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feat: Add vector embeddings for core memory semantic search
- Add memory_chunks table for storing chunked content with embeddings - Create Python embedder script (memory_embedder.py) using CodexLens fastembed - Add TypeScript bridge (memory-embedder-bridge.ts) for Python interop - Implement content chunking with paragraph/sentence-aware splitting - Add vectorSimilarity dimension to clustering (weight 0.3) - New CLI commands: ccw memory embed, search, embed-status - Extend core-memory MCP tool with embed/search/embed_status operations Clustering improvement: max relevance 0.388 → 0.809 (+109%) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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catlog22
362
ccw/scripts/memory_embedder.py
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362
ccw/scripts/memory_embedder.py
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#!/usr/bin/env python3
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"""
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Memory Embedder - Bridge CCW to CodexLens semantic search
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This script generates and searches embeddings for memory chunks stored in CCW's
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SQLite database using CodexLens's embedder.
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Usage:
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python memory_embedder.py embed <db_path> [--source-id ID] [--batch-size N] [--force]
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python memory_embedder.py search <db_path> <query> [--top-k N] [--min-score F] [--type TYPE]
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python memory_embedder.py status <db_path>
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"""
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import argparse
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import json
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import sqlite3
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import sys
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import time
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from pathlib import Path
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from typing import List, Dict, Any, Optional, Tuple
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try:
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import numpy as np
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except ImportError:
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print("Error: numpy is required. Install with: pip install numpy", file=sys.stderr)
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sys.exit(1)
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try:
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from codexlens.semantic.embedder import get_embedder
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except ImportError:
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print("Error: CodexLens not found. Install with: pip install codexlens[semantic]", file=sys.stderr)
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sys.exit(1)
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class MemoryEmbedder:
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"""Generate and search embeddings for memory chunks."""
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EMBEDDING_DIM = 768 # jina-embeddings-v2-base-code dimension
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def __init__(self, db_path: str):
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"""Initialize embedder with database path."""
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self.db_path = Path(db_path)
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if not self.db_path.exists():
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raise FileNotFoundError(f"Database not found: {db_path}")
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self.conn = sqlite3.connect(str(self.db_path))
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self.conn.row_factory = sqlite3.Row
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# Initialize embedder (uses cached singleton)
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self.embedder = get_embedder(profile="code")
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def close(self):
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"""Close database connection."""
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if self.conn:
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self.conn.close()
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def embed_chunks(
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self,
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source_id: Optional[str] = None,
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batch_size: int = 8,
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force: bool = False
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) -> Dict[str, Any]:
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"""
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Generate embeddings for unembedded chunks.
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Args:
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source_id: Only process chunks from this source
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batch_size: Number of chunks to process in each batch
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force: Re-embed chunks that already have embeddings
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Returns:
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Result dict with success, chunks_processed, chunks_failed, elapsed_time
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"""
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start_time = time.time()
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# Build query
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query = "SELECT id, source_id, source_type, chunk_index, content FROM memory_chunks"
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params = []
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if force:
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# Process all chunks (with optional source filter)
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if source_id:
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query += " WHERE source_id = ?"
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params.append(source_id)
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else:
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# Only process chunks without embeddings
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query += " WHERE embedding IS NULL"
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if source_id:
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query += " AND source_id = ?"
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params.append(source_id)
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query += " ORDER BY id"
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cursor = self.conn.cursor()
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cursor.execute(query, params)
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chunks_processed = 0
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chunks_failed = 0
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batch = []
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batch_ids = []
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for row in cursor:
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batch.append(row["content"])
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batch_ids.append(row["id"])
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# Process batch when full
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if len(batch) >= batch_size:
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processed, failed = self._process_batch(batch, batch_ids)
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chunks_processed += processed
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chunks_failed += failed
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batch = []
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batch_ids = []
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# Process remaining chunks
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if batch:
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processed, failed = self._process_batch(batch, batch_ids)
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chunks_processed += processed
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chunks_failed += failed
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elapsed_time = time.time() - start_time
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return {
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"success": chunks_failed == 0,
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"chunks_processed": chunks_processed,
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"chunks_failed": chunks_failed,
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"elapsed_time": round(elapsed_time, 2)
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}
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def _process_batch(self, texts: List[str], ids: List[int]) -> Tuple[int, int]:
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"""Process a batch of texts and update embeddings."""
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try:
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# Generate embeddings for batch
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embeddings = self.embedder.embed(texts)
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processed = 0
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failed = 0
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# Update database
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cursor = self.conn.cursor()
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for chunk_id, embedding in zip(ids, embeddings):
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try:
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# Convert to numpy array and store as bytes
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emb_array = np.array(embedding, dtype=np.float32)
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emb_bytes = emb_array.tobytes()
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cursor.execute(
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"UPDATE memory_chunks SET embedding = ? WHERE id = ?",
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(emb_bytes, chunk_id)
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)
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processed += 1
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except Exception as e:
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print(f"Error updating chunk {chunk_id}: {e}", file=sys.stderr)
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failed += 1
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self.conn.commit()
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return processed, failed
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except Exception as e:
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print(f"Error processing batch: {e}", file=sys.stderr)
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return 0, len(ids)
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def search(
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self,
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query: str,
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top_k: int = 10,
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min_score: float = 0.3,
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source_type: Optional[str] = None
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) -> Dict[str, Any]:
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"""
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Perform semantic search on memory chunks.
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Args:
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query: Search query text
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top_k: Number of results to return
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min_score: Minimum similarity score (0-1)
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source_type: Filter by source type (core_memory, workflow, cli_history)
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Returns:
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Result dict with success and matches list
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"""
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try:
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# Generate query embedding
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query_embedding = self.embedder.embed_single(query)
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query_array = np.array(query_embedding, dtype=np.float32)
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# Build database query
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sql = """
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SELECT id, source_id, source_type, chunk_index, content, embedding
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FROM memory_chunks
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WHERE embedding IS NOT NULL
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"""
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params = []
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if source_type:
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sql += " AND source_type = ?"
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params.append(source_type)
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cursor = self.conn.cursor()
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cursor.execute(sql, params)
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# Calculate similarities
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matches = []
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for row in cursor:
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# Load embedding from bytes
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emb_bytes = row["embedding"]
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emb_array = np.frombuffer(emb_bytes, dtype=np.float32)
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# Cosine similarity
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score = float(
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np.dot(query_array, emb_array) /
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(np.linalg.norm(query_array) * np.linalg.norm(emb_array))
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)
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if score >= min_score:
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# Generate restore command
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restore_command = self._get_restore_command(
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row["source_id"],
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row["source_type"]
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)
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matches.append({
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"source_id": row["source_id"],
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"source_type": row["source_type"],
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"chunk_index": row["chunk_index"],
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"content": row["content"],
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"score": round(score, 4),
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"restore_command": restore_command
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})
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# Sort by score and limit
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matches.sort(key=lambda x: x["score"], reverse=True)
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matches = matches[:top_k]
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return {
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"success": True,
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"matches": matches
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}
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except Exception as e:
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return {
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"success": False,
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"error": str(e),
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"matches": []
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}
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def _get_restore_command(self, source_id: str, source_type: str) -> str:
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"""Generate restore command for a source."""
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if source_type in ("core_memory", "cli_history"):
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return f"ccw memory export {source_id}"
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elif source_type == "workflow":
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return f"ccw session resume {source_id}"
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else:
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return f"# Unknown source type: {source_type}"
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def get_status(self) -> Dict[str, Any]:
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"""Get embedding status statistics."""
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cursor = self.conn.cursor()
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# Total chunks
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cursor.execute("SELECT COUNT(*) as count FROM memory_chunks")
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total_chunks = cursor.fetchone()["count"]
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# Embedded chunks
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cursor.execute("SELECT COUNT(*) as count FROM memory_chunks WHERE embedding IS NOT NULL")
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embedded_chunks = cursor.fetchone()["count"]
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# By type
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cursor.execute("""
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SELECT
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source_type,
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COUNT(*) as total,
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SUM(CASE WHEN embedding IS NOT NULL THEN 1 ELSE 0 END) as embedded
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FROM memory_chunks
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GROUP BY source_type
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""")
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by_type = {}
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for row in cursor:
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by_type[row["source_type"]] = {
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"total": row["total"],
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"embedded": row["embedded"],
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"pending": row["total"] - row["embedded"]
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}
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return {
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"total_chunks": total_chunks,
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"embedded_chunks": embedded_chunks,
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"pending_chunks": total_chunks - embedded_chunks,
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"by_type": by_type
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}
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def main():
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"""Main entry point."""
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parser = argparse.ArgumentParser(
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description="Memory Embedder - Bridge CCW to CodexLens semantic search"
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)
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subparsers = parser.add_subparsers(dest="command", help="Command to execute")
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subparsers.required = True
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# Embed command
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embed_parser = subparsers.add_parser("embed", help="Generate embeddings for chunks")
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embed_parser.add_argument("db_path", help="Path to SQLite database")
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embed_parser.add_argument("--source-id", help="Only process chunks from this source")
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embed_parser.add_argument("--batch-size", type=int, default=8, help="Batch size (default: 8)")
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embed_parser.add_argument("--force", action="store_true", help="Re-embed existing chunks")
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# Search command
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search_parser = subparsers.add_parser("search", help="Semantic search")
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search_parser.add_argument("db_path", help="Path to SQLite database")
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search_parser.add_argument("query", help="Search query")
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search_parser.add_argument("--top-k", type=int, default=10, help="Number of results (default: 10)")
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search_parser.add_argument("--min-score", type=float, default=0.3, help="Minimum score (default: 0.3)")
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search_parser.add_argument("--type", dest="source_type", help="Filter by source type")
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# Status command
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status_parser = subparsers.add_parser("status", help="Get embedding status")
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status_parser.add_argument("db_path", help="Path to SQLite database")
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args = parser.parse_args()
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try:
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embedder = MemoryEmbedder(args.db_path)
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if args.command == "embed":
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result = embedder.embed_chunks(
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source_id=args.source_id,
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batch_size=args.batch_size,
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force=args.force
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)
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print(json.dumps(result, indent=2))
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elif args.command == "search":
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result = embedder.search(
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query=args.query,
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top_k=args.top_k,
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min_score=args.min_score,
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source_type=args.source_type
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)
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print(json.dumps(result, indent=2))
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elif args.command == "status":
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result = embedder.get_status()
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print(json.dumps(result, indent=2))
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embedder.close()
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# Exit with error code if operation failed
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if "success" in result and not result["success"]:
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sys.exit(1)
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except Exception as e:
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print(json.dumps({
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"success": False,
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"error": str(e)
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}, indent=2), file=sys.stderr)
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sys.exit(1)
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if __name__ == "__main__":
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main()
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