gristlabs_grist-core/sandbox/grist/main.py
Jarosław Sadziński 6e3f0f2b35 (core) Porting back AI formula backend
Summary: This is a backend part for the formula AI.

Test Plan: New tests

Reviewers: paulfitz

Reviewed By: paulfitz

Subscribers: cyprien

Differential Revision: https://phab.getgrist.com/D3786
2023-02-08 17:15:59 +01:00

161 lines
4.9 KiB
Python

"""
This module defines what sandbox functions are made available to the Node controller,
and starts the grist sandbox. See engine.py for the API documentation.
"""
import os
import random
import sys
sys.path.append('thirdparty')
# pylint: disable=wrong-import-position
import logging
import marshal
import functools
import six
import actions
import engine
import formula_prompt
import migrations
import schema
import useractions
import objtypes
from acl_formula import parse_acl_formula
from sandbox import get_default_sandbox
from imports.register import register_import_parsers
import logger
log = logger.Logger(__name__, logger.INFO)
# Configure logging module to behave similarly to logger. (It may be OK to get rid of logger.)
logging.basicConfig(format="[%(levelname)s] [%(name)s] %(message)s")
def table_data_from_db(table_name, table_data_repr):
if table_data_repr is None:
return actions.TableData(table_name, [], {})
table_data_parsed = marshal.loads(table_data_repr)
table_data_parsed = {key.decode("utf8"): value for key, value in table_data_parsed.items()}
id_col = table_data_parsed.pop("id")
return actions.TableData(table_name, id_col,
actions.decode_bulk_values(table_data_parsed, _decode_db_value))
def _decode_db_value(value):
# Decode database values received from SQLite's allMarshal() call. These are encoded by
# marshalling certain types and storing as BLOBs (received in Python as binary strings, as
# opposed to text which is received as unicode). See also encodeValue() in DocStorage.js
t = type(value)
if t == six.binary_type:
return objtypes.decode_object(marshal.loads(value))
else:
return value
def run(sandbox):
eng = engine.Engine()
def export(method):
# Wrap each method so that it logs a message that it's being called.
@functools.wraps(method)
def wrapper(*args, **kwargs):
log.debug("calling %s" % method.__name__)
return method(*args, **kwargs)
sandbox.register(method.__name__, wrapper)
def load_and_record_table_data(table_name, table_data_repr):
result = table_data_from_db(table_name, table_data_repr)
eng.record_table_stats(result, table_data_repr)
return result
@export
def apply_user_actions(action_reprs, user=None):
action_group = eng.apply_user_actions([useractions.from_repr(u) for u in action_reprs], user)
result = dict(
rowCount=eng.count_rows(),
**eng.acl_split(action_group).to_json_obj()
)
if action_group.requests:
result["requests"] = action_group.requests
return result
@export
def fetch_table(table_id, formulas=True, query=None):
return actions.get_action_repr(eng.fetch_table(table_id, formulas=formulas, query=query))
@export
def fetch_table_schema():
return eng.fetch_table_schema()
@export
def autocomplete(txt, table_id, column_id, row_id, user):
return eng.autocomplete(txt, table_id, column_id, row_id, user)
@export
def find_col_from_values(values, n, opt_table_id):
return eng.find_col_from_values(values, n, opt_table_id)
@export
def fetch_meta_tables(formulas=True):
return {table_id: actions.get_action_repr(table_data)
for (table_id, table_data) in six.iteritems(eng.fetch_meta_tables(formulas))}
@export
def load_meta_tables(meta_tables, meta_columns):
return eng.load_meta_tables(load_and_record_table_data("_grist_Tables", meta_tables),
load_and_record_table_data("_grist_Tables_column", meta_columns))
@export
def load_table(table_name, table_data):
return eng.load_table(load_and_record_table_data(table_name, table_data))
@export
def get_table_stats():
return eng.get_table_stats()
@export
def create_migrations(all_tables, metadata_only=False):
doc_actions = migrations.create_migrations(
{t: table_data_from_db(t, data) for t, data in six.iteritems(all_tables)}, metadata_only)
return [actions.get_action_repr(action) for action in doc_actions]
@export
def get_version():
return schema.SCHEMA_VERSION
@export
def initialize(doc_url):
if os.environ.get("DETERMINISTIC_MODE"):
random.seed(1)
else:
# Make sure we have randomness, even if we are being cloned from a checkpoint
random.seed()
if doc_url:
os.environ['DOC_URL'] = doc_url
@export
def get_formula_error(table_id, col_id, row_id):
return objtypes.encode_object(eng.get_formula_error(table_id, col_id, row_id))
@export
def get_formula_prompt(table_id, col_id, description):
return formula_prompt.get_formula_prompt(eng, table_id, col_id, description)
@export
def convert_formula_completion(completion):
return formula_prompt.convert_completion(completion)
export(parse_acl_formula)
export(eng.load_empty)
export(eng.load_done)
register_import_parsers(sandbox)
log.info("Ready") # This log message is significant for checkpointing.
sandbox.run()
def main():
run(get_default_sandbox())
if __name__ == "__main__":
main()