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trajectory_compressor.py
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1447 lines (1174 loc) Β· 60.2 KB
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#!/usr/bin/env python3
"""
Trajectory Compressor
Post-processes completed agent trajectories to compress them within a target
token budget while preserving training signal quality.
Compression Strategy:
1. Protect first turns (system, human, first gpt, first tool)
2. Protect last N turns (final actions and conclusions)
3. Compress MIDDLE turns only, starting from 2nd tool response
4. Compress only as much as needed to fit under target
5. Replace compressed region with a single human summary message
6. Keep remaining tool calls intact (model continues working after summary)
Usage:
# Compress a directory of JSONL files
python trajectory_compressor.py --input=data/my_run
# Compress a single JSONL file
python trajectory_compressor.py --input=data/trajectories.jsonl
# Compress 15% sample of a file
python trajectory_compressor.py --input=data/trajectories.jsonl --sample_percent=15
# Compress with custom output and token target
python trajectory_compressor.py --input=data/trajectories.jsonl --output=compressed.jsonl --target_max_tokens=16000
# Compress 10% sample from a directory
python trajectory_compressor.py --input=data/my_run --sample_percent=10
"""
import json
import os
import re
import time
import yaml
import logging
import asyncio
from pathlib import Path
from typing import List, Dict, Any, Optional, Tuple, Callable
from dataclasses import dataclass, field
from datetime import datetime
import fire
from rich.progress import Progress, SpinnerColumn, TextColumn, BarColumn, TaskProgressColumn, TimeElapsedColumn, TimeRemainingColumn
from rich.console import Console
from hermes_constants import OPENROUTER_BASE_URL
# Load environment variables
from dotenv import load_dotenv
load_dotenv()
@dataclass
class CompressionConfig:
"""Configuration for trajectory compression."""
# Tokenizer
tokenizer_name: str = "moonshotai/Kimi-K2-Thinking"
trust_remote_code: bool = True
# Compression targets
target_max_tokens: int = 15250
summary_target_tokens: int = 750
# Protected turns
protect_first_system: bool = True
protect_first_human: bool = True
protect_first_gpt: bool = True
protect_first_tool: bool = True
protect_last_n_turns: int = 4
# Summarization (OpenRouter)
summarization_model: str = "google/gemini-3-flash-preview"
base_url: str = OPENROUTER_BASE_URL
api_key_env: str = "OPENROUTER_API_KEY"
temperature: float = 0.3
max_retries: int = 3
retry_delay: int = 2
# Output
add_summary_notice: bool = True
summary_notice_text: str = "\n\nSome of your previous tool responses may be summarized to preserve context."
output_suffix: str = "_compressed"
# Processing
num_workers: int = 4
max_concurrent_requests: int = 50 # Max concurrent API calls for summarization
skip_under_target: bool = True
save_over_limit: bool = True
per_trajectory_timeout: int = 300 # Timeout per trajectory in seconds (default: 5 min)
# Metrics
metrics_enabled: bool = True
metrics_per_trajectory: bool = True
metrics_output_file: str = "compression_metrics.json"
@classmethod
def from_yaml(cls, yaml_path: str) -> "CompressionConfig":
"""Load configuration from YAML file."""
with open(yaml_path, 'r') as f:
data = yaml.safe_load(f)
config = cls()
# Tokenizer
if 'tokenizer' in data:
config.tokenizer_name = data['tokenizer'].get('name', config.tokenizer_name)
config.trust_remote_code = data['tokenizer'].get('trust_remote_code', config.trust_remote_code)
# Compression
if 'compression' in data:
config.target_max_tokens = data['compression'].get('target_max_tokens', config.target_max_tokens)
config.summary_target_tokens = data['compression'].get('summary_target_tokens', config.summary_target_tokens)
# Protected turns
if 'protected_turns' in data:
config.protect_first_system = data['protected_turns'].get('first_system', config.protect_first_system)
config.protect_first_human = data['protected_turns'].get('first_human', config.protect_first_human)
config.protect_first_gpt = data['protected_turns'].get('first_gpt', config.protect_first_gpt)
config.protect_first_tool = data['protected_turns'].get('first_tool', config.protect_first_tool)
config.protect_last_n_turns = data['protected_turns'].get('last_n_turns', config.protect_last_n_turns)
# Summarization
if 'summarization' in data:
config.summarization_model = data['summarization'].get('model', config.summarization_model)
config.base_url = data['summarization'].get('base_url', config.base_url)
config.api_key_env = data['summarization'].get('api_key_env', config.api_key_env)
config.temperature = data['summarization'].get('temperature', config.temperature)
config.max_retries = data['summarization'].get('max_retries', config.max_retries)
config.retry_delay = data['summarization'].get('retry_delay', config.retry_delay)
# Output
if 'output' in data:
config.add_summary_notice = data['output'].get('add_summary_notice', config.add_summary_notice)
config.summary_notice_text = data['output'].get('summary_notice_text', config.summary_notice_text)
config.output_suffix = data['output'].get('output_suffix', config.output_suffix)
# Processing
if 'processing' in data:
config.num_workers = data['processing'].get('num_workers', config.num_workers)
config.max_concurrent_requests = data['processing'].get('max_concurrent_requests', config.max_concurrent_requests)
config.skip_under_target = data['processing'].get('skip_under_target', config.skip_under_target)
config.save_over_limit = data['processing'].get('save_over_limit', config.save_over_limit)
# Metrics
if 'metrics' in data:
config.metrics_enabled = data['metrics'].get('enabled', config.metrics_enabled)
config.metrics_per_trajectory = data['metrics'].get('per_trajectory', config.metrics_per_trajectory)
config.metrics_output_file = data['metrics'].get('output_file', config.metrics_output_file)
return config
@dataclass
class TrajectoryMetrics:
"""Metrics for a single trajectory compression."""
original_tokens: int = 0
compressed_tokens: int = 0
tokens_saved: int = 0
compression_ratio: float = 1.0
original_turns: int = 0
compressed_turns: int = 0
turns_removed: int = 0
turns_compressed_start_idx: int = -1
turns_compressed_end_idx: int = -1
turns_in_compressed_region: int = 0
was_compressed: bool = False
still_over_limit: bool = False
skipped_under_target: bool = False
summarization_api_calls: int = 0
summarization_errors: int = 0
def to_dict(self) -> Dict[str, Any]:
return {
"original_tokens": self.original_tokens,
"compressed_tokens": self.compressed_tokens,
"tokens_saved": self.tokens_saved,
"compression_ratio": round(self.compression_ratio, 4),
"original_turns": self.original_turns,
"compressed_turns": self.compressed_turns,
"turns_removed": self.turns_removed,
"compression_region": {
"start_idx": self.turns_compressed_start_idx,
"end_idx": self.turns_compressed_end_idx,
"turns_count": self.turns_in_compressed_region,
},
"was_compressed": self.was_compressed,
"still_over_limit": self.still_over_limit,
"skipped_under_target": self.skipped_under_target,
"summarization_api_calls": self.summarization_api_calls,
"summarization_errors": self.summarization_errors,
}
@dataclass
class AggregateMetrics:
"""Aggregate metrics across all trajectories."""
total_trajectories: int = 0
trajectories_compressed: int = 0
trajectories_skipped_under_target: int = 0
trajectories_still_over_limit: int = 0
trajectories_failed: int = 0
total_tokens_before: int = 0
total_tokens_after: int = 0
total_tokens_saved: int = 0
total_turns_before: int = 0
total_turns_after: int = 0
total_turns_removed: int = 0
total_summarization_calls: int = 0
total_summarization_errors: int = 0
# Distribution stats
compression_ratios: List[float] = field(default_factory=list)
tokens_saved_list: List[int] = field(default_factory=list)
turns_removed_list: List[int] = field(default_factory=list)
processing_start_time: str = ""
processing_end_time: str = ""
processing_duration_seconds: float = 0.0
def add_trajectory_metrics(self, metrics: TrajectoryMetrics):
"""Add a trajectory's metrics to the aggregate."""
self.total_trajectories += 1
self.total_tokens_before += metrics.original_tokens
self.total_tokens_after += metrics.compressed_tokens
self.total_tokens_saved += metrics.tokens_saved
self.total_turns_before += metrics.original_turns
self.total_turns_after += metrics.compressed_turns
self.total_turns_removed += metrics.turns_removed
self.total_summarization_calls += metrics.summarization_api_calls
self.total_summarization_errors += metrics.summarization_errors
if metrics.was_compressed:
self.trajectories_compressed += 1
self.compression_ratios.append(metrics.compression_ratio)
self.tokens_saved_list.append(metrics.tokens_saved)
self.turns_removed_list.append(metrics.turns_removed)
if metrics.skipped_under_target:
self.trajectories_skipped_under_target += 1
if metrics.still_over_limit:
self.trajectories_still_over_limit += 1
def to_dict(self) -> Dict[str, Any]:
avg_compression_ratio = (
sum(self.compression_ratios) / len(self.compression_ratios)
if self.compression_ratios else 1.0
)
avg_tokens_saved = (
sum(self.tokens_saved_list) / len(self.tokens_saved_list)
if self.tokens_saved_list else 0
)
avg_turns_removed = (
sum(self.turns_removed_list) / len(self.turns_removed_list)
if self.turns_removed_list else 0
)
return {
"summary": {
"total_trajectories": self.total_trajectories,
"trajectories_compressed": self.trajectories_compressed,
"trajectories_skipped_under_target": self.trajectories_skipped_under_target,
"trajectories_still_over_limit": self.trajectories_still_over_limit,
"trajectories_failed": self.trajectories_failed,
"compression_rate": round(self.trajectories_compressed / max(self.total_trajectories, 1), 4),
},
"tokens": {
"total_before": self.total_tokens_before,
"total_after": self.total_tokens_after,
"total_saved": self.total_tokens_saved,
"overall_compression_ratio": round(self.total_tokens_after / max(self.total_tokens_before, 1), 4),
},
"turns": {
"total_before": self.total_turns_before,
"total_after": self.total_turns_after,
"total_removed": self.total_turns_removed,
},
"averages": {
"avg_compression_ratio": round(avg_compression_ratio, 4),
"avg_tokens_saved_per_compressed": round(avg_tokens_saved, 1),
"avg_turns_removed_per_compressed": round(avg_turns_removed, 2),
},
"summarization": {
"total_api_calls": self.total_summarization_calls,
"total_errors": self.total_summarization_errors,
"success_rate": round(1 - (self.total_summarization_errors / max(self.total_summarization_calls, 1)), 4),
},
"processing": {
"start_time": self.processing_start_time,
"end_time": self.processing_end_time,
"duration_seconds": round(self.processing_duration_seconds, 2),
},
}
class TrajectoryCompressor:
"""
Compresses agent trajectories to fit within a target token budget.
Compression strategy:
1. Keep protected head turns (system, human, first gpt+tool)
2. Keep protected tail turns (last N turns)
3. From the compressible middle region, compress only as much as needed
4. Replace compressed turns with a single human summary message
5. Keep remaining middle turns intact (model continues with tools)
"""
def __init__(self, config: CompressionConfig):
"""Initialize the compressor."""
self.config = config
self.aggregate_metrics = AggregateMetrics()
# Initialize tokenizer
self._init_tokenizer()
# Initialize OpenRouter client
self._init_summarizer()
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s',
datefmt='%H:%M:%S'
)
self.logger = logging.getLogger(__name__)
def _init_tokenizer(self):
"""Initialize HuggingFace tokenizer for token counting."""
try:
from transformers import AutoTokenizer
self.tokenizer = AutoTokenizer.from_pretrained(
self.config.tokenizer_name,
trust_remote_code=self.config.trust_remote_code
)
print(f"β
Loaded tokenizer: {self.config.tokenizer_name}")
except Exception as e:
raise RuntimeError(f"Failed to load tokenizer '{self.config.tokenizer_name}': {e}")
def _init_summarizer(self):
"""Initialize OpenRouter client for summarization (sync and async)."""
api_key = os.getenv(self.config.api_key_env)
if not api_key:
raise RuntimeError(f"Missing API key. Set {self.config.api_key_env} environment variable.")
from openai import OpenAI, AsyncOpenAI
# OpenRouter app attribution headers (only for OpenRouter endpoints)
extra = {}
if "openrouter" in self.config.base_url.lower():
extra["default_headers"] = {
"HTTP-Referer": "https://github.com/NousResearch/hermes-agent",
"X-OpenRouter-Title": "Hermes Agent",
"X-OpenRouter-Categories": "productivity,cli-agent",
}
# Sync client (for backwards compatibility)
self.client = OpenAI(
api_key=api_key,
base_url=self.config.base_url,
**extra,
)
# Async client for parallel processing
self.async_client = AsyncOpenAI(
api_key=api_key,
base_url=self.config.base_url,
**extra,
)
print(f"β
Initialized OpenRouter client: {self.config.summarization_model}")
print(f" Max concurrent requests: {self.config.max_concurrent_requests}")
def count_tokens(self, text: str) -> int:
"""Count tokens in text using the configured tokenizer."""
if not text:
return 0
try:
return len(self.tokenizer.encode(text))
except Exception:
# Fallback to character estimate
return len(text) // 4
def count_trajectory_tokens(self, trajectory: List[Dict[str, str]]) -> int:
"""Count total tokens in a trajectory."""
return sum(self.count_tokens(turn.get("value", "")) for turn in trajectory)
def count_turn_tokens(self, trajectory: List[Dict[str, str]]) -> List[int]:
"""Count tokens for each turn in a trajectory."""
return [self.count_tokens(turn.get("value", "")) for turn in trajectory]
def _find_protected_indices(self, trajectory: List[Dict[str, str]]) -> Tuple[set, int, int]:
"""
Find indices of protected turns.
Returns:
Tuple of (protected_set, compressible_start, compressible_end)
"""
n = len(trajectory)
protected = set()
# Track first occurrences
first_system = first_human = first_gpt = first_tool = None
for i, turn in enumerate(trajectory):
role = turn.get("from", "")
if role == "system" and first_system is None:
first_system = i
elif role == "human" and first_human is None:
first_human = i
elif role == "gpt" and first_gpt is None:
first_gpt = i
elif role == "tool" and first_tool is None:
first_tool = i
# Protect first turns
if self.config.protect_first_system and first_system is not None:
protected.add(first_system)
if self.config.protect_first_human and first_human is not None:
protected.add(first_human)
if self.config.protect_first_gpt and first_gpt is not None:
protected.add(first_gpt)
if self.config.protect_first_tool and first_tool is not None:
protected.add(first_tool)
# Protect last N turns
for i in range(max(0, n - self.config.protect_last_n_turns), n):
protected.add(i)
# Determine compressible region
# Start after the last protected head turn
head_protected = [i for i in protected if i < n // 2]
tail_protected = [i for i in protected if i >= n // 2]
compressible_start = max(head_protected) + 1 if head_protected else 0
compressible_end = min(tail_protected) if tail_protected else n
return protected, compressible_start, compressible_end
def _extract_turn_content_for_summary(self, trajectory: List[Dict[str, str]], start: int, end: int) -> str:
"""
Extract content from turns to be summarized.
Args:
trajectory: Full trajectory
start: Start index (inclusive)
end: End index (exclusive)
Returns:
Formatted string of turn contents for summarization
"""
parts = []
for i in range(start, end):
turn = trajectory[i]
role = turn.get("from", "unknown")
value = turn.get("value", "")
# Truncate very long values for the summary prompt
if len(value) > 3000:
value = value[:1500] + "\n...[truncated]...\n" + value[-500:]
parts.append(f"[Turn {i} - {role.upper()}]:\n{value}")
return "\n\n".join(parts)
def _generate_summary(self, content: str, metrics: TrajectoryMetrics) -> str:
"""
Generate a summary of the compressed turns using OpenRouter.
Args:
content: The content to summarize
metrics: Metrics object to update
Returns:
Summary string
"""
prompt = f"""Summarize the following agent conversation turns concisely. This summary will replace these turns in the conversation history.
Write the summary from a neutral perspective describing what the assistant did and learned. Include:
1. What actions the assistant took (tool calls, searches, file operations)
2. Key information or results obtained
3. Any important decisions or findings
4. Relevant data, file names, values, or outputs
Keep the summary factual and informative. Target approximately {self.config.summary_target_tokens} tokens.
---
TURNS TO SUMMARIZE:
{content}
---
Write only the summary, starting with "[CONTEXT SUMMARY]:" prefix."""
for attempt in range(self.config.max_retries):
try:
metrics.summarization_api_calls += 1
response = self.client.chat.completions.create(
model=self.config.summarization_model,
messages=[{"role": "user", "content": prompt}],
temperature=self.config.temperature,
max_tokens=self.config.summary_target_tokens * 2,
)
summary = response.choices[0].message.content.strip()
# Ensure it starts with the prefix
if not summary.startswith("[CONTEXT SUMMARY]:"):
summary = "[CONTEXT SUMMARY]: " + summary
return summary
except Exception as e:
metrics.summarization_errors += 1
self.logger.warning(f"Summarization attempt {attempt + 1} failed: {e}")
if attempt < self.config.max_retries - 1:
time.sleep(self.config.retry_delay * (attempt + 1))
else:
# Fallback: create a basic summary
return "[CONTEXT SUMMARY]: [Summary generation failed - previous turns contained tool calls and responses that have been compressed to save context space.]"
async def _generate_summary_async(self, content: str, metrics: TrajectoryMetrics) -> str:
"""
Generate a summary of the compressed turns using OpenRouter (async version).
Args:
content: The content to summarize
metrics: Metrics object to update
Returns:
Summary string
"""
prompt = f"""Summarize the following agent conversation turns concisely. This summary will replace these turns in the conversation history.
Write the summary from a neutral perspective describing what the assistant did and learned. Include:
1. What actions the assistant took (tool calls, searches, file operations)
2. Key information or results obtained
3. Any important decisions or findings
4. Relevant data, file names, values, or outputs
Keep the summary factual and informative. Target approximately {self.config.summary_target_tokens} tokens.
---
TURNS TO SUMMARIZE:
{content}
---
Write only the summary, starting with "[CONTEXT SUMMARY]:" prefix."""
for attempt in range(self.config.max_retries):
try:
metrics.summarization_api_calls += 1
response = await self.async_client.chat.completions.create(
model=self.config.summarization_model,
messages=[{"role": "user", "content": prompt}],
temperature=self.config.temperature,
max_tokens=self.config.summary_target_tokens * 2,
)
summary = response.choices[0].message.content.strip()
# Ensure it starts with the prefix
if not summary.startswith("[CONTEXT SUMMARY]:"):
summary = "[CONTEXT SUMMARY]: " + summary
return summary
except Exception as e:
metrics.summarization_errors += 1
self.logger.warning(f"Summarization attempt {attempt + 1} failed: {e}")
if attempt < self.config.max_retries - 1:
await asyncio.sleep(self.config.retry_delay * (attempt + 1))
else:
# Fallback: create a basic summary
return "[CONTEXT SUMMARY]: [Summary generation failed - previous turns contained tool calls and responses that have been compressed to save context space.]"
def compress_trajectory(
self,
trajectory: List[Dict[str, str]]
) -> Tuple[List[Dict[str, str]], TrajectoryMetrics]:
"""
Compress a single trajectory to fit within target token budget.
Algorithm:
1. Count total tokens
2. If under target, skip
3. Find compressible region (between protected head and tail)
4. Calculate how many tokens need to be saved
5. Accumulate turns from start of compressible region until savings met
6. Replace accumulated turns with single human summary
7. Keep remaining turns intact
Args:
trajectory: List of conversation turns
Returns:
Tuple of (compressed_trajectory, metrics)
"""
metrics = TrajectoryMetrics()
metrics.original_turns = len(trajectory)
# Count tokens per turn
turn_tokens = self.count_turn_tokens(trajectory)
total_tokens = sum(turn_tokens)
metrics.original_tokens = total_tokens
# Check if compression needed
if total_tokens <= self.config.target_max_tokens:
metrics.skipped_under_target = True
metrics.compressed_tokens = total_tokens
metrics.compressed_turns = len(trajectory)
metrics.compression_ratio = 1.0
return trajectory, metrics
# Find protected regions
protected, compress_start, compress_end = self._find_protected_indices(trajectory)
# Check if there's anything to compress
if compress_start >= compress_end:
# Nothing to compress, return as-is
metrics.compressed_tokens = total_tokens
metrics.compressed_turns = len(trajectory)
metrics.still_over_limit = total_tokens > self.config.target_max_tokens
return trajectory, metrics
# Calculate how much we need to save
tokens_to_save = total_tokens - self.config.target_max_tokens
# We'll replace N turns with 1 summary turn
# Net savings = (sum of N turns' tokens) - summary_target_tokens
# We need: net_savings >= tokens_to_save
# So: sum of turns >= tokens_to_save + summary_target_tokens
target_tokens_to_compress = tokens_to_save + self.config.summary_target_tokens
# Accumulate turns from compress_start until we have enough savings
accumulated_tokens = 0
compress_until = compress_start
for i in range(compress_start, compress_end):
accumulated_tokens += turn_tokens[i]
compress_until = i + 1 # Exclusive end
# Check if we have enough savings
if accumulated_tokens >= target_tokens_to_compress:
break
# If we still don't have enough savings, compress the entire compressible region
if accumulated_tokens < target_tokens_to_compress and compress_until < compress_end:
compress_until = compress_end
accumulated_tokens = sum(turn_tokens[compress_start:compress_end])
# Record compression region
metrics.turns_compressed_start_idx = compress_start
metrics.turns_compressed_end_idx = compress_until
metrics.turns_in_compressed_region = compress_until - compress_start
# Extract content for summary
content_to_summarize = self._extract_turn_content_for_summary(
trajectory, compress_start, compress_until
)
# Generate summary
summary = self._generate_summary(content_to_summarize, metrics)
# Build compressed trajectory
compressed = []
# Add head (turns before compression region)
for i in range(compress_start):
turn = trajectory[i].copy()
# Add notice to system message
if turn.get("from") == "system" and self.config.add_summary_notice:
turn["value"] = turn["value"] + self.config.summary_notice_text
compressed.append(turn)
# Add summary as human message
compressed.append({
"from": "human",
"value": summary
})
# Add tail (turns after compression region)
for i in range(compress_until, len(trajectory)):
compressed.append(trajectory[i].copy())
# Calculate final metrics
metrics.compressed_turns = len(compressed)
metrics.compressed_tokens = self.count_trajectory_tokens(compressed)
metrics.turns_removed = metrics.original_turns - metrics.compressed_turns
metrics.tokens_saved = metrics.original_tokens - metrics.compressed_tokens
metrics.compression_ratio = metrics.compressed_tokens / max(metrics.original_tokens, 1)
metrics.was_compressed = True
metrics.still_over_limit = metrics.compressed_tokens > self.config.target_max_tokens
return compressed, metrics
async def compress_trajectory_async(
self,
trajectory: List[Dict[str, str]]
) -> Tuple[List[Dict[str, str]], TrajectoryMetrics]:
"""
Compress a single trajectory to fit within target token budget (async version).
Same algorithm as compress_trajectory but uses async API calls for summarization.
"""
metrics = TrajectoryMetrics()
metrics.original_turns = len(trajectory)
# Count tokens per turn
turn_tokens = self.count_turn_tokens(trajectory)
total_tokens = sum(turn_tokens)
metrics.original_tokens = total_tokens
# Check if compression needed
if total_tokens <= self.config.target_max_tokens:
metrics.skipped_under_target = True
metrics.compressed_tokens = total_tokens
metrics.compressed_turns = len(trajectory)
metrics.compression_ratio = 1.0
return trajectory, metrics
# Find protected regions
protected, compress_start, compress_end = self._find_protected_indices(trajectory)
# Check if there's anything to compress
if compress_start >= compress_end:
metrics.compressed_tokens = total_tokens
metrics.compressed_turns = len(trajectory)
metrics.still_over_limit = total_tokens > self.config.target_max_tokens
return trajectory, metrics
# Calculate how much we need to save
tokens_to_save = total_tokens - self.config.target_max_tokens
target_tokens_to_compress = tokens_to_save + self.config.summary_target_tokens
# Accumulate turns from compress_start until we have enough savings
accumulated_tokens = 0
compress_until = compress_start
for i in range(compress_start, compress_end):
accumulated_tokens += turn_tokens[i]
compress_until = i + 1
if accumulated_tokens >= target_tokens_to_compress:
break
# If we still don't have enough savings, compress the entire compressible region
if accumulated_tokens < target_tokens_to_compress and compress_until < compress_end:
compress_until = compress_end
accumulated_tokens = sum(turn_tokens[compress_start:compress_end])
# Record compression region
metrics.turns_compressed_start_idx = compress_start
metrics.turns_compressed_end_idx = compress_until
metrics.turns_in_compressed_region = compress_until - compress_start
# Extract content for summary
content_to_summarize = self._extract_turn_content_for_summary(
trajectory, compress_start, compress_until
)
# Generate summary (ASYNC)
summary = await self._generate_summary_async(content_to_summarize, metrics)
# Build compressed trajectory
compressed = []
# Add head (turns before compression region)
for i in range(compress_start):
turn = trajectory[i].copy()
if turn.get("from") == "system" and self.config.add_summary_notice:
turn["value"] = turn["value"] + self.config.summary_notice_text
compressed.append(turn)
# Add summary as human message
compressed.append({
"from": "human",
"value": summary
})
# Add tail (turns after compression region)
for i in range(compress_until, len(trajectory)):
compressed.append(trajectory[i].copy())
# Calculate final metrics
metrics.compressed_turns = len(compressed)
metrics.compressed_tokens = self.count_trajectory_tokens(compressed)
metrics.turns_removed = metrics.original_turns - metrics.compressed_turns
metrics.tokens_saved = metrics.original_tokens - metrics.compressed_tokens
metrics.compression_ratio = metrics.compressed_tokens / max(metrics.original_tokens, 1)
metrics.was_compressed = True
metrics.still_over_limit = metrics.compressed_tokens > self.config.target_max_tokens
return compressed, metrics
async def process_entry_async(self, entry: Dict[str, Any]) -> Tuple[Dict[str, Any], TrajectoryMetrics]:
"""
Process a single JSONL entry (async version).
"""
if "conversations" not in entry:
metrics = TrajectoryMetrics()
return entry, metrics
trajectory = entry["conversations"]
compressed_trajectory, metrics = await self.compress_trajectory_async(trajectory)
# Create new entry with compressed trajectory
result = entry.copy()
result["conversations"] = compressed_trajectory
# Add compression metadata if enabled
if self.config.metrics_per_trajectory and metrics.was_compressed:
result["compression_metrics"] = metrics.to_dict()
return result, metrics
def process_entry(self, entry: Dict[str, Any]) -> Tuple[Dict[str, Any], TrajectoryMetrics]:
"""
Process a single JSONL entry.
Args:
entry: JSONL entry containing 'conversations' field
Returns:
Tuple of (processed_entry, metrics)
"""
if "conversations" not in entry:
metrics = TrajectoryMetrics()
return entry, metrics
trajectory = entry["conversations"]
compressed_trajectory, metrics = self.compress_trajectory(trajectory)
# Create new entry with compressed trajectory
result = entry.copy()
result["conversations"] = compressed_trajectory
# Add compression metadata if enabled
if self.config.metrics_per_trajectory and metrics.was_compressed:
result["compression_metrics"] = metrics.to_dict()
return result, metrics
def process_file(
self,
input_path: Path,
output_path: Path,
progress_callback: Optional[Callable[[TrajectoryMetrics], None]] = None
) -> List[TrajectoryMetrics]:
"""
Process a single JSONL file.
Args:
input_path: Path to input JSONL file
output_path: Path to output JSONL file
progress_callback: Optional callback called after each entry with its metrics
Returns:
List of metrics for each trajectory
"""
file_metrics = []
# Read all entries
entries = []
with open(input_path, 'r', encoding='utf-8') as f:
for line_num, line in enumerate(f, 1):
line = line.strip()
if line:
try:
entries.append(json.loads(line))
except json.JSONDecodeError as e:
self.logger.warning(f"Skipping invalid JSON at {input_path}:{line_num}: {e}")
# Process entries
processed_entries = []
for entry in entries:
try:
processed_entry, metrics = self.process_entry(entry)
processed_entries.append(processed_entry)
file_metrics.append(metrics)
self.aggregate_metrics.add_trajectory_metrics(metrics)
# Call progress callback if provided
if progress_callback:
progress_callback(metrics)
except Exception as e:
self.logger.error(f"Error processing entry: {e}")
self.aggregate_metrics.trajectories_failed += 1
# Keep original entry on error
processed_entries.append(entry)
empty_metrics = TrajectoryMetrics()
file_metrics.append(empty_metrics)
if progress_callback:
progress_callback(empty_metrics)
# Write output
output_path.parent.mkdir(parents=True, exist_ok=True)
with open(output_path, 'w', encoding='utf-8') as f:
for entry in processed_entries:
f.write(json.dumps(entry, ensure_ascii=False) + '\n')
return file_metrics
def process_directory(self, input_dir: Path, output_dir: Path):
"""
Process all JSONL files in a directory using async parallel processing.
Args:
input_dir: Input directory containing JSONL files
output_dir: Output directory for compressed files
"""
# Run the async version
asyncio.run(self._process_directory_async(input_dir, output_dir))
async def _process_directory_async(self, input_dir: Path, output_dir: Path):
"""
Async implementation of directory processing with parallel API calls.
"""
console = Console()
# Record start time
self.aggregate_metrics.processing_start_time = datetime.now().isoformat()
start_time = time.time()
# Find all JSONL files
jsonl_files = sorted(input_dir.glob("*.jsonl"))
if not jsonl_files:
self.logger.warning(f"No JSONL files found in {input_dir}")
return
# Load ALL entries from all files
console.print("\n[dim]Loading all entries...[/dim]")
all_entries = [] # List of (file_path, entry_idx, entry)
for file_path in jsonl_files:
with open(file_path, 'r', encoding='utf-8') as f:
for line_num, line in enumerate(f):
line = line.strip()
if line:
try:
entry = json.loads(line)
all_entries.append((file_path, line_num, entry))
except json.JSONDecodeError as e:
self.logger.warning(f"Skipping invalid JSON at {file_path}:{line_num}: {e}")
total_entries = len(all_entries)
console.print(f"\n{'='*60}")
console.print(f"π Input: {input_dir}")
console.print(f"π Output: {output_dir}")
console.print(f"π Files to process: {len(jsonl_files)}")
console.print(f"π Total trajectories: {total_entries:,}")
console.print(f"π― Target max tokens: {self.config.target_max_tokens:,}")
console.print(f"π Summary target tokens: {self.config.summary_target_tokens}")
console.print(f"β‘ Max concurrent API calls: {self.config.max_concurrent_requests}")
console.print(f"{'='*60}\n")
# Create semaphore for rate limiting
semaphore = asyncio.Semaphore(self.config.max_concurrent_requests)
# Tracking for progress display (thread-safe with lock)
progress_lock = asyncio.Lock()
compressed_count = 0
skipped_count = 0
api_calls = 0
in_flight = 0
# Results storage: {file_path: {entry_idx: (processed_entry, metrics)}}
results = {f: {} for f in jsonl_files}
# Track timeouts separately
timeout_count = 0
async def process_single(file_path: Path, entry_idx: int, entry: Dict,
progress, main_task, status_task):
"""Process a single entry with semaphore rate limiting and timeout."""
nonlocal compressed_count, skipped_count, api_calls, in_flight, timeout_count
async with semaphore:
# Track in-flight
async with progress_lock:
in_flight += 1
try:
# Apply per-trajectory timeout
processed_entry, metrics = await asyncio.wait_for(
self.process_entry_async(entry),
timeout=self.config.per_trajectory_timeout
)