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feat: add db driver env variable (#5967)

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* Add option to disable prepared statements

* [autofix.ci] apply automated fixes

* use generic connection driver env var

* simplify and allow usage for sqlite

* allow pool class to be set to kwargs

* simplify pool class selection

* [autofix.ci] apply automated fixes

* use reflection to get poolclass

* rebase fixes

* [autofix.ci] apply automated fixes

* [autofix.ci] apply automated fixes (attempt 2/3)

---------

Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
This commit is contained in:
Jordan Frazier 2025-02-18 09:03:07 -08:00 • committed by GitHub
commit 45bac3714a
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5 changed files with 53 additions and 17 deletions
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6
src/backend/base/langflow/initial_setup/starter_projects/Financial Report Parser.json
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@ -960,7 +960,9 @@
"key": "ParseData", "key": "ParseData",
"legacy": false, "legacy": false,
"lf_version": "1.1.5", "lf_version": "1.1.5",
"metadata": {}, "metadata": {
"legacy_name": "Parse Data"
},
"minimized": false, "minimized": false,
"output_types": [], "output_types": [],
"outputs": [ "outputs": [
@ -1011,7 +1013,7 @@
"show": true, "show": true,
"title_case": false, "title_case": false,
"type": "code", "type": "code",
"value": "from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text, data_to_text_list\nfrom langflow.io import DataInput, MultilineInput, Output, StrInput\nfrom langflow.schema import Data\nfrom langflow.schema.message import Message\n\n\nclass ParseDataComponent(Component):\n display_name = \"Data to Message\"\n description = \"Convert Data objects into Messages using any {field_name} from input data.\"\n icon = \"message-square\"\n name = \"ParseData\"\n\n inputs = [\n DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\", is_list=True, required=True),\n MultilineInput(\n name=\"template\",\n display_name=\"Template\",\n info=\"The template to use for formatting the data. \"\n \"It can contain the keys {text}, {data} or any other key in the Data.\",\n value=\"{text}\",\n required=True,\n ),\n StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n ]\n\n outputs = [\n Output(\n display_name=\"Message\",\n name=\"text\",\n info=\"Data as a single Message, with each input Data separated by Separator\",\n method=\"parse_data\",\n ),\n Output(\n display_name=\"Data List\",\n name=\"data_list\",\n info=\"Data as a list of new Data, each having `text` formatted by Template\",\n method=\"parse_data_as_list\",\n ),\n ]\n\n def _clean_args(self) -> tuple[list[Data], str, str]:\n data = self.data if isinstance(self.data, list) else [self.data]\n template = self.template\n sep = self.sep\n return data, template, sep\n\n def parse_data(self) -> Message:\n data, template, sep = self._clean_args()\n result_string = data_to_text(template, data, sep)\n self.status = result_string\n return Message(text=result_string)\n\n def parse_data_as_list(self) -> list[Data]:\n data, template, _ = self._clean_args()\n text_list, data_list = data_to_text_list(template, data)\n for item, text in zip(data_list, text_list, strict=True):\n item.set_text(text)\n self.status = data_list\n return data_list\n" "value": "from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text, data_to_text_list\nfrom langflow.io import DataInput, MultilineInput, Output, StrInput\nfrom langflow.schema import Data\nfrom langflow.schema.message import Message\n\n\nclass ParseDataComponent(Component):\n display_name = \"Data to Message\"\n description = \"Convert Data objects into Messages using any {field_name} from input data.\"\n icon = \"message-square\"\n name = \"ParseData\"\n metadata = {\n \"legacy_name\": \"Parse Data\",\n }\n\n inputs = [\n DataInput(\n name=\"data\",\n display_name=\"Data\",\n info=\"The data to convert to text.\",\n is_list=True,\n required=True,\n ),\n MultilineInput(\n name=\"template\",\n display_name=\"Template\",\n info=\"The template to use for formatting the data. \"\n \"It can contain the keys {text}, {data} or any other key in the Data.\",\n value=\"{text}\",\n required=True,\n ),\n StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n ]\n\n outputs = [\n Output(\n display_name=\"Message\",\n name=\"text\",\n info=\"Data as a single Message, with each input Data separated by Separator\",\n method=\"parse_data\",\n ),\n Output(\n display_name=\"Data List\",\n name=\"data_list\",\n info=\"Data as a list of new Data, each having `text` formatted by Template\",\n method=\"parse_data_as_list\",\n ),\n ]\n\n def _clean_args(self) -> tuple[list[Data], str, str]:\n data = self.data if isinstance(self.data, list) else [self.data]\n template = self.template\n sep = self.sep\n return data, template, sep\n\n def parse_data(self) -> Message:\n data, template, sep = self._clean_args()\n result_string = data_to_text(template, data, sep)\n self.status = result_string\n return Message(text=result_string)\n\n def parse_data_as_list(self) -> list[Data]:\n data, template, _ = self._clean_args()\n text_list, data_list = data_to_text_list(template, data)\n for item, text in zip(data_list, text_list, strict=True):\n item.set_text(text)\n self.status = data_list\n return data_list\n"
}, },
"data": { "data": {
"_input_type": "DataInput", "_input_type": "DataInput",

6
src/backend/base/langflow/initial_setup/starter_projects/Meeting Summary.json
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@ -452,7 +452,9 @@
"icon": "message-square", "icon": "message-square",
"legacy": false, "legacy": false,
"lf_version": "1.1.5", "lf_version": "1.1.5",
"metadata": {}, "metadata": {
"legacy_name": "Parse Data"
},
"minimized": false, "minimized": false,
"output_types": [], "output_types": [],
"outputs": [ "outputs": [
@ -502,7 +504,7 @@
"show": true, "show": true,
"title_case": false, "title_case": false,
"type": "code", "type": "code",
"value": "from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text, data_to_text_list\nfrom langflow.io import DataInput, MultilineInput, Output, StrInput\nfrom langflow.schema import Data\nfrom langflow.schema.message import Message\n\n\nclass ParseDataComponent(Component):\n display_name = \"Data to Message\"\n description = \"Convert Data objects into Messages using any {field_name} from input data.\"\n icon = \"message-square\"\n name = \"ParseData\"\n\n inputs = [\n DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\", is_list=True, required=True),\n MultilineInput(\n name=\"template\",\n display_name=\"Template\",\n info=\"The template to use for formatting the data. \"\n \"It can contain the keys {text}, {data} or any other key in the Data.\",\n value=\"{text}\",\n required=True,\n ),\n StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n ]\n\n outputs = [\n Output(\n display_name=\"Message\",\n name=\"text\",\n info=\"Data as a single Message, with each input Data separated by Separator\",\n method=\"parse_data\",\n ),\n Output(\n display_name=\"Data List\",\n name=\"data_list\",\n info=\"Data as a list of new Data, each having `text` formatted by Template\",\n method=\"parse_data_as_list\",\n ),\n ]\n\n def _clean_args(self) -> tuple[list[Data], str, str]:\n data = self.data if isinstance(self.data, list) else [self.data]\n template = self.template\n sep = self.sep\n return data, template, sep\n\n def parse_data(self) -> Message:\n data, template, sep = self._clean_args()\n result_string = data_to_text(template, data, sep)\n self.status = result_string\n return Message(text=result_string)\n\n def parse_data_as_list(self) -> list[Data]:\n data, template, _ = self._clean_args()\n text_list, data_list = data_to_text_list(template, data)\n for item, text in zip(data_list, text_list, strict=True):\n item.set_text(text)\n self.status = data_list\n return data_list\n" "value": "from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text, data_to_text_list\nfrom langflow.io import DataInput, MultilineInput, Output, StrInput\nfrom langflow.schema import Data\nfrom langflow.schema.message import Message\n\n\nclass ParseDataComponent(Component):\n display_name = \"Data to Message\"\n description = \"Convert Data objects into Messages using any {field_name} from input data.\"\n icon = \"message-square\"\n name = \"ParseData\"\n metadata = {\n \"legacy_name\": \"Parse Data\",\n }\n\n inputs = [\n DataInput(\n name=\"data\",\n display_name=\"Data\",\n info=\"The data to convert to text.\",\n is_list=True,\n required=True,\n ),\n MultilineInput(\n name=\"template\",\n display_name=\"Template\",\n info=\"The template to use for formatting the data. \"\n \"It can contain the keys {text}, {data} or any other key in the Data.\",\n value=\"{text}\",\n required=True,\n ),\n StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n ]\n\n outputs = [\n Output(\n display_name=\"Message\",\n name=\"text\",\n info=\"Data as a single Message, with each input Data separated by Separator\",\n method=\"parse_data\",\n ),\n Output(\n display_name=\"Data List\",\n name=\"data_list\",\n info=\"Data as a list of new Data, each having `text` formatted by Template\",\n method=\"parse_data_as_list\",\n ),\n ]\n\n def _clean_args(self) -> tuple[list[Data], str, str]:\n data = self.data if isinstance(self.data, list) else [self.data]\n template = self.template\n sep = self.sep\n return data, template, sep\n\n def parse_data(self) -> Message:\n data, template, sep = self._clean_args()\n result_string = data_to_text(template, data, sep)\n self.status = result_string\n return Message(text=result_string)\n\n def parse_data_as_list(self) -> list[Data]:\n data, template, _ = self._clean_args()\n text_list, data_list = data_to_text_list(template, data)\n for item, text in zip(data_list, text_list, strict=True):\n item.set_text(text)\n self.status = data_list\n return data_list\n"
}, },
"data": { "data": {
"_input_type": "DataInput", "_input_type": "DataInput",

28
src/backend/base/langflow/initial_setup/starter_projects/Youtube Analysis.json
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27
src/backend/base/langflow/services/database/service.py
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@ -124,6 +124,15 @@ class DatabaseService(Service):
# if the user specifies an empty dict, we allow it. # if the user specifies an empty dict, we allow it.
kwargs = self._build_connection_kwargs() kwargs = self._build_connection_kwargs()
poolclass_key = kwargs.get("poolclass")
if poolclass_key is not None:
pool_class = getattr(sa, poolclass_key, None)
if pool_class and isinstance(pool_class(), sa.pool.Pool):
logger.debug(f"Using poolclass: {poolclass_key}.")
kwargs["poolclass"] = pool_class
else:
logger.error(f"Invalid poolclass '{poolclass_key}' specified. Using default pool class.")
return create_async_engine( return create_async_engine(
self.database_url, self.database_url,
connect_args=self._get_connect_args(), connect_args=self._get_connect_args(),
@ -136,16 +145,18 @@ class DatabaseService(Service):
return self._create_engine() return self._create_engine()
def _get_connect_args(self): def _get_connect_args(self):
if self.settings_service.settings.database_url and self.settings_service.settings.database_url.startswith( settings = self.settings_service.settings
"sqlite"
): if settings.db_driver_connection_settings is not None:
connect_args = { return settings.db_driver_connection_settings
if settings.database_url and settings.database_url.startswith("sqlite"):
return {
"check_same_thread": False, "check_same_thread": False,
"timeout": self.settings_service.settings.db_connect_timeout, "timeout": settings.db_connect_timeout,
} }
else:
connect_args = {} return {}
return connect_args
def on_connection(self, dbapi_connection, _connection_record) -> None: def on_connection(self, dbapi_connection, _connection_record) -> None:
if isinstance(dbapi_connection, sqlite3.Connection | dialect_sqlite.aiosqlite.AsyncAdapt_aiosqlite_connection): if isinstance(dbapi_connection, sqlite3.Connection | dialect_sqlite.aiosqlite.AsyncAdapt_aiosqlite_connection):

3
src/backend/base/langflow/services/settings/base.py
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@ -90,6 +90,9 @@ class Settings(BaseSettings):
sqlite_pragmas: dict | None = {"synchronous": "NORMAL", "journal_mode": "WAL"} sqlite_pragmas: dict | None = {"synchronous": "NORMAL", "journal_mode": "WAL"}
"""SQLite pragmas to use when connecting to the database.""" """SQLite pragmas to use when connecting to the database."""
db_driver_connection_settings: dict | None = None
"""Database driver connection settings."""
db_connection_settings: dict | None = { db_connection_settings: dict | None = {
"pool_size": 20, # Match the pool_size above "pool_size": 20, # Match the pool_size above
"max_overflow": 30, # Match the max_overflow above "max_overflow": 30, # Match the max_overflow above