"""Abstractions for documents and their extractions."""
import json
import logging
from datetime import datetime
from enum import Enum
from typing import Any, Optional
from uuid import UUID, uuid4
from pydantic import Field
from .base import R2RSerializable
from .llm import GenerationConfig
logger = logging.getLogger()
class DocumentType(str, Enum):
"""Types of documents that can be stored."""
# Audio
MP3 = "mp3"
# CSV
CSV = "csv"
# Email
EML = "eml"
MSG = "msg"
P7S = "p7s"
# EPUB
EPUB = "epub"
# Excel
XLS = "xls"
XLSX = "xlsx"
# HTML
HTML = "html"
HTM = "htm"
# Image
BMP = "bmp"
HEIC = "heic"
JPEG = "jpeg"
PNG = "png"
TIFF = "tiff"
JPG = "jpg"
SVG = "svg"
# Markdown
MD = "md"
# Org Mode
ORG = "org"
# Open Office
ODT = "odt"
# PDF
PDF = "pdf"
# Plain text
TXT = "txt"
JSON = "json"
# PowerPoint
PPT = "ppt"
PPTX = "pptx"
# reStructured Text
RST = "rst"
# Rich Text
RTF = "rtf"
# TSV
TSV = "tsv"
# Video/GIF
GIF = "gif"
# Word
DOC = "doc"
DOCX = "docx"
# XML
XML = "xml"
class Document(R2RSerializable):
id: UUID = Field(default_factory=uuid4)
collection_ids: list[UUID]
owner_id: UUID
document_type: DocumentType
metadata: dict
class Config:
arbitrary_types_allowed = True
ignore_extra = False
json_encoders = {
UUID: str,
}
populate_by_name = True
class IngestionStatus(str, Enum):
"""Status of document processing."""
PENDING = "pending"
PARSING = "parsing"
EXTRACTING = "extracting"
CHUNKING = "chunking"
EMBEDDING = "embedding"
AUGMENTING = "augmenting"
STORING = "storing"
ENRICHING = "enriching"
FAILED = "failed"
SUCCESS = "success"
def __str__(self):
return self.value
@classmethod
def table_name(cls) -> str:
return "documents"
@classmethod
def id_column(cls) -> str:
return "document_id"
class GraphExtractionStatus(str, Enum):
"""Status of graph creation per document."""
PENDING = "pending"
PROCESSING = "processing"
SUCCESS = "success"
ENRICHED = "enriched"
FAILED = "failed"
def __str__(self):
return self.value
@classmethod
def table_name(cls) -> str:
return "documents"
@classmethod
def id_column(cls) -> str:
return "id"
class GraphConstructionStatus(str, Enum):
"""Status of graph enrichment per collection."""
PENDING = "pending"
PROCESSING = "processing"
OUTDATED = "outdated"
SUCCESS = "success"
FAILED = "failed"
def __str__(self):
return self.value
@classmethod
def table_name(cls) -> str:
return "collections"
@classmethod
def id_column(cls) -> str:
return "id"
class DocumentResponse(R2RSerializable):
"""Base class for document information handling."""
id: UUID
collection_ids: list[UUID]
owner_id: UUID
document_type: DocumentType
metadata: dict
title: Optional[str] = None
version: str
size_in_bytes: Optional[int]
ingestion_status: IngestionStatus = IngestionStatus.PENDING
extraction_status: GraphExtractionStatus = GraphExtractionStatus.PENDING
created_at: Optional[datetime] = None
updated_at: Optional[datetime] = None
ingestion_attempt_number: Optional[int] = None
summary: Optional[str] = None
summary_embedding: Optional[list[float]] = None # Add optional embedding
total_tokens: Optional[int] = None
chunks: Optional[list] = None
def convert_to_db_entry(self):
"""Prepare the document info for database entry, extracting certain
fields from metadata."""
now = datetime.now()
# Format the embedding properly for Postgres vector type
embedding = None
if self.summary_embedding is not None:
embedding = f"[{','.join(str(x) for x in self.summary_embedding)}]"
return {
"id": self.id,
"collection_ids": self.collection_ids,
"owner_id": self.owner_id,
"document_type": self.document_type,
"metadata": json.dumps(self.metadata),
"title": self.title or "N/A",
"version": self.version,
"size_in_bytes": self.size_in_bytes,
"ingestion_status": self.ingestion_status.value,
"extraction_status": self.extraction_status.value,
"created_at": self.created_at or now,
"updated_at": self.updated_at or now,
"ingestion_attempt_number": self.ingestion_attempt_number or 0,
"summary": self.summary,
"summary_embedding": embedding,
"total_tokens": self.total_tokens or 0, # ensure we pass 0 if None
}
class Config:
json_schema_extra = {
"example": {
"id": "123e4567-e89b-12d3-a456-426614174000",
"collection_ids": ["123e4567-e89b-12d3-a456-426614174000"],
"owner_id": "123e4567-e89b-12d3-a456-426614174000",
"document_type": "pdf",
"metadata": {"title": "Sample Document"},
"title": "Sample Document",
"version": "1.0",
"size_in_bytes": 123456,
"ingestion_status": "pending",
"extraction_status": "pending",
"created_at": "2021-01-01T00:00:00",
"updated_at": "2021-01-01T00:00:00",
"ingestion_attempt_number": 0,
"summary": "A summary of the document",
"summary_embedding": [0.1, 0.2, 0.3],
"total_tokens": 1000,
}
}
class UnprocessedChunk(R2RSerializable):
"""An extraction from a document."""
id: Optional[UUID] = None
document_id: Optional[UUID] = None
collection_ids: list[UUID] = []
metadata: dict = {}
text: str
class UpdateChunk(R2RSerializable):
"""An extraction from a document."""
id: UUID
metadata: Optional[dict] = None
text: str
class DocumentChunk(R2RSerializable):
"""An extraction from a document."""
id: UUID
document_id: UUID
collection_ids: list[UUID]
owner_id: UUID
data: str | bytes
metadata: dict
class RawChunk(R2RSerializable):
text: str
class IngestionMode(str, Enum):
hi_res = "hi-res"
fast = "fast"
custom = "custom"
class ChunkEnrichmentSettings(R2RSerializable):
"""Settings for chunk enrichment."""
enable_chunk_enrichment: bool = Field(
default=False,
description="Whether to enable chunk enrichment or not",
)
n_chunks: int = Field(
default=2,
description="The number of preceding and succeeding chunks to include. Defaults to 2.",
)
generation_config: Optional[GenerationConfig] = Field(
default=None,
description="The generation config to use for chunk enrichment",
)
chunk_enrichment_prompt: Optional[str] = Field(
default="chunk_enrichment",
description="The prompt to use for chunk enrichment",
)
class IngestionConfig(R2RSerializable):
provider: str = "r2r"
excluded_parsers: list[str] = ["mp4"]
chunking_strategy: str = "recursive"
chunk_enrichment_settings: ChunkEnrichmentSettings = (
ChunkEnrichmentSettings()
)
extra_parsers: dict[str, Any] = {}
audio_transcription_model: str = ""
vision_img_prompt_name: str = "vision_img"
vision_pdf_prompt_name: str = "vision_pdf"
skip_document_summary: bool = False
document_summary_system_prompt: str = "system"
document_summary_task_prompt: str = "summary"
chunks_for_document_summary: int = 128
document_summary_model: str = ""
@property
def supported_providers(self) -> list[str]:
return ["r2r", "unstructured_local", "unstructured_api"]
def validate_config(self) -> None:
if self.provider not in self.supported_providers:
raise ValueError(f"Provider {self.provider} is not supported.")
@classmethod
def get_default(cls, mode: str) -> "IngestionConfig":
"""Return default ingestion configuration for a given mode."""
if mode == "hi-res":
# More thorough parsing, no skipping summaries, possibly larger `chunks_for_document_summary`.
return cls(
provider="r2r",
excluded_parsers=["mp4"],
chunk_enrichment_settings=ChunkEnrichmentSettings(), # default
extra_parsers={},
audio_transcription_model="",
vision_img_prompt_name="vision_img",
vision_pdf_prompt_name="vision_pdf",
skip_document_summary=False,
document_summary_system_prompt="system",
document_summary_task_prompt="summary",
chunks_for_document_summary=256, # larger for hi-res
document_summary_model="",
)
elif mode == "fast":
# Skip summaries and other enrichment steps for speed.
return cls(
provider="r2r",
excluded_parsers=["mp4"],
chunk_enrichment_settings=ChunkEnrichmentSettings(), # default
extra_parsers={},
audio_transcription_model="",
vision_img_prompt_name="vision_img",
vision_pdf_prompt_name="vision_pdf",
skip_document_summary=True, # skip summaries
document_summary_system_prompt="system",
document_summary_task_prompt="summary",
chunks_for_document_summary=64,
document_summary_model="",
)
else:
# For `custom` or any unrecognized mode, return a base config
return cls()
