705 lines
32 KiB
Python
705 lines
32 KiB
Python
"""
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Document processing method module.
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Handles document operations using the document service.
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"""
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import logging
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from typing import Dict, Any, List, Optional
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from datetime import datetime, UTC
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from modules.chat.methodBase import MethodBase, ActionResult, action
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logger = logging.getLogger(__name__)
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class MethodDocument(MethodBase):
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"""Document method implementation for document operations"""
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def __init__(self, serviceCenter: Any):
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"""Initialize the document method"""
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super().__init__(serviceCenter)
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self.name = "document"
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self.description = "Handle document operations like extraction and analysis"
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@action
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async def extract(self, parameters: Dict[str, Any]) -> ActionResult:
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"""
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Extract specific content from document with AI prompt and return it in the specified format.
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Parameters:
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documentList (str): Reference to the document list to extract content from
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aiPrompt (str): AI prompt for content extraction
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expectedDocumentFormats (list, optional): Expected document formats with extension, mimeType, description
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includeMetadata (bool, optional): Whether to include metadata (default: True)
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"""
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try:
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documentList = parameters.get("documentList")
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aiPrompt = parameters.get("aiPrompt")
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expectedDocumentFormats = parameters.get("expectedDocumentFormats", [])
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includeMetadata = parameters.get("includeMetadata", True)
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if not documentList:
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return self._createResult(
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success=False,
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data={},
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error="Document list reference is required"
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)
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if not aiPrompt:
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return self._createResult(
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success=False,
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data={},
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error="AI prompt is required"
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)
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chatDocuments = self.service.getChatDocumentsFromDocumentList(documentList)
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if not chatDocuments:
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return self._createResult(
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success=False,
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data={},
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error="No documents found for the provided reference"
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)
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# Extract content from all documents using AI
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all_extracted_content = []
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file_infos = []
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for chatDocument in chatDocuments:
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fileId = chatDocument.fileId
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file_data = self.service.getFileData(fileId)
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file_info = self.service.getFileInfo(fileId)
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if not file_data:
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logger.warning(f"File not found or empty for fileId: {fileId}")
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continue
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extracted_content = await self.service.extractContentFromFileData(
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prompt=aiPrompt,
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fileData=file_data,
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filename=file_info.get('name', 'document'),
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mimeType=file_info.get('mimeType', 'application/octet-stream'),
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base64Encoded=False,
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documentId=chatDocument.id
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)
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all_extracted_content.append(extracted_content)
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if includeMetadata:
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file_infos.append(file_info)
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if not all_extracted_content:
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return self._createResult(
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success=False,
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data={},
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error="No content could be extracted from any documents"
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)
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# Process each document individually with its own format conversion
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output_documents = []
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for i, (chatDocument, extracted_content) in enumerate(zip(chatDocuments, all_extracted_content)):
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# Extract text content from this document
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text_content = ""
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if hasattr(extracted_content, 'contents') and extracted_content.contents:
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# Extract text from ContentItem objects
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text_parts = []
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for content_item in extracted_content.contents:
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if hasattr(content_item, 'data') and content_item.data:
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text_parts.append(content_item.data)
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text_content = "\n".join(text_parts)
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elif isinstance(extracted_content, str):
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text_content = extracted_content
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else:
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text_content = str(extracted_content)
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# Get the expected format for this document (or use default)
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target_format = None
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if expectedDocumentFormats and i < len(expectedDocumentFormats):
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target_format = expectedDocumentFormats[i]
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elif expectedDocumentFormats and len(expectedDocumentFormats) > 0:
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# If fewer formats than documents, use the last format for remaining documents
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target_format = expectedDocumentFormats[-1]
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# Determine output format and filename
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if target_format:
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target_extension = target_format.get("extension", ".txt")
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target_mime_type = target_format.get("mimeType", "text/plain")
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# Check if format conversion is needed
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if target_extension not in [".txt", ".text"] or target_mime_type != "text/plain":
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logger.info(f"Converting document {i+1} to format: {target_extension} ({target_mime_type})")
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# Use AI to convert format
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formatted_content = await self._convertContentToFormat(text_content, target_format)
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final_content = formatted_content
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final_mime_type = target_mime_type
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final_extension = target_extension
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else:
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logger.info(f"Document {i+1}: No format conversion needed, using plain text")
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final_content = text_content
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final_mime_type = "text/plain"
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final_extension = ".txt"
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else:
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logger.info(f"Document {i+1}: No expected format specified, using plain text")
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final_content = text_content
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final_mime_type = "text/plain"
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final_extension = ".txt"
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# Create output filename based on original filename and target format
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original_filename = chatDocument.filename
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base_name = original_filename.rsplit('.', 1)[0] if '.' in original_filename else original_filename
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output_filename = f"{base_name}_extracted_{datetime.now(UTC).strftime('%Y%m%d_%H%M%S')}{final_extension}"
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# Create result data for this document
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result_data = {
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"documentCount": 1,
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"content": final_content,
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"originalFilename": original_filename,
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"fileInfos": [file_infos[i]] if includeMetadata and i < len(file_infos) else None,
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"timestamp": datetime.now(UTC).isoformat()
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}
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logger.info(f"Created output document: {output_filename} with {len(final_content)} characters")
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output_documents.append({
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"documentName": output_filename,
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"documentData": result_data,
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"mimeType": final_mime_type
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})
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return self._createResult(
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success=True,
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data={
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"documents": output_documents
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}
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)
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except Exception as e:
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logger.error(f"Error extracting content: {str(e)}")
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return self._createResult(
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success=False,
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data={},
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error=str(e)
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)
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@action
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async def generate(self, parameters: Dict[str, Any]) -> ActionResult:
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"""
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Generate documents in specific formats from document references.
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This action automatically extracts content from documents and converts it to the specified format.
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Parameters:
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documentList (list): List of document references to extract content from
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expectedDocumentFormats (list): Expected document formats with extension, mimeType, description
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originalDocuments (list, optional): List of original document names
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includeMetadata (bool, optional): Whether to include metadata (default: True)
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"""
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try:
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document_list = parameters.get("documentList", [])
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expected_document_formats = parameters.get("expectedDocumentFormats", [])
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original_documents = parameters.get("originalDocuments", [])
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include_metadata = parameters.get("includeMetadata", True)
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if not document_list:
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return self._createResult(
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success=False,
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data={},
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error="Document list is required for generation"
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)
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if not expected_document_formats or len(expected_document_formats) == 0:
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return self._createResult(
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success=False,
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data={},
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error="Expected document formats specification is required"
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)
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# Get chat documents for original documents list
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chat_documents = self.service.getChatDocumentsFromDocumentList(document_list)
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logger.info(f"Found {len(chat_documents)} chat documents")
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if not chat_documents:
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return self._createResult(
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success=False,
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data={},
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error="No documents found for the provided documentList reference"
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)
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# Update original documents list if not provided
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if not original_documents:
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original_documents = [doc.filename if hasattr(doc, 'filename') else str(doc.id) for doc in chat_documents]
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# Process each document individually with its own format conversion
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output_documents = []
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for i, chat_document in enumerate(chat_documents):
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# Extract content from this document
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# ChatDocument is just a reference, so we need to get file data using fileId
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content = ""
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if hasattr(chat_document, 'fileId') and chat_document.fileId:
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# Need to get file data
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file_data = self.service.getFileData(chat_document.fileId)
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if file_data:
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if isinstance(file_data, bytes):
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content = file_data.decode('utf-8', errors='ignore')
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else:
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content = str(file_data)
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else:
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logger.warning(f"Could not get file data for document {i+1}, skipping")
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continue
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else:
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logger.warning(f"Document {i+1} has no fileId, skipping")
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continue
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if not content:
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logger.warning(f"Could not extract content from document {i+1}, skipping")
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continue
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logger.info(f"Extracted content from document {i+1}: {len(content)} characters")
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# Get the expected format for this document (or use default)
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target_format = None
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if i < len(expected_document_formats):
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target_format = expected_document_formats[i]
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elif len(expected_document_formats) > 0:
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# If fewer formats than documents, use the last format for remaining documents
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target_format = expected_document_formats[-1]
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if not target_format:
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logger.warning(f"No expected format for document {i+1}, skipping")
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continue
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# Use AI to convert format
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formatted_content = await self._convertContentToFormat(content, target_format)
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if not formatted_content:
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logger.warning(f"Failed to format document {i+1}, skipping")
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continue
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target_extension = target_format.get("extension", ".txt")
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target_mime_type = target_format.get("mimeType", "text/plain")
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# Create output filename
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timestamp = datetime.now(UTC).strftime('%Y%m%d_%H%M%S')
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if i < len(original_documents):
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base_name = original_documents[i].rsplit('.', 1)[0] if '.' in original_documents[i] else original_documents[i]
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else:
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base_name = f"document_{i+1}"
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output_filename = f"{base_name}_generated_{timestamp}{target_extension}"
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# Create result data
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result_data = {
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"documentCount": 1,
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"content": formatted_content,
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"outputFormat": target_format,
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"originalDocument": original_documents[i] if i < len(original_documents) else f"document_{i+1}",
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"timestamp": datetime.now(UTC).isoformat()
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}
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logger.info(f"Generated document: {output_filename} with {len(formatted_content)} characters")
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output_documents.append({
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"documentName": output_filename,
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"documentData": result_data,
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"mimeType": target_mime_type
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})
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if not output_documents:
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return self._createResult(
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success=False,
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data={},
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error="No documents could be generated"
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)
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return self._createResult(
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success=True,
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data={
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"documents": output_documents
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}
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)
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except Exception as e:
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logger.error(f"Error generating document: {str(e)}")
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return self._createResult(
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success=False,
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data={},
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error=str(e)
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)
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async def _convertContentToFormat(self, content: str, target_format: Dict[str, Any]) -> str:
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"""
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Helper function to convert content to the specified format using AI.
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"""
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try:
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extension = target_format.get("extension", ".txt")
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mime_type = target_format.get("mimeType", "text/plain")
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logger.info(f"Converting content to format: {extension} ({mime_type})")
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# Create AI prompt for format conversion
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format_prompts = {
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".csv": f"""
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Convert the following content into a proper CSV format.
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Requirements:
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1. Output ONLY the CSV data without any markdown, code blocks, or additional text
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2. Use appropriate headers based on the content
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3. Ensure proper CSV formatting with commas and quotes where needed
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4. Make the data easily readable and importable into spreadsheet applications
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Content to convert:
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{content}
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Generate ONLY the CSV data:
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""",
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".json": f"""
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Convert the following content into a proper JSON format.
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Requirements:
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1. Output ONLY the JSON data without any markdown, code blocks, or additional text
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2. Structure the data logically with appropriate keys and values
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3. Ensure valid JSON syntax
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4. Make the data easily parseable and readable
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Content to convert:
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{content}
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Generate ONLY the JSON data:
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""",
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".xml": f"""
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Convert the following content into a proper XML format.
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Requirements:
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1. Output ONLY the XML data without any markdown, code blocks, or additional text
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2. Use appropriate XML tags and structure
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3. Ensure valid XML syntax
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4. Make the data easily parseable and readable
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Content to convert:
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{content}
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Generate ONLY the XML data:
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""",
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".html": f"""
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Convert the following content into a proper HTML format.
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Requirements:
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1. Output ONLY the HTML data without any markdown, code blocks, or additional text
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2. Use appropriate HTML tags and structure
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3. Ensure valid HTML syntax
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4. Make the data easily readable in web browsers
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Content to convert:
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{content}
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Generate ONLY the HTML data:
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""",
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".md": f"""
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Convert the following content into a proper Markdown format.
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Requirements:
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1. Output ONLY the Markdown data without any code blocks or additional text
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2. Use appropriate Markdown syntax for headers, lists, emphasis, etc.
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3. Structure the content logically
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4. Make the data easily readable and convertible to other formats
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Content to convert:
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{content}
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Generate ONLY the Markdown data:
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"""
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}
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# Get the appropriate prompt for the target format
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if extension in format_prompts:
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ai_prompt = format_prompts[extension]
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else:
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# Generic format conversion
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ai_prompt = f"""
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Convert the following content into {extension.upper()} format.
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Requirements:
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1. Output ONLY the {extension.upper()} data without any markdown, code blocks, or additional text
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2. Use appropriate formatting for {extension.upper()} files
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3. Ensure the output is valid and usable
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4. Make the data easily readable and importable
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Content to convert:
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{content}
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Generate ONLY the {extension.upper()} data:
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"""
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# Call AI to generate the formatted content
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logger.info(f"Calling AI for {extension} format conversion")
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formatted_content = await self.service.callAiTextBasic(ai_prompt, content)
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if not formatted_content or formatted_content.strip() == "":
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logger.warning("AI format conversion failed, using fallback")
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return self._generateFallbackFormattedContent(content, extension, mime_type)
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# Clean up the AI response
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formatted_content = formatted_content.strip()
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# Remove markdown code blocks if present
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if formatted_content.startswith("```") and formatted_content.endswith("```"):
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lines = formatted_content.split('\n')
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if len(lines) > 2:
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formatted_content = '\n'.join(lines[1:-1])
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return formatted_content
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except Exception as e:
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logger.error(f"Error in AI format conversion: {str(e)}")
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return self._generateFallbackFormattedContent(content, extension, mime_type)
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def _generateFallbackFormattedContent(self, content: str, extension: str, mime_type: str) -> str:
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"""
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Generate fallback formatted content when AI conversion fails.
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"""
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try:
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if extension == ".csv":
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# Simple CSV fallback - split by lines and create basic CSV
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lines = content.strip().split('\n')
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if lines:
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# Create a simple CSV with line numbers and content
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csv_lines = ["Line,Content"]
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for i, line in enumerate(lines, 1):
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# Escape quotes and wrap in quotes if comma present
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if ',' in line:
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line = f'"{line.replace(chr(34), chr(34) + chr(34))}"'
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csv_lines.append(f"{i},{line}")
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return '\n'.join(csv_lines)
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return "Line,Content\n1,No content available"
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elif extension == ".json":
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# Simple JSON fallback
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content_escaped = content.replace('"', '\\"')
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timestamp = datetime.now(UTC).isoformat()
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return f'{{"content": "{content_escaped}", "format": "json", "timestamp": "{timestamp}"}}'
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elif extension == ".xml":
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# Simple XML fallback
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timestamp = datetime.now(UTC).isoformat()
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return f'<?xml version="1.0" encoding="UTF-8"?>\n<document>\n<content>{content}</content>\n<format>xml</format>\n<timestamp>{timestamp}</timestamp>\n</document>'
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elif extension == ".html":
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# Simple HTML fallback
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timestamp = datetime.now(UTC).strftime('%Y-%m-%d %H:%M:%S UTC')
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return f'<!DOCTYPE html>\n<html>\n<head><meta charset="UTF-8"><title>Generated Document</title></head>\n<body>\n<pre>{content}</pre>\n<p><em>Generated on {timestamp}</em></p>\n</body>\n</html>'
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elif extension == ".md":
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# Simple Markdown fallback
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timestamp = datetime.now(UTC).strftime('%Y-%m-%d %H:%M:%S UTC')
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return f"# Generated Document\n\n{content}\n\n---\n*Generated on {timestamp}*"
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else:
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# Generic fallback - return content as-is
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return content
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except Exception as e:
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logger.error(f"Error in fallback format conversion: {str(e)}")
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return content
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@action
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async def generateReport(self, parameters: Dict[str, Any]) -> ActionResult:
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"""
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Generate a comprehensive, professional HTML report from multiple documents, consolidating and summarizing all findings using AI.
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Parameters:
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documentList (str): Reference to the document list to create the report from
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title (str, optional): Title for the report (default: "Summary Report")
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includeMetadata (bool, optional): Whether to include metadata (default: True)
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"""
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try:
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documentList = parameters.get("documentList")
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title = parameters.get("title", "Summary Report")
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includeMetadata = parameters.get("includeMetadata", True)
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if not documentList:
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return self._createResult(
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success=False,
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data={},
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error="Document list reference is required"
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)
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chatDocuments = self.service.getChatDocumentsFromDocumentList(documentList)
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logger.info(f"Retrieved {len(chatDocuments)} chat documents for report generation")
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if not chatDocuments:
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return self._createResult(
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success=False,
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data={},
|
|
error="No documents found for the provided reference"
|
|
)
|
|
|
|
# Generate HTML report
|
|
html_content = await self._generateHtmlReport(chatDocuments, title, includeMetadata)
|
|
|
|
# Create output filename
|
|
timestamp = datetime.now(UTC).strftime('%Y%m%d_%H%M%S')
|
|
output_filename = f"report_{timestamp}.html"
|
|
|
|
result_data = {
|
|
"documentCount": len(chatDocuments),
|
|
"content": html_content,
|
|
"title": title,
|
|
"timestamp": datetime.now(UTC).isoformat()
|
|
}
|
|
|
|
logger.info(f"Generated HTML report: {output_filename} with {len(html_content)} characters")
|
|
|
|
return self._createResult(
|
|
success=True,
|
|
data={
|
|
"documents": [{
|
|
"documentName": output_filename,
|
|
"documentData": result_data,
|
|
"mimeType": "text/html"
|
|
}]
|
|
}
|
|
)
|
|
except Exception as e:
|
|
logger.error(f"Error generating report: {str(e)}")
|
|
return self._createResult(
|
|
success=False,
|
|
data={},
|
|
error=str(e)
|
|
)
|
|
|
|
async def _generateHtmlReport(self, chatDocuments: List[Any], title: str, includeMetadata: bool) -> str:
|
|
"""
|
|
Generate a comprehensive HTML report using AI from all input documents.
|
|
"""
|
|
try:
|
|
# Filter out empty documents and collect content
|
|
validDocuments = []
|
|
allContent = []
|
|
|
|
for doc in chatDocuments:
|
|
content = ""
|
|
logger.info(f"Processing document: type={type(doc)}")
|
|
|
|
# Get actual file content using the fileId reference
|
|
try:
|
|
file_data = self.service.getFileData(doc.fileId)
|
|
if file_data:
|
|
# Convert bytes to string
|
|
if isinstance(file_data, bytes):
|
|
content = file_data.decode('utf-8')
|
|
else:
|
|
content = str(file_data)
|
|
logger.info(f" Retrieved content from file: {len(content)} characters")
|
|
else:
|
|
logger.warning(f" No file data found for fileId: {doc.fileId}")
|
|
except Exception as e:
|
|
logger.error(f" Error retrieving file data: {str(e)}")
|
|
|
|
# Skip empty documents
|
|
if content:
|
|
validDocuments.append(doc)
|
|
allContent.append(f"Document: {doc.filename}\n{content}\n")
|
|
logger.info(f" Added document to valid documents list")
|
|
else:
|
|
logger.warning(f" Skipping document with no content")
|
|
|
|
if not validDocuments:
|
|
# If no valid documents, create a simple report
|
|
html = ["<html><head><meta charset='utf-8'><title>" + title + "</title></head><body>"]
|
|
html.append(f"<h1>{title}</h1>")
|
|
html.append(f"<p><b>Generated:</b> {datetime.now(UTC).strftime('%Y-%m-%d %H:%M:%S UTC')}</p>")
|
|
html.append("<p><em>No content available in the provided documents.</em></p>")
|
|
html.append("</body></html>")
|
|
return '\n'.join(html)
|
|
|
|
# Create AI prompt for comprehensive report generation
|
|
combinedContent = "\n\n".join(allContent)
|
|
aiPrompt = f"""
|
|
Create a comprehensive, well-structured HTML report based on the following documents and content.
|
|
|
|
Report Title: {title}
|
|
|
|
Requirements:
|
|
1. Create a professional, well-formatted HTML report
|
|
2. Include an executive summary at the beginning
|
|
3. Organize information logically with clear sections
|
|
4. Highlight key findings and insights
|
|
5. Include relevant data, statistics, and conclusions
|
|
6. Use proper HTML formatting with headers, lists, and styling
|
|
7. Make it readable and professional
|
|
|
|
Document Content:
|
|
{combinedContent}
|
|
|
|
Generate a complete HTML report that integrates all the information into a cohesive, professional document.
|
|
"""
|
|
|
|
# Call AI to generate the report
|
|
logger.info(f"Generating AI report for {len(validDocuments)} documents")
|
|
aiReport = await self.service.callAiTextBasic(aiPrompt, combinedContent)
|
|
|
|
# If AI call fails, fall back to basic HTML
|
|
if not aiReport or aiReport.strip() == "":
|
|
logger.warning("AI report generation failed, using fallback HTML")
|
|
return self._generateFallbackHtmlReport(validDocuments, title, includeMetadata)
|
|
|
|
# Clean up the AI response and ensure it's valid HTML
|
|
if not aiReport.strip().startswith('<html'):
|
|
# Wrap the AI content in proper HTML structure
|
|
html = ["<html><head><meta charset='utf-8'><title>" + title + "</title></head><body>"]
|
|
html.append(f"<h1>{title}</h1>")
|
|
html.append(f"<p><b>Generated:</b> {datetime.now(UTC).strftime('%Y-%m-%d %H:%M:%S UTC')}</p>")
|
|
html.append(f"<p><b>Total Documents Analyzed:</b> {len(validDocuments)}</p>")
|
|
html.append("<hr>")
|
|
html.append(aiReport)
|
|
html.append("</body></html>")
|
|
return '\n'.join(html)
|
|
else:
|
|
# AI returned complete HTML, use it directly
|
|
return aiReport
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error generating AI report: {str(e)}")
|
|
# Fall back to basic HTML report
|
|
return self._generateFallbackHtmlReport(chatDocuments, title, includeMetadata)
|
|
|
|
def _generateFallbackHtmlReport(self, chatDocuments: List[Any], title: str, includeMetadata: bool) -> str:
|
|
"""
|
|
Generate a basic HTML report as fallback when AI generation fails.
|
|
"""
|
|
html = ["<html><head><meta charset='utf-8'><title>" + title + "</title></head><body>"]
|
|
html.append(f"<h1>{title}</h1>")
|
|
html.append(f"<p><b>Generated:</b> {datetime.now(UTC).strftime('%Y-%m-%d %H:%M:%S UTC')}</p>")
|
|
html.append(f"<p><b>Total Documents:</b> {len(chatDocuments)}</p>")
|
|
|
|
for i, doc in enumerate(chatDocuments, 1):
|
|
html.append(f"<h2>Document {i}: {doc.filename}</h2>")
|
|
|
|
if includeMetadata:
|
|
html.append("<ul>")
|
|
html.append(f"<li><b>ID:</b> {doc.id}</li>")
|
|
html.append(f"<li><b>File ID:</b> {doc.fileId}</li>")
|
|
html.append(f"<li><b>Filename:</b> {doc.filename}</li>")
|
|
if hasattr(doc, 'createdAt'):
|
|
html.append(f"<li><b>Created:</b> {doc.createdAt}</li>")
|
|
html.append("</ul>")
|
|
|
|
# Add document content if available
|
|
content = ""
|
|
if hasattr(doc, 'fileId') and doc.fileId:
|
|
# ChatDocument is just a reference, so we need to get file data using fileId
|
|
try:
|
|
file_data = self.service.getFileData(doc.fileId)
|
|
if file_data:
|
|
if isinstance(file_data, bytes):
|
|
content = file_data.decode('utf-8')
|
|
else:
|
|
content = str(file_data)
|
|
except Exception as e:
|
|
logger.warning(f"Could not retrieve content for document {doc.filename}: {str(e)}")
|
|
|
|
if content:
|
|
html.append(f"<div style='white-space:pre-wrap; border:1px solid #ccc; padding:0.5em; margin-bottom:1em; background-color:#f9f9f9;'>{content}</div>")
|
|
else:
|
|
html.append("<p><em>No content available</em></p>")
|
|
|
|
html.append("</body></html>")
|
|
return '\n'.join(html)
|