"""Pebblo's safe dataloader is a wrapper for document loaders"""
import json
import logging
import os
import uuid
from http import HTTPStatus
from typing import Any, Dict, Iterator, List, Optional
import requests # type: ignore
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
from langchain_community.utilities.pebblo import (
APP_DISCOVER_URL,
BATCH_SIZE_BYTES,
CLASSIFIER_URL,
LOADER_DOC_URL,
PEBBLO_CLOUD_URL,
PLUGIN_VERSION,
App,
Doc,
IndexedDocument,
generate_size_based_batches,
get_full_path,
get_loader_full_path,
get_loader_type,
get_runtime,
)
logger = logging.getLogger(__name__)
[docs]class PebbloSafeLoader(BaseLoader):
"""Pebblo Safe Loader class is a wrapper around document loaders enabling the data
to be scrutinized.
"""
_discover_sent: bool = False
_loader_sent: bool = False
[docs] def __init__(
self,
langchain_loader: BaseLoader,
name: str,
owner: str = "",
description: str = "",
api_key: Optional[str] = None,
load_semantic: bool = False,
classifier_url: Optional[str] = None,
*,
classifier_location: str = "local",
):
if not name or not isinstance(name, str):
raise NameError("Must specify a valid name.")
self.app_name = name
self.api_key = os.environ.get("PEBBLO_API_KEY") or api_key
self.load_id = str(uuid.uuid4())
self.loader = langchain_loader
self.load_semantic = os.environ.get("PEBBLO_LOAD_SEMANTIC") or load_semantic
self.owner = owner
self.description = description
self.source_path = get_loader_full_path(self.loader)
self.source_owner = PebbloSafeLoader.get_file_owner_from_path(self.source_path)
self.docs: List[Document] = []
self.docs_with_id: List[IndexedDocument] = []
loader_name = str(type(self.loader)).split(".")[-1].split("'")[0]
self.source_type = get_loader_type(loader_name)
self.source_path_size = self.get_source_size(self.source_path)
self.source_aggregate_size = 0
self.classifier_url = classifier_url or CLASSIFIER_URL
self.classifier_location = classifier_location
self.batch_size = BATCH_SIZE_BYTES
self.loader_details = {
"loader": loader_name,
"source_path": self.source_path,
"source_type": self.source_type,
**(
{"source_path_size": str(self.source_path_size)}
if self.source_path_size > 0
else {}
),
}
# generate app
self.app = self._get_app_details()
self._send_discover()
[docs] def load(self) -> List[Document]:
"""Load Documents.
Returns:
list: Documents fetched from load method of the wrapped `loader`.
"""
self.docs = self.loader.load()
# Classify docs in batches
self.classify_in_batches()
return self.docs
[docs] def classify_in_batches(self) -> None:
"""
Classify documents in batches.
This is to avoid API timeouts when sending large number of documents.
Batches are generated based on the page_content size.
"""
batches: List[List[Document]] = generate_size_based_batches(
self.docs, self.batch_size
)
processed_docs: List[Document] = []
total_batches = len(batches)
for i, batch in enumerate(batches):
is_last_batch: bool = i == total_batches - 1
self.docs = batch
self.docs_with_id = self._index_docs()
classified_docs = self._classify_doc(loading_end=is_last_batch)
self._add_pebblo_specific_metadata(classified_docs)
if self.load_semantic:
batch_processed_docs = self._add_semantic_to_docs(classified_docs)
else:
batch_processed_docs = self._unindex_docs()
processed_docs.extend(batch_processed_docs)
self.docs = processed_docs
[docs] def lazy_load(self) -> Iterator[Document]:
"""Load documents in lazy fashion.
Raises:
NotImplementedError: raised when lazy_load id not implemented
within wrapped loader.
Yields:
list: Documents from loader's lazy loading.
"""
try:
doc_iterator = self.loader.lazy_load()
except NotImplementedError as exc:
err_str = f"{self.loader.__class__.__name__} does not implement lazy_load()"
logger.error(err_str)
raise NotImplementedError(err_str) from exc
while True:
try:
doc = next(doc_iterator)
except StopIteration:
self.docs = []
break
self.docs = list((doc,))
self.docs_with_id = self._index_docs()
classified_doc = self._classify_doc()
self._add_pebblo_specific_metadata(classified_doc)
if self.load_semantic:
self.docs = self._add_semantic_to_docs(classified_doc)
else:
self.docs = self._unindex_docs()
yield self.docs[0]
[docs] @classmethod
def set_discover_sent(cls) -> None:
cls._discover_sent = True
[docs] @classmethod
def set_loader_sent(cls) -> None:
cls._loader_sent = True
def _classify_doc(self, loading_end: bool = False) -> dict:
"""Send documents fetched from loader to pebblo-server. Then send
classified documents to Daxa cloud(If api_key is present). Internal method.
Args:
loading_end (bool, optional): Flag indicating the halt of data
loading by loader. Defaults to False.
"""
headers = {
"Accept": "application/json",
"Content-Type": "application/json",
}
if loading_end is True:
PebbloSafeLoader.set_loader_sent()
doc_content = [doc.dict() for doc in self.docs_with_id]
docs = []
for doc in doc_content:
doc_metadata = doc.get("metadata", {})
doc_authorized_identities = doc_metadata.get("authorized_identities", [])
doc_source_path = get_full_path(
doc_metadata.get(
"full_path", doc_metadata.get("source", self.source_path)
)
)
doc_source_owner = doc_metadata.get(
"owner", PebbloSafeLoader.get_file_owner_from_path(doc_source_path)
)
doc_source_size = doc_metadata.get(
"size", self.get_source_size(doc_source_path)
)
page_content = str(doc.get("page_content"))
page_content_size = self.calculate_content_size(page_content)
self.source_aggregate_size += page_content_size
doc_id = doc.get("pb_id", None) or 0
docs.append(
{
"doc": page_content,
"source_path": doc_source_path,
"pb_id": doc_id,
"last_modified": doc.get("metadata", {}).get("last_modified"),
"file_owner": doc_source_owner,
**(
{"authorized_identities": doc_authorized_identities}
if doc_authorized_identities
else {}
),
**(
{"source_path_size": doc_source_size}
if doc_source_size is not None
else {}
),
}
)
payload: Dict[str, Any] = {
"name": self.app_name,
"owner": self.owner,
"docs": docs,
"plugin_version": PLUGIN_VERSION,
"load_id": self.load_id,
"loader_details": self.loader_details,
"loading_end": "false",
"source_owner": self.source_owner,
"classifier_location": self.classifier_location,
}
if loading_end is True:
payload["loading_end"] = "true"
if "loader_details" in payload:
payload["loader_details"]["source_aggregate_size"] = (
self.source_aggregate_size
)
payload = Doc(**payload).dict(exclude_unset=True)
classified_docs = {}
# Raw payload to be sent to classifier
if self.classifier_location == "local":
load_doc_url = f"{self.classifier_url}{LOADER_DOC_URL}"
try:
pebblo_resp = requests.post(
load_doc_url, headers=headers, json=payload, timeout=300
)
# Updating the structure of pebblo response docs for efficient searching
for classified_doc in json.loads(pebblo_resp.text).get("docs", []):
classified_docs.update({classified_doc["pb_id"]: classified_doc})
if pebblo_resp.status_code not in [
HTTPStatus.OK,
HTTPStatus.BAD_GATEWAY,
]:
logger.warning(
"Received unexpected HTTP response code: %s",
pebblo_resp.status_code,
)
logger.debug(
"send_loader_doc[local]: request url %s, body %s len %s\
response status %s body %s",
pebblo_resp.request.url,
str(pebblo_resp.request.body),
str(
len(
pebblo_resp.request.body if pebblo_resp.request.body else []
)
),
str(pebblo_resp.status_code),
pebblo_resp.json(),
)
except requests.exceptions.RequestException:
logger.warning("Unable to reach pebblo server.")
except Exception as e:
logger.warning("An Exception caught in _send_loader_doc: local %s", e)
if self.api_key:
if self.classifier_location == "local":
docs = payload["docs"]
for doc_data in docs:
classified_data = classified_docs.get(doc_data["pb_id"], {})
doc_data.update(
{
"pb_checksum": classified_data.get("pb_checksum", None),
"loader_source_path": classified_data.get(
"loader_source_path", None
),
"entities": classified_data.get("entities", {}),
"topics": classified_data.get("topics", {}),
}
)
doc_data.pop("doc")
headers.update({"x-api-key": self.api_key})
pebblo_cloud_url = f"{PEBBLO_CLOUD_URL}{LOADER_DOC_URL}"
try:
pebblo_cloud_response = requests.post(
pebblo_cloud_url, headers=headers, json=payload, timeout=20
)
logger.debug(
"send_loader_doc[cloud]: request url %s, body %s len %s\
response status %s body %s",
pebblo_cloud_response.request.url,
str(pebblo_cloud_response.request.body),
str(
len(
pebblo_cloud_response.request.body
if pebblo_cloud_response.request.body
else []
)
),
str(pebblo_cloud_response.status_code),
pebblo_cloud_response.json(),
)
except requests.exceptions.RequestException:
logger.warning("Unable to reach Pebblo cloud server.")
except Exception as e:
logger.warning("An Exception caught in _send_loader_doc: cloud %s", e)
elif self.classifier_location == "pebblo-cloud":
logger.warning("API key is missing for sending docs to Pebblo cloud.")
raise NameError("API key is missing for sending docs to Pebblo cloud.")
return classified_docs
[docs] @staticmethod
def calculate_content_size(page_content: str) -> int:
"""Calculate the content size in bytes:
- Encode the string to bytes using a specific encoding (e.g., UTF-8)
- Get the length of the encoded bytes.
Args:
page_content (str): Data string.
Returns:
int: Size of string in bytes.
"""
# Encode the content to bytes using UTF-8
encoded_content = page_content.encode("utf-8")
size = len(encoded_content)
return size
def _send_discover(self) -> None:
"""Send app discovery payload to pebblo-server. Internal method."""
pebblo_resp = None
headers = {
"Accept": "application/json",
"Content-Type": "application/json",
}
payload = self.app.dict(exclude_unset=True)
# Raw discover payload to be sent to classifier
if self.classifier_location == "local":
app_discover_url = f"{self.classifier_url}{APP_DISCOVER_URL}"
try:
pebblo_resp = requests.post(
app_discover_url, headers=headers, json=payload, timeout=20
)
logger.debug(
"send_discover[local]: request url %s, body %s len %s\
response status %s body %s",
pebblo_resp.request.url,
str(pebblo_resp.request.body),
str(
len(
pebblo_resp.request.body if pebblo_resp.request.body else []
)
),
str(pebblo_resp.status_code),
pebblo_resp.json(),
)
if pebblo_resp.status_code in [HTTPStatus.OK, HTTPStatus.BAD_GATEWAY]:
PebbloSafeLoader.set_discover_sent()
else:
logger.warning(
f"Received unexpected HTTP response code:\
{pebblo_resp.status_code}"
)
except requests.exceptions.RequestException:
logger.warning("Unable to reach pebblo server.")
except Exception as e:
logger.warning("An Exception caught in _send_discover: local %s", e)
if self.api_key:
try:
headers.update({"x-api-key": self.api_key})
# If the pebblo_resp is None,
# then the pebblo server version is not available
if pebblo_resp:
pebblo_server_version = json.loads(pebblo_resp.text).get(
"pebblo_server_version"
)
payload.update({"pebblo_server_version": pebblo_server_version})
payload.update({"pebblo_client_version": PLUGIN_VERSION})
pebblo_cloud_url = f"{PEBBLO_CLOUD_URL}{APP_DISCOVER_URL}"
pebblo_cloud_response = requests.post(
pebblo_cloud_url, headers=headers, json=payload, timeout=20
)
logger.debug(
"send_discover[cloud]: request url %s, body %s len %s\
response status %s body %s",
pebblo_cloud_response.request.url,
str(pebblo_cloud_response.request.body),
str(
len(
pebblo_cloud_response.request.body
if pebblo_cloud_response.request.body
else []
)
),
str(pebblo_cloud_response.status_code),
pebblo_cloud_response.json(),
)
except requests.exceptions.RequestException:
logger.warning("Unable to reach Pebblo cloud server.")
except Exception as e:
logger.warning("An Exception caught in _send_discover: cloud %s", e)
def _get_app_details(self) -> App:
"""Fetch app details. Internal method.
Returns:
App: App details.
"""
framework, runtime = get_runtime()
app = App(
name=self.app_name,
owner=self.owner,
description=self.description,
load_id=self.load_id,
runtime=runtime,
framework=framework,
plugin_version=PLUGIN_VERSION,
)
return app
[docs] @staticmethod
def get_file_owner_from_path(file_path: str) -> str:
"""Fetch owner of local file path.
Args:
file_path (str): Local file path.
Returns:
str: Name of owner.
"""
try:
import pwd
file_owner_uid = os.stat(file_path).st_uid
file_owner_name = pwd.getpwuid(file_owner_uid).pw_name
except Exception:
file_owner_name = "unknown"
return file_owner_name
[docs] def get_source_size(self, source_path: str) -> int:
"""Fetch size of source path. Source can be a directory or a file.
Args:
source_path (str): Local path of data source.
Returns:
int: Source size in bytes.
"""
if not source_path:
return 0
size = 0
if os.path.isfile(source_path):
size = os.path.getsize(source_path)
elif os.path.isdir(source_path):
total_size = 0
for dirpath, _, filenames in os.walk(source_path):
for f in filenames:
fp = os.path.join(dirpath, f)
if not os.path.islink(fp):
total_size += os.path.getsize(fp)
size = total_size
return size
def _index_docs(self) -> List[IndexedDocument]:
"""
Indexes the documents and returns a list of IndexedDocument objects.
Returns:
List[IndexedDocument]: A list of IndexedDocument objects with unique IDs.
"""
docs_with_id = [
IndexedDocument(pb_id=str(i), **doc.dict())
for i, doc in enumerate(self.docs)
]
return docs_with_id
def _add_semantic_to_docs(self, classified_docs: Dict) -> List[Document]:
"""
Adds semantic metadata to the given list of documents.
Args:
classified_docs (Dict): A dictionary of dictionaries containing the
classified documents with pb_id as key.
Returns:
List[Document]: A list of Document objects with added semantic metadata.
"""
indexed_docs = {
doc.pb_id: Document(page_content=doc.page_content, metadata=doc.metadata)
for doc in self.docs_with_id
}
for classified_doc in classified_docs.values():
doc_id = classified_doc.get("pb_id")
if doc_id in indexed_docs:
self._add_semantic_to_doc(indexed_docs[doc_id], classified_doc)
semantic_metadata_docs = [doc for doc in indexed_docs.values()]
return semantic_metadata_docs
def _unindex_docs(self) -> List[Document]:
"""
Converts a list of IndexedDocument objects to a list of Document objects.
Returns:
List[Document]: A list of Document objects.
"""
docs = [
Document(page_content=doc.page_content, metadata=doc.metadata)
for i, doc in enumerate(self.docs_with_id)
]
return docs
def _add_semantic_to_doc(self, doc: Document, classified_doc: dict) -> Document:
"""
Adds semantic metadata to the given document in-place.
Args:
doc (Document): A Document object.
classified_doc (dict): A dictionary containing the classified document.
Returns:
Document: The Document object with added semantic metadata.
"""
doc.metadata["pebblo_semantic_entities"] = list(
classified_doc.get("entities", {}).keys()
)
doc.metadata["pebblo_semantic_topics"] = list(
classified_doc.get("topics", {}).keys()
)
return doc
def _add_pebblo_specific_metadata(self, classified_docs: dict) -> None:
"""Add Pebblo specific metadata to documents."""
for doc in self.docs_with_id:
doc_metadata = doc.metadata
doc_metadata["full_path"] = get_full_path(
doc_metadata.get(
"full_path", doc_metadata.get("source", self.source_path)
)
)
doc_metadata["pb_checksum"] = classified_docs.get(doc.pb_id, {}).get(
"pb_checksum", None
)
