WangShen007
20fef2b9f3
One word everywhere: ReplyPier in UI copy, package and image names, the replypier.site domains (app/api/docs/help), REPLYPIER_* settings and the --rp-* design tokens. - public widget API is window.ReplyPier with replypierId / replypierBaseUrl / replypierConfig / replypierToken, loader at webclient/replypier.min.js; both bundles rebuilt from source - NOTICE, llms.txt, CONTRIBUTING, HELP_CENTER_INFRA and the demo runtime config rewritten; the demo config ships no third-party keys - migration adds the ReplyPier managed model value to the AI model enum - local compose project, database, role, volume and Docker network carry the same name
82 lines
3.1 KiB
Python
82 lines
3.1 KiB
Python
"""
|
|
Copyright 2024-2026 ReplyPier
|
|
|
|
Licensed under the Apache License, Version 2.0 (the "License");
|
|
you may not use this file except in compliance with the License.
|
|
You may obtain a copy of the License at
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
Unless required by applicable law or agreed to in writing, software
|
|
distributed under the License is distributed on an "AS IS" BASIS,
|
|
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
See the License for the specific language governing permissions and
|
|
limitations under the License.
|
|
"""
|
|
|
|
from typing import Any, Dict, Iterator, List, Optional
|
|
from agno.knowledge.pdf import PDFKnowledgeBase
|
|
from agno.document import Document
|
|
from app.core.logger import get_logger
|
|
|
|
logger = get_logger(__name__)
|
|
|
|
|
|
class EnhancedPDFKnowledgeBase(PDFKnowledgeBase):
|
|
"""Enhanced PDF knowledge base that supports metadata filters"""
|
|
|
|
def load(
|
|
self,
|
|
recreate: bool = False,
|
|
upsert: bool = True,
|
|
skip_existing: bool = True,
|
|
filters: Optional[Dict[str, Any]] = None,
|
|
) -> None:
|
|
"""
|
|
Load the PDF contents to the vector db with metadata filters
|
|
|
|
Args:
|
|
recreate (bool, optional): Whether to recreate the collection. Defaults to False.
|
|
upsert (bool, optional): Whether to upsert documents. Defaults to True.
|
|
skip_existing (bool, optional): Whether to skip existing documents. Defaults to True.
|
|
filters (Optional[Dict[str, Any]], optional): Metadata filters to apply to the documents. Defaults to None.
|
|
"""
|
|
if self.vector_db is None:
|
|
logger.warning("No vector db provided")
|
|
return
|
|
|
|
if recreate:
|
|
self.vector_db.drop()
|
|
self.vector_db.create()
|
|
elif not self.vector_db.exists():
|
|
self.vector_db.create()
|
|
|
|
logger.info(f"Loading PDF knowledge base: {self.path}")
|
|
|
|
# Read documents from PDF
|
|
if self.reader is None:
|
|
logger.warning("No reader provided")
|
|
return
|
|
|
|
# Get documents and embed them
|
|
for document_list in self.document_lists:
|
|
# Embed documents if embedder is available
|
|
if self.vector_db and hasattr(self.vector_db, 'embedder'):
|
|
for document in document_list:
|
|
if document.embedding is None:
|
|
try:
|
|
document.embed(embedder=self.vector_db.embedder)
|
|
except Exception as e:
|
|
logger.error(f"Error embedding document {document.id}: {str(e)}")
|
|
|
|
# Upsert documents with filters
|
|
if upsert and self.vector_db:
|
|
try:
|
|
logger.debug(f"Upserting {len(document_list)} documents with filters: {filters}")
|
|
self.vector_db.upsert(documents=document_list, filters=filters)
|
|
except Exception as e:
|
|
logger.error(f"Error upserting documents: {str(e)}")
|
|
|
|
logger.info(f"Completed loading PDF knowledge base: {self.path}")
|
|
|