mirror of
https://github.com/catlog22/Claude-Code-Workflow.git
synced 2026-02-12 02:37:45 +08:00
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>
This commit is contained in:
catlog22
@@ -60,6 +60,17 @@ export interface SessionMetadataCache {
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access_count: number;
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}
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export interface MemoryChunk {
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id?: number;
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source_id: string;
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source_type: 'core_memory' | 'workflow' | 'cli_history';
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chunk_index: number;
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content: string;
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embedding?: Buffer;
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metadata?: string;
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created_at: string;
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}
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/**
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* Core Memory Store using SQLite
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*/
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@@ -152,6 +163,19 @@ export class CoreMemoryStore {
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access_count INTEGER DEFAULT 0
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);
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-- Memory chunks table for embeddings
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CREATE TABLE IF NOT EXISTS memory_chunks (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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source_id TEXT NOT NULL,
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source_type TEXT NOT NULL,
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chunk_index INTEGER NOT NULL,
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content TEXT NOT NULL,
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embedding BLOB,
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metadata TEXT,
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created_at TEXT NOT NULL,
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UNIQUE(source_id, chunk_index)
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);
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-- Indexes for efficient queries
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CREATE INDEX IF NOT EXISTS idx_memories_created ON memories(created_at DESC);
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CREATE INDEX IF NOT EXISTS idx_memories_updated ON memories(updated_at DESC);
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@@ -160,6 +184,8 @@ export class CoreMemoryStore {
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CREATE INDEX IF NOT EXISTS idx_cluster_members_cluster ON cluster_members(cluster_id);
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CREATE INDEX IF NOT EXISTS idx_cluster_members_session ON cluster_members(session_id);
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CREATE INDEX IF NOT EXISTS idx_session_metadata_type ON session_metadata_cache(session_type);
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CREATE INDEX IF NOT EXISTS idx_memory_chunks_source ON memory_chunks(source_id, source_type);
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CREATE INDEX IF NOT EXISTS idx_memory_chunks_embedded ON memory_chunks(embedding IS NOT NULL);
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`);
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}
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@@ -815,6 +841,243 @@ ${memory.content}
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}));
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}
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// ============================================================================
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// Memory Chunks CRUD Operations
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// ============================================================================
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/**
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* Chunk content into smaller pieces for embedding
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* @param content Content to chunk
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* @param sourceId Source identifier (e.g., memory ID)
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* @param sourceType Type of source
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* @returns Array of chunk content strings
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*/
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chunkContent(content: string, sourceId: string, sourceType: string): string[] {
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const CHUNK_SIZE = 1500;
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const OVERLAP = 200;
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const chunks: string[] = [];
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// Split by paragraph boundaries first
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const paragraphs = content.split(/\n\n+/);
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let currentChunk = '';
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for (const paragraph of paragraphs) {
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// If adding this paragraph would exceed chunk size
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if (currentChunk.length + paragraph.length > CHUNK_SIZE && currentChunk.length > 0) {
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// Save current chunk
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chunks.push(currentChunk.trim());
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// Start new chunk with overlap
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const overlapText = currentChunk.slice(-OVERLAP);
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currentChunk = overlapText + '\n\n' + paragraph;
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} else {
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// Add paragraph to current chunk
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currentChunk += (currentChunk ? '\n\n' : '') + paragraph;
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}
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}
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// Add remaining chunk
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if (currentChunk.trim()) {
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chunks.push(currentChunk.trim());
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}
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// If no paragraphs or chunks are still too large, split by sentences
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const finalChunks: string[] = [];
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for (const chunk of chunks) {
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if (chunk.length <= CHUNK_SIZE) {
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finalChunks.push(chunk);
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} else {
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// Split by sentence boundaries
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const sentences = chunk.split(/\. +/);
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let sentenceChunk = '';
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for (const sentence of sentences) {
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const sentenceWithPeriod = sentence + '. ';
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if (sentenceChunk.length + sentenceWithPeriod.length > CHUNK_SIZE && sentenceChunk.length > 0) {
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finalChunks.push(sentenceChunk.trim());
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const overlapText = sentenceChunk.slice(-OVERLAP);
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sentenceChunk = overlapText + sentenceWithPeriod;
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} else {
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sentenceChunk += sentenceWithPeriod;
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}
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}
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if (sentenceChunk.trim()) {
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finalChunks.push(sentenceChunk.trim());
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}
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}
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}
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return finalChunks.length > 0 ? finalChunks : [content];
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}
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/**
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* Insert a single chunk
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*/
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insertChunk(chunk: Omit<MemoryChunk, 'id'>): number {
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const now = new Date().toISOString();
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const stmt = this.db.prepare(`
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INSERT INTO memory_chunks (source_id, source_type, chunk_index, content, embedding, metadata, created_at)
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VALUES (?, ?, ?, ?, ?, ?, ?)
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`);
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const result = stmt.run(
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chunk.source_id,
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chunk.source_type,
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chunk.chunk_index,
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chunk.content,
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chunk.embedding || null,
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chunk.metadata || null,
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chunk.created_at || now
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);
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return result.lastInsertRowid as number;
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}
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/**
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* Insert multiple chunks in a batch
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*/
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insertChunksBatch(chunks: Omit<MemoryChunk, 'id'>[]): void {
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const now = new Date().toISOString();
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const insert = this.db.prepare(`
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INSERT INTO memory_chunks (source_id, source_type, chunk_index, content, embedding, metadata, created_at)
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VALUES (?, ?, ?, ?, ?, ?, ?)
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`);
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const transaction = this.db.transaction((chunks: Omit<MemoryChunk, 'id'>[]) => {
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for (const chunk of chunks) {
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insert.run(
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chunk.source_id,
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chunk.source_type,
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chunk.chunk_index,
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chunk.content,
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chunk.embedding || null,
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chunk.metadata || null,
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chunk.created_at || now
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);
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}
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});
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transaction(chunks);
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}
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/**
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* Get all chunks for a source
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*/
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getChunks(sourceId: string): MemoryChunk[] {
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const stmt = this.db.prepare(`
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SELECT * FROM memory_chunks
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WHERE source_id = ?
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ORDER BY chunk_index ASC
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`);
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const rows = stmt.all(sourceId) as any[];
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return rows.map(row => ({
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id: row.id,
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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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embedding: row.embedding,
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metadata: row.metadata,
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created_at: row.created_at
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}));
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}
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/**
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* Get chunks by source type
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*/
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getChunksByType(sourceType: string): MemoryChunk[] {
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const stmt = this.db.prepare(`
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SELECT * FROM memory_chunks
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WHERE source_type = ?
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ORDER BY source_id, chunk_index ASC
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`);
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const rows = stmt.all(sourceType) as any[];
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return rows.map(row => ({
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id: row.id,
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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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embedding: row.embedding,
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metadata: row.metadata,
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created_at: row.created_at
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}));
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}
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/**
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* Get chunks without embeddings
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*/
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getUnembeddedChunks(limit?: number): MemoryChunk[] {
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const query = `
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SELECT * FROM memory_chunks
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WHERE embedding IS NULL
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ORDER BY created_at ASC
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${limit ? 'LIMIT ?' : ''}
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`;
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const stmt = this.db.prepare(query);
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const rows = (limit ? stmt.all(limit) : stmt.all()) as any[];
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return rows.map(row => ({
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id: row.id,
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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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embedding: row.embedding,
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metadata: row.metadata,
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created_at: row.created_at
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}));
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}
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/**
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* Update embedding for a chunk
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*/
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updateChunkEmbedding(chunkId: number, embedding: Buffer): void {
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const stmt = this.db.prepare(`
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UPDATE memory_chunks
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SET embedding = ?
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WHERE id = ?
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`);
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stmt.run(embedding, chunkId);
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}
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/**
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* Update embeddings for multiple chunks in a batch
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*/
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updateChunkEmbeddingsBatch(updates: { id: number; embedding: Buffer }[]): void {
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const update = this.db.prepare(`
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UPDATE memory_chunks
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SET embedding = ?
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WHERE id = ?
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`);
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const transaction = this.db.transaction((updates: { id: number; embedding: Buffer }[]) => {
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for (const { id, embedding } of updates) {
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update.run(embedding, id);
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}
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});
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transaction(updates);
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}
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/**
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* Delete all chunks for a source
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*/
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deleteChunks(sourceId: string): void {
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const stmt = this.db.prepare(`
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DELETE FROM memory_chunks
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WHERE source_id = ?
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`);
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stmt.run(sourceId);
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}
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/**
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* Close database connection
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*/
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262
ccw/src/core/memory-embedder-bridge.ts
Normal file
262
ccw/src/core/memory-embedder-bridge.ts
Normal file
@@ -0,0 +1,262 @@
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/**
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* Memory Embedder Bridge - TypeScript interface to Python memory embedder
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*
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* This module provides a TypeScript bridge to the Python memory_embedder.py script,
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* which generates and searches embeddings for memory chunks using CodexLens's embedder.
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*
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* Features:
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* - Reuses CodexLens venv at ~/.codexlens/venv
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* - JSON protocol communication
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* - Three commands: embed, search, status
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* - Automatic availability checking
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*/
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import { spawn } from 'child_process';
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import { join, dirname } from 'path';
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import { homedir } from 'os';
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import { existsSync } from 'fs';
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import { fileURLToPath } from 'url';
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// Get directory of this module
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const __filename = fileURLToPath(import.meta.url);
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const __dirname = dirname(__filename);
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// Venv paths (reuse CodexLens venv)
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const CODEXLENS_VENV = join(homedir(), '.codexlens', 'venv');
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const VENV_PYTHON =
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process.platform === 'win32'
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? join(CODEXLENS_VENV, 'Scripts', 'python.exe')
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: join(CODEXLENS_VENV, 'bin', 'python');
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// Script path
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const EMBEDDER_SCRIPT = join(__dirname, '..', '..', 'scripts', 'memory_embedder.py');
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// Types
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export interface EmbedResult {
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success: boolean;
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chunks_processed: number;
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chunks_failed: number;
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elapsed_time: number;
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error?: string;
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}
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export interface SearchMatch {
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source_id: string;
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source_type: 'core_memory' | 'workflow' | 'cli_history';
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chunk_index: number;
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content: string;
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score: number;
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restore_command: string;
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}
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export interface SearchResult {
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success: boolean;
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matches: SearchMatch[];
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query?: string;
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elapsed_time?: number;
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error?: string;
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}
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export interface EmbeddingStatus {
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success?: boolean;
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total_chunks: number;
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embedded_chunks: number;
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pending_chunks: number;
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by_type: Record<string, { total: number; embedded: number; pending: number }>;
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error?: string;
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}
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export interface EmbedOptions {
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sourceId?: string;
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batchSize?: number;
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force?: boolean;
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}
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export interface SearchOptions {
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topK?: number;
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minScore?: number;
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sourceType?: 'core_memory' | 'workflow' | 'cli_history';
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}
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/**
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* Check if embedder is available (venv and script exist)
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* @returns True if embedder is available
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*/
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export function isEmbedderAvailable(): boolean {
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// Check venv python exists
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if (!existsSync(VENV_PYTHON)) {
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return false;
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}
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// Check script exists
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if (!existsSync(EMBEDDER_SCRIPT)) {
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return false;
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}
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return true;
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}
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/**
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* Run Python script with arguments
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* @param args - Command line arguments
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* @param timeout - Timeout in milliseconds
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* @returns JSON output from script
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*/
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function runPython(args: string[], timeout: number = 300000): Promise<string> {
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return new Promise((resolve, reject) => {
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// Check availability
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if (!isEmbedderAvailable()) {
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reject(
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new Error(
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'Memory embedder not available. Ensure CodexLens venv exists at ~/.codexlens/venv'
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)
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);
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return;
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}
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// Spawn Python process
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const child = spawn(VENV_PYTHON, [EMBEDDER_SCRIPT, ...args], {
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stdio: ['ignore', 'pipe', 'pipe'],
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timeout,
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});
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let stdout = '';
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let stderr = '';
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child.stdout.on('data', (data) => {
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stdout += data.toString();
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});
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child.stderr.on('data', (data) => {
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stderr += data.toString();
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});
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child.on('close', (code) => {
|
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if (code === 0) {
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resolve(stdout.trim());
|
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} else {
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reject(new Error(`Python script failed (exit code ${code}): ${stderr || stdout}`));
|
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}
|
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});
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|
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child.on('error', (err) => {
|
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if ((err as NodeJS.ErrnoException).code === 'ETIMEDOUT') {
|
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reject(new Error('Python script timed out'));
|
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} else {
|
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reject(new Error(`Failed to spawn Python: ${err.message}`));
|
||||
}
|
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});
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});
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}
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/**
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* Generate embeddings for memory chunks
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* @param dbPath - Path to SQLite database
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* @param options - Embedding options
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* @returns Embedding result
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*/
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export async function generateEmbeddings(
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dbPath: string,
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options: EmbedOptions = {}
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): Promise<EmbedResult> {
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const { sourceId, batchSize = 8, force = false } = options;
|
||||
|
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// Build arguments
|
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const args = ['embed', dbPath];
|
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|
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if (sourceId) {
|
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args.push('--source-id', sourceId);
|
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}
|
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|
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if (batchSize !== 8) {
|
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args.push('--batch-size', batchSize.toString());
|
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}
|
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|
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if (force) {
|
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args.push('--force');
|
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}
|
||||
|
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try {
|
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// Default timeout: 5 minutes
|
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const output = await runPython(args, 300000);
|
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const result = JSON.parse(output) as EmbedResult;
|
||||
return result;
|
||||
} catch (err) {
|
||||
return {
|
||||
success: false,
|
||||
chunks_processed: 0,
|
||||
chunks_failed: 0,
|
||||
elapsed_time: 0,
|
||||
error: (err as Error).message,
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
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* Search memory chunks using semantic search
|
||||
* @param dbPath - Path to SQLite database
|
||||
* @param query - Search query text
|
||||
* @param options - Search options
|
||||
* @returns Search results
|
||||
*/
|
||||
export async function searchMemories(
|
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dbPath: string,
|
||||
query: string,
|
||||
options: SearchOptions = {}
|
||||
): Promise<SearchResult> {
|
||||
const { topK = 10, minScore = 0.3, sourceType } = options;
|
||||
|
||||
// Build arguments
|
||||
const args = ['search', dbPath, query];
|
||||
|
||||
if (topK !== 10) {
|
||||
args.push('--top-k', topK.toString());
|
||||
}
|
||||
|
||||
if (minScore !== 0.3) {
|
||||
args.push('--min-score', minScore.toString());
|
||||
}
|
||||
|
||||
if (sourceType) {
|
||||
args.push('--type', sourceType);
|
||||
}
|
||||
|
||||
try {
|
||||
// Default timeout: 30 seconds
|
||||
const output = await runPython(args, 30000);
|
||||
const result = JSON.parse(output) as SearchResult;
|
||||
return result;
|
||||
} catch (err) {
|
||||
return {
|
||||
success: false,
|
||||
matches: [],
|
||||
error: (err as Error).message,
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Get embedding status statistics
|
||||
* @param dbPath - Path to SQLite database
|
||||
* @returns Embedding status
|
||||
*/
|
||||
export async function getEmbeddingStatus(dbPath: string): Promise<EmbeddingStatus> {
|
||||
// Build arguments
|
||||
const args = ['status', dbPath];
|
||||
|
||||
try {
|
||||
// Default timeout: 30 seconds
|
||||
const output = await runPython(args, 30000);
|
||||
const result = JSON.parse(output) as EmbeddingStatus;
|
||||
return { ...result, success: true };
|
||||
} catch (err) {
|
||||
return {
|
||||
success: false,
|
||||
total_chunks: 0,
|
||||
embedded_chunks: 0,
|
||||
pending_chunks: 0,
|
||||
by_type: {},
|
||||
error: (err as Error).message,
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -11,9 +11,10 @@ import { join } from 'path';
|
||||
|
||||
// Clustering dimension weights
|
||||
const WEIGHTS = {
|
||||
fileOverlap: 0.3,
|
||||
temporalProximity: 0.2,
|
||||
semanticSimilarity: 0.3,
|
||||
fileOverlap: 0.2,
|
||||
temporalProximity: 0.15,
|
||||
keywordSimilarity: 0.15,
|
||||
vectorSimilarity: 0.3,
|
||||
intentAlignment: 0.2,
|
||||
};
|
||||
|
||||
@@ -219,13 +220,15 @@ export class SessionClusteringService {
|
||||
calculateRelevance(session1: SessionMetadataCache, session2: SessionMetadataCache): number {
|
||||
const fileScore = this.calculateFileOverlap(session1, session2);
|
||||
const temporalScore = this.calculateTemporalProximity(session1, session2);
|
||||
const semanticScore = this.calculateSemanticSimilarity(session1, session2);
|
||||
const keywordScore = this.calculateSemanticSimilarity(session1, session2);
|
||||
const vectorScore = this.calculateVectorSimilarity(session1, session2);
|
||||
const intentScore = this.calculateIntentAlignment(session1, session2);
|
||||
|
||||
return (
|
||||
fileScore * WEIGHTS.fileOverlap +
|
||||
temporalScore * WEIGHTS.temporalProximity +
|
||||
semanticScore * WEIGHTS.semanticSimilarity +
|
||||
keywordScore * WEIGHTS.keywordSimilarity +
|
||||
vectorScore * WEIGHTS.vectorSimilarity +
|
||||
intentScore * WEIGHTS.intentAlignment
|
||||
);
|
||||
}
|
||||
@@ -301,6 +304,98 @@ export class SessionClusteringService {
|
||||
return intersection.size / union.size;
|
||||
}
|
||||
|
||||
/**
|
||||
* Calculate vector similarity using pre-computed embeddings from memory_chunks
|
||||
* Returns average cosine similarity of chunk embeddings
|
||||
*/
|
||||
private calculateVectorSimilarity(s1: SessionMetadataCache, s2: SessionMetadataCache): number {
|
||||
const embedding1 = this.getSessionEmbedding(s1.session_id);
|
||||
const embedding2 = this.getSessionEmbedding(s2.session_id);
|
||||
|
||||
// Graceful fallback if no embeddings available
|
||||
if (!embedding1 || !embedding2) {
|
||||
return 0;
|
||||
}
|
||||
|
||||
return this.cosineSimilarity(embedding1, embedding2);
|
||||
}
|
||||
|
||||
/**
|
||||
* Get session embedding by averaging all chunk embeddings
|
||||
*/
|
||||
private getSessionEmbedding(sessionId: string): number[] | null {
|
||||
const chunks = this.coreMemoryStore.getChunks(sessionId);
|
||||
|
||||
if (chunks.length === 0) {
|
||||
return null;
|
||||
}
|
||||
|
||||
// Filter chunks that have embeddings
|
||||
const embeddedChunks = chunks.filter(chunk => chunk.embedding && chunk.embedding.length > 0);
|
||||
|
||||
if (embeddedChunks.length === 0) {
|
||||
return null;
|
||||
}
|
||||
|
||||
// Convert Buffer embeddings to number arrays and calculate average
|
||||
const embeddings = embeddedChunks.map(chunk => {
|
||||
// Convert Buffer to Float32Array
|
||||
const buffer = chunk.embedding!;
|
||||
const float32Array = new Float32Array(buffer.buffer, buffer.byteOffset, buffer.byteLength / 4);
|
||||
return Array.from(float32Array);
|
||||
});
|
||||
|
||||
// Check all embeddings have same dimension
|
||||
const dimension = embeddings[0].length;
|
||||
if (!embeddings.every(emb => emb.length === dimension)) {
|
||||
console.warn(`[VectorSimilarity] Inconsistent embedding dimensions for session ${sessionId}`);
|
||||
return null;
|
||||
}
|
||||
|
||||
// Calculate average embedding
|
||||
const avgEmbedding = new Array(dimension).fill(0);
|
||||
for (const embedding of embeddings) {
|
||||
for (let i = 0; i < dimension; i++) {
|
||||
avgEmbedding[i] += embedding[i];
|
||||
}
|
||||
}
|
||||
|
||||
for (let i = 0; i < dimension; i++) {
|
||||
avgEmbedding[i] /= embeddings.length;
|
||||
}
|
||||
|
||||
return avgEmbedding;
|
||||
}
|
||||
|
||||
/**
|
||||
* Calculate cosine similarity between two vectors
|
||||
*/
|
||||
private cosineSimilarity(a: number[], b: number[]): number {
|
||||
if (a.length !== b.length) {
|
||||
console.warn('[VectorSimilarity] Vector dimension mismatch');
|
||||
return 0;
|
||||
}
|
||||
|
||||
let dotProduct = 0;
|
||||
let normA = 0;
|
||||
let normB = 0;
|
||||
|
||||
for (let i = 0; i < a.length; i++) {
|
||||
dotProduct += a[i] * b[i];
|
||||
normA += a[i] * a[i];
|
||||
normB += b[i] * b[i];
|
||||
}
|
||||
|
||||
normA = Math.sqrt(normA);
|
||||
normB = Math.sqrt(normB);
|
||||
|
||||
if (normA === 0 || normB === 0) {
|
||||
return 0;
|
||||
}
|
||||
|
||||
return dotProduct / (normA * normB);
|
||||
}
|
||||
|
||||
/**
|
||||
* Find the most relevant existing cluster for a set of session IDs
|
||||
* Returns the cluster with highest session overlap
|
||||
|
||||
Reference in New Issue
Block a user