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loader.py
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loader.py
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# Copyright 2024 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import json
from typing import (
Any,
AsyncIterator,
Callable,
Dict,
Iterable,
Iterator,
List,
Optional,
)
import sqlalchemy
from langchain_community.document_loaders.base import BaseLoader
from langchain_core.documents import Document
from .engine import PostgresEngine
DEFAULT_CONTENT_COL = "page_content"
DEFAULT_METADATA_COL = "langchain_metadata"
def text_formatter(row, content_columns) -> str:
return " ".join(str(row[column]) for column in content_columns if column in row)
def csv_formatter(row, content_columns) -> str:
return ", ".join(str(row[column]) for column in content_columns if column in row)
def yaml_formatter(row, content_columns) -> str:
return "\n".join(
f"{column}: {str(row[column])}" for column in content_columns if column in row
)
def json_formatter(row, content_columns) -> str:
dictionary = {}
for column in content_columns:
if column in row:
dictionary[column] = row[column]
return json.dumps(dictionary)
def _parse_doc_from_row(
content_columns: Iterable[str],
metadata_columns: Iterable[str],
row: dict,
metadata_json_column: Optional[str] = DEFAULT_METADATA_COL,
formatter: Callable = text_formatter,
) -> Document:
page_content = formatter(row, content_columns)
metadata: Dict[str, Any] = {}
# unnest metadata from langchain_metadata column
if metadata_json_column and row.get(metadata_json_column):
for k, v in row[metadata_json_column].items():
metadata[k] = v
# load metadata from other columns
for column in metadata_columns:
if column in row and column != metadata_json_column:
metadata[column] = row[column]
return Document(page_content=page_content, metadata=metadata)
def _parse_row_from_doc(
doc: Document,
column_names: Iterable[str],
content_column: str = DEFAULT_CONTENT_COL,
metadata_json_column: Optional[str] = DEFAULT_METADATA_COL,
) -> Dict:
doc_metadata = doc.metadata.copy()
row: Dict[str, Any] = {content_column: doc.page_content}
for entry in doc.metadata:
if entry in column_names:
row[entry] = doc_metadata[entry]
del doc_metadata[entry]
# store extra metadata in langchain_metadata column in json format
if metadata_json_column:
row[metadata_json_column] = doc_metadata
return row
class PostgresLoader(BaseLoader):
"""Load documents from PostgreSQL`.
Each document represents one row of the result. The `content_columns` are
written into the `content_columns`of the document. The `metadata_columns` are written
into the `metadata_columns` of the document. By default, first columns is written into
the `page_content` and everything else into the `metadata`.
"""
__create_key = object()
def __init__(
self,
key,
engine: PostgresEngine,
query: str,
content_columns: List[str],
metadata_columns: List[str],
formatter: Callable,
metadata_json_column: Optional[str] = None,
) -> None:
if key != PostgresLoader.__create_key:
raise Exception(
"Only create class through 'create' or 'create_sync' methods!"
)
self.engine = engine
self.query = query
self.content_columns = content_columns
self.metadata_columns = metadata_columns
self.formatter = formatter
self.metadata_json_column = metadata_json_column
@classmethod
async def create(
cls,
engine: PostgresEngine,
query: Optional[str] = None,
table_name: Optional[str] = None,
content_columns: Optional[List[str]] = None,
metadata_columns: Optional[List[str]] = None,
metadata_json_column: Optional[str] = None,
format: Optional[str] = None,
formatter: Optional[Callable] = None,
):
"""Constructor for PostgresLoader
Args:
engine (PostgresEngine):AsyncEngine with pool connection to the postgres database
query (Optional[str], optional): SQL query. Defaults to None.
table_name (Optional[str], optional): Name of table to query. Defaults to None.
content_columns (Optional[List[str]], optional): Column that represent a Document's page_content. Defaults to the first column.
metadata_columns (Optional[List[str]], optional): Column(s) that represent a Document's metadata.. Defaults to None.
metadata_json_column (Optional[str], optional): Column to store metadata as JSON. Defaults to "langchain_metadata".
format (Optional[str], optional): Format of page content (OneOf: text, csv, YAML, JSON). Defaults to 'text'.
formatter (Optional[Callable], optional): A function to format page content (OneOf: format, formatter). Defaults to None.
Returns:
PostgresLoader
"""
if table_name and query:
raise ValueError("Only one of 'table_name' or 'query' should be specified.")
if not table_name and not query:
raise ValueError(
"At least one of the parameters 'table_name' or 'query' needs to be provided"
)
if format and formatter:
raise ValueError("Only one of 'format' or 'formatter' should be specified.")
if format and format not in ["csv", "text", "JSON", "YAML"]:
raise ValueError("format must be type: 'csv', 'text', 'JSON', 'YAML'")
if formatter:
formatter = formatter
elif format == "csv":
formatter = csv_formatter
elif format == "YAML":
formatter = yaml_formatter
elif format == "JSON":
formatter = json_formatter
else:
formatter = text_formatter
if not query:
query = f'SELECT * FROM "{table_name}"'
stmt = sqlalchemy.text(query)
async with engine._engine.connect() as connection:
result_proxy = await connection.execute(stmt)
column_names = list(result_proxy.keys())
# Select content or default to first column
content_columns = content_columns or [column_names[0]]
# Select metadata columns
metadata_columns = metadata_columns or [
col for col in column_names if col not in content_columns
]
# Check validity of metadata json column
if metadata_json_column and metadata_json_column not in column_names:
raise ValueError(
f"Column {metadata_json_column} not found in query result {column_names}."
)
# use default metadata json column if not specified
if metadata_json_column and metadata_json_column in column_names:
metadata_json_column = metadata_json_column
elif DEFAULT_METADATA_COL in column_names:
metadata_json_column = DEFAULT_METADATA_COL
else:
metadata_json_column = None
# check validity of other column
all_names = content_columns + metadata_columns
for name in all_names:
if name not in column_names:
raise ValueError(
f"Column {name} not found in query result {column_names}."
)
return cls(
cls.__create_key,
engine,
query,
content_columns,
metadata_columns,
formatter,
metadata_json_column,
)
@classmethod
def create_sync(
cls,
engine: PostgresEngine,
query: Optional[str] = None,
table_name: Optional[str] = None,
content_columns: Optional[List[str]] = None,
metadata_columns: Optional[List[str]] = None,
metadata_json_column: Optional[str] = None,
format: Optional[str] = None,
formatter: Optional[Callable] = None,
):
coro = cls.create(
engine,
query,
table_name,
content_columns,
metadata_columns,
metadata_json_column,
format,
formatter,
)
return engine._run_as_sync(coro)
async def _collect_async_items(self, docs_generator):
return [doc async for doc in docs_generator]
def load(self) -> List[Document]:
"""Load PostgreSQL data into Document objects."""
documents = self.engine._run_as_sync(
self._collect_async_items(self.alazy_load())
)
return documents
async def aload(self) -> List[Document]:
"""Load PostgreSQL data into Document objects."""
return [doc async for doc in self.alazy_load()]
def lazy_load(self) -> Iterator[Document]:
"""Load PostgreSQL data into Document objects lazily."""
yield from self.engine._run_as_sync(
self._collect_async_items(self.alazy_load())
)
async def alazy_load(self) -> AsyncIterator[Document]:
"""Load PostgreSQL data into Document objects lazily."""
stmt = sqlalchemy.text(self.query)
async with self.engine._engine.connect() as connection:
result_proxy = await connection.execute(stmt)
# load document one by one
while True:
row = result_proxy.fetchone()
if not row:
break
row_data = {}
column_names = self.content_columns + self.metadata_columns
column_names += (
[self.metadata_json_column] if self.metadata_json_column else []
)
for column in column_names:
value = getattr(row, column)
row_data[column] = value
yield _parse_doc_from_row(
self.content_columns,
self.metadata_columns,
row_data,
self.metadata_json_column,
self.formatter,
)
class PostgresDocumentSaver:
"""A class for saving langchain documents into a PostgreSQL database table."""
__create_key = object()
def __init__(
self,
key,
engine: PostgresEngine,
table_name: str,
content_column: str,
metadata_columns: List[str] = [],
metadata_json_column: Optional[str] = None,
):
if key != PostgresDocumentSaver.__create_key:
raise Exception(
"Only create class through 'create' or 'create_sync' methods!"
)
self.engine = engine
self.table_name = table_name
self.content_column = content_column
self.metadata_columns = metadata_columns
self.metadata_json_column = metadata_json_column
@classmethod
async def create(
cls,
engine: PostgresEngine,
table_name: str,
content_column: str = DEFAULT_CONTENT_COL,
metadata_columns: List[str] = [],
metadata_json_column: Optional[str] = DEFAULT_METADATA_COL,
):
table_schema = await engine._aload_table_schema(table_name)
column_names = table_schema.columns.keys()
if content_column not in column_names:
raise ValueError(f"Content column, {content_column}, does not exist.")
# Set metadata columns to all columns if not set
if len(metadata_columns) == 0:
metadata_columns = [
column
for column in column_names
if column != content_column and column != metadata_json_column
]
# Check and set metadata json column
for column in metadata_columns:
if column not in column_names:
raise ValueError(f"Metadata column, {column}, does not exist.")
if (
metadata_json_column
and metadata_json_column != DEFAULT_METADATA_COL
and metadata_json_column not in column_names
):
raise ValueError(f"Metadata JSON column, {column}, does not exist.")
elif metadata_json_column not in column_names:
metadata_json_column = None
return cls(
cls.__create_key,
engine,
table_name,
content_column,
metadata_columns,
metadata_json_column,
)
@classmethod
def create_sync(
cls,
engine: PostgresEngine,
table_name: str,
content_column: str = DEFAULT_CONTENT_COL,
metadata_columns: List[str] = [],
metadata_json_column: str = DEFAULT_METADATA_COL,
):
coro = cls.create(
engine,
table_name,
content_column,
metadata_columns,
metadata_json_column,
)
return engine._run_as_sync(coro)
async def aadd_documents(self, docs: List[Document]) -> None:
"""
Save documents in the DocumentSaver table. Document’s metadata is added to columns if found or
stored in langchain_metadata JSON column.
Args:
docs (List[langchain_core.documents.Document]): a list of documents to be saved.
"""
for doc in docs:
row = _parse_row_from_doc(
doc,
self.metadata_columns,
self.content_column,
self.metadata_json_column,
)
for key, value in row.items():
if isinstance(value, dict):
row[key] = json.dumps(value)
# Create list of column names
insert_stmt = f'INSERT INTO "{self.table_name}"({self.content_column}'
values_stmt = f"VALUES (:{self.content_column}"
# Add metadata
for metadata_column in self.metadata_columns:
if metadata_column in doc.metadata:
insert_stmt += f", {metadata_column}"
values_stmt += f", :{metadata_column}"
# Add JSON column and/or close statement
insert_stmt += (
f", {self.metadata_json_column})" if self.metadata_json_column else ")"
)
if self.metadata_json_column:
values_stmt += f", :{self.metadata_json_column})"
else:
values_stmt += ")"
query = insert_stmt + values_stmt
await self.engine._aexecute(query, row)
def add_documents(self, docs: List[Document]) -> None:
self.engine._run_as_sync(self.aadd_documents(docs))
async def adelete(self, docs: List[Document]) -> None:
"""
Delete all instances of a document from the DocumentSaver table by matching the entire Document
object.
Args:
docs (List[langchain_core.documents.Document]): a list of documents to be deleted.
"""
for doc in docs:
row = _parse_row_from_doc(
doc,
self.metadata_columns,
self.content_column,
self.metadata_json_column,
)
# delete by matching all fields of document
where_conditions_list = []
for key, value in row.items():
if isinstance(value, dict):
where_conditions_list.append(
f"{key}::jsonb @> '{json.dumps(value)}'::jsonb"
)
else:
# Handle simple key-value pairs
where_conditions_list.append(f"{key} = :{key}")
where_conditions = " AND ".join(where_conditions_list)
stmt = f'DELETE FROM "{self.table_name}" WHERE {where_conditions};'
values = {}
for key, value in row.items():
if type(value) is int:
values[key] = str(value)
else:
values[key] = value
await self.engine._aexecute(stmt, values)
def delete(self, docs: List[Document]) -> None:
self.engine._run_as_sync(self.adelete(docs))
async def _aload_table_schema(self) -> sqlalchemy.Table:
"""
Load table schema from existing table in PgSQL database.
Returns:
(sqlalchemy.Table): The loaded table.
"""
metadata = sqlalchemy.MetaData()
async with self.engine._engine.connect() as conn:
await conn.run_sync(metadata.reflect, only=[self.table_name])
table = sqlalchemy.Table(self.table_name, metadata)
# Extract the schema information
schema = []
for column in table.columns:
schema.append(
{
"name": column.name,
"type": column.type.python_type,
"max_length": getattr(column.type, "length", None),
"nullable": not column.nullable,
}
)
return metadata.tables[self.table_name]