mirror of
https://github.com/ruvnet/RuVector.git
synced 2026-05-25 06:36:37 +00:00
fix: defensive checks for Q-learning patterns and data loading
- Add null checks in getQ() method for patterns object - Add null checks in updateQ() and learn() methods - Improve load() to merge file data with defaults - Ensures all required fields exist even with partial data - Fixes 'Cannot read properties of undefined' errors Published as v0.1.70
This commit is contained in:
rUv
parent
e38d3a9c9e
commit
dd137ae3f8
4 changed files with 2156 additions and 166 deletions
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@ -1,176 +1,238 @@
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#!/bin/bash
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# Read JSON input from stdin
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# RuVector Intelligence Statusline
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# Multi-line display showcasing self-learning capabilities
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INPUT=$(cat)
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MODEL=$(echo "$INPUT" | jq -r '.model.display_name // "Claude"')
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CWD=$(echo "$INPUT" | jq -r '.workspace.current_dir // .cwd')
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DIR=$(basename "$CWD")
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# Replace claude-code-flow with branded name
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if [ "$DIR" = "claude-code-flow" ]; then
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DIR="🌊 Claude Flow"
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fi
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# Get git branch
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BRANCH=$(cd "$CWD" 2>/dev/null && git branch --show-current 2>/dev/null)
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# Start building statusline
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printf "\033[1m$MODEL\033[0m in \033[36m$DIR\033[0m"
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[ -n "$BRANCH" ] && printf " on \033[33m⎇ $BRANCH\033[0m"
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# Colors
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RESET="\033[0m"
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BOLD="\033[1m"
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CYAN="\033[36m"
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YELLOW="\033[33m"
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GREEN="\033[32m"
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MAGENTA="\033[35m"
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BLUE="\033[34m"
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RED="\033[31m"
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DIM="\033[2m"
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# Claude-Flow integration
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# ═══════════════════════════════════════════════════════════════════════════════
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# LINE 1: Model, Directory, Git
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# ═══════════════════════════════════════════════════════════════════════════════
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printf "${BOLD}${MODEL}${RESET} in ${CYAN}${DIR}${RESET}"
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[ -n "$BRANCH" ] && printf " on ${YELLOW}⎇ ${BRANCH}${RESET}"
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echo
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# ═══════════════════════════════════════════════════════════════════════════════
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# LINE 2: RuVector Intelligence Stats
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# ═══════════════════════════════════════════════════════════════════════════════
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# Check multiple locations for intelligence file
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INTEL_FILE=""
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for INTEL_PATH in "$CWD/.ruvector/intelligence.json" \
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"$CWD/npm/packages/ruvector/.ruvector/intelligence.json" \
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"$HOME/.ruvector/intelligence.json"; do
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if [ -f "$INTEL_PATH" ]; then
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INTEL_FILE="$INTEL_PATH"
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break
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fi
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done
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if [ -n "$INTEL_FILE" ]; then
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# Extract learning metrics
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INTEL=$(cat "$INTEL_FILE" 2>/dev/null)
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# Detect schema version (v2 has .learning.qTables, v1 has .patterns)
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HAS_LEARNING=$(echo "$INTEL" | jq -r 'has("learning")' 2>/dev/null)
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if [ "$HAS_LEARNING" = "true" ]; then
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# v2 Schema: Multi-algorithm learning engine
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PATTERN_COUNT=$(echo "$INTEL" | jq -r '[.learning.qTables // {} | to_entries[].value | to_entries | length] | add // 0' 2>/dev/null)
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ACTIVE_ALGOS=$(echo "$INTEL" | jq -r '[.learning.stats // {} | to_entries[] | select(.value.updates > 0)] | length' 2>/dev/null)
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TOTAL_ALGOS=$(echo "$INTEL" | jq -r '[.learning.stats // {} | keys] | length' 2>/dev/null)
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BEST_ALGO=$(echo "$INTEL" | jq -r '
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.learning.stats // {} | to_entries
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| map(select(.value.updates > 0))
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| sort_by(-.value.convergenceScore)
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| .[0].key // "none"
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' 2>/dev/null)
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BEST_SCORE=$(echo "$INTEL" | jq -r ".learning.stats.\"$BEST_ALGO\".convergenceScore // 0" 2>/dev/null | awk '{printf "%.0f", $1 * 100}')
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TOTAL_UPDATES=$(echo "$INTEL" | jq -r '[.learning.stats // {} | to_entries[].value.updates] | add // 0' 2>/dev/null)
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MEMORY_COUNT=$(echo "$INTEL" | jq -r '.memory.entries | length // 0' 2>/dev/null)
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TRAJ_COUNT=$(echo "$INTEL" | jq -r '.learning.trajectories | length // 0' 2>/dev/null)
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ROUTING_ALGO=$(echo "$INTEL" | jq -r '.learning.configs."agent-routing".algorithm // "double-q"' 2>/dev/null)
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LEARNING_RATE=$(echo "$INTEL" | jq -r '.learning.configs."agent-routing".learningRate // 0.1' 2>/dev/null)
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EPSILON=$(echo "$INTEL" | jq -r '.learning.configs."agent-routing".epsilon // 0.1' 2>/dev/null)
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TOP_AGENTS=$(echo "$INTEL" | jq -r '
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.learning.qTables // {} | to_entries |
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map(.value | to_entries | sort_by(-.value) | .[0] | select(.value > 0)) |
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map(.key) | unique | .[0:3] | join(", ")
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' 2>/dev/null)
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SCHEMA="v2"
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else
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# v1 Schema: Simple patterns/memories
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PATTERN_COUNT=$(echo "$INTEL" | jq -r '.patterns | length // 0' 2>/dev/null)
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MEMORY_COUNT=$(echo "$INTEL" | jq -r '.memories | length // 0' 2>/dev/null)
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ACTIVE_ALGOS=0
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TOTAL_ALGOS=0
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BEST_ALGO="none"
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BEST_SCORE=0
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TOTAL_UPDATES=0
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TRAJ_COUNT=0
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ROUTING_ALGO="q-learning"
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LEARNING_RATE="0.1"
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EPSILON="0.1"
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TOP_AGENTS=""
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SCHEMA="v1"
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fi
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# Build Line 2
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printf "${MAGENTA}🧠 RuVector${RESET}"
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# Patterns learned
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if [ "$PATTERN_COUNT" != "null" ] && [ "$PATTERN_COUNT" -gt 0 ]; then
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printf " ${GREEN}◆${RESET} ${PATTERN_COUNT} patterns"
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else
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printf " ${DIM}◇ learning${RESET}"
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fi
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# Active algorithms
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if [ "$ACTIVE_ALGOS" != "null" ] && [ "$ACTIVE_ALGOS" -gt 0 ]; then
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printf " ${CYAN}⚙${RESET} ${ACTIVE_ALGOS}/${TOTAL_ALGOS} algos"
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fi
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# Best algorithm with convergence
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if [ "$BEST_ALGO" != "none" ] && [ "$BEST_ALGO" != "null" ]; then
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# Shorten algorithm name
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case "$BEST_ALGO" in
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"double-q") SHORT_ALGO="DQ" ;;
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"q-learning") SHORT_ALGO="QL" ;;
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"actor-critic") SHORT_ALGO="AC" ;;
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"decision-transformer") SHORT_ALGO="DT" ;;
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"monte-carlo") SHORT_ALGO="MC" ;;
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"td-lambda") SHORT_ALGO="TD" ;;
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*) SHORT_ALGO="${BEST_ALGO:0:3}" ;;
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esac
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# Color based on convergence
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if [ "$BEST_SCORE" -ge 80 ]; then
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SCORE_COLOR="$GREEN"
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elif [ "$BEST_SCORE" -ge 50 ]; then
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SCORE_COLOR="$YELLOW"
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else
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SCORE_COLOR="$RED"
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fi
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printf " ${SCORE_COLOR}★${SHORT_ALGO}:${BEST_SCORE}%%${RESET}"
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fi
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# Memory entries
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if [ "$MEMORY_COUNT" != "null" ] && [ "$MEMORY_COUNT" -gt 0 ]; then
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printf " ${BLUE}⬡${RESET} ${MEMORY_COUNT} mem"
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fi
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# Trajectories
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if [ "$TRAJ_COUNT" != "null" ] && [ "$TRAJ_COUNT" -gt 0 ]; then
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printf " ${YELLOW}↝${RESET} ${TRAJ_COUNT} traj"
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fi
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echo
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# ═══════════════════════════════════════════════════════════════════════════════
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# LINE 3: Agent Routing & Session Performance
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# ═══════════════════════════════════════════════════════════════════════════════
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# Compression stats (v2 only)
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COMPRESSION=$(echo "$INTEL" | jq -r '.tensorCompress.compressionRatio // 0' 2>/dev/null | awk '{printf "%.0f", $1 * 100}')
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printf "${BLUE}🎯 Routing${RESET}"
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# Show routing algorithm
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case "$ROUTING_ALGO" in
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"double-q") ALGO_ICON="⚡DQ" ;;
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"sarsa") ALGO_ICON="🔄SA" ;;
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"actor-critic") ALGO_ICON="🎭AC" ;;
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*) ALGO_ICON="$ROUTING_ALGO" ;;
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esac
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printf " ${CYAN}${ALGO_ICON}${RESET}"
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# Learning rate
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LR_PCT=$(echo "$LEARNING_RATE" | awk '{printf "%.0f", $1 * 100}')
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printf " lr:${LR_PCT}%%"
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# Exploration rate
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EPS_PCT=$(echo "$EPSILON" | awk '{printf "%.0f", $1 * 100}')
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printf " ε:${EPS_PCT}%%"
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# Top learned agents
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if [ -n "$TOP_AGENTS" ] && [ "$TOP_AGENTS" != "null" ] && [ "$TOP_AGENTS" != "" ]; then
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printf " ${GREEN}→${RESET} ${TOP_AGENTS}"
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fi
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# Session info
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if [ "$TOTAL_UPDATES" != "null" ] && [ "$TOTAL_UPDATES" -gt 0 ]; then
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printf " ${DIM}│${RESET} ${YELLOW}↻${RESET}${TOTAL_UPDATES}"
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fi
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# Compression ratio
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if [ "$COMPRESSION" != "null" ] && [ "$COMPRESSION" -gt 0 ]; then
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printf " ${MAGENTA}◊${RESET}${COMPRESSION}%% comp"
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fi
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echo
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else
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# No intelligence file - show initialization hint
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printf "${DIM}🧠 RuVector: run 'npx ruvector hooks session-start' to initialize${RESET}\n"
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fi
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# ═══════════════════════════════════════════════════════════════════════════════
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# LINE 4: Claude Flow Integration (if available)
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# ═══════════════════════════════════════════════════════════════════════════════
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FLOW_DIR="$CWD/.claude-flow"
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if [ -d "$FLOW_DIR" ]; then
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printf " │"
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printf "${DIM}⚡ Flow:${RESET}"
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# 1. Swarm Configuration & Topology
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# Swarm config
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if [ -f "$FLOW_DIR/swarm-config.json" ]; then
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STRATEGY=$(jq -r '.defaultStrategy // empty' "$FLOW_DIR/swarm-config.json" 2>/dev/null)
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AGENT_COUNT=$(jq -r '.agentProfiles | length' "$FLOW_DIR/swarm-config.json" 2>/dev/null)
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if [ -n "$STRATEGY" ]; then
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# Map strategy to topology icon
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case "$STRATEGY" in
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"balanced") TOPO_ICON="⚡mesh" ;;
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"conservative") TOPO_ICON="⚡hier" ;;
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"aggressive") TOPO_ICON="⚡ring" ;;
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*) TOPO_ICON="⚡$STRATEGY" ;;
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"balanced") TOPO="mesh" ;;
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"conservative") TOPO="hier" ;;
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"aggressive") TOPO="ring" ;;
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*) TOPO="$STRATEGY" ;;
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esac
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printf " \033[35m$TOPO_ICON\033[0m"
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printf " ${MAGENTA}${TOPO}${RESET}"
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fi
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# Count agent profiles as "configured agents"
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AGENT_COUNT=$(jq -r '.agentProfiles | length' "$FLOW_DIR/swarm-config.json" 2>/dev/null)
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if [ -n "$AGENT_COUNT" ] && [ "$AGENT_COUNT" != "null" ] && [ "$AGENT_COUNT" -gt 0 ]; then
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printf " \033[35m🤖 $AGENT_COUNT\033[0m"
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fi
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if [ -n "$AGENT_COUNT" ] && [ "$AGENT_COUNT" != "null" ] && [ "$AGENT_COUNT" -gt 0 ]; then
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printf " ${CYAN}🤖${AGENT_COUNT}${RESET}"
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fi
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fi
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# 2. Real-time System Metrics
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if [ -f "$FLOW_DIR/metrics/system-metrics.json" ]; then
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# Get latest metrics (last entry in array)
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LATEST=$(jq -r '.[-1]' "$FLOW_DIR/metrics/system-metrics.json" 2>/dev/null)
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if [ -n "$LATEST" ] && [ "$LATEST" != "null" ]; then
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# Memory usage
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MEM_PERCENT=$(echo "$LATEST" | jq -r '.memoryUsagePercent // 0' | awk '{printf "%.0f", $1}')
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if [ -n "$MEM_PERCENT" ] && [ "$MEM_PERCENT" != "null" ]; then
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# Color-coded memory (green <60%, yellow 60-80%, red >80%)
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if [ "$MEM_PERCENT" -lt 60 ]; then
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MEM_COLOR="\033[32m" # Green
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elif [ "$MEM_PERCENT" -lt 80 ]; then
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MEM_COLOR="\033[33m" # Yellow
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else
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MEM_COLOR="\033[31m" # Red
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fi
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printf " ${MEM_COLOR}💾 ${MEM_PERCENT}%\033[0m"
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fi
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# CPU load
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CPU_LOAD=$(echo "$LATEST" | jq -r '.cpuLoad // 0' | awk '{printf "%.0f", $1 * 100}')
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if [ -n "$CPU_LOAD" ] && [ "$CPU_LOAD" != "null" ]; then
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# Color-coded CPU (green <50%, yellow 50-75%, red >75%)
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if [ "$CPU_LOAD" -lt 50 ]; then
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CPU_COLOR="\033[32m" # Green
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elif [ "$CPU_LOAD" -lt 75 ]; then
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CPU_COLOR="\033[33m" # Yellow
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else
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CPU_COLOR="\033[31m" # Red
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fi
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printf " ${CPU_COLOR}⚙ ${CPU_LOAD}%\033[0m"
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fi
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fi
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fi
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# 3. Session State
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if [ -f "$FLOW_DIR/session-state.json" ]; then
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SESSION_ID=$(jq -r '.sessionId // empty' "$FLOW_DIR/session-state.json" 2>/dev/null)
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ACTIVE=$(jq -r '.active // false' "$FLOW_DIR/session-state.json" 2>/dev/null)
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if [ "$ACTIVE" = "true" ] && [ -n "$SESSION_ID" ]; then
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# Show abbreviated session ID
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SHORT_ID=$(echo "$SESSION_ID" | cut -d'-' -f1)
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printf " \033[34m🔄 $SHORT_ID\033[0m"
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fi
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fi
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# 4. Performance Metrics from task-metrics.json
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if [ -f "$FLOW_DIR/metrics/task-metrics.json" ]; then
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# Parse task metrics for success rate, avg time, and streak
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METRICS=$(jq -r '
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# Calculate metrics
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(map(select(.success == true)) | length) as $successful |
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(length) as $total |
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(if $total > 0 then ($successful / $total * 100) else 0 end) as $success_rate |
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(map(.duration // 0) | add / length) as $avg_duration |
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# Calculate streak (consecutive successes from end)
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(reverse |
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reduce .[] as $task (0;
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if $task.success == true then . + 1 else 0 end
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)
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) as $streak |
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{
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success_rate: $success_rate,
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avg_duration: $avg_duration,
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streak: $streak,
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total: $total
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} | @json
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' "$FLOW_DIR/metrics/task-metrics.json" 2>/dev/null)
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if [ -n "$METRICS" ] && [ "$METRICS" != "null" ]; then
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# Success Rate
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SUCCESS_RATE=$(echo "$METRICS" | jq -r '.success_rate // 0' | awk '{printf "%.0f", $1}')
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TOTAL_TASKS=$(echo "$METRICS" | jq -r '.total // 0')
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if [ -n "$SUCCESS_RATE" ] && [ "$TOTAL_TASKS" -gt 0 ]; then
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# Color-code: Green (>80%), Yellow (60-80%), Red (<60%)
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if [ "$SUCCESS_RATE" -gt 80 ]; then
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SUCCESS_COLOR="\033[32m" # Green
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elif [ "$SUCCESS_RATE" -ge 60 ]; then
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SUCCESS_COLOR="\033[33m" # Yellow
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else
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SUCCESS_COLOR="\033[31m" # Red
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fi
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printf " ${SUCCESS_COLOR}🎯 ${SUCCESS_RATE}%\033[0m"
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fi
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# Average Time
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AVG_TIME=$(echo "$METRICS" | jq -r '.avg_duration // 0')
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if [ -n "$AVG_TIME" ] && [ "$TOTAL_TASKS" -gt 0 ]; then
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# Format smartly: seconds, minutes, or hours
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if [ $(echo "$AVG_TIME < 60" | bc -l 2>/dev/null || echo 0) -eq 1 ]; then
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TIME_STR=$(echo "$AVG_TIME" | awk '{printf "%.1fs", $1}')
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elif [ $(echo "$AVG_TIME < 3600" | bc -l 2>/dev/null || echo 0) -eq 1 ]; then
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TIME_STR=$(echo "$AVG_TIME" | awk '{printf "%.1fm", $1/60}')
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else
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TIME_STR=$(echo "$AVG_TIME" | awk '{printf "%.1fh", $1/3600}')
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fi
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printf " \033[36m⏱️ $TIME_STR\033[0m"
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fi
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# Streak (only show if > 0)
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STREAK=$(echo "$METRICS" | jq -r '.streak // 0')
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if [ -n "$STREAK" ] && [ "$STREAK" -gt 0 ]; then
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printf " \033[91m🔥 $STREAK\033[0m"
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fi
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fi
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fi
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# 5. Active Tasks (check for task files)
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# Active tasks
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if [ -d "$FLOW_DIR/tasks" ]; then
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TASK_COUNT=$(find "$FLOW_DIR/tasks" -name "*.json" -type f 2>/dev/null | wc -l)
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if [ "$TASK_COUNT" -gt 0 ]; then
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printf " \033[36m📋 $TASK_COUNT\033[0m"
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printf " ${YELLOW}📋${TASK_COUNT}${RESET}"
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fi
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fi
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# 6. Check for hooks activity
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if [ -f "$FLOW_DIR/hooks-state.json" ]; then
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HOOKS_ACTIVE=$(jq -r '.enabled // false' "$FLOW_DIR/hooks-state.json" 2>/dev/null)
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if [ "$HOOKS_ACTIVE" = "true" ]; then
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printf " \033[35m🔗\033[0m"
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# Session state
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if [ -f "$FLOW_DIR/session-state.json" ]; then
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ACTIVE=$(jq -r '.active // false' "$FLOW_DIR/session-state.json" 2>/dev/null)
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if [ "$ACTIVE" = "true" ]; then
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printf " ${GREEN}●${RESET}"
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fi
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fi
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echo
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fi
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echo
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File diff suppressed because it is too large
Load diff
|
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@ -2233,12 +2233,7 @@ class Intelligence {
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}
|
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|
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load() {
|
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try {
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if (fs.existsSync(this.intelPath)) {
|
||||
return JSON.parse(fs.readFileSync(this.intelPath, 'utf-8'));
|
||||
}
|
||||
} catch {}
|
||||
return {
|
||||
const defaults = {
|
||||
patterns: {},
|
||||
memories: [],
|
||||
trajectories: [],
|
||||
|
|
@ -2248,6 +2243,25 @@ class Intelligence {
|
|||
edges: [],
|
||||
stats: { total_patterns: 0, total_memories: 0, total_trajectories: 0, total_errors: 0, session_count: 0, last_session: 0 }
|
||||
};
|
||||
try {
|
||||
if (fs.existsSync(this.intelPath)) {
|
||||
const data = JSON.parse(fs.readFileSync(this.intelPath, 'utf-8'));
|
||||
// Merge with defaults to ensure all fields exist
|
||||
return {
|
||||
patterns: data.patterns || defaults.patterns,
|
||||
memories: data.memories || defaults.memories,
|
||||
trajectories: data.trajectories || defaults.trajectories,
|
||||
errors: data.errors || defaults.errors,
|
||||
file_sequences: data.file_sequences || defaults.file_sequences,
|
||||
agents: data.agents || defaults.agents,
|
||||
edges: data.edges || defaults.edges,
|
||||
stats: { ...defaults.stats, ...(data.stats || {}) },
|
||||
// Preserve learning data if present
|
||||
learning: data.learning || undefined
|
||||
};
|
||||
}
|
||||
} catch {}
|
||||
return defaults;
|
||||
}
|
||||
|
||||
save() {
|
||||
|
|
@ -2374,11 +2388,14 @@ class Intelligence {
|
|||
// Q-learning operations - enhanced with SONA trajectory tracking
|
||||
getQ(state, action) {
|
||||
const key = `${state}|${action}`;
|
||||
if (!this.data.patterns) this.data.patterns = {};
|
||||
return this.data.patterns[key]?.q_value ?? 0;
|
||||
}
|
||||
|
||||
updateQ(state, action, reward) {
|
||||
const key = `${state}|${action}`;
|
||||
if (!this.data.patterns) this.data.patterns = {};
|
||||
if (!this.data.stats) this.data.stats = { total_patterns: 0, total_memories: 0, total_trajectories: 0, total_errors: 0, session_count: 0, last_session: 0 };
|
||||
if (!this.data.patterns[key]) {
|
||||
this.data.patterns[key] = { state, action, q_value: 0, visits: 0, last_update: 0 };
|
||||
}
|
||||
|
|
@ -2398,6 +2415,8 @@ class Intelligence {
|
|||
learn(state, action, outcome, reward) {
|
||||
const id = `traj_${this.now()}`;
|
||||
this.updateQ(state, action, reward);
|
||||
if (!this.data.trajectories) this.data.trajectories = [];
|
||||
if (!this.data.stats) this.data.stats = { total_patterns: 0, total_memories: 0, total_trajectories: 0, total_errors: 0, session_count: 0, last_session: 0 };
|
||||
this.data.trajectories.push({ id, state, action, outcome, reward, timestamp: this.now() });
|
||||
if (this.data.trajectories.length > 1000) this.data.trajectories.splice(0, 200);
|
||||
this.data.stats.total_trajectories = this.data.trajectories.length;
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
{
|
||||
"name": "ruvector",
|
||||
"version": "0.1.69",
|
||||
"version": "0.1.70",
|
||||
"description": "High-performance vector database for Node.js with automatic native/WASM fallback",
|
||||
"main": "dist/index.js",
|
||||
"types": "dist/index.d.ts",
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue