pdf-brain AGENTS.md

pdf-brain Agent Notes

libSQL Quirks

COUNT(*) returns 0 for vector tables - libSQL's vector extension has a quirk where SELECT COUNT(*) FROM embeddings returns 0. Always count a specific column instead:

-- WRONG: returns 0
SELECT COUNT(*) FROM embeddings

-- CORRECT: returns actual count
SELECT COUNT(chunk_id) FROM embeddings

Vector index shadow tables are MASSIVE - The *_idx_shadow tables store neighbor graphs for HNSW search. Each row averages ~100KB. For 500k embeddings, expect ~48GB just for the index.

┌─────────────────────────────────────────────────────────────────────┐
│                    DB SIZE BREAKDOWN (500k chunks)                  │
├─────────────────────────────────────────────────────────────────────┤
│  embeddings_idx_shadow    ~48GB (92%)  - HNSW neighbor graphs       │
│  embeddings               ~1.9GB (4%)  - 500k × 1024 dims × 4 bytes │
│  chunks                   ~180MB (<1%) - actual text content        │
│  chunks_fts               ~200MB       - full-text search index     │
└─────────────────────────────────────────────────────────────────────┘

Potential optimizations:

• Use compress_neighbors=float8 in index (already enabled)
• Consider smaller embedding models (384 dims vs 1024)
• Batch similar documents to reduce total chunks
• Use partial indexing (only index recent/important docs)

AI SDK Pattern

Use the simple model string pattern with Vercel AI Gateway:

import { generateObject } from "ai";
import { z } from "zod";

const { object } = await generateObject({
  model: "anthropic/claude-haiku-4-5",
  schema: MyZodSchema,
  prompt: "...",
});

No provider setup needed - uses AI_GATEWAY_API_KEY env var automatically.

Key Files

• src/services/LibSQLDatabase.ts - Database layer with Effect
• src/services/AutoTagger.ts - LLM enrichment logic
• src/services/TaxonomyService.ts - SKOS concept management
• src/cli.ts - CLI commands
• data/taxonomy.json - Starter taxonomy seed data