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jyong
2025-05-15 16:44:55 +08:00
parent e710a8402c
commit 93ac6d37e9
5 changed files with 58 additions and 67 deletions
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62
api/core/workflow/nodes/knowledge_index/knowledge_index_node.py
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@ -1,7 +1,13 @@
import datetime
import logging
import time
from typing import Any, cast
from flask_login import current_user
from core.model_manager import ModelManager
from core.model_runtime.entities.model_entities import ModelType
from core.rag.index_processor.index_processor_factory import IndexProcessorFactory
from core.rag.retrieval.retrieval_methods import RetrievalMethod
from core.variables.segments import ObjectSegment
from core.workflow.entities.node_entities import NodeRunResult
@ -11,7 +17,7 @@ from extensions.ext_database import db
from extensions.ext_redis import redis_client
from models.dataset import Dataset, Document, RateLimitLog
from models.workflow import WorkflowNodeExecutionStatus
from services.dataset_service import DocumentService
from services.dataset_service import DatasetCollectionBindingService
from services.feature_service import FeatureService
from .entities import KnowledgeIndexNodeData
@ -109,14 +115,52 @@ class KnowledgeIndexNode(LLMNode):
if not document:
raise KnowledgeIndexNodeError(f"Document {node_data.document_id} not found.")
DocumentService.invoke_knowledge_index(
dataset=dataset,
document=document,
chunks=chunks,
chunk_structure=node_data.chunk_structure,
index_method=node_data.index_method,
retrieval_setting=node_data.retrieval_setting,
)
retrieval_setting = node_data.retrieval_setting
index_method = node_data.index_method
if not dataset.indexing_technique:
if node_data.index_method.indexing_technique not in Dataset.INDEXING_TECHNIQUE_LIST:
raise ValueError("Indexing technique is invalid")
dataset.indexing_technique = index_method.indexing_technique
if index_method.indexing_technique == "high_quality":
model_manager = ModelManager()
if (
index_method.embedding_setting.embedding_model
and index_method.embedding_setting.embedding_model_provider
):
dataset_embedding_model = index_method.embedding_setting.embedding_model
dataset_embedding_model_provider = index_method.embedding_setting.embedding_model_provider
else:
embedding_model = model_manager.get_default_model_instance(
tenant_id=current_user.current_tenant_id, model_type=ModelType.TEXT_EMBEDDING
)
dataset_embedding_model = embedding_model.model
dataset_embedding_model_provider = embedding_model.provider
dataset.embedding_model = dataset_embedding_model
dataset.embedding_model_provider = dataset_embedding_model_provider
dataset_collection_binding = DatasetCollectionBindingService.get_dataset_collection_binding(
dataset_embedding_model_provider, dataset_embedding_model
)
dataset.collection_binding_id = dataset_collection_binding.id
if not dataset.retrieval_model:
default_retrieval_model = {
"search_method": RetrievalMethod.SEMANTIC_SEARCH.value,
"reranking_enable": False,
"reranking_model": {"reranking_provider_name": "", "reranking_model_name": ""},
"top_k": 2,
"score_threshold_enabled": False,
}
dataset.retrieval_model = (
retrieval_setting.model_dump() if retrieval_setting else default_retrieval_model
) # type: ignore
index_processor = IndexProcessorFactory(node_data.chunk_structure).init_index_processor()
index_processor.index(dataset, document, chunks)
# update document status
document.indexing_status = "completed"
document.completed_at = datetime.datetime.now(datetime.UTC).replace(tzinfo=None)
db.session.commit()
return {
"dataset_id": dataset.id,