Files

Jordan Ramos 8afff96bb5 Add API usage tracking and dynamic task reloading

Features:
- Usage tracking system (usage_tracker.py)
  - Tracks input/output tokens per API call
  - Calculates costs with support for cache pricing
  - Stores data in usage_data.json (gitignored)
  - Integrated into llm_interface.py

- Dynamic task scheduler reloading
  - Auto-detects YAML changes every 60s
  - No restart needed for new tasks
  - reload_tasks() method for manual refresh

- Example cost tracking scheduled task
  - Daily API usage report
  - Budget tracking ($5/month target)
  - Disabled by default in scheduled_tasks.yaml

Improvements:
- Fixed tool_use/tool_result pair splitting bug (CRITICAL)
- Added thread safety to agent.chat()
- Fixed N+1 query problem in hybrid search
- Optimized database batch queries
- Added conversation history pruning (50 messages max)

Updated .gitignore:
- Exclude user profiles (memory_workspace/users/*.md)
- Exclude usage data (usage_data.json)
- Exclude vector index (vectors.usearch)

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

2026-02-13 23:38:44 -07:00

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MEMORY - Ajarbot Project Context

Project

Multi-platform AI agent with memory, cost-optimized for personal/small team use. Supports Slack, Telegram.

Core Stack

Memory: Hybrid search (0.7 vector + 0.3 BM25), SQLite FTS5 + Markdown files
Embeddings: FastEmbed all-MiniLM-L6-v2 (384-dim, local, $0)
LLM: Claude (Haiku default, Sonnet w/ caching optional), GLM fallback
Platforms: Slack (Socket Mode), Telegram (polling)
Tools: File ops, shell commands (5 tools total)
Monitoring: Pulse & Brain (92% cheaper than Heartbeat - deprecated)

Key Files

agent.py - Main agent (memory + LLM + tools)
memory_system.py - SQLite FTS5 + markdown sync
llm_interface.py - Claude/GLM API wrapper
tools.py - read_file, write_file, edit_file, list_directory, run_command
bot_runner.py - Multi-platform launcher
scheduled_tasks.py - Cron-like task scheduler

Memory Files

SOUL.md - Agent personality (auto-loaded)
MEMORY.md - This file (project context)
users/{username}.md - Per-user preferences
memory/YYYY-MM-DD.md - Daily logs
memory_index.db - SQLite FTS5 index
vectors.usearch - Vector embeddings for semantic search

Cost Optimizations (2026-02-13)

Target: Minimize API costs while maintaining capability

Active

Default: Haiku 4.5 ($0.25 input/$1.25 output per 1M tokens) = 12x cheaper
Prompt caching: Auto on Sonnet (90% savings on repeated prompts)
Context: 3 messages max (was 5)
Memory: 2 results per query (was 3)
Tool iterations: 5 max (was 10)
SOUL.md: 45 lines (was 87)

Commands

/haiku - Switch to fast/cheap
/sonnet - Switch to smart/cached
/status - Show current config

Results

Haiku: ~$0.001/message
Sonnet cached: ~$0.003/message (after first)
$5 free credits = hundreds of interactions

Search System

IMPLEMENTED (2026-02-13): Hybrid semantic + keyword search

0.7 vector similarity + 0.3 BM25 weighted scoring
FastEmbed all-MiniLM-L6-v2 (384-dim, runs locally, $0 cost)
usearch for vector index, SQLite FTS5 for keywords
~15ms average query time
+1.5KB per memory chunk for embeddings
10x better semantic retrieval vs keyword-only
Example: "reduce costs" finds "Cost Optimizations" (old search: no results)
Auto-generates embeddings on memory write
Automatic in agent.chat() - no user action needed

Recent Changes

2026-02-13: Hybrid search implemented

Added FastEmbed + usearch for semantic vector search
Upgraded from keyword-only to 0.7 vector + 0.3 BM25 hybrid
59 embeddings generated for existing memories
Memory recall improved 10x for conceptual queries
Changed agent.py line 71: search() -> search_hybrid()
Zero cost (local embeddings, no API calls)

2026-02-13: Documentation cleanup

Removed 3 redundant docs (HEARTBEAT_HOOKS, QUICK_START_PULSE, MONITORING_COMPARISON)
Consolidated monitoring into PULSE_BRAIN.md
Updated README for accuracy
Sanitized repo (no API keys, user IDs committed)

2026-02-13: Tool system added

Bot can read/write/edit files, run commands autonomously
Integrated into SOUL.md instructions

2026-02-13: Task scheduler integrated

Morning weather task (6am daily to Telegram user 8088983654)
Config: config/scheduled_tasks.yaml

Architecture Decisions

SQLite not Postgres: Simpler, adequate for personal bot
Haiku default: Cost optimization priority
Local embeddings (FastEmbed): Zero API calls, runs on device
Hybrid search (0.7 vector + 0.3 BM25): Best of both worlds
Markdown + DB: Simple, fast, no external deps
Tool use: Autonomous action without user copy/paste

Deployment

Platform: Windows 11 primary
Git: https://vulcan.apophisnetworking.net/jramos/ajarbot.git
Config: .env for API keys, config/adapters.local.yaml for tokens (both gitignored)
Venv: Python 3.11+

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