81 lines
3.0 KiB
Python
81 lines
3.0 KiB
Python
"""
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Copyright 2024-2026 ReplyPier
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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from typing import Any, Dict, Optional
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from agno.knowledge.pdf import PDFKnowledgeBase
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from app.core.logger import get_logger
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logger = get_logger(__name__)
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class EnhancedPDFKnowledgeBase(PDFKnowledgeBase):
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"""Enhanced PDF knowledge base that supports metadata filters"""
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def load(
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self,
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recreate: bool = False,
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upsert: bool = True,
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skip_existing: bool = True,
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filters: Optional[Dict[str, Any]] = None,
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) -> None:
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"""
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Load the PDF contents to the vector db with metadata filters
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Args:
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recreate (bool, optional): Whether to recreate the collection. Defaults to False.
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upsert (bool, optional): Whether to upsert documents. Defaults to True.
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skip_existing (bool, optional): Whether to skip existing documents. Defaults to True.
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filters (Optional[Dict[str, Any]], optional): Metadata filters to apply to the documents. Defaults to None.
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"""
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if self.vector_db is None:
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logger.warning("No vector db provided")
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return
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if recreate:
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self.vector_db.drop()
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self.vector_db.create()
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elif not self.vector_db.exists():
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self.vector_db.create()
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logger.info(f"Loading PDF knowledge base: {self.path}")
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# Read documents from PDF
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if self.reader is None:
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logger.warning("No reader provided")
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return
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# Get documents and embed them
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for document_list in self.document_lists:
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# Embed documents if embedder is available
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if self.vector_db and hasattr(self.vector_db, 'embedder'):
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for document in document_list:
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if document.embedding is None:
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try:
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document.embed(embedder=self.vector_db.embedder)
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except Exception as e:
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logger.error(f"Error embedding document {document.id}: {str(e)}")
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# Upsert documents with filters
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if upsert and self.vector_db:
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try:
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logger.debug(f"Upserting {len(document_list)} documents with filters: {filters}")
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self.vector_db.upsert(documents=document_list, filters=filters)
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except Exception as e:
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logger.error(f"Error upserting documents: {str(e)}")
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logger.info(f"Completed loading PDF knowledge base: {self.path}")
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