feat(system): seedgo v2 operational, full system audit, 14/15 branches at 99%
Three days of intensive work bringing seedgo to full operational status and driving all branches through comprehensive standards compliance. Seedgo v2.0.0: - 22 checkers active (up from 20), standards pack fully operational - New introspection standard researched from Dev-Pass, FPLAN-0017 open - Bypass system for false positives (.seedgo config) - Standards query and audit commands fully functional System-wide audit (FPLAN-0016): - All 14 auditable branches at 99%+ compliance - CLI imports standardized across all branches (console from cli.apps.modules) - handle_command(command, args) → bool contract added to all modules - print_help() function naming fixed for checker pattern matching - Handler extraction: large modules split, file I/O moved to handler layer - New handlers created across ai_mail, backup, daemon, flow, skills, spawn, seedgo Branch-specific highlights: - ai_mail: email.py split 840→420 lines, 4 new handlers - flow: dplan_flow.py 688→591 lines, 4 new handlers - seedgo: massive restructure — standards moved to handlers/aipass_standards/, old standards/ tree removed, bypass system added, diagnostics module - commons: database module added, CLI imports fixed - skills: 5 handle_commands added, help function renamed - trigger: error reporter handler, handle_command routing - All branches: consistent architecture, clean drone routing Culture doc (CLAUDE.md) added — documents AIPass philosophy, identity, memory system, and collaboration principles. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
AIOSAI
co-authored by
Claude Opus 4.6
Claude Opus 4.6
parent
09e759a8a4
commit
babedd9c64
@@ -1,19 +1,9 @@
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# ===================AIPASS====================
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# META DATA HEADER
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# Name: search.py - Search Orchestration Module
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# Date: 2025-11-27
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# Version: 0.2.0
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# Category: memory/modules
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#
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# CHANGELOG (Max 5 entries):
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# - v0.2.0 (2026-03-06): Adapted for AIPass public repo - removed internal deps
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# - v0.1.0 (2025-11-27): Initial version - orchestrate semantic search
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#
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# CODE STANDARDS:
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# - Thin orchestration: Delegate all logic to handlers
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# - No business logic: Only coordinate workflow
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# - handle_command() pattern
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# =================== AIPass ====================
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# Name: search.py
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# Description: Search Orchestration Module
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# Version: 0.4.0
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# Created: 2025-11-27
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# Modified: 2026-03-08
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# =============================================
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"""
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@@ -30,75 +20,22 @@ Purpose:
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"""
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import sys
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import os
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import logging
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import subprocess
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import json
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from pathlib import Path
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from typing import List
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from rich.console import Console
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from rich.panel import Panel
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from rich import box
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from aipass.prax import logger
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from aipass.cli.apps.modules import console
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# =============================================================================
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# INFRASTRUCTURE SETUP
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# =============================================================================
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logger = logging.getLogger(__name__)
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console = Console()
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# Handler imports (relative within the memory package)
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from ..handlers.vector import embedder
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# ChromaDB search via subprocess
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_HANDLERS_DIR = Path(__file__).resolve().parent.parent / "handlers"
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CHROMA_SUBPROCESS_SCRIPT = _HANDLERS_DIR / "storage" / "chroma_subprocess.py"
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# Use system python by default; can be overridden via environment variable
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MEMORY_PYTHON = os.environ.get("AIPASS_MEMORY_PYTHON", sys.executable)
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def _search_vectors_subprocess(
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query_embedding: list,
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branch: str | None = None,
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memory_type: str | None = None,
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n_results: int = 5,
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db_path: str | Path | None = None
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) -> dict:
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"""
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Search vectors via subprocess.
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This ensures ChromaDB compatibility regardless of calling Python version.
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"""
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input_data = {
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'operation': 'search_vectors',
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'query_embedding': query_embedding,
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'branch': branch,
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'memory_type': memory_type,
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'n_results': n_results,
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'db_path': str(db_path) if db_path else None
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}
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try:
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result = subprocess.run(
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[str(MEMORY_PYTHON), str(CHROMA_SUBPROCESS_SCRIPT)],
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input=json.dumps(input_data),
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capture_output=True,
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text=True,
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timeout=60
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)
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if result.returncode != 0:
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return {'success': False, 'error': result.stderr or 'Subprocess failed'}
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return json.loads(result.stdout)
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except subprocess.TimeoutExpired:
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return {'success': False, 'error': 'Search operation timed out'}
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except json.JSONDecodeError as e:
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return {'success': False, 'error': f'Invalid JSON response: {e}'}
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except Exception as e:
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return {'success': False, 'error': str(e)}
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# Handler imports
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from aipass.memory.apps.handlers.search.query_executor import (
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execute_search as _handler_execute_search,
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)
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# =============================================================================
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@@ -160,7 +97,7 @@ def handle_command(command: str, args: List[str]) -> bool:
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console.print("[red]Error:[/red] Search query required")
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return True
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execute_search(query, branch=branch, memory_type=memory_type, n_results=n_results)
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show_search_results(query, branch=branch, memory_type=memory_type, n_results=n_results)
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return True
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return False
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@@ -205,17 +142,17 @@ def print_help() -> None:
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# =============================================================================
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# SEARCH ORCHESTRATION
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# SEARCH RESULTS DISPLAY
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# =============================================================================
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def execute_search(query: str, branch: str | None = None, memory_type: str | None = None, n_results: int = 5) -> bool:
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def show_search_results(
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query: str,
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branch: str | None = None,
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memory_type: str | None = None,
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n_results: int = 5
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) -> bool:
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"""
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Execute semantic search and display results
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Workflow:
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1. Encode query to embedding vector
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2. Search ChromaDB via subprocess
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3. Format and display results with Rich
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Execute semantic search via handler and display results with Rich.
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Args:
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query: Search query text
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@@ -234,7 +171,7 @@ def execute_search(query: str, branch: str | None = None, memory_type: str | Non
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))
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console.print()
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# Step 1: Encode query
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# Display query info
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console.print(f"[cyan]Query:[/cyan] {query}")
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if branch:
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console.print(f"[cyan]Branch:[/cyan] {branch}")
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@@ -243,52 +180,29 @@ def execute_search(query: str, branch: str | None = None, memory_type: str | Non
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console.print()
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console.print("[dim]Encoding query...[/dim]")
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embed_result = embedder.encode_batch([query])
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if not embed_result['success']:
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error_msg = embed_result.get('error', 'Unknown error')
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logger.error(f"[search] Failed to encode query: {error_msg}")
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console.print(f"[red]x[/red] Failed to encode query: {error_msg}")
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return False
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embeddings = embed_result.get('embeddings', [])
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if not embeddings:
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console.print("[red]x[/red] No embedding generated")
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return False
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query_embedding = embeddings[0]
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# Convert numpy array to list for JSON serialization
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if hasattr(query_embedding, 'tolist'):
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query_embedding = query_embedding.tolist()
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logger.info(f"[search] Encoded query to {len(query_embedding)}-dim vector")
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# Step 2: Search via subprocess
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console.print("[dim]Searching collections...[/dim]")
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search_result = _search_vectors_subprocess(
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query_embedding=query_embedding,
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# Delegate to handler
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result = _handler_execute_search(
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query=query,
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branch=branch,
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memory_type=memory_type,
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n_results=n_results
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)
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if not search_result['success']:
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error_msg = search_result.get('error', 'Unknown error')
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logger.error(f"[search] Search failed: {error_msg}")
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console.print(f"[red]x[/red] Search failed: {error_msg}")
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if not result['success']:
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console.print(f"[red]x[/red] {result.get('error', 'Unknown error')}")
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return False
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results = search_result.get('results', [])
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collections_searched = search_result.get('collections_searched', 0)
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total_results = search_result.get('total_results', 0)
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collections_searched = result.get('collections_searched', 0)
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total_results = result.get('total_results', 0)
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filtered_results = result.get('results', [])
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logger.info(f"[search] Found {total_results} results across {collections_searched} collections")
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# Step 3: Display results
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# Display summary
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console.print(f"[green]>[/green] Found {total_results} results in {collections_searched} collections")
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console.print()
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if not results:
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if not filtered_results and total_results == 0:
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console.print("[yellow]No matching memories found[/yellow]")
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console.print()
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console.print("[dim]Try:[/dim]")
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@@ -297,27 +211,6 @@ def execute_search(query: str, branch: str | None = None, memory_type: str | Non
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console.print(" * Check if memories have been rolled over (drone @memory status)")
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return True
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# Minimum similarity threshold - filter out irrelevant results
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MIN_SIMILARITY_THRESHOLD = 0.40 # 40% minimum relevance
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# Filter and process results
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filtered_results = []
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for result in results[:n_results]:
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document = result.get('document', '')
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distance = result.get('distance', 0)
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# Calculate similarity (ChromaDB L2 distance: 0=identical, ~2=very different)
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similarity = max(0, 1 - (distance / 2))
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# Skip empty documents and low-relevance results
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if not document or not document.strip():
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continue
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if similarity < MIN_SIMILARITY_THRESHOLD:
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continue
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result['similarity'] = similarity
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filtered_results.append(result)
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if not filtered_results:
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console.print("[yellow]No relevant memories found[/yellow]")
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console.print()
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@@ -325,11 +218,11 @@ def execute_search(query: str, branch: str | None = None, memory_type: str | Non
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console.print("[dim]Try more specific search terms related to your AIPass work.[/dim]")
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return True
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for i, result in enumerate(filtered_results, 1):
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collection = result.get('collection', 'unknown')
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document = result.get('document', '')
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metadata = result.get('metadata', {})
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similarity = result.get('similarity', 0)
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for i, item in enumerate(filtered_results, 1):
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collection = item.get('collection', 'unknown')
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document = item.get('document', '')
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metadata = item.get('metadata', {})
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similarity = item.get('similarity', 0)
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# Parse collection name
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parts = collection.split('_')
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@@ -360,11 +253,25 @@ def execute_search(query: str, branch: str | None = None, memory_type: str | Non
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))
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console.print()
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logger.info(f"[search] Displayed {len(filtered_results)} results")
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return True
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# =============================================================================
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# INTROSPECTION
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# =============================================================================
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def print_introspection():
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"""Display module introspection info."""
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console.print()
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console.print("search Module")
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console.print("Orchestrates semantic search across memory collections via vector embeddings and ChromaDB")
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console.print()
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console.print("Connected Handlers:")
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console.print(" handlers/search/")
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console.print(" - query_executor.py (execute_search — encode query, search collections, filter results by similarity)")
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console.print()
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# =============================================================================
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# STANDALONE EXECUTION
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# =============================================================================
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