[紫薇六大资产·代码资产] semantic_search.py

#!/usr/bin/env python3
“”“
L3冷记忆层 - 语义搜索接口

功能:

• top-k语义搜索
• type过滤 (monthly_archive, session_log等)
• 返回score+content+metadata
• 支持 –dry-run

用法: python semantic_search.py [–dry-run] [–query “关键词”] [–type monthly_archive] [–top-k 10]
“”“
import sys
import os
import argparse
import logging
from pathlib import Path
from datetime import datetime, timezone, timedelta
from typing import Dict, List, Optional

sys.path.insert(0, str(Path(file).resolve().parent.parent))

try:
from l3_cold.config import TOP_K, SIMILARITY_THRESHOLD, CST
from l3_cold.chroma_store import ChromaStore
from l3_cold.embedder import Embedder
except ImportError:
from config import TOP_K, SIMILARITY_THRESHOLD, CST
from chroma_store import ChromaStore
from embedder import Embedder

MEMORY_BASE = Path.home() / ‘.hermes’ / ‘memory’
LOG_DIR = MEMORY_BASE / ‘logs’

def setup_logging():
LOG_DIR.mkdir(parents=True, exist_ok=True)
logging.basicConfig(
level=logging.INFO,
format=’%(asctime)s [%(levelname)s] %(message)s’,
handlers=[
logging.FileHandler(LOG_DIR / ‘error.log’, encoding=‘utf-8’),
logging.StreamHandler(sys.stdout),
]
)
return logging.getLogger(name)

class SemanticSearch:
“”“
语义搜索接口

基于ChromaDB的向量相似度搜索
支持元数据过滤和结果排序
"""

def __init__(self, dry_run: bool = False):
    self.dry_run = dry_run
    self.logger = logging.getLogger(__name__)
    self.store = ChromaStore(dry_run=dry_run)
    self.embedder = Embedder(dry_run=dry_run)

def search(
    self,
    query: str,
    top_k: int = TOP_K,
    filter_type: Optional[str] = None,
    filter_task: Optional[str] = None,
    threshold: float = SIMILARITY_THRESHOLD,
) -> List[Dict]:
    """
    语义搜索

    Args:
        query: 查询文本
        top_k: 返回结果数
        filter_type: 按type过滤 (如 monthly_archive)
        filter_task: 按task_id过滤 (如 T001)
        threshold: 相似度最低阈值

    Returns:
        List[Dict]: 搜索结果 [{id, document, metadata, score}]
    """
    if self.dry_run:
        self.logger.info(f"[DRY-RUN] 语义搜索: '{query[:50]}...'")
        return []

    # 初始化
    if not self.store.init():
        return []
    if not self.embedder.load():
        return []

    # 计算查询向量
    query_embedding = self.embedder.embed(query)

    # 构建过滤条件
    where = {}
    if filter_type:
        where['type'] = filter_type
    if filter_task:
        where['task_id'] = filter_task

    # 搜索
    results = self.store.search(
        query=query,
        top_k=top_k,
        where=where if where else None,
        query_embedding=query_embedding,
    )

    # 格式化和过滤
    formatted = []
    for r in results:
        distance = r.get('distance', 1.0)
        # 转换距离为相似度分数 (cosine distance → similarity)
        if distance is not None:
            score = 1.0 - distance
        else:
            score = 0.5

        if score >= threshold:
            formatted.append({
                'id': r['id'],
                'content': r['document'],
                'metadata': r.get('metadata', {}),
                'score': round(score, 4),
            })

    # 按分数降序
    formatted.sort(key=lambda x: x['score'], reverse=True)
    self.logger.info(f"搜索完成: {len(formatted)}/{len(results)} 条结果 (阈值: {threshold})")

    return formatted

def contextual_search(self, query: str, context_hints: List[str] = None) -> List[Dict]:
    """
    上下文增强搜索

    使用多个提示文本加权搜索

    Args:
        query: 主查询
        context_hints: 上下文提示列表

    Returns:
        List[Dict]: 综合搜索结果
    """
    if not context_hints:
        return self.search(query)

    # 主查询 + 所有提示的搜索结果
    all_results = []
    all_ids = set()

    # 主查询
    main_results = self.search(query, top_k=TOP_K * 2)
    for r in main_results:
        r['source_query'] = 'main'
        all_results.append(r)
        all_ids.add(r['id'])

    # 上下文提示
    for hint in context_hints[:3]:  # 最多3个提示
        hint_results = self.search(hint, top_k=TOP_K)
        for r in hint_results:
            if r['id'] not in all_ids:
                r['source_query'] = 'context'
                r['score'] *= 0.7  # 上下文查询降权
                all_results.append(r)
                all_ids.add(r['id'])

    # 去重排序
    all_results.sort(key=lambda x: x['score'], reverse=True)
    return all_results[:TOP_K]

def get_similar(self, document_id: str, top_k: int = 10) -> List[Dict]:
    """
    查找相似记忆

    Args:
        document_id: 文档ID
        top_k: 返回数

    Returns:
        List[Dict]: 相似文档
    """
    if self.dry_run:
        return []

    # 获取该文档的元数据作为搜索上下文
    results = self.store.search(query=document_id, top_k=1)
    if results:
        doc_text = results[0].get('document', '')
        return self.search(doc_text, top_k=top_k)
    return []

def main():
parser = argparse.ArgumentParser(description=‘L3语义搜索’)
parser.add_argument(’–dry-run’, action=‘store_true’, help=‘只输出操作预览’)
parser.add_argument(’–query’, type=str, default=None, help=‘搜索查询’)
parser.add_argument(’–type’, type=str, default=None, help=‘按type过滤’)
parser.add_argument(’–task’, type=str, default=None, help=‘按task_id过滤’)
parser.add_argument(’–top-k’, type=int, default=TOP_K, help=‘返回结果数’)

args = parser.parse_args()
logger = setup_logging()
logger.info("L3 语义搜索")

search = SemanticSearch(dry_run=args.dry_run)

query = args.query or "Hermes记忆系统架构"

logger.info(f"查询: '{query}' (top-{args.top_k})")
if args.type:
    logger.info(f"过滤type: {args.type}")
if args.task:
    logger.info(f"过滤task: {

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