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langchain/libs/community/langchain_community/document_loaders/astradb.py

144 lines
4.7 KiB
Python

from __future__ import annotations
import json
import logging
import threading
from queue import Queue
from typing import (
TYPE_CHECKING,
Any,
AsyncIterator,
Callable,
Dict,
Iterator,
List,
Optional,
)
from langchain_core.documents import Document
from langchain_core.runnables import run_in_executor
from langchain_community.document_loaders.base import BaseLoader
from langchain_community.utilities.astradb import AstraDBEnvironment
if TYPE_CHECKING:
from astrapy.db import AstraDB, AsyncAstraDB
logger = logging.getLogger(__name__)
class AstraDBLoader(BaseLoader):
"""Load DataStax Astra DB documents."""
def __init__(
self,
collection_name: str,
token: Optional[str] = None,
api_endpoint: Optional[str] = None,
astra_db_client: Optional[AstraDB] = None,
async_astra_db_client: Optional[AsyncAstraDB] = None,
namespace: Optional[str] = None,
filter_criteria: Optional[Dict[str, Any]] = None,
projection: Optional[Dict[str, Any]] = None,
find_options: Optional[Dict[str, Any]] = None,
nb_prefetched: int = 1000,
extraction_function: Callable[[Dict], str] = json.dumps,
) -> None:
astra_env = AstraDBEnvironment(
token=token,
api_endpoint=api_endpoint,
astra_db_client=astra_db_client,
async_astra_db_client=async_astra_db_client,
namespace=namespace,
)
self.astra_env = astra_env
self.collection = astra_env.astra_db.collection(collection_name)
self.collection_name = collection_name
self.filter = filter_criteria
self.projection = projection
self.find_options = find_options or {}
self.nb_prefetched = nb_prefetched
self.extraction_function = extraction_function
def load(self) -> List[Document]:
"""Eagerly load the content."""
return list(self.lazy_load())
def lazy_load(self) -> Iterator[Document]:
queue = Queue(self.nb_prefetched) # type: ignore
t = threading.Thread(target=self.fetch_results, args=(queue,))
t.start()
while True:
doc = queue.get()
if doc is None:
break
yield doc
t.join()
async def aload(self) -> List[Document]:
"""Load data into Document objects."""
return [doc async for doc in self.alazy_load()]
async def alazy_load(self) -> AsyncIterator[Document]:
if not self.astra_env.async_astra_db:
iterator = run_in_executor(
None,
self.collection.paginated_find,
filter=self.filter,
options=self.find_options,
projection=self.projection,
sort=None,
prefetched=True,
)
done = object()
while True:
item = await run_in_executor(None, lambda it: next(it, done), iterator)
if item is done:
break
yield item # type: ignore[misc]
return
async_collection = await self.astra_env.async_astra_db.collection(
self.collection_name
)
async for doc in async_collection.paginated_find(
filter=self.filter,
options=self.find_options,
projection=self.projection,
sort=None,
prefetched=True,
):
yield Document(
page_content=self.extraction_function(doc),
metadata={
"namespace": async_collection.astra_db.namespace,
"api_endpoint": async_collection.astra_db.base_url,
"collection": self.collection_name,
},
)
def fetch_results(self, queue: Queue): # type: ignore[no-untyped-def]
self.fetch_page_result(queue)
while self.find_options.get("pageState"):
self.fetch_page_result(queue)
queue.put(None)
def fetch_page_result(self, queue: Queue): # type: ignore[no-untyped-def]
res = self.collection.find(
filter=self.filter,
options=self.find_options,
projection=self.projection,
sort=None,
)
self.find_options["pageState"] = res["data"].get("nextPageState")
for doc in res["data"]["documents"]:
queue.put(
Document(
page_content=self.extraction_function(doc),
metadata={
"namespace": self.collection.astra_db.namespace,
"api_endpoint": self.collection.astra_db.base_url,
"collection": self.collection.collection_name,
},
)
)