vishalakshi

Install

pip install vishalakshi

The loop

Vault() with no argument uses ~/.vishalakshi/vault.db. This page uses a throwaway file against this repository’s own docs and source.

from tempfile import mkdtemp
from litesearch import repo_root
from vishalakshi import Vault

root = str(repo_root() or Path('..'))   # works from nbs/ or the repo root
v = Vault(Path(mkdtemp())/'vault.db')
v.enc.note

'minishlab/potion-multilingual-128M (256d, float16, model2vec)'

add takes a directory, a file, or text. grab routes an arXiv id, YouTube link, GitHub repo, PDF, file, or directory.

v.add(root)                       # README.md and every notebook under nbs/
v.note('federate fuses the legs by rank because they share no vector space: the vault embeds '
       'prose, kosha embeds identifiers, ripgrep embeds nothing.', tags=['retrieval', 'design'])
v.stats()

{'docs': 19,
 'nodes': 166,
 'chunks': 672,
 'encoder': 'model2vec',
 'entities': 0,
 'path': '/var/folders/kg/9vdw4mdd1fs58svgh4k1qhr09x7dqh/T/tmp0ufdxnt4/vault.db',
 'by_kind': {'notebook': 12, 'md': 5, 'txt': 1, 'note': 1}}

Default model: gemma-4-E2B on LiteRT GPU, no API key. Name any rishi model id/path; chat_kw= reaches its constructor. $VISHALAKSHI_MODEL replaces the id; $VISHALAKSHI_GPU=0 puts LiteRT on CPU. Context budget is sections=4, max_chars=1500.

r = v.ask('why are rankings fused instead of distances?')
print(r.model, '·', r.runtime)
print(r.answer)
print(L(r.cited).map(lambda c: f"{c['n']} {c['breadcrumb']}"))
if (hit := first(r.cited)): print(v.read(hit['node_id'])['text'][:400])

litert/litert-community/gemma-4-E2B-it-litert-lm · litert
The provided sections do not explicitly state why rankings are fused instead of distances. However, one section mentions that `federate` fuses rankings with RRF and never distances [2].
['2 03 code › Fusing legs that share no vector space']
The vault embeds prose, kosha embeds identifiers, ripgrep embeds nothing. Legs share no vector space, so `federate` fuses rankings with RRF and never distances.


#| export
def _prose(v, q, n, kind=None, source:str='prose') -> L:
    'Vault sections, normalised to the federated row shape. `source` names the shelf they came from.'
    return L(v.sections(q, limit=n, kind=kind)).map(
        lambda 

PII and noise

ask, extract and explain share one gate: 1.000 precision at recall 1.000 on a held-out corpus run once, 0.996 on the corpus it was tuned against (evals/pii.py, which reports both and says which is which). Digits are read in context, so EN 60601-1 is a standard rather than a ZIP and a ten-digit page id in a URL is not an NHS number. Thirty-four kinds, including nineteen regional identifiers with their own checksums: Aadhaar, PAN and GSTIN, Australian TFN, ABN and Medicare, Singapore NRIC, Thai national ID, Dutch BSN, French NIR, Spanish DNI and NIE, Italian codice fiscale, Polish PESEL, Swedish personnummer, Norwegian fødselsnummer, German Steuer-IdNr, UK NINO, IMEI.

Names are opt-in (ner=True, honorific-anchored); scanned_ner says whether anything looked. Answers are re-scanned with names on.

Two learned detectors were measured and neither is shipped: both lose the gate to the patterns on precision and recall, and are 200 times slower. A DeBERTa-v3 ONNX classifier earns its gigabyte only on names no honorific introduces (2/8 to 5/8), and Liquid's 350M tflite encoder finds no names at all. Both live in evals/backends.py with their numbers in evals/pii_model.py, so the wheel stays arithmetic.

Junk is separate: suggest_noisy scores 0.988 AUC with no labels; mark_noisy excludes. See pii and quality.

from vishalakshi.pii import pii_report

r = pii_report('Invoice for ada@example.com. Card 4111 1111 1111 1111. KEY=sk-abcdefghijklmnopqrstuvwxyz123456')
r.has_pii, r.identifying, r.kinds

(True,
 {'email': 1, 'card': 1, 'secret': 1},
 {'email': 1, 'card': 1, 'secret': 1})

# a person's judgement, over the top of the arithmetic
v.add('A letter about Jane, and what she said on Tuesday.',
      title='letter', source='/inbox/letter.md')
v.add('Cookie policy. All rights reserved. Privacy. Terms. Contact us.',
      title='footer', source='/inbox/footer.md')
v.mark_pii('/inbox/letter.md', reason='names a person')     # private even though nothing matched
v.mark_noisy('/inbox/footer.md', reason='site furniture')   # out of search, sections, context, ask

r = v.pii('/inbox/letter.md', ner=True)   # scanned_ner says whether names were looked for
r.scanned_ner, r.detected, r.has_pii, any('footer' in h['breadcrumb'] for h in v.search('cookie policy'))

pii=	what happens
local (default)	local model answers; shape and quantity, not detail. Structured fields are scrubbed too
redact	mask recognised spans, then any model may answer. Names are not masked
refuse	return the finding, no answer
off	do not look

v.mark_not_pii(doc_id, reason='my own invoice')
v.accept_noisy(k=10, reason='reviewed')   # mark the current top suggestions
v.fit_noise(save=True); v.use_noise(True) # optional; fitted blend 0.996 AUC

Paperwork

kind is how a document arrived. categorize says what it is (cues first; model only on ties). extract returns fields; omit schema and the doctype picks one. ask_doc / ref= answer from named documents with the vault behind them. Depth on extract and ask.

INVOICE = '''# INVOICE
Invoice No: ACM-2024-0117
Date: 2024-03-01
Bill to: Contoso GmbH, Berlin
From: Acme Supplies Ltd
Total due: 1,240.00 EUR
Due date: 2024-03-31
Items: widgets x10 @ 124.00
'''
v.add(INVOICE, title='Acme invoice 0117', source='/inbox/acme-0117.md')
v.categorize_all(llm='never')
v.doctypes()

{'code': 13, 'documentation': 4, 'invoice': 1, 'paper': 1, 'other': 1}

from dataclasses import fields
from vishalakshi.extract import SCHEMAS, as_schema

print(L(SCHEMAS), L(fields(as_schema('invoice'))).attrgot('name'))
e = v.extract('/inbox/acme-0117.md')                    # doctype picks schema
a = v.ask_doc('/inbox/acme-0117.md', 'what is owed, to whom, and by when?',
              schema='amount:float, payee:str, due:str')
e.schema, e.fields, a

[{'invoice': <class 'vishalakshi.extract.Invoice'>, 'purchase_order': <class 'vishalakshi.extract.Invoice'>, 'quote': <class 'vishalakshi.extract.Invoice'>, 'receipt': <class 'vishalakshi.extract.Receipt'>, 'catalogue': <class 'vishalakshi.extract.Catalogue'>, 'contract': <class 'vishalakshi.extract.Contract'>, 'resume': <class 'vishalakshi.extract.Resume'>, 'paper': <class 'vishalakshi.extract.Paper'>, 'meeting_notes': <class 'vishalakshi.extract.MeetingNotes'>, 'other': <class 'vishalakshi.extract.Summary'>}] ['number', 'date', 'due_date', 'vendor', 'vendor_tax_id', 'bill_to', 'ship_to', 'currency', 'subtotal', 'tax', 'total', 'payment_terms', 'items']

/Users/71293/code/personal/orgs/vishalakshi/vishalakshi/extract.py:580: UserWarning: ValueError on a constrained call for Answer (model neither called the tool nor returned JSON; reply: 'The invoice details are as follows:\n*   **Invoice No:** ACM-2024-0117 [1]\n*   **Date:** 202); retrying as a JSON reply.
  warnings.warn(f'{type(e).__name__} on a constrained call for {schema.__name__} '

('Invoice',
 {'number': 'ACM-2024-0117',
  'date': '2024-03-01',
  'due_date': '',
  'vendor': 'Acme Supplies Ltd',
  'vendor_tax_id': '',
  'bill_to': 'Contoso GmbH, Berlin',
  'ship_to': '',
  'currency': 'EUR',
  'subtotal': 1240.0,
  'tax': 0,
  'total': 1240.0,
  'payment_terms': '',
  'items': ['widgets x10 @ 124.00']},
 {'question': 'what is owed, to whom, and by when?',
  'model': 'litert/litert-community/gemma-4-E2B-it-litert-lm',
  'runtime': 'litert',
  'context': {'results': [{'node_id': '4c1bdc4b329e3c3d#0', 'title': 'Acme invoice 0117', 'doc_id': '4c1bdc4b329e3c3d', 'breadcrumb': 'Acme invoice 0117', 'filename': '/inbox/acme-0117.md', 'pages': None, 'text': '# INVOICE\nInvoice No: ACM-2024-0117\nDate: 2024-03-01\nBill to: Contoso GmbH, Berlin\nFrom: Acme Supplies Ltd\nTotal due: 1,240.00 EUR\nDue date: 2024-03-31\nItems: widgets x10 @ 124.00'}, {'node_id': 'e8024c5880508178#0', 'title': '05 mcp', 'doc_id': 'e8024c5880508178', 'breadcrumb': '05 mcp', 'filename': None, 'pages': (0, 0), 'text': "    'files anything you point it at; `note` writes your own conclusions back so they are searched '\n    'alongside the sources. `add_tree` points at a directory and splits it: documents into the '\n    'vault, source files into kosha. When kosha has indexed '\n    'the repo, `context` and `ask` add code sections to what they retrieve on their own, so a '\n    'question about the user\\'s own system is\n\n    'For one document rather than the corpus: `document` returns the whole of it, `categorize` says '\n    'what kind of thing it is (invoice, catalogue, contract, paper, …), `extract` pulls its fields '\n    'out against a schema (a name like `invoice`, or a spec like `vendor:str, total:float` you '\n    'make up on the spot), and `ask_doc` answers a question about that document with the rest of '\n "}, {'node_id': 'bd083e908521c339#24', 'title': '`chat_kw` reaches the rest of the constructor: `CachedChat` takes `path`, and pointing it at', 'doc_id': 'bd083e908521c339', 'breadcrumb': '06 extract › `chat_kw` reaches the rest of the constructor: `CachedChat` takes `path`, and pointing it at', 'filename': None, 'pages': (0, 0), 'text': "test_eq(a.cited.attrgot('node_id')[0], f'{a.doc_id}#0')\nassert 'VAT (20%)' in a.prompt, 'the whole document must reach the model, not a retrieved chunk'\nassert a.prompt.endswith('Question: what is the total due?')\n# the same question as data instead of prose, through a schema built at the moment of asking\nd = _x.ask_doc('/inbox/acme-0117.md', 'what is owed and to whom?',\n               schema='amo\n\ntest_eq(_x.document(_p).origin, 'disk')\ntest_eq(_x.categorize(str(_p), llm='never', save=False).doctype, 'meeting_notes')\n\n#| eval:false\n#| hide\n# the route that had to wait: what acquisition could not tell at the door, the doctype can\nv.shelf('papers', offline=True)          # pre-registered, so this test pays for no encoder download\nr = v.reshelf('Acme invoice', llm='never')\ntest_eq((r.doctype, \n\nanswer), ('Answer', ['amount', 'currency', 'owed_to'], None))\ntest_eq(d.fields['amount'], 180.0)\n# and it is `ask` underneath\ntest_eq(_x.ask('what is the total due?', ref='/inbox/acme-0117.md', related=2, **_m).answer, a.answer)\n\n# a file the vault has never seen, straight off disk: no model needed to prove where it came from\nfrom fastcore.all import Path\nfrom tempfile import mkdtemp\n_p = Path(mkd"}, {'node_id': '10bdb7299a36ecf7#0', 'title': '02 ask', 'doc_id': '10bdb7299a36ecf7', 'breadcrumb': '02 ask', 'filename': None, 'pages': (0, 0), 'text': 'transcripts, files and their own notes. Answer only from those sections.\n\nRules:\n- Cite every claim with the bracketed number of the section it came from, like [2]. A sentence\n  drawing on two sections cites both.\n- If the sections do not answer the question, say exactly what is missing rather than filling the\n  gap from memory. A vault that admits a hole is useful; one that guesses is not.\n- Sect\n\n- Say what you are holding back and why, in one line, so the questioner knows something is \\\nthere rather than assuming there is nothing.\n- If the question cannot be answered without a personal detail, do not answer it. Say what \\\ninstruction would let you answer it usefully (a count, a comparison, a yes or no, a total) \\\nand stop. The questioner will send that instruction back and you will get an\n\nwhich must never be shown any of it.\n\nRules, in order of importance:\n- Never reproduce a personal detail. No names, addresses, emails, phone numbers, account or \\\ncard numbers, dates of birth, or medical specifics: not in your answer, not as an example, \\\nnot to show your working, not even partially or obfuscated.\n- Answer at the level of shape and quantity instead: how many, what kind, which peri'}],
   'related': [],
   'encoder': 'minishlab/potion-multilingual-128M (256d, float16, model2vec)',
   'doc': {'doc_id': '4c1bdc4b329e3c3d',
    'title': 'Acme invoice 0117',
    'source': '/inbox/acme-0117.md',
    'kind': 'file',
    'meta': {'doctype': 'invoice',
     'doctype_by': 'cues (ner+regex)',
     'doctype_score': 0.7},
    'pages': 1,
    'origin': 'vault',
    'nodes': 2,
    'chars': 179,
    'truncated': False,
    'text': '# INVOICE\nInvoice No: ACM-2024-0117\nDate: 2024-03-01\nBill to: Contoso GmbH, Berlin\nFrom: Acme Supplies Ltd\nTotal due: 1,240.00 EUR\nDue date: 2024-03-31\nItems: widgets x10 @ 124.00'},
   'docs': [{'doc_id': '4c1bdc4b329e3c3d', 'title': 'Acme invoice 0117', 'source': '/inbox/acme-0117.md', 'kind': 'file', 'meta': {'doctype': 'invoice', 'doctype_by': 'cues (ner+regex)', 'doctype_score': 0.7}, 'pages': 1, 'origin': 'vault', 'nodes': 2, 'chars': 179, 'truncated': False, 'text': '# INVOICE\nInvoice No: ACM-2024-0117\nDate: 2024-03-01\nBill to: Contoso GmbH, Berlin\nFrom: Acme Supplies Ltd\nTotal due: 1,240.00 EUR\nDue date: 2024-03-31\nItems: widgets x10 @ 124.00'}],
   'n_docs': 1,
   'note': '[1] is the document being asked about; the rest is context from elsewhere in the vault.\n\n'},
  'encoder': 'minishlab/potion-multilingual-128M (256d, float16, model2vec)',
  'prompt': "[1] Acme invoice 0117\n(source: /inbox/acme-0117.md)\n\n# INVOICE\nInvoice No: ACM-2024-0117\nDate: 2024-03-01\nBill to: Contoso GmbH, Berlin\nFrom: Acme Supplies Ltd\nTotal due: 1,240.00 EUR\nDue date: 2024-03-31\nItems: widgets x10 @ 124.00\n\n---\n\n[2] 05 mcp\n(source: e8024c5880508178, pages 0–0)\n\n    'files anything you point it at; `note` writes your own conclusions back so they are searched '\n    'alongside the sources. `add_tree` points at a directory and splits it: documents into the '\n    'vault, source files into kosha. When kosha has indexed '\n    'the repo, `context` and `ask` add code sections to what they retrieve on their own, so a '\n    'question about the user\\'s own system is\n\n    'For one document rather than the corpus: `document` returns the whole of it, `categorize` says '\n    'what kind of thing it is (invoice, catalogue, contract, paper, …), `extract` pulls its fields '\n    'out against a schema (a name like `invoice`, or a spec like `vendor:str, total:float` you '\n    'make up on the spot), and `ask_doc` answers a question about that document with the rest of '\n \n\n---\n\n[3] 06 extract › `chat_kw` reaches the rest of the constructor: `CachedChat` takes `path`, and pointing it at\n(source: bd083e908521c339, pages 0–0)\n\ntest_eq(a.cited.attrgot('node_id')[0], f'{a.doc_id}#0')\nassert 'VAT (20%)' in a.prompt, 'the whole document must reach the model, not a retrieved chunk'\nassert a.prompt.endswith('Question: what is the total due?')\n# the same question as data instead of prose, through a schema built at the moment of asking\nd = _x.ask_doc('/inbox/acme-0117.md', 'what is owed and to whom?',\n               schema='amo\n\ntest_eq(_x.document(_p).origin, 'disk')\ntest_eq(_x.categorize(str(_p), llm='never', save=False).doctype, 'meeting_notes')\n\n#| eval:false\n#| hide\n# the route that had to wait: what acquisition could not tell at the door, the doctype can\nv.shelf('papers', offline=True)          # pre-registered, so this test pays for no encoder download\nr = v.reshelf('Acme invoice', llm='never')\ntest_eq((r.doctype, \n\nanswer), ('Answer', ['amount', 'currency', 'owed_to'], None))\ntest_eq(d.fields['amount'], 180.0)\n# and it is `ask` underneath\ntest_eq(_x.ask('what is the total due?', ref='/inbox/acme-0117.md', related=2, **_m).answer, a.answer)\n\n# a file the vault has never seen, straight off disk: no model needed to prove where it came from\nfrom fastcore.all import Path\nfrom tempfile import mkdtemp\n_p = Path(mkd\n\n---\n\n[4] 02 ask\n(source: 10bdb7299a36ecf7, pages 0–0)\n\ntranscripts, files and their own notes. Answer only from those sections.\n\nRules:\n- Cite every claim with the bracketed number of the section it came from, like [2]. A sentence\n  drawing on two sections cites both.\n- If the sections do not answer the question, say exactly what is missing rather than filling the\n  gap from memory. A vault that admits a hole is useful; one that guesses is not.\n- Sect\n\n- Say what you are holding back and why, in one line, so the questioner knows something is \\\nthere rather than assuming there is nothing.\n- If the question cannot be answered without a personal detail, do not answer it. Say what \\\ninstruction would let you answer it usefully (a count, a comparison, a yes or no, a total) \\\nand stop. The questioner will send that instruction back and you will get an\n\nwhich must never be shown any of it.\n\nRules, in order of importance:\n- Never reproduce a personal detail. No names, addresses, emails, phone numbers, account or \\\ncard numbers, dates of birth, or medical specifics: not in your answer, not as an example, \\\nnot to show your working, not even partially or obfuscated.\n- Answer at the level of shape and quantity instead: how many, what kind, which peri\n\n---\n\n[1] is the document being asked about; the rest is context from elsewhere in the vault.\n\nQuestion: what is owed, to whom, and by when?",
  'answer': None,
  'thinking': '',
  'cited': [],
  'schema': 'Answer',
  'fields': {'amount': 1240.0,
   'payee': 'Contoso GmbH, Berlin',
   'due': '2024-03-31'},
  'doc_id': '4c1bdc4b329e3c3d',
  'title': 'Acme invoice 0117',
  'source': '/inbox/acme-0117.md',
  'origin': 'vault',
  'chars': 179,
  'truncated': False,
  'pii': {'has_pii': False,
   'kinds': {},
   'identifying': {},
   'n': 0,
   'scanned': 3395,
   'density': 0.0,
   'spans': []},
  'usage': total=1,674|in=1,623|out=51|turns=1})

Code

index_code fills kosha; context(..., code=n) then appends code sections beside prose. grep is ripgrep on disk (no index). federate is on code.

v.index_code(root)                # fills .kosha/; context then appends code sections
c = v.context('where does the entity graph get rebuilt?', sections=3, related=0, code=3, dir=root)
# code hits have no node_id; their handle is path:line on disk
c.code, L(c.results).filter(lambda r: r.node_id is None).attrgot('breadcrumb')

parse files from /Users/71293/code/personal/orgs/vishalakshi: 100%|██████████| 18/18 [00:00<00:00, 225.97it/s]

<style> progress { appearance: none; border: none; border-radius: 4px; width: 300px; height: 20px; vertical-align: middle; background: #e0e0e0; } progress::-webkit-progress-bar { background: #e0e0e0; border-radius: 4px; } progress::-webkit-progress-value { background: #2196F3; border-radius: 4px; } progress::-moz-progress-bar { background: #2196F3; border-radius: 4px; } progress:not([value]) { background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px); } progress.progress-bar-interrupted::-webkit-progress-value { background: #F44336; } progress.progress-bar-interrupted::-moz-progress-value { background: #F44336; } progress.progress-bar-interrupted::-webkit-progress-bar { background: #F44336; } progress.progress-bar-interrupted::-moz-progress-bar { background: #F44336; } progress.progress-bar-interrupted { background: #F44336; } table.fastprogress { border-collapse: collapse; margin: 1em 0; font-size: 0.9em; } table.fastprogress th, table.fastprogress td { padding: 8px 12px; border: 1px solid #ddd; text-align: left; } table.fastprogress thead tr { background: #f8f9fa; font-weight: bold; } table.fastprogress tbody tr:nth-of-type(even) { background: #f8f9fa; } </style> <style> progress { appearance: none; border: none; border-radius: 4px; width: 300px; height: 20px; vertical-align: middle; background: #e0e0e0; } progress::-webkit-progress-bar { background: #e0e0e0; border-radius: 4px; } progress::-webkit-progress-value { background: #2196F3; border-radius: 4px; } progress::-moz-progress-bar { background: #2196F3; border-radius: 4px; } progress:not([value]) { background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px); } progress.progress-bar-interrupted::-webkit-progress-value { background: #F44336; } progress.progress-bar-interrupted::-moz-progress-value { background: #F44336; } progress.progress-bar-interrupted::-webkit-progress-bar { background: #F44336; } progress.progress-bar-interrupted::-moz-progress-bar { background: #F44336; } progress.progress-bar-interrupted { background: #F44336; } table.fastprogress { border-collapse: collapse; margin: 1em 0; font-size: 0.9em; } table.fastprogress th, table.fastprogress td { padding: 8px 12px; border: 1px solid #ddd; text-align: left; } table.fastprogress thead tr { background: #f8f9fa; font-weight: bold; } table.fastprogress tbody tr:nth-of-type(even) { background: #f8f9fa; } </style>

(3,
 ['repo › /Users/71293/code/personal/orgs/vishalakshi/vishalakshi/core.py:520', 'grep › README.md:119', 'repo › /Users/71293/code/personal/orgs/vishalakshi/vishalakshi/core.py:547'])

L(v.grep('rrf_all', root, limit=4)).attrgot('where')   # ripgrep; no kosha needed

['README.md:125', 'nbs/index.ipynb:573', 'vishalakshi/code.py:12', 'vishalakshi/code.py:128']

Watches and the queue

An action is an acquisition method name. poll() is the tick (cron, scheduler, or button): it reclaims what a dead worker was holding, enqueues what has come due, and drains. A failed fetch retries with backoff and dead-letters after five attempts, so jobs(state='dead') is where work goes when it will not succeed, rather than nowhere. See jobs.

v.watch('https://example.com/changelog', action='url', every='6h')
v.watch('late chunking retrieval', action='web', every='1d', n=5)
v.watch('Re-read the evals', action='remind', every='1w')
L(v.watches()).map(lambda w: (w['action'], w['target'][:34], w['every'], w['params']))

[('url', 'https://example.com/changelog', 21600.0, {}), ('web', 'late chunking retrieval', 86400.0, {'n': 5}), ('remind', 'Re-read the evals', 604800.0, {})]

The rest

page	what is on it
core	Vault, shelves, context, entity graph, document
acquire	grab, url, web, crawl, arxiv, pdf, youtube, github, apis, harvest, watches
ask	ask, ask_doc, citations, model plumbing, CachedChat
code	kosha, symbol, where_to_add, grep, federate
cli	every Vault method as a command
mcp	vishalakshi-mcp
extract	categorize, extract, extract_all, schemas
concepts	encoders, shelves, backends, reshelf
skill	agent cheat sheet (exported skill)
pii	patterns, checksums, context guards, nineteen regional identifiers, secret, mark_pii / mark_not_pii, redact
jobs	Queue, retries, leases, the dead letter
quality	suggest_noisy / accept_noisy, fit_noise, ranker

vishalakshi grab https://example.com/post
vishalakshi ask "why does late chunking help"

variable	what it sets
$VISHALAKSHI_VAULT	the vault file
$VISHALAKSHI_MODEL	the model ask uses
$VISHALAKSHI_PII_MODEL	local model when sections are private
$VISHALAKSHI_GPU=0	put LiteRT on the CPU
$VISHALAKSHI_OFFLINE	never download; hashing encoder

MCP client config is on mcp. Retrieval trade-offs and measured defaults are in concepts and evals/RESULTS.md.

Development

The notebooks in nbs/ are the source; the modules are generated.

pip install -e .
nbdev-prepare