Files

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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novel-rag: Hybrid BM25 + Dense Retrieval over SQLite FTS5 + sqlite-vec

Metadata

Field	Value
Institute	Molodetz Institute — Research & Retrieval Division
Subject	sqlite-rag-hybrid-search
Document type	Source Dossier
Tier	1
Source title	novel-rag: Hybrid BM25 + dense retrieval (RAG) over a fiction library -> SQLite FTS5 + sqlite-vec, fused with Reciprocal Rank Fusion, with a Hit@K/MRR eval harness
Source URL	https://github.com/Rohit-rockan/novel-rag
Source publisher / author	GitHub user Rohit-rockan
Source type	repository
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	Medium
Status	Active

Summary

A personal/hobby RAG project that retrieves passages from a fiction library using a hybrid pipeline: SQLite FTS5 for lexical (BM25) search, sqlite-vec for dense vector search, the two fused with Reciprocal Rank Fusion, evaluated with a Hit@K / MRR harness over the fiction corpus.

Key findings

Demonstrates the "SQLite FTS5 + sqlite-vec + RRF" pattern as something an individual developer can assemble without a dedicated vector database service.
Explicitly builds an evaluation harness (Hit@K, MRR) rather than shipping the hybrid design untested — a methodological point worth noting independent of the specific repo's code quality.
Confirms RRF (not score-weighted fusion) is a viable, implementable choice for fusing a lexical and a dense ranking from the same SQLite file.

Pros / strengths

Concrete, runnable existence proof that the FTS5+sqlite-vec+RRF combination works end-to-end in a single SQLite file, which is the architecture family this project's "tai" vault already resembles (FTS5 is already in use).
Including an eval harness is a good practice signal — the repo isn't just claiming the hybrid approach works, it measures it.

Cons / limitations / caveats

A single hobby/personal repository, not a peer-reviewed or widely-adopted reference implementation; no independent confirmation of the eval numbers was found during this research pass.
No information was gathered on its actual Hit@K/MRR results, chunking strategy, or RRF constant (k) choice — only the high-level architecture description was available from the search result.
"Fiction library" is a narrow, favorable domain for lexical search (named characters, specific phrases) and may not generalize to the kind of operational/log text tai needs to search.

Relevance to tai

Served as the first concrete evidence, early in this research pass, that "SQLite FTS5 + vector extension + RRF" is a real, assembled pattern other developers use for local-first RAG — this grounded the initial (later superseded) proposal to give tai a hybrid BM25+vector search layer. See conclusions/no-vectors-bm25-chunked-rag.md for why that proposal was ultimately not adopted.

Changelog

Date	Change
2026-10-09	Initial dossier created.

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