Incremental / large document loading (#659)

Tech spec

BlobStore (trustgraph-flow/trustgraph/librarian/blob_store.py):
- get_stream() - yields document content in chunks for streaming retrieval
- create_multipart_upload() - initializes S3 multipart upload, returns
  upload_id
- upload_part() - uploads a single part, returns etag
- complete_multipart_upload() - finalizes upload with part etags
- abort_multipart_upload() - cancels and cleans up

Cassandra schema (trustgraph-flow/trustgraph/tables/library.py):
- New upload_session table with 24-hour TTL
- Index on user for listing sessions
- Prepared statements for all operations
- Methods: create_upload_session(), get_upload_session(),
  update_upload_session_chunk(), delete_upload_session(),
  list_upload_sessions()

- Schema extended with UploadSession, UploadProgress, and new
  request/response fields
- Librarian methods: begin_upload, upload_chunk, complete_upload,
  abort_upload, get_upload_status, list_uploads
- Service routing for all new operations
- Python SDK with transparent chunked upload:
  - add_document() auto-switches to chunked for files > 10MB
  - Progress callback support (on_progress)
  - get_pending_uploads(), get_upload_status(), abort_upload(),
    resume_upload()

- Document table: Added parent_id and document_type columns with index
- Document schema (knowledge/document.py): Added document_id field for
  streaming retrieval
- Librarian operations:
  - add-child-document for extracted PDF pages
  - list-children to get child documents
  - stream-document for chunked content retrieval
  - Cascade delete removes children when parent is deleted
  - list-documents filters children by default
- PDF decoder (decoding/pdf/pdf_decoder.py): Updated to stream large
  documents from librarian API to temp file
- Librarian service (librarian/service.py): Sends document_id instead of
  content for large PDFs (>2MB)
- Deprecated tools (load_pdf.py, load_text.py): Added deprecation
  warnings directing users to tg-add-library-document +
  tg-start-library-processing

Remove load_pdf and load_text utils

Move chunker/librarian comms to base class

Updating tests
This commit is contained in:
cybermaggedon 2026-03-04 16:57:58 +00:00 committed by GitHub
parent a38ca9474f
commit a630e143ef
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21 changed files with 3164 additions and 650 deletions

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@ -1,20 +1,37 @@
"""
Base chunking service that provides parameter specification functionality
for chunk-size and chunk-overlap parameters
for chunk-size and chunk-overlap parameters, and librarian client for
fetching large document content.
"""
import asyncio
import base64
import logging
import uuid
from .flow_processor import FlowProcessor
from .parameter_spec import ParameterSpec
from .consumer import Consumer
from .producer import Producer
from .metrics import ConsumerMetrics, ProducerMetrics
from ..schema import LibrarianRequest, LibrarianResponse
from ..schema import librarian_request_queue, librarian_response_queue
# Module logger
logger = logging.getLogger(__name__)
default_librarian_request_queue = librarian_request_queue
default_librarian_response_queue = librarian_response_queue
class ChunkingService(FlowProcessor):
"""Base service for chunking processors with parameter specification support"""
def __init__(self, **params):
id = params.get("id", "chunker")
# Call parent constructor
super(ChunkingService, self).__init__(**params)
@ -27,8 +44,122 @@ class ChunkingService(FlowProcessor):
ParameterSpec(name="chunk-overlap")
)
# Librarian client for fetching document content
librarian_request_q = params.get(
"librarian_request_queue", default_librarian_request_queue
)
librarian_response_q = params.get(
"librarian_response_queue", default_librarian_response_queue
)
librarian_request_metrics = ProducerMetrics(
processor=id, flow=None, name="librarian-request"
)
self.librarian_request_producer = Producer(
backend=self.pubsub,
topic=librarian_request_q,
schema=LibrarianRequest,
metrics=librarian_request_metrics,
)
librarian_response_metrics = ConsumerMetrics(
processor=id, flow=None, name="librarian-response"
)
self.librarian_response_consumer = Consumer(
taskgroup=self.taskgroup,
backend=self.pubsub,
flow=None,
topic=librarian_response_q,
subscriber=f"{id}-librarian",
schema=LibrarianResponse,
handler=self.on_librarian_response,
metrics=librarian_response_metrics,
)
# Pending librarian requests: request_id -> asyncio.Future
self.pending_requests = {}
logger.debug("ChunkingService initialized with parameter specifications")
async def start(self):
await super(ChunkingService, self).start()
await self.librarian_request_producer.start()
await self.librarian_response_consumer.start()
async def on_librarian_response(self, msg, consumer, flow):
"""Handle responses from the librarian service."""
response = msg.value()
request_id = msg.properties().get("id")
if request_id and request_id in self.pending_requests:
future = self.pending_requests.pop(request_id)
future.set_result(response)
else:
logger.warning(f"Received unexpected librarian response: {request_id}")
async def fetch_document_content(self, document_id, user, timeout=120):
"""
Fetch document content from librarian via Pulsar.
"""
request_id = str(uuid.uuid4())
request = LibrarianRequest(
operation="get-document-content",
document_id=document_id,
user=user,
)
# Create future for response
future = asyncio.get_event_loop().create_future()
self.pending_requests[request_id] = future
try:
# Send request
await self.librarian_request_producer.send(
request, properties={"id": request_id}
)
# Wait for response
response = await asyncio.wait_for(future, timeout=timeout)
if response.error:
raise RuntimeError(
f"Librarian error: {response.error.type}: {response.error.message}"
)
return response.content
except asyncio.TimeoutError:
self.pending_requests.pop(request_id, None)
raise RuntimeError(f"Timeout fetching document {document_id}")
async def get_document_text(self, doc):
"""
Get text content from a TextDocument, fetching from librarian if needed.
Args:
doc: TextDocument with either inline text or document_id
Returns:
str: The document text content
"""
if doc.document_id and not doc.text:
logger.info(f"Fetching document {doc.document_id} from librarian...")
content = await self.fetch_document_content(
document_id=doc.document_id,
user=doc.metadata.user,
)
# Content is base64 encoded
if isinstance(content, str):
content = content.encode('utf-8')
text = base64.b64decode(content).decode("utf-8")
logger.info(f"Fetched {len(text)} characters from librarian")
return text
else:
return doc.text.decode("utf-8")
async def chunk_document(self, msg, consumer, flow, default_chunk_size, default_chunk_overlap):
"""
Extract chunk parameters from flow and return effective values
@ -59,4 +190,16 @@ class ChunkingService(FlowProcessor):
@staticmethod
def add_args(parser):
"""Add chunking service arguments to parser"""
FlowProcessor.add_args(parser)
FlowProcessor.add_args(parser)
parser.add_argument(
'--librarian-request-queue',
default=default_librarian_request_queue,
help=f'Librarian request queue (default: {default_librarian_request_queue})',
)
parser.add_argument(
'--librarian-response-queue',
default=default_librarian_response_queue,
help=f'Librarian response queue (default: {default_librarian_response_queue})',
)