mirror of
https://github.com/infiniflow/ragflow.git
synced 2025-11-04 03:39:41 +00:00
85083ad400
### What problem does this PR solve? Validate returned chunk at list_chunks and add_chunk ### Type of change - [x] Refactoring
1395 lines
46 KiB
Python
1395 lines
46 KiB
Python
#
|
|
# Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
|
|
#
|
|
# 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.
|
|
#
|
|
import pathlib
|
|
import datetime
|
|
|
|
from api.db.services.dialog_service import keyword_extraction
|
|
from rag.app.qa import rmPrefix, beAdoc
|
|
from rag.nlp import rag_tokenizer
|
|
from api.db import LLMType, ParserType
|
|
from api.db.services.llm_service import TenantLLMService
|
|
from api import settings
|
|
import xxhash
|
|
import re
|
|
from api.utils.api_utils import token_required
|
|
from api.db.db_models import Task
|
|
from api.db.services.task_service import TaskService, queue_tasks
|
|
from api.utils.api_utils import server_error_response
|
|
from api.utils.api_utils import get_result, get_error_data_result
|
|
from io import BytesIO
|
|
from flask import request, send_file
|
|
from api.db import FileSource, TaskStatus, FileType
|
|
from api.db.db_models import File
|
|
from api.db.services.document_service import DocumentService
|
|
from api.db.services.file2document_service import File2DocumentService
|
|
from api.db.services.file_service import FileService
|
|
from api.db.services.knowledgebase_service import KnowledgebaseService
|
|
from api.utils.api_utils import construct_json_result, get_parser_config
|
|
from rag.nlp import search
|
|
from rag.utils import rmSpace
|
|
from rag.utils.storage_factory import STORAGE_IMPL
|
|
|
|
from pydantic import BaseModel, Field, validator
|
|
|
|
MAXIMUM_OF_UPLOADING_FILES = 256
|
|
|
|
|
|
class Chunk(BaseModel):
|
|
id: str = ""
|
|
content: str = ""
|
|
document_id: str = ""
|
|
docnm_kwd: str = ""
|
|
important_keywords: list = Field(default_factory=list)
|
|
questions: list = Field(default_factory=list)
|
|
question_tks: str = ""
|
|
image_id: str = ""
|
|
available: bool = True
|
|
positions: list[list[int]] = Field(default_factory=list)
|
|
|
|
@validator('positions')
|
|
def validate_positions(cls, value):
|
|
for sublist in value:
|
|
if len(sublist) != 5:
|
|
raise ValueError("Each sublist in positions must have a length of 5")
|
|
return value
|
|
|
|
@manager.route("/datasets/<dataset_id>/documents", methods=["POST"]) # noqa: F821
|
|
@token_required
|
|
def upload(dataset_id, tenant_id):
|
|
"""
|
|
Upload documents to a dataset.
|
|
---
|
|
tags:
|
|
- Documents
|
|
security:
|
|
- ApiKeyAuth: []
|
|
parameters:
|
|
- in: path
|
|
name: dataset_id
|
|
type: string
|
|
required: true
|
|
description: ID of the dataset.
|
|
- in: header
|
|
name: Authorization
|
|
type: string
|
|
required: true
|
|
description: Bearer token for authentication.
|
|
- in: formData
|
|
name: file
|
|
type: file
|
|
required: true
|
|
description: Document files to upload.
|
|
responses:
|
|
200:
|
|
description: Successfully uploaded documents.
|
|
schema:
|
|
type: object
|
|
properties:
|
|
data:
|
|
type: array
|
|
items:
|
|
type: object
|
|
properties:
|
|
id:
|
|
type: string
|
|
description: Document ID.
|
|
name:
|
|
type: string
|
|
description: Document name.
|
|
chunk_count:
|
|
type: integer
|
|
description: Number of chunks.
|
|
token_count:
|
|
type: integer
|
|
description: Number of tokens.
|
|
dataset_id:
|
|
type: string
|
|
description: ID of the dataset.
|
|
chunk_method:
|
|
type: string
|
|
description: Chunking method used.
|
|
run:
|
|
type: string
|
|
description: Processing status.
|
|
"""
|
|
if "file" not in request.files:
|
|
return get_error_data_result(
|
|
message="No file part!", code=settings.RetCode.ARGUMENT_ERROR
|
|
)
|
|
file_objs = request.files.getlist("file")
|
|
for file_obj in file_objs:
|
|
if file_obj.filename == "":
|
|
return get_result(
|
|
message="No file selected!", code=settings.RetCode.ARGUMENT_ERROR
|
|
)
|
|
'''
|
|
# total size
|
|
total_size = 0
|
|
for file_obj in file_objs:
|
|
file_obj.seek(0, os.SEEK_END)
|
|
total_size += file_obj.tell()
|
|
file_obj.seek(0)
|
|
MAX_TOTAL_FILE_SIZE = 10 * 1024 * 1024
|
|
if total_size > MAX_TOTAL_FILE_SIZE:
|
|
return get_result(
|
|
message=f"Total file size exceeds 10MB limit! ({total_size / (1024 * 1024):.2f} MB)",
|
|
code=settings.RetCode.ARGUMENT_ERROR,
|
|
)
|
|
'''
|
|
e, kb = KnowledgebaseService.get_by_id(dataset_id)
|
|
if not e:
|
|
raise LookupError(f"Can't find the dataset with ID {dataset_id}!")
|
|
err, files = FileService.upload_document(kb, file_objs, tenant_id)
|
|
if err:
|
|
return get_result(message="\n".join(err), code=settings.RetCode.SERVER_ERROR)
|
|
# rename key's name
|
|
renamed_doc_list = []
|
|
for file in files:
|
|
doc = file[0]
|
|
key_mapping = {
|
|
"chunk_num": "chunk_count",
|
|
"kb_id": "dataset_id",
|
|
"token_num": "token_count",
|
|
"parser_id": "chunk_method",
|
|
}
|
|
renamed_doc = {}
|
|
for key, value in doc.items():
|
|
new_key = key_mapping.get(key, key)
|
|
renamed_doc[new_key] = value
|
|
renamed_doc["run"] = "UNSTART"
|
|
renamed_doc_list.append(renamed_doc)
|
|
return get_result(data=renamed_doc_list)
|
|
|
|
|
|
@manager.route("/datasets/<dataset_id>/documents/<document_id>", methods=["PUT"]) # noqa: F821
|
|
@token_required
|
|
def update_doc(tenant_id, dataset_id, document_id):
|
|
"""
|
|
Update a document within a dataset.
|
|
---
|
|
tags:
|
|
- Documents
|
|
security:
|
|
- ApiKeyAuth: []
|
|
parameters:
|
|
- in: path
|
|
name: dataset_id
|
|
type: string
|
|
required: true
|
|
description: ID of the dataset.
|
|
- in: path
|
|
name: document_id
|
|
type: string
|
|
required: true
|
|
description: ID of the document to update.
|
|
- in: header
|
|
name: Authorization
|
|
type: string
|
|
required: true
|
|
description: Bearer token for authentication.
|
|
- in: body
|
|
name: body
|
|
description: Document update parameters.
|
|
required: true
|
|
schema:
|
|
type: object
|
|
properties:
|
|
name:
|
|
type: string
|
|
description: New name of the document.
|
|
parser_config:
|
|
type: object
|
|
description: Parser configuration.
|
|
chunk_method:
|
|
type: string
|
|
description: Chunking method.
|
|
responses:
|
|
200:
|
|
description: Document updated successfully.
|
|
schema:
|
|
type: object
|
|
"""
|
|
req = request.json
|
|
if not KnowledgebaseService.query(id=dataset_id, tenant_id=tenant_id):
|
|
return get_error_data_result(message="You don't own the dataset.")
|
|
doc = DocumentService.query(kb_id=dataset_id, id=document_id)
|
|
if not doc:
|
|
return get_error_data_result(message="The dataset doesn't own the document.")
|
|
doc = doc[0]
|
|
if "chunk_count" in req:
|
|
if req["chunk_count"] != doc.chunk_num:
|
|
return get_error_data_result(message="Can't change `chunk_count`.")
|
|
if "token_count" in req:
|
|
if req["token_count"] != doc.token_num:
|
|
return get_error_data_result(message="Can't change `token_count`.")
|
|
if "progress" in req:
|
|
if req["progress"] != doc.progress:
|
|
return get_error_data_result(message="Can't change `progress`.")
|
|
|
|
if "name" in req and req["name"] != doc.name:
|
|
if (
|
|
pathlib.Path(req["name"].lower()).suffix
|
|
!= pathlib.Path(doc.name.lower()).suffix
|
|
):
|
|
return get_result(
|
|
message="The extension of file can't be changed",
|
|
code=settings.RetCode.ARGUMENT_ERROR,
|
|
)
|
|
for d in DocumentService.query(name=req["name"], kb_id=doc.kb_id):
|
|
if d.name == req["name"]:
|
|
return get_error_data_result(
|
|
message="Duplicated document name in the same dataset."
|
|
)
|
|
if not DocumentService.update_by_id(document_id, {"name": req["name"]}):
|
|
return get_error_data_result(message="Database error (Document rename)!")
|
|
|
|
informs = File2DocumentService.get_by_document_id(document_id)
|
|
if informs:
|
|
e, file = FileService.get_by_id(informs[0].file_id)
|
|
FileService.update_by_id(file.id, {"name": req["name"]})
|
|
if "parser_config" in req:
|
|
DocumentService.update_parser_config(doc.id, req["parser_config"])
|
|
if "chunk_method" in req:
|
|
valid_chunk_method = {
|
|
"naive",
|
|
"manual",
|
|
"qa",
|
|
"table",
|
|
"paper",
|
|
"book",
|
|
"laws",
|
|
"presentation",
|
|
"picture",
|
|
"one",
|
|
"knowledge_graph",
|
|
"email",
|
|
}
|
|
if req.get("chunk_method") not in valid_chunk_method:
|
|
return get_error_data_result(
|
|
f"`chunk_method` {req['chunk_method']} doesn't exist"
|
|
)
|
|
if doc.parser_id.lower() == req["chunk_method"].lower():
|
|
return get_result()
|
|
|
|
if doc.type == FileType.VISUAL or re.search(r"\.(ppt|pptx|pages)$", doc.name):
|
|
return get_error_data_result(message="Not supported yet!")
|
|
|
|
e = DocumentService.update_by_id(
|
|
doc.id,
|
|
{
|
|
"parser_id": req["chunk_method"],
|
|
"progress": 0,
|
|
"progress_msg": "",
|
|
"run": TaskStatus.UNSTART.value,
|
|
},
|
|
)
|
|
if not e:
|
|
return get_error_data_result(message="Document not found!")
|
|
req["parser_config"] = get_parser_config(
|
|
req["chunk_method"], req.get("parser_config")
|
|
)
|
|
DocumentService.update_parser_config(doc.id, req["parser_config"])
|
|
if doc.token_num > 0:
|
|
e = DocumentService.increment_chunk_num(
|
|
doc.id,
|
|
doc.kb_id,
|
|
doc.token_num * -1,
|
|
doc.chunk_num * -1,
|
|
doc.process_duation * -1,
|
|
)
|
|
if not e:
|
|
return get_error_data_result(message="Document not found!")
|
|
settings.docStoreConn.delete({"doc_id": doc.id}, search.index_name(tenant_id), dataset_id)
|
|
|
|
return get_result()
|
|
|
|
|
|
@manager.route("/datasets/<dataset_id>/documents/<document_id>", methods=["GET"]) # noqa: F821
|
|
@token_required
|
|
def download(tenant_id, dataset_id, document_id):
|
|
"""
|
|
Download a document from a dataset.
|
|
---
|
|
tags:
|
|
- Documents
|
|
security:
|
|
- ApiKeyAuth: []
|
|
produces:
|
|
- application/octet-stream
|
|
parameters:
|
|
- in: path
|
|
name: dataset_id
|
|
type: string
|
|
required: true
|
|
description: ID of the dataset.
|
|
- in: path
|
|
name: document_id
|
|
type: string
|
|
required: true
|
|
description: ID of the document to download.
|
|
- in: header
|
|
name: Authorization
|
|
type: string
|
|
required: true
|
|
description: Bearer token for authentication.
|
|
responses:
|
|
200:
|
|
description: Document file stream.
|
|
schema:
|
|
type: file
|
|
400:
|
|
description: Error message.
|
|
schema:
|
|
type: object
|
|
"""
|
|
if not KnowledgebaseService.query(id=dataset_id, tenant_id=tenant_id):
|
|
return get_error_data_result(message=f"You do not own the dataset {dataset_id}.")
|
|
doc = DocumentService.query(kb_id=dataset_id, id=document_id)
|
|
if not doc:
|
|
return get_error_data_result(
|
|
message=f"The dataset not own the document {document_id}."
|
|
)
|
|
# The process of downloading
|
|
doc_id, doc_location = File2DocumentService.get_storage_address(
|
|
doc_id=document_id
|
|
) # minio address
|
|
file_stream = STORAGE_IMPL.get(doc_id, doc_location)
|
|
if not file_stream:
|
|
return construct_json_result(
|
|
message="This file is empty.", code=settings.RetCode.DATA_ERROR
|
|
)
|
|
file = BytesIO(file_stream)
|
|
# Use send_file with a proper filename and MIME type
|
|
return send_file(
|
|
file,
|
|
as_attachment=True,
|
|
download_name=doc[0].name,
|
|
mimetype="application/octet-stream", # Set a default MIME type
|
|
)
|
|
|
|
|
|
@manager.route("/datasets/<dataset_id>/documents", methods=["GET"]) # noqa: F821
|
|
@token_required
|
|
def list_docs(dataset_id, tenant_id):
|
|
"""
|
|
List documents in a dataset.
|
|
---
|
|
tags:
|
|
- Documents
|
|
security:
|
|
- ApiKeyAuth: []
|
|
parameters:
|
|
- in: path
|
|
name: dataset_id
|
|
type: string
|
|
required: true
|
|
description: ID of the dataset.
|
|
- in: query
|
|
name: id
|
|
type: string
|
|
required: false
|
|
description: Filter by document ID.
|
|
- in: query
|
|
name: page
|
|
type: integer
|
|
required: false
|
|
default: 1
|
|
description: Page number.
|
|
- in: query
|
|
name: page_size
|
|
type: integer
|
|
required: false
|
|
default: 30
|
|
description: Number of items per page.
|
|
- in: query
|
|
name: orderby
|
|
type: string
|
|
required: false
|
|
default: "create_time"
|
|
description: Field to order by.
|
|
- in: query
|
|
name: desc
|
|
type: boolean
|
|
required: false
|
|
default: true
|
|
description: Order in descending.
|
|
- in: header
|
|
name: Authorization
|
|
type: string
|
|
required: true
|
|
description: Bearer token for authentication.
|
|
responses:
|
|
200:
|
|
description: List of documents.
|
|
schema:
|
|
type: object
|
|
properties:
|
|
total:
|
|
type: integer
|
|
description: Total number of documents.
|
|
docs:
|
|
type: array
|
|
items:
|
|
type: object
|
|
properties:
|
|
id:
|
|
type: string
|
|
description: Document ID.
|
|
name:
|
|
type: string
|
|
description: Document name.
|
|
chunk_count:
|
|
type: integer
|
|
description: Number of chunks.
|
|
token_count:
|
|
type: integer
|
|
description: Number of tokens.
|
|
dataset_id:
|
|
type: string
|
|
description: ID of the dataset.
|
|
chunk_method:
|
|
type: string
|
|
description: Chunking method used.
|
|
run:
|
|
type: string
|
|
description: Processing status.
|
|
"""
|
|
if not KnowledgebaseService.accessible(kb_id=dataset_id, user_id=tenant_id):
|
|
return get_error_data_result(message=f"You don't own the dataset {dataset_id}. ")
|
|
id = request.args.get("id")
|
|
name = request.args.get("name")
|
|
if not DocumentService.query(id=id, kb_id=dataset_id):
|
|
return get_error_data_result(message=f"You don't own the document {id}.")
|
|
if not DocumentService.query(name=name, kb_id=dataset_id):
|
|
return get_error_data_result(message=f"You don't own the document {name}.")
|
|
page = int(request.args.get("page", 1))
|
|
keywords = request.args.get("keywords", "")
|
|
page_size = int(request.args.get("page_size", 30))
|
|
orderby = request.args.get("orderby", "create_time")
|
|
if request.args.get("desc") == "False":
|
|
desc = False
|
|
else:
|
|
desc = True
|
|
docs, tol = DocumentService.get_list(
|
|
dataset_id, page, page_size, orderby, desc, keywords, id, name
|
|
)
|
|
|
|
# rename key's name
|
|
renamed_doc_list = []
|
|
for doc in docs:
|
|
key_mapping = {
|
|
"chunk_num": "chunk_count",
|
|
"kb_id": "dataset_id",
|
|
"token_num": "token_count",
|
|
"parser_id": "chunk_method",
|
|
}
|
|
run_mapping = {
|
|
"0": "UNSTART",
|
|
"1": "RUNNING",
|
|
"2": "CANCEL",
|
|
"3": "DONE",
|
|
"4": "FAIL",
|
|
}
|
|
renamed_doc = {}
|
|
for key, value in doc.items():
|
|
if key == "run":
|
|
renamed_doc["run"] = run_mapping.get(str(value))
|
|
new_key = key_mapping.get(key, key)
|
|
renamed_doc[new_key] = value
|
|
if key == "run":
|
|
renamed_doc["run"] = run_mapping.get(value)
|
|
renamed_doc_list.append(renamed_doc)
|
|
return get_result(data={"total": tol, "docs": renamed_doc_list})
|
|
|
|
|
|
@manager.route("/datasets/<dataset_id>/documents", methods=["DELETE"]) # noqa: F821
|
|
@token_required
|
|
def delete(tenant_id, dataset_id):
|
|
"""
|
|
Delete documents from a dataset.
|
|
---
|
|
tags:
|
|
- Documents
|
|
security:
|
|
- ApiKeyAuth: []
|
|
parameters:
|
|
- in: path
|
|
name: dataset_id
|
|
type: string
|
|
required: true
|
|
description: ID of the dataset.
|
|
- in: body
|
|
name: body
|
|
description: Document deletion parameters.
|
|
required: true
|
|
schema:
|
|
type: object
|
|
properties:
|
|
ids:
|
|
type: array
|
|
items:
|
|
type: string
|
|
description: List of document IDs to delete.
|
|
- in: header
|
|
name: Authorization
|
|
type: string
|
|
required: true
|
|
description: Bearer token for authentication.
|
|
responses:
|
|
200:
|
|
description: Documents deleted successfully.
|
|
schema:
|
|
type: object
|
|
"""
|
|
if not KnowledgebaseService.accessible(kb_id=dataset_id, user_id=tenant_id):
|
|
return get_error_data_result(message=f"You don't own the dataset {dataset_id}. ")
|
|
req = request.json
|
|
if not req:
|
|
doc_ids = None
|
|
else:
|
|
doc_ids = req.get("ids")
|
|
if not doc_ids:
|
|
doc_list = []
|
|
docs = DocumentService.query(kb_id=dataset_id)
|
|
for doc in docs:
|
|
doc_list.append(doc.id)
|
|
else:
|
|
doc_list = doc_ids
|
|
root_folder = FileService.get_root_folder(tenant_id)
|
|
pf_id = root_folder["id"]
|
|
FileService.init_knowledgebase_docs(pf_id, tenant_id)
|
|
errors = ""
|
|
for doc_id in doc_list:
|
|
try:
|
|
e, doc = DocumentService.get_by_id(doc_id)
|
|
if not e:
|
|
return get_error_data_result(message="Document not found!")
|
|
tenant_id = DocumentService.get_tenant_id(doc_id)
|
|
if not tenant_id:
|
|
return get_error_data_result(message="Tenant not found!")
|
|
|
|
b, n = File2DocumentService.get_storage_address(doc_id=doc_id)
|
|
|
|
if not DocumentService.remove_document(doc, tenant_id):
|
|
return get_error_data_result(
|
|
message="Database error (Document removal)!"
|
|
)
|
|
|
|
f2d = File2DocumentService.get_by_document_id(doc_id)
|
|
FileService.filter_delete(
|
|
[
|
|
File.source_type == FileSource.KNOWLEDGEBASE,
|
|
File.id == f2d[0].file_id,
|
|
]
|
|
)
|
|
File2DocumentService.delete_by_document_id(doc_id)
|
|
|
|
STORAGE_IMPL.rm(b, n)
|
|
except Exception as e:
|
|
errors += str(e)
|
|
|
|
if errors:
|
|
return get_result(message=errors, code=settings.RetCode.SERVER_ERROR)
|
|
|
|
return get_result()
|
|
|
|
|
|
@manager.route("/datasets/<dataset_id>/chunks", methods=["POST"]) # noqa: F821
|
|
@token_required
|
|
def parse(tenant_id, dataset_id):
|
|
"""
|
|
Start parsing documents into chunks.
|
|
---
|
|
tags:
|
|
- Chunks
|
|
security:
|
|
- ApiKeyAuth: []
|
|
parameters:
|
|
- in: path
|
|
name: dataset_id
|
|
type: string
|
|
required: true
|
|
description: ID of the dataset.
|
|
- in: body
|
|
name: body
|
|
description: Parsing parameters.
|
|
required: true
|
|
schema:
|
|
type: object
|
|
properties:
|
|
document_ids:
|
|
type: array
|
|
items:
|
|
type: string
|
|
description: List of document IDs to parse.
|
|
- in: header
|
|
name: Authorization
|
|
type: string
|
|
required: true
|
|
description: Bearer token for authentication.
|
|
responses:
|
|
200:
|
|
description: Parsing started successfully.
|
|
schema:
|
|
type: object
|
|
"""
|
|
if not KnowledgebaseService.accessible(kb_id=dataset_id, user_id=tenant_id):
|
|
return get_error_data_result(message=f"You don't own the dataset {dataset_id}.")
|
|
req = request.json
|
|
if not req.get("document_ids"):
|
|
return get_error_data_result("`document_ids` is required")
|
|
for id in req["document_ids"]:
|
|
doc = DocumentService.query(id=id, kb_id=dataset_id)
|
|
if not doc:
|
|
return get_error_data_result(message=f"You don't own the document {id}.")
|
|
if doc[0].progress != 0.0:
|
|
return get_error_data_result(
|
|
"Can't stop parsing document with progress at 0 or 100"
|
|
)
|
|
info = {"run": "1", "progress": 0}
|
|
info["progress_msg"] = ""
|
|
info["chunk_num"] = 0
|
|
info["token_num"] = 0
|
|
DocumentService.update_by_id(id, info)
|
|
settings.docStoreConn.delete({"doc_id": id}, search.index_name(tenant_id), dataset_id)
|
|
TaskService.filter_delete([Task.doc_id == id])
|
|
e, doc = DocumentService.get_by_id(id)
|
|
doc = doc.to_dict()
|
|
doc["tenant_id"] = tenant_id
|
|
bucket, name = File2DocumentService.get_storage_address(doc_id=doc["id"])
|
|
queue_tasks(doc, bucket, name)
|
|
return get_result()
|
|
|
|
|
|
@manager.route("/datasets/<dataset_id>/chunks", methods=["DELETE"]) # noqa: F821
|
|
@token_required
|
|
def stop_parsing(tenant_id, dataset_id):
|
|
"""
|
|
Stop parsing documents into chunks.
|
|
---
|
|
tags:
|
|
- Chunks
|
|
security:
|
|
- ApiKeyAuth: []
|
|
parameters:
|
|
- in: path
|
|
name: dataset_id
|
|
type: string
|
|
required: true
|
|
description: ID of the dataset.
|
|
- in: body
|
|
name: body
|
|
description: Stop parsing parameters.
|
|
required: true
|
|
schema:
|
|
type: object
|
|
properties:
|
|
document_ids:
|
|
type: array
|
|
items:
|
|
type: string
|
|
description: List of document IDs to stop parsing.
|
|
- in: header
|
|
name: Authorization
|
|
type: string
|
|
required: true
|
|
description: Bearer token for authentication.
|
|
responses:
|
|
200:
|
|
description: Parsing stopped successfully.
|
|
schema:
|
|
type: object
|
|
"""
|
|
if not KnowledgebaseService.accessible(kb_id=dataset_id, user_id=tenant_id):
|
|
return get_error_data_result(message=f"You don't own the dataset {dataset_id}.")
|
|
req = request.json
|
|
if not req.get("document_ids"):
|
|
return get_error_data_result("`document_ids` is required")
|
|
for id in req["document_ids"]:
|
|
doc = DocumentService.query(id=id, kb_id=dataset_id)
|
|
if not doc:
|
|
return get_error_data_result(message=f"You don't own the document {id}.")
|
|
if int(doc[0].progress) == 1 or int(doc[0].progress) == 0:
|
|
return get_error_data_result(
|
|
"Can't stop parsing document with progress at 0 or 1"
|
|
)
|
|
info = {"run": "2", "progress": 0, "chunk_num": 0}
|
|
DocumentService.update_by_id(id, info)
|
|
settings.docStoreConn.delete({"doc_id": doc.id}, search.index_name(tenant_id), dataset_id)
|
|
return get_result()
|
|
|
|
|
|
@manager.route("/datasets/<dataset_id>/documents/<document_id>/chunks", methods=["GET"]) # noqa: F821
|
|
@token_required
|
|
def list_chunks(tenant_id, dataset_id, document_id):
|
|
"""
|
|
List chunks of a document.
|
|
---
|
|
tags:
|
|
- Chunks
|
|
security:
|
|
- ApiKeyAuth: []
|
|
parameters:
|
|
- in: path
|
|
name: dataset_id
|
|
type: string
|
|
required: true
|
|
description: ID of the dataset.
|
|
- in: path
|
|
name: document_id
|
|
type: string
|
|
required: true
|
|
description: ID of the document.
|
|
- in: query
|
|
name: page
|
|
type: integer
|
|
required: false
|
|
default: 1
|
|
description: Page number.
|
|
- in: query
|
|
name: page_size
|
|
type: integer
|
|
required: false
|
|
default: 30
|
|
description: Number of items per page.
|
|
- in: header
|
|
name: Authorization
|
|
type: string
|
|
required: true
|
|
description: Bearer token for authentication.
|
|
responses:
|
|
200:
|
|
description: List of chunks.
|
|
schema:
|
|
type: object
|
|
properties:
|
|
total:
|
|
type: integer
|
|
description: Total number of chunks.
|
|
chunks:
|
|
type: array
|
|
items:
|
|
type: object
|
|
properties:
|
|
id:
|
|
type: string
|
|
description: Chunk ID.
|
|
content:
|
|
type: string
|
|
description: Chunk content.
|
|
document_id:
|
|
type: string
|
|
description: ID of the document.
|
|
important_keywords:
|
|
type: array
|
|
items:
|
|
type: string
|
|
description: Important keywords.
|
|
image_id:
|
|
type: string
|
|
description: Image ID associated with the chunk.
|
|
doc:
|
|
type: object
|
|
description: Document details.
|
|
"""
|
|
if not KnowledgebaseService.accessible(kb_id=dataset_id, user_id=tenant_id):
|
|
return get_error_data_result(message=f"You don't own the dataset {dataset_id}.")
|
|
doc = DocumentService.query(id=document_id, kb_id=dataset_id)
|
|
if not doc:
|
|
return get_error_data_result(
|
|
message=f"You don't own the document {document_id}."
|
|
)
|
|
doc = doc[0]
|
|
req = request.args
|
|
doc_id = document_id
|
|
page = int(req.get("page", 1))
|
|
size = int(req.get("page_size", 30))
|
|
question = req.get("keywords", "")
|
|
query = {
|
|
"doc_ids": [doc_id],
|
|
"page": page,
|
|
"size": size,
|
|
"question": question,
|
|
"sort": True,
|
|
}
|
|
key_mapping = {
|
|
"chunk_num": "chunk_count",
|
|
"kb_id": "dataset_id",
|
|
"token_num": "token_count",
|
|
"parser_id": "chunk_method",
|
|
}
|
|
run_mapping = {
|
|
"0": "UNSTART",
|
|
"1": "RUNNING",
|
|
"2": "CANCEL",
|
|
"3": "DONE",
|
|
"4": "FAIL",
|
|
}
|
|
doc = doc.to_dict()
|
|
renamed_doc = {}
|
|
for key, value in doc.items():
|
|
new_key = key_mapping.get(key, key)
|
|
renamed_doc[new_key] = value
|
|
if key == "run":
|
|
renamed_doc["run"] = run_mapping.get(str(value))
|
|
|
|
res = {"total": 0, "chunks": [], "doc": renamed_doc}
|
|
origin_chunks = []
|
|
if settings.docStoreConn.indexExist(search.index_name(tenant_id), dataset_id):
|
|
sres = settings.retrievaler.search(query, search.index_name(tenant_id), [dataset_id], emb_mdl=None,
|
|
highlight=True)
|
|
res["total"] = sres.total
|
|
sign = 0
|
|
for id in sres.ids:
|
|
d = {
|
|
"id": id,
|
|
"content_with_weight": (
|
|
rmSpace(sres.highlight[id])
|
|
if question and id in sres.highlight
|
|
else sres.field[id].get("content_with_weight", "")
|
|
),
|
|
"doc_id": sres.field[id]["doc_id"],
|
|
"docnm_kwd": sres.field[id]["docnm_kwd"],
|
|
"important_kwd": sres.field[id].get("important_kwd", []),
|
|
"question_kwd": sres.field[id].get("question_kwd", []),
|
|
"img_id": sres.field[id].get("img_id", ""),
|
|
"available_int": sres.field[id].get("available_int", 1),
|
|
"positions": sres.field[id].get("position_int", []),
|
|
}
|
|
origin_chunks.append(d)
|
|
if req.get("id"):
|
|
if req.get("id") == id:
|
|
origin_chunks.clear()
|
|
origin_chunks.append(d)
|
|
sign = 1
|
|
break
|
|
if req.get("id"):
|
|
if sign == 0:
|
|
return get_error_data_result(f"Can't find this chunk {req.get('id')}")
|
|
|
|
for chunk in origin_chunks:
|
|
key_mapping = {
|
|
"id": "id",
|
|
"content_with_weight": "content",
|
|
"doc_id": "document_id",
|
|
"important_kwd": "important_keywords",
|
|
"question_kwd": "questions",
|
|
"img_id": "image_id",
|
|
"available_int": "available",
|
|
}
|
|
renamed_chunk = {}
|
|
for key, value in chunk.items():
|
|
new_key = key_mapping.get(key, key)
|
|
renamed_chunk[new_key] = value
|
|
if renamed_chunk["available"] == 0:
|
|
renamed_chunk["available"] = False
|
|
if renamed_chunk["available"] == 1:
|
|
renamed_chunk["available"] = True
|
|
res["chunks"].append(renamed_chunk)
|
|
_ = Chunk(**renamed_chunk) # validate the chunk
|
|
return get_result(data=res)
|
|
|
|
|
|
@manager.route( # noqa: F821
|
|
"/datasets/<dataset_id>/documents/<document_id>/chunks", methods=["POST"]
|
|
)
|
|
@token_required
|
|
def add_chunk(tenant_id, dataset_id, document_id):
|
|
"""
|
|
Add a chunk to a document.
|
|
---
|
|
tags:
|
|
- Chunks
|
|
security:
|
|
- ApiKeyAuth: []
|
|
parameters:
|
|
- in: path
|
|
name: dataset_id
|
|
type: string
|
|
required: true
|
|
description: ID of the dataset.
|
|
- in: path
|
|
name: document_id
|
|
type: string
|
|
required: true
|
|
description: ID of the document.
|
|
- in: body
|
|
name: body
|
|
description: Chunk data.
|
|
required: true
|
|
schema:
|
|
type: object
|
|
properties:
|
|
content:
|
|
type: string
|
|
required: true
|
|
description: Content of the chunk.
|
|
important_keywords:
|
|
type: array
|
|
items:
|
|
type: string
|
|
description: Important keywords.
|
|
- in: header
|
|
name: Authorization
|
|
type: string
|
|
required: true
|
|
description: Bearer token for authentication.
|
|
responses:
|
|
200:
|
|
description: Chunk added successfully.
|
|
schema:
|
|
type: object
|
|
properties:
|
|
chunk:
|
|
type: object
|
|
properties:
|
|
id:
|
|
type: string
|
|
description: Chunk ID.
|
|
content:
|
|
type: string
|
|
description: Chunk content.
|
|
document_id:
|
|
type: string
|
|
description: ID of the document.
|
|
important_keywords:
|
|
type: array
|
|
items:
|
|
type: string
|
|
description: Important keywords.
|
|
"""
|
|
if not KnowledgebaseService.accessible(kb_id=dataset_id, user_id=tenant_id):
|
|
return get_error_data_result(message=f"You don't own the dataset {dataset_id}.")
|
|
doc = DocumentService.query(id=document_id, kb_id=dataset_id)
|
|
if not doc:
|
|
return get_error_data_result(
|
|
message=f"You don't own the document {document_id}."
|
|
)
|
|
doc = doc[0]
|
|
req = request.json
|
|
if not req.get("content"):
|
|
return get_error_data_result(message="`content` is required")
|
|
if "important_keywords" in req:
|
|
if not isinstance(req["important_keywords"], list):
|
|
return get_error_data_result(
|
|
"`important_keywords` is required to be a list"
|
|
)
|
|
if "questions" in req:
|
|
if not isinstance(req["questions"], list):
|
|
return get_error_data_result(
|
|
"`questions` is required to be a list"
|
|
)
|
|
chunk_id = xxhash.xxh64((req["content"] + document_id).encode("utf-8")).hexdigest()
|
|
d = {
|
|
"id": chunk_id,
|
|
"content_ltks": rag_tokenizer.tokenize(req["content"]),
|
|
"content_with_weight": req["content"],
|
|
}
|
|
d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"])
|
|
d["important_kwd"] = req.get("important_keywords", [])
|
|
d["important_tks"] = rag_tokenizer.tokenize(
|
|
" ".join(req.get("important_keywords", []))
|
|
)
|
|
d["question_kwd"] = req.get("questions", [])
|
|
d["question_tks"] = rag_tokenizer.tokenize(
|
|
"\n".join(req.get("questions", []))
|
|
)
|
|
d["create_time"] = str(datetime.datetime.now()).replace("T", " ")[:19]
|
|
d["create_timestamp_flt"] = datetime.datetime.now().timestamp()
|
|
d["kb_id"] = dataset_id
|
|
d["docnm_kwd"] = doc.name
|
|
d["doc_id"] = document_id
|
|
embd_id = DocumentService.get_embd_id(document_id)
|
|
embd_mdl = TenantLLMService.model_instance(
|
|
tenant_id, LLMType.EMBEDDING.value, embd_id
|
|
)
|
|
v, c = embd_mdl.encode([doc.name, req["content"] if not d["question_kwd"] else "\n".join(d["question_kwd"])])
|
|
v = 0.1 * v[0] + 0.9 * v[1]
|
|
d["q_%d_vec" % len(v)] = v.tolist()
|
|
settings.docStoreConn.insert([d], search.index_name(tenant_id), dataset_id)
|
|
|
|
DocumentService.increment_chunk_num(doc.id, doc.kb_id, c, 1, 0)
|
|
# rename keys
|
|
key_mapping = {
|
|
"id": "id",
|
|
"content_with_weight": "content",
|
|
"doc_id": "document_id",
|
|
"important_kwd": "important_keywords",
|
|
"question_kwd": "questions",
|
|
"kb_id": "dataset_id",
|
|
"create_timestamp_flt": "create_timestamp",
|
|
"create_time": "create_time",
|
|
"document_keyword": "document",
|
|
}
|
|
renamed_chunk = {}
|
|
for key, value in d.items():
|
|
if key in key_mapping:
|
|
new_key = key_mapping.get(key, key)
|
|
renamed_chunk[new_key] = value
|
|
_ = Chunk(**renamed_chunk) # validate the chunk
|
|
return get_result(data={"chunk": renamed_chunk})
|
|
# return get_result(data={"chunk_id": chunk_id})
|
|
|
|
|
|
@manager.route( # noqa: F821
|
|
"datasets/<dataset_id>/documents/<document_id>/chunks", methods=["DELETE"]
|
|
)
|
|
@token_required
|
|
def rm_chunk(tenant_id, dataset_id, document_id):
|
|
"""
|
|
Remove chunks from a document.
|
|
---
|
|
tags:
|
|
- Chunks
|
|
security:
|
|
- ApiKeyAuth: []
|
|
parameters:
|
|
- in: path
|
|
name: dataset_id
|
|
type: string
|
|
required: true
|
|
description: ID of the dataset.
|
|
- in: path
|
|
name: document_id
|
|
type: string
|
|
required: true
|
|
description: ID of the document.
|
|
- in: body
|
|
name: body
|
|
description: Chunk removal parameters.
|
|
required: true
|
|
schema:
|
|
type: object
|
|
properties:
|
|
chunk_ids:
|
|
type: array
|
|
items:
|
|
type: string
|
|
description: List of chunk IDs to remove.
|
|
- in: header
|
|
name: Authorization
|
|
type: string
|
|
required: true
|
|
description: Bearer token for authentication.
|
|
responses:
|
|
200:
|
|
description: Chunks removed successfully.
|
|
schema:
|
|
type: object
|
|
"""
|
|
if not KnowledgebaseService.accessible(kb_id=dataset_id, user_id=tenant_id):
|
|
return get_error_data_result(message=f"You don't own the dataset {dataset_id}.")
|
|
req = request.json
|
|
condition = {"doc_id": document_id}
|
|
if "chunk_ids" in req:
|
|
condition["id"] = req["chunk_ids"]
|
|
chunk_number = settings.docStoreConn.delete(condition, search.index_name(tenant_id), dataset_id)
|
|
if chunk_number != 0:
|
|
DocumentService.decrement_chunk_num(document_id, dataset_id, 1, chunk_number, 0)
|
|
if "chunk_ids" in req and chunk_number != len(req["chunk_ids"]):
|
|
return get_error_data_result(message=f"rm_chunk deleted chunks {chunk_number}, expect {len(req['chunk_ids'])}")
|
|
return get_result(message=f"deleted {chunk_number} chunks")
|
|
|
|
|
|
@manager.route( # noqa: F821
|
|
"/datasets/<dataset_id>/documents/<document_id>/chunks/<chunk_id>", methods=["PUT"]
|
|
)
|
|
@token_required
|
|
def update_chunk(tenant_id, dataset_id, document_id, chunk_id):
|
|
"""
|
|
Update a chunk within a document.
|
|
---
|
|
tags:
|
|
- Chunks
|
|
security:
|
|
- ApiKeyAuth: []
|
|
parameters:
|
|
- in: path
|
|
name: dataset_id
|
|
type: string
|
|
required: true
|
|
description: ID of the dataset.
|
|
- in: path
|
|
name: document_id
|
|
type: string
|
|
required: true
|
|
description: ID of the document.
|
|
- in: path
|
|
name: chunk_id
|
|
type: string
|
|
required: true
|
|
description: ID of the chunk to update.
|
|
- in: body
|
|
name: body
|
|
description: Chunk update parameters.
|
|
required: true
|
|
schema:
|
|
type: object
|
|
properties:
|
|
content:
|
|
type: string
|
|
description: Updated content of the chunk.
|
|
important_keywords:
|
|
type: array
|
|
items:
|
|
type: string
|
|
description: Updated important keywords.
|
|
available:
|
|
type: boolean
|
|
description: Availability status of the chunk.
|
|
- in: header
|
|
name: Authorization
|
|
type: string
|
|
required: true
|
|
description: Bearer token for authentication.
|
|
responses:
|
|
200:
|
|
description: Chunk updated successfully.
|
|
schema:
|
|
type: object
|
|
"""
|
|
chunk = settings.docStoreConn.get(chunk_id, search.index_name(tenant_id), [dataset_id])
|
|
if chunk is None:
|
|
return get_error_data_result(f"Can't find this chunk {chunk_id}")
|
|
if not KnowledgebaseService.accessible(kb_id=dataset_id, user_id=tenant_id):
|
|
return get_error_data_result(message=f"You don't own the dataset {dataset_id}.")
|
|
doc = DocumentService.query(id=document_id, kb_id=dataset_id)
|
|
if not doc:
|
|
return get_error_data_result(
|
|
message=f"You don't own the document {document_id}."
|
|
)
|
|
doc = doc[0]
|
|
req = request.json
|
|
if "content" in req:
|
|
content = req["content"]
|
|
else:
|
|
content = chunk.get("content_with_weight", "")
|
|
d = {"id": chunk_id, "content_with_weight": content}
|
|
d["content_ltks"] = rag_tokenizer.tokenize(d["content_with_weight"])
|
|
d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"])
|
|
if "important_keywords" in req:
|
|
if not isinstance(req["important_keywords"], list):
|
|
return get_error_data_result("`important_keywords` should be a list")
|
|
d["important_kwd"] = req.get("important_keywords", [])
|
|
d["important_tks"] = rag_tokenizer.tokenize(" ".join(req["important_keywords"]))
|
|
if "questions" in req:
|
|
if not isinstance(req["questions"], list):
|
|
return get_error_data_result("`questions` should be a list")
|
|
d["question_kwd"] = req.get("questions")
|
|
d["question_tks"] = rag_tokenizer.tokenize("\n".join(req["questions"]))
|
|
if "available" in req:
|
|
d["available_int"] = int(req["available"])
|
|
embd_id = DocumentService.get_embd_id(document_id)
|
|
embd_mdl = TenantLLMService.model_instance(
|
|
tenant_id, LLMType.EMBEDDING.value, embd_id
|
|
)
|
|
if doc.parser_id == ParserType.QA:
|
|
arr = [t for t in re.split(r"[\n\t]", d["content_with_weight"]) if len(t) > 1]
|
|
if len(arr) != 2:
|
|
return get_error_data_result(
|
|
message="Q&A must be separated by TAB/ENTER key."
|
|
)
|
|
q, a = rmPrefix(arr[0]), rmPrefix(arr[1])
|
|
d = beAdoc(
|
|
d, arr[0], arr[1], not any([rag_tokenizer.is_chinese(t) for t in q + a])
|
|
)
|
|
|
|
v, c = embd_mdl.encode([doc.name, d["content_with_weight"] if not d.get("question_kwd") else "\n".join(d["question_kwd"])])
|
|
v = 0.1 * v[0] + 0.9 * v[1] if doc.parser_id != ParserType.QA else v[1]
|
|
d["q_%d_vec" % len(v)] = v.tolist()
|
|
settings.docStoreConn.update({"id": chunk_id}, d, search.index_name(tenant_id), dataset_id)
|
|
return get_result()
|
|
|
|
|
|
@manager.route("/retrieval", methods=["POST"]) # noqa: F821
|
|
@token_required
|
|
def retrieval_test(tenant_id):
|
|
"""
|
|
Retrieve chunks based on a query.
|
|
---
|
|
tags:
|
|
- Retrieval
|
|
security:
|
|
- ApiKeyAuth: []
|
|
parameters:
|
|
- in: body
|
|
name: body
|
|
description: Retrieval parameters.
|
|
required: true
|
|
schema:
|
|
type: object
|
|
properties:
|
|
dataset_ids:
|
|
type: array
|
|
items:
|
|
type: string
|
|
required: true
|
|
description: List of dataset IDs to search in.
|
|
question:
|
|
type: string
|
|
required: true
|
|
description: Query string.
|
|
document_ids:
|
|
type: array
|
|
items:
|
|
type: string
|
|
description: List of document IDs to filter.
|
|
similarity_threshold:
|
|
type: number
|
|
format: float
|
|
description: Similarity threshold.
|
|
vector_similarity_weight:
|
|
type: number
|
|
format: float
|
|
description: Vector similarity weight.
|
|
top_k:
|
|
type: integer
|
|
description: Maximum number of chunks to return.
|
|
highlight:
|
|
type: boolean
|
|
description: Whether to highlight matched content.
|
|
- in: header
|
|
name: Authorization
|
|
type: string
|
|
required: true
|
|
description: Bearer token for authentication.
|
|
responses:
|
|
200:
|
|
description: Retrieval results.
|
|
schema:
|
|
type: object
|
|
properties:
|
|
chunks:
|
|
type: array
|
|
items:
|
|
type: object
|
|
properties:
|
|
id:
|
|
type: string
|
|
description: Chunk ID.
|
|
content:
|
|
type: string
|
|
description: Chunk content.
|
|
document_id:
|
|
type: string
|
|
description: ID of the document.
|
|
dataset_id:
|
|
type: string
|
|
description: ID of the dataset.
|
|
similarity:
|
|
type: number
|
|
format: float
|
|
description: Similarity score.
|
|
"""
|
|
req = request.json
|
|
if not req.get("dataset_ids"):
|
|
return get_error_data_result("`dataset_ids` is required.")
|
|
kb_ids = req["dataset_ids"]
|
|
if not isinstance(kb_ids, list):
|
|
return get_error_data_result("`dataset_ids` should be a list")
|
|
kbs = KnowledgebaseService.get_by_ids(kb_ids)
|
|
for id in kb_ids:
|
|
if not KnowledgebaseService.accessible(kb_id=id, user_id=tenant_id):
|
|
return get_error_data_result(f"You don't own the dataset {id}.")
|
|
embd_nms = list(set([kb.embd_id for kb in kbs]))
|
|
if len(embd_nms) != 1:
|
|
return get_result(
|
|
message='Datasets use different embedding models."',
|
|
code=settings.RetCode.AUTHENTICATION_ERROR,
|
|
)
|
|
if "question" not in req:
|
|
return get_error_data_result("`question` is required.")
|
|
page = int(req.get("page", 1))
|
|
size = int(req.get("page_size", 30))
|
|
question = req["question"]
|
|
doc_ids = req.get("document_ids", [])
|
|
if not isinstance(doc_ids, list):
|
|
return get_error_data_result("`documents` should be a list")
|
|
doc_ids_list = KnowledgebaseService.list_documents_by_ids(kb_ids)
|
|
for doc_id in doc_ids:
|
|
if doc_id not in doc_ids_list:
|
|
return get_error_data_result(
|
|
f"The datasets don't own the document {doc_id}"
|
|
)
|
|
similarity_threshold = float(req.get("similarity_threshold", 0.2))
|
|
vector_similarity_weight = float(req.get("vector_similarity_weight", 0.3))
|
|
top = int(req.get("top_k", 1024))
|
|
if req.get("highlight") == "False" or req.get("highlight") == "false":
|
|
highlight = False
|
|
else:
|
|
highlight = True
|
|
try:
|
|
e, kb = KnowledgebaseService.get_by_id(kb_ids[0])
|
|
if not e:
|
|
return get_error_data_result(message="Dataset not found!")
|
|
embd_mdl = TenantLLMService.model_instance(
|
|
kb.tenant_id, LLMType.EMBEDDING.value, llm_name=kb.embd_id
|
|
)
|
|
|
|
rerank_mdl = None
|
|
if req.get("rerank_id"):
|
|
rerank_mdl = TenantLLMService.model_instance(
|
|
kb.tenant_id, LLMType.RERANK.value, llm_name=req["rerank_id"]
|
|
)
|
|
|
|
if req.get("keyword", False):
|
|
chat_mdl = TenantLLMService.model_instance(kb.tenant_id, LLMType.CHAT)
|
|
question += keyword_extraction(chat_mdl, question)
|
|
|
|
retr = settings.retrievaler if kb.parser_id != ParserType.KG else settings.kg_retrievaler
|
|
ranks = retr.retrieval(
|
|
question,
|
|
embd_mdl,
|
|
kb.tenant_id,
|
|
kb_ids,
|
|
page,
|
|
size,
|
|
similarity_threshold,
|
|
vector_similarity_weight,
|
|
top,
|
|
doc_ids,
|
|
rerank_mdl=rerank_mdl,
|
|
highlight=highlight,
|
|
)
|
|
for c in ranks["chunks"]:
|
|
c.pop("vector", None)
|
|
|
|
##rename keys
|
|
renamed_chunks = []
|
|
for chunk in ranks["chunks"]:
|
|
key_mapping = {
|
|
"chunk_id": "id",
|
|
"content_with_weight": "content",
|
|
"doc_id": "document_id",
|
|
"important_kwd": "important_keywords",
|
|
"question_kwd": "questions",
|
|
"docnm_kwd": "document_keyword",
|
|
}
|
|
rename_chunk = {}
|
|
for key, value in chunk.items():
|
|
new_key = key_mapping.get(key, key)
|
|
rename_chunk[new_key] = value
|
|
renamed_chunks.append(rename_chunk)
|
|
ranks["chunks"] = renamed_chunks
|
|
return get_result(data=ranks)
|
|
except Exception as e:
|
|
if str(e).find("not_found") > 0:
|
|
return get_result(
|
|
message="No chunk found! Check the chunk status please!",
|
|
code=settings.RetCode.DATA_ERROR,
|
|
)
|
|
return server_error_response(e)
|