[ADD]Add functions related to knowledge base graph:
Add functions related to knowledge base graph: 1. Entity type generation, 2. Knowledge base graph acquisition, 3. Hard deletion of knowledge base graph, 4. Knowledge base graph reconstruction (asynchronous)
This commit is contained in:
lixiangcheng1
438
api/app/tasks.py
438
api/app/tasks.py
@@ -1,4 +1,5 @@
|
||||
import asyncio
|
||||
import trio
|
||||
import json
|
||||
import os
|
||||
import time
|
||||
@@ -17,8 +18,10 @@ from app.core.config import settings
|
||||
from app.core.rag.graphrag.utils import get_llm_cache, set_llm_cache
|
||||
from app.core.rag.llm.chat_model import Base
|
||||
from app.core.rag.llm.cv_model import QWenCV
|
||||
from app.core.rag.llm.embedding_model import OpenAIEmbed
|
||||
from app.core.rag.llm.sequence2txt_model import QWenSeq2txt
|
||||
from app.core.rag.models.chunk import DocumentChunk
|
||||
from app.core.rag.graphrag.general.index import init_graphrag, run_graphrag_for_kb
|
||||
from app.core.rag.prompts.generator import question_proposal
|
||||
from app.core.rag.vdb.elasticsearch.elasticsearch_vector import (
|
||||
ElasticSearchVectorFactory,
|
||||
@@ -52,138 +55,325 @@ def parse_document(file_path: str, document_id: uuid.UUID):
|
||||
"""
|
||||
Document parsing, vectorization, and storage
|
||||
"""
|
||||
with get_db_context() as db:
|
||||
db_document = None
|
||||
db_knowledge = None
|
||||
progress_msg = f"{datetime.now().strftime('%H:%M:%S')} Task has been received.\n"
|
||||
try:
|
||||
db_document = db.query(Document).filter(Document.id == document_id).first()
|
||||
db_knowledge = db.query(Knowledge).filter(Knowledge.id == db_document.kb_id).first()
|
||||
# 1. Document parsing & segmentation
|
||||
progress_msg += f"{datetime.now().strftime('%H:%M:%S')} Start to parse.\n"
|
||||
start_time = time.time()
|
||||
db_document.progress = 0.0
|
||||
db_document.progress_msg = progress_msg
|
||||
db_document.process_begin_at = datetime.now(tz=timezone.utc)
|
||||
db_document.process_duration = 0.0
|
||||
db_document.run = 1
|
||||
db.commit()
|
||||
db.refresh(db_document)
|
||||
db = next(get_db()) # Manually call the generator
|
||||
db_document = None
|
||||
db_knowledge = None
|
||||
progress_msg = f"{datetime.now().strftime('%H:%M:%S')} Task has been received.\n"
|
||||
try:
|
||||
db_document = db.query(Document).filter(Document.id == document_id).first()
|
||||
db_knowledge = db.query(Knowledge).filter(Knowledge.id == db_document.kb_id).first()
|
||||
# 1. Document parsing & segmentation
|
||||
progress_msg += f"{datetime.now().strftime('%H:%M:%S')} Start to parse.\n"
|
||||
start_time = time.time()
|
||||
db_document.progress = 0.0
|
||||
db_document.progress_msg = progress_msg
|
||||
db_document.process_begin_at = datetime.now(tz=timezone.utc)
|
||||
db_document.process_duration = 0.0
|
||||
db_document.run = 1
|
||||
db.commit()
|
||||
db.refresh(db_document)
|
||||
|
||||
def progress_callback(prog=None, msg=None):
|
||||
nonlocal progress_msg # Declare the use of an external progress_msg variable
|
||||
progress_msg += f"{datetime.now().strftime('%H:%M:%S')} parse progress: {prog} msg: {msg}.\n"
|
||||
# Prepare to configure chat_mdl、vision_model information
|
||||
chat_model = Base(
|
||||
key=db_knowledge.llm.api_keys[0].api_key,
|
||||
model_name=db_knowledge.llm.api_keys[0].model_name,
|
||||
base_url=db_knowledge.llm.api_keys[0].api_base
|
||||
)
|
||||
vision_model = QWenCV(
|
||||
key=db_knowledge.image2text.api_keys[0].api_key,
|
||||
model_name=db_knowledge.image2text.api_keys[0].model_name,
|
||||
def progress_callback(prog=None, msg=None):
|
||||
nonlocal progress_msg # Declare the use of an external progress_msg variable
|
||||
progress_msg += f"{datetime.now().strftime('%H:%M:%S')} parse progress: {prog} msg: {msg}.\n"
|
||||
|
||||
# Prepare to configure chat_mdl、embedding_model、vision_model information
|
||||
chat_model = Base(
|
||||
key=db_knowledge.llm.api_keys[0].api_key,
|
||||
model_name=db_knowledge.llm.api_keys[0].model_name,
|
||||
base_url=db_knowledge.llm.api_keys[0].api_base
|
||||
)
|
||||
embedding_model = OpenAIEmbed(
|
||||
key=db_knowledge.embedding.api_keys[0].api_key,
|
||||
model_name=db_knowledge.embedding.api_keys[0].model_name,
|
||||
base_url=db_knowledge.embedding.api_keys[0].api_base
|
||||
)
|
||||
vision_model = QWenCV(
|
||||
key=db_knowledge.image2text.api_keys[0].api_key,
|
||||
model_name=db_knowledge.image2text.api_keys[0].model_name,
|
||||
lang="Chinese",
|
||||
base_url=db_knowledge.image2text.api_keys[0].api_base
|
||||
)
|
||||
if re.search(r"\.(da|wave|wav|mp3|aac|flac|ogg|aiff|au|midi|wma|realaudio|vqf|oggvorbis|ape?)$", file_path,
|
||||
re.IGNORECASE):
|
||||
vision_model = QWenSeq2txt(
|
||||
key=os.getenv("QWEN3_OMNI_API_KEY", ""),
|
||||
model_name=os.getenv("QWEN3_OMNI_MODEL_NAME", "qwen3-omni-flash"),
|
||||
lang="Chinese",
|
||||
base_url=db_knowledge.image2text.api_keys[0].api_base
|
||||
base_url=os.getenv("QWEN3_OMNI_BASE_URL", "https://dashscope.aliyuncs.com/compatible-mode/v1"),
|
||||
)
|
||||
if re.search(r"\.(da|wave|wav|mp3|aac|flac|ogg|aiff|au|midi|wma|realaudio|vqf|oggvorbis|ape?)$", file_path, re.IGNORECASE):
|
||||
vision_model = QWenSeq2txt(
|
||||
key=os.getenv("QWEN3_OMNI_API_KEY", ""),
|
||||
model_name=os.getenv("QWEN3_OMNI_MODEL_NAME", "qwen3-omni-flash"),
|
||||
lang="Chinese",
|
||||
base_url=os.getenv("QWEN3_OMNI_BASE_URL", "https://dashscope.aliyuncs.com/compatible-mode/v1"),
|
||||
)
|
||||
elif re.search(r"\.(png|jpeg|jpg|gif|bmp|svg|mp4|mov|avi|flv|mpeg|mpg|webm|wmv|3gp|3gpp|mkv?)$", file_path, re.IGNORECASE):
|
||||
vision_model = QWenCV(
|
||||
key=os.getenv("QWEN3_OMNI_API_KEY", ""),
|
||||
model_name=os.getenv("QWEN3_OMNI_MODEL_NAME", "qwen3-omni-flash"),
|
||||
lang="Chinese",
|
||||
base_url=os.getenv("QWEN3_OMNI_BASE_URL", "https://dashscope.aliyuncs.com/compatible-mode/v1"),
|
||||
)
|
||||
else:
|
||||
print(file_path)
|
||||
from app.core.rag.app.naive import chunk
|
||||
res = chunk(filename=file_path,
|
||||
from_page=0,
|
||||
to_page=100000,
|
||||
callback=progress_callback,
|
||||
vision_model=vision_model,
|
||||
parser_config=db_document.parser_config,
|
||||
is_root=False)
|
||||
elif re.search(r"\.(png|jpeg|jpg|gif|bmp|svg|mp4|mov|avi|flv|mpeg|mpg|webm|wmv|3gp|3gpp|mkv?)$", file_path,
|
||||
re.IGNORECASE):
|
||||
vision_model = QWenCV(
|
||||
key=os.getenv("QWEN3_OMNI_API_KEY", ""),
|
||||
model_name=os.getenv("QWEN3_OMNI_MODEL_NAME", "qwen3-omni-flash"),
|
||||
lang="Chinese",
|
||||
base_url=os.getenv("QWEN3_OMNI_BASE_URL", "https://dashscope.aliyuncs.com/compatible-mode/v1"),
|
||||
)
|
||||
else:
|
||||
print(file_path)
|
||||
|
||||
progress_msg += f"{datetime.now().strftime('%H:%M:%S')} Finish parsing.\n"
|
||||
db_document.progress = 0.8
|
||||
from app.core.rag.app.naive import chunk
|
||||
res = chunk(filename=file_path,
|
||||
from_page=0,
|
||||
to_page=100000,
|
||||
callback=progress_callback,
|
||||
vision_model=vision_model,
|
||||
parser_config=db_document.parser_config,
|
||||
is_root=False)
|
||||
|
||||
progress_msg += f"{datetime.now().strftime('%H:%M:%S')} Finish parsing.\n"
|
||||
db_document.progress = 0.8
|
||||
db_document.progress_msg = progress_msg
|
||||
db.commit()
|
||||
db.refresh(db_document)
|
||||
|
||||
# 2. Document vectorization and storage
|
||||
total_chunks = len(res)
|
||||
progress_msg += f"{datetime.now().strftime('%H:%M:%S')} Generate {total_chunks} chunks.\n"
|
||||
batch_size = 100
|
||||
total_batches = ceil(total_chunks / batch_size)
|
||||
progress_per_batch = 0.2 / total_batches # Progress of each batch
|
||||
vector_service = ElasticSearchVectorFactory().init_vector(knowledge=db_knowledge)
|
||||
# 2.1 Delete document vector index
|
||||
vector_service.delete_by_metadata_field(key="document_id", value=str(document_id))
|
||||
# 2.2 Vectorize and import batch documents
|
||||
for batch_start in range(0, total_chunks, batch_size):
|
||||
batch_end = min(batch_start + batch_size, total_chunks) # prevent out-of-bounds
|
||||
batch = res[batch_start: batch_end] # Retrieve the current batch
|
||||
chunks = []
|
||||
|
||||
# Process the current batch
|
||||
for idx_in_batch, item in enumerate(batch):
|
||||
global_idx = batch_start + idx_in_batch # Calculate global index
|
||||
metadata = {
|
||||
"doc_id": uuid.uuid4().hex,
|
||||
"file_id": str(db_document.file_id),
|
||||
"file_name": db_document.file_name,
|
||||
"file_created_at": int(db_document.created_at.timestamp() * 1000),
|
||||
"document_id": str(db_document.id),
|
||||
"knowledge_id": str(db_document.kb_id),
|
||||
"sort_id": global_idx,
|
||||
"status": 1,
|
||||
}
|
||||
if db_document.parser_config.get("auto_questions", 0):
|
||||
topn = db_document.parser_config["auto_questions"]
|
||||
cached = get_llm_cache(chat_model.model_name, item["content_with_weight"], "question",
|
||||
{"topn": topn})
|
||||
if not cached:
|
||||
cached = question_proposal(chat_model, item["content_with_weight"], topn)
|
||||
set_llm_cache(chat_model.model_name, item["content_with_weight"], cached, "question",
|
||||
{"topn": topn})
|
||||
chunks.append(
|
||||
DocumentChunk(page_content=f"question: {cached} answer: {item['content_with_weight']}",
|
||||
metadata=metadata))
|
||||
else:
|
||||
chunks.append(DocumentChunk(page_content=item["content_with_weight"], metadata=metadata))
|
||||
|
||||
# Bulk segmented vector import
|
||||
vector_service.add_chunks(chunks)
|
||||
|
||||
# Update progress
|
||||
db_document.progress += progress_per_batch
|
||||
progress_msg += f"{datetime.now().strftime('%H:%M:%S')} Embedding progress ({db_document.progress}).\n"
|
||||
db_document.progress_msg = progress_msg
|
||||
db.commit()
|
||||
db.refresh(db_document)
|
||||
|
||||
# 2. Document vectorization and storage
|
||||
total_chunks = len(res)
|
||||
progress_msg += f"{datetime.now().strftime('%H:%M:%S')} Generate {total_chunks} chunks.\n"
|
||||
batch_size = 100
|
||||
total_batches = ceil(total_chunks / batch_size)
|
||||
progress_per_batch = 0.2 / total_batches # Progress of each batch
|
||||
vector_service = ElasticSearchVectorFactory().init_vector(knowledge=db_knowledge)
|
||||
# 2.1 Delete document vector index
|
||||
vector_service.delete_by_metadata_field(key="document_id", value=str(document_id))
|
||||
# 2.2 Vectorize and import batch documents
|
||||
for batch_start in range(0, total_chunks, batch_size):
|
||||
batch_end = min(batch_start + batch_size, total_chunks) # prevent out-of-bounds
|
||||
batch = res[batch_start: batch_end] # Retrieve the current batch
|
||||
chunks = []
|
||||
|
||||
# Process the current batch
|
||||
for idx_in_batch, item in enumerate(batch):
|
||||
global_idx = batch_start + idx_in_batch # Calculate global index
|
||||
metadata = {
|
||||
"doc_id": uuid.uuid4().hex,
|
||||
"file_id": str(db_document.file_id),
|
||||
"file_name": db_document.file_name,
|
||||
"file_created_at": int(db_document.created_at.timestamp() * 1000),
|
||||
"document_id": str(db_document.id),
|
||||
"knowledge_id": str(db_document.kb_id),
|
||||
"sort_id": global_idx,
|
||||
"status": 1,
|
||||
}
|
||||
if db_document.parser_config.get("auto_questions", 0):
|
||||
topn = db_document.parser_config["auto_questions"]
|
||||
cached = get_llm_cache(chat_model.model_name, item["content_with_weight"], "question", {"topn": topn})
|
||||
if not cached:
|
||||
cached = question_proposal(chat_model, item["content_with_weight"], topn)
|
||||
set_llm_cache(chat_model.model_name, item["content_with_weight"], cached, "question", {"topn": topn})
|
||||
chunks.append(DocumentChunk(page_content=f"question: {cached} answer: {item['content_with_weight']}", metadata=metadata))
|
||||
else:
|
||||
chunks.append(DocumentChunk(page_content=item["content_with_weight"], metadata=metadata))
|
||||
|
||||
# Bulk segmented vector import
|
||||
vector_service.add_chunks(chunks)
|
||||
|
||||
# Update progress
|
||||
db_document.progress += progress_per_batch
|
||||
progress_msg += f"{datetime.now().strftime('%H:%M:%S')} Embedding progress ({db_document.progress}).\n"
|
||||
db_document.progress_msg = progress_msg
|
||||
db_document.process_duration = time.time() - start_time
|
||||
db_document.run = 0
|
||||
db.commit()
|
||||
db.refresh(db_document)
|
||||
|
||||
# Vectorization and data entry completed
|
||||
progress_msg += f"{datetime.now().strftime('%H:%M:%S')} Indexing done.\n"
|
||||
db_document.chunk_num = total_chunks
|
||||
db_document.progress = 1.0
|
||||
db_document.process_duration = time.time() - start_time
|
||||
progress_msg += f"{datetime.now().strftime('%H:%M:%S')} Task done ({db_document.process_duration}s).\n"
|
||||
db_document.progress_msg = progress_msg
|
||||
db_document.run = 0
|
||||
db.commit()
|
||||
result = f"parse document '{db_document.file_name}' processed successfully."
|
||||
return result
|
||||
except Exception as e:
|
||||
if 'db_document' in locals():
|
||||
db_document.progress_msg += f"Failed to vectorize and import the parsed document:{str(e)}\n"
|
||||
db_document.run = 0
|
||||
db.commit()
|
||||
result = f"parse document '{db_document.file_name}' failed."
|
||||
return result
|
||||
db.refresh(db_document)
|
||||
|
||||
# Vectorization and data entry completed
|
||||
progress_msg += f"{datetime.now().strftime('%H:%M:%S')} Indexing done.\n"
|
||||
db_document.chunk_num = total_chunks
|
||||
db_document.progress = 1.0
|
||||
db_document.process_duration = time.time() - start_time
|
||||
progress_msg += f"{datetime.now().strftime('%H:%M:%S')} Task done ({db_document.process_duration}s).\n"
|
||||
db_document.progress_msg = progress_msg
|
||||
db_document.run = 0
|
||||
db.commit()
|
||||
|
||||
# using graphrag
|
||||
if db_knowledge.parser_config.get("graphrag", {}).get("use_graphrag", False):
|
||||
graphrag_conf = db_knowledge.parser_config.get("graphrag", {})
|
||||
with_resolution = graphrag_conf.get("resolution", False)
|
||||
with_community = graphrag_conf.get("community", False)
|
||||
|
||||
def callback(msg=None):
|
||||
nonlocal progress_msg
|
||||
progress_msg += f"{datetime.now().strftime('%H:%M:%S')} run graphrag msg: {msg}.\n"
|
||||
|
||||
progress_msg += f"{datetime.now().strftime('%H:%M:%S')} Start to run graphrag.\n"
|
||||
start_time = time.time()
|
||||
db_document.progress_msg = progress_msg
|
||||
db.commit()
|
||||
db.refresh(db_document)
|
||||
|
||||
task = {
|
||||
"id": str(db_document.id),
|
||||
"workspace_id": str(db_knowledge.workspace_id),
|
||||
"kb_id": str(db_knowledge.id),
|
||||
"parser_config": db_knowledge.parser_config,
|
||||
}
|
||||
|
||||
# init_graphrag
|
||||
vts, _ = embedding_model.encode(["ok"])
|
||||
vector_size = len(vts[0])
|
||||
init_graphrag(task, vector_size)
|
||||
|
||||
async def _run(row: dict, document_ids: list[str], language: str, parser_config: dict, vector_service,
|
||||
chat_model, embedding_model, callback, with_resolution: bool = True,
|
||||
with_community: bool = True, ) -> dict:
|
||||
nonlocal progress_msg # Declare the use of an external progress_msg variable
|
||||
result = await run_graphrag_for_kb(
|
||||
row=row,
|
||||
document_ids=document_ids,
|
||||
language=language,
|
||||
parser_config=parser_config,
|
||||
vector_service=vector_service,
|
||||
chat_model=chat_model,
|
||||
embedding_model=embedding_model,
|
||||
callback=callback,
|
||||
with_resolution=with_resolution,
|
||||
with_community=with_community,
|
||||
)
|
||||
progress_msg += f"{datetime.now().strftime('%H:%M:%S')} GraphRAG task result for task {task}:\n{result}\n"
|
||||
return result
|
||||
|
||||
try:
|
||||
trio.run(
|
||||
lambda: _run(
|
||||
row=task,
|
||||
document_ids=[str(db_document.id)],
|
||||
language="Chinese",
|
||||
parser_config=db_knowledge.parser_config,
|
||||
vector_service=vector_service,
|
||||
chat_model=chat_model,
|
||||
embedding_model=embedding_model,
|
||||
callback=callback,
|
||||
with_resolution=with_resolution,
|
||||
with_community=with_community,
|
||||
)
|
||||
)
|
||||
except Exception as e:
|
||||
progress_msg += f"{datetime.now().strftime('%H:%M:%S')} GraphRAG task failed for task {task}:\n{str(e)}\n"
|
||||
progress_msg += f"{datetime.now().strftime('%H:%M:%S')} Knowledge Graph done ({time.time() - start_time}s)"
|
||||
db_document.progress_msg = progress_msg
|
||||
db.commit()
|
||||
db.refresh(db_document)
|
||||
|
||||
result = f"parse document '{db_document.file_name}' processed successfully."
|
||||
return result
|
||||
except Exception as e:
|
||||
if 'db_document' in locals():
|
||||
db_document.progress_msg += f"Failed to vectorize and import the parsed document:{str(e)}\n"
|
||||
db_document.run = 0
|
||||
db.commit()
|
||||
result = f"parse document '{db_document.file_name}' failed."
|
||||
return result
|
||||
finally:
|
||||
db.close()
|
||||
|
||||
|
||||
@celery_app.task(name="app.core.rag.tasks.build_graphrag_for_kb")
|
||||
def build_graphrag_for_kb(kb_id: uuid.UUID):
|
||||
"""
|
||||
build knowledge graph
|
||||
"""
|
||||
db = next(get_db()) # Manually call the generator
|
||||
db_knowledge = None
|
||||
try:
|
||||
db_knowledge = db.query(Knowledge).filter(Knowledge.id == kb_id).first()
|
||||
# 1. Prepare to configure chat_mdl、embedding_model、vision_model information
|
||||
chat_model = Base(
|
||||
key=db_knowledge.llm.api_keys[0].api_key,
|
||||
model_name=db_knowledge.llm.api_keys[0].model_name,
|
||||
base_url=db_knowledge.llm.api_keys[0].api_base
|
||||
)
|
||||
embedding_model = OpenAIEmbed(
|
||||
key=db_knowledge.embedding.api_keys[0].api_key,
|
||||
model_name=db_knowledge.embedding.api_keys[0].model_name,
|
||||
base_url=db_knowledge.embedding.api_keys[0].api_base
|
||||
)
|
||||
vision_model = QWenCV(
|
||||
key=db_knowledge.image2text.api_keys[0].api_key,
|
||||
model_name=db_knowledge.image2text.api_keys[0].model_name,
|
||||
lang="Chinese",
|
||||
base_url=db_knowledge.image2text.api_keys[0].api_base
|
||||
)
|
||||
|
||||
# 2. get all document_ids from knowledge base
|
||||
vector_service = ElasticSearchVectorFactory().init_vector(knowledge=db_knowledge)
|
||||
total, items = vector_service.search_by_segment(document_id=None, query=None, pagesize=9999, page=1, asc=True)
|
||||
document_ids = [item.metadata["document_id"] for item in items]
|
||||
|
||||
# 2. using graphrag
|
||||
if db_knowledge.parser_config.get("graphrag", {}).get("use_graphrag", False):
|
||||
graphrag_conf = db_knowledge.parser_config.get("graphrag", {})
|
||||
with_resolution = graphrag_conf.get("resolution", False)
|
||||
with_community = graphrag_conf.get("community", False)
|
||||
|
||||
def callback(msg=None):
|
||||
print(f"{datetime.now().strftime('%H:%M:%S')} run graphrag msg: {msg}.\n")
|
||||
|
||||
start_time = time.time()
|
||||
task = {
|
||||
"id": str(db_knowledge.id),
|
||||
"workspace_id": str(db_knowledge.workspace_id),
|
||||
"kb_id": str(db_knowledge.id),
|
||||
"parser_config": db_knowledge.parser_config,
|
||||
}
|
||||
|
||||
# init_graphrag
|
||||
vts, _ = embedding_model.encode(["ok"])
|
||||
vector_size = len(vts[0])
|
||||
init_graphrag(task, vector_size)
|
||||
|
||||
async def _run(row: dict, document_ids: list[str], language: str, parser_config: dict, vector_service,
|
||||
chat_model, embedding_model, callback, with_resolution: bool = True,
|
||||
with_community: bool = True, ) -> dict:
|
||||
result = await run_graphrag_for_kb(
|
||||
row=row,
|
||||
document_ids=document_ids,
|
||||
language=language,
|
||||
parser_config=parser_config,
|
||||
vector_service=vector_service,
|
||||
chat_model=chat_model,
|
||||
embedding_model=embedding_model,
|
||||
callback=callback,
|
||||
with_resolution=with_resolution,
|
||||
with_community=with_community,
|
||||
)
|
||||
print(f"{datetime.now().strftime('%H:%M:%S')} GraphRAG task result for task {task}:\n{result}\n")
|
||||
return result
|
||||
|
||||
try:
|
||||
trio.run(
|
||||
lambda: _run(
|
||||
row=task,
|
||||
document_ids=document_ids,
|
||||
language="Chinese",
|
||||
parser_config=db_knowledge.parser_config,
|
||||
vector_service=vector_service,
|
||||
chat_model=chat_model,
|
||||
embedding_model=embedding_model,
|
||||
callback=callback,
|
||||
with_resolution=with_resolution,
|
||||
with_community=with_community,
|
||||
)
|
||||
)
|
||||
except Exception as e:
|
||||
print(f"{datetime.now().strftime('%H:%M:%S')} GraphRAG task failed for task {task}:\n{str(e)}\n")
|
||||
print(f"{datetime.now().strftime('%H:%M:%S')} Knowledge Graph done ({time.time() - start_time}s)")
|
||||
|
||||
result = f"build knowledge graph '{db_knowledge.name}' processed successfully."
|
||||
return result
|
||||
except Exception as e:
|
||||
if 'db_knowledge' in locals():
|
||||
print(f"Failed to build knowledge grap:{str(e)}\n")
|
||||
result = f"build knowledge grap '{db_knowledge.name}' failed."
|
||||
return result
|
||||
finally:
|
||||
db.close()
|
||||
|
||||
|
||||
@celery_app.task(name="app.core.memory.agent.read_message", bind=True)
|
||||
|
||||
Reference in New Issue
Block a user