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185c6a0c71
### What problem does this PR solve? Unified API response json schema ### Type of change - [x] Refactoring
1405 lines
45 KiB
Python
1405 lines
45 KiB
Python
#
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# Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
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#
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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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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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import pathlib
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import datetime
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from api.db.services.dialog_service import keyword_extraction
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from rag.app.qa import rmPrefix, beAdoc
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from rag.nlp import rag_tokenizer
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from api.db import LLMType, ParserType
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from api.db.services.llm_service import TenantLLMService
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from api.settings import kg_retrievaler
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import hashlib
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import re
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from api.utils.api_utils import token_required
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from api.db.db_models import Task
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from api.db.services.task_service import TaskService, queue_tasks
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from api.utils.api_utils import server_error_response
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from api.utils.api_utils import get_result, get_error_data_result
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from io import BytesIO
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from elasticsearch_dsl import Q
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from flask import request, send_file
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from api.db import FileSource, TaskStatus, FileType
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from api.db.db_models import File
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from api.db.services.document_service import DocumentService
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from api.db.services.file2document_service import File2DocumentService
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from api.db.services.file_service import FileService
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from api.db.services.knowledgebase_service import KnowledgebaseService
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from api.settings import RetCode, retrievaler
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from api.utils.api_utils import construct_json_result, get_parser_config
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from rag.nlp import search
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from rag.utils import rmSpace
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from rag.utils.es_conn import ELASTICSEARCH
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from rag.utils.storage_factory import STORAGE_IMPL
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import os
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MAXIMUM_OF_UPLOADING_FILES = 256
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@manager.route("/datasets/<dataset_id>/documents", methods=["POST"])
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@token_required
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def upload(dataset_id, tenant_id):
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"""
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Upload documents to a dataset.
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---
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tags:
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- Documents
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security:
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- ApiKeyAuth: []
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parameters:
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- in: path
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name: dataset_id
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type: string
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required: true
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description: ID of the dataset.
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- in: header
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name: Authorization
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type: string
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required: true
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description: Bearer token for authentication.
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- in: formData
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name: file
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type: file
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required: true
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description: Document files to upload.
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responses:
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200:
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description: Successfully uploaded documents.
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schema:
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type: object
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properties:
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data:
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type: array
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items:
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type: object
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properties:
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id:
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type: string
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description: Document ID.
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name:
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type: string
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description: Document name.
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chunk_count:
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type: integer
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description: Number of chunks.
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token_count:
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type: integer
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description: Number of tokens.
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dataset_id:
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type: string
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description: ID of the dataset.
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chunk_method:
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type: string
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description: Chunking method used.
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run:
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type: string
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description: Processing status.
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"""
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if "file" not in request.files:
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return get_error_data_result(
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message="No file part!", code=RetCode.ARGUMENT_ERROR
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)
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file_objs = request.files.getlist("file")
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for file_obj in file_objs:
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if file_obj.filename == "":
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return get_result(
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message="No file selected!", code=RetCode.ARGUMENT_ERROR
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)
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# total size
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total_size = 0
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for file_obj in file_objs:
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file_obj.seek(0, os.SEEK_END)
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total_size += file_obj.tell()
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file_obj.seek(0)
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MAX_TOTAL_FILE_SIZE = 10 * 1024 * 1024
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if total_size > MAX_TOTAL_FILE_SIZE:
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return get_result(
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message=f"Total file size exceeds 10MB limit! ({total_size / (1024 * 1024):.2f} MB)",
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code=RetCode.ARGUMENT_ERROR,
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)
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e, kb = KnowledgebaseService.get_by_id(dataset_id)
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if not e:
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raise LookupError(f"Can't find the dataset with ID {dataset_id}!")
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err, files = FileService.upload_document(kb, file_objs, tenant_id)
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if err:
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return get_result(message="\n".join(err), code=RetCode.SERVER_ERROR)
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# rename key's name
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renamed_doc_list = []
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for file in files:
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doc = file[0]
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key_mapping = {
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"chunk_num": "chunk_count",
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"kb_id": "dataset_id",
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"token_num": "token_count",
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"parser_id": "chunk_method",
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}
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renamed_doc = {}
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for key, value in doc.items():
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new_key = key_mapping.get(key, key)
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renamed_doc[new_key] = value
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renamed_doc["run"] = "UNSTART"
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renamed_doc_list.append(renamed_doc)
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return get_result(data=renamed_doc_list)
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@manager.route("/datasets/<dataset_id>/documents/<document_id>", methods=["PUT"])
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@token_required
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def update_doc(tenant_id, dataset_id, document_id):
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"""
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Update a document within a dataset.
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---
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tags:
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- Documents
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security:
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- ApiKeyAuth: []
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parameters:
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- in: path
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name: dataset_id
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type: string
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required: true
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description: ID of the dataset.
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- in: path
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name: document_id
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type: string
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required: true
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description: ID of the document to update.
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- in: header
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name: Authorization
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type: string
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required: true
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description: Bearer token for authentication.
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- in: body
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name: body
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description: Document update parameters.
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required: true
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schema:
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type: object
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properties:
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name:
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type: string
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description: New name of the document.
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parser_config:
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type: object
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description: Parser configuration.
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chunk_method:
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type: string
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description: Chunking method.
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responses:
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200:
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description: Document updated successfully.
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schema:
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type: object
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"""
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req = request.json
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if not KnowledgebaseService.query(id=dataset_id, tenant_id=tenant_id):
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return get_error_data_result(message="You don't own the dataset.")
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doc = DocumentService.query(kb_id=dataset_id, id=document_id)
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if not doc:
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return get_error_data_result(message="The dataset doesn't own the document.")
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doc = doc[0]
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if "chunk_count" in req:
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if req["chunk_count"] != doc.chunk_num:
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return get_error_data_result(message="Can't change `chunk_count`.")
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if "token_count" in req:
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if req["token_count"] != doc.token_num:
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return get_error_data_result(message="Can't change `token_count`.")
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if "progress" in req:
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if req["progress"] != doc.progress:
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return get_error_data_result(message="Can't change `progress`.")
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if "name" in req and req["name"] != doc.name:
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if (
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pathlib.Path(req["name"].lower()).suffix
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!= pathlib.Path(doc.name.lower()).suffix
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):
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return get_result(
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message="The extension of file can't be changed",
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code=RetCode.ARGUMENT_ERROR,
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)
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for d in DocumentService.query(name=req["name"], kb_id=doc.kb_id):
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if d.name == req["name"]:
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return get_error_data_result(
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message="Duplicated document name in the same dataset."
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)
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if not DocumentService.update_by_id(document_id, {"name": req["name"]}):
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return get_error_data_result(message="Database error (Document rename)!")
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informs = File2DocumentService.get_by_document_id(document_id)
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if informs:
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e, file = FileService.get_by_id(informs[0].file_id)
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FileService.update_by_id(file.id, {"name": req["name"]})
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if "parser_config" in req:
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DocumentService.update_parser_config(doc.id, req["parser_config"])
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if "chunk_method" in req:
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valid_chunk_method = {
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"naive",
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"manual",
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"qa",
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"table",
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"paper",
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"book",
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"laws",
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"presentation",
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"picture",
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"one",
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"knowledge_graph",
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"email",
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}
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if req.get("chunk_method") not in valid_chunk_method:
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return get_error_data_result(
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f"`chunk_method` {req['chunk_method']} doesn't exist"
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)
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if doc.parser_id.lower() == req["chunk_method"].lower():
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return get_result()
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if doc.type == FileType.VISUAL or re.search(r"\.(ppt|pptx|pages)$", doc.name):
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return get_error_data_result(message="Not supported yet!")
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e = DocumentService.update_by_id(
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doc.id,
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{
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"parser_id": req["chunk_method"],
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"progress": 0,
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"progress_msg": "",
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"run": TaskStatus.UNSTART.value,
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},
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)
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if not e:
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return get_error_data_result(message="Document not found!")
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req["parser_config"] = get_parser_config(
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req["chunk_method"], req.get("parser_config")
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)
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DocumentService.update_parser_config(doc.id, req["parser_config"])
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if doc.token_num > 0:
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e = DocumentService.increment_chunk_num(
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doc.id,
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doc.kb_id,
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doc.token_num * -1,
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doc.chunk_num * -1,
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doc.process_duation * -1,
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)
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if not e:
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return get_error_data_result(message="Document not found!")
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ELASTICSEARCH.deleteByQuery(
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Q("match", doc_id=doc.id), idxnm=search.index_name(tenant_id)
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)
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return get_result()
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@manager.route("/datasets/<dataset_id>/documents/<document_id>", methods=["GET"])
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@token_required
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def download(tenant_id, dataset_id, document_id):
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"""
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Download a document from a dataset.
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---
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tags:
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- Documents
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security:
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- ApiKeyAuth: []
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produces:
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- application/octet-stream
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parameters:
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- in: path
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name: dataset_id
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type: string
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required: true
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description: ID of the dataset.
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- in: path
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name: document_id
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type: string
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required: true
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description: ID of the document to download.
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- in: header
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name: Authorization
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type: string
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required: true
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description: Bearer token for authentication.
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responses:
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200:
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description: Document file stream.
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schema:
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type: file
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400:
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description: Error message.
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schema:
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type: object
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"""
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if not KnowledgebaseService.query(id=dataset_id, tenant_id=tenant_id):
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return get_error_data_result(message=f"You do not own the dataset {dataset_id}.")
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doc = DocumentService.query(kb_id=dataset_id, id=document_id)
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if not doc:
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return get_error_data_result(
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message=f"The dataset not own the document {document_id}."
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)
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# The process of downloading
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doc_id, doc_location = File2DocumentService.get_storage_address(
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doc_id=document_id
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) # minio address
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file_stream = STORAGE_IMPL.get(doc_id, doc_location)
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if not file_stream:
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return construct_json_result(
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message="This file is empty.", code=RetCode.DATA_ERROR
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)
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file = BytesIO(file_stream)
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# Use send_file with a proper filename and MIME type
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return send_file(
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file,
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as_attachment=True,
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download_name=doc[0].name,
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mimetype="application/octet-stream", # Set a default MIME type
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)
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@manager.route("/datasets/<dataset_id>/documents", methods=["GET"])
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@token_required
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def list_docs(dataset_id, tenant_id):
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"""
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List documents in a dataset.
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---
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tags:
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- Documents
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security:
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- ApiKeyAuth: []
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parameters:
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- in: path
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name: dataset_id
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type: string
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required: true
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description: ID of the dataset.
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- in: query
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name: id
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type: string
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required: false
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description: Filter by document ID.
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- in: query
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name: offset
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type: integer
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required: false
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default: 1
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description: Page number.
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- in: query
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name: limit
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type: integer
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required: false
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default: 1024
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description: Number of items per page.
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- in: query
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name: orderby
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type: string
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required: false
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default: "create_time"
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description: Field to order by.
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- in: query
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name: desc
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type: boolean
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required: false
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default: true
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description: Order in descending.
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- in: header
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name: Authorization
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type: string
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required: true
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description: Bearer token for authentication.
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responses:
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200:
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description: List of documents.
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schema:
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type: object
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properties:
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total:
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type: integer
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description: Total number of documents.
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docs:
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type: array
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items:
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type: object
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properties:
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id:
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type: string
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description: Document ID.
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name:
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type: string
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description: Document name.
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chunk_count:
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type: integer
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description: Number of chunks.
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token_count:
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type: integer
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description: Number of tokens.
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dataset_id:
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type: string
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description: ID of the dataset.
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chunk_method:
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type: string
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description: Chunking method used.
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run:
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type: string
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description: Processing status.
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"""
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if not KnowledgebaseService.query(id=dataset_id, tenant_id=tenant_id):
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return get_error_data_result(message=f"You don't own the dataset {dataset_id}. ")
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id = request.args.get("id")
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name = request.args.get("name")
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if not DocumentService.query(id=id, kb_id=dataset_id):
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return get_error_data_result(message=f"You don't own the document {id}.")
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if not DocumentService.query(name=name, kb_id=dataset_id):
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return get_error_data_result(message=f"You don't own the document {name}.")
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page = int(request.args.get("page", 1))
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keywords = request.args.get("keywords", "")
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page_size = int(request.args.get("page_size", 1024))
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orderby = request.args.get("orderby", "create_time")
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if request.args.get("desc") == "False":
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desc = False
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else:
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desc = True
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docs, tol = DocumentService.get_list(
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dataset_id, page, page_size, orderby, desc, keywords, id, name
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)
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# rename key's name
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renamed_doc_list = []
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for doc in docs:
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key_mapping = {
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"chunk_num": "chunk_count",
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"kb_id": "dataset_id",
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"token_num": "token_count",
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"parser_id": "chunk_method",
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}
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run_mapping = {
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"0": "UNSTART",
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"1": "RUNNING",
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"2": "CANCEL",
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"3": "DONE",
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"4": "FAIL",
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}
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renamed_doc = {}
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for key, value in doc.items():
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if key == "run":
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renamed_doc["run"] = run_mapping.get(str(value))
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new_key = key_mapping.get(key, key)
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renamed_doc[new_key] = value
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if key == "run":
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renamed_doc["run"] = run_mapping.get(value)
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renamed_doc_list.append(renamed_doc)
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return get_result(data={"total": tol, "docs": renamed_doc_list})
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|
|
|
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@manager.route("/datasets/<dataset_id>/documents", methods=["DELETE"])
|
|
@token_required
|
|
def delete(tenant_id, dataset_id):
|
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"""
|
|
Delete documents from a dataset.
|
|
---
|
|
tags:
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- Documents
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|
security:
|
|
- ApiKeyAuth: []
|
|
parameters:
|
|
- in: path
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|
name: dataset_id
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type: string
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required: true
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description: ID of the dataset.
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- in: body
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name: body
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description: Document deletion parameters.
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required: true
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schema:
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type: object
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properties:
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ids:
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type: array
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items:
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type: string
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description: List of document IDs to delete.
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- in: header
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name: Authorization
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type: string
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required: true
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description: Bearer token for authentication.
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responses:
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200:
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description: Documents deleted successfully.
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schema:
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type: object
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"""
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if not KnowledgebaseService.query(id=dataset_id, tenant_id=tenant_id):
|
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return get_error_data_result(message=f"You don't own the dataset {dataset_id}. ")
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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=RetCode.SERVER_ERROR)
|
|
|
|
return get_result()
|
|
|
|
|
|
@manager.route("/datasets/<dataset_id>/chunks", methods=["POST"])
|
|
@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.query(id=dataset_id, tenant_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)
|
|
ELASTICSEARCH.deleteByQuery(
|
|
Q("match", doc_id=id), idxnm=search.index_name(tenant_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"])
|
|
@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.query(id=dataset_id, tenant_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)
|
|
ELASTICSEARCH.deleteByQuery(
|
|
Q("match", doc_id=id), idxnm=search.index_name(tenant_id)
|
|
)
|
|
return get_result()
|
|
|
|
|
|
@manager.route("/datasets/<dataset_id>/documents/<document_id>/chunks", methods=["GET"])
|
|
@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: offset
|
|
type: integer
|
|
required: false
|
|
default: 1
|
|
description: Page number.
|
|
- in: query
|
|
name: limit
|
|
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.query(id=dataset_id, tenant_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,
|
|
}
|
|
sres = retrievaler.search(query, search.index_name(tenant_id), highlight=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": sres.total, "chunks": [], "doc": renamed_doc}
|
|
origin_chunks = []
|
|
sign = 0
|
|
for id in sres.ids:
|
|
d = {
|
|
"chunk_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", []),
|
|
"img_id": sres.field[id].get("img_id", ""),
|
|
"available_int": sres.field[id].get("available_int", 1),
|
|
"positions": sres.field[id].get("position_int", "").split("\t"),
|
|
}
|
|
if len(d["positions"]) % 5 == 0:
|
|
poss = []
|
|
for i in range(0, len(d["positions"]), 5):
|
|
poss.append(
|
|
[
|
|
float(d["positions"][i]),
|
|
float(d["positions"][i + 1]),
|
|
float(d["positions"][i + 2]),
|
|
float(d["positions"][i + 3]),
|
|
float(d["positions"][i + 4]),
|
|
]
|
|
)
|
|
d["positions"] = poss
|
|
|
|
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 = {
|
|
"chunk_id": "id",
|
|
"content_with_weight": "content",
|
|
"doc_id": "document_id",
|
|
"important_kwd": "important_keywords",
|
|
"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)
|
|
return get_result(data=res)
|
|
|
|
|
|
@manager.route(
|
|
"/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.query(id=dataset_id, tenant_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 type(req["important_keywords"]) != list:
|
|
return get_error_data_result(
|
|
"`important_keywords` is required to be a list"
|
|
)
|
|
md5 = hashlib.md5()
|
|
md5.update((req["content"] + document_id).encode("utf-8"))
|
|
|
|
chunk_id = md5.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["create_time"] = str(datetime.datetime.now()).replace("T", " ")[:19]
|
|
d["create_timestamp_flt"] = datetime.datetime.now().timestamp()
|
|
d["kb_id"] = [doc.kb_id]
|
|
d["docnm_kwd"] = doc.name
|
|
d["doc_id"] = doc.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"]])
|
|
v = 0.1 * v[0] + 0.9 * v[1]
|
|
d["q_%d_vec" % len(v)] = v.tolist()
|
|
ELASTICSEARCH.upsert([d], search.index_name(tenant_id))
|
|
|
|
DocumentService.increment_chunk_num(doc.id, doc.kb_id, c, 1, 0)
|
|
d["chunk_id"] = chunk_id
|
|
d["kb_id"] = doc.kb_id
|
|
# rename keys
|
|
key_mapping = {
|
|
"chunk_id": "id",
|
|
"content_with_weight": "content",
|
|
"doc_id": "document_id",
|
|
"important_kwd": "important_keywords",
|
|
"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
|
|
return get_result(data={"chunk": renamed_chunk})
|
|
# return get_result(data={"chunk_id": chunk_id})
|
|
|
|
|
|
@manager.route(
|
|
"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.query(id=dataset_id, tenant_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("chunk_ids"):
|
|
return get_error_data_result("`chunk_ids` is required")
|
|
query = {"doc_ids": [doc.id], "page": 1, "size": 1024, "question": "", "sort": True}
|
|
sres = retrievaler.search(query, search.index_name(tenant_id), highlight=True)
|
|
if not req:
|
|
chunk_ids = None
|
|
else:
|
|
chunk_ids = req.get("chunk_ids")
|
|
if not chunk_ids:
|
|
chunk_list = sres.ids
|
|
else:
|
|
chunk_list = chunk_ids
|
|
for chunk_id in chunk_list:
|
|
if chunk_id not in sres.ids:
|
|
return get_error_data_result(f"Chunk {chunk_id} not found")
|
|
if not ELASTICSEARCH.deleteByQuery(
|
|
Q("ids", values=chunk_list), search.index_name(tenant_id)
|
|
):
|
|
return get_error_data_result(message="Index updating failure")
|
|
deleted_chunk_ids = chunk_list
|
|
chunk_number = len(deleted_chunk_ids)
|
|
DocumentService.decrement_chunk_num(doc.id, doc.kb_id, 1, chunk_number, 0)
|
|
return get_result()
|
|
|
|
|
|
@manager.route(
|
|
"/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
|
|
"""
|
|
try:
|
|
res = ELASTICSEARCH.get(chunk_id, search.index_name(tenant_id))
|
|
except Exception:
|
|
return get_error_data_result(f"Can't find this chunk {chunk_id}")
|
|
if not KnowledgebaseService.query(id=dataset_id, tenant_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]
|
|
query = {
|
|
"doc_ids": [document_id],
|
|
"page": 1,
|
|
"size": 1024,
|
|
"question": "",
|
|
"sort": True,
|
|
}
|
|
sres = retrievaler.search(query, search.index_name(tenant_id), highlight=True)
|
|
if chunk_id not in sres.ids:
|
|
return get_error_data_result(f"You don't own the chunk {chunk_id}")
|
|
req = request.json
|
|
content = res["_source"].get("content_with_weight")
|
|
d = {"id": chunk_id, "content_with_weight": req.get("content", 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 "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"]])
|
|
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()
|
|
ELASTICSEARCH.upsert([d], search.index_name(tenant_id))
|
|
return get_result()
|
|
|
|
|
|
@manager.route("/retrieval", methods=["POST"])
|
|
@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.query(id=id, tenant_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=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", 1024))
|
|
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 = retrievaler if kb.parser_id != ParserType.KG else 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"]:
|
|
if "vector" in c:
|
|
del c["vector"]
|
|
|
|
##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",
|
|
"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=RetCode.DATA_ERROR,
|
|
)
|
|
return server_error_response(e)
|