sync: bug fixes + eval system from private repo
- Fix chunk_status: pass chunk_status/chunk/metadata to context builder - Fix vector search: scope by node_id BEFORE similarity search - Add eval logging system (RAH_EVALS_LOG=1) - Add eval dashboard at /evals - Add vitest for testing 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
“BeeRad”
co-authored by
Claude Opus 4.5
Claude Opus 4.5
parent
e15f223ed8
commit
2f2ef10ec9
@@ -12,6 +12,10 @@ interface RequestContext {
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openai?: string;
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anthropic?: string;
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};
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helperName?: string;
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evalDatasetId?: string;
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evalScenarioId?: string;
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evalChatSpanId?: string;
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}
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let currentContext: RequestContext = {};
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@@ -26,11 +30,11 @@ export const RequestContext = {
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}
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});
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},
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get(): RequestContext {
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return currentContext;
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},
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clear() {
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currentContext = {};
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},
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@@ -170,35 +170,57 @@ export class ChunkService {
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// PostgreSQL path removed in SQLite-only consolidation
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private async searchChunksSQLite(
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queryEmbedding: number[],
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queryEmbedding: number[],
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similarityThreshold = 0.5,
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matchCount = 5,
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nodeIds?: number[]
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): Promise<Array<Chunk & { similarity: number }>> {
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const sqlite = getSQLiteClient();
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// Step 1: Determine vector search limit - more conservative approach
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let vectorLimit = 50; // Start smaller for efficiency
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if (nodeIds && nodeIds.length > 0) {
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// When searching specific nodes, get exact count and use reasonable multiplier
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const candidateCountQuery = `SELECT COUNT(*) as count FROM chunks WHERE node_id IN (${nodeIds.map(() => '?').join(',')})`;
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const candidateResult = sqlite.query<{count: number}>(candidateCountQuery, nodeIds);
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const candidateCount = Number(candidateResult.rows[0].count);
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// Use 2x the candidate count but cap at reasonable limits
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vectorLimit = Math.min(Math.max(candidateCount * 2, 50), 1000);
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console.log(`🔍 Node-scoped search: ${candidateCount} candidates, using vector limit ${vectorLimit}`);
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} else {
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// For global search, use adaptive limit based on expected results
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vectorLimit = Math.max(matchCount * 10, 50);
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}
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const startTime = Date.now();
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// Step 2: Use CTE-based query to avoid UNION ALL explosion
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const vectorString = `[${queryEmbedding.join(',')}]`;
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let query = `
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// When searching specific nodes, first get their chunk_ids to scope the vector search
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if (nodeIds && nodeIds.length > 0) {
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// Get chunk IDs for the target nodes
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const chunkIdsQuery = `SELECT id FROM chunks WHERE node_id IN (${nodeIds.map(() => '?').join(',')})`;
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const chunkIdsResult = sqlite.query<{id: number}>(chunkIdsQuery, nodeIds);
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const chunkIds = chunkIdsResult.rows.map(r => r.id);
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if (chunkIds.length === 0) {
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console.log(`🔍 Node-scoped search: no chunks found for nodes ${nodeIds.join(', ')}`);
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return [];
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}
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console.log(`🔍 Node-scoped search: ${chunkIds.length} chunks in nodes ${nodeIds.join(', ')}`);
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// Search ONLY within those chunks using rowid filter
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const query = `
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SELECT c.*, (1.0 / (1.0 + v.distance)) AS similarity
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FROM vec_chunks v
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JOIN chunks c ON c.id = v.chunk_id
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WHERE v.embedding MATCH ?
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AND v.chunk_id IN (${chunkIds.map(() => '?').join(',')})
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AND (1.0 / (1.0 + v.distance)) >= ?
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ORDER BY v.distance
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LIMIT ?
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`;
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const params = [vectorString, ...chunkIds, similarityThreshold, matchCount];
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const result = sqlite.query<Chunk & { similarity: number }>(query, params);
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const searchTime = Date.now() - startTime;
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console.log(`📊 Vector search (node-scoped): ${result.rows.length} chunks, threshold=${similarityThreshold}, time=${searchTime}ms`);
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if (result.rows.length > 0) {
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console.log(`🎯 Top result: chunk ${result.rows[0].id} (similarity: ${result.rows[0].similarity.toFixed(3)})`);
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}
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return result.rows;
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}
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// Global search (no node filter)
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const vectorLimit = Math.max(matchCount * 10, 50);
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const query = `
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WITH vector_results AS (
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SELECT chunk_id, distance
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FROM vec_chunks
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@@ -209,36 +231,20 @@ export class ChunkService {
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SELECT c.*, (1.0 / (1.0 + vr.distance)) AS similarity
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FROM vector_results vr
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JOIN chunks c ON c.id = vr.chunk_id
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WHERE (1.0 / (1.0 + vr.distance)) >= ?
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ORDER BY similarity DESC
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LIMIT ?
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`;
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const params: any[] = [vectorString, vectorLimit];
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const conditions = [];
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// Node ID filter if provided
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if (nodeIds && nodeIds.length > 0) {
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conditions.push(`c.node_id IN (${nodeIds.map(() => '?').join(',')})`);
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params.push(...nodeIds);
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}
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// Similarity threshold filter
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conditions.push(`(1.0 / (1.0 + vr.distance)) >= ?`);
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params.push(similarityThreshold);
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if (conditions.length > 0) {
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query += ` WHERE ${conditions.join(' AND ')}`;
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}
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query += ` ORDER BY similarity DESC LIMIT ?`;
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params.push(matchCount);
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const params = [vectorString, vectorLimit, similarityThreshold, matchCount];
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const result = sqlite.query<Chunk & { similarity: number }>(query, params);
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const searchTime = Date.now() - startTime;
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console.log(`📊 Vector search: ${result.rows.length}/${vectorLimit} chunks, threshold=${similarityThreshold}, time=${searchTime}ms`);
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console.log(`📊 Vector search (global): ${result.rows.length}/${vectorLimit} chunks, threshold=${similarityThreshold}, time=${searchTime}ms`);
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if (result.rows.length > 0) {
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console.log(`🎯 Top result: chunk ${result.rows[0].id} (similarity: ${result.rows[0].similarity.toFixed(3)})`);
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}
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return result.rows;
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}
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@@ -0,0 +1,269 @@
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import Database from 'better-sqlite3';
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import fs from 'fs';
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import path from 'path';
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import { randomUUID } from 'crypto';
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import { RequestContext } from '@/services/context/requestContext';
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type EvalToolCallLog = {
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toolName: string;
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args?: unknown;
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result?: unknown;
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error?: unknown;
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latencyMs?: number;
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};
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type EvalChatLog = {
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traceId?: string;
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spanId?: string;
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helperName?: string;
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model?: string;
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promptVersion?: string;
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systemMessage?: string | null;
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userMessage?: string | null;
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assistantMessage?: string | null;
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inputTokens?: number;
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outputTokens?: number;
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totalTokens?: number;
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cacheWriteTokens?: number;
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cacheReadTokens?: number;
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cacheHit?: boolean;
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cacheSavingsPct?: number;
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estimatedCostUsd?: number;
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provider?: string | null;
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mode?: string | null;
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workflowKey?: string | null;
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workflowNodeId?: number | null;
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latencyMs?: number;
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success?: boolean;
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error?: string | null;
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};
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const EVALS_LOG_FLAG = process.env.RAH_EVALS_LOG;
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const EVALS_LOG_ENABLED = EVALS_LOG_FLAG === '1' || EVALS_LOG_FLAG === 'true';
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const LOG_DIR = path.join(process.cwd(), 'logs');
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const DB_PATH = path.join(LOG_DIR, 'evals.sqlite');
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let evalsDb: Database.Database | null = null;
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function shouldLogEvals() {
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// Log ALL interactions when RAH_EVALS_LOG=1
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// - Real app interactions: scenario_id will be NULL
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// - Synthetic scenarios: scenario_id will be set
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return EVALS_LOG_ENABLED;
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}
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function ensureSchema(db: Database.Database) {
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db.exec(`
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CREATE TABLE IF NOT EXISTS tool_calls (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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ts TEXT NOT NULL,
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trace_id TEXT,
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span_id TEXT,
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parent_span_id TEXT,
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helper_name TEXT,
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tool_name TEXT NOT NULL,
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args_json TEXT,
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result_json TEXT,
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success INTEGER,
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latency_ms INTEGER,
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error TEXT,
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dataset_id TEXT,
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scenario_id TEXT
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);
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CREATE INDEX IF NOT EXISTS idx_tool_calls_trace ON tool_calls(trace_id);
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CREATE INDEX IF NOT EXISTS idx_tool_calls_scenario ON tool_calls(scenario_id);
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`);
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db.exec(`
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CREATE TABLE IF NOT EXISTS llm_chats (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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ts TEXT NOT NULL,
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trace_id TEXT,
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span_id TEXT,
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helper_name TEXT,
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model TEXT,
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prompt_version TEXT,
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system_message TEXT,
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user_message TEXT,
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assistant_message TEXT,
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input_tokens INTEGER,
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output_tokens INTEGER,
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total_tokens INTEGER,
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cache_write_tokens INTEGER,
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cache_read_tokens INTEGER,
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cache_hit INTEGER,
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cache_savings_pct INTEGER,
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estimated_cost_usd REAL,
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provider TEXT,
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mode TEXT,
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workflow_key TEXT,
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workflow_node_id INTEGER,
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latency_ms INTEGER,
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success INTEGER,
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error TEXT,
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dataset_id TEXT,
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scenario_id TEXT
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);
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CREATE INDEX IF NOT EXISTS idx_llm_chats_trace ON llm_chats(trace_id);
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CREATE INDEX IF NOT EXISTS idx_llm_chats_scenario ON llm_chats(scenario_id);
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`);
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const columns = db.prepare(`PRAGMA table_info(llm_chats);`).all() as { name: string }[];
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const columnNames = new Set(columns.map(column => column.name));
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if (!columnNames.has('system_message')) {
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db.exec(`ALTER TABLE llm_chats ADD COLUMN system_message TEXT;`);
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}
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if (!columnNames.has('cache_write_tokens')) {
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db.exec(`ALTER TABLE llm_chats ADD COLUMN cache_write_tokens INTEGER;`);
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}
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if (!columnNames.has('cache_read_tokens')) {
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db.exec(`ALTER TABLE llm_chats ADD COLUMN cache_read_tokens INTEGER;`);
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}
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if (!columnNames.has('cache_hit')) {
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db.exec(`ALTER TABLE llm_chats ADD COLUMN cache_hit INTEGER;`);
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}
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if (!columnNames.has('cache_savings_pct')) {
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db.exec(`ALTER TABLE llm_chats ADD COLUMN cache_savings_pct INTEGER;`);
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}
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if (!columnNames.has('estimated_cost_usd')) {
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db.exec(`ALTER TABLE llm_chats ADD COLUMN estimated_cost_usd REAL;`);
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}
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if (!columnNames.has('provider')) {
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db.exec(`ALTER TABLE llm_chats ADD COLUMN provider TEXT;`);
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}
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if (!columnNames.has('mode')) {
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db.exec(`ALTER TABLE llm_chats ADD COLUMN mode TEXT;`);
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}
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if (!columnNames.has('workflow_key')) {
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db.exec(`ALTER TABLE llm_chats ADD COLUMN workflow_key TEXT;`);
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}
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if (!columnNames.has('workflow_node_id')) {
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db.exec(`ALTER TABLE llm_chats ADD COLUMN workflow_node_id INTEGER;`);
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}
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}
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function getDb() {
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if (!EVALS_LOG_ENABLED) return null;
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if (evalsDb) return evalsDb;
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fs.mkdirSync(LOG_DIR, { recursive: true });
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evalsDb = new Database(DB_PATH);
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evalsDb.pragma('journal_mode = WAL');
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evalsDb.pragma('synchronous = NORMAL');
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evalsDb.pragma('busy_timeout = 3000');
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ensureSchema(evalsDb);
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return evalsDb;
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}
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function stringifySafe(value: unknown) {
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if (value === undefined) return null;
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try {
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return JSON.stringify(value);
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} catch (error) {
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return JSON.stringify({
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error: 'Failed to serialize payload',
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message: error instanceof Error ? error.message : String(error),
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});
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}
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}
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export function logEvalToolCall(entry: EvalToolCallLog) {
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if (!shouldLogEvals()) return;
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const context = RequestContext.get();
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const traceId = context.traceId;
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if (!traceId) return;
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const db = getDb();
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if (!db) return;
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const now = new Date().toISOString();
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const spanId = randomUUID();
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const parentSpanId = context.evalChatSpanId || null;
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const helperName = context.helperName || null;
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const datasetId = context.evalDatasetId || null;
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const scenarioId = context.evalScenarioId || null;
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const errorMessage = entry.error instanceof Error
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? entry.error.message
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: entry.error
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? String(entry.error)
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: null;
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const success = typeof entry.error === 'undefined'
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? entry.result && typeof (entry.result as any).success === 'boolean'
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? ((entry.result as any).success ? 1 : 0)
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: 1
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: 0;
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db.prepare(`
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INSERT INTO tool_calls (
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ts, trace_id, span_id, parent_span_id, helper_name,
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tool_name, args_json, result_json, success, latency_ms, error,
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dataset_id, scenario_id
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) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
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`).run(
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now,
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traceId,
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spanId,
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parentSpanId,
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helperName,
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entry.toolName,
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stringifySafe(entry.args),
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stringifySafe(entry.result),
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success,
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entry.latencyMs ?? null,
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errorMessage,
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datasetId,
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scenarioId
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);
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}
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export function logEvalChat(entry: EvalChatLog) {
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if (!shouldLogEvals()) return;
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const context = RequestContext.get();
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const traceId = entry.traceId || context.traceId;
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if (!traceId) return;
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const db = getDb();
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if (!db) return;
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const now = new Date().toISOString();
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const spanId = entry.spanId || context.evalChatSpanId || randomUUID();
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const datasetId = context.evalDatasetId || null;
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const scenarioId = context.evalScenarioId || null;
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db.prepare(`
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INSERT INTO llm_chats (
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ts, trace_id, span_id, helper_name, model, prompt_version, system_message,
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user_message, assistant_message, input_tokens, output_tokens, total_tokens,
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cache_write_tokens, cache_read_tokens, cache_hit, cache_savings_pct,
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estimated_cost_usd, provider, mode, workflow_key, workflow_node_id,
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latency_ms, success, error, dataset_id, scenario_id
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) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
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`).run(
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now,
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traceId,
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spanId,
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entry.helperName ?? null,
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entry.model ?? null,
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entry.promptVersion ?? null,
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entry.systemMessage ?? null,
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entry.userMessage ?? null,
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entry.assistantMessage ?? null,
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entry.inputTokens ?? null,
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entry.outputTokens ?? null,
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entry.totalTokens ?? null,
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entry.cacheWriteTokens ?? null,
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entry.cacheReadTokens ?? null,
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typeof entry.cacheHit === 'boolean' ? (entry.cacheHit ? 1 : 0) : null,
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entry.cacheSavingsPct ?? null,
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entry.estimatedCostUsd ?? null,
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entry.provider ?? null,
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entry.mode ?? null,
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entry.workflowKey ?? null,
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entry.workflowNodeId ?? null,
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entry.latencyMs ?? null,
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typeof entry.success === 'boolean' ? (entry.success ? 1 : 0) : null,
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entry.error ?? null,
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datasetId,
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scenarioId
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);
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}
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@@ -0,0 +1,141 @@
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import Database from 'better-sqlite3';
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import fs from 'fs';
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import path from 'path';
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export type EvalChatRow = {
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id: number;
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ts: string;
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trace_id: string;
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span_id: string | null;
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helper_name: string | null;
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model: string | null;
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prompt_version: string | null;
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system_message: string | null;
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user_message: string | null;
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assistant_message: string | null;
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input_tokens: number | null;
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output_tokens: number | null;
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total_tokens: number | null;
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cache_write_tokens: number | null;
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cache_read_tokens: number | null;
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cache_hit: number | null;
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cache_savings_pct: number | null;
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estimated_cost_usd: number | null;
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provider: string | null;
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mode: string | null;
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workflow_key: string | null;
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workflow_node_id: number | null;
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latency_ms: number | null;
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success: number | null;
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error: string | null;
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dataset_id: string | null;
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scenario_id: string | null;
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};
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export type EvalToolCallRow = {
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id: number;
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ts: string;
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trace_id: string;
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span_id: string | null;
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parent_span_id: string | null;
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helper_name: string | null;
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tool_name: string;
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args_json: string | null;
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result_json: string | null;
|
||||
success: number | null;
|
||||
latency_ms: number | null;
|
||||
error: string | null;
|
||||
dataset_id: string | null;
|
||||
scenario_id: string | null;
|
||||
};
|
||||
|
||||
export type EvalTrace = {
|
||||
chat: EvalChatRow;
|
||||
toolCalls: EvalToolCallRow[];
|
||||
comment: string | null;
|
||||
};
|
||||
|
||||
const LOG_DB_PATH = path.join(process.cwd(), 'logs', 'evals.sqlite');
|
||||
|
||||
function ensureCommentSchema(db: Database.Database) {
|
||||
db.exec(`
|
||||
CREATE TABLE IF NOT EXISTS eval_comments (
|
||||
trace_id TEXT PRIMARY KEY,
|
||||
scenario_id TEXT,
|
||||
comment TEXT,
|
||||
updated_at TEXT
|
||||
);
|
||||
CREATE INDEX IF NOT EXISTS idx_eval_comments_scenario ON eval_comments(scenario_id);
|
||||
`);
|
||||
}
|
||||
|
||||
export function upsertEvalComment(traceId: string, scenarioId: string | null, comment: string) {
|
||||
if (!fs.existsSync(LOG_DB_PATH)) {
|
||||
return;
|
||||
}
|
||||
const db = new Database(LOG_DB_PATH);
|
||||
ensureCommentSchema(db);
|
||||
const now = new Date().toISOString();
|
||||
db.prepare(`
|
||||
INSERT INTO eval_comments (trace_id, scenario_id, comment, updated_at)
|
||||
VALUES (?, ?, ?, ?)
|
||||
ON CONFLICT(trace_id) DO UPDATE SET
|
||||
comment = excluded.comment,
|
||||
updated_at = excluded.updated_at,
|
||||
scenario_id = excluded.scenario_id
|
||||
`).run(traceId, scenarioId, comment, now);
|
||||
}
|
||||
|
||||
export function loadEvalTraces(limit = 25): EvalTrace[] {
|
||||
if (!fs.existsSync(LOG_DB_PATH)) {
|
||||
return [];
|
||||
}
|
||||
|
||||
const db = new Database(LOG_DB_PATH, { readonly: true, fileMustExist: true });
|
||||
const chats = db.prepare(`
|
||||
SELECT *
|
||||
FROM llm_chats
|
||||
ORDER BY ts DESC
|
||||
LIMIT ?
|
||||
`).all(limit) as EvalChatRow[];
|
||||
|
||||
if (chats.length === 0) return [];
|
||||
|
||||
const traceIds = Array.from(new Set(chats.map(chat => chat.trace_id)));
|
||||
const placeholders = traceIds.map(() => '?').join(', ');
|
||||
const toolCalls = db.prepare(`
|
||||
SELECT *
|
||||
FROM tool_calls
|
||||
WHERE trace_id IN (${placeholders})
|
||||
ORDER BY ts ASC
|
||||
`).all(...traceIds) as EvalToolCallRow[];
|
||||
|
||||
const toolCallsByTrace = new Map<string, EvalToolCallRow[]>();
|
||||
toolCalls.forEach(call => {
|
||||
const list = toolCallsByTrace.get(call.trace_id) || [];
|
||||
list.push(call);
|
||||
toolCallsByTrace.set(call.trace_id, list);
|
||||
});
|
||||
|
||||
const commentsByTrace = new Map<string, string | null>();
|
||||
if (placeholders.length > 0) {
|
||||
try {
|
||||
const commentRows = db.prepare(`
|
||||
SELECT trace_id, comment
|
||||
FROM eval_comments
|
||||
WHERE trace_id IN (${placeholders})
|
||||
`).all(...traceIds) as Array<{ trace_id: string; comment: string | null }>;
|
||||
commentRows.forEach(row => {
|
||||
commentsByTrace.set(row.trace_id, row.comment ?? null);
|
||||
});
|
||||
} catch {
|
||||
// Comments table doesn't exist yet in readonly mode.
|
||||
}
|
||||
}
|
||||
|
||||
return chats.map(chat => ({
|
||||
chat,
|
||||
toolCalls: toolCallsByTrace.get(chat.trace_id) || [],
|
||||
comment: commentsByTrace.get(chat.trace_id) ?? null,
|
||||
}));
|
||||
}
|
||||
Reference in New Issue
Block a user