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T

retoorandClaude Sonnet 5 9dbd1fe667 Add plexer subsystem, container test infra, and last-resort opencode box backend

- Plexer: always-active terminal multiplexing. NativePlexerController/
  TmuxPlexerController share one interface, chosen by which env var is
  already set (TAI_PLEXER_SOCKET vs TMUX_PANE); NativePlexerBackend is
  the pty.fork()-based wrapper (raw mode, SIGWINCH resize forwarding,
  a Unix-socket control protocol, continuous session logging, a
  Ctrl+A prefix key distinct from tmux's own Ctrl+B). Auto-wraps new
  interactive shells via a .bashrc hook mirroring the existing
  command-not-found install pattern, gated on $TMUX/$TAI_PLEXER_ACTIVE/
  a $HOME/.tai-no-plexer bypass. New plexer_send/read/type/status
  tools, env-gated on a detected controller.
- tests/container.py + docker-compose.yml + tests/docker/Dockerfile:
  a disposable podman/docker image for the tests that must never touch
  a real machine (installing the plexer hook into a real ~/.bashrc, a
  live tmux session). Required suite (test_seal.py, test_tai.py,
  tests/plexer.py, tests/bashrc.py) gates the result; tests/rag.py
  runs as best-effort since its real-LLM dependency can't be satisfied
  inside a disposable container with no nested engine.
- tests/bashrc.py: real end-to-end hook install + bash -i auto-wrap,
  anti-recursion, tmux-active skip, non-interactive skip, a live
  TmuxPlexerController test, and an install/uninstall round-trip.
- tests/plexer.py: NativePlexerBackend under a real pty, plus a
  dedicated robustness pass (file permissions, >64KB payload framing,
  a malformed control request, a genuine self-deadlock fix in the
  write-to-child path).
- tai_test.py: the shared async process-driving framework (pipe/pty
  transport, group-wide kill-and-reap, a StdinBridge for --interactive)
  both test files build on.
- ChatClient last-resort backend: when there's no local opencode CLI
  but the sandbox box is already running, install opencode into it
  (official Node tarball, not apt -- apt's nodejs/npm pull in
  webpack/babel/jest as transitive deps) and route through it instead
  of giving up. Only reachable after the first retry round already
  failed everything else; never creates the box itself. TmuxPlexerController.status()
  fixed to use `list-panes` instead of `display-message -p`, which
  silently fell back to the default pane instead of erroring against
  a dead target.
- Fix 4 pre-existing test_tai.py bugs surfaced by running it in a
  clean environment for the first time: two stale mock signatures
  (subprocess.run's stdout= kwarg, Chat.complete's deadline= kwarg),
  one $TMUX-dependent catalog assertion, one tty-dependent color
  assertion.
- research/: a sourced research archive (Source Dossier/Citation
  Card/Conclusion tiers) backing the above design decisions, with its
  own audit.py consistency checker.
- .gitignore: fix a blanket *.md rule that was about to silently drop
  the entire research/ archive from version control; add __pycache__/
  and the one-off debug transcript this session started from.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

2026-10-10 04:36:05 +02:00

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I Ditched Cloud Vector Databases for SQLite FTS5 — and My RAG Pipeline Got 10x Better

Metadata

Field	Value
Institute	Molodetz Institute — Research & Retrieval Division
Subject	sqlite-rag-hybrid-search
Document type	Source Dossier
Tier	1
Source title	I Ditched Cloud Vector Databases for SQLite FTS5 — and My RAG Pipeline Got 10x Better
Source URL	https://dev.to/cagrik34/i-ditched-cloud-vector-databases-for-sqlite-fts5-and-my-rag-pipeline-got-10x-better-759
Source publisher / author	dev.to user cagrik34
Source type	blog post
Source date (published)	Unknown — not stated
Date researched	2026-10-09
Researched by	tai (agent), model: Claude Sonnet 5, on behalf of retoor
Research method	web search synthesis
Confidence	Low
Status	Active

Summary

A first-person account of replacing a cloud vector database with plain SQLite FTS5 for a RAG pipeline, with the headline claim of a 10x improvement. The specific metric being 10x'd (latency, cost, accuracy, or something else) was not recoverable from the search synthesis alone.

Key findings

The author's lexical-search-only pipeline reportedly outperformed their prior cloud-vector-database pipeline for their specific workload.
Positioned as direct practitioner evidence that BM25-only search over SQLite FTS5 is not automatically inferior to a vector pipeline, at least for some real workloads.

Pros / strengths

First-person, concrete practitioner experience rather than vendor marketing — useful as a counterweight to the general industry assumption that "more vector search is always better."
Directly relevant case: a developer actively chose to remove vector search and keep FTS5, which is close to the decision tai ultimately made (see conclusion).

Cons / limitations / caveats

The "10x better" claim's exact metric, workload, and corpus size were not established during this research pass — this is a title-level claim, not an independently verified benchmark.
Single-author blog post, not peer-reviewed, no reproduction data available to this research pass.
Should not be treated as generalizable evidence that FTS5 beats vector search broadly — only as one data point that it can, for some workloads.

Relevance to tai

Directly supportive, anecdotally, of the path tai ultimately took: dropping vector search entirely in favor of SQLite FTS5 + BM25. Used as corroborating (not conclusive) evidence in conclusions/no-vectors-bm25-chunked-rag.md.

Changelog

Date	Change
2026-10-09	Initial dossier created.

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