary == "Second compression summary (includes first)"
assert len(recent) == 2 # Q9 and Q10 (after compression point at index 7)
assert recent[0]["prompt"] == "Q9"
assert recent[1]["prompt"] == "Q10"
def test_extract_summary_with_tags(self, compression_service):
"""Test summary extraction with analysis and summary tags"""
llm_response = """
<analysis>
This is my analysis of the conversation.
It has multiple lines.
</analysis>
<summary>
This is the actual summary.
It should be extracted.
</summary>
"""
result = compression_service._extract_summary(llm_response)
assert "<analysis>" not in result
assert "This is the actual summary" in result
assert "my analysis" not in result
def test_extract_summary_without_tags(self, compression_service):
"""Test summary extraction when no tags present"""
llm_response = "This is a plain summary without tags."
result = compression_service._extract_summary(llm_response)
assert result == "This is a plain summary without tags."
def test_count_tokens_in_queries(self, sample_conversation):
"""Test token counting in queries"""
queries = sample_conversation["queries"]
token_count = TokenCounter.count_query_tokens(queries)
# Should count all prompts and responses
assert token_count > 0
def test_count_tokens_with_tool_calls(self):
"""Test token counting includes tool calls"""
queries = [
{
"prompt": "Test prompt",
"response": "Test response",
"tool_calls": [
{
"tool_name": "test_tool",
"action_name": "test_action",
"arguments": {"arg": "value"},
"result": "Tool result",
}
],
}
]
token_count_with_tools = TokenCounter.count_query_tokens(
queries, include_tool_calls=True
)
token_count_without_tools = TokenCounter.count_query_tokens(
queries, include_tool_calls=False
)
assert token_count_with_tools > token_count_without_tools
def test_format_conversation_for_compression(
self, prompt_builder, sample_conversation
):
"""Test conversation formatting for compression prompt"""
queries = sample_conversation["queries"]
formatted = prompt_builder._format_conversation(queries)
# Verify formatting includes all messages
assert "Message 1" in formatted
assert "What is Python?" in formatted
assert "Python is a high-level programming language" in formatted
assert "Message 2" in formatted
assert "How do I install it?" in formatted
def test_build_compression_prompt_basic(self, prompt_builder):
"""Test compression prompt building"""
queries = [
{"prompt": "Q1", "response": "A1", "tool_calls": [], "sources": []},
{"prompt": "Q2", "response": "A2", "tool_calls": [], "sources": []},
]
messages = prompt_builder.build_prompt(queries)
assert len(messages) == 2 # System and user messages
assert messages[0]["role"] == "system"
assert messages[1]["role"] == "user"
assert "conversation to summarize" in messages[1]["content"]
def test_build_compression_prompt_with_existing_compressions(
self, prompt_builder
):
"""Test compression prompt building with existing compressions"""
queries = [
{"prompt": "Q3", "response": "A3", "tool_calls": [], "sources": []},
{"prompt": "Q4", "response": "A4", "tool_calls": [], "sources": []},
]
existing_compressions = [
{
"query_index": 1,
"compressed_summary": "Previous compression summary",
"timestamp": datetime.now(timezone.utc),
}
]
messages = prompt_builder.build_prompt(
queries, existing_compressions
)
user_content = messages[1]["content"]
# Should mention existing compression
assert "compressed before" in user_content
assert "Previous compression summary" in user_content
assert "NEW summary" in user_content
def test_calculate_conversation_tokens(
self, sample_conversation
):
"""Test conversation token calculation"""
token_count = TokenCounter.count_conversation_tokens(
sample_conversation, include_system_prompt=False
)
assert token_count > 0
# With system prompt should be higher
token_count_with_system = TokenCounter.count_conversation_tokens(
sample_conversation, include_system_prompt=True
)
assert token_count_with_system > token_count
@patch("application.api.answer.services.compression.threshold_checker.logger")
def test_error_handling_in_should_compress(
self, mock_logger, threshold_checker, sample_conversation
):
"""Test error handling in should_compress"""
# Force an error by making get_token_limit raise an exception
with patch(
"application.api.answer.services.compression.threshold_checker.get_token_limit",
side_effect=Exception("Test error"),
):
result = threshold_checker.should_compress(
sample_conversation, model_id="gpt-4o"
)
# Should return False on error
assert result is False
# Should log the error
assert mock_logger.error.called
@patch("application.api.answer.services.compression.service.logger")
def test_error_handling_in_get_compressed_context(
self, mock_logger, compression_service
):
"""Test error handling in get_compressed_context"""
# Malformed conversation
malformed_conversation = {"queries": None}
summary, recent = compression_service.get_compressed_context(
malformed_conversation
)
# Should return safe defaults
assert summary is None
assert recent == []
# Should log the error
assert mock_logger.error.called
def test_compression_points_array_limiting(self, compression_service):
"""Test that only the most recent compression points are kept"""
# Simulate a conversation with 3 previous compressions
conversation = {
"queries": [
{"prompt": f"Q{i}", "response": f"A{i}"} for i in range(1, 11)
],
"compression_metadata": {
"is_compressed": True,
"last_compression_at": datetime.now(timezone.utc),
"compression_points": [
{
"timestamp": datetime.now(timezone.utc),
"query_index": 2,
"compressed_summary": "First compression summary",
"original_token_count": 100,
"compressed_token_count": 20,
},
{
"timestamp": datetime.now(timezone.utc),
"query_index": 5,
"compressed_summary": "Second compression summary",
"original_token_count": 150,
"compressed_token_count": 30,
},
{
"timestamp": datetime.now(timezone.utc),
"query_index": 7,
"compressed_summary": "Third compression summary",
"original_token_count": 200,
"compressed_token_count": 40,
},
],
},
}
# The service should use the most recent compression
summary, recent = compression_service.get_compressed_context(
conversation
)
# Should use the most recent (third) compression
assert summary == "Third compression summary"
assert len(recent) == 2 # Q9 and Q10 (after compression point at index 7)
assert recent[0]["prompt"] == "Q9"
assert recent[1]["prompt"] == "Q10"
def test_compression_with_heavy_tool_usage(self, compression_service):
"""Test compression when conversation has many tool calls with large responses
Scenario: User asks agent to scrape all files in a GitHub repo, generating
dozens of tool calls with file contents as responses. This tests the system's
ability to compress tool-heavy conversations that hit token limits.
"""
# Simulate a conversation where agent scraped 50 files from DocsGPT repo
queries = []
# Initial user request
queries.append({
"prompt": "Please analyze all Python files in the https://github.com/arc53/DocsGPT repository",
"response": "I'll scrape all the Python files from the DocsGPT repository and analyze them.",
"tool_calls": []
})
# Simulate 50 file scraping tool calls with realistic file contents
file_paths = [
"application/app.py",
"application/api/answer/routes.py",
"application/api/answer/services/conversation_service.py",
"application/api/answer/services/compression_service.py",
"application/api/answer/services/stream_processor.py",
"application/agents/base.py",
"application/agents/react.py",
"application/llm/handlers/base.py",
"application/llm/llm_creator.py",
"application/core/settings.py",
"application/core/model_configs.py",
"application/utils.py",
"application/vectorstore/base.py",
"application/parser/file_parser.py",
"tests/test_compression_service.py",
"tests/test_agent_token_tracking.py",
"frontend/src/App.tsx",
"frontend/src/store/index.ts",
"deployment/docker-compose.yaml",
"setup.py",
]
tool_calls = []
for i, file_path in enumerate(file_paths[:20]): # First 20 files
# Each tool call with realistic file content (simulating ~500-1000 tokens per file)
file_content = f"""
# {file_path}
import os
import sys
from typing import Dict, List, Optional, Any
from datetime import datetime
class {file_path.split('/')[-1].replace('.py', '').title()}:
'''
This is a module that handles various operations for the DocsGPT application.
It contains multiple classes and functions for processing data.
'''
def __init__(self, config: Dict[str, Any]):
self.config = config
self.initialized = False
self.data_store = {{}}
def process_data(self, input_data: List[str]) -> Dict[str, Any]:
'''Process input data and return results'''
results = {{}}
for item in input_data:
# Complex processing logic here
processed = self._transform_item(item)
results[item] = processed
return results
def _transform_item(self, item: str) -> str:
'''Internal transformation logic'''
# Multiple lines of transformation code
transformed = item.upper().strip()
transformed = transformed.replace(' ', '_')
return transformed
def validate_config(self) -> bool:
'''Validate configuration settings'''
required_keys = ['api_key', 'endpoint', 'model_id']
return all(key in self.config for key in required_keys)
# Additional helper functions
def utility_function_one(param: str) -> str:
return param.strip().lower()
def utility_function_two(data: Dict) -> List:
return list(data.values())
def main():
config = {{'api_key': 'test', 'endpoint': 'http://localhost', 'model_id': 'gpt-4'}}
instance = {file_path.split('/')[-1].replace('.py', '').title()}(config)
instance.process_data(['item1', 'item2', 'item3'])
""" * 2 # Double it to simulate ~1000-1500 tokens per response
tool_calls.append({
"call_id": f"call_{i}",
"tool_name": "github_file_scraper",
"action_name": "read_file",
"arguments": {"file_path": file_path},
"result": {"content": file_content, "status": "success"},
"status": "success"
})
# Add query with all tool calls
queries.append({
"prompt": "[Agent continues processing]",
"response": "I've scraped 20 Python files. Let me analyze the patterns...",
"tool_calls": tool_calls
})
# Add analysis response
queries.append({
"prompt": "[Agent continues analysis]",
"response": """Based on my analysis of the 20 Python files:
1. Architecture: The codebase follows a modular architecture with clear separation between API, agents, LLM handlers, and utilities.
2. Key patterns identified:
- Heavy use of type hints (typing module)
- Consistent error handling patterns
- Service-based architecture for API endpoints
- Factory pattern for LLM creation
- Abstract base classes for extensibility
3. Core components:
- Agent system with tool integration
- LLM provider abstraction
- Compression service for context management
- Stream processing for real-time responses
4. Code quality observations:
- Comprehensive docstrings
- Good test coverage
- Clear naming conventions
- Proper separation of concerns""",
"tool_calls": []
})
conversation = {"queries": queries}
# Mock LLM response for compression
mock_summary = """<summary>
User requested analysis of all Python files in DocsGPT GitHub repository.
Agent scraped 20 files including app.py, API routes, services, agents, and tests.
Analysis revealed modular architecture with service-based design, type hints,
factory patterns, and agent system with tool integration. Code quality is high
with comprehensive docstrings and test coverage.
</summary>"""
compression_service.llm.gen.return_value = mock_summary
# Compress the heavy tool usage
result = compression_service.compress_conversation(
conversation=conversation,
compress_up_to_index=1 # Compress first 2 queries (including all tool calls)
)
# Verify compression handled tool calls properly
assert result.query_index == 1
assert result.compressed_summary is not None
# Verify the compression prompt included tool call information
call_args = compression_service.llm.gen.call_args
messages = call_args[1]["messages"]
user_message = messages[1]["content"]
# Should include tool calls section
assert "Tool Calls:" in user_message
assert "github_file_scraper" in user_message
# Verify compression ratio (should be significant with all that tool data)
original_tokens = result.original_token_count
compressed_tokens = result.compressed_token_count
compression_ratio = result.compression_ratio
# With 20 large tool responses, original should be substantial
assert original_tokens > 5000 # At least 5k tokens from tool responses
assert compressed_tokens < 500 # Summary should be much smaller
assert compression_ratio > 10 # Should achieve >10x compression
def test_compression_with_needle_in_haystack(self, compression_service):
"""Test compression preserves important information buried in long conversation
Scenario: User has long conversation with verbose responses, hiding critical
information in the middle. Tests that compression correctly identifies and
preserves key details even when surrounded by lengthy content.
"""
# Create a long conversation with important info buried in the middle
queries = []
# Query 1: Long general discussion
queries.append({
"prompt": "Tell me about Python programming best practices",
"response": """Python best practices encompass a wide range of principles and patterns.
Let me provide a comprehensive overview:
1. Code Style and Formatting:
- Follow PEP 8 style guide for consistent formatting
- Use 4 spaces for indentation (not tabs)
- Limit lines to 79 characters for code, 72 for docstrings
- Use meaningful variable names that describe their purpose
- Add whitespace around operators and after commas
- Group imports: standard library, third-party, local
2. Documentation:
- Write clear docstrings for all functions, classes, and modules
- Use type hints for better code clarity and IDE support
- Include examples in docstrings when helpful
- Keep comments up-to-date with code changes
3. Error Handling:
- Use specific exceptions rather than bare except clauses
- Create custom exceptions for domain-specific errors
- Always clean up resources with context managers (with statement)
- Log errors appropriately for debugging
4. Testing:
- Write unit tests for all critical functionality
- Aim for high test coverage (80%+)
- Use pytest for modern testing features
- Mock external dependencies in tests
5. Code Organization:
- Keep functions small and focused on single tasks
- Use classes to group related functionality
- Avoid deep nesting (max 3-4 levels)
- Extract complex conditions into well-named variables
6. Performance:
- Use list comprehensions for simple transformations
- Avoid premature optimization
- Profile code before optimizing
- Use generators for large datasets
These practices help maintain readable, maintainable, and efficient code.""",
"tool_calls": []
})
# Query 2: Another long response
queries.append({
"prompt": "What about Python data structures?",
"response": """Python provides several built-in data structures, each optimized for different use cases:
1. Lists:
- Ordered, mutable sequences
- Dynamic sizing with amortized O(1) append
- Access by index in O(1)
- Insertion/deletion in middle is O(n)
- Use cases: ordered collections, stacks, queues
- Methods: append(), extend(), insert(), remove(), pop(), sort()
2. Tuples:
- Ordered, immutable sequences
- Slightly more memory efficient than lists
- Can be used as dictionary keys (if contents are hashable)
- Use cases: fixed collections, function return values, dictionary keys
3. Dictionaries:
- Unordered (ordered in Python 3.7+) key-value mappings
- Average O(1) lookup, insertion, deletion
- Keys must be hashable
- Use cases: lookups, caching, counting, grouping
- Methods: get(), keys(), values(), items(), update(), pop()
4. Sets:
- Unordered collections of unique elements
- Average O(1) membership testing
- Efficient for removing duplicates
- Support set operations: union, intersection, difference
- Use cases: membership testing, removing duplicates, set mathematics
5. Collections module extensions:
- defaultdict: dict with default values for missing keys
- Counter: dict subclass for counting hashable objects
- deque: double-ended queue with O(1) append/pop from both ends
- OrderedDict: maintains insertion order (less relevant in Python 3.7+)
- namedtuple: tuple subclass with named fields
6. Performance considerations:
- Lists for ordered data with frequent append operations
- Dictionaries for key-based lookups
- Sets for membership testing and uniqueness
- Deques for queue operations from both ends
- Tuples for immutable data
Understanding these data structures is crucial for writing efficient Python code.""",
"tool_calls": []
})
# Query 3: THE CRITICAL INFORMATION (needle in the haystack)
queries.append({
"prompt": "I need to remember this important detail",
"response": """I'll make a note of that important detail.
CRITICAL INFORMATION TO REMEMBER:
The production database password is stored in the environment variable DB_PASSWORD_PROD.
The backup schedule is set to run daily at 3:00 AM UTC.
The API rate limit for premium users is 10,000 requests per hour.
The encryption key rotation happens every 90 days.
The primary contact for incidents is: ops-team@example.com
I've recorded this information for our conversation. These operational details are important for system administration and should be referenced when needed.""",
"tool_calls": []
})
# Query 4: More long content after the important info
queries.append({
"prompt": "Explain Python decorators in detail",
"response": """Python decorators are a powerful feature that allows you to modify or enhance functions and classes. Here's a comprehensive explanation:
1. Basic Concept:
- Decorators are functions that take another function as input
- They return a modified version of that function
- Syntax: @decorator above function definition
- They implement the decorator design pattern
2. Function Decorators:
```python
def my_decorator(func):
def wrapper(*args, **kwargs):
# Code before function
result = func(*args, **kwargs)
# Code after function
return result
return wrapper
@my_decorator
def my_function():
pass
```
3. Common Use Cases:
- Logging: Record function calls and results
- Timing: Measure execution time
- Authentication: Check permissions before execution
- Caching: Store and return cached results
- Validation: Check input parameters
- Rate limiting: Throttle function calls
4. Decorators with Arguments:
```python
def repeat(times):
def decorator(func):
def wrapper(*args, **kwargs):
for _ in range(times):
result = func(*args, **kwargs)
return result
return wrapper
return decorator
@repeat(3)
def greet():
print("Hello")
```
5. Class Decorators:
- Can decorate entire classes
- Useful for adding methods or attributes
- Can enforce patterns like singleton
6. Built-in Decorators:
- @property: Create managed attributes
- @staticmethod: Define static methods
- @classmethod: Define class methods
- @abstractmethod: Define abstract methods
7. functools.wraps:
- Preserves original function metadata
- Should be used in decorator implementations
- Maintains __name__, __doc__, etc.
8. Practical Examples:
- @login_required for web routes
- @cache for memoization
- @retry for resilient API calls
- @deprecated for marking old code
Decorators are essential for writing clean, maintainable Python code with separation of concerns.""",
"tool_calls": []
})
# Query 5: Final long response
queries.append({
"prompt": "What about Python async programming?",
"response": """Asynchronous programming in Python allows for concurrent execution of I/O-bound operations:
1. Core Concepts:
- Event loop: Manages and executes async tasks
- Coroutines: Functions defined with async def
- await: Pauses coroutine until awaitable completes
- Tasks: Wrapper for coroutines to run concurrently
2. Basic Syntax:
```python
import asyncio
async def fetch_data():
await asyncio.sleep(1)
return "data"
async def main():
result = await fetch_data()
print(result)
asyncio.run(main())
```
3. When to Use Async:
- I/O-bound operations (network requests, file I/O, database queries)
- Multiple concurrent operations
- Real-time applications (websockets, streaming)
- NOT for CPU-bound tasks (use multiprocessing instead)
4. Common Patterns:
- Gather: Run multiple coroutines concurrently
- create_task: Schedule coroutine execution
- Semaphore: Limit concurrent operations
- Queue: Producer-consumer patterns
5. Async Libraries:
- aiohttp: Async HTTP client/server
- asyncpg: Async PostgreSQL driver
- motor: Async MongoDB driver
- aioredis: Async Redis client
6. Error Handling:
- Use try/except in coroutines
- Tasks can be cancelled with task.cancel()
- Timeouts with asyncio.wait_for()
Understanding async programming is crucial for building scalable Python applications.""",
"tool_calls": []
})
conversation = {"queries": queries}
# Mock LLM response that MUST preserve the critical information
mock_summary = """<summary>
User asked about Python best practices, data structures, decorators, and async programming.
Discussed code style, testing, documentation standards, and various Python data structures.
CRITICAL OPERATIONAL DETAILS PROVIDED:
- Production database password stored in DB_PASSWORD_PROD environment variable
- Backup schedule: daily at 3:00 AM UTC
- Premium API rate limit: 10,000 requests/hour
- Encryption key rotation: every 90 days
- Incident contact: ops-team@example.com
Also covered decorators for code enhancement and async programming for I/O-bound operations.
</summary>"""
compression_service.llm.gen.return_value = mock_summary
# Compress everything except the last query
result = compression_service.compress_conversation(
conversation=conversation,
compress_up_to_index=3 # Compress first 4 queries (includes the critical info)
)
# Verify compression happened
assert result.query_index == 3
assert result.compressed_summary is not None
# Get the compressed context
conversation["compression_metadata"] = {
"is_compressed": True,
"last_compression_at": datetime.now(timezone.utc),
"compression_points": [result.to_dict()]
}
summary, recent = compression_service.get_compressed_context(
conversation
)
# Verify critical information is in the summary
assert summary is not None
assert "DB_PASSWORD_PROD" in summary or "database password" in summary.lower()
assert "3:00 AM UTC" in summary or "backup" in summary.lower()
assert "10,000" in summary or "rate limit" in summary.lower()
assert "ops-team@example.com" in summary or "incident contact" in summary.lower()
# Verify only the last query is in recent
assert len(recent) == 1
assert "async programming" in recent[0]["prompt"].lower()
# The compression should be substantial (long responses compressed to summary)
assert result.original_token_count > 1300 # 4 long responses
assert result.compressed_token_count < 300 # Summary should be concise
assert result.compression_ratio > 4 # At least 4x compression
if __name__ == "__main__":
pytest.main([__file__, "-v"])
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