🧠 Brain of Reese

A chippy, honest RAG chatbot over your ~/Homelab and ~/Deployments projects. Point it at your notes — markdown, YAML, JSON, Python, plain text — ask it anything, and it retrieves the relevant chunks with hybrid search (pgvector cosine ∪ Postgres full-text search, fused with Reciprocal Rank Fusion), feeds the whole relevant document to a self-hosted LLM (turbo via https://aipi.reeseapps.com/v1), and streams a grounded answer back.

If it doesn't have notes for your question, it admits it — and offers 2-3 concrete alternative questions about things it does have notes on.

Image support is opt-in (BOR_IMAGES): standalone images become retrievable documents, and you can attach an image to a question — see Image support.

Updated your notes? Re-run the import — it's idempotent and only re-embeds what changed:

uv run python -m scripts.import_docs --prune

See Updating the documents for details.

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Quick Start

# 1. Dependencies
uv sync

# 2. Configure (copy and edit)
cp .env.example .env
# Set BOR_LLM_API_KEY — your aipi key (falls back to $AIPI_KEY)

# 3. Start the database
podman compose up -d db
podman compose ps          # wait until "healthy"

# 4. Apply migrations & import
uv run alembic upgrade head
uv run python -m scripts.import_docs

# 5. Run the app
uv run uvicorn app.main:app --reload
# → http://localhost:8000  (chat)

Stack: FastAPI · Pydantic v2 · SQLAlchemy 2 · Alembic · pgvector · vanilla HTML/CSS/JS (no CDN) · Playwright E2E

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Pages at a glance

The app is a single-page application — all views load from / with a client-side router. Direct URLs deep-link to the matching view.

Page	URL	Who can see it
Chat	/	Everyone (token gate for non-shared)
RAG / Knowledge base	/sources.html	Admin only
Git sources	/git-sources.html	Admin only
Tuning	/tuning.html	Admin only
Saved chats	/history.html	Admin only
Access tokens	/tokens.html	Admin only
Document viewer	/document.html?source=X&path=Y	Token users & admins
Login	/login.html	Everyone
Shared chat	/shared/<token>	Everyone (anonymous)
Edit doc	/doc-edit.html	Admin only

Chat (/)

Ask questions; answers stream in with source chips that cite the exact documents used. Clicking a chip opens the document in an almost-fullscreen modal on the same page (no new tab). The onboarding suggestion chips follow the opening questions of the 3 most recent saved chats (each chat's first question — a follow-up never chips, phase 103) — on a fresh deployment they seed from BOR_SUGGESTIONS.

RAG / Knowledge base (/sources.html)

The indexed document list. The Path column opens each document in the same almost-fullscreen modal. Admin-only — anonymous visitors see a sign-in gate instead.

Git sources (/git-sources.html)

The admin-managed source registry: add or remove git repositories, upload source archives (.tar, .tar.gz, .tgz, .zip), and register local directories — all in one table with a kind discriminator. No .env editing, no restart. The Sync sources button clones/pulls/walks every source and re-imports in one click.

Tuning (/tuning.html)

Set global instructions that are injected into the system prompt of every chat turn. Add notes like "be more concise" or "assume I'm on NixOS". List, edit, or remove them here — no conversation required.

Saved chats (/history.html)

Every conversation is saved automatically. Click a title to return to that chat. Admin-only view.

Access tokens (/tokens.html)

Generate tokens and hand them out — a token opens chat, the answers, and the documents they cite. Shared chats stay open to everyone.

Shared chat (/shared/<token>)

A read-only snapshot of any conversation, shareable via a link. Open to everyone — no login or token required.

Edit doc (/doc-edit.html)

Review and adjust an AI-generated answer before committing it to the docs repository. Admin-only flow page.

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Admin & Sign-in

Brain of Reese has exactly one account: the admin (you). Signing in unlocks the full Sources catalog, the answer-tuning controls, and token management. Shared chats are the only content that stays open to anonymous visitors.

Setup (one-time)

python -c 'import secrets;print(secrets.token_hex(32))'   # → paste into .env

BOR_ADMIN_PASSWORD=your-password        # plaintext — homelab scope, by design
BOR_SESSION_SECRET=<the hex from above> # signs the session cookie

Fail-loud: while either variable is empty the app refuses to start, naming the missing one(s).

How authentication works

Endpoint	Purpose
POST /api/login	Password → signed bor_session cookie
POST /api/logout	Clears session cookie
GET /api/whoami	`{"authenticated": bool, "role": "admin"
POST /api/token-auth	Token (bor_…) → same cookie, role user

The cookie uses same_site="lax", https_only off — no HTTPS enforcement (homelab HTTP; the cookie is single-admin convenience, not a cloud boundary). Max age is BOR_SESSION_MAX_AGE (default 43200 = 12 h).

Access tokens

The admin can hand out access without sharing the admin password.

1. Generate: sign in → Tokens in the navbar. Type a label and hit Generate — a bor_ + 32-hex token appears in the shown once block. Copy it now: only its SHA-256 hash is stored.
2. Use: the holder enters the token at the in-app gate. The browser caches it in localStorage["bor.token"] for silent re-auth on reloads.
3. Revoke: click Revoke on the token's row. Revocation is immediate.

A token user can chat and open documents — nothing else. The Sources catalog, git sources, tuning, and history stay admin-only.

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Using the App

Updating the documents

The knowledge base is refreshed by re-running the import. It is idempotent and delta-based (sha256 per file), so a refresh after a normal editing session takes seconds:

# After editing/adding/removing notes:
uv run python -m scripts.import_docs                 # re-index what changed
uv run python -m scripts.import_docs --prune         # also drop deleted files

# Point it at extra directories (repeatable):
uv run python -m scripts.import_docs --source ~/SomeOtherDocs

With git-based sources each run first pulls the latest commits of your repos, so this same command is the whole update loop: commit → re-run.

Then check the Sources page (http://localhost:8000/sources.html): the documents / chunks counters and last indexed timestamp should reflect the new files.

• The import prints one line per file and ends with a greppable summary, so it is safe to run from cron or after every commit.
• Indexed formats: md, markdown, txt, yaml, yml, json, py, the Podman quadlet family (container, network, volume, image, pod, kube, swap, os, endpoint), and j2 Jinja templates (case-insensitive).
• Hidden files and common cache dirs (.venv, node_modules, .git, __pycache__, etc.) are skipped.
• Non-markdown files get format-aware chunking (YAML keys, JSON keys, Python classes/defs).
• With BOR_IMAGES=1, standalone images (png, jpg, jpeg, webp, gif, bmp) are indexed as documents too — see Image support.

Git-based sources

Rather than pointing the import at local folders, point it at git repositories. The admin Git sources page (/git-sources.html) is the primary management surface — add or remove repositories there and the list is stored in Postgres.

# .env — the fallback list (fresh setups, or until the admin page
# stores a source; the page becomes the source of truth)
BOR_GIT_SOURCES=https://git.reeseapps.com/reese/homelab.git,git@github.com:reese/deployments.git
BOR_SOURCES_DIR=~/bor-sources   # default; each repo lands in <dir>/<repo-name>/

• Auth is whatever the machine supplies — HTTPS via the OS credential helper, or SSH via your key; no credentials are stored in the app.
• Every run clones (first time, shallow --depth 1) or pulls (git pull --ff-only) each repo, then indexes the checkout.
• A failed sync aborts the run — no partial junk.

Archive upload sources

Upload a .tar, .tar.gz, .tgz, or .zip archive to make it a source. The form on the Git sources page — and the POST /api/git-sources/upload route behind it — accepts archives and unpacks + scans them immediately.

• Re-uploading the same filename replaces the source in place (no second folder, no duplicate row).
• Zip-bomb guard: absolute member paths, .. traversal, and symlinks escaping the unpack folder are rejected.
• Archives unpack under BOR_UPLOAD_DIR (default ~/bor-sources/uploads).
• BOR_UPLOAD_MAX_MB (default 512) caps both compressed and extracted size.

Local directory sources

A plain directory can be a first-class source too. It shares the git sources' one table (the git_sources registry with a kind discriminator: git | local), one admin page, and one Sync button.

• Register via API: POST /api/git-sources with {"kind": "local", "path": …}.
• Sync walks it directly — no clone, no checkout copy.
• The directory is re-verified to exist at sync time; a missing directory fails the run loudly.

Sync from the UI

The Sync sources button on the Sources page (admin only) runs the whole git-source refresh in one click:

1. clone/pull + walk every configured source — git repos and local directories, including uploaded archives.
2. re-import with prune — files deleted upstream leave the index.
3. regenerate the KB overview (the <knowledge_base> outline every chat turn injects) — only when the import changed the knowledge base.

• States: the button shows Syncing… with a spinner while polling GET /api/sync/status every 2 s. No client-side timeout — a clone + embed can legitimately take minutes.
• One sync at a time: a second trigger while a run is in flight gets a 409.

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Thinking

The self-hosted turbo model reasons before it answers. That reasoning is streamed with the turn as thinking SSE events and shown in a collapsible "Thinking" block above the answer bubble: it opens and fills in live while the model thinks, tucks itself away the moment the first answer token lands, and stays click-toggleable afterwards.

To hide it, set BOR_STREAM_THINKING=0.

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Agent document tools (ls + read + grep)

Retrieval only puts the top documents in context. When an answer depends on a file a note references ("the exact JSON shape is in example-record-file.json"), the model can extend its own context with three server-side tools — on grounded (high-relevance) turns only:

Tool	Description
ls	Lists every indexed document (source: X | path: Y | title: Z); pass a source name as path to list one source
read(path)	Appends the full text of one indexed document to context (never truncated); path is the combined source/path string
grep(pattern, path?)	Searches indexed documents for an exact string (case-insensitive fixed substring); returns up to 20 source/path:line: text matches; an optional path limits to one document

Each call is executed against Postgres only (no extra LLM round trip) and streamed as an SSE tool frame. In the chat, each call shows a transient calling-tool status alongside "thinking" (the send button keeps its busy state — "Stop" — for the whole turn).

The model may call tools as many times as needed, bounded by a round cap:

Env	Default	Meaning
BOR_AGENT_MAX_ROUNDS	10	hard cap on agent tool rounds per grounded turn (0 = no tools, the kill switch)

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Tuning your answers

Admin-only — sign in first. If an answer isn't quite right — too chatty, wrong assumption, missing context — tune Brain right there:

1. Press "Tune" in the meta row under any completed answer (deflected ones included).
2. Type a short instruction (1–2000 chars), e.g. "be more concise" or "assume I'm on NixOS", and Save.

The note is stored in Postgres (steering_notes) and read into the system prompt of every subsequent chat turn as a <tuning> section (numbered, oldest first, capped at BOR_STEERING_MAX_CHARS chars — default 8000, overflow marked […truncated…]).

List or remove notes from the "Tuning" button in the chat header (count badge, newest-first, per-note delete). The API is stateless JSON if you prefer curl:

curl -s localhost:8000/api/steering                                  # list
curl -s -X POST localhost:8000/api/steering \
     -H 'Content-Type: application/json' -d '{"note": "be more concise"}'
curl -s -X DELETE localhost:8000/api/steering/<note-id>              # remove

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How retrieval works (hybrid)

Every question is embedded and also lexically tokenized (OR-joined, English stemming) and searched twice against Postgres:

1. Vector — pgvector cosine top-N (default BOR_HYBRID_VECTOR_CANDIDATES=100)
2. Lexical — a stored tsvector (GIN-indexed) matched with to_tsquery, top-N by ts_rank (default BOR_HYBRID_LEXICAL_CANDIDATES=30)

The two ranked lists are fused with Reciprocal Rank Fusion (score = Σ 1/(k + rank), BOR_RRF_K=60) — a chunk in both lists scores nearly double, which lets a name-your-tool question ("gitlab") find its own document even when the question embeds close to generic templates.

The honesty gate (A8) then answers (HIGH) when the best cosine is ≥ BOR_RELEVANCE_THRESHOLD (default 0.62) or when at least one chunk matched lexically (fts_hits > 0) and the best cosine clears BOR_LEXICAL_SUPPORT_FLOOR (default 0.35) — the vector signal must corroborate a lexical hit (A8 revised 2026-09-14). It deflects (LOW) otherwise, including a weak single-token hit with vector-unsupported docs. The top BOR_TOP_N_DOCS full documents are what the LLM sees.

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Document summaries (non-markdown)

Raw yaml/json/py/txt embeds badly — flags and keys are not language, so retrieval can miss exactly the documents that are all configuration. At import time, every non-markdown A9 document is summarized by the aipi lite model (BOR_LLM_SUMMARY_MODEL, default lite):

• The summary is stored on documents.summary and indexed as one extra embedded chunk (chunks.is_summary, position −1), so hybrid search has a natural-language target to hit instead of the raw text.
• The last line is a code-deterministic pointer — Source: <source>/<path> — appended by the app, never model-generated.
• The model only sees the first BOR_SUMMARY_MAX_CHARS (default 12000) characters; overflow is cut and marked with […truncated…].

Summary generation is best-effort: if lite fails, the document is still indexed (without a summary), the failure is logged, and counted in the import summary line (summaries=N summary_errors=N).

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Image support

One toggle gates both image capabilities: BOR_IMAGES (default off). Enable it only when your chat model (BOR_LLM_CHAT_MODEL) supports vision — the chat model is the vision model: it describes image documents and answers questions with attached images. The embedding model never sees pixels in either flow.

Image documents

With BOR_IMAGES=1, a standalone image file — inside an uploaded source archive, or as a file in a git/local source walk — becomes a first-class document:

• Described by the chat model. On first index, a single multimodal chat-model call describes the image. The description becomes the document's content and summary — and is the only text that gets embedded, so hybrid search has a natural-language target to hit instead of pixels.
• The bytes persist. Each ingested image is copied to BOR_IMAGE_DIR/<uuid>.<ext> and served from that copy (uploads are replaced and checkouts re-cloned, so the served image outlives its source file).
• Retrieved & displayed. Image docs appear in the Sources catalog with a small lazy-loaded thumbnail (progressive enhancement — a fetch failure falls back to the doc icon), the document viewer renders the image with the description below it, and a cited image doc renders inline in the chat's sources block (alt text = the description). The agent's read tool returns the description with a marker telling the model it is reading an image document.
• Prune guard. While BOR_IMAGES is off, syncs never prune existing image documents — an image doc is invisible to the walk, not a deleted file. With the toggle on, a deleted image file prunes its doc and its copy.
• Fail-soft. A failed or empty description skips that image (no row, no copy), logs a warning, and counts in the import summary line (images_failed=N) — the sync continues.

Accepted formats are png, jpg, jpeg, webp, gif, bmp (BOR_IMAGE_EXTENSIONS, case-insensitive) — a separate set from BOR_IMPORT_EXTENSIONS; images are never added via that variable, the toggle is the single knob.

Question images

With the toggle on, the composer gains a paperclip button. Attach one image to a question and the chat model answers with text and image:

• Server-stored, path-persisted. On send the file is first uploaded to POST /api/chat-images (capped at BOR_CHAT_IMAGE_MAX_MB, default 10 MiB — over is a 413) and stored under BOR_CHAT_IMAGE_DIR. Saved and shared chats carry the returned path — never base64.
• Current turn only. The model receives a multimodal message for this question; prior turns' images are never replayed into history (the text of a prior turn stands alone).
• Rendered everywhere. The image renders in the user bubble, after a page reload, and on the shared chat page; a failed load degrades to a small "image unavailable" note (never a broken-image icon).
• Blocked on failure. If the image upload fails, the send is blocked with a banner — the question is never sent without the image the user attached.
• Toggle off ⇒ the attach control is absent, and an API request carrying an image gets an error frame with the hint: Enable BOR_IMAGES in the server's .env (and restart) to ask with an image.

A question image is never indexed as a document — it is turn-local storage, not a source.

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Caching & deploys

A deploy is a commit — and the browser must see it without a hard refresh. One Starlette middleware (app/core/caching.py) applies the rule at the transport layer:

• HTML pages ship Cache-Control: no-cache, no etag, no last-modified — each visit always gets a fresh 200 body.
• Assets are versioned and cached for a year. Pages reference their CSS/JS with a token (/assets/styles.css?v=<token>), and every /assets/* response ships Cache-Control: public, max-age=31536000, immutable.
• The token is the deploy. In a git checkout it is the short SHA of HEAD — so every commit/deploy flips the token and the versioned asset URLs change with it. A checkout without .git falls back to a stable content hash of the frontend/ tree.

No CDN, no new services, no build-step change: the middleware rewrites the asset references of the known pages in flight.

Deploy note: the very first deploy onto this scheme needs one normal page visit, so the browser revalidates the HTML once and starts requesting the versioned assets; every commit after that is picked up automatically.

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Configuration reference

Everything is an environment variable (the BOR_ prefix, plus DEBUGPY*) read from .env. Every variable in .env.example is documented below — copy that file to .env and adjust.

App & branding

Env	Default	Meaning
BOR_APP_NAME	Brain of Reese	Display name everywhere (page titles, header brand, status labels)
BOR_INPUT_PLACEHOLDER	Ask me anything…	Chat composer placeholder
BOR_FOOTER_TEXT	Powered by self-hosted models	Footer line on every page
BOR_ENVIRONMENT	development	development or production (the prod compose profile sets production)
BOR_LOG_LEVEL	INFO	App log level
BOR_STATIC_DIR	frontend	Where static files are served from (the container image sets /app/static)

Database & concurrency

Env	Default	Meaning
BOR_DATABASE_URL	local compose URL	SQLAlchemy URL (psycopg)
BOR_DB_POOL_SIZE	5	DB connection pool size
BOR_DB_POOL_MAX_OVERFLOW	10	Overflow connections above BOR_DB_POOL_SIZE
BOR_DB_POOL_RECYCLE	3600	Recycle pool connections after N seconds
BOR_CHAT_MAX_CONCURRENT	10	Max concurrent /api/chat turns

LLM (aipi)

Env	Default	Meaning
BOR_LLM_BASE_URL	https://aipi.reeseapps.com/v1	OpenAI-compatible endpoint
BOR_LLM_API_KEY	— (falls back to $AIPI_KEY)	aipi API key
BOR_LLM_CHAT_MODEL	turbo	Chat model — must support vision when BOR_IMAGES=1
BOR_LLM_EMBED_MODEL	embed	Embedding model
BOR_LLM_SUMMARY_MODEL	lite	One-shot completions: document summaries at import, KB overview
BOR_EMBEDDING_DIM	768	Vector dimension (fixed at table creation)
BOR_LLM_RETRIES	3	Retries: chat-turn stream before the first token + empty one-shot summary replies; 0 = off
BOR_LLM_RETRY_DELAY	5	Seconds between LLM retries
BOR_LLM_TIMEOUT	120	HTTP timeout (s) for LLM API calls
BOR_STREAM_THINKING	1	Stream the model's thinking as thinking SSE events (0 = suppress)

RAG & prompt budgets

Env	Default	Meaning
BOR_TOP_N_DOCS	2	Ceiling for the cited tier, not a quota (phase 113) — a single strong doc yields one citation
BOR_RELEVANCE_THRESHOLD	0.62	Answer when best cosine ≥ this or a lexical hit corroborated by cosine ≥ BOR_LEXICAL_SUPPORT_FLOOR; otherwise honest deflection
BOR_LEXICAL_SUPPORT_FLOOR	0.35	Best-cosine floor an FTS hit must clear to flip the gate HIGH (A8 revised 2026-09-14)
BOR_SOURCE_USEFULNESS_FLOOR	0.35	Cosine bar for a citation slot (phase 113): a doc earns the cited tier only when its best-chunk cosine clears it; 0 = no bar
BOR_RELATED_MAX_DOCS	2	Cap on the related-doc tier (the de-emphasized "nearby docs" row — never a citation chip); 0 = no related docs
BOR_SUGGESTED_DOCS	5	Cap on the "start here" suggestion tier (phase 118): top-N doc summaries seeded into the grounded prompt; no cosine floor; ≥ 1
BOR_SUGGESTION_PREVIEW_CHARS	400	NULL-summary fallback preview in a suggestion block (phase 118): first N content chars + truncation marker; > 0
BOR_MAX_OUTPUT_TOKENS	32768	Max answer length in tokens (answers must not be cut off)
BOR_STEERING_MAX_CHARS	8000	Char budget for the <tuning> (steering notes) prompt section
BOR_SUMMARY_MAX_CHARS	12000	Cap on document content sent to the lite summary model
BOR_KB_OVERVIEW_MAX_CHARS	4000	Char budget for the <knowledge_base> prompt section
BOR_OVERVIEW_INPUT_MAX_CHARS	40000	Cap on the document list sent to lite for KB overview generation
BOR_FOLDER_SUMMARY_INPUT_MAX_CHARS	8000	Cap on a folder's document list sent to lite for folder summaries (phase 94)

Chunking & embedding

Env	Default	Meaning
BOR_CHUNK_TARGET_CHARS	2000	Target chunk size (chars)
BOR_CHUNK_OVERLAP_CHARS	200	Overlap between adjacent chunks (chars)
BOR_EMBED_BATCH_SIZE	16	Chunks per embedding API call
BOR_EMBED_QUESTION_MAX_CHARS	1200	Chat question-embed prefix cap (phase 114): the embed step embeds at most this many chars of the question — the full question still reaches the LLM prompt

Hybrid retrieval

Env	Default	Meaning
BOR_HYBRID_VECTOR_CANDIDATES	100	Cosine list width for the RRF fusion
BOR_HYBRID_LEXICAL_CANDIDATES	30	FTS list width for the RRF fusion
BOR_RRF_K	60	RRF damping constant (1/(k + rank))
BOR_RECENCY_BOOST	0.0007	Max additive fused-score bonus a zero-age doc gets (phase 106); 0 = off
BOR_RECENCY_HALF_LIFE_DAYS	365	Recency decay timescale, days (phase 106) — the boost decays by e^-1 (~0.37x) per timescale of age
BOR_NAME_HIT_BONUS	0.005	Selection-time bonus a name-hit doc's best fused score gets (phase 119); 0 = off

Agent tools

Env	Default	Meaning
BOR_AGENT_MAX_ROUNDS	10	Hard cap on agent tool rounds per grounded turn (0 = no tools)
BOR_READ_MAX_CHARS	128000	Cap on a read tool result (chars); over-cap reads truncate + a grep-pointer notice (phase 95)

Import & sources

Env	Default	Meaning
BOR_IMPORT_EXTENSIONS	A9 family	Importable text formats — the A9 default is md,markdown,txt,yaml,yml,json,py,container,network,volume,image,pod,kube,swap,os,endpoint,j2; any well-formed extension is allowed. Images are not added here — see BOR_IMAGES
BOR_GIT_SOURCES	— (empty)	CSV of git repo URLs — fallback while the admin Git sources page's list is empty
BOR_SOURCES_DIR	~/bor-sources	Where git repos are cloned/pulled
BOR_UPLOAD_DIR	~/bor-sources/uploads	Where uploaded source archives are unpacked
BOR_UPLOAD_MAX_MB	512	Cap (MiB) for uploaded source archives (zip-bomb guard)
BOR_SUGGESTIONS	built-in list	JSON seed for onboarding chips (shown before the first saved question)

Image support (phases 122/123)

Env	Default	Meaning
BOR_IMAGES	0	Master switch for image support — off by default; enable only when BOR_LLM_CHAT_MODEL supports vision. Gates both image documents and question images
BOR_IMAGE_EXTENSIONS	png,jpg,jpeg,webp,gif,bmp	Extensions indexed as image documents (case-insensitive; separate from BOR_IMPORT_EXTENSIONS)
BOR_IMAGE_DIR	~/bor-sources/images	Persistent home for the served image bytes (uploads are replaced, checkouts re-cloned)
BOR_CHAT_IMAGE_DIR	~/bor-sources/chat-images	Where question images are stored (saved/shared chats carry the path, never base64)
BOR_CHAT_IMAGE_MAX_MB	10	Question-image upload cap (MiB; must be > 0)

Docs push

Env	Default	Meaning
BOR_DOCS_REPO	— (empty)	The git repo chat answers can be committed to (URL or local path); while empty "Save as doc" is hidden and the push endpoint 409s
BOR_DOCS_BRANCH	bor-docs	Branch pushes land on (git push --ff-only — you open the PR yourself)
BOR_DOCS_BASE_BRANCH	main	Branch each push is cut fresh from
BOR_DOCS_WORK_DIR	~/bor-docs	Where BOR_DOCS_REPO is checked out on the server

Admin & debugging

Env	Default	Meaning
BOR_ADMIN_PASSWORD	(required)	The single admin's password — app refuses to start when empty
BOR_SESSION_SECRET	(required)	Signing key for the bor_session cookie
BOR_SESSION_MAX_AGE	43200	Session-cookie lifetime in seconds (12 h, sliding)
DEBUGPY	0	1 ⇒ attach-on-demand debugpy on DEBUGPY_PORT
DEBUGPY_PORT	5678	debugpy listen port

Customizing the look

Every identity string is an env var: BOR_APP_NAME (display name), BOR_INPUT_PLACEHOLDER (chat composer placeholder), and BOR_FOOTER_TEXT (footer line) — all served by GET /api/config and applied by assets/brand.js at boot.

The colors are set from the admin Theme tab (/theme.html, admin-only — phase 91): the 8 identity colors plus the three strings above are edited with text fields and color pickers, persisted in the ui_settings table, and injected into every served page as an inline <style> BEFORE first paint (no red flash, no pop-in). The old CSS-file theming is retired — a leftover BOR_THEME line in a deployment's .env is simply ignored. Leave everything unset and the app renders the built-in dark-tech palette byte-identically.

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Checking retrieval quality

Ask the real pipeline (live aipi embeddings + the current KB) whether a question lands on the right document, with gate verdict and per-document cosine / FTS / fused scores:

uv run python -m scripts.eval_retrieval "How did I install gitlab?"
uv run python -m scripts.eval_retrieval --from-file questions.txt --top 8

Requires AIPI_KEY in the environment and an imported knowledge base.

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Debugging

debugpy is off by default and never imported unless you opt in:

DEBUGPY=1 uv run uvicorn app.main:app
# → log line: debugpy: remote debugging ENABLED, listening on 0.0.0.0:5678

Attach from VS Code (.vscode/launch.json):

{
  "name": "Attach to Brain of Reese",
  "type": "debugpy",
  "request": "attach",
  "connect": { "host": "localhost", "port": 5678 },
  "pathMappings": [
    { "localRoot": "${workspaceFolder}", "remoteRoot": "/app" }
  ]
}

The port is non-blocking and attach-on-demand: the app keeps running normally until you attach. Override the port with DEBUGPY_PORT.

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QA / Testing

Three layers — the project rule is one story, one phase, one Playwright suite (see AGENTS.md):

# Unit + integration (FastAPI TestClient)
uv run pytest

# Coverage gate (phases require >90% on app/)
uv run pytest --cov=app --cov-report=term-missing

# Lint + static types
uv run ruff check .
uv run pyright

# Playwright E2E — install the browser once:
uv run playwright install chromium

# Each story's E2E runs IN ISOLATION:
uv run pytest tests/e2e/test_import_documents.py -v --no-cov
uv run pytest tests/e2e/test_chat_rag.py -v --no-cov
# ...one file per story in .agents/user_stories/ (see .agents/phases/todo/)

Deterministic E2E: by default the E2E app talks to a local mock aipi (tests/e2e/mock_llm.py) whose embeddings are real token-overlap vectors — so the cosine relevance threshold behaves like production. To run E2E against the live self-hosted models instead:

E2E_REAL_LLM=1 uv run pytest tests/e2e/test_chat_rag.py -v --no-cov

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Production Deployment

Build the multi-stage image (frontend minified by esbuild in the builder stage, deps installed by uv, non-root runtime):

podman build -t brain-of-reese/app:latest .

Run standalone (bring your own Postgres + pgvector):

podman run -d --name brain-of-reese \
  -p 8000:8000 \
  -e BOR_DATABASE_URL=postgresql+psycopg://reese:SECRETPASSWORD@dbhost:5432/brain_of_reese \
  -e BOR_LLM_BASE_URL=https://aipi.reeseapps.com/v1 \
  -e BOR_LLM_API_KEY=$AIPI_KEY \
  brain-of-reese/app:latest

The entrypoint runs alembic upgrade head automatically on start.

Or run the whole stack from compose (app + db):

podman compose --profile prod up -d --build

Production hardening: app runs as non-root (uid 10001), slim image, healthcheck on /api/health, debugpy off unless DEBUGPY=1, all assets served locally (no CDN), BOR_ENVIRONMENT=production.

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Troubleshooting

• 401 from aipi — set BOR_LLM_API_KEY (or $AIPI_KEY).
• litellm.UnsupportedParamsError … encoding_format — the aipi proxy (litellm openai_like) rejects the encoding_format parameter. The app already works around this by POSTing a minimal {model, input} payload. If you see this, you are likely calling the endpoint with a different client — drop the parameter (or set litellm.drop_params = True on the proxy).
• Embedding dimension mismatch — aipi changed models; run uv run python -m scripts.llm_probe, update BOR_EMBEDDING_DIM, then drop + recreate the chunks table.
• No attach button / images ignored on import — image support is off (the default). Set BOR_IMAGES=1 in .env and restart — only when BOR_LLM_CHAT_MODEL supports vision. Turning the toggle off never destroys existing image documents (syncs prune-guard them).
• Honest deflection (the amber deflection bubble) — every question passes the honesty gate: deflection happens when the best cosine similarity is below BOR_RELEVANCE_THRESHOLD (default 0.62) and the lexical signal is absent or not vector-corroborated (fts_hits = 0, or best cosine below BOR_LEXICAL_SUPPORT_FLOOR — default 0.35 — A8 revised 2026-09-14). A weak cosine with a lexical hit (name-your-tool questions) still gets a grounded answer, provided the best cosine clears the floor. When it does deflect, the LLM prompt carries weak-hit titles only (no document content), the reply admits it has no notes on that and offers 2-3 concrete alternative questions about things it does have notes on, the bubble renders amber with "Maybe try" chips derived from the closest indexed titles, and the query_log row records deflected=true. This is a feature, not a bug. With a small local model, a rare turn may still answer from general knowledge with an explicit disclosure when retrieval was borderline — the gate (A8 revised) minimizes this by keeping vector-unsupported docs out of the grounded prompt, and the disclosure is surfaced, never silent.
• Answers deflect too often / too rarely — tune BOR_RELEVANCE_THRESHOLD (lower = answers more, higher = more honest deflection) and BOR_LEXICAL_SUPPORT_FLOOR (the best-cosine floor a lexical hit must clear to flip HIGH — lower = lexical hits ground more easily): 0.0 ⇒ the gate leans entirely on FTS hits; 1.0 ⇒ everything deflects unless a lexical hit is vector-corroborated. The embed model's cosines cluster in a ~0.6–0.85 band on the live KB, so the default is 0.62. Check real scores:

  SELECT question, top_score, fts_hits, deflected
  FROM query_log ORDER BY created_at DESC LIMIT 20;
  

• KB offline banner in the chat — Postgres isn't running: podman compose up -d db.
• Stuck "Thinking…" — the LLM is slow or down; a 120s client timeout turns it into an error banner automatically.

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Planning & Architecture

The architecture, LOCKED decisions, and the phase roadmap live in .agents/PLAN.md; per-story specs in .agents/user_stories/.