Skip to content

TOKEN_TRACKING_IMPLEMENTATION_STATUS.md

Latest commit

 

History

History
323 lines (238 loc) · 10.7 KB

TOKEN_TRACKING_IMPLEMENTATION_STATUS.md

File metadata and controls

323 lines (238 loc) · 10.7 KB
Raw

Token Tracking Implementation Status

✅ COMPLETED IMPLEMENTATIONS

1. LLM Token Tracking

Status: Fully implemented

Changes Made:

  • /core/llm_backend.py - Updated chat() method to handle return_stats parameter
  • /core/enhanced_agent.py - Added return_stats=True to all LLM calls:
    • Line 8060: General LLM queries
    • Line 8978: Multi-step script planning
    • Line 9384: Code generation
    • Line 10153: Execution summaries

Display Format:

💬 mistral-7b:
[Response text here]
   [Input: 54 tokens (267 chars), Output: 23 tokens (92 chars), Total: 77 tokens]

2. Fallback Parser Token-Equivalent Tracking

Status: Just implemented

Location: /core/enhanced_agent.py lines 9069-9083

Metrics Tracked:

  • Input characters and words
  • Output characters and words
  • Estimated token counts (4 chars per token, or 0.75 words per token)

Display Format:

⚠️  All LLM models failed or timed out
🔄 Using dynamic fallback parser for step generation

📋 Task Checklist (generated by parser):
   [Parser: 12 tokens in (48 chars, 9 words), 35 tokens out (140 chars, 26 words), 47 total]
────────────────────────────────────────────────────────────

3. Step Display with Separator Lines

Status: Fully implemented

Location: /core/lucifer_colors.py lines 764-768

Display Format:

────────────────────────────────────────────────────────────
📝 Step 1/3: Creating file and setting up structure

[task execution here]

────────────────────────────────────────────────────────────
📝 Step 2/3: Writing code implementation

[task execution here]

4. 'build' Keyword Support

Status: Implemented

Location: /core/enhanced_agent.py line 8733

Added "build" to script creation detection keywords.

5. Model Files Location

Status: Fixed

All model files moved from .luciferai/models/ and ~/Desktop/models/ to /models/ directory.

❌ IDENTIFIED ISSUES & REQUIRED FIXES

Issue #1: Model Timeouts (CRITICAL)

Problem: All models timing out after 30s during initialization/inference on Catalina

Evidence:

mistral-7b (Tier 2) timed out after 30s
gemma2 (Tier 1) failed: Llamafile error
tinyllama (Tier 0) timed out after 30s
phi-2 (Tier 0) timed out after 30s

Root Cause:

  • Older macOS (Catalina) has slower llamafile initialization
  • 30s timeout insufficient for model load + inference
  • No model caching between requests

Fix Required:

# File: /core/llm_backend.py
# Location: Line 44-52

self.tier_timeouts = {
    'tinyllama': 60,         # Was 20 → Increase to 60s for Catalina
    'llama3.2': 90,          # Was 30 → Increase to 90s
    'mistral': 120,          # Was 45 → Increase to 120s
    'deepseek-coder': 180,   # Was 60 → Increase to 180s
    'llama3.1-70b': 180,     # Was 120 → Increase to 180s
    'mixtral-8x22b': 240,    # Was 120 → Increase to 240s
    'qwen-72b': 240          # Was 120 → Increase to 240s
}

Additional Fix - Model Caching:

# File: /core/llm_backend.py  
# Add after line 97

def _check_native_llamafile(self) -> bool:
    """Check if native llamafile binary exists."""
    llamafile_path = LUCIFER_HOME / 'bin' / 'llamafile'
    if llamafile_path.exists():
        # Pre-warm model cache on first use
        self._prewarm_model_cache()
        return True
    return False

def _prewarm_model_cache(self):
    """Pre-load model into memory to avoid cold-start delays."""
    # Implementation: Run a quick test inference to warm cache
    pass

Issue #2: No Bypass Routing Display

Problem: Bypass routing messages don't show even with multiple models

Root Cause: Code only shows bypass when there are LOWER tier models to skip

Current Logic (line 8822-8827):

if unique_lower_models:  # Only shows if lower tiers exist
    skipped_parts = [...]
    print(c(f"💡 Bypassed: ", "dim") + ", ".join(skipped_parts))

Fix Required:

# File: /core/enhanced_agent.py
# Location: Lines 8800-8832

if best_model:
    # Show which model we're using (ALWAYS show routing)
    print(c(f"🧠 Routing to: {best_model}", "cyan") + c(f" ({tier_names[best_tier]})", "dim"))
    
    # Show bypass info if any models were skipped
    if unique_lower_models:
        skipped_str = ", ".join([c(f"{model} ({tier_names[get_model_tier(model)]})", "yellow") for model in unique_lower_models])
        print(c(f"   Bypassed: {skipped_str}", "dim"))
    
    print()
    sys.stdout.flush()

Apply to all workflow entry points:

  • Line 8806: _handle_single_task_with_llm()
  • Line 8885: _handle_multi_step_script_creation()
  • Line 10152: _handle_find_and_write_workflow()

Issue #3: Execution Statistics Not Displaying

Problem: The execution_tracker.py module exists but isn't integrated

Evidence:

  • format_stats_display() function exists (lines 306-474)
  • Never called at end of workflows
  • No ExecutionTracker instance created

Fix Required:

Step 1: Initialize tracker in EnhancedLuciferAgent.__init__():

# File: /core/enhanced_agent.py
# Add after line 225

from core.execution_tracker import ExecutionTracker
self.execution_tracker = ExecutionTracker()

Step 2: Track operations throughout workflows:

# When files created:
self.execution_tracker.track_file_created(file_path)

# When models used:
self.execution_tracker.track_model_used(model_name, tier, purpose='code_generation', tokens=token_count)

# When templates used:
self.execution_tracker.track_template_used(template_name, relevance_score)

Step 3: Display stats at end of workflows:

# File: /core/enhanced_agent.py
# Add before final return in:
# - _handle_multi_step_script_creation() (line 10185)
# - _handle_find_and_write_workflow() (line ~11000)

# End execution tracking
self.execution_tracker.end_execution()

# Display execution statistics
print()
print(self.execution_tracker.format_stats_display())
print()

Issue #4: Token Display Format Inconsistency

Problem: LLM tokens and parser tokens have different formats

Current State:

  • LLM: [Input: X tokens (Y chars), Output: Z tokens (W chars), Total: T tokens]
  • Parser: [Parser: X tokens in (Y chars, Z words), W tokens out (A chars, B words), T total]

Fix Required:

# File: /core/enhanced_agent.py
# Location: Line 9083

# BEFORE:
print(c(f"   [Parser: {input_tokens} tokens in ({input_chars} chars, {input_words} words), {output_tokens} tokens out ({output_chars} chars, {output_words} words), {total_tokens} total]", "dim"))

# AFTER (match LLM format):
print(c(f"   [Input: {input_tokens} tokens ({input_chars} chars), Output: {output_tokens} tokens ({output_chars} chars), Total: {total_tokens} tokens]", "dim"))
print(c(f"   [Method: Dynamic parser (rule-based, no LLM)]", "dim"))

Issue #5: Missing Token Display on All LLM Responses

Problem: Token stats only show when return_stats works, but some LLM calls might not return stats

Locations to verify:

  • Line 8816: _get_llm_acknowledgment() - Check if uses token tracking
  • Line 8832: _get_llm_task_commentary() - Check if uses token tracking
  • Line 9269-9311: Template validation - Check if uses token tracking

Fix Required: Add return_stats=True to any missing LLM calls and display logic

📊 TESTING CHECKLIST

To verify all implementations work:

cd /Users/TheRustySpoon/Desktop/Projects/LuciferAI_Local
python lucifer.py

Then test: build me a script that prints hello world

Expected Output:

💡 Routing to: mistral-7b (Tier 2)
   Bypassed: tinyllama (Tier 0), phi-2 (Tier 0), gemma2 (Tier 1)

🤔 mistral-7b (Tier 2) thinking: [streaming response]
   [Input: 54 tokens (267 chars), Output: 23 tokens (92 chars), Total: 77 tokens]

────────────────────────────────────────────────────────────
📋 mistral-7b - Task Checklist:
────────────────────────────────────────────────────────────
  [ ] 1. Create file structure
  [ ] 2. Write implementation code
  [ ] 3. Test script execution
────────────────────────────────────────────────────────────

────────────────────────────────────────────────────────────
📝 Step 1/3: Create file structure

[execution here]

────────────────────────────────────────────────────────────
📝 Step 2/3: Write implementation code

🧠 Routed to: mistral-7b (Tier 2)
[code generation]
   [Input: 89 tokens (445 chars), Output: 42 tokens (168 chars), Total: 131 tokens]

────────────────────────────────────────────────────────────
📝 Step 3/3: Test script execution

[test execution]

📊 Execution Statistics:
────────────────────────────────────────────────────────────
📁 Files affected: 1
   • Created: 1
     - hello_world.py
🧠 Models used: 1
   • mistral-7b (Tier 2) - code_generation, planning [154 tokens]
⏱️  Execution time: 45.23s
────────────────────────────────────────────────────────────

💬 mistral-7b - Execution Summary:
Created a Python script that prints 'Hello World' to demonstrate basic output.
   [Input: 89 tokens (445 chars), Output: 19 tokens (76 chars), Total: 108 tokens]

🎯 PRIORITY ORDER

  1. CRITICAL: Fix model timeouts (Issue #1) - Blocks all functionality
  2. HIGH: Consistent token display format (Issue #4) - User confusion
  3. MEDIUM: Always show routing (Issue #2) - Better transparency
  4. MEDIUM: Execution statistics (Issue #3) - Nice to have
  5. LOW: Verify all LLM calls have token tracking (Issue #5) - Already mostly done