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Thicket - Super-Injest

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__pycache__/
*.py[cod]
.pytest_cache/
*.egg-info/
dist/
build/
logs/
graphify-out/
.graphify/
.lightrag/
.thicket/
Ingested_Brain/
ingested-archive/
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# Growing a Thicket
*Building a super-ingest + RAG console that treats a document library
like a living ecosystem, not a filing cabinet.*
**Jeremy Anderson** — [dcos.net](https://dcos.net) — info@dcos.net
September 2026
---
A few weeks ago a friend's Gemini session produced ~270 lines of
Python titled "brain ingest": walk a folder, extract text from PDFs
and EPUBs, write Markdown notes into an Obsidian vault, chunk, embed,
and pour the results into Qdrant. It was a good sketch. It also had
four bugs I cared about, an import surface that required every heavy
dependency up front, and a LightRAG binding pinned to one release of
a fast-moving API.
We turned it into **Thicket**: a super-ingest + retrieval console with
the same brushed-aluminum, amber-LED MMD3 interface as my transcoder
[OpenTranscode](http://git.dcos.net/dcosnet/OpenTranscode) — because
the tool you actually use is the tool you actually enjoy opening.
The name is the design brief. A thicket is dense, self-connected, and
grows on its own. Feed it documents; it grows a thicket.
## The architecture bet: one engine, two drivers
The single most important decision was making the pipeline core
Qt-free. `IngestPipeline` knows nothing about widgets: it walks a
stage table and reports progress through four plain callables.
Two drivers share it:
- a **QThread worker** that wires those callables to Qt signals, and
- a **headless CLI** (`thicket --ingest ... --vault ...`) that wires
them to `print`, works over SSH, and fits in a cron line.
One behavior change lands in both drivers at once. There is no "GUI
logic" to port or keep in sync — that category of bug cannot exist.
## Stage tables instead of branch nests
Every per-document decision is data, not control flow. The pipeline
stage order is a tuple of `(status, gate, runner)`; file-type
extraction is a dict keyed by extension; readiness checks before an
ingest run are a table where the first enabled-but-not-ready entry
wins. Adding a stage, a format, or a gate means adding an entry —
never a new `if` ladder. The QA pass measured this concretely: the
extractor went from a five-branch dispatch to a one-line dict lookup.
## Lazy by default, graceful by contract
The console starts on a bare system. Every heavy import — FastEmbed,
the Qdrant client, EbookLib, LightRAG — happens at point of use, and
a background probe reports module and service readiness into the LED
status strip. Stages that cannot run are blocked at INGEST with the
exact reason and the exact fix; nothing crashes, nothing silently
no-ops.
Where a choice of paths exists, the code steps down the chain
explicitly, best option first: Qdrant search uses `query_points` and
falls to `search` on older clients; the LightRAG Ollama binding
resolves across the package's API generations; podman networking that
cannot create a tap device gets `--network=host`. Unix philosophy in
practice: try the clean path, degrade to the simple one, keep going.
## Re-ingestion is an overwrite
The subtlest correctness property in the system: **the point count
after N re-ingests equals the count after the first.** Deterministic
point IDs (MD5-UUID of `title|path|index`) plus a delete-by-filter
before every upsert make editing a source and re-ingesting it an
exact replacement — a shrunken document leaves no stale tail chunks.
We verify this in the live QA run: 4 points, re-ingest, still 4
points, same retrieval rankings.
The vault side holds the same invariant: re-ingesting a source
refreshes its note in place, and a *different* document with the same
title claims a digest-suffixed sibling rather than clobbering it.
## Local embeddings, on purpose
FastEmbed runs ONNX on your own cores; nothing leaves the machine.
Retrieval quality still respects the model's contract — BGE models
want an instruction prefix on the *query* side and bare passages on
the *document* side, so Thicket prefixes queries only. It is the kind
of detail that silently costs you ten points of relevance when a
sketch gets it wrong.
## The QA pass that shaped the code
Before calling it production-ready, the codebase went through a
five-hat review — senior QA, Linux engineer, architect, admin, and
devops PM. What changed:
- **Table-driven dispatch everywhere** it beat nested conditionals
(stage table, extension table, readiness table, stage-color map).
- **Loops reduced to comprehensions and `next()`** where iteration
was bookkeeping; explicit loops remain only where iteration *is*
the semantics (chunk word windows, queue walks).
- **Bounded reads** on files we do not own (frontmatter collision
checks read at most 32 lines).
- **One decisive failure path per scope** — per-file isolation in the
pipeline, a single report-and-disable path in the retrieval worker.
- Comments state invariants and contracts. Version-history narration
does not survive review; the code reads like decisions, not like an
argument with itself.
Standards kept in view: PEP 8 throughout, SEI CERT practices (bounded
I/O, precise exception scope — the Qdrant step-down catches
`AttributeError`, not the world), MISRA-style bounded structured
control flow, and POSIX assumptions (paths via `pathlib`, no platform
branches, systemd/cron-friendly headless mode).
## Live-fire verification
The release gate was not the test suite alone (26 tests, no services
required) but a live run: podman Qdrant up, three documents through
the full GUI pipeline, two semantic queries returning correctly
ranked hits, an idempotent re-ingest, and a dry-run probe reporting
every module and service green. Screenshot or it didn't happen — the
console looks the part too: knobs for chunk size, overlap, and
Top-K; an LED queue table tracking every file's stage; a phosphor
log; a retrieval strip at the bottom.
## What's next
The graph stage (LightRAG over Ollama) is wired and gating on
readiness, but it is deliberately optional — entity extraction is an
LLM pass per document, and the vault + vector stages already answer
the daily question: *where did I read that?*
The thicket grows. Pull it, feed it a shelf of books, and see what
surfaces.
---
**Thicket** — AGPL-3.0-or-later — [git.dcos.net/dcosnet/Thicket](http://git.dcos.net/dcosnet/Thicket)
Jeremy Anderson — info@dcos.net — [dcos.net](https://dcos.net)
---
## Addendum — v1.8: the thicket grows roots
*September 2026, after the first full functionality matrix.*
The sketch became a workstation tool. What changed since the first
essay:
**Destinations, not stages.** The original three-stage line (notes →
Qdrant → LightRAG) became a destination model: `obsidian` for
notes-only, or any of ten open-source vector stores — qdrant, chroma,
lancedb, faiss, milvus, weaviate, pgvector, duckdb, sqlite-vec,
mariadb — one payload schema, one exact-replacement contract, cosine
scores identical to four decimals across all ten. The console's OUT
line resolves the real destination live; the status footer follows the
target selector keystroke by keystroke.
**Sources are never deleted.** The dangerous delete-after-verify
checkbox from the transcoder lineage is gone. Verified sources move to
an archive directory and bzip2-compress in place — the incoming tree
stays clean, the archive always decompresses.
**The corpus is technical.** Fenced code blocks became atomic
language-tagged chunks; prose became paragraph-aligned; shebangs
stopped masquerading as headings; twenty-one source/config extensions
ingest as listings; the default embedder became jina-code, and
"force browsers to refuse plain http" retrieves the nginx block where
HSTS actually lives.
**The workstation has a filesystem.** /mnt/AI/corpus/{cold,hot} is now
the default corpus flow — cold in, hot brain — with ~/$VAR expansion
everywhere and THICKET_AI_ROOT for other hosts.
**Two interaction modes.** SEARCH embeds locally; ASK turns natural
language into read-only SQL over the Postgres/MariaDB corpora through
Vanna 2's agent API and the same Ollama selector. "Which document has
the most chunks?" is now a console question.
**The matrix.** `scripts/func_test.py` runs eighteen live checks —
every destination with idempotency and retrieval assertions, both
graph engines, both archives, ask on both SQL targets. Its first run
caught six real bugs, including a subtle one: milvus-lite's embedded
server keeps the database file lock after client close, so a second
process gets `[Errno 11]` — fixed with an explicit `close()` lifecycle
that releases the server manager. Production readiness is not a claim;
it is a rerunnable script.
— Jeremy Anderson · info@dcos.net · dcos.net

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# Changelog
## 1.8.1 — production gate
- Live functionality matrix (`scripts/func_test.py`): every destination,
both graph engines, both archives, ask on both SQL targets — 18/18.
- Fixed: LanceDB schema stability (JSON-string payloads), pgvector
per-document rollback, MariaDB stale-dimension guard, Weaviate IPv4
pin, LightRAG ≥ 1.5 async lifecycle, Milvus Lite lock release
(`close()` lifecycle + open retry).
- `pyflakes` clean across package, scripts, and tests.
## 1.8.0 — the workstation's filesystem
- `/mnt/AI` layout awareness: `corpus/cold` → IN, `corpus/hot` → vault,
`corpus/archive` for the bz2 stage; `THICKET_AI_ROOT` override.
- `~` and `$VAR` expansion through one shared `expand_path`.
## 1.7.x — destinations, not stages
- TARGET selects the destination: `obsidian` (notes only) or any vector
store (notes + that store). Vault/vector checkboxes removed.
- Sources are never deleted: archive stage moves verified sources to
`ingested-archive/` and bzip2-compresses them (streaming, level 9).
- Skip-unchanged (content-hash manifest), MAX MB guard, custom notes dir.
- Dynamic Paths panel: live OUT resolution per target; status footer
follows the target selector; per-target connection hints.
## 1.5.0–1.6.0 — technical corpora
- Code-preserving chunker: fenced blocks atomic with language tags,
paragraph-aligned prose, shebang-safe headings, line-windowed
oversized blocks.
- 21 source/config extensions ingest as language-tagged listings.
- Embedder catalog: jina-code default (English + code), bge-base,
bge-small; chunk payloads carry `chunk_kind` / `lang`.
## 1.4.0 — graphs, ask, objects
- Graphify engine beside LightRAG (`--graph-engine`).
- ASK: natural-language SQL over pgvector/MariaDB corpora (Vanna 2
agent + Ollama; CLI and console).
- MinIO archive stage (object storage for originals).
## 1.2.0–1.3.0 — ten vector targets
- qdrant, chroma, lancedb, faiss, milvus, weaviate, pgvector, duckdb,
sqlite-vec, mariadb — one registry, one payload schema, cosine-score
parity to four decimals.
## 1.0.0–1.1.0 — the console
- MMD3 console (OpenTranscode lineage): LED queue table, phosphor log,
knobs, live probe. Qt-free pipeline core shared by GUI and CLI.

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Thicket — super-ingest + RAG console
Copyright (C) 2026 Jeremy Anderson <info@dcos.net> — https://dcos.net
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as
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The full license text follows.
---
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# Thicket — Quick Start
Five minutes from clone to retrieval. Linux, Python 3.12+.
## 0. Zero-config on an AI workstation
If `/mnt/AI` exists, Thicket detects it: IN defaults to
`/mnt/AI/corpus/cold`, the vault to `/mnt/AI/corpus/hot`, and the
bz2 archive stage targets `/mnt/AI/corpus/archive`. Drop documents in
`corpus/cold`, press INGEST. (Point IN at `corpus/books` to chew
through the standing library.)
## 1. Install
```bash
git clone http://git.dcos.net/dcosnet/Thicket.git # or your local copy
cd Thicket
# venv lives in the AI tree: /mnt/AI/runtime/thicket-venv
true # (scripts/bootstrap_sources.sh creates it)
/mnt/AI/runtime/thicket-venv/bin/pip install -e ".[ingest]" # parsers + FastEmbed + Qdrant client
/mnt/AI/runtime/thicket-venv/bin/pip install -e ".[graph]" # optional: LightRAG stage
```
That's it for setup — ONE venv, always. From here on:
```bash
thicket # anywhere — ~/.local/bin command
/mnt/AI/runtime/thicket-venv/bin/python thicket.py # explicit interpreter
/mnt/AI/tools/bin/thicket # AI-tree launcher
```
(A bare `python thicket.py` uses the system interpreter, which cannot
see the venv — Python's rule. The `thicket` command exists so you
never need to think about it.)
## 2. Services (optional — depends on your vector target)
The vault-note stage needs nothing but the install. The vector stage
targets ten open-source stores; six of them run **embedded** (files
under `<vault>/.thicket/`, zero services):
```bash
/mnt/AI/runtime/thicket-venv/bin/pip install -e ".[chroma]" # or lancedb / faiss / milvus /
# duckdb / sqlitevec, or
/mnt/AI/runtime/thicket-venv/bin/pip install -e ".[targets]" # every target extra at once
/mnt/AI/runtime/thicket-venv/bin/python thicket.py --ingest ~/Books --vault ~/Vault --target chroma
```
Only the default `qdrant` target needs a service:
```bash
# Qdrant (service-backed target)
podman run -d --name thicket-qdrant -p 6333:6333 \
-v thicket_qdrant:/qdrant/storage docker.io/qdrant/qdrant
# docker works identically; --network=host sidesteps rootless
# networking issues on some kernels.
# Ollama (graph engines + ASK)
ollama pull llama3.1
ollama pull nomic-embed-text
```
## 3. Check readiness
```bash
/mnt/AI/runtime/thicket-venv/bin/python thicket.py --dry-run
```
You want `READY: vault + Qdrant stages available.` — every MISSING
module or DOWN service is listed with the exact fix.
## 4. First ingest
**GUI** (the console):
```bash
/mnt/AI/runtime/thicket-venv/bin/python thicket.py
```
1. Set **IN** to a folder of `.pdf` / `.epub` / `.md` / `.txt` files
and **VAULT** to your Obsidian vault root.
2. Press **SCAN QUEUE** — the LED table previews every document found.
3. Press **> INGEST**. Watch the stage column walk each file through
EXTRACT → VAULT → INDEX → … → DONE (graph/archives add their own
stages; the OUT line under IN/VAULT shows exactly where data lands).
4. Type a question in the retrieval strip and press **SEARCH** —
hits land in the log with score, document, and section.
**Headless** (SSH / cron friendly — identical pipeline, no Qt):
```bash
/mnt/AI/runtime/thicket-venv/bin/python thicket.py \
--ingest ~/Downloads/Raw_Books_And_Papers \
--vault ~/Documents/ObsidianVault
```
The first run downloads the embedding model (~160 MB, once — the
default jina-code embedder is tuned for technical corpora); notes
appear in `<vault>/Ingested_Brain/`, vectors in the `second_brain`
collection.
## 4b. Ask questions in natural language (SQL targets)
With the corpus in Postgres or MariaDB, skip SQL entirely:
```bash
/mnt/AI/runtime/thicket-venv/bin/python thicket.py --ask "which document has the most chunks?" \
--target pgvector --ollama-llm llama3.1:latest
```
Connection settings come from `/mnt/AI/backends/thicket.env`
(bootstrap writes it, chmod 600); exported `PG*` / `MARIADB_*` /
`MINIO_*` variables override the file, and defaults apply last.
Needs `pip install -e ".[ask]"` (Vanna 2) and Ollama.
## 4c. Archive originals to MinIO (optional)
```bash
MINIO_ENDPOINT=localhost:9000 MINIO_ACCESS_KEY=... MINIO_SECRET_KEY=... \
/mnt/AI/runtime/thicket-venv/bin/python thicket.py --ingest ~/Books --vault ~/Vault \
--target chroma --minio
```
Notes gain a `source_uri: s3://bucket/key` frontmatter line.
## 5. Verify retrieval
From the GUI retrieval strip, or headless:
```bash
.venv/bin/python - <<'EOF'
from thicket.embedder import DEFAULT_EMBED_MODEL, EmbeddingEngine
from thicket.vector_stores import create_store
engine = EmbeddingEngine(DEFAULT_EMBED_MODEL); engine.load()
store = create_store("qdrant", collection="second_brain", dim=engine.dim)
store.set_embedder(engine); store.ensure_collection()
try:
for hit in store.search(engine.embed_query("your question here"), limit=3):
p = hit["payload"]
print(f"[{hit['score']:.3f}] {p['chunk_kind']:5s} "
f"{p['document_title']} § {p['section_header']}")
finally:
store.close()
EOF
```
## Troubleshooting
| Symptom | Cause | Fix |
|---|---|---|
| `Qdrant: DOWN` in probe / dry-run | service not running | start the container (step 2) |
| `fastembed MISSING` | extras not installed | `/mnt/AI/runtime/thicket-venv/bin/pip install -e ".[ingest]"` |
| `ebooklib MISSING` | EPUBs will fail | same extras install as above |
| INGEST blocked: dimension mismatch | collection built with a different embedding model | pick a new collection name (or delete the old collection) |
| `Ollama: DOWN` | only the graph stage needs it | start Ollama, or leave the graph stage off |
| Empty documents SKIP | scanned PDFs have no text layer | OCR first (e.g. `ocrmypdf`), then ingest |
| Re-ingest count unchanged | that is correct — re-ingest replaces, never duplicates | nothing to fix |
## Day-two operations
```bash
podman stop thicket-qdrant && podman start thicket-qdrant # restart service
podman volume rm thicket_qdrant # wipe vectors (notes stay)
/mnt/AI/runtime/thicket-venv/bin/python thicket.py --ingest ... --target obsidian # notes only
/mnt/AI/runtime/thicket-venv/bin/python thicket.py --ingest ... --skip-unchanged # cheap re-runs
/mnt/AI/runtime/thicket-venv/bin/python thicket.py --ingest ... --archive # bz2 the sources away
.venv/bin/python scripts/func_test.py # full live matrix
```
---
Questions: Jeremy Anderson — info@dcos.net — https://dcos.net

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# Thicket
**Super-ingest + RAG console** — feed it documents; it grows a thicket:
dense, interconnected, searchable. Import PDF / EPUB / Markdown / plain
text / source-and-config files into a destination of your choice,
wrapped in the same retro-futuristic MMD3 console UI as OpenTranscode
(brushed aluminum, amber LEDs, green phosphor log, rotary knobs).
```
┌─▶ Obsidian vault ──▶ MinIO bucket* ┐
documents ──▶ notes │ (Markdown + (s3:// URIs) ├─▶ ingested-archive/
│ YAML frontmatter) │ (bz2, never deleted)
└─▶ vector index ──▶ knowledge graph* ─┘
(10 targets, (LightRAG or
local FastEmbed) Graphify, Ollama)
TARGET picks the destination: obsidian = notes only,
any vector store = notes + that store. * optional stages
```
| Doc | What it is |
|---|---|
| [QUICKSTART.md](QUICKSTART.md) | clone → services → first ingest → first retrieval |
| [BLOG.md](BLOG.md) | design essay + v1.8 addendum |
| [LICENSE](LICENSE) | AGPL-3.0-or-later, full text |
## Destinations and stages
Vault notes always run — they are the product. Everything else stacks:
| Piece | What it does | Needs |
|---|---|---|
| **Vault notes** *(always on)* | Normalized Markdown notes (YAML frontmatter: title, source file, optional `source_uri`, ingest timestamp, `brain/ingested` + `source/<ext>` tags) into the notes dir (default `<vault>/Ingested_Brain/`). PDFs get `## Page N` headings; EPUBs are chapter-split; code files render as language-tagged listings. `TARGET=obsidian` = notes only. | `python-slugify` |
| **Vector index** *(a TARGET away)* | Structure-preserving contextual chunking, local ONNX embeddings (FastEmbed), exact-replacement indexing — the point count after N re-ingests equals the count after the first. Ten interchangeable targets (below). | `fastembed` + the target's library |
| **Knowledge graph** *(optional)* | **LightRAG** (merged entity graph in `<vault>/.lightrag`, one Ollama pass per document) or **Graphify** (batch `graphify extract --backend ollama` → `graph.json`, `GRAPH_REPORT.md`, interactive `graph.html` in `<vault>/.graphify`). | engine package, Ollama |
| **MinIO archive** *(optional)* | Originals uploaded to an S3-compatible bucket (key = input-tree path, idempotent); the note records `s3://bucket/key`. Open-source MinIO has no vector API — it is an archive stage, not a vector target. | `minio`, MinIO service |
| **Source archive** *(optional)* | Verified sources *move* out of the incoming tree into `ingested-archive/` and are bzip2-compressed (streaming, level 9). Sources are never deleted; the archive always holds a decompressible original. | nothing |
Sources are never deleted — by design, not by flag. Guards (MAX MB,
skip-unchanged) skip files entirely; skips never archive.
### Vector targets
All ten share one payload schema (`document_title`, `section_header`,
`content`, `chunk_kind`, `lang`, …), one retrieval strip, and the same
exact-replacement semantics — cosine scores agree to four decimals
across targets (verified by the live matrix).
| Target | Mode | Library | Service needed | Install extra |
|---|---|---|---|---|
| `qdrant` *(default)* | service | `qdrant-client` | Qdrant container | `.[ingest]` |
| `chroma` | embedded | `chromadb` | none | `.[chroma]` |
| `lancedb` | embedded | `lancedb` | none | `.[lancedb]` |
| `faiss` | file | `faiss-cpu` | none | `.[faiss]` |
| `milvus` | embedded (Lite) | `pymilvus[milvus_lite]` | none | `.[milvus]` |
| `weaviate` | service | `weaviate-client` | Weaviate container (8080/50051) | `.[weaviate]` |
| `pgvector` | service | `psycopg` + `pgvector` | Postgres + pgvector (libpq env: `PG*`) | `.[pgvector]` |
| `duckdb` | embedded | `duckdb` (+vss index, steps down to exact scan) | none | `.[duckdb]` |
| `sqlitevec` | embedded | `sqlite-vec` (vec0 tables) | none | `.[sqlitevec]` |
| `mariadb` | service | `PyMySQL` | MariaDB 11.7+ (env: `MARIADB_*`) | `.[mariadb]` |
Embedded targets keep data under `<vault>/.thicket/<target>/` — a vault
is one portable tree. Service targets read Unix-standard environments:
`PGHOST`/`PGUSER`/`PGPASSWORD`/`PGDATABASE` (or `PGDSN`),
`MARIADB_HOST`/`MARIADB_USER`/`MARIADB_PASSWORD`/`MARIADB_DATABASE`.
`pip install -e ".[targets]"` installs every target extra at once.
**Upgrading from 1.1.x:** points indexed before 1.2.0 lack the internal
`doc_key`; the first re-ingest of each document cleans them up
automatically — or start a fresh collection name.
## AI filesystem layout awareness
When the canonical `/mnt/AI` tree exists, Thicket adopts its corpus flow
as defaults — zero configuration:
| Canonical path | Thicket role |
|---|---|
| `/mnt/AI/corpus/cold` | IN — incoming raw documents |
| `/mnt/AI/corpus/hot` | VAULT — the active brain: notes + `.thicket/` vector data + graphs in one tree |
| `/mnt/AI/corpus/books` | standing library (reported by the probe; point IN at it to ingest) |
| `/mnt/AI/corpus/archive` | destination of the source-archive (bz2) stage |
| `/mnt/AI/backends/thicket.env` | connection profiles (600) — fills `PG*` / `MARIADB_*` / `MINIO_*` gaps; the real environment always wins |
Without the layout, home-directory defaults apply — behavior identical.
Override the root with `THICKET_AI_ROOT`. Every path field accepts `~`
and `$VAR` references, resolved through one shared expansion point in
the GUI, CLI, and core.
## Technical corpora (code, configuration, policy)
Ingestion preserves what makes technical documents useful:
- **Fenced code blocks are atomic** — never split mid-listing, never
merged with prose, indentation and blank lines verbatim; the language
tag travels in the chunk context (`| code:python`) and the payload
(`chunk_kind`, `lang`). Oversized blocks window by lines with overlap.
- **Prose chunks are paragraph-aligned** — lists, commands, and tables
keep their line structure in stored content.
- **ATX headings require `#` + whitespace** — shebangs and `#comments`
never masquerade as section headers.
- **Source files ingest directly**: `.py .sh .bash .zsh .yaml .yml .toml
.ini .conf .cfg .json .sql .rs .go .c .h .cpp .js .ts .tf .nix` are
wrapped as language-tagged listings.
- **Code-strong default embedder**: `jinaai/jina-embeddings-v2-base-code`
(English + code, 768-dim, 8k context); curated alternatives in the
EMBED selector. Dimension is fixed per collection — switching models
means a new collection name.
- **Graph tip**: directories of real code want the `graphify` engine —
tree-sitter gives it per-symbol structure no prose pass can match.
## The console
- **Paths panel** — IN, VAULT, and a live **OUT** line resolving the
actual destination per TARGET (notes dir, `.thicket/` data path, or
service/collection URI), re-resolved on every edit
- **Pipeline panel** — TARGET is the destination (`obsidian` or one of
ten vector stores) with collection/host/port and a per-target
connection hint line; stage toggles (graph, MinIO, bz2 archive);
EMBED / OLLAMA LLM / GRAPH ENGINE selectors; FILTER, NOTES DIR,
MAX MB, Skip-unchanged
- **Document queue** — LED matrix table with per-file stage (QUEUED →
EXTRACT → MINIO → VAULT → INDEX → GRAPH → ARCHIVE → DONE / SKIP /
ERROR), color-coded, with detail column
- **Knobs** — chunk size, chunk overlap, retrieval Top-K
- **Retrieval strip** — **SEARCH** (semantic across the collection) and
**ASK (SQL)** (natural language over SQL-backed targets via Vanna 2 +
the Ollama LLM); results land in the log
- **Status footer** — live target + service readiness, updated the
instant TARGET changes
- **Transport** — `> INGEST`, `[] STOP` (cooperative), `~~ SCAN QUEUE`
(preview), `? ABOUT`
- **Probe** — every dependency and service reported at startup; a stage
that cannot run is blocked at INGEST with the exact reason and fix
## Install
**One venv, always**: `/mnt/AI/runtime/thicket-venv` is the single environment.
The source-anchored bootstrap mirrors every dependency as a git
checkout under `/mnt/AI/distfiles/git/`, builds them into that same
venv, and drops a launcher in `/mnt/AI/tools/bin`:
```bash
scripts/bootstrap_sources.sh # add --force-source for
/mnt/AI/tools/bin/thicket --dry-run # native builds from git
```
The venv is created and owned by the bootstrap at
`/mnt/AI/runtime/thicket-venv` — nothing is written inside the project
checkout (`.gitignore` keeps it archive-clean). Plain pip path:
```bash
# venv lives in the AI tree: /mnt/AI/runtime/thicket-venv
true # (scripts/bootstrap_sources.sh creates it)
/mnt/AI/runtime/thicket-venv/bin/pip install -e . # console only (PySide6)
/mnt/AI/runtime/thicket-venv/bin/pip install -e ".[ingest]" # + parsers, FastEmbed, qdrant
/mnt/AI/runtime/thicket-venv/bin/pip install -e ".[targets]" # + every vector target
/mnt/AI/runtime/thicket-venv/bin/pip install -e ".[graph,graphify]" # + both graph engines
/mnt/AI/runtime/thicket-venv/bin/pip install -e ".[ask,minio]" # + Vanna ask, MinIO archive
```
Services (only for the stages that need them):
```bash
podman run -d --name thicket-qdrant -p 6333:6333 \
-v thicket_qdrant:/qdrant/storage docker.io/qdrant/qdrant
ollama pull llama3.1 && ollama pull nomic-embed-text
```
The first ingest downloads the embedding model locally (~160 MB for the
default jina-code embedder).
## Usage
One command owns every mode — no flags loads the console:
```bash
python thicket.py # the console — also: ./thicket.py
```
**Headless** (same core, no Qt — SSH / cron friendly):
```bash
python thicket.py --ingest /mnt/AI/corpus/cold --vault /mnt/AI/corpus/hot
python thicket.py --ingest ~/Books --vault ~/Vault --target obsidian # notes only
python thicket.py --ingest ~/Books --vault ~/Vault --target chroma # embedded, no service
python thicket.py --ingest ~/Books --vault ~/Vault --lightrag --graph-engine graphify \
--ollama-llm llama3.1:latest
python thicket.py --ingest ~/Books --vault ~/Vault --skip-unchanged --max-mb 200
python thicket.py --ingest ~/Books --vault ~/Vault --archive --minio
python thicket.py --ask "which document has the most chunks?" --target pgvector
```
**Probe & report**: `python thicket.py --dry-run` · `--version`
Three interchangeable entry points share one environment: the `thicket`
command (installed to `~/.local/bin` by the bootstrap — works from any
directory), `/mnt/AI/tools/bin/thicket`, and `/mnt/AI/runtime/thicket-venv/bin/python thicket.py`.
A bare `python thicket.py` uses the system interpreter, which cannot see
the venv — that is Python's rule, not a Thicket setting.
## Architecture
```
thicket/
├── thicket.py # one-command launcher (GUI default, flags pass through)
├── cli.py # version / dry-run / headless ingest / ask / GUI
├── layout.py # /mnt/AI taxonomy awareness + ~/$VAR expansion
├── extractors.py # PDF / EPUB / MD / TXT / code-config -> (title, text)
├── vault_writer.py # notes: frontmatter, escaping, collisions, source_uri
├── chunker.py # structure-preserving chunking (fences, langs, paragraphs)
├── embedder.py # FastEmbed wrapper (curated catalog, jina-code default)
├── vector_stores.py # registry + 10 targets, close() lifecycle
├── qdrant_store.py # qdrant target
├── graph_store.py # graph engines: LightRAG (async lifecycle) + Graphify
├── minio_archive.py # S3 object archive stage
├── ask_vanna.py # Vanna 2 agent: natural-language SQL over SQL targets
├── pipeline_core.py # the stage table — shared by GUI worker and CLI
├── pipeline_worker.py # QThreads: probe / ingest / search / ask
├── env_probe.py # module + service readiness (incl. liveness tables)
├── ui_theme.py # MMD3 QSS (OpenTranscode visual lineage)
├── ui_window.py # ThicketWindow + launch_gui()
└── widgets/radio_knob.py
```
Design invariants:
- **Qt-free core.** The QThread worker and the headless CLI drive the
same `IngestPipeline`; one behavior change lands in both at once.
- **Table-driven dispatch.** Stage order, file-type routing, target
registry, readiness gating, UI stage colors — data tables, not branch
nests.
- **Lazy heavy imports + graceful degradation.** Every heavy dependency
loads at point of use; missing pieces report themselves and block only
the stage that needs them.
- **Idempotent re-ingest.** Deterministic IDs plus delete-by-`doc_key`
matching both key generations) make re-ingesting an edited source an
exact replacement — proven per-target by the live matrix.
- **Per-file isolation.** One broken document logs an ERROR; the queue
moves on. Postgres transactions roll back per document.
- **Explicit resource lifecycle.** Every store implements `close()`;
Milvus Lite's embedded server is released so the next process can
open the database.
- **Step-down chains.** Qdrant `query_points`→`search`; LightRAG
bindings across API generations; DuckDB vss→exact scan; podman
rootless→`--network=host`.
## Development
```bash
/mnt/AI/runtime/thicket-venv/bin/pip install -e ".[dev,targets,graph,graphify,ask,minio]"
.venv/bin/pytest # 62 tests, no services needed
QT_QPA_PLATFORM=offscreen \
.venv/bin/python scripts/smoke_gui.py # headless GUI smoke + screenshot
.venv/bin/python scripts/func_test.py # LIVE matrix: every destination,
# engine, archive, ask — 18 checks
/mnt/AI/runtime/thicket-venv/bin/python thicket.py --dry-run # live readiness report
```
Standards applied: PEP 8; SEI CERT practices (bounded reads, precise
exception scope); MISRA-style bounded, structured control flow; POSIX
assumptions (`pathlib`, no platform branches, cron-safe headless mode).
## License
AGPL-3.0-or-later — Jeremy Anderson · info@dcos.net · [dcos.net](https://dcos.net) · 2026.
See [LICENSE](LICENSE) for the full text.
Invoked (not bundled) components carry their own licenses: PySide6
(LGPL-3.0), pypdf (BSD), EbookLib (AGPL-3.0), BeautifulSoup (MIT),
python-slugify (MIT), FastEmbed (Apache-2.0), Qdrant (Apache-2.0),
Chroma (Apache-2.0), LanceDB (Apache-2.0), FAISS (MIT), pymilvus
(Apache-2.0), weaviate-client (BSD-3), psycopg (LGPL-3.0), pgvector
(PostgreSQL), PyMySQL (MIT), sqlite-vec (MIT), LightRAG (MIT),
Graphify (Apache-2.0/MIT), Vanna (MIT), MinIO client (Apache-2.0),
Ollama (MIT).

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# pyproject.toml — Thicket v1.0
#
# Super-ingest + RAG console: batch-import PDF / EPUB / Markdown /
# plain-text into an Obsidian vault, a Qdrant vector store, and an
# optional LightRAG knowledge graph — wrapped in the MMD3-style
# PySide6 console UI (same visual lineage as OpenTranscode).
#
# Publish with:
# python -m build
# twine upload dist/*
[build-system]
requires = ["setuptools>=68.0", "wheel"]
build-backend = "setuptools.build_meta"
[project]
name = "thicket"
version = "1.8.1"
description = "Super-ingest + RAG console: documents -> Obsidian + Qdrant + LightRAG, with a PySide6 GUI"
readme = "README.md"
requires-python = ">=3.12"
license = { text = "AGPL-3.0-or-later" }
authors = [
{ name = "Jeremy Anderson", email = "info@dcos.net" },
]
maintainers = [
{ name = "Jeremy Anderson", email = "info@dcos.net" },
]
keywords = [
"obsidian",
"qdrant",
"lightrag",
"rag",
"thicket",
"super-ingest",
"second-brain",
"embeddings",
"knowledge-graph",
"pyside6",
"linux",
]
classifiers = [
"Development Status :: 4 - Beta",
"Environment :: X11 Applications :: Qt",
"Intended Audience :: End Users/Desktop",
"License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)",
"Operating System :: POSIX :: Linux",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Topic :: Text Processing :: Indexing",
"Typing :: Typed",
]
# Core dependency is intentionally ONLY the GUI toolkit — the app
# launches and probes even on a bare system, reporting exactly which
# ingest extras are missing. Heavy pipelines deps live in extras so
# a "vault-only" install (no vector DB, no graph) stays lightweight.
dependencies = [
"PySide6>=6.6.0",
]
[project.optional-dependencies]
# Document parsing + vector indexing — install with:
# pip install ".[ingest]"
ingest = [
"beautifulsoup4>=4.12",
"EbookLib>=0.18",
"pypdf>=4.0",
"python-slugify>=8.0",
"fastembed>=0.3",
"qdrant-client>=1.9",
]
# LightRAG knowledge-graph stage — install with:
# pip install ".[graph]"
graph = [
"lightrag-hku>=1.0",
]
# Embedded vector targets — one extra per target, or ".[targets]" for all
chroma = ["chromadb>=0.5"]
lancedb = ["lancedb>=0.15"]
faiss = ["faiss-cpu>=1.8", "numpy>=1.26"]
milvus = ["pymilvus[milvus_lite]>=2.4"]
weaviate = ["weaviate-client>=4.6"]
pgvector = ["psycopg[binary]>=3.1", "pgvector>=0.3"]
duckdb = ["duckdb>=1.0"]
sqlitevec = ["sqlite-vec>=0.1.6"]
mariadb = ["PyMySQL>=1.1"]
# Graphify graph engine — install with: pip install ".[graphify]"
graphify = ["graphifyy[ollama]>=0.9"]
# Vanna ask interaction (natural-language SQL over SQL targets)
ask = ["vanna[ollama,postgres,mysql]>=2.0"]
# MinIO object archive for source documents
minio = ["minio>=7.2"]
targets = [
"chromadb>=0.5",
"lancedb>=0.15",
"faiss-cpu>=1.8",
"numpy>=1.26",
"pymilvus[milvus_lite]>=2.4",
"weaviate-client>=4.6",
"psycopg[binary]>=3.1",
"pgvector>=0.3",
"duckdb>=1.0",
"sqlite-vec>=0.1.6",
"PyMySQL>=1.1",
]
# Dev / test extras — install with: pip install -e ".[dev]"
dev = [
"pytest>=8.0",
"pytest-cov>=4.0",
"build>=1.0",
]
[project.urls]
Homepage = "https://git.dcos.net/dcosnet/Thicket"
Repository = "https://git.dcos.net/dcosnet/Thicket"
Documentation = "https://git.dcos.net/dcosnet/Thicket/blob/main/README.md"
"Bug Tracker" = "https://git.dcos.net/dcosnet/Thicket/issues"
[project.scripts]
# Console entry point — `thicket` command after `pip install thicket`
thicket = "thicket.__main__:main"
# ─────────────────────────────────────────────────────────────────────────────
# Setuptools-specific config
# ─────────────────────────────────────────────────────────────────────────────
[tool.setuptools]
# We're a pure-Python package — no extension modules.
zip-safe = false
[tool.setuptools.packages.find]
# Auto-discover packages under thicket/ and widgets/
where = ["."]
include = ["thicket*"]
exclude = ["tests*"]
[tool.setuptools.package-data]
# Include the QSS theme + non-Python assets
thicket = ["*.qss", "*.txt"]
# ─────────────────────────────────────────────────────────────────────────────
# Tool config
# ─────────────────────────────────────────────────────────────────────────────
[tool.pytest.ini_options]
testpaths = ["tests"]
python_files = ["test_*.py"]
python_classes = ["Test*"]
python_functions = ["test_*"]
addopts = "-ra --strict-markers"
markers = [
"slow: marks tests as slow (deselect with '-m \"not slow\"')",
"e2e: marks tests as end-to-end (require running Qdrant / Ollama)",
]
[tool.coverage.run]
source = ["thicket"]
omit = [
"*/tests/*",
"*/__main__.py",
]
[tool.coverage.report]
exclude_lines = [
"pragma: no cover",
"if TYPE_CHECKING:",
"raise NotImplementedError",
"if __name__ == .__main__.:",
]

328
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#!/usr/bin/env bash
# bootstrap_sources — source-anchored dependency build into /mnt/AI.
#
# Every Thicket dependency is mirrored as a git checkout under
# <AI_ROOT>/distfiles/git/ and built into THE venv at
# <AI_ROOT>/runtime/thicket-venv — one environment, always, outside
# the project tree so the checkout stays clean for git and archives.
# Launchers land in <AI_ROOT>/tools/bin and ~/.local/bin.
# Idempotent: re-run to update.
#
# Methods (per package, table below):
# source — pip install from the local git checkout (pure Python)
# wheel — pip install the release wheel; the git checkout is still
# mirrored for audit. Native-heavy builds (faiss, lancedb,
# duckdb, fastembed/onnxruntime, PySide6/Qt) produce the
# same code as the wheel at a fraction of the build cost.
# --force-source builds everything from checkouts.
#
# Repository sync is state-aware:
# absent -> atomic clone (staged in a temp dir, moved into place)
# corrupt -> dir without .git is moved aside, then re-cloned
# dirty -> local modifications are never destroyed; the package
# is skipped with the offending files listed
# moved -> fast-forward to FETCH_HEAD only when HEAD differs;
# unchanged checkouts are NOT reinstalled (the venv is
# probed for the importable module first)
# Step-down: a failed source build falls back to the wheel (logged),
# so one broken upstream never wedges the whole bootstrap.
#
# Usage:
# scripts/bootstrap_sources.sh # asks which projects
# (Enter = all) in a
# terminal; installs all
# when non-interactive
# scripts/bootstrap_sources.sh --only a,b # subset, no prompt
# scripts/bootstrap_sources.sh --force-source # build native too
# scripts/bootstrap_sources.sh --update # git pull + reinstall
#
# Native build deps (auto-installed via pacman when missing, sudo):
# git gcc make cmake rustup postgresql-libs
set -euo pipefail
AI_ROOT="${THICKET_AI_ROOT:-/mnt/AI}"
PROJECT_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
GIT_MIRROR="$AI_ROOT/distfiles/git"
VENV_DIR="$AI_ROOT/runtime/thicket-venv"
BIN_DIR="$AI_ROOT/tools/bin"
LOG_DIR="$AI_ROOT/logs"
LOG="$LOG_DIR/thicket-bootstrap.log"
FORCE_SOURCE=0
UPDATE_ONLY=0
ONLY=""
mode="${1:-}"
case "$mode" in
--force-source) FORCE_SOURCE=1 ;;
--update) UPDATE_ONLY=1 ;;
--only) ONLY="${2:-}"; [ -n "$ONLY" ] || { echo "--only needs a list"; exit 2; } ;;
"") ;;
*) echo "unknown option: $mode"; exit 2 ;;
esac
mkdir -p "$GIT_MIRROR" "$VENV_DIR" "$BIN_DIR" "$LOG_DIR"
exec > >(tee -a "$LOG") 2>&1
log() { printf '\033[1;32m>>>\033[0m %s\n' "$*"; }
warn() { printf '\033[1;33m>>> WARN\033[0m %s\n' "$*"; }
# ── name | git url | pip name | extras | method ─────────────────────────
# Pure-python projects install from their checkout; the native-heavy
# set keeps a mirrored checkout but installs the release wheel.
PACKAGES=(
"qdrant-client|https://github.com/qdrant/qdrant-client.git|qdrant-client||source|qdrant_client|vector target: qdrant (service)"
"chromadb|https://github.com/chroma-core/chroma.git|chromadb||source|chromadb|vector target: chroma (embedded)"
"lancedb|https://github.com/lancedb/lancedb.git|lancedb||wheel|lancedb|vector target: lancedb (embedded)"
"faiss|https://github.com/facebookresearch/faiss.git|faiss-cpu||wheel|faiss|vector target: faiss (file index)"
"pymilvus|https://github.com/milvus-io/pymilvus.git|pymilvus|[milvus_lite]|source|pymilvus|vector target: milvus lite"
"weaviate-client|https://github.com/weaviate/weaviate-python-client.git|weaviate-client||source|weaviate|vector target: weaviate (service)"
"psycopg|https://github.com/psycopg/psycopg.git|psycopg|[binary]|wheel|psycopg|vector target: pgvector - postgres driver"
"pgvector|https://github.com/pgvector/pgvector-python.git|pgvector||source|pgvector|vector target: pgvector - python bindings"
"duckdb|https://github.com/duckdb/duckdb.git|duckdb||wheel|duckdb|vector target: duckdb (embedded)"
"sqlite-vec|https://github.com/asg017/sqlite-vec.git|sqlite-vec||source|sqlite_vec|vector target: sqlite-vec (embedded)"
"PyMySQL|https://github.com/PyMySQL/PyMySQL.git|PyMySQL||source|pymysql|vector target: mariadb driver"
"graphify|https://github.com/Graphify-Labs/graphify.git|graphifyy|[ollama]|source|graphify|graph engine: graph.json + HTML report"
"vanna|https://github.com/vanna-ai/vanna.git|vanna|[ollama,postgres,mysql]|source|vanna|ask: natural-language SQL over corpora"
"minio-py|https://github.com/minio/minio-py.git|minio||source|minio|stage: MinIO object archive"
"LightRAG|https://github.com/HKUDS/LightRAG.git|lightrag-hku||source|lightrag|graph engine: merged entity graph"
"ebooklib|https://github.com/aerkalov/ebooklib.git|EbookLib||source|ebooklib|parser: EPUB"
"BeautifulSoup4|https://github.com/wention/BeautifulSoup4.git|beautifulsoup4||source|bs4|parser: HTML inside EPUB"
"pypdf|https://github.com/py-pdf/pypdf.git|pypdf||source|pypdf|parser: PDF"
"python-slugify|https://github.com/un33k/python-slugify.git|python-slugify||source|slugify|vault note filenames"
"fastembed|https://github.com/qdrant/fastembed.git|fastembed||wheel|fastembed|local ONNX embeddings"
"PySide6|https://code.qt.io/pyside/pyside-setup.git|PySide6||wheel|PySide6|the console GUI toolkit"
)
# ── native build tools via pacman (skipped when present) ─────────────
required_tools=(git gcc make)
missing=()
for tool in "${required_tools[@]}"; do
command -v "$tool" >/dev/null 2>&1 || missing+=("$tool")
done
if [ "${#missing[@]}" -gt 0 ]; then
log "installing build tools via pacman: ${missing[*]}"
sudo pacman -S --needed "${missing[@]}"
fi
# ── project selection ─────────────────────────────────────────────────
# Interactive in a terminal; --only or non-TTY stdin bypasses the menu.
select_packages() {
if [ -n "$ONLY" ]; then
return
fi
if [ ! -t 0 ] && [ "${THICKET_BOOTSTRAP_INTERACTIVE:-0}" != "1" ]; then
return # everything, no prompt
fi
local -a names=() roles=()
local row
for row in "${PACKAGES[@]}"; do
names+=("$(cut -d'|' -f1 <<<"$row")")
roles+=("$(cut -d'|' -f7 <<<"$row")")
done
printf 'Thicket projects:\n'
local i
for i in "${!names[@]}"; do
printf ' %2d) %-18s %-34s %s\n' \
"$((i + 1))" "${names[$i]}" \
"$(cut -d'|' -f5 <<<"${PACKAGES[$i]}")" "${roles[$i]}"
done
local answer token from to picked valid
while :; do
read -r -p $'Install which? (numbers/names/ranges, "all", Enter = all): ' answer || return
[ -z "$answer" ] && return
[ "$answer" = "all" ] && return
picked=""
for token in ${answer//,/ }; do
valid=""
if [[ "$token" =~ ^[0-9]+$ ]] && [ "$token" -ge 1 ] \
&& [ "$token" -le "${#names[@]}" ]; then
picked+=",${names[$((token - 1))]}"; valid=1
elif [[ "$token" =~ ^([0-9]+)-([0-9]+)$ ]]; then
from="${BASH_REMATCH[1]}"; to="${BASH_REMATCH[2]}"
if [ "$from" -ge 1 ] && [ "$to" -le "${#names[@]}" ] \
&& [ "$from" -le "$to" ]; then
for ((i = from; i <= to; i++)); do
picked+=",${names[$((i - 1))]}"
done
valid=1
fi
else
for i in "${!names[@]}"; do
if [ "${names[$i],,}" = "${token,,}" ]; then
picked+=",${names[$i]}"; valid=1; break
fi
done
fi
[ -n "$valid" ] || warn "ignored unknown selection: $token"
done
if [ -n "$picked" ]; then
ONLY="${picked#,}"
log "selected: ${ONLY//,/ }"
return
fi
done
}
select_packages
# ── the venv ──────────────────────────────────────────────────────────
if [ ! -x "$VENV_DIR/bin/python" ]; then
log "creating venv at $VENV_DIR"
python3 -m venv "$VENV_DIR"
fi
PIP=("$VENV_DIR/bin/pip")
"${PIP[@]}" install -q --upgrade pip setuptools wheel
# ── repository sync: absent | corrupt | dirty | unchanged | moved ────
# Prints the resulting state; never destroys uncommitted work.
sync_repo() {
local name="$1" url="$2"
local dir="$GIT_MIRROR/$name"
if [ ! -e "$dir" ]; then
local stage="$dir.clone.$$"
if git clone --quiet --depth 1 "$url" "$stage"; then
mv "$stage" "$dir"
echo "cloned"
else
rm -rf "$stage"
echo "clone-failed"
fi
return
fi
if [ ! -d "$dir/.git" ]; then
local aside="$dir.corrupt-$(date +%s)"
warn "[$name] not a git checkout — moved aside to $(basename "$aside")"
mv "$dir" "$aside"
local stage="$dir.clone.$$"
if git clone --quiet --depth 1 "$url" "$stage"; then
mv "$stage" "$dir"
echo "recloned"
else
rm -rf "$stage"
echo "clone-failed"
fi
return
fi
local origin_url
origin_url="$(git -C "$dir" remote get-url origin 2>/dev/null || true)"
if [ "$origin_url" != "$url" ]; then
warn "[$name] remote is '$origin_url', table says '$url' — skipping"
echo "url-mismatch"
return
fi
if [ -n "$(git -C "$dir" status --porcelain 2>/dev/null)" ]; then
warn "[$name] local modifications — not touching them:"
git -C "$dir" status --porcelain | sed 's/^/ /'
echo "dirty"
return
fi
local branch refspec
branch="$(git -C "$dir" rev-parse --abbrev-ref HEAD)"
if [ "$branch" = "HEAD" ]; then
refspec="HEAD" # detached shallow clone
else
refspec="$branch"
fi
if ! git -C "$dir" fetch --quiet --depth 1 origin "$refspec"; then
warn "[$name] fetch failed (offline?)"
echo "unchanged"
return
fi
local head fetched
head="$(git -C "$dir" rev-parse HEAD)"
fetched="$(git -C "$dir" rev-parse FETCH_HEAD)"
if [ "$head" = "$fetched" ]; then
echo "unchanged"
return
fi
git -C "$dir" reset --hard --quiet "$fetched"
echo "moved"
}
module_importable() {
"$VENV_DIR/bin/python" -c "import $1" >/dev/null 2>&1
}
# ── per-package install with step-down ────────────────────────────────
install_one() {
local name="$1" url="$2" pipname="$3" extras="$4" method="$5" module="$6"
local dir="$GIT_MIRROR/$name"
if [ -n "$ONLY" ]; then
case ",$ONLY," in *",$name,"*) ;; *) return 0 ;; esac
fi
local state
state="$(sync_repo "$name" "$url")"
case "$state" in
clone-failed)
warn "[$name] clone failed — skipping"
return 0 ;;
url-mismatch | dirty)
return 0 ;; # never clobber; report above
esac
if [ "$UPDATE_ONLY" -eq 1 ]; then
log "[$name] $state"
return 0
fi
# Unchanged checkout + already importable: nothing to do.
if [ "$state" = "unchanged" ] && module_importable "$module"; then
log "[$name] up to date ($(git -C "$dir" rev-parse --short HEAD))"
return 0
fi
if [ "$method" = "source" ] || [ "$FORCE_SOURCE" -eq 1 ]; then
log "[$name] $state — installing from source checkout"
# Local-path extras: pip accepts "<dir>[extra1,extra2]".
if "${PIP[@]}" install -q "${dir}${extras}"; then
return 0
fi
warn "[$name] source build failed — stepping down to wheel"
fi
log "[$name] $state — installing ${pipname}${extras} (wheel)"
"${PIP[@]}" install -q "${pipname}${extras}"
}
for row in "${PACKAGES[@]}"; do
IFS='|' read -r name url pipname extras method module _ <<<"$row"
install_one "$name" "$url" "$pipname" "$extras" "$method" "$module"
done
# ── thicket itself, editable from this checkout ───────────────────────
log "[thicket] installing project (editable)"
"${PIP[@]}" install -q -e "$PROJECT_ROOT"
# ── launcher in the AI tree ───────────────────────────────────────────
cat > "$BIN_DIR/thicket" <<WRAPPER
#!/usr/bin/env bash
# Thicket launcher — /mnt/AI venv, project checkout, one command.
exec "$VENV_DIR/bin/python" "$PROJECT_ROOT/thicket.py" "\$@"
WRAPPER
chmod +x "$BIN_DIR/thicket"
# PATH command (~/.local/bin) — same venv, same interpreter.
# Nothing is ever written inside the project checkout.
mkdir -p "$HOME/.local/bin"
cat > "$HOME/.local/bin/thicket" <<USERWRAPPER
#!/usr/bin/env bash
# thicket — console/CLI with the project venv's interpreter.
exec "$VENV_DIR/bin/python" "$PROJECT_ROOT/thicket.py" "\$@"
USERWRAPPER
chmod +x "$HOME/.local/bin/thicket"
log "bootstrap complete"
log " venv : $VENV_DIR"
log " launcher : $BIN_DIR/thicket"
log " mirror : $GIT_MIRROR ($(ls "$GIT_MIRROR" | wc -l) checkouts)"
log "verify : $BIN_DIR/thicket --dry-run"

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#!/usr/bin/env python3
"""Functionality matrix — every destination, live.
One PASS/FAIL/SKIP row per target or stage against real services:
destinations : obsidian (notes only) + ten vector stores
per target : ingest → re-ingest idempotency → retrieval assertions
graphs : LightRAG and Graphify engines (one document each)
archives : MinIO object stage + filesystem bz2 stage
ask : Vanna natural-language SQL (pgvector + mariadb)
Exit code 0 iff no FAIL. Services read the standard env (PG*,
MARIADB_*, MINIO_*); missing services degrade to SKIP, never crash.
Run: .venv/bin/python scripts/func_test.py
"""
from __future__ import annotations
import os
import shutil
import subprocess
import sys
import tempfile
import time
from pathlib import Path
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT))
LAUNCHER = ROOT / "thicket.py"
RESULTS: list[tuple[str, str, str]] = [] # (section, name, outcome)
# Local service credentials for the standing test stack.
os.environ.setdefault("PGHOST", "localhost")
os.environ.setdefault("PGUSER", "thicket")
os.environ.setdefault("PGPASSWORD", "thicket")
os.environ.setdefault("PGDATABASE", "thicket")
os.environ.setdefault("MARIADB_HOST", "127.0.0.1")
os.environ.setdefault("MARIADB_USER", "root")
os.environ.setdefault("MARIADB_DATABASE", "thicket")
os.environ.setdefault("MINIO_ENDPOINT", "127.0.0.1:9000")
os.environ.setdefault("MINIO_ACCESS_KEY", "thicket")
os.environ.setdefault("MINIO_SECRET_KEY", "thicket-secret")
EMBED_MODEL = "jinaai/jina-embeddings-v2-base-code"
COLLECTION = "thicket_func"
CORPUS = {
"tls-rotation.sh": (
"#!/usr/bin/env bash\n"
"set -euo pipefail\n"
"# rotate tls certificates weekly and reload the edge\n"
"certbot renew --quiet --deploy-hook \"systemctl reload nginx\"\n"
"find /etc/letsencrypt/archive -mtime +90 -delete\n"
),
"timeout-policy.md": (
"# Edge Timeout Policy\n\n"
"Production proxies apply strict connection timeouts.\n\n"
"## nginx block\n\n"
"```nginx\n"
"proxy_connect_timeout 300s;\n"
"proxy_read_timeout 300s;\n"
"client_max_body_size 25m;\n"
"```\n\n"
"The 300 second ceiling bounds slow-loris exposure.\n"
),
"pasta.txt": (
"Kitchen notes: the asparagus pasta with basil pesto needs the\n"
"noodles pulled a minute early — residual heat finishes them.\n"
),
}
QUERIES = [ # (query, expected document stem)
("rotate tls certificates weekly", "tls-rotation"),
("connection timeout 300 nginx", "timeout-policy"),
("asparagus pasta recipe", "pasta"),
]
def record(section: str, name: str, outcome: str, detail: str = "") -> None:
tag = outcome if not detail else f"{outcome} — {detail}"
RESULTS.append((section, name, tag))
print(f" [{outcome:4s}] {name:28s} {detail}")
def run_cli(args: list[str], timeout: int = 600) -> tuple[int, str]:
proc = subprocess.run(
[sys.executable, str(LAUNCHER), *args],
capture_output=True, text=True, timeout=timeout, cwd=ROOT,
)
return proc.returncode, proc.stdout + proc.stderr
def make_corpus(base: Path) -> Path:
docs = base / "cold"
docs.mkdir(parents=True)
for name, body in CORPUS.items():
(docs / name).write_text(body, encoding="utf-8")
return docs
def fresh_vault(base: Path, name: str) -> Path:
vault = base / f"vault-{name}"
vault.mkdir(parents=True, exist_ok=True)
return vault
def search_once(target: str, vault: Path, query: str, port: int = 6333):
from thicket.vector_stores import create_store
engine = search_once._engine
store = create_store(target, collection=COLLECTION, dim=engine.dim,
host="localhost", port=port,
data_dir=vault / ".thicket" / target)
store.set_embedder(engine)
try:
store.ensure_collection()
return store.search(engine.embed_query(query), limit=1)
finally:
store.close()
def drop_stale() -> None:
"""The matrix owns its collection name; any earlier state at that
name (including other dimension eras) goes before the run."""
try:
from qdrant_client import QdrantClient
c = QdrantClient(url="http://localhost:6333",
check_compatibility=False)
if c.collection_exists(COLLECTION):
c.delete_collection(COLLECTION)
except Exception:
pass
try:
import psycopg
with psycopg.connect(connect_timeout=3) as conn:
conn.execute(f'DROP TABLE IF EXISTS "{COLLECTION}"')
conn.commit()
except Exception:
pass
try:
import pymysql
conn = pymysql.connect(host="127.0.0.1", user="root",
database="thicket", connect_timeout=3)
with conn.cursor() as cur:
cur.execute(f"DROP TABLE IF EXISTS `{COLLECTION}`")
conn.commit(); conn.close()
except Exception:
pass
try:
import weaviate
client = weaviate.connect_to_local(
host="127.0.0.1", port=8080, grpc_port=50051)
mapped = "Thicket_func"
if client.collections.exists(mapped):
client.collections.delete(mapped)
client.close()
except Exception:
pass
def phase_destinations(base: Path) -> None:
from thicket.embedder import EmbeddingEngine
from thicket.vector_stores import TARGETS
drop_stale()
engine = EmbeddingEngine(EMBED_MODEL)
engine.load()
search_once._engine = engine
print("\n== destinations ==")
# obsidian: notes only
docs = make_corpus(base / "obsidian")
vault = fresh_vault(base, "obsidian")
rc, out = run_cli(["--ingest", str(docs), "--vault", str(vault),
"--target", "obsidian", "--no-qdrant"])
notes = sorted(p.name for p in (vault / "Ingested_Brain").glob("*.md"))
ok = rc == 0 and len(notes) == 3 and "INDEX" not in out
record("destinations", "obsidian",
"PASS" if ok else "FAIL", f"notes={notes}" if ok else out[-160:])
parity: dict[str, float] = {}
for target in TARGETS:
docs = make_corpus(base / target)
vault = fresh_vault(base, target)
port = 8080 if target == "weaviate" else 6333
rc, out = run_cli(["--ingest", str(docs), "--vault", str(vault),
"--target", target, "--port", str(port),
"--collection", COLLECTION])
if rc != 0 or "0 failed" not in out:
record("destinations", target, "FAIL", out.strip()[-140:])
continue
try:
first = search_once(target, vault, QUERIES[0][0], port)
rc2, _ = run_cli(["--ingest", str(docs), "--vault", str(vault),
"--target", target, "--port", str(port),
"--collection", COLLECTION])
second = search_once(target, vault, QUERIES[0][0], port)
except Exception as e: # noqa: BLE001
record("destinations", target, "FAIL", f"search: {e}")
continue
hit_ok = all(
search_once(target, vault, q, port)[0]["payload"]
["document_title"].lower().replace(" ", "-").startswith(exp.split("-")[0])
for q, exp in QUERIES[:1]
)
idem = (rc2 == 0 and abs(first[0]["score"] - second[0]["score"]) < 1e-3
and second[0]["payload"]["document_title"]
== first[0]["payload"]["document_title"])
parity[target] = first[0]["score"]
record("destinations", target,
"PASS" if (hit_ok and idem) else "FAIL",
f"top={first[0]['payload']['document_title'][:22]!r} "
f"score={first[0]['score']:.3f} idempotent={idem}")
if len(parity) >= 2:
scores = sorted(parity.values())
spread = scores[-1] - scores[0]
record("destinations", "score parity (all)",
"PASS" if spread <= 0.02 else "WARN", f"spread={spread:.4f}")
def phase_graphs(base: Path) -> None:
print("\n== graph engines ==")
for engine, marker in (("lightrag", ".lightrag"), ("graphify", ".graphify")):
docs = make_corpus(base / f"g-{engine}")
docs_extra = docs / "tls-rotation.sh"
docs_extra.write_text(CORPUS["tls-rotation.sh"], encoding="utf-8")
vault = fresh_vault(base, f"g-{engine}")
rc, out = run_cli(["--ingest", str(docs), "--vault", str(vault),
"--target", "obsidian", "--no-qdrant",
"--lightrag", "--graph-engine", engine,
"--ollama-llm", "llama3.1:latest"],
timeout=900)
if rc != 0:
record("graphs", engine, "FAIL", out.strip()[-160:])
continue
if engine == "graphify":
ok = (vault / ".graphify" / "graphify-out" / "graph.json").exists()
else:
graph_dir = vault / ".lightrag"
ok = graph_dir.exists() and any(graph_dir.iterdir())
record("graphs", engine, "PASS" if ok else "FAIL",
f"artifacts={'yes' if ok else 'missing'}")
def phase_archives(base: Path) -> None:
import bz2
print("\n== archive stages ==")
# filesystem bz2
docs = make_corpus(base / "fsarch")
vault = fresh_vault(base, "fsarch")
archive = base / "ingested-archive"
rc, out = run_cli(["--ingest", str(docs), "--vault", str(vault),
"--target", "obsidian", "--no-qdrant", "--archive",
"--archive-dir", str(archive)])
packed = sorted(archive.glob("*.bz2")) if archive.exists() else []
roundtrip = bool(packed) and all(
bz2.decompress(p.read_bytes()) for p in packed)
empty_in = not any(docs.iterdir())
record("archives", "filesystem bz2",
"PASS" if (rc == 0 and roundtrip and empty_in) else "FAIL",
f"{len(packed)} objects, roundtrip={roundtrip}")
# MinIO object stage
try:
from minio import Minio
client = Minio("127.0.0.1:9000", access_key="thicket",
secret_key="thicket-secret", secure=False)
if not client.bucket_exists("thicket-corpus"):
client.make_bucket("thicket-corpus")
except Exception as e: # noqa: BLE001
record("archives", "minio objects", "SKIP", f"service: {e}")
return
docs = make_corpus(base / "minioarch")
vault = fresh_vault(base, "minioarch")
rc, out = run_cli(["--ingest", str(docs), "--vault", str(vault),
"--target", "obsidian", "--no-qdrant", "--minio"])
objects = {o.object_name for o in
client.list_objects("thicket-corpus", recursive=True)}
uris = "source_uri" in (vault / "Ingested_Brain" / "pasta.md").read_text()
expected = set(CORPUS)
ok = rc == 0 and uris and expected & objects == expected
record("archives", "minio objects",
"PASS" if ok else "FAIL",
f"{len(expected & objects)}/{len(expected)} objects, uri_in_note={uris}")
def phase_ask(base: Path) -> None:
print("\n== ask (Vanna + Ollama) ==")
from thicket.ask_vanna import SQL_TARGETS, ask
for target in SQL_TARGETS:
try:
answer = ask("How many rows are in the corpus table?",
target=target, llm_model="llama3.1:latest")
ok = bool(answer.strip()) and "ERROR" not in answer
record("ask", target, "PASS" if ok else "FAIL",
answer.strip().replace("\n", " ")[:60])
except Exception as e: # noqa: BLE001
record("ask", target, "FAIL", str(e)[:100])
def main() -> int:
started = time.time()
print(f"thicket functionality matrix — {time.strftime('%Y-%m-%d %H:%M')}")
base = Path(tempfile.mkdtemp(prefix="thicket-func-"))
try:
phase_destinations(base)
phase_graphs(base)
phase_archives(base)
phase_ask(base)
finally:
shutil.rmtree(base, ignore_errors=True)
fails = [r for r in RESULTS if r[2].startswith("FAIL")]
print(f"\n{'=' * 60}")
print(f"{len(RESULTS)} checks, {len(fails)} failed "
f"({time.time() - started:.0f}s)")
return 1 if fails else 0
if __name__ == "__main__":
sys.exit(main())

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#!/usr/bin/env python3
"""Offscreen GUI smoke test — instantiates the full window, lets the
probe thread settle, populates a demo queue, and grabs a screenshot.
Run: QT_QPA_PLATFORM=offscreen python3 scripts/smoke_gui.py
"""
import os
import sys
import tempfile
from pathlib import Path
os.environ.setdefault("QT_QPA_PLATFORM", "offscreen")
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
from PySide6.QtCore import QTimer
from PySide6.QtWidgets import QApplication
from thicket.ui_window import ThicketWindow
OUT = Path("/tmp/thicket_smoke.png")
def main() -> int:
app = QApplication(sys.argv)
# Demo input dir with a few plausible documents so the queue table
# shows real rows in the screenshot.
demo = Path(tempfile.mkdtemp(prefix="bf_smoke_"))
(demo / "attention-is-all-you-need.pdf").write_bytes(b"%PDF-1.4 stub")
(demo / "thinking-fast-and-slow.epub").write_bytes(b"PK stub")
(demo / "zettelkasten-method.md").write_text("# Zettelkasten\n\nnotes.\n")
(demo / "reading backlog.txt").write_text("todo list\n")
window = ThicketWindow()
window.in_path_edit.setText(str(demo))
window.show()
def settle():
window._scan()
window._on_file_status(str(demo / "zettelkasten-method.md"), "DONE")
window._on_file_detail(str(demo / "zettelkasten-method.md"), "14 chunks indexed")
window._on_file_status(str(demo / "thinking-fast-and-slow.epub"), "EXTRACT")
window._on_file_detail(str(demo / "thinking-fast-and-slow.epub"), "extracting text")
window.query_edit.setText("how do transformers handle attention?")
window.log_box.append("> [1] 0.873 Attention Is All You Need § Page 3")
QTimer.singleShot(1500, settle)
def finish():
ok_run = window.btn_run.isEnabled()
window.grab().save(str(OUT))
print(f"btn_run enabled: {ok_run}")
print(f"status bar: {window.status_label.text()!r}")
print(f"queue rows: {window.queue_table.rowCount()}")
print(f"screenshot: {OUT}")
results["ok"] = bool(ok_run and window.queue_table.rowCount() == 4)
window.close()
QTimer.singleShot(0, app.quit)
results = {}
QTimer.singleShot(3000, finish)
app.exec()
return 0 if results.get("ok") else 1
if __name__ == "__main__":
sys.exit(main())

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"""Shared fixtures — temp vault + sample documents."""
from __future__ import annotations
from pathlib import Path
import pytest
@pytest.fixture
def sample_docs(tmp_path: Path) -> Path:
"""Input directory with a couple of small markdown/txt documents."""
docs = tmp_path / "docs"
docs.mkdir()
(docs / "deep-learning-notes.md").write_text(
"# Deep Learning\n\n"
"Neural networks learn representations via gradient descent.\n\n"
"## Optimizers\n\n"
"SGD is simple. Adam adapts per-parameter learning rates.\n\n"
"## Regularization\n\n"
"Dropout randomly masks units during training to reduce overfit.\n",
encoding="utf-8",
)
(docs / "reading-list.txt").write_text(
"Books to read this year, in no particular order.\n\n"
"The Pragmatic Programmer. Structure and Interpretation.\n",
encoding="utf-8",
)
return docs
@pytest.fixture
def vault(tmp_path: Path) -> Path:
vault_dir = tmp_path / "ObsidianVault"
vault_dir.mkdir()
return vault_dir

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"""ContextualChunker — structure preservation for technical corpora."""
from __future__ import annotations
from thicket.chunker import ContextualChunker
def _filler(n: int, tag: str = "w") -> str:
return " ".join(f"{tag}{i}" for i in range(n))
def test_empty_text_yields_no_chunks():
assert ContextualChunker().chunk("T", "") == []
assert ContextualChunker().chunk("T", "\n\n\n") == []
def test_fenced_block_is_atomic_and_verbatim():
code = "\n".join([
"```python",
"def harden(host):",
" if host == 'prod':",
" sys.exit('no')",
"",
" return True",
"```",
])
chunks = ContextualChunker(chunk_size=10, overlap=0).chunk("T", code)
assert len(chunks) == 1
assert chunks[0].kind == "code"
assert chunks[0].lang == "python"
assert " if host == 'prod':" in chunks[0].text # indentation kept
assert "\n\n" in chunks[0].text # blank line kept
assert "| code:python]" in chunks[0].contextual_text
def test_code_never_merges_with_prose():
doc = "Intro prose here.\n\n```bash\nls -la\n```\n\nOutro prose here."
chunks = ContextualChunker(chunk_size=100, overlap=0).chunk("T", doc)
kinds = [c.kind for c in chunks]
assert kinds.count("code") == 1
code = next(c for c in chunks if c.kind == "code")
assert "Intro" not in code.text and "Outro" not in code.text
def test_shebang_and_comments_are_not_headings():
doc = "#!/usr/bin/env python3\nimport os\n\n#no-space-comment\n\n## Real Heading\n\nBody text."
chunks = ContextualChunker(chunk_size=100).chunk("T", doc)
assert chunks[-1].header == "Real Heading"
def test_prose_paragraph_lines_are_preserved():
doc = "# H\n\nFirst paragraph.\nSecond line of same paragraph."
chunks = ContextualChunker(chunk_size=100).chunk("T", doc)
assert "First paragraph.\nSecond line of same paragraph." in chunks[0].text
def test_paragraph_aligned_budget():
paras = [_filler(40, f"p{i}_") for i in range(6)] # 6 x 40 words
doc = "# H\n\n" + "\n\n".join(paras)
chunks = ContextualChunker(chunk_size=100, overlap=0).chunk("T", doc)
# No paragraph is ever split: each chunk is 2 paragraphs (80 words).
assert all(c.text.count("_") >= 40 for c in chunks)
assert all(len(c.text.split()) <= 100 for c in chunks)
assert len(chunks) == 3
def test_oversized_code_is_windowed_with_lang_on_every_window():
lines = [f"x_{i} = {i} # {'filler ' * 20}" for i in range(60)]
doc = "```python\n" + "\n".join(lines) + "\n```"
chunks = ContextualChunker(chunk_size=40, overlap=0).chunk("T", doc)
assert len(chunks) > 1
assert all(c.kind == "code" and c.lang == "python" for c in chunks)
# Window overlap: first line of window N+1 appeared in window N.
assert any(chunks[i + 1].text.split("\n")[0] in chunks[i].text
for i in range(len(chunks) - 1))
def test_section_boundaries_do_not_straddle():
doc = ("# A\n\n" + _filler(30, "a") + "\n\n"
"# B\n\n" + _filler(30, "b"))
chunks = ContextualChunker(chunk_size=100, overlap=50).chunk("T", doc)
assert len(chunks) == 2
assert chunks[0].header == "A" and chunks[1].header == "B"
assert "b0" not in chunks[0].text
def test_indented_block_treated_as_code():
doc = "Prose.\n\n permit root no\n retries 3\n\nMore prose."
chunks = ContextualChunker(chunk_size=100).chunk("T", doc)
code = [c for c in chunks if c.kind == "code"]
assert code and "permit root no" in code[0].text
def test_default_header_is_introduction():
chunks = ContextualChunker(chunk_size=50).chunk("T", _filler(10))
assert chunks[0].header == "Introduction"
def test_source_provenance_attaches_to_every_chunk():
doc = "# H\n\nprose\n\n```python\nx = 1\n```"
chunks = ContextualChunker(chunk_size=100).chunk(
"T", doc, source_path="sub/deploy.py")
assert {c.source for c in chunks} == {"sub/deploy.py"}
assert ContextualChunker().chunk("T", doc)[0].source is None

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"""DocumentExtractor — dispatch, md/txt extraction, scanning."""
from __future__ import annotations
import pytest
from thicket.extractors import (
ExtractionError, DocumentExtractor, scan_files,
)
def test_md_extraction_title_from_stem(sample_docs):
title, text = DocumentExtractor.extract(sample_docs / "deep-learning-notes.md")
assert title == "Deep Learning Notes"
assert "# Deep Learning" in text
assert "## Optimizers" in text
def test_txt_extraction(sample_docs):
title, text = DocumentExtractor.extract(sample_docs / "reading-list.txt")
assert title == "Reading List"
assert "Pragmatic Programmer" in text
def test_unsupported_extension_raises(tmp_path):
bogus = tmp_path / "photo.jpg"
bogus.write_bytes(b"\xff\xd8fake")
with pytest.raises(ExtractionError):
DocumentExtractor.extract(bogus)
def test_scan_files_sorted_and_filtered(sample_docs, tmp_path):
(sample_docs / "nested").mkdir()
(sample_docs / "nested" / "zz-last.md").write_text("x", encoding="utf-8")
(sample_docs / "image.png").write_bytes(b"\x89PNG") # ignored
files = scan_files(sample_docs)
names = [f.name for f in files]
assert "image.png" not in names
# Sorted by full path (case-insensitive), so the nested file sits
# between the top-level entries alphabetically.
assert names == ["deep-learning-notes.md", "zz-last.md", "reading-list.txt"]
def test_scan_files_custom_extensions(sample_docs):
files = scan_files(sample_docs, {".txt"})
assert [f.name for f in files] == ["reading-list.txt"]

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"""Graph engines, MinIO archiver, and the ask bridge — contract tests
that run without any service or heavy dependency installed."""
from __future__ import annotations
import pytest
from thicket.graph_store import GRAPH_ENGINES, GraphUnavailable, create_graph
def test_graph_engine_registry():
assert sorted(GRAPH_ENGINES) == ["graphify", "lightrag"]
assert GRAPH_ENGINES["lightrag"]["modules"] == ("lightrag",)
assert GRAPH_ENGINES["graphify"]["modules"] == ("graphify",)
def test_unknown_graph_engine_names_choices(tmp_path):
with pytest.raises(GraphUnavailable, match="known:"):
create_graph("memgraph", working_dir=tmp_path, llm_model="m",
embed_model="e")
def test_graphify_engine_stages_documents(tmp_path):
pytest.importorskip("graphify")
store = create_graph("graphify", working_dir=tmp_path, llm_model="m",
embed_model="e")
store.ingest_document("Doc One", "alpha text")
store.ingest_document("Doc Two", "beta text")
staged = list((tmp_path / ".graphify" / "corpus").glob("*.md"))
assert len(staged) == 2
store.ingest_document("Doc One", "different content this time")
# same title + different content -> a digest sibling, not a clobber
assert len(list((tmp_path / ".graphify" / "corpus").glob("doc-one*.md"))) == 2
def test_minio_archiver_requires_package():
from thicket.minio_archive import ArchiveUnavailable, MinioArchiver
import importlib.util
if importlib.util.find_spec("minio"):
pytest.skip("minio installed — guard path covered live")
with pytest.raises(ArchiveUnavailable, match="pip install"):
MinioArchiver()
def test_ask_rejects_non_sql_targets():
from thicket.ask_vanna import AskUnavailable, SQL_TARGETS, target_ddl
assert SQL_TARGETS == ("pgvector", "mariadb")
with pytest.raises(AskUnavailable, match="no SQL corpus"):
target_ddl("chroma")
def test_ask_ddl_documents_payload_fields():
from thicket.ask_vanna import target_ddl
for target in ("pgvector", "mariadb"):
ddl = target_ddl(target, "second_brain")
assert "second_brain" in ddl
assert "document_title" in ddl and "chunk_index" in ddl

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"""Root launcher — thicket.py must own every mode from any cwd."""
from __future__ import annotations
import subprocess
import sys
from pathlib import Path
LAUNCHER = Path(__file__).resolve().parents[1] / "thicket.py"
def _run(args: list[str], cwd: Path) -> subprocess.CompletedProcess:
return subprocess.run(
[sys.executable, str(LAUNCHER), *args],
capture_output=True, text=True, timeout=60, cwd=cwd,
)
def test_launcher_version_from_project_root():
from thicket import __version__
result = _run(["--version"], LAUNCHER.parent)
assert result.returncode == 0
assert result.stdout.strip() == f"thicket {__version__}"
def test_launcher_version_from_unrelated_cwd(tmp_path):
"""By-path invocation must resolve the in-tree package anywhere."""
result = _run(["--version"], tmp_path)
from thicket import __version__
assert result.returncode == 0
assert f"thicket {__version__}" in result.stdout
def test_launcher_dry_run_reports_probe():
result = _run(["--dry-run"], LAUNCHER.parent)
assert result.returncode == 0
assert "Thicket environment probe" in result.stdout
assert "Qdrant:" in result.stdout and "Ollama:" in result.stdout

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"""AI layout awareness and path expansion."""
from __future__ import annotations
from pathlib import Path
import pytest
import thicket.layout as layout
from thicket.pipeline_core import IngestConfig
@pytest.fixture
def fake_ai(tmp_path: Path, monkeypatch):
(tmp_path / "corpus" / "cold").mkdir(parents=True)
(tmp_path / "corpus" / "hot").mkdir(parents=True)
(tmp_path / "corpus" / "books").mkdir(parents=True)
monkeypatch.setattr(layout, "AI_ROOT", tmp_path)
return tmp_path
def test_layout_detected_with_corpus_taxonomy(fake_ai):
profile = layout.detect_layout()
assert profile is not None
assert profile.corpus_cold == fake_ai / "corpus" / "cold"
assert profile.corpus_hot == fake_ai / "corpus" / "hot"
assert profile.books_present is True
assert profile.archive == fake_ai / "corpus" / "archive"
def test_layout_absent_without_corpus(tmp_path, monkeypatch):
monkeypatch.setattr(layout, "AI_ROOT", tmp_path)
assert layout.detect_layout() is None
def test_defaults_follow_the_layout(fake_ai):
assert layout.default_input() == fake_ai / "corpus" / "cold"
assert layout.default_vault() == fake_ai / "corpus" / "hot"
assert layout.default_archive(Path("/anywhere/in")) == fake_ai / "corpus" / "archive"
def test_defaults_fall_home_without_layout(tmp_path, monkeypatch):
monkeypatch.setattr(layout, "AI_ROOT", tmp_path)
assert layout.default_input() == Path.home() / "Downloads" / "Raw_Books_And_Papers"
assert layout.default_vault() == Path.home() / "Documents" / "ObsidianVault"
assert layout.default_archive(Path("/data/in")) == Path("/data/ingested-archive")
def test_archive_falls_back_to_sibling_without_layout(tmp_path, monkeypatch):
monkeypatch.setattr(layout, "AI_ROOT", tmp_path)
assert layout.default_archive(Path("/data/in")) == Path("/data/ingested-archive")
def test_expand_path_tilde_and_env(monkeypatch):
monkeypatch.setenv("THICKET_TEST_DIR", "/tmp/thicket-expanded")
assert layout.expand_path("~/books") == Path.home() / "books"
assert layout.expand_path("$THICKET_TEST_DIR/x") == Path("/tmp/thicket-expanded/x")
assert layout.expand_path("/plain/path") == Path("/plain/path")
def test_ingest_config_expands_paths(monkeypatch):
monkeypatch.setenv("THICKET_TEST_DIR", "/tmp/thicket-expanded")
config = IngestConfig(input_dir="~/in", vault_dir="$THICKET_TEST_DIR/vault",
archive_dir="$THICKET_TEST_DIR/archive",
use_qdrant=False)
assert config.input_dir == Path.home() / "in"
assert config.vault_dir == Path("/tmp/thicket-expanded/vault")
assert config.archive_dir == Path("/tmp/thicket-expanded/archive")
def test_backend_profiles_fill_gaps_env_wins(tmp_path, monkeypatch):
import os
import thicket.layout as layout
backends = tmp_path / "backends"
backends.mkdir()
(backends / "thicket.env").write_text(
"# profile\n"
"PGHOST=db.local\n"
"PGUSER='fileuser'\n"
"MINIO_ENDPOINT=\"127.0.0.1:9000\"\n"
"NOT_A_LINE\n", encoding="utf-8")
monkeypatch.setattr(layout, "AI_ROOT", tmp_path)
for key in ("PGHOST", "PGUSER", "MINIO_ENDPOINT"):
monkeypatch.delenv(key, raising=False)
applied = layout.apply_backend_profiles()
assert applied == {"PGHOST": "db.local", "PGUSER": "fileuser",
"MINIO_ENDPOINT": "127.0.0.1:9000"}
assert os.environ["PGUSER"] == "fileuser"
monkeypatch.setenv("PGUSER", "shelluser")
monkeypatch.delenv("PGHOST", raising=False)
applied = layout.apply_backend_profiles()
assert "PGUSER" not in applied # environment wins
assert applied["PGHOST"] == "db.local" # gaps still filled
assert os.environ["PGUSER"] == "shelluser"
def test_backend_profiles_absent_file_is_noop(tmp_path, monkeypatch):
import thicket.layout as layout
monkeypatch.setattr(layout, "AI_ROOT", tmp_path)
assert layout.apply_backend_profiles() == {}

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"""Package + probe smoke tests (no services required)."""
from __future__ import annotations
import re
from pathlib import Path
from thicket import __version__
def test_version_string():
assert isinstance(__version__, str)
assert __version__.count(".") == 2
def test_version_matches_pyproject():
pyproject = Path(__file__).resolve().parents[1] / "pyproject.toml"
match = re.search(r'^version = "(.+)"$', pyproject.read_text(), re.M)
assert match is not None, "pyproject version line missing"
assert match.group(1) == __version__
def test_env_probe_reports_module_table():
from thicket.env_probe import PROBED_MODULES, probe_environment
env = probe_environment(qdrant_host="localhost", qdrant_port=1) # dead port
assert set(env.modules.keys()) == set(PROBED_MODULES.keys())
assert all(isinstance(v, bool) for v in env.modules.values())
# known-good modules on this interpreter
assert env.modules["pypdf"] is True
assert env.modules["slugify"] is True
def test_ui_modules_import_without_heavy_deps():
# GUI must import on a bare system (heavy deps are lazy).
import thicket.ui_theme # noqa: F401
import thicket.ui_window # noqa: F401
import thicket.widgets
assert hasattr(thicket.widgets, "RadioKnob")

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"""IngestPipeline — vault-only end-to-end run (no services needed)."""
from __future__ import annotations
from thicket.pipeline_core import (
IngestConfig, IngestPipeline, PipelineCallbacks,
)
class RecordingCallbacks:
def __init__(self):
self.logs: list[str] = []
self.statuses: list[tuple[str, str]] = []
self.details: list[tuple[str, str]] = []
self.progress: list[tuple[str, int, int]] = []
def bind(self) -> PipelineCallbacks:
return PipelineCallbacks(
log=self.logs.append,
file_status=lambda p, s: self.statuses.append((p, s)),
file_detail=lambda p, d: self.details.append((p, d)),
progress=lambda n, c, t: self.progress.append((n, c, t)),
)
def test_vault_only_pipeline_processes_every_document(sample_docs, vault):
config = IngestConfig(
input_dir=sample_docs, vault_dir=vault,
use_qdrant=False, use_lightrag=False,
)
cb = RecordingCallbacks()
ok, fail = IngestPipeline(config, cb.bind()).run()
assert (ok, fail) == (2, 0)
assert (vault / "Ingested_Brain" / "deep-learning-notes.md").exists()
assert (vault / "Ingested_Brain" / "reading-list.md").exists()
# every file walked the full status lifecycle
for path in {p for p, _ in cb.statuses}:
stages = [s for p, s in cb.statuses if p == path]
assert stages[0] == "QUEUED"
assert stages[-1] == "DONE"
assert "EXTRACT" in stages and "VAULT" in stages
# progress counted 1..2
assert cb.progress[-1][1:] == (2, 2)
def test_empty_file_is_skip_not_failure(sample_docs, vault):
(sample_docs / "blank.md").write_text("", encoding="utf-8")
config = IngestConfig(input_dir=sample_docs, vault_dir=vault,
use_qdrant=False, use_lightrag=False)
cb = RecordingCallbacks()
ok, fail = IngestPipeline(config, cb.bind()).run()
assert (ok, fail) == (3, 0)
assert ("SKIP" in [s for _, s in cb.statuses])
def test_cooperative_stop_between_files(sample_docs, vault):
config = IngestConfig(input_dir=sample_docs, vault_dir=vault,
use_qdrant=False, use_lightrag=False)
pipeline = IngestPipeline(config, PipelineCallbacks.quiet())
original = pipeline._process_one
calls = {"n": 0}
def stop_after_first(filepath):
calls["n"] += 1
result = original(filepath)
pipeline.request_stop()
return result
pipeline._process_one = stop_after_first
ok, fail = pipeline.run()
assert calls["n"] == 1 # stopped before the second file
assert (ok, fail) == (1, 0)
def test_broken_document_fails_alone_and_queue_continues(sample_docs, vault):
(sample_docs / "broken.epub").write_bytes(b"not really an epub")
config = IngestConfig(input_dir=sample_docs, vault_dir=vault,
use_qdrant=False, use_lightrag=False)
cb = RecordingCallbacks()
ok, fail = IngestPipeline(config, cb.bind()).run()
# the two good files still succeeded; the epub failed in isolation
assert ok == 2 and fail == 1
assert any("ERROR processing broken.epub" in line for line in cb.logs)
def test_vault_stage_disabled_writes_no_notes(sample_docs, vault):
config = IngestConfig(input_dir=sample_docs, vault_dir=vault,
use_vault=False, use_qdrant=False, use_lightrag=False)
cb = RecordingCallbacks()
ok, fail = IngestPipeline(config, cb.bind()).run()
assert (ok, fail) == (2, 0)
assert not (vault / "Ingested_Brain").exists() or \
not any((vault / "Ingested_Brain").iterdir())
assert not any("Vault note written" in line for line in cb.logs)
# VAULT never appears in the stage lifecycle
assert "VAULT" not in [s for _, s in cb.statuses]
def test_skip_unchanged_skips_second_run(sample_docs, vault):
config = IngestConfig(input_dir=sample_docs, vault_dir=vault,
use_qdrant=False, use_lightrag=False,
skip_unchanged=True)
cb = RecordingCallbacks()
first = IngestPipeline(config, cb.bind()).run()
second = IngestPipeline(config, cb.bind()).run()
assert first == (2, 0)
assert second == (2, 0)
assert [s for _, s in cb.statuses].count("SKIP") == 2
assert any("unchanged" in d for _, d in cb.details)
# Editing a file brings it back into the queue.
(sample_docs / "reading-list.txt").write_text("new content\n",
encoding="utf-8")
IngestPipeline(config, cb.bind()).run()
assert [s for _, s in cb.statuses].count("DONE") == 3
def test_fs_archive_moves_and_compresses(sample_docs, vault, tmp_path):
archive = tmp_path / "ingested-archive"
config = IngestConfig(input_dir=sample_docs, vault_dir=vault,
use_qdrant=False, use_lightrag=False,
use_fs_archive=True, archive_dir=archive)
ok, fail = IngestPipeline(config, PipelineCallbacks.quiet()).run()
assert (ok, fail) == (2, 0)
# Incoming tree is empty; the archive holds bz2 payloads only.
assert not any(p.exists() for p in sample_docs.iterdir() if p.is_file())
packed = sorted(archive.glob("*.bz2"))
assert len(packed) == 2 and not any(p.suffix != ".bz2" for p in archive.iterdir())
# Content round-trips through bunzip2.
import bz2
text = bz2.decompress(packed[0].read_bytes()).decode("utf-8")
assert text.strip() # real content survived
def test_obsidian_target_is_notes_only(sample_docs, vault):
config = IngestConfig(input_dir=sample_docs, vault_dir=vault,
target="obsidian", use_qdrant=False,
use_lightrag=False)
cb = RecordingCallbacks()
ok, fail = IngestPipeline(config, cb.bind()).run()
assert (ok, fail) == (2, 0)
assert (vault / "Ingested_Brain" / "reading-list.md").exists()
assert "INDEX" not in [s_ for _, s_ in cb.statuses] # no vector stage
def test_max_mb_guards_oversized_files(sample_docs, vault):
(sample_docs / "huge.txt").write_text("x" * 1_200_000, encoding="utf-8")
config = IngestConfig(input_dir=sample_docs, vault_dir=vault,
use_qdrant=False, use_lightrag=False, max_mb=1)
cb = RecordingCallbacks()
ok, fail = IngestPipeline(config, cb.bind()).run()
assert (ok, fail) == (3, 0)
details = [d for _, d in cb.details]
assert any("exceeds" in d for d in details)
assert (sample_docs / "huge.txt").exists() # guards skip, never delete
def test_custom_notes_dir(sample_docs, vault):
config = IngestConfig(input_dir=sample_docs, vault_dir=vault,
use_qdrant=False, use_lightrag=False,
notes_dir="Technical_Shots")
IngestPipeline(config, PipelineCallbacks.quiet()).run()
assert (vault / "Technical_Shots" / "reading-list.md").exists()

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"""ObsidianVaultWriter — frontmatter, escaping, collision handling."""
from __future__ import annotations
import re
from thicket.vault_writer import ObsidianVaultWriter
def test_note_written_with_frontmatter_and_header(vault, sample_docs):
writer = ObsidianVaultWriter(vault)
source = sample_docs / "reading-list.txt"
note = writer.write("Reading List", "body text", source)
assert note.parent == vault / "Ingested_Brain"
text = note.read_text(encoding="utf-8")
assert text.startswith("---\n")
assert re.search(r'^title: "Reading List"$', text, re.M)
assert re.search(r'^source_file: "reading-list\.txt"$', text, re.M)
assert "tags:" in text and "brain/ingested" in text and "source/txt" in text
assert "# Reading List" in text
assert "*Source document: `reading-list.txt`*" in text
assert text.endswith("body text")
def test_title_with_quotes_survives_frontmatter(vault, sample_docs):
writer = ObsidianVaultWriter(vault)
source = sample_docs / "reading-list.txt"
note = writer.write('The "Real" Deal', "x", source)
text = note.read_text(encoding="utf-8")
fm = text.split("---")[1]
assert 'title: "The \\"Real\\" Deal"' in fm
def test_reingest_same_source_overwrites(vault, sample_docs):
writer = ObsidianVaultWriter(vault)
source = sample_docs / "reading-list.txt"
first = writer.write("Same Title", "v1", source)
second = writer.write("Same Title", "v2", source)
assert first == second
assert "v2" in first.read_text(encoding="utf-8")
def test_same_title_different_source_gets_suffix(vault, sample_docs):
writer = ObsidianVaultWriter(vault)
a = writer.write("Same Title", "from A", sample_docs / "reading-list.txt")
b = writer.write("Same Title", "from B", sample_docs / "deep-learning-notes.md")
assert a != b
assert a.exists() and b.exists()
# suffix is a 6-hex digest of the source name
assert re.search(r"-[0-9a-f]{6}\.md$", b.name)
def test_symbol_only_title_falls_back_to_untitled(vault, sample_docs):
writer = ObsidianVaultWriter(vault)
source = sample_docs / "reading-list.txt"
note = writer.write("###", "x", source)
assert note.stem.startswith("untitled")

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"""Vector store targets — registry, doc_key contract, and per-target
roundtrips against a deterministic stub engine (no model download).
The per-target roundtrips are skipped automatically when a target's
library is not installed; the registry tests always run.
"""
from __future__ import annotations
import pytest
from thicket.chunker import Chunk
from thicket.vector_stores import (
TARGETS, VectorStoreError, create_store, doc_key,
)
CHUNKS = [
Chunk(header="Alpha", text="alpha content one",
contextual_text="[Source: T | Section: Alpha]\nalpha content one"),
Chunk(header="Beta", text="beta content two",
contextual_text="[Source: T | Section: Beta]\nbeta content two"),
]
class StubEngine:
"""Deterministic 4-dim embeddings: direction keyed by first word."""
model_name = "stub-model"
dim = 4
_BASIS = {
"alpha": [1.0, 0.0, 0.0, 0.0],
"beta": [0.0, 1.0, 0.0, 0.0],
}
def embed(self, texts: list[str]) -> list[list[float]]:
return [self._vector(t) for t in texts]
def embed_query(self, text: str) -> list[float]:
return self._vector(text)
@classmethod
def _vector(cls, text: str) -> list[float]:
for word, vec in cls._BASIS.items():
if word in text.lower():
return list(vec)
return [0.0, 0.0, 1.0, 0.0]
def _roundtrip(store) -> None:
store.set_embedder(StubEngine())
store.ensure_collection()
written = store.replace_document("Doc One", "Ingested_Brain/doc-one.md", CHUNKS)
assert written == 2
hits = store.search(StubEngine().embed_query("alpha query"), limit=2)
assert hits, "expected at least one hit"
top = hits[0]
assert top["payload"]["document_title"] == "Doc One"
assert top["payload"]["section_header"] == "Alpha"
assert "doc_key" not in top["payload"] # internal key never surfaces
# Exact replacement: shrink to one chunk, count must follow exactly.
store.replace_document("Doc One", "Ingested_Brain/doc-one.md", CHUNKS[:1])
hits_after = store.search(StubEngine().embed_query("alpha query"), limit=10)
alpha_hits = [h for h in hits_after
if h["payload"]["section_header"] == "Alpha"]
beta_hits = [h for h in hits_after
if h["payload"]["section_header"] == "Beta"]
assert len(alpha_hits) == 1 and not beta_hits, \
"re-ingest must replace exactly, leaving no stale chunks"
# ── registry (always runs) ──
def test_registry_covers_ten_targets():
assert sorted(TARGETS) == [
"chroma", "duckdb", "faiss", "lancedb", "mariadb", "milvus",
"pgvector", "qdrant", "sqlitevec", "weaviate",
]
def test_service_requirements_are_exact():
services = {key: spec.service for key, spec in TARGETS.items()}
assert services == {
"qdrant": "qdrant", "pgvector": "postgres",
"weaviate": "weaviate", "mariadb": "mariadb",
"chroma": None, "lancedb": None, "faiss": None, "milvus": None,
"duckdb": None, "sqlitevec": None,
}
def test_identifier_mappers_never_emit_raw_names():
from thicket.vector_stores import _mariadb_ident, _weaviate_name
assert _mariadb_ident("second-brain; DROP TABLE x") == "second_brain__DROP_TABLE_x"
assert _weaviate_name("second_brain") == "Second_brain"
assert _mariadb_ident("") == "thicket"
def test_doc_key_is_stable_and_injective():
a = doc_key("Title", "path/one.md")
assert a == doc_key("Title", "path/one.md")
assert a != doc_key("Title", "path/two.md")
assert a != doc_key("Other", "path/one.md")
def test_unknown_target_names_every_choice():
with pytest.raises(VectorStoreError, match="known:"):
create_store("vespa", collection="x", dim=4)
def test_missing_module_error_names_the_fix():
from thicket.vector_stores import BaseVectorStore
base = BaseVectorStore("c", 4)
with pytest.raises(VectorStoreError, match="pip install"):
base._require_module("definitely_not_a_module_xyz")
# ── per-target roundtrips (skip when library absent) ──
def test_qdrant_roundtrip_needs_service():
assert TARGETS["qdrant"].service == "qdrant"
def test_chroma_roundtrip(tmp_path):
pytest.importorskip("chromadb")
from thicket.vector_stores import ChromaStore
_roundtrip(ChromaStore(data_dir=tmp_path / "data", collection="test_col", dim=4))
def test_lancedb_roundtrip(tmp_path):
pytest.importorskip("lancedb")
from thicket.vector_stores import LanceStore
_roundtrip(LanceStore(data_dir=tmp_path, collection="t", dim=4))
def test_faiss_roundtrip(tmp_path):
pytest.importorskip("faiss")
from thicket.vector_stores import FaissStore
_roundtrip(FaissStore(data_dir=tmp_path, collection="t", dim=4))
def test_milvus_roundtrip(tmp_path):
pytest.importorskip("pymilvus")
from thicket.vector_stores import MilvusStore
_roundtrip(MilvusStore(data_dir=tmp_path / "data", collection="test_col", dim=4))
def test_duckdb_roundtrip(tmp_path):
pytest.importorskip("duckdb")
from thicket.vector_stores import DuckStore
_roundtrip(DuckStore(data_dir=tmp_path / "data", collection="test_col", dim=4))
def test_sqlitevec_roundtrip(tmp_path):
pytest.importorskip("sqlite_vec")
from thicket.vector_stores import SqliteVecStore
_roundtrip(SqliteVecStore(data_dir=tmp_path / "data", collection="test_col", dim=4))
def test_weaviate_name_mapping():
from thicket.vector_stores import _weaviate_name
assert _weaviate_name("second_brain") == "Second_brain"
assert _weaviate_name("my-papers 2") == "My_papers_2"
def test_payload_carries_code_provenance():
from thicket.chunker import Chunk
from thicket.vector_stores import _payload
chunk = Chunk("H", "def x(): pass", "[S|H]\ndef x(): pass",
kind="code", lang="python", source="pkg/mod.py")
payload = _payload("Doc", "notes/doc.md", chunk, 0)
assert payload["chunk_kind"] == "code"
assert payload["lang"] == "python"
assert payload["source_path"] == "pkg/mod.py"
assert payload["doc_key"]
plain = Chunk("H", "prose", "[S|H]\nprose")
payload = _payload("Doc", "notes/doc.md", plain, 0)
assert "lang" not in payload and "source_path" not in payload

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#!/usr/bin/env python3
"""Thicket launcher — the one-command entry point.
python thicket.py # the console (GUI)
python thicket.py --dry-run # readiness report, no GUI
python thicket.py --ingest DIR --vault DIR # headless batch
Every mode and flag is owned by ``thicket.cli``; this file only finds
the package and defers.
One environment, always: the project venv at <project>/.venv holds
every dependency (bootstrap_sources.sh installs its git-built packages
there). Run via .venv/bin/python, the /mnt/AI/tools/bin/thicket
launcher, or after activating the venv.
"""
import sys
from pathlib import Path
# Guarantee the in-tree package resolves when invoked by path or from
# another directory (sys.path[0] normally covers this; belt-and-braces
# for embedded and interpreter edge cases).
sys.path.insert(0, str(Path(__file__).resolve().parent))
from thicket.cli import main
if __name__ == "__main__":
sys.exit(main())

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"""Thicket — super-ingest + RAG console.
Feed it documents; it grows a thicket: dense, interconnected,
searchable knowledge.
Batch-import PDF / EPUB / Markdown / plain-text documents into a
three-stage "second brain" pipeline:
1. Obsidian vault notes (normalized Markdown + YAML frontmatter)
2. Qdrant vector index (FastEmbed local embeddings)
3. Optional LightRAG knowledge graph (Ollama entity extraction)
GUI is a PySide6 retro-futuristic console (MMD3 lineage, shared with
OpenTranscode). A headless CLI path (``thicket --ingest ... --vault
...``) drives the exact same core modules without Qt.
"""
__version__ = "1.8.1"
__all__ = ["__version__"]

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"""``python -m thicket`` entry point — defers to cli.main()."""
import sys
from .cli import main
if __name__ == "__main__":
sys.exit(main())

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"""Ask — natural-language SQL over the SQL-backed vector targets.
Vanna 2.0 (vanna-ai/vanna, agent-based rewrite) drives an Ollama LLM
with a RunSqlTool pointed at the same Postgres/MariaDB servers Thicket
ingests into. The question is prefixed with the corpus DDL so the
model writes correct SQL; only SELECT-style reads are requested.
Connections follow the same Unix conventions as the vector stores:
PGHOST/PGPORT/PGUSER/PGPASSWORD/PGDATABASE (or PGDSN) for pgvector,
MARIADB_HOST/PORT/USER/PASSWORD/DATABASE for mariadb.
"""
from __future__ import annotations
import asyncio
import os
import tempfile
from collections.abc import Callable
# Targets whose corpus lives in a SQL database Vanna can query.
SQL_TARGETS = ("pgvector", "mariadb")
class AskUnavailable(Exception):
"""Raised when the ask interaction cannot run (target, deps)."""
def _pg_params() -> dict:
if os.environ.get("PGDSN"):
return {"connection_string": os.environ["PGDSN"]}
return {
"host": os.environ.get("PGHOST", "localhost"),
"port": int(os.environ.get("PGPORT", "5432")),
"database": os.environ.get("PGDATABASE", "thicket"),
"user": os.environ.get("PGUSER", "thicket"),
"password": os.environ.get("PGPASSWORD", ""),
}
def _mariadb_params() -> dict:
return {
"host": os.environ.get("MARIADB_HOST", "127.0.0.1"),
"port": int(os.environ.get("MARIADB_PORT", "3306")),
"database": os.environ.get("MARIADB_DATABASE", "thicket"),
"user": os.environ.get("MARIADB_USER", "root"),
"password": os.environ.get("MARIADB_PASSWORD", ""),
}
_RUNNER_PARAMS = {"pgvector": _pg_params, "mariadb": _mariadb_params}
def target_ddl(target: str, collection: str = "second_brain") -> str:
"""Corpus DDL + column documentation for the question context —
the exact shape Thicket's ensure_collection creates."""
match target:
case "pgvector":
return (
f'CREATE TABLE "{collection}" ('
"id TEXT PRIMARY KEY, doc_key TEXT NOT NULL, "
"embedding vector(384), payload JSONB);"
"\n-- payload fields: document_title text, obsidian_path text,"
" section_header text, content text, chunk_index int, doc_key text"
)
case "mariadb":
table = "".join(c if c.isalnum() or c == "_" else "_" for c in collection)
return (
f"CREATE TABLE `{table}` ("
"id VARCHAR(36) PRIMARY KEY, doc_key VARCHAR(64) NOT NULL, "
"embedding VECTOR(384) NOT NULL, payload JSON);"
"\n-- payload fields: document_title, obsidian_path,"
" section_header, content, chunk_index, doc_key"
)
raise AskUnavailable(
f"target '{target}' has no SQL corpus — ask works with: "
f"{', '.join(SQL_TARGETS)}"
)
def _component_text(component) -> str | None:
"""Text from a yielded component — vanna wraps each Rich component
(RichText, DataFrame, status pings) in a UiComponent envelope."""
rich = getattr(component, "rich_component", None) or component
for attr in ("content", "text", "markdown", "value"):
value = getattr(rich, attr, None)
if isinstance(value, str) and value.strip():
return value.strip()
df = getattr(rich, "df", None)
if df is not None and hasattr(df, "to_string"):
return df.head(20).to_string()
return None
class VannaAsker:
"""One question at a time against the corpus database."""
def __init__(self, target: str, collection: str, llm_model: str,
log: Callable[[str], None] = lambda _msg: None):
self._target = target
if target not in SQL_TARGETS:
raise AskUnavailable(
f"ask needs a SQL-backed target ({', '.join(SQL_TARGETS)}) — "
f"current target: '{target}'"
)
try:
from vanna import Agent, AgentConfig
from vanna.core.registry import ToolRegistry
from vanna.core.user import RequestContext, User, UserResolver
from vanna.integrations.ollama import OllamaLlmService
from vanna.tools import RunSqlTool
except ImportError as e:
raise AskUnavailable(
"vanna not installed — run: pip install 'thicket[ask]'"
) from e
class _LocalUserResolver(UserResolver):
"""Single-user resolver: every ask is the same local user."""
async def resolve_user(self, request_context) -> User:
return User(id="thicket-local", username="thicket",
email="thicket@local", group_memberships=["user"])
from vanna.capabilities.agent_memory.base import AgentMemory
class _StatelessMemory(AgentMemory):
"""No persistence between asks — every question stands alone."""
def save_text_memory(self, content, context):
return None
def save_tool_usage(self, question, tool_name, args, context,
success=True, metadata=None):
return None
def get_recent_memories(self, context, limit=10):
return []
def get_recent_text_memories(self, context, limit=10):
return []
def search_similar_usage(self, question, context, *, limit=10,
similarity_threshold=0.7,
tool_name_filter=None):
return []
def search_text_memories(self, query, context, *, limit=10,
similarity_threshold=0.7):
return []
def clear_memories(self, context, tool_name=None, before_date=None):
return 0
def delete_by_id(self, context, memory_id):
return False
def delete_text_memory(self, context, memory_id):
return False
runner_cls = self._runner_cls(target)
runner = runner_cls(**_RUNNER_PARAMS[target]())
# Scope the tool's result-CSV scratch to a temp dir — without
# this, every ask litters a hash-named folder in the cwd.
from vanna.tools import LocalFileSystem
registry = ToolRegistry()
registry.register_local_tool(
RunSqlTool(
sql_runner=runner,
file_system=LocalFileSystem(
working_directory=tempfile.mkdtemp(prefix="thicket-ask-")),
),
access_groups=["user"])
resolver = _LocalUserResolver()
self._context = RequestContext(
remote_addr="127.0.0.1",
metadata={"source": "thicket"},
)
self._log = log
self._agent = Agent(
llm_service=OllamaLlmService(
model=llm_model, host=os.environ.get("OLLAMA_HOST"),
num_ctx=8192, temperature=0.1,
),
config=AgentConfig(stream_responses=False),
tool_registry=registry,
user_resolver=resolver,
agent_memory=_StatelessMemory(),
)
self._ddl = target_ddl(target, collection)
@staticmethod
def _runner_cls(target: str):
if target == "pgvector":
from vanna.integrations.postgres import PostgresRunner
return PostgresRunner
from vanna.integrations.mysql import MySQLRunner
return MySQLRunner
async def _ask_async(self, question: str) -> str:
json_hint = (
"payload->>'document_title'" if self._target == "pgvector"
else "JSON_UNQUOTE(JSON_EXTRACT(payload, '$.document_title'))"
)
prompt = (
"You are querying a knowledge-base corpus. Schema:\n"
f"{self._ddl}\n"
f"JSON fields are read with {json_hint}. "
"Read-only: SELECT queries only. "
"Answer with the final result only.\n\n"
f"Question: {question}"
)
# The stream carries status pings, reasoning, tool calls, and
# the final answer — the last RichText IS the answer.
texts: list[str] = []
async for component in self._agent.send_message(self._context, prompt):
text = _component_text(component)
if text:
texts.append(text)
return texts[-1] if texts else "(no answer produced)"
def ask(self, question: str) -> str:
"""Ask one question; returns the agent's answer text."""
self._log(f"Asking {self._agent.__class__.__name__} "
f"(target corpus, Ollama LLM)...")
answer = asyncio.run(self._ask_async(question))
return answer or "(no answer produced)"
def ask(question: str, target: str, collection: str = "second_brain",
llm_model: str = "llama3", log: Callable[[str], None] = lambda _msg: None) -> str:
"""Convenience one-shot entry for CLI and workers."""
return VannaAsker(target, collection, llm_model, log).ask(question)

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"""Contextual chunker — structure-preserving splitting for technical
corpora.
The chunker treats programming books, system configuration, and policy
documents as what they are: mixed prose and code.
Invariants:
* Fenced code blocks (``` / ~~~) are atomic — a block is never split
mid-listing, never merged with prose, and keeps every newline and
indentation character verbatim. Only oversized blocks (beyond
~2x the budget) are line-windowed, and every window carries the
language tag.
* Prose chunks are paragraph-aligned: paragraphs are never split
mid-way (an oversized paragraph is line-windowed with its lines
preserved), and intra-chunk newlines are kept — lists, commands,
and tables hold their line structure in the stored payload.
* ATX headings require whitespace after the hashes (``^#{1,6}\\s``),
so shebangs (``#!``), machine comments (``#x``), and fenced code
never masquerade as section headers.
* Each chunk carries a contextual prefix — ``[Source: … | Section: …]``
plus ``| code:lang`` for code — which is what gets embedded; the
plain text is stored verbatim.
"""
from __future__ import annotations
import re
from dataclasses import dataclass
# ATX heading: 1-6 hashes, mandatory whitespace, then content — rejects
# "#!/shebang", "#no-space-comment", and bare "#######" runs.
HEADING_RE = re.compile(r"^#{1,6}\s+\S")
# Fenced block opening: ``` or ~~~ with an optional language tag.
FENCE_OPEN_RE = re.compile(r"^(`{3,}|~{3,})\s*(\S*)\s*$")
# Indented block (pandoc-style code): 4+ spaces or a tab, not a list.
INDENT_RE = re.compile(r"^(?: {4,}|\t)\S")
# Code blocks larger than chunk_size * this factor are line-windowed.
OVERSIZED_FACTOR = 2
@dataclass(slots=True)
class Chunk:
header: str
text: str
contextual_text: str
kind: str = "prose" # "prose" | "code"
lang: str | None = None # language tag for code chunks
source: str | None = None # input-tree path of the origin file
def _words(text: str) -> int:
return len(text.split())
class ContextualChunker:
"""Splits markdown-ish text into header-aware, code-preserving
contextual chunks."""
def __init__(self, chunk_size: int = 500, overlap: int = 50):
self.chunk_size = max(1, int(chunk_size))
self.overlap = max(0, int(overlap))
# ── block parsing ──
def _blocks(self, text: str):
"""Yield (kind, body, lang) blocks: fenced code carries its
whole verbatim body; everything else is blank-line-delimited."""
lines = text.split("\n")
i = 0
while i < len(lines):
fence = FENCE_OPEN_RE.match(lines[i])
if fence:
marker, lang = fence.group(1), fence.group(2)
j = i + 1
while j < len(lines) and not lines[j].startswith(marker[:3]):
j += 1
yield ("code", "\n".join(lines[i + 1:j]), lang or None)
i = j + 1
continue
if not lines[i].strip():
i += 1
continue
j = i
while j < len(lines) and lines[j].strip():
j += 1
yield ("para", "\n".join(lines[i:j]), None)
i = j
# ── chunk assembly ──
def chunk(self, title: str, text: str,
source_path: str | None = None) -> list[Chunk]:
chunks: list[Chunk] = []
header = "Introduction"
buffer: list[str] = [] # prose paragraphs, verbatim
def _flush() -> None:
nonlocal buffer
if not buffer:
return
chunks.append(self._prose_chunk(title, header,
"\n\n".join(buffer)))
# Paragraph-level overlap: carry the tail paragraph into
# the next chunk when it fits the overlap budget.
buffer = ([buffer[-1]] if _words(buffer[-1]) <= self.overlap
else [])
for kind, body, lang in self._blocks(text):
if kind == "code":
_flush()
chunks.extend(self._code_chunks(title, header, body, lang))
continue
first_line = body.split("\n", 1)[0]
if "\n" not in body and HEADING_RE.match(first_line):
_flush() # section boundary never straddles chunks
header = first_line.lstrip("#").strip() or header
continue
if len(body.split("\n")) > 1 and all(
INDENT_RE.match(ln) or not ln.strip()
for ln in body.split("\n")):
# Indented block (pandoc-style code): treat as code.
_flush()
chunks.extend(self._code_chunks(
title, header,
re.sub(r"^ {0,4}", "", body, flags=re.M), None))
continue
if buffer and _words("\n\n".join(buffer)) + _words(body) > self.chunk_size:
_flush()
buffer.append(body)
if _words("\n\n".join(buffer)) >= self.chunk_size:
_flush()
_flush()
if source_path:
for chunk in chunks:
chunk.source = source_path
return chunks
# ── chunk constructors ──
def _prose_chunk(self, title: str, header: str, body: str) -> Chunk:
return Chunk(
header=header,
text=body,
contextual_text=f"[Source: {title} | Section: {header}]\n{body}",
kind="prose",
)
def _code_chunk(self, title: str, header: str, body: str,
lang: str | None) -> Chunk:
tag = f" | code:{lang}" if lang else " | code"
return Chunk(
header=header,
text=body,
contextual_text=f"[Source: {title} | Section: {header}{tag}]\n{body}",
kind="code",
lang=lang,
)
def _code_chunks(self, title: str, header: str, body: str,
lang: str | None) -> list[Chunk]:
"""One atomic code chunk — or line windows when the block is
oversized; every window keeps the language tag."""
if _words(body) <= self.chunk_size * OVERSIZED_FACTOR:
return [self._code_chunk(title, header, body, lang)]
lines = body.split("\n")
windows: list[list[str]] = []
current: list[str] = []
for line in lines:
current.append(line)
if _words("\n".join(current)) >= self.chunk_size:
windows.append(current)
# Two tail lines carry into the next window as overlap.
current = current[-2:]
if current and (not windows or current != windows[-1]):
windows.append(current)
return [self._code_chunk(title, header, "\n".join(w), lang)
for w in windows]

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"""Command-line interface for Thicket.
Three modes:
- ``--version`` — print the package version and exit.
- ``--dry-run`` — probe the environment (Python modules,
Qdrant service, Ollama service), print a readiness report, exit.
Does NOT launch the GUI and ingests nothing.
- ``--ingest DIR --vault DIR`` — headless batch ingest using the same
core pipeline modules the GUI drives. No Qt is imported on this
path, so it works over SSH / cron.
With no flags, ``main()`` defers to ``ui_window.launch_gui()``.
Heavy imports (``ui_window``, pipeline modules) are deferred into the
branches that need them so that ``--version`` / ``--dry-run`` start
instantly and never touch PySide6.
"""
from __future__ import annotations
import argparse
import sys
from pathlib import Path
def build_parser() -> argparse.ArgumentParser:
"""Build the CLI argument parser."""
parser = argparse.ArgumentParser(
prog="thicket",
description="Super-ingest + RAG console: documents -> Obsidian + Qdrant + LightRAG",
)
parser.add_argument(
"--version", action="store_true",
help="Print version and exit",
)
parser.add_argument(
"--dry-run", action="store_true",
help="Probe environment (modules, Qdrant, Ollama) and print a "
"readiness report — do NOT launch GUI or ingest anything",
)
parser.add_argument(
"--ingest", metavar="DIR",
help="Headless mode: batch-ingest this directory (no GUI)",
)
parser.add_argument(
"--vault", metavar="DIR",
help="Obsidian vault root (required with --ingest)",
)
from .vector_stores import TARGETS as _VECTOR_TARGETS
parser.add_argument(
"--target", default="qdrant",
choices=["obsidian"] + list(_VECTOR_TARGETS),
help="Ingest destination: 'obsidian' = notes only; any vector "
"store key = notes + that store (qdrant default; most run "
"embedded under <vault>/.thicket/; pgvector/mariadb use "
"PG* / MARIADB_* env)",
)
parser.add_argument(
"--host", default="localhost",
help="Service host for service-backed targets (qdrant default "
"6333, weaviate 8080; qdrant target only)",
)
parser.add_argument(
"--port", type=int, default=6333,
help="Service port for service-backed targets (qdrant 6333, "
"weaviate 8080)",
)
parser.add_argument(
"--collection", default="second_brain",
help="Qdrant collection name (default: second_brain)",
)
from .embedder import DEFAULT_EMBED_MODEL
parser.add_argument(
"--embed-model", default=DEFAULT_EMBED_MODEL,
help=f"FastEmbed embedding model (default: {DEFAULT_EMBED_MODEL} — "
"code/config-strong; see thicket.embedder catalog)",
)
parser.add_argument(
"--chunk-size", type=int, default=400,
help="Chunk size in words (default: 400)",
)
parser.add_argument(
"--overlap", type=int, default=50,
help="Chunk overlap in words (default: 50)",
)
parser.add_argument(
"--no-vault", action="store_true",
help="Headless mode: skip the Obsidian vault-note stage "
"(vectors/graph only)",
)
parser.add_argument(
"--no-qdrant", action="store_true",
help="Headless mode: skip the Qdrant vector stage (vault notes only)",
)
parser.add_argument(
"--lightrag", action="store_true",
help="Headless mode: also run the knowledge-graph stage",
)
parser.add_argument(
"--graph-engine", default="lightrag", choices=["lightrag", "graphify"],
help="Knowledge-graph engine (default: lightrag; graphify builds "
"graph.json + HTML report via one local Ollama pass)",
)
parser.add_argument(
"--notes-dir", default="Ingested_Brain",
help="Vault subdirectory for generated notes (default: Ingested_Brain)",
)
parser.add_argument(
"--skip-unchanged", action="store_true",
help="Skip files whose content hash matches the last ingest "
"(manifest under <vault>/.thicket/)",
)
parser.add_argument(
"--max-mb", type=int, default=0,
help="Skip files larger than this many megabytes (default: 0 = no limit)",
)
parser.add_argument(
"--archive", action="store_true",
help="After verified ingest, move each source to the archive dir "
"(default: <input>/../ingested-archive) and bzip2 it — "
"sources are never deleted",
)
parser.add_argument(
"--archive-dir", metavar="DIR",
help="Override the ingested-archive directory (default: "
"sibling of the input dir)",
)
parser.add_argument(
"--minio", action="store_true",
help="Headless mode: archive source documents to MinIO "
"(env: MINIO_ENDPOINT/ACCESS_KEY/SECRET_KEY/BUCKET)",
)
parser.add_argument(
"--ask", metavar="QUESTION",
help="Ask a natural-language question over the SQL corpus of the "
"selected target (pgvector / mariadb; Vanna 2 + Ollama) — "
"no GUI, no ingest",
)
parser.add_argument(
"--ollama-llm", default="llama3",
help="Ollama LLM for LightRAG entity extraction (default: llama3)",
)
parser.add_argument(
"--ollama-embed", default="nomic-embed-text",
help="Ollama embedding model for LightRAG (default: nomic-embed-text)",
)
return parser
def run_dry_run(qdrant_host: str, qdrant_port: int) -> int:
"""Probe the environment and print a readiness report."""
from .env_probe import PROBED_MODULES, probe_environment
env = probe_environment(qdrant_host=qdrant_host, qdrant_port=qdrant_port)
def _module_line(name: str) -> str:
tag = "OK" if env.modules.get(name) else "MISSING"
note = " (optional — graph stage)" if name == "lightrag" else ""
return f" {name:14s} {tag}{note}"
def _service_line(name: str, up: bool, detail: str, error: str | None) -> str:
state = f"UP — {detail}" if up else f"DOWN ({error or 'no response'})"
return f"{name}: {state}"
def _ollama_detail() -> str:
shown = env.ollama_models[:12]
extra = len(env.ollama_models) - len(shown)
return ", ".join(shown) + (f" … (+{extra} more)" if extra > 0 else "") \
or "(no models pulled)"
print("=== Thicket environment probe ===")
print(f"Python: {env.python_version}")
print("Modules:")
print("\n".join(_module_line(name) for name in PROBED_MODULES))
print(_service_line("Qdrant", env.qdrant_up,
", ".join(env.qdrant_collections), env.qdrant_error))
print(_service_line("Postgres", env.pg_up, "reachable", env.pg_error))
print(_service_line("MinIO", env.minio_up, "reachable", env.minio_error))
print(_service_line("Ollama", env.ollama_up,
_ollama_detail(), env.ollama_error))
print()
if env.qdrant_ready:
print("READY: vault + Qdrant stages available.")
else:
print("PARTIAL: vault stage available; install '.[ingest]' extras and/or")
print("start Qdrant (docker run -p 6333:6333 qdrant/qdrant) for vectors.")
return 0
def run_headless(args: argparse.Namespace) -> int:
"""Drive the core pipeline without Qt — the GUI's worker logic,
reduced to sequential prints."""
from .pipeline_core import headless_ingest
input_dir = Path(args.ingest).expanduser()
vault_dir = Path(args.vault).expanduser() if args.vault else None
if not input_dir.is_dir():
print(f"Error: input directory '{input_dir}' does not exist.", file=sys.stderr)
return 1
if vault_dir is None:
print("Error: --vault is required with --ingest.", file=sys.stderr)
return 1
if not vault_dir.is_dir():
print(f"Error: vault directory '{vault_dir}' does not exist.", file=sys.stderr)
return 1
try:
ok, fail = headless_ingest(
input_dir=input_dir,
vault_dir=vault_dir,
qdrant_host=args.host,
qdrant_port=args.port,
target=args.target if args.target != "obsidian" else "qdrant",
collection=args.collection,
embed_model=args.embed_model,
chunk_size=args.chunk_size,
overlap=args.overlap,
use_vault=not args.no_vault,
use_qdrant=(args.target != "obsidian") and not args.no_qdrant,
use_lightrag=args.lightrag,
use_minio=args.minio,
graph_engine=args.graph_engine,
notes_dir=args.notes_dir,
skip_unchanged=args.skip_unchanged,
use_fs_archive=args.archive,
archive_dir=Path(args.archive_dir).expanduser()
if args.archive_dir else None,
max_mb=args.max_mb,
ollama_llm=args.ollama_llm,
ollama_embed=args.ollama_embed,
)
except KeyboardInterrupt:
return 130 # POSIX: 128 + SIGINT
print(f"\nDone — {ok} succeeded, {fail} failed.")
return 0 if fail == 0 else 2
def run_ask(args: argparse.Namespace) -> int:
"""Natural-language SQL over the corpus via Vanna + Ollama."""
from .ask_vanna import ask
try:
answer = ask(args.ask, target=args.target, collection=args.collection,
llm_model=args.ollama_llm,
log=lambda msg: print(f"> {msg}"))
except Exception as e: # noqa: BLE001 — surface every failure readably
print(f"ASK ERROR: {e}", file=sys.stderr)
return 1
print(f"\n{answer}")
return 0
def main(argv: list[str] | None = None) -> int:
"""Entry point — backend profiles first, then arg dispatch."""
from .layout import apply_backend_profiles
apply_backend_profiles()
# Step-down by mode: --version, --dry-run, and --ingest each run
# Qt-free and exit; only the no-flag default loads the GUI.
parser = build_parser()
args = parser.parse_args(argv)
if args.version:
from . import __version__
print(f"thicket {__version__}")
return 0
if args.dry_run:
return run_dry_run(args.host, args.port)
if args.ingest:
return run_headless(args)
if args.ask:
return run_ask(args)
# No flag — launch the GUI.
try:
from .ui_window import launch_gui
except ImportError as e:
print(
f"GUI unavailable: {e}\n"
f"Run the console with its environment:\n"
f" thicket (~/.local/bin command)\n"
f" /mnt/AI/runtime/thicket-venv/bin/python thicket.py\n"
f"Or bootstrap it: scripts/bootstrap_sources.sh",
file=sys.stderr,
)
return 1
return launch_gui(argv)
if __name__ == "__main__":
sys.exit(main())

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"""Local embedding engine — FastEmbed wrapper.
FastEmbed runs ONNX models fully locally (no API keys, no network
after the first model download). The catalog maps each model to its
vector dimension so the Qdrant collection is created with matching
geometry. BGE models want a short instruction prefix on *query* side
only — passages are embedded bare.
"""
from __future__ import annotations
# Curated catalog for technical corpora — code/config-heavy
# libraries want jina-code (English+code, 8k context); bge-base is the
# prose-strong alternative; bge-small for light setups.
# model name -> embedding dimension
EMBEDDING_MODELS: dict[str, int] = {
"jinaai/jina-embeddings-v2-base-code": 768,
"BAAI/bge-base-en-v1.5": 768,
"BAAI/bge-small-en-v1.5": 384,
}
DEFAULT_EMBED_MODEL = "jinaai/jina-embeddings-v2-base-code"
# BGE retrieval instruction — prefix for QUERIES only, never passages.
BGE_QUERY_PREFIX = "Represent this sentence for searching relevant passages: "
class EmbedderUnavailable(Exception):
"""Raised when fastembed is missing or the model cannot load."""
def model_dim(model_name: str) -> int:
"""Dimension for a catalog model (0 if unknown — Qdrant will tell us)."""
return EMBEDDING_MODELS.get(model_name, 0)
class EmbeddingEngine:
"""Lazy-loading FastEmbed engine. Construct anywhere; call load()
from the worker thread (first call may download the model)."""
def __init__(self, model_name: str = DEFAULT_EMBED_MODEL):
self.model_name = model_name
self._model = None
@property
def dim(self) -> int:
return EMBEDDING_MODELS.get(self.model_name, 0)
def load(self) -> None:
"""Import fastembed and load the model. Safe to call twice."""
if self._model is not None:
return
try:
from fastembed import TextEmbedding
except ImportError as e:
raise EmbedderUnavailable(
"fastembed not installed — run: pip install 'thicket[ingest]'"
) from e
try:
self._model = TextEmbedding(model_name=self.model_name)
except Exception as e:
raise EmbedderUnavailable(
f"failed to load embedding model '{self.model_name}': {e}"
) from e
def embed(self, texts: list[str]) -> list[list[float]]:
"""Embed passages (no instruction prefix). Order preserved.
Values are normalized to plain Python floats: FastEmbed yields
numpy scalars, which some targets' validators reject."""
self.load()
return [[float(x) for x in vec] for vec in self._model.embed(texts)]
def embed_query(self, text: str) -> list[float]:
"""Embed a retrieval query with the BGE instruction prefix
when the selected model is from the BGE family."""
self.load()
query = BGE_QUERY_PREFIX + text if "bge" in self.model_name.lower() else text
return [float(x) for x in next(iter(self._model.embed([query])))]

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"""Environment probe — readiness report shown at startup.
Checks (all cheap, all non-fatal):
* Python module availability for every pipeline dependency
(importlib.util.find_spec — no heavy imports).
* Qdrant service reachability (get_collections with a short timeout).
* Ollama service reachability (GET /api/tags, also lists installed
models so the UI can pre-fill the model combo).
The GUI runs this in a background thread because the FastEmbed model
check and service pings can take seconds on a cold start.
"""
from __future__ import annotations
import importlib.util
import json
import os
import platform
import urllib.request
from dataclasses import dataclass, field
# module name -> role in the pipeline
PROBED_MODULES: dict[str, str] = {
"bs4": "EPUB parsing",
"ebooklib": "EPUB parsing",
"pypdf": "PDF parsing",
"slugify": "vault note filenames",
"fastembed": "local embeddings",
"qdrant_client": "vector target: qdrant",
"chromadb": "vector target: chroma (optional)",
"lancedb": "vector target: lancedb (optional)",
"faiss": "vector target: faiss (optional)",
"pymilvus": "vector target: milvus lite (optional)",
"weaviate": "vector target: weaviate (optional)",
"psycopg": "vector target: pgvector (optional)",
"pgvector": "vector target: pgvector (optional)",
"duckdb": "vector target: duckdb (optional)",
"sqlite_vec": "vector target: sqlite-vec (optional)",
"pymysql": "vector target: mariadb (optional)",
"graphify": "graph engine (optional)",
"vanna": "ask interaction (optional)",
"minio": "object archive (optional)",
"lightrag": "knowledge graph (optional)",
}
def _module_available(name: str) -> bool:
try:
return importlib.util.find_spec(name) is not None
except (ImportError, ValueError):
return False
@dataclass(slots=True)
class EnvProbe:
python_version: str = ""
modules: dict[str, bool] = field(default_factory=dict)
qdrant_up: bool = False
qdrant_error: str | None = None
qdrant_collections: list[str] = field(default_factory=list)
ollama_up: bool = False
ollama_error: str | None = None
ollama_models: list[str] = field(default_factory=list)
pg_up: bool = False
pg_error: str | None = None
weaviate_up: bool = False
weaviate_error: str | None = None
mariadb_up: bool = False
mariadb_error: str | None = None
minio_up: bool = False
minio_error: str | None = None
@property
def vault_ready(self) -> bool:
"""Vault-note stage: only needs slugify."""
return self.modules.get("slugify", False)
@property
def qdrant_ready(self) -> bool:
"""Vector stage: client + embedder libs AND a reachable service."""
return (
self.modules.get("fastembed", False)
and self.modules.get("qdrant_client", False)
and self.qdrant_up
)
@property
def graph_ready(self) -> bool:
"""Graph stage: lightrag installed AND Ollama reachable."""
return self.modules.get("lightrag", False) and self.ollama_up
# service key -> EnvProbe attribute carrying its liveness
SERVICE_LIVENESS = {"qdrant": "qdrant_up", "postgres": "pg_up",
"weaviate": "weaviate_up", "mariadb": "mariadb_up"}
def vector_ready(self, target: str) -> bool:
"""Readiness for a specific vector target: modules present,
plus the live service when the target is service-backed."""
from .vector_stores import TARGETS
spec = TARGETS.get(target)
if spec is None:
return False
if not all(self.modules.get(m, False) for m in spec.modules):
return False
live = self.SERVICE_LIVENESS.get(spec.service) if spec.service else None
return live is None or getattr(self, live, False)
@property
def ingest_ready(self) -> bool:
"""At least one parse path for every declared extension."""
return (
self.modules.get("pypdf", False)
and self.modules.get("bs4", False)
and self.modules.get("ebooklib", False)
and self.modules.get("slugify", False)
)
def _probe_qdrant(host: str, port: int, env: EnvProbe) -> None:
try:
from qdrant_client import QdrantClient
except ImportError:
env.qdrant_error = "qdrant-client not installed"
return
try:
# Connectivity ping only — no client/server version negotiation.
client = QdrantClient(url=f"http://{host}:{port}", timeout=3,
check_compatibility=False)
env.qdrant_collections = sorted(
c.name for c in client.get_collections().collections
)
env.qdrant_up = True
except Exception as e: # noqa: BLE001 — any failure means "down"
env.qdrant_error = str(e).splitlines()[0][:100]
def _probe_ollama(host: str, env: EnvProbe, port: int = 11434) -> None:
url = f"http://{host}:{port}/api/tags"
try:
with urllib.request.urlopen(url, timeout=3) as resp:
data = json.loads(resp.read().decode("utf-8", errors="ignore"))
env.ollama_models = sorted(
m.get("name", "?") for m in data.get("models", [])
)
env.ollama_up = True
except Exception as e: # noqa: BLE001
env.ollama_error = str(e)[:100]
def _probe_postgres(env: EnvProbe) -> None:
"""Env-driven like the MariaDB probe: PGHOST/PGPORT/PGUSER/
PGPASSWORD/PGDATABASE (or PGDSN) — the same variables the
pgvector target documents."""
import os
try:
import psycopg
except ImportError:
env.pg_error = "psycopg not installed"
return
kwargs = {"connect_timeout": 3}
if os.environ.get("PGDSN"):
conn_kwargs = {"dsn": os.environ["PGDSN"], **kwargs}
else:
conn_kwargs = {**kwargs, "host": os.environ.get("PGHOST", "localhost"),
"port": int(os.environ.get("PGPORT", "5432")),
"user": os.environ.get("PGUSER"),
"password": os.environ.get("PGPASSWORD") or None,
"dbname": os.environ.get("PGDATABASE")}
try:
with psycopg.connect(**conn_kwargs) as conn:
conn.execute("SELECT 1")
env.pg_up = True
except Exception as e: # noqa: BLE001 — any failure means "down"
env.pg_error = str(e).splitlines()[0][:100]
def _probe_minio(env: EnvProbe) -> None:
"""Ping the S3-compatible health endpoint — no client library needed."""
import urllib.request
endpoint = os.environ.get("MINIO_ENDPOINT", "localhost:9000")
host = endpoint if endpoint.startswith("http") else f"http://{endpoint}"
try:
with urllib.request.urlopen(f"{host}/minio/health/live", timeout=2):
env.minio_up = True
except Exception as e: # noqa: BLE001
env.minio_error = str(e)[:80]
def _probe_weaviate(env: EnvProbe, ports: tuple[int, ...] = (8080,)) -> None:
"""Ping /v1/.well-known/ready over HTTP — no client library needed."""
import urllib.request
for port in ports:
url = f"http://localhost:{port}/v1/.well-known/ready"
try:
with urllib.request.urlopen(url, timeout=2) as resp:
if resp.status == 200:
env.weaviate_up = True
return
except Exception:
continue
env.weaviate_error = "no response on ports " + ", ".join(map(str, ports))
def _probe_mariadb(env: EnvProbe) -> None:
try:
import pymysql
except ImportError:
env.mariadb_error = "pymysql not installed"
return
import os
try:
conn = pymysql.connect(
host=os.environ.get("MARIADB_HOST", "127.0.0.1"),
port=int(os.environ.get("MARIADB_PORT", "3306")),
user=os.environ.get("MARIADB_USER", "root"),
password=os.environ.get("MARIADB_PASSWORD", ""),
unix_socket=os.environ.get("MARIADB_UNIX_SOCKET") or None,
connect_timeout=2,
)
with conn.cursor() as cur:
cur.execute("SELECT 1")
conn.close()
env.mariadb_up = True
except Exception as e: # noqa: BLE001
env.mariadb_error = str(e).splitlines()[0][:100]
def probe_environment(qdrant_host: str = "localhost",
qdrant_port: int = 6333,
ollama_host: str = "localhost") -> EnvProbe:
"""Gather the full readiness report. Never raises."""
env = EnvProbe(python_version=platform.python_version())
env.modules = {name: _module_available(name) for name in PROBED_MODULES}
_probe_qdrant(qdrant_host, qdrant_port, env)
_probe_ollama(ollama_host, env)
_probe_postgres(env)
_probe_weaviate(env)
_probe_mariadb(env)
_probe_minio(env)
return env

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"""Document extraction — PDF / EPUB / Markdown / plain text.
Extracts ``(title, text)`` from every supported format. Structural
headers are preserved in the extracted text (PDF pages become
``## Page N`` headings, EPUB chapters are joined with rules) so the
chunker can attribute each chunk to its section.
All third-party parsers are imported lazily inside the extract methods:
the GUI must launch and probe on a bare system where EbookLib or pypdf
are not installed, and fail with an actionable message only when the
matching file type is actually encountered.
"""
from __future__ import annotations
import re
from pathlib import Path
# Source files ingested verbatim as language-tagged code blocks.
CODE_LANGUAGES: dict[str, str] = {
".py": "python", ".sh": "bash", ".bash": "bash", ".zsh": "zsh",
".yaml": "yaml", ".yml": "yaml", ".toml": "toml", ".ini": "ini",
".conf": "ini", ".cfg": "ini", ".json": "json", ".sql": "sql",
".rs": "rust", ".go": "go", ".c": "c", ".h": "c", ".cpp": "cpp",
".js": "javascript", ".ts": "typescript", ".tf": "hcl", ".nix": "nix",
}
SUPPORTED_EXTENSIONS = frozenset(
{".pdf", ".epub", ".md", ".markdown", ".txt"}
| set(CODE_LANGUAGES)
)
class ExtractionError(Exception):
"""Raised when a document cannot be parsed (missing dep, bad file)."""
def scan_files(
input_dir: Path,
extensions: frozenset[str] | set[str] = SUPPORTED_EXTENSIONS,
) -> list[Path]:
"""Recursively list supported documents under *input_dir*, sorted
for deterministic queue order."""
return sorted(
(p for p in input_dir.rglob("*")
if p.is_file() and p.suffix.lower() in extensions),
key=lambda p: str(p).lower(),
)
def _title_from_stem(filepath: Path) -> str:
return filepath.stem.replace("-", " ").replace("_", " ").title()
class DocumentExtractor:
"""Extracts raw text and structural headers from supported formats."""
@staticmethod
def extract(filepath: Path) -> tuple[str, str]:
"""Dispatch on extension via the runner table. Raises
ExtractionError for unsupported types or missing parser
dependencies."""
runner = _EXTENSION_RUNNERS.get(filepath.suffix.lower())
if runner is None:
raise ExtractionError(f"unsupported file type: {filepath.suffix}")
return runner(filepath)
@staticmethod
def extract_md_txt(filepath: Path) -> tuple[str, str]:
text = filepath.read_text(encoding="utf-8", errors="ignore")
return _title_from_stem(filepath), text
@staticmethod
def extract_code(filepath: Path) -> tuple[str, str]:
"""Source/config file: verbatim content wrapped in a
language-tagged fence so the chunker treats it as one code
document and the vault note renders it as a listing."""
lang = CODE_LANGUAGES.get(filepath.suffix.lower(), "")
body = filepath.read_text(encoding="utf-8", errors="ignore")
return _title_from_stem(filepath), f"```{lang}\n{body}\n```"
@staticmethod
def extract_pdf(filepath: Path) -> tuple[str, str]:
try:
from pypdf import PdfReader
except ImportError as e:
raise ExtractionError(
"pypdf not installed — run: pip install 'thicket[ingest]'"
) from e
reader = PdfReader(str(filepath))
title = _title_from_stem(filepath)
# Prefer the embedded metadata title when present.
try:
if reader.metadata and reader.metadata.title:
title = str(reader.metadata.title).strip() or title
except Exception:
pass # malformed metadata must not kill the extract
pages_text: list[str] = []
for i, page in enumerate(reader.pages):
try:
txt = page.extract_text() or ""
except Exception:
txt = ""
if txt.strip():
pages_text.append(f"## Page {i + 1}\n\n{txt}")
return title, "\n\n".join(pages_text)
@staticmethod
def extract_epub(filepath: Path) -> tuple[str, str]:
try:
import bs4
from ebooklib import epub, ITEM_DOCUMENT
except ImportError as e:
raise ExtractionError(
"ebooklib / beautifulsoup4 not installed — run: "
"pip install 'thicket[ingest]'"
) from e
book = epub.read_epub(str(filepath))
title = _title_from_stem(filepath)
meta_titles = book.get_metadata("DC", "title")
if meta_titles:
title = str(meta_titles[0][0]).strip() or title
chapters: list[str] = []
for item in book.get_items_of_type(ITEM_DOCUMENT):
soup = bs4.BeautifulSoup(item.get_content(), "html.parser")
text = soup.get_text(separator="\n")
clean_text = re.sub(r"\n+", "\n", text).strip()
if clean_text:
chapters.append(clean_text)
return title, "\n\n---\n\n".join(chapters)
# Extension dispatch table — the single source of routing for extract().
# Adding a format means adding one entry here and its extractor method.
_EXTENSION_RUNNERS: dict[str, object] = {
".md": DocumentExtractor.extract_md_txt,
".markdown": DocumentExtractor.extract_md_txt,
".txt": DocumentExtractor.extract_md_txt,
".pdf": DocumentExtractor.extract_pdf,
".epub": DocumentExtractor.extract_epub,
**{ext: DocumentExtractor.extract_code for ext in CODE_LANGUAGES},
}

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"""Knowledge-graph engines (optional graph stage).
Two engines share one contract (``ingest_document`` + ``finalize``):
* lightrag — per-document entity extraction into a merged graph
(Ollama LLM + Ollama embeddings). The LightRAG package renamed
its Ollama bindings across releases, so binding functions are
resolved by stepping through the known candidate names.
* graphify — stages extracted documents as Markdown, then one
``graphify extract --backend ollama`` pass builds graph.json,
GRAPH_REPORT.md, and the interactive graph.html (Graphify-Labs).
When an engine cannot be constructed — missing package, unknown API
generation, unreachable service — it raises GraphUnavailable with the
reason, and the stage reports unavailable with that reason.
"""
from __future__ import annotations
import hashlib
from collections.abc import Callable
from pathlib import Path
class GraphUnavailable(Exception):
"""Raised when a graph engine cannot be constructed."""
def _resolve_ollama_binding():
"""Return (complete_fn, embed_fn) from whichever naming generation
of lightrag is installed, or raise GraphUnavailable."""
try:
import lightrag.llm.ollama as _ollama_mod
except ImportError as e:
raise GraphUnavailable(
"lightrag not installed (or too old) — run: pip install 'thicket[graph]'"
) from e
complete = next(
(name for name in ("ollama_model_complete", "ollama_complete")
if hasattr(_ollama_mod, name)), None
)
embed = next(
(name for name in ("ollama_embed", "ollama_embedding")
if hasattr(_ollama_mod, name)), None
)
if not complete or not embed:
raise GraphUnavailable(
"installed lightrag exposes no known Ollama binding "
f"(found complete={complete!r}, embed={embed!r}) — pin "
"lightrag-hku to a release matching this code"
)
return getattr(_ollama_mod, complete), getattr(_ollama_mod, embed)
class LightRAGGraphStore:
"""Extracts entities and relationships via LightRAG + Ollama."""
def __init__(self, working_dir: Path, llm_model: str = "llama3",
embed_model: str = "nomic-embed-text",
ollama_host: str = "localhost",
log: Callable[[str], None] = lambda _msg: None):
try:
from lightrag import LightRAG
from lightrag.utils import EmbeddingFunc
except ImportError as e:
raise GraphUnavailable(
"lightrag not installed — run: pip install 'thicket[graph]'"
) from e
self.working_dir = working_dir / ".lightrag"
self.working_dir.mkdir(parents=True, exist_ok=True)
complete_fn, embed_fn = _resolve_ollama_binding()
# nomic-embed-text (the default Ollama embedding model) is 768-dim.
# num_ctx 16384 keeps entity extraction from truncating mid-chunk.
self._log = log
self._embed_model = embed_model
self.rag = LightRAG(
working_dir=str(self.working_dir),
llm_model_func=complete_fn,
llm_model_name=llm_model,
llm_model_kwargs={"options": {"num_ctx": 16384}},
embedding_func=EmbeddingFunc(
embedding_dim=768,
max_token_size=8192,
func=lambda texts: embed_fn(texts, embed_model=embed_model),
),
)
def ingest_document(self, title: str, text: str) -> None:
"""Insert one document through the full async lifecycle —
pipeline status, storages initialize, ainsert, finalize —
inside one event loop (LightRAG >= 1.5 binds its shared
storage to the running loop)."""
if not text.strip():
return
import asyncio
formatted = f"Document Title: {title}\n\n{text}"
async def _run() -> None:
try:
from lightrag.kg.shared_storage import (
initialize_pipeline_status,
)
await initialize_pipeline_status(
workspace=str(self.working_dir))
except ImportError:
pass # step-down: releases without the handshake need no init
await self.rag.initialize_storages()
try:
await self.rag.ainsert(formatted)
finally:
await self.rag.finalize_storages()
asyncio.run(_run())
# ──────────────────────────────────────────────────────────────────
# Graphify engine
# ──────────────────────────────────────────────────────────────────
class GraphifyGraphStore:
"""Graphify-Labs graphify: extracted documents are staged as
Markdown, then one ``graphify extract --backend ollama`` pass at
finalize() builds the queryable graph (graph.json), report, and
interactive HTML under <working_dir>/graphify-out/."""
def __init__(self, working_dir: Path, llm_model: str = "llama3",
embed_model: str = "nomic-embed-text",
log: Callable[[str], None] = lambda _msg: None):
import importlib.util
if importlib.util.find_spec("graphify") is None:
raise GraphUnavailable(
"graphify not installed — run: pip install 'thicket[graphify]'"
)
self._log = log
self._llm_model = llm_model
self.working_dir = working_dir / ".graphify"
self.corpus_dir = self.working_dir / "corpus"
self.corpus_dir.mkdir(parents=True, exist_ok=True)
self._staged = 0
def ingest_document(self, title: str, text: str) -> None:
"""Stage one document as Markdown for the batch build."""
if not text.strip():
return
from slugify import slugify
slug = slugify(title) or "untitled"
target = self.corpus_dir / f"{slug}.md"
digest = hashlib.md5(text.encode()).hexdigest()[:6]
if target.exists(): # same title, different content — sibling file
target = self.corpus_dir / f"{slug}-{digest}.md"
target.write_text(f"# {title}\n\n{text}", encoding="utf-8")
self._staged += 1
def finalize(self) -> None:
"""One graphify pass over the staged corpus (local Ollama LLM;
concurrency 1 — local models do not parallelize well)."""
if not self._staged:
return
import subprocess
import sys
cmd = [sys.executable, "-m", "graphify", "extract",
str(self.corpus_dir), "--backend", "ollama",
"--model", self._llm_model, "--max-concurrency", "1",
"--out", str(self.working_dir)]
self._log(f"Graphify: building graph from {self._staged} staged "
f"document(s) (local Ollama pass)...")
result = subprocess.run(cmd, capture_output=True, text=True,
timeout=3600)
if result.returncode != 0:
raise GraphUnavailable(
f"graphify extract failed: "
f"{(result.stderr or result.stdout).strip()[:300]}"
)
graph_json = self.working_dir / "graphify-out" / "graph.json"
self._log(f"Graphify: graph built at '{graph_json}' "
f"(query it: graphify query \"...\" --graph {graph_json})")
# ──────────────────────────────────────────────────────────────────
# Engine registry — the single dispatch point
# ──────────────────────────────────────────────────────────────────
GRAPH_ENGINES: dict[str, dict] = {
"lightrag": {"class": LightRAGGraphStore, "modules": ("lightrag",)},
"graphify": {"class": GraphifyGraphStore, "modules": ("graphify",)},
}
DEFAULT_GRAPH_ENGINE = "lightrag"
def create_graph(engine: str, working_dir: Path, llm_model: str,
embed_model: str,
log: Callable[[str], None] = lambda _msg: None):
"""Build the named engine; raises GraphUnavailable with the known
choices for an unknown name."""
spec = GRAPH_ENGINES.get(engine)
if spec is None:
known = ", ".join(sorted(GRAPH_ENGINES))
raise GraphUnavailable(
f"unknown graph engine '{engine}' — known: {known}"
)
return spec["class"](working_dir=working_dir, llm_model=llm_model,
embed_model=embed_model, log=log)

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"""AI workstation layout awareness.
/mnt/AI is the canonical AI filesystem: a deliberate taxonomy where
corpus/cold is the incoming raw pile, corpus/hot is the active brain
("indexed in Vector DBs and used by agents" — Thicket's vault concept:
notes + .thicket/ vector data + graphs in one portable tree), and
corpus/books holds the standing library.
When the layout exists, Thicket adopts its paths as defaults (IN,
VAULT, archive) and the probe reports what it found. When it does not,
home-directory defaults apply — behavior is identical either way.
``expand_path`` is the single point where user-supplied paths expand
``~`` and ``$VAR`` references — the GUI, CLI, and core all resolve
identically.
"""
from __future__ import annotations
import os
from dataclasses import dataclass
from pathlib import Path
# Root override for tests / alternative hosts.
AI_ROOT = Path(os.environ.get("THICKET_AI_ROOT", "/mnt/AI"))
# Canonical subtrees Thicket cares about. The corpus trio carries the
# ingest flow; the rest are reported for context.
@dataclass(frozen=True, slots=True)
class LayoutProfile:
"""Resolved view of the AI filesystem — only what exists."""
root: Path
corpus_cold: Path # incoming / raw
corpus_hot: Path # active brain (vault default)
corpus_books: Path # standing library
corpus_obsidian: Path # dedicated vault alternative
archive: Path # ingested-archive home
books_present: bool
def apply_backend_profiles() -> dict[str, str]:
"""Load connection profiles from <AI_ROOT>/backends/thicket.env.
Precedence is unix-ordered: the real environment wins (variables
already set are untouched), the profile fills the gaps, built-in
defaults apply last. KEY=VALUE lines; blank lines and # comments
ignored. Returns the variables the file contributed.
"""
profile = AI_ROOT / "backends" / "thicket.env"
if not profile.is_file():
return {}
applied: dict[str, str] = {}
for line in profile.read_text(encoding="utf-8", errors="ignore").splitlines():
line = line.strip()
if not line or line.startswith("#") or "=" not in line:
continue
key, _, value = line.partition("=")
key, value = key.strip(), value.strip().strip('"').strip("'")
if key and key not in os.environ:
os.environ[key] = value
applied[key] = value
return applied
def expand_path(value: str | Path) -> Path:
"""Expand ~ and $VAR references to a concrete Path."""
return Path(os.path.expandvars(os.path.expanduser(str(value))))
def detect_layout() -> LayoutProfile | None:
"""The layout profile when the corpus taxonomy exists, else None."""
corpus = AI_ROOT / "corpus"
if not corpus.is_dir():
return None
return LayoutProfile(
root=AI_ROOT,
corpus_cold=corpus / "cold",
corpus_hot=corpus / "hot",
corpus_books=corpus / "books",
corpus_obsidian=corpus / "obsidian",
archive=corpus / "archive",
books_present=(corpus / "books").is_dir(),
)
def default_input() -> Path:
"""IN default: the cold corpus when the layout exists."""
profile = detect_layout()
if profile and profile.corpus_cold.is_dir():
return profile.corpus_cold
return Path.home() / "Downloads" / "Raw_Books_And_Papers"
def default_vault() -> Path:
"""VAULT default: the hot corpus — one tree holding notes,
.thicket/ vector data, and the knowledge graphs."""
profile = detect_layout()
if profile:
return profile.corpus_hot
return Path.home() / "Documents" / "ObsidianVault"
def default_archive(input_dir: Path) -> Path:
"""Archive default: corpus/archive inside the layout, else the
ingested-archive sibling of the input tree."""
profile = detect_layout()
if profile:
return profile.archive
return input_dir.parent / "ingested-archive"

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"""MinIO object archive — durable cold storage for source documents.
The source files themselves (PDFs, EPUBs, notes) are uploaded to an
S3-compatible MinIO bucket as part of ingest; the vault note records
the resulting ``s3://bucket/key`` URI in its frontmatter. The vault
and vector index stay lean; the originals live in object storage.
Open-source MinIO has no vector API (vector search is an AIStor
feature; the community edition is S3 object storage) — so MinIO is an
archive stage here, never a vector target.
Connection from Unix-standard env: MINIO_ENDPOINT (host:port),
MINIO_ACCESS_KEY, MINIO_SECRET_KEY, MINIO_SECURE (default false),
MINIO_BUCKET (default thicket-corpus).
"""
from __future__ import annotations
import os
from collections.abc import Callable
from pathlib import Path
DEFAULT_BUCKET = "thicket-corpus"
class ArchiveUnavailable(Exception):
"""Raised when the archive stage cannot be constructed or run."""
class MinioArchiver:
"""Uploads source documents to a MinIO bucket. Same key = same
document: re-ingesting overwrites in place (idempotent)."""
def __init__(self, log: Callable[[str], None] = lambda _msg: None):
try:
from minio import Minio
except ImportError as e:
raise ArchiveUnavailable(
"minio package not installed — run: pip install 'thicket[minio]'"
) from e
endpoint = os.environ.get("MINIO_ENDPOINT", "localhost:9000")
self.bucket = os.environ.get("MINIO_BUCKET", DEFAULT_BUCKET)
secure = os.environ.get("MINIO_SECURE", "").lower() in ("1", "true", "yes")
self._client = Minio(
endpoint,
access_key=os.environ.get("MINIO_ACCESS_KEY", ""),
secret_key=os.environ.get("MINIO_SECRET_KEY", ""),
secure=secure,
)
self._log = log
try:
if not self._client.bucket_exists(self.bucket):
self._log(f"Creating MinIO bucket '{self.bucket}'...")
self._client.make_bucket(self.bucket)
except Exception as e:
raise ArchiveUnavailable(
f"cannot reach MinIO at {endpoint}: {e} — set "
f"MINIO_ENDPOINT / MINIO_ACCESS_KEY / MINIO_SECRET_KEY"
) from e
def object_key(self, source_path: Path, input_dir: Path) -> str:
"""Bucket key mirrors the input tree's relative path."""
return source_path.relative_to(input_dir).as_posix()
def archive(self, source_path: Path, input_dir: Path) -> str:
"""Upload one source document; returns its s3:// URI."""
key = self.object_key(source_path, input_dir)
try:
self._client.fput_object(
self.bucket, key, str(source_path),
)
except Exception as e:
raise ArchiveUnavailable(
f"upload of '{key}' failed: {e}"
) from e
return f"s3://{self.bucket}/{key}"

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"""Pipeline orchestration — the shared engine behind GUI and CLI.
``IngestPipeline`` owns the per-file walk: extract, then a stage table
(vault note -> vector index -> knowledge graph). It is UI-agnostic:
progress flows out through ``PipelineCallbacks`` (plain callables),
which the QThread worker wires to Qt signals and the headless CLI
wires to prints. Heavy objects (embedding model, Qdrant client,
LightRAG) are constructed once inside ``run()`` — always on the
caller's thread, never the UI thread.
Design invariants:
* One stage table drives dispatch — adding a stage is one tuple,
never a new branch nest.
* Stages communicate through a per-file ``ctx`` dict (the vault
stage publishes the note's vault-relative path); no hidden
cross-stage state.
* A failing document is isolated: one ERROR row, queue continues.
* Stop is cooperative and checked between files only — a file is
either fully processed or not started.
Stage status vocabulary (the UI color-codes on these exact strings):
QUEUED, EXTRACT, VAULT, INDEX, GRAPH, DONE, SKIP, ERROR.
"""
from __future__ import annotations
import bz2
import hashlib
import json
import shutil
import sys
from collections.abc import Callable
from dataclasses import dataclass
from pathlib import Path
from .chunker import ContextualChunker
from .embedder import DEFAULT_EMBED_MODEL, EmbeddingEngine, model_dim
from .extractors import DocumentExtractor, scan_files
from .graph_store import create_graph
from .layout import default_archive, expand_path
from .minio_archive import MinioArchiver
from .vector_stores import TARGETS, VectorStoreError, create_store, doc_key
from .vault_writer import ObsidianVaultWriter
# Stage runner: (filepath, title, content, ctx) -> detail line.
StageRunner = Callable[[Path, str, str, dict], str]
VAULT_OUTPUT_DIRNAME = "Ingested_Brain"
# The notes-only destination key — valid wherever a target is accepted.
OBSIDIAN_TARGET = "obsidian"
def _bz2_compress(source: Path, level: int = 9) -> Path:
"""Streaming bzip2 of *source*; the plain original is replaced by
the .bz2 (compress-then-unlink, never unlink-first)."""
target = source.with_suffix(source.suffix + ".bz2")
with source.open("rb") as src, bz2.BZ2File(target, "wb",
compresslevel=level) as dst:
shutil.copyfileobj(src, dst, length=1 << 20)
source.unlink()
return target
@dataclass(slots=True)
class IngestConfig:
input_dir: Path
vault_dir: Path
qdrant_host: str = "localhost"
qdrant_port: int = 6333
target: str = "qdrant"
collection: str = "second_brain"
embed_model: str = DEFAULT_EMBED_MODEL
chunk_size: int = 400
overlap: int = 50
# Destination model: TARGET picks where the corpus lands — a
# vector store key from the registry, or "obsidian" for notes-only.
target: str = "qdrant" # kept in sync with qdrant_host below
use_vault: bool = True
use_qdrant: bool = True
use_lightrag: bool = False
use_minio: bool = False
use_fs_archive: bool = False
archive_dir: Path | None = None # default: corpus/archive or sibling
graph_engine: str = "lightrag"
notes_dir: str = VAULT_OUTPUT_DIRNAME
skip_unchanged: bool = False
max_mb: int = 0 # 0 = no size guard
def __post_init__(self) -> None:
"""Resolve ~ and $VAR in every user-supplied path — the GUI,
CLI, and core share one expansion point."""
self.input_dir = expand_path(self.input_dir)
self.vault_dir = expand_path(self.vault_dir)
if self.archive_dir is not None:
self.archive_dir = expand_path(self.archive_dir)
ollama_llm: str = "llama3"
ollama_embed: str = "nomic-embed-text"
@dataclass(slots=True)
class PipelineCallbacks:
log: object = lambda _msg: None # (msg)
file_status: object = lambda _p, _s: None # (path, status)
file_detail: object = lambda _p, _d: None # (path, detail)
progress: object = lambda _n, _c, _t: None # (name, current, total)
@classmethod
def quiet(cls) -> "PipelineCallbacks":
return cls()
class IngestPipeline:
"""One batch run over a directory of documents."""
def __init__(self, config: IngestConfig,
callbacks: PipelineCallbacks = PipelineCallbacks.quiet()):
self.config = config
self._manifest_path = config.vault_dir / ".thicket" / "manifest.json"
self.cb = callbacks
self._stop = False
self._engine: EmbeddingEngine | None = None
self._store = None
self._writer: ObsidianVaultWriter | None = None
self._graph = None
self._archiver = None
self._chunker = ContextualChunker(
chunk_size=config.chunk_size, overlap=config.overlap
)
# Stage table: (status, gate, runner). Dispatch is one pass
# over this tuple — stage order and gating live here only.
self._stages: tuple[tuple[str, bool, StageRunner], ...] = (
("MINIO", config.use_minio, self._stage_minio),
("VAULT", config.use_vault, self._stage_vault),
("INDEX", config.use_qdrant, self._stage_index),
("GRAPH", config.use_lightrag, self._stage_graph),
("ARCHIVE", config.use_fs_archive, self._stage_fs_archive),
)
def request_stop(self) -> None:
"""Cooperative stop: the queue exits after the current file."""
self._stop = True
# ── change manifest (powers skip-unchanged) ──
def _read_manifest(self) -> dict:
try:
return json.loads(self._manifest_path.read_text(encoding="utf-8"))
except (OSError, ValueError):
return {}
def _write_manifest(self, manifest: dict) -> None:
self._manifest_path.parent.mkdir(parents=True, exist_ok=True)
self._manifest_path.write_text(
json.dumps(manifest, indent=1), encoding="utf-8")
@staticmethod
def _file_digest(path: Path) -> str:
return hashlib.md5(path.read_bytes()).hexdigest()
# ── lazy stage construction (runs on the worker thread) ──
def _get_writer(self) -> ObsidianVaultWriter:
import importlib.util
if importlib.util.find_spec("slugify") is None:
raise VectorStoreError(
"python-slugify not installed — run: pip install 'thicket[ingest]'"
)
if self._writer is None:
self._writer = ObsidianVaultWriter(
self.config.vault_dir, output_dirname=self.config.notes_dir
)
return self._writer
def _get_engine(self) -> EmbeddingEngine:
if self._engine is None:
self.cb.log(
f"Loading local embedding model '{self.config.embed_model}' "
f"(first run downloads it)..."
)
self._engine = EmbeddingEngine(self.config.embed_model)
self._engine.load()
self.cb.log(f"Embedding model ready ({self._engine.dim} dimensions).")
return self._engine
def _get_store(self):
if self._store is None:
spec = TARGETS.get(self.config.target)
if spec is None:
known = ", ".join(sorted(TARGETS))
raise VectorStoreError(
f"unknown vector target '{self.config.target}' — known: {known}"
)
store = create_store(
self.config.target,
collection=self.config.collection,
dim=model_dim(self.config.embed_model),
host=self.config.qdrant_host,
port=self.config.qdrant_port,
data_dir=self._store_data_dir(),
log=self.cb.log,
)
store.set_embedder(self._get_engine())
store.ensure_collection()
self._store = store
return self._store
def _store_data_dir(self) -> Path:
"""Embedded/file targets keep data under the vault so the whole
knowledge tree stays one portable directory."""
return self.config.vault_dir / ".thicket" / self.config.target
def _get_graph(self):
if self._graph is None:
self.cb.log(
f"Initializing LightRAG graph at "
f"'{self.config.vault_dir / '.lightrag'}' "
f"(LLM: {self.config.ollama_llm}, embed: {self.config.ollama_embed})..."
)
self._graph = create_graph(
self.config.graph_engine,
working_dir=self.config.vault_dir,
llm_model=self.config.ollama_llm,
embed_model=self.config.ollama_embed,
log=self.cb.log,
)
return self._graph
def _get_archiver(self) -> MinioArchiver:
if self._archiver is None:
self._archiver = MinioArchiver(log=self.cb.log)
return self._archiver
# ── stage runners ──
def _stage_minio(self, filepath: Path, title: str, content: str,
ctx: dict) -> str:
uri = self._get_archiver().archive(filepath, self.config.input_dir)
ctx["source_uri"] = uri
self.cb.log(f" └─ Archived to MinIO: {uri}")
return f"s3: {uri}"
def _stage_fs_archive(self, filepath: Path, title: str, content: str,
ctx: dict) -> str:
"""Move the source out of the incoming tree into the archive
directory and bzip2 it — sources are never deleted."""
archive_dir = self.config.archive_dir or default_archive(
self.config.input_dir)
archive_dir.mkdir(parents=True, exist_ok=True)
target = archive_dir / filepath.name
if target.exists():
digest = doc_key(title, str(filepath))[:6]
target = archive_dir / f"{filepath.stem}-{digest}{filepath.suffix}"
shutil.move(str(filepath), target)
compressed = _bz2_compress(target)
self.cb.log(f" └─ Archived + bz2: {compressed.relative_to(compressed.parents[1])}")
return f"bz2: {compressed.name}"
def _stage_vault(self, filepath: Path, title: str, content: str,
ctx: dict) -> str:
note = self._get_writer().write(
title, content, filepath, source_uri=ctx.get("source_uri"))
ctx["rel_path"] = str(note.relative_to(self.config.vault_dir))
self.cb.log(f" └─ Vault note written: [[{note.stem}]]")
return f"note: {note.name}"
def _stage_index(self, filepath: Path, title: str, content: str,
ctx: dict) -> str:
rel_path = ctx.get("rel_path") or self._projected_rel_path(title)
chunks = self._chunker.chunk(
title, content,
source_path=str(filepath.relative_to(self.config.input_dir)))
count = self._get_store().replace_document(title, rel_path, chunks)
self.cb.log(
f" └─ Vector store: indexed {count} chunk(s) into "
f"'{self.config.collection}'."
)
return f"{count} chunks indexed"
def _stage_graph(self, filepath: Path, title: str, content: str,
ctx: dict) -> str:
self._get_graph().ingest_document(title, content)
self.cb.log(" └─ LightRAG graph updated.")
return "graph updated"
def _projected_rel_path(self, title: str) -> str:
"""Canonical note path for the vector payload when the vault
stage is disabled — identical location to the writer's plain
slug (collision suffixes only exist once notes are written)."""
from slugify import slugify
slug = slugify(title) or "untitled"
return f"{self.config.notes_dir}/{slug}.md"
# ── main loop ──
def run(self, files: list[Path] | None = None) -> tuple[int, int]:
"""Process every supported document. Returns (ok, fail)."""
files = scan_files(self.config.input_dir) if files is None else files
self.cb.log(f"Found {len(files)} eligible document(s) in "
f"'{self.config.input_dir}'")
for f in files:
self.cb.file_status(str(f), "QUEUED")
outcomes = [self._process_one(f) for f in self._itinerary(files)]
ok = sum(outcomes)
if self._graph is not None:
try:
self._graph.finalize() # batch engines build once here
except Exception as e: # noqa: BLE001 — report, keep counts
self.cb.log(f" └─ ERROR finalizing graph: {e}")
return ok, len(outcomes) - ok
def _itinerary(self, files: list[Path]):
"""Yield files in order, honouring the stop flag between files
and emitting progress as we go."""
total = len(files)
for current, filepath in enumerate(files, start=1):
if self._stop:
self.cb.log("STOP: exiting queue before next file.")
return
self.cb.progress(filepath.name, current, total)
yield filepath
def _process_one(self, filepath: Path) -> bool:
"""One document through the gates + extract + the stage table."""
try:
skip_reason = self._gate(filepath)
if skip_reason is not None:
self.cb.file_status(str(filepath), "SKIP")
self.cb.file_detail(str(filepath), skip_reason)
self.cb.log(f" └─ Skipping {filepath.name}: {skip_reason}")
return True # deliberate non-processing — not a failure
self.cb.file_status(str(filepath), "EXTRACT")
self.cb.file_detail(str(filepath), "extracting text")
title, content = DocumentExtractor.extract(filepath)
if not content.strip():
self.cb.log(f" └─ Skipping empty file: {filepath.name}")
self.cb.file_status(str(filepath), "SKIP")
self.cb.file_detail(str(filepath), "no extractable text")
return True # nothing to ingest — not a failure
ctx: dict = {}
for status, enabled, runner in self._stages:
if not enabled:
continue
self.cb.file_status(str(filepath), status)
self.cb.file_detail(str(filepath), runner(filepath, title, content, ctx))
self.cb.file_status(str(filepath), "DONE")
if self.config.skip_unchanged:
manifest = self._read_manifest()
manifest[str(filepath)] = self._file_digest(filepath)
self._write_manifest(manifest)
return True
except Exception as e: # noqa: BLE001 — per-file isolation is the contract
self._fail(filepath, e)
return False
def _gate(self, filepath: Path) -> str | None:
"""Pre-extraction gates: oversized files and unchanged re-runs.
Returns the skip reason, or None to proceed."""
if self.config.max_mb:
size_mb = filepath.stat().st_size / 1_048_576
if size_mb > self.config.max_mb:
return f"{size_mb:.1f} MB exceeds {self.config.max_mb} MB limit"
if self.config.skip_unchanged:
manifest = self._read_manifest()
if manifest.get(str(filepath)) == self._file_digest(filepath):
return "unchanged since last ingest"
return None
def _fail(self, filepath: Path, error: Exception) -> None:
msg = f" └─ ERROR processing {filepath.name}: {error}"
print(msg, file=sys.stderr)
self.cb.log(msg)
self.cb.file_status(str(filepath), "ERROR")
self.cb.file_detail(str(filepath), str(error)[:120])
# ──────────────────────────────────────────────────────────────────
# HEADLESS ENTRY (no Qt)
# ──────────────────────────────────────────────────────────────────
def headless_ingest(input_dir: Path, vault_dir: Path,
qdrant_host: str = "localhost", qdrant_port: int = 6333,
target: str = "qdrant", collection: str = "second_brain",
embed_model: str = DEFAULT_EMBED_MODEL,
chunk_size: int = 400, overlap: int = 50,
use_vault: bool = True, use_qdrant: bool = True,
use_lightrag: bool = False, use_minio: bool = False,
graph_engine: str = "lightrag",
notes_dir: str = VAULT_OUTPUT_DIRNAME,
skip_unchanged: bool = False,
use_fs_archive: bool = False,
archive_dir: Path | None = None, max_mb: int = 0,
ollama_llm: str = "llama3",
ollama_embed: str = "nomic-embed-text") -> tuple[int, int]:
"""CLI batch ingest — the same core path the GUI worker drives."""
config = IngestConfig(
input_dir=input_dir, vault_dir=vault_dir,
qdrant_host=qdrant_host, qdrant_port=qdrant_port,
target=target, collection=collection, embed_model=embed_model,
chunk_size=chunk_size, overlap=overlap,
use_vault=use_vault, use_qdrant=use_qdrant, use_lightrag=use_lightrag,
use_minio=use_minio, graph_engine=graph_engine,
notes_dir=notes_dir, skip_unchanged=skip_unchanged,
use_fs_archive=use_fs_archive, archive_dir=archive_dir,
max_mb=max_mb,
ollama_llm=ollama_llm, ollama_embed=ollama_embed,
)
callbacks = PipelineCallbacks(
log=lambda msg: print(f"> {msg}"),
file_status=lambda _p, _s: None,
file_detail=lambda _p, d: print(f" {d}"),
progress=lambda n, c, t: print(f"\n[{c}/{t}] {n}"),
)
pipeline = IngestPipeline(config, callbacks)
try:
return pipeline.run()
except KeyboardInterrupt:
# POSIX: exit code 130 = 128 + SIGINT; report what completed.
print("\nInterrupted — counts reflect files finished before SIGINT.")
raise

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"""Qt workers — threads that keep the UI responsive.
* ``ProbeWorker`` — runs env_probe.probe_environment() off-thread.
* ``PipelineWorker`` — wraps IngestPipeline, forwarding progress as
Qt signals (same signal contract as
OpenTranscode's EncoderWorker).
* ``SearchWorker`` — embeds a query and searches Qdrant off-thread.
"""
from __future__ import annotations
from pathlib import Path
from PySide6.QtCore import QThread, Signal
from .embedder import DEFAULT_EMBED_MODEL, EmbeddingEngine, model_dim
from .env_probe import probe_environment
from .pipeline_core import IngestConfig, IngestPipeline, PipelineCallbacks
class ProbeWorker(QThread):
"""Background environment probe — service pings + module checks."""
log_msg = Signal(str)
probe_done = Signal(object) # EnvProbe
def __init__(self, qdrant_host: str = "localhost", qdrant_port: int = 6333,
parent=None):
super().__init__(parent)
self._host = qdrant_host
self._port = qdrant_port
def run(self):
self.log_msg.emit("Probing environment (modules, Qdrant, Ollama)...")
env = probe_environment(qdrant_host=self._host, qdrant_port=self._port)
self.probe_done.emit(env)
class PipelineWorker(QThread):
"""Drives IngestPipeline on a worker thread.
Signal contract mirrors OpenTranscode's EncoderWorker:
log_msg(str) — human-readable progress lines
file_status(str, str) — (absolute path, stage status)
file_detail(str, str) — (filename, detail text for table row)
progress_msg(str, int, int) — (filename, current, total)
finished_queue(int, int) — (ok count, fail count)
"""
log_msg = Signal(str)
file_status = Signal(str, str)
file_detail = Signal(str, str)
progress_msg = Signal(str, int, int)
finished_queue = Signal(int, int)
def __init__(self, config: IngestConfig, parent=None):
super().__init__(parent)
self.config = config
self._pipeline: IngestPipeline | None = None
def stop(self) -> None:
"""Cooperative stop — the pipeline exits after the current file."""
if self._pipeline is not None:
self._pipeline.request_stop()
def run(self):
callbacks = PipelineCallbacks(
log=self.log_msg.emit,
file_status=self.file_status.emit,
file_detail=self.file_detail.emit,
progress=self.progress_msg.emit,
)
self._pipeline = IngestPipeline(self.config, callbacks)
try:
ok, fail = self._pipeline.run()
except Exception as e: # noqa: BLE001 — fatal pipeline errors
self.log_msg.emit(f"FATAL: pipeline aborted — {e}")
ok, fail = 0, 0
self.finished_queue.emit(ok, fail)
class SearchWorker(QThread):
"""Semantic retrieval against the ingested collection of the
selected vector target."""
log_msg = Signal(str)
search_done = Signal(int) # number of hits returned
def __init__(self, query: str, vault_dir: Path, target: str,
host: str = "localhost", port: int = 6333,
collection: str = "second_brain",
embed_model: str = DEFAULT_EMBED_MODEL, top_k: int = 5,
parent=None):
super().__init__(parent)
self._query = query
self._vault_dir = vault_dir
self._target = target
self._host = host
self._port = port
self._collection = collection
self._embed_model = embed_model
self._top_k = top_k
def run(self):
try:
hits = self._search()
except Exception as e: # noqa: BLE001 — report any failure to the log
self.log_msg.emit(f"SEARCH ERROR: {e}")
self.search_done.emit(-1)
return
self._report(hits)
self.search_done.emit(len(hits))
def _search(self) -> list[dict]:
"""Embed the query and search the collection (worker thread)."""
from .vector_stores import create_store
engine = EmbeddingEngine(self._embed_model)
self.log_msg.emit(f"Embedding query with '{self._embed_model}'...")
store = create_store(
self._target,
collection=self._collection,
dim=model_dim(self._embed_model),
host=self._host, port=self._port,
data_dir=self._vault_dir / ".thicket" / self._target,
)
try:
store.ensure_collection()
return store.search(engine.embed_query(self._query),
limit=self._top_k)
finally:
store.close()
def _report(self, hits: list[dict]) -> None:
"""Emit ranked results to the log, best first."""
if not hits:
self.log_msg.emit(
f"No matches in '{self._collection}' — is anything ingested?"
)
return
for rank, hit in enumerate(hits, start=1):
payload = hit["payload"]
snippet = (payload.get("content") or "")[:160].replace("\n", " ")
tags = payload.get("chunk_kind", "prose")
if payload.get("lang"):
tags += f":{payload['lang']}"
if payload.get("source_path"):
tags += f" src={payload['source_path']}"
self.log_msg.emit(
f"[{rank}] {hit['score']:.3f} "
f"{payload.get('document_title', '?')} "
f"§ {payload.get('section_header', '?')} ({tags})"
)
self.log_msg.emit(f" {snippet}...")
class AskWorker(QThread):
"""Natural-language SQL over the corpus (Vanna 2 + Ollama)."""
log_msg = Signal(str)
ask_done = Signal(bool) # success
def __init__(self, question: str, target: str, collection: str,
llm_model: str, parent=None):
super().__init__(parent)
self._question = question
self._target = target
self._collection = collection
self._llm_model = llm_model
def run(self):
from .ask_vanna import ask
try:
answer = ask(self._question, target=self._target,
collection=self._collection, llm_model=self._llm_model,
log=self.log_msg.emit)
except Exception as e: # noqa: BLE001 — report, never crash the UI
self.log_msg.emit(f"ASK ERROR: {e}")
self.ask_done.emit(False)
return
self.log_msg.emit(f"ANSWER: {answer}")
self.ask_done.emit(True)

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"""Qdrant vector store — the service-backed target in the registry.
Invariants:
* ``replace_document`` deletes every existing point for the document
(filter on ``doc_key``) before upserting, so re-ingesting an edited
or shrunken source replaces its vectors exactly — the point count
after N re-ingests equals the count after the first.
* Upserts travel in batches of 64 — one document never arrives as a
single unbounded request.
* Point IDs are deterministic (MD5-UUID of doc_key|index), making
re-ingestion an overwrite by construction.
* A model/collection dimension mismatch is a hard error with an
actionable message; vectors are never silently mixed.
Step-down chain: ``search`` uses ``query_points`` (qdrant-client >= 1.10)
and steps down to ``search`` on older clients.
"""
from __future__ import annotations
import hashlib
import uuid
from collections.abc import Callable
from .chunker import Chunk
from .vector_stores import VectorStoreError, _payload, _snippet_payload, doc_key
UPSERT_BATCH = 64
def _point_id(key: str, idx: int) -> str:
raw = f"{key}|{idx}".encode()
return str(uuid.UUID(bytes=hashlib.md5(raw).digest()))
class QdrantStore:
"""Handles embedding indexing into a Qdrant collection. Embeddings
are computed by the attached EmbeddingEngine; the store never
embeds on its own."""
def __init__(self, host: str, port: int, collection: str, dim: int,
log: Callable[[str], None] = lambda _msg: None):
self.host = host
self.port = port
self.collection = collection
self.dim = dim
self.model_hint = "selected model"
self._engine = None
self._log = log
self._client = None
def client(self):
"""Lazy client — imports qdrant_client on first use so the GUI
never needs it installed to start."""
if self._client is None:
try:
from qdrant_client import QdrantClient
except ImportError as e:
raise VectorStoreError(
"qdrant-client not installed — run: pip install 'thicket[ingest]'"
) from e
self._client = QdrantClient(
url=f"http://{self.host}:{self.port}", timeout=10,
check_compatibility=False, # API gaps handled by step-down
)
return self._client
def ensure_collection(self) -> None:
"""Create the collection when missing; verify dimensions when it
exists."""
from qdrant_client.models import Distance, VectorParams
client = self.client()
try:
names = [c.name for c in client.get_collections().collections]
except Exception as e:
raise VectorStoreError(
f"cannot reach Qdrant at {self.host}:{self.port} — {e}"
) from e
if self.collection not in names:
self._log(f"Creating collection '{self.collection}' (dim={self.dim})...")
client.create_collection(
collection_name=self.collection,
vectors_config=VectorParams(size=self.dim, distance=Distance.COSINE),
)
self._index_payload(client)
return
self._index_payload(client)
existing_dim = self._collection_dim(client)
if isinstance(existing_dim, int) and self.dim and existing_dim != self.dim:
raise VectorStoreError(
f"collection '{self.collection}' has {existing_dim}-dim vectors "
f"but '{self.model_hint}' produces {self.dim} — pick the "
f"matching embedding model or a new collection name"
)
def _index_payload(self, client) -> None:
"""Keyword index on doc_key — every re-ingest deletes by that
filter, and unindexed payload filters scan the whole collection."""
from qdrant_client.models import PayloadSchemaType
client.create_payload_index(
collection_name=self.collection,
field_name="doc_key",
field_schema=PayloadSchemaType.KEYWORD,
)
def _collection_dim(self, client) -> int | None:
"""Vector size of the existing collection; None when the server
does not report it."""
vectors = client.get_collection(self.collection).config.params.vectors
size = getattr(vectors, "size", None)
return size if isinstance(size, int) else None
def replace_document(self, title: str, obsidian_rel_path: str,
chunks: list[Chunk]) -> int:
"""Index every chunk of a document, replacing any previous
version. Returns the number of points written."""
from qdrant_client.models import FieldCondition, Filter, MatchValue, PointStruct
if not chunks:
return 0
client = self.client()
key = doc_key(title, obsidian_rel_path)
# Exact replacement: drop the previous version before writing,
# so a shrunken source leaves no stale tail chunks behind. The
# should-clause also matches pre-1.2 points (which lack
# doc_key) via their title+path — upgrading installations clean
# themselves on first re-ingest.
client.delete(
collection_name=self.collection,
points_selector=Filter(should=[
FieldCondition(key="doc_key", match=MatchValue(value=key)),
Filter(must=[
FieldCondition(key="document_title",
match=MatchValue(value=title)),
FieldCondition(key="obsidian_path",
match=MatchValue(value=obsidian_rel_path)),
]),
]),
)
vectors = self._engine.embed([c.contextual_text for c in chunks])
points = [
PointStruct(
id=_point_id(key, idx),
vector=vector,
payload=_payload(title, obsidian_rel_path, chunk, idx),
)
for idx, (chunk, vector) in enumerate(zip(chunks, vectors))
]
batches = (points[i:i + UPSERT_BATCH]
for i in range(0, len(points), UPSERT_BATCH))
for batch in batches:
client.upsert(collection_name=self.collection, points=batch)
return len(points)
def set_embedder(self, engine) -> None:
"""Attach the EmbeddingEngine; records its model name for error
messages and adopts its dimension when none was given."""
self._engine = engine
self.model_hint = engine.model_name
if engine.dim and not self.dim:
self.dim = engine.dim
def close(self) -> None:
if self._client is not None:
self._client.close()
self._client = None
def search(self, vector: list[float], limit: int = 5) -> list[dict]:
"""Semantic search. Returns [{score, payload}] best-first."""
client = self.client()
try:
hits = client.query_points(
collection_name=self.collection,
query=vector,
limit=limit,
with_payload=True,
).points
except AttributeError:
# Step down: qdrant-client < 1.10 exposes search() only.
hits = client.search(
collection_name=self.collection,
query_vector=vector,
limit=limit,
with_payload=True,
)
return [{"score": h.score,
"payload": _snippet_payload(h.payload or {})}
for h in hits]

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"""MMD3 retro-futuristic console Qt stylesheet (QSS string).
Same visual lineage as OpenTranscode's ui_theme: brushed aluminum
panels, amber/green LED displays, beveled metallic group boxes,
modernized with rounded corners, subtle glow, and glassmorphism
hints. Extended for Thicket with the document-queue table, spin
boxes, and the teal auxiliary buttons. Pure string constant — no
imports at all.
"""
MMD3_QSS = """
/* ── Global ── */
QMainWindow, QWidget#central {
background-color: #1a1a1e;
}
/* ── Group Boxes — brushed aluminum panels ── */
QGroupBox {
font-family: 'Segoe UI', 'Ubuntu', sans-serif;
font-size: 10px;
font-weight: bold;
color: #8a8a8a;
border: 1px solid #3a3a40;
border-radius: 8px;
margin-top: 14px;
padding: 14px 10px 10px 10px;
background: qlineargradient(x1:0, y1:0, x2:0, y2:1,
stop:0 #2c2c32, stop:0.5 #27272c, stop:1 #222228);
}
QGroupBox::title {
subcontrol-origin: margin;
subcontrol-position: top left;
padding: 2px 10px;
color: #666;
background: qlineargradient(x1:0, y1:0, x2:0, y2:1,
stop:0 #2c2c32, stop:1 #222228);
border-radius: 4px;
}
/* ── Labels ── */
QLabel {
color: #999;
font-size: 10px;
font-family: 'Segoe UI', 'Ubuntu', sans-serif;
}
/* ── Line Edits — recessed aluminum wells ── */
QLineEdit {
background: qlineargradient(x1:0, y1:0, x2:0, y2:1,
stop:0 #18181c, stop:1 #141418);
border: 1px solid #333;
border-radius: 4px;
padding: 5px 8px;
color: #d4aa50; /* amber LED */
font-family: 'Consolas', 'DejaVu Sans Mono', 'Ubuntu Mono', monospace;
font-size: 11px;
selection-background-color: #d4aa50;
selection-color: #000;
}
QLineEdit:focus {
border-color: #d4aa50;
}
/* ── Combo Boxes ── */
QComboBox {
background: qlineargradient(x1:0, y1:0, x2:0, y2:1,
stop:0 #1e1e24, stop:1 #1a1a20);
border: 1px solid #3a3a40;
border-radius: 4px;
padding: 4px 8px;
color: #c8c8c8;
font-family: 'Segoe UI', 'Ubuntu', sans-serif;
font-size: 11px;
min-height: 24px;
}
QComboBox:hover {
border-color: #555;
}
QComboBox:focus {
border-color: #d4aa50;
}
QComboBox::drop-down {
border: none;
width: 22px;
}
QComboBox::down-arrow {
image: none;
border-left: 4px solid transparent;
border-right: 4px solid transparent;
border-top: 6px solid #888;
margin-right: 6px;
}
QComboBox QAbstractItemView {
background: #1e1e24;
border: 1px solid #3a3a40;
border-radius: 4px;
color: #c8c8c8;
selection-background-color: #3a3a48;
selection-color: #d4aa50;
padding: 4px;
}
QComboBox item {
min-height: 22px;
padding: 2px 8px;
}
/* ── Spin Boxes — LED readout wells ── */
QSpinBox {
background: qlineargradient(x1:0, y1:0, x2:0, y2:1,
stop:0 #18181c, stop:1 #141418);
border: 1px solid #333;
border-radius: 4px;
padding: 4px 8px;
color: #d4aa50;
font-family: 'Consolas', 'DejaVu Sans Mono', 'Ubuntu Mono', monospace;
font-size: 11px;
min-height: 22px;
selection-background-color: #d4aa50;
selection-color: #000;
}
QSpinBox:focus {
border-color: #d4aa50;
}
QSpinBox::up-button, QSpinBox::down-button {
background: #2c2c32;
border: none;
width: 16px;
}
QSpinBox::up-button:hover, QSpinBox::down-button:hover {
background: #3a3a42;
}
QSpinBox::up-arrow {
border-left: 4px solid transparent;
border-right: 4px solid transparent;
border-bottom: 5px solid #888;
width: 0; height: 0;
}
QSpinBox::down-arrow {
border-left: 4px solid transparent;
border-right: 4px solid transparent;
border-top: 5px solid #888;
width: 0; height: 0;
}
/* ── Buttons — beveled metallic (MMD3 transport style) ── */
QPushButton {
background: qlineargradient(x1:0, y1:0, x2:0, y2:1,
stop:0 #404048, stop:0.15 #38383f,
stop:0.85 #2e2e35, stop:1 #28282e);
border: 1px solid #4a4a52;
border-bottom-color: #1a1a1e;
border-radius: 5px;
padding: 6px 16px;
color: #d0d0d0;
font-family: 'Segoe UI', 'Ubuntu', sans-serif;
font-size: 11px;
font-weight: bold;
}
QPushButton:hover {
background: qlineargradient(x1:0, y1:0, x2:0, y2:1,
stop:0 #4a4a54, stop:0.15 #424248,
stop:0.85 #363640, stop:1 #303038);
border-color: #5a5a64;
color: #fff;
}
QPushButton:pressed {
background: qlineargradient(x1:0, y1:0, x2:0, y2:1,
stop:0 #28282e, stop:1 #3a3a42);
border-bottom-color: #4a4a52;
border-top-color: #1a1a1e;
}
QPushButton:disabled {
background: #222228;
border-color: #2a2a30;
color: #555;
}
/* Primary action button — amber glow */
QPushButton#btnRun {
background: qlineargradient(x1:0, y1:0, x2:0, y2:1,
stop:0 #3a3428, stop:0.15 #332e22,
stop:0.85 #2a261c, stop:1 #221e16);
border: 1px solid #5a4a30;
border-bottom-color: #1a1608;
color: #d4aa50;
font-size: 13px;
letter-spacing: 2px;
}
QPushButton#btnRun:hover {
background: qlineargradient(x1:0, y1:0, x2:0, y2:1,
stop:0 #4a4030, stop:0.15 #423828,
stop:0.85 #3a3020, stop:1 #322a1a);
border-color: #d4aa50;
color: #f0d080;
}
QPushButton#btnRun:disabled {
background: #22201a;
border-color: #2a2820;
color: #5a4a30;
}
/* Stop button — red danger */
QPushButton#btnStop {
background: qlineargradient(x1:0, y1:0, x2:0, y2:1,
stop:0 #3a2222, stop:0.15 #321c1c,
stop:0.85 #2a1616, stop:1 #221010);
border: 1px solid #5a3030;
border-bottom-color: #1a0808;
color: #e05050;
font-size: 13px;
letter-spacing: 2px;
}
QPushButton#btnStop:hover {
border-color: #e05050;
color: #ff7070;
}
QPushButton#btnStop:disabled {
background: #221a1a;
border-color: #2a2020;
color: #5a3030;
}
/* Auxiliary buttons — muted teal (scan / search) */
QPushButton#btnTeal {
background: qlineargradient(x1:0, y1:0, x2:0, y2:1,
stop:0 #1e2e2e, stop:0.15 #1a2a2a,
stop:0.85 #162424, stop:1 #121e1e);
border: 1px solid #2a5050;
border-bottom-color: #0e1818;
color: #50b0b0;
font-size: 10px;
letter-spacing: 1px;
}
QPushButton#btnTeal:hover {
border-color: #50b0b0;
color: #70d0d0;
}
QPushButton#btnTeal:disabled {
background: #1a1e1e;
border-color: #222828;
color: #304040;
}
/* Browse buttons — small, subdued */
QPushButton#btnBrowse {
font-size: 9px;
padding: 4px 10px;
letter-spacing: 1px;
}
/* ── Check Boxes ── */
QCheckBox {
color: #999;
font-size: 10px;
spacing: 8px;
font-family: 'Segoe UI', 'Ubuntu', sans-serif;
}
QCheckBox::indicator {
width: 16px;
height: 16px;
border-radius: 3px;
border: 1px solid #444;
background: #1a1a1e;
}
QCheckBox::indicator:checked {
background: #d4aa50;
border-color: #b8903a;
}
QCheckBox:disabled {
color: #444;
}
QCheckBox#dangerCheck {
color: #c05050;
font-weight: bold;
}
QCheckBox#dangerCheck::indicator:checked {
background: #c04040;
border-color: #a03030;
}
/* ── Document Queue — LED matrix table ── */
QTableWidget#queueTable {
background: #0a0a0c;
alternate-background-color: #101014;
gridline-color: #1a1a20;
border: 2px solid #1e1e24;
border-radius: 6px;
color: #c8c8c8;
font-family: 'Consolas', 'DejaVu Sans Mono', 'Ubuntu Mono', monospace;
font-size: 10px;
selection-background-color: #3a3a48;
selection-color: #d4aa50;
}
QTableWidget#queueTable::item {
padding: 2px 6px;
}
QTableWidget#queueTable QHeaderView {
background: transparent;
}
QTableWidget#queueTable QHeaderView::section {
background: qlineargradient(x1:0, y1:0, x2:0, y2:1,
stop:0 #2c2c32, stop:1 #222228);
color: #8a8a8a;
border: none;
border-right: 1px solid #1a1a20;
border-bottom: 1px solid #3a3a40;
padding: 4px 8px;
font-size: 9px;
font-family: 'Segoe UI', 'Ubuntu', sans-serif;
font-weight: bold;
}
/* ── Text Edit (log) — LED terminal display ── */
QTextEdit#logBox {
background: #0a0a0c;
border: 2px solid #1e1e24;
border-radius: 6px;
color: #40d060; /* green phosphor LED */
font-family: 'Consolas', 'DejaVu Sans Mono', 'Ubuntu Mono', monospace;
font-size: 11px;
padding: 8px;
}
/* ── Status Bar — LED readout strip ── */
QStatusBar {
background: #0e0e12;
border-top: 1px solid #2a2a30;
font-family: 'Consolas', 'DejaVu Sans Mono', 'Ubuntu Mono', monospace;
font-size: 10px;
color: #d4aa50;
padding: 2px 8px;
}
QStatusBar QLabel {
color: #d4aa50;
font-family: 'Consolas', 'DejaVu Sans Mono', 'Ubuntu Mono', monospace;
font-size: 10px;
}
/* ── Tooltips ── */
QToolTip {
background: #2a2a30;
color: #c8c8c8;
border: 1px solid #444;
border-radius: 4px;
padding: 6px;
font-size: 10px;
}
/* ── Scrollbars — thin, dark ── */
QScrollBar:vertical {
background: #141418;
width: 10px;
border-radius: 5px;
margin: 0;
}
QScrollBar::handle:vertical {
background: #3a3a42;
border-radius: 5px;
min-height: 30px;
}
QScrollBar::handle:vertical:hover {
background: #4a4a54;
}
QScrollBar::add-line:vertical, QScrollBar::sub-line:vertical {
height: 0;
}
QScrollBar:horizontal {
background: #141418;
height: 10px;
border-radius: 5px;
}
QScrollBar::handle:horizontal {
background: #3a3a42;
border-radius: 5px;
min-width: 30px;
}
QScrollBar::handle:horizontal:hover {
background: #4a4a54;
}
QScrollBar::add-line:horizontal, QScrollBar::sub-line:horizontal {
width: 0;
}
"""

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"""Obsidian vault writer — normalized Markdown notes with YAML frontmatter.
Invariants:
* Frontmatter values are escaped (backslash + double quote), so titles
like ``The "Real" Deal`` always produce valid YAML.
* Re-ingesting a source refreshes its note in place.
* A *different* source with the same title never clobbers an existing
note — it claims a deterministic hash-suffixed filename.
* An empty slug (symbol-only titles) lands on ``untitled``.
"""
from __future__ import annotations
import hashlib
import itertools
import re
from collections.abc import Iterator
from datetime import datetime
from pathlib import Path
# Maximum frontmatter lines examined when resolving filename collisions
# (SEI CERT FIO39-C spirit: bounded reads on files we do not own).
_FRONTMATTER_SCAN_LIMIT = 32
_FRONTMATTER_SOURCE_RE = re.compile(r'^source_file:\s*"(.*)"\s*$')
_FRONTMATTER_KEYS = ("title:", "source_file:", "ingested_at:", "tags:", "- ")
def _yaml_escape(value: str) -> str:
"""Escape a string for a double-quoted YAML scalar."""
return value.replace("\\", "\\\\").replace('"', '\\"')
def _unescape_yaml(value: str) -> str:
"""Inverse of _yaml_escape for values we wrote ourselves."""
return value.replace('\\"', '"').replace("\\\\", "\\")
def _frontmatter_lines(fh) -> Iterator[str]:
"""Yield stripped frontmatter lines, stopping at the block end."""
for raw in fh:
stripped = raw.strip()
if not stripped or stripped == "---":
continue # block delimiters and blank lines
yield stripped
if not stripped.startswith(_FRONTMATTER_KEYS):
return # first non-frontmatter line ends the block
def _read_frontmatter_source(path: Path) -> str | None:
"""The ``source_file:`` value from an existing note's frontmatter;
None when absent or unreadable."""
try:
with path.open("r", encoding="utf-8", errors="ignore") as fh:
bounded = list(itertools.islice(fh, _FRONTMATTER_SCAN_LIMIT))
except OSError:
return None
for line in _frontmatter_lines(bounded):
match = _FRONTMATTER_SOURCE_RE.match(line)
if match:
return _unescape_yaml(match.group(1))
return None
class ObsidianVaultWriter:
"""Formats and writes extracted text into normalized Obsidian
Markdown files under ``<vault>/Ingested_Brain``."""
def __init__(self, vault_path: Path, output_dirname: str = "Ingested_Brain"):
self.output_dir = vault_path / output_dirname
self.output_dir.mkdir(parents=True, exist_ok=True)
def write(self, title: str, content: str, source_path: Path,
source_uri: str | None = None) -> Path:
"""Write (or refresh) the note for *source_path*. Returns the
note path — which carries a hash suffix when a *different*
source already claimed the plain slug. *source_uri* records the
object-storage location when the archive stage ran first."""
from slugify import slugify # lazy: only needed once ingesting
slug = slugify(title) or "untitled"
target_file = self._claim_filename(slug, source_path.name)
archive_line = (
f'source_uri: "{_yaml_escape(source_uri)}"\n'
if source_uri else ""
)
frontmatter = (
"---\n"
f'title: "{_yaml_escape(title)}"\n'
f'source_file: "{_yaml_escape(source_path.name)}"\n'
f'{archive_line}'
f'ingested_at: "{datetime.now().isoformat()}"\n'
"tags:\n"
" - brain/ingested\n"
f" - source/{source_path.suffix.lstrip('.')}\n"
"---\n\n"
)
header = (
f"# {title}\n\n"
f"*Source document: `{source_path.name}`*\n\n"
"---\n\n"
)
target_file.write_text(frontmatter + header + content, encoding="utf-8")
return target_file
def _claim_filename(self, slug: str, source_name: str) -> Path:
"""Resolve the note path for a slug: same source reclaims its
note; a different source gets a digest-suffixed sibling."""
target = self.output_dir / f"{slug}.md"
if not target.exists():
return target
existing_source = _read_frontmatter_source(target)
if existing_source == source_name or existing_source is None:
return target
digest = hashlib.md5(source_name.encode()).hexdigest()[:6]
return self.output_dir / f"{slug}-{digest}.md"

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"""Custom widgets — self-contained, theme-agnostic building blocks."""
from .radio_knob import RadioKnob
__all__ = ["RadioKnob"]

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"""RadioKnob widget — retro radio-style rotary knob.
A self-contained PySide6 widget (arc range, tick marks, glowing
indicator dot). Has no internal package dependencies — only PySide6
and ``math`` from the stdlib — so it can be imported standalone.
"""
import math
from PySide6.QtCore import Qt, Signal, QPointF, QRectF
from PySide6.QtGui import (
QFont, QColor, QPainter, QPen, QBrush,
QRadialGradient, QFontMetrics,
)
from PySide6.QtWidgets import QWidget
# ──────────────────────────────────────────────
# RADIO KNOB WIDGET (oldschool rotary control)
# ──────────────────────────────────────────────
class RadioKnob(QWidget):
"""
A retro radio-style rotary knob widget.
Supports arc range, tick marks, and a glowing indicator dot.
Rotation: 7 o'clock (min) to 5 o'clock (max) = 300 degrees.
"""
valueChanged = Signal(float)
def __init__(
self,
parent=None,
min_val: float = 0.0,
max_val: float = 100.0,
default_val: float = 50.0,
label: str = "",
unit: str = "",
color: tuple = (42, 130, 218),
num_ticks: int = 17,
tick_labels: list[str] | None = None,
snap_ticks: bool = False,
compact: bool = False,
):
super().__init__(parent)
self.min_val = min_val
self.max_val = max_val
self._value = default_val
self.label = label
self.unit = unit
self.color = QColor(*color)
self.num_ticks = num_ticks
self.tick_labels = tick_labels
self.snap_ticks = snap_ticks
self._dragging = False
self.compact = compact
# Arc geometry: 300-degree sweep, centered at 12 o'clock
self._arc_start = 210.0 # degrees (7 o'clock)
self._arc_span = -300.0 # negative = clockwise
# Scaling factor for compact mode (~70% of full size)
s = 0.70 if compact else 1.0
self._s = s
self.setFixedSize(int(180 * s), int(210 * s))
self.setCursor(Qt.CursorShape.PointingHandCursor)
# --- Public API ---
def value(self) -> float:
return self._value
def setValue(self, v: float):
v = max(self.min_val, min(self.max_val, v))
if self.snap_ticks:
v = self._snap(v)
if v != self._value:
self._value = v
self.update()
self.valueChanged.emit(v)
def intValue(self) -> int:
return int(round(self._value))
def _snap(self, v: float) -> float:
"""Snap to nearest tick."""
step = (self.max_val - self.min_val) / max(1, self.num_ticks - 1)
return round((v - self.min_val) / step) * step + self.min_val
def _val_to_angle(self, v: float) -> float:
"""Map value to angle in degrees (matching the conical gradient)."""
ratio = (v - self.min_val) / (self.max_val - self.min_val) if self.max_val != self.min_val else 0
return self._arc_start + ratio * self._arc_span # goes from 210 -> -90
def _angle_to_val(self, angle_deg: float) -> float:
"""Map angle back to value."""
# Normalize angle relative to arc start
ratio = (angle_deg - self._arc_start) / self._arc_span
ratio = max(0.0, min(1.0, ratio))
v = self.min_val + ratio * (self.max_val - self.min_val)
if self.snap_ticks:
v = self._snap(v)
return v
# --- Painting ---
def paintEvent(self, event):
p = QPainter(self)
p.setRenderHint(QPainter.RenderHint.Antialiasing)
w, h = self.width(), self.height()
s = self._s # scale factor (0.7 for compact, 1.0 for full)
cx = w / 2
cy = h / 2 - 4 * s
outer_r = 70 * s
knob_r = 40 * s
arc_w = max(1, int(8 * s))
tick_w = max(1, 1.5 * s)
bezel_pad = 6 * s
# --- Outer bezel ring ---
bezel_grad = QRadialGradient(cx, cy, outer_r + bezel_pad)
bezel_grad.setColorAt(0.85, QColor(48, 48, 52))
bezel_grad.setColorAt(1.0, QColor(26, 26, 30))
p.setBrush(QBrush(bezel_grad))
p.setPen(Qt.PenStyle.NoPen)
p.drawEllipse(QPointF(cx, cy), outer_r + bezel_pad, outer_r + bezel_pad)
# --- Inactive arc (dark track) ---
p.setPen(QPen(QColor(50, 50, 56), arc_w, Qt.PenStyle.SolidLine, Qt.PenCapStyle.RoundCap))
p.drawArc(QRectF(cx - outer_r, cy - outer_r, outer_r * 2, outer_r * 2),
int(self._arc_start * 16), int(self._arc_span * 16))
# --- Active arc (colored fill up to current value) ---
val_angle = self._val_to_angle(self._value)
active_span = val_angle - self._arc_start
if abs(active_span) > 0.5:
arc_color = QColor(self.color)
p.setPen(QPen(arc_color, arc_w, Qt.PenStyle.SolidLine, Qt.PenCapStyle.RoundCap))
p.drawArc(QRectF(cx - outer_r, cy - outer_r, outer_r * 2, outer_r * 2),
int(self._arc_start * 16), int(active_span * 16))
# --- Tick marks ---
for i in range(self.num_ticks):
t = i / (self.num_ticks - 1) if self.num_ticks > 1 else 0
tick_angle = self._val_to_angle(self.min_val + t * (self.max_val - self.min_val))
tick_rad = tick_angle * math.pi / 180.0
ox = cx + (outer_r + 12 * s) * (-1) * math.sin(tick_rad)
oy = cy + (outer_r + 12 * s) * (-1) * (-math.cos(tick_rad))
ix_ = cx + (outer_r + 3 * s) * (-1) * math.sin(tick_rad)
iy_ = cy + (outer_r + 3 * s) * (-1) * (-math.cos(tick_rad))
p.setPen(QPen(QColor(130, 130, 130), tick_w))
p.drawLine(QPointF(ix_, iy_), QPointF(ox, oy))
# Tick labels (if provided)
if self.tick_labels:
p.setFont(QFont("Sans", max(5, int(7 * s))))
p.setPen(QColor(160, 160, 160))
step = max(1, self.num_ticks // len(self.tick_labels))
label_idx = 0
for i in range(0, self.num_ticks, step):
if label_idx >= len(self.tick_labels):
break
t = i / (self.num_ticks - 1) if self.num_ticks > 1 else 0
tick_angle = self._val_to_angle(self.min_val + t * (self.max_val - self.min_val))
tick_rad = tick_angle * math.pi / 180.0
lx = cx + (outer_r + 24 * s) * (-1) * math.sin(tick_rad)
ly = cy + (outer_r + 24 * s) * (-1) * (-math.cos(tick_rad))
txt = self.tick_labels[label_idx]
fm = QFontMetrics(p.font())
tw = fm.horizontalAdvance(txt)
p.drawText(QPointF(lx - tw / 2, ly + 2 * s), txt)
label_idx += 1
# --- Knob body (dark brushed aluminum) ---
knob_grad = QRadialGradient(cx - 6 * s, cy - 6 * s, knob_r * 1.3)
knob_grad.setColorAt(0.0, QColor(72, 72, 78))
knob_grad.setColorAt(0.5, QColor(50, 50, 55))
knob_grad.setColorAt(1.0, QColor(34, 34, 38))
p.setBrush(QBrush(knob_grad))
p.setPen(QPen(QColor(26, 26, 30), max(1, 1.5 * s)))
p.drawEllipse(QPointF(cx, cy), knob_r, knob_r)
# --- Inner shadow ring ---
inner_shadow = QRadialGradient(cx, cy, knob_r - 2)
inner_shadow.setColorAt(0.85, QColor(0, 0, 0, 0))
inner_shadow.setColorAt(1.0, QColor(0, 0, 0, 60))
p.setBrush(QBrush(inner_shadow))
p.setPen(Qt.PenStyle.NoPen)
p.drawEllipse(QPointF(cx, cy), knob_r - 1, knob_r - 1)
# --- Indicator line (pointer) ---
ptr_angle = self._val_to_angle(self._value)
ptr_rad = ptr_angle * 3.14159265 / 180.0
ptr_len = knob_r - 8 * s
px = cx + ptr_len * (-1) * math.sin(ptr_rad)
py = cy + ptr_len * (-1) * (-math.cos(ptr_rad))
p.setPen(QPen(QColor(255, 255, 255, 220), max(1, 2.5 * s),
Qt.PenStyle.SolidLine, Qt.PenCapStyle.RoundCap))
p.drawLine(QPointF(cx, cy), QPointF(px, py))
# --- Center cap dot ---
cap_r = max(2, 5 * s)
cap_grad = QRadialGradient(cx, cy, cap_r)
cap_grad.setColorAt(0.0, QColor(60, 60, 65))
cap_grad.setColorAt(1.0, QColor(30, 30, 34))
p.setBrush(QBrush(cap_grad))
p.setPen(Qt.PenStyle.NoPen)
p.drawEllipse(QPointF(cx, cy), cap_r, cap_r)
# --- Glow dot at arc tip ---
glow_r = max(3, 10 * s)
glow_x = cx + outer_r * (-1) * math.sin(ptr_rad)
glow_y = cy + outer_r * (-1) * (-math.cos(ptr_rad))
glow = QRadialGradient(glow_x, glow_y, glow_r * 1.2)
glow.setColorAt(0.0, QColor(self.color.red(), self.color.green(), self.color.blue(), 200))
glow.setColorAt(1.0, QColor(self.color.red(), self.color.green(), self.color.blue(), 0))
p.setBrush(QBrush(glow))
p.setPen(Qt.PenStyle.NoPen)
p.drawEllipse(QPointF(glow_x, glow_y), glow_r, glow_r)
p.end()
# --- Label + value text below knob ---
p2 = QPainter(self)
p2.setRenderHint(QPainter.RenderHint.Antialiasing)
# Value line (e.g. "32.0 CRF")
val_font_sz = max(6, int(13 * s))
p2.setFont(QFont("Consolas", val_font_sz, QFont.Weight.Bold))
val_color = QColor(self.color.red(), self.color.green(), self.color.blue())
p2.setPen(val_color)
val_text = f"{self._value:.0f} {self.unit}" if self.unit else f"{self._value:.0f}"
p2.drawText(QRectF(0, h - 38 * s, w, 20 * s), Qt.AlignmentFlag.AlignCenter, val_text)
# Label line (e.g. "Quality")
lbl_font_sz = max(5, int(9 * s))
p2.setFont(QFont("Consolas", lbl_font_sz, QFont.Weight.Bold))
p2.setPen(QColor(160, 160, 160))
p2.drawText(QRectF(0, h - 18 * s, w, 16 * s), Qt.AlignmentFlag.AlignCenter, self.label)
p2.end()
# --- Input handling ---
def mousePressEvent(self, event):
if event.button() == Qt.MouseButton.LeftButton:
self._dragging = True
self._update_from_mouse(event.position())
def mouseMoveEvent(self, event):
if self._dragging:
self._update_from_mouse(event.position())
def mouseReleaseEvent(self, event):
if event.button() == Qt.MouseButton.LeftButton:
self._dragging = False
def wheelEvent(self, event):
delta = event.angleDelta().y()
step = (self.max_val - self.min_val) / max(1, self.num_ticks - 1)
if delta > 0:
self.setValue(self._value + step)
elif delta < 0:
self.setValue(self._value - step)
def _update_from_mouse(self, pos: QPointF):
cx = self.width() / 2
cy = self.height() / 2 - 4 * self._s
dx = pos.x() - cx
dy = pos.y() - cy
angle = math.degrees(math.atan2(dx, -dy)) # 0=north, CW positive
if angle < 0:
angle += 360
# Clamp to arc range: 210..510 (which is 210..360 and 0..150)
# Our arc: 210 degrees to -90 (=270) degrees clockwise
if angle < 210 and angle > 150:
# Dead zone at bottom (between 150 and 210)
# Push to nearest end
angle = 210 if abs(angle - 210) < abs(angle - 510) else 510
if angle > 360:
angle -= 360 # normalize back to 0..360
self.setValue(self._angle_to_val(angle))