gbrain/TODOS.md

TODOS

P1

Batch embedding queue across files

What: Shared embedding queue that collects chunks from all parallel import workers and flushes to OpenAI in batches of 100, instead of each worker batching independently.

Why: With 4 workers importing files that average 5 chunks each, you get 4 concurrent OpenAI API calls with small batches (5-10 chunks). A shared queue would batch 100 chunks across workers into one API call, cutting embedding cost and latency roughly in half.

Pros: Fewer API calls (500 chunks = 5 calls instead of ~100), lower cost, faster embedding.

Cons: Adds coordination complexity: backpressure when queue is full, error attribution back to source file, worker pausing. Medium implementation effort.

Context: Deferred during eng review because per-worker embedding is simpler and the parallel workers themselves are the bigger speed win (network round-trips). Revisit after profiling real import workloads to confirm embedding is actually the bottleneck. If most imports use --no-embed, this matters less.

Implementation sketch: src/core/embedding-queue.ts with a Promise-based semaphore. Workers await queue.submit(chunks) which resolves when the queue has room. Queue flushes to OpenAI in batches of 100 with max 2-3 concurrent API calls. Track source file per chunk for error propagation.

Depends on: Part 5 (parallel import with per-worker engines) -- already shipped.

P0

ChatGPT MCP support (OAuth 2.1)

What: Add OAuth 2.1 with Dynamic Client Registration to the Edge Function so ChatGPT can connect.

Why: ChatGPT requires OAuth 2.1 for MCP connectors. Bearer token auth is NOT supported. This is the only major AI client that can't use GBrain remotely.

Pros: Completes the "every AI client" promise. ChatGPT has the largest user base.

Cons: OAuth 2.1 is a significant implementation: authorization endpoint, token endpoint, PKCE flow, dynamic client registration. Estimated CC: ~3-4 hours.

Context: Discovered during DX review (2026-04-10). All other clients (Claude Desktop/Code/Cowork, Perplexity) work with bearer tokens. See docs/mcp/CHATGPT.md for current status.

Depends on: v0.6.0 remote MCP server (shipped).

P2

Fly.io HTTP server as alternative deployment

What: Add gbrain serve --http and a Dockerfile/fly.toml for users who prefer a traditional server over Edge Functions.

Why: Avoids the Deno bundling seam. Bun runs natively. No 60s timeout. No cold start. Codex flagged the bundle strategy as "permanent maintenance tax."

Pros: Simpler code path, no edge-entry.ts needed, no Deno compat concerns. Supports sync_brain and file_upload remotely.

Cons: Users need a Fly.io account. Not zero-infra.

Context: From CEO review (2026-04-10). Edge Functions are the primary path. Fly.io is for power users who want full operation support remotely.

Depends on: v0.6.0 remote MCP server (shipped).

Completed

Implement AWS Signature V4 for S3 storage backend

Completed: v0.6.0 (2026-04-10) — replaced with @aws-sdk/client-s3 for proper SigV4 signing.