116 lines
4.7 KiB
Python
116 lines
4.7 KiB
Python
from typing import List
|
|
|
|
from letta.log import get_logger
|
|
from letta.schemas.agent import AgentState
|
|
from letta.schemas.enums import FileProcessingStatus
|
|
from letta.schemas.file import FileMetadata
|
|
from letta.schemas.passage import Passage
|
|
from letta.schemas.user import User
|
|
from letta.server.server import SyncServer
|
|
from letta.services.file_manager import FileManager
|
|
from letta.services.file_processor.chunker.line_chunker import LineChunker
|
|
from letta.services.file_processor.chunker.llama_index_chunker import LlamaIndexChunker
|
|
from letta.services.file_processor.embedder.openai_embedder import OpenAIEmbedder
|
|
from letta.services.file_processor.parser.mistral_parser import MistralFileParser
|
|
from letta.services.job_manager import JobManager
|
|
from letta.services.passage_manager import PassageManager
|
|
from letta.services.source_manager import SourceManager
|
|
|
|
logger = get_logger(__name__)
|
|
|
|
|
|
class FileProcessor:
|
|
"""Main PDF processing orchestrator"""
|
|
|
|
def __init__(
|
|
self,
|
|
file_parser: MistralFileParser,
|
|
text_chunker: LlamaIndexChunker,
|
|
embedder: OpenAIEmbedder,
|
|
actor: User,
|
|
max_file_size: int = 50 * 1024 * 1024, # 50MB default
|
|
):
|
|
self.file_parser = file_parser
|
|
self.text_chunker = text_chunker
|
|
self.line_chunker = LineChunker()
|
|
self.embedder = embedder
|
|
self.max_file_size = max_file_size
|
|
self.file_manager = FileManager()
|
|
self.source_manager = SourceManager()
|
|
self.passage_manager = PassageManager()
|
|
self.job_manager = JobManager()
|
|
self.actor = actor
|
|
|
|
# TODO: Factor this function out of SyncServer
|
|
async def process(
|
|
self, server: SyncServer, agent_states: List[AgentState], source_id: str, content: bytes, file_metadata: FileMetadata
|
|
) -> List[Passage]:
|
|
filename = file_metadata.file_name
|
|
|
|
# Create file as early as possible with no content
|
|
file_metadata.processing_status = FileProcessingStatus.PARSING # Parsing now
|
|
file_metadata = await self.file_manager.create_file(file_metadata, self.actor)
|
|
|
|
try:
|
|
# Ensure we're working with bytes
|
|
if isinstance(content, str):
|
|
content = content.encode("utf-8")
|
|
|
|
if len(content) > self.max_file_size:
|
|
raise ValueError(f"PDF size exceeds maximum allowed size of {self.max_file_size} bytes")
|
|
|
|
logger.info(f"Starting OCR extraction for {filename}")
|
|
ocr_response = await self.file_parser.extract_text(content, mime_type=file_metadata.file_type)
|
|
|
|
# update file with raw text
|
|
raw_markdown_text = "".join([page.markdown for page in ocr_response.pages])
|
|
file_metadata = await self.file_manager.update_file_status(
|
|
file_id=file_metadata.id, actor=self.actor, processing_status=FileProcessingStatus.EMBEDDING
|
|
)
|
|
file_metadata = await self.file_manager.upsert_file_content(file_id=file_metadata.id, text=raw_markdown_text, actor=self.actor)
|
|
|
|
await server.insert_file_into_context_windows(
|
|
source_id=source_id,
|
|
file_metadata_with_content=file_metadata,
|
|
actor=self.actor,
|
|
agent_states=agent_states,
|
|
)
|
|
|
|
if not ocr_response or len(ocr_response.pages) == 0:
|
|
raise ValueError("No text extracted from PDF")
|
|
|
|
logger.info("Chunking extracted text")
|
|
all_passages = []
|
|
|
|
for page in ocr_response.pages:
|
|
chunks = self.text_chunker.chunk_text(page)
|
|
|
|
if not chunks:
|
|
raise ValueError("No chunks created from text")
|
|
|
|
passages = await self.embedder.generate_embedded_passages(
|
|
file_id=file_metadata.id, source_id=source_id, chunks=chunks, actor=self.actor
|
|
)
|
|
all_passages.extend(passages)
|
|
|
|
all_passages = await self.passage_manager.create_many_source_passages_async(
|
|
passages=all_passages, file_metadata=file_metadata, actor=self.actor
|
|
)
|
|
|
|
logger.info(f"Successfully processed {filename}: {len(all_passages)} passages")
|
|
|
|
# update job status
|
|
await self.file_manager.update_file_status(
|
|
file_id=file_metadata.id, actor=self.actor, processing_status=FileProcessingStatus.COMPLETED
|
|
)
|
|
|
|
return all_passages
|
|
|
|
except Exception as e:
|
|
logger.error(f"File processing failed for {filename}: {str(e)}")
|
|
await self.file_manager.update_file_status(
|
|
file_id=file_metadata.id, actor=self.actor, processing_status=FileProcessingStatus.ERROR, error_message=str(e)
|
|
)
|
|
|
|
return []
|