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#
# 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.
#
from flask import request
from api.db import StatusEnum
from api.db.db_models import TenantLLM
from api.db.services.dialog_service import DialogService
from api.db.services.knowledgebase_service import KnowledgebaseService
from api.db.services.llm_service import LLMService, TenantLLMService
from api.db.services.user_service import TenantService
from api.settings import RetCode
from api.utils import get_uuid
from api.utils.api_utils import get_data_error_result, token_required
from api.utils.api_utils import get_json_result
@manager.route('/save', methods=['POST'])
@token_required
def save(tenant_id):
req = request.json
# dataset
if req.get("knowledgebases") == []:
return get_data_error_result(retmsg="knowledgebases can not be empty list")
kb_list = []
if req.get("knowledgebases"):
for kb in req.get("knowledgebases"):
if not kb["id"]:
return get_data_error_result(retmsg="knowledgebase needs id")
if not KnowledgebaseService.query(id=kb["id"], tenant_id=tenant_id):
return get_data_error_result(retmsg="you do not own the knowledgebase")
# if not DocumentService.query(kb_id=kb["id"]):
# return get_data_error_result(retmsg="There is a invalid knowledgebase")
kb_list.append(kb["id"])
req["kb_ids"] = kb_list
# llm
llm = req.get("llm")
if llm:
if "model_name" in llm:
req["llm_id"] = llm.pop("model_name")
req["llm_setting"] = req.pop("llm")
e, tenant = TenantService.get_by_id(tenant_id)
if not e:
return get_data_error_result(retmsg="Tenant not found!")
# prompt
prompt = req.get("prompt")
key_mapping = {"parameters": "variables",
"prologue": "opener",
"quote": "show_quote",
"system": "prompt",
"rerank_id": "rerank_model",
"vector_similarity_weight": "keywords_similarity_weight"}
key_list = ["similarity_threshold", "vector_similarity_weight", "top_n", "rerank_id"]
if prompt:
for new_key, old_key in key_mapping.items():
if old_key in prompt:
prompt[new_key] = prompt.pop(old_key)
for key in key_list:
if key in prompt:
req[key] = prompt.pop(key)
req["prompt_config"] = req.pop("prompt")
# create
if "id" not in req:
# dataset
if not kb_list:
return get_data_error_result(retmsg="knowledgebases are required!")
# init
req["id"] = get_uuid()
req["description"] = req.get("description", "A helpful Assistant")
req["icon"] = req.get("avatar", "")
req["top_n"] = req.get("top_n", 6)
req["top_k"] = req.get("top_k", 1024)
req["rerank_id"] = req.get("rerank_id", "")
if req.get("llm_id"):
if not TenantLLMService.query(llm_name=req["llm_id"]):
return get_data_error_result(retmsg="the model_name does not exist.")
else:
req["llm_id"] = tenant.llm_id
if not req.get("name"):
return get_data_error_result(retmsg="name is required.")
if DialogService.query(name=req["name"], tenant_id=tenant_id, status=StatusEnum.VALID.value):
return get_data_error_result(retmsg="Duplicated assistant name in creating dataset.")
# tenant_id
if req.get("tenant_id"):
return get_data_error_result(retmsg="tenant_id must not be provided.")
req["tenant_id"] = tenant_id
# prompt more parameter
default_prompt = {
"system": """你是一个智能助手，请总结知识库的内容来回答问题，请列举知识库中的数据详细回答。当所有知识库内容都与问题无关时，你的回答必须包括“知识库中未找到您要的答案！”这句话。回答需要考虑聊天历史。
以下是知识库：
{knowledge}
以上是知识库。""",
"prologue": "您好，我是您的助手小樱，长得可爱又善良，can I help you?",
"parameters": [
{"key": "knowledge", "optional": False}
],
"empty_response": "Sorry! 知识库中未找到相关内容！"
}
key_list_2 = ["system", "prologue", "parameters", "empty_response"]
if "prompt_config" not in req:
req['prompt_config'] = {}
for key in key_list_2:
temp = req['prompt_config'].get(key)
if not temp:
req['prompt_config'][key] = default_prompt[key]
for p in req['prompt_config']["parameters"]:
if p["optional"]:
continue
if req['prompt_config']["system"].find("{%s}" % p["key"]) < 0:
return get_data_error_result(
retmsg="Parameter '{}' is not used".format(p["key"]))
# save
if not DialogService.save(**req):
return get_data_error_result(retmsg="Fail to new an assistant!")
# response
e, res = DialogService.get_by_id(req["id"])
if not e:
return get_data_error_result(retmsg="Fail to new an assistant!")
res = res.to_json()
renamed_dict = {}
for key, value in res["prompt_config"].items():
new_key = key_mapping.get(key, key)
renamed_dict[new_key] = value
res["prompt"] = renamed_dict
del res["prompt_config"]
new_dict = {"similarity_threshold": res["similarity_threshold"],
"keywords_similarity_weight": res["vector_similarity_weight"],
"top_n": res["top_n"],
"rerank_model": res['rerank_id']}
res["prompt"].update(new_dict)
for key in key_list:
del res[key]
res["llm"] = res.pop("llm_setting")
res["llm"]["model_name"] = res.pop("llm_id")
del res["kb_ids"]
res["knowledgebases"] = req["knowledgebases"]
res["avatar"] = res.pop("icon")
return get_json_result(data=res)
else:
# authorization
if not DialogService.query(tenant_id=tenant_id, id=req["id"], status=StatusEnum.VALID.value):
return get_json_result(data=False, retmsg='You do not own the assistant', retcode=RetCode.OPERATING_ERROR)
# prompt
if not req["id"]:
return get_data_error_result(retmsg="id can not be empty")
e, res = DialogService.get_by_id(req["id"])
res = res.to_json()
if "llm_id" in req:
if not TenantLLMService.query(llm_name=req["llm_id"]):
return get_data_error_result(retmsg="the model_name does not exist.")
if "name" in req:
if not req.get("name"):
return get_data_error_result(retmsg="name is not empty.")
if req["name"].lower() != res["name"].lower() \
and len(
DialogService.query(name=req["name"], tenant_id=tenant_id, status=StatusEnum.VALID.value)) > 0:
return get_data_error_result(retmsg="Duplicated assistant name in updating dataset.")
if "prompt_config" in req:
res["prompt_config"].update(req["prompt_config"])
for p in res["prompt_config"]["parameters"]:
if p["optional"]:
continue
if res["prompt_config"]["system"].find("{%s}" % p["key"]) < 0:
return get_data_error_result(retmsg="Parameter '{}' is not used".format(p["key"]))
if "llm_setting" in req:
res["llm_setting"].update(req["llm_setting"])
req["prompt_config"] = res["prompt_config"]
req["llm_setting"] = res["llm_setting"]
# avatar
if "avatar" in req:
req["icon"] = req.pop("avatar")
assistant_id = req.pop("id")
if "knowledgebases" in req:
req.pop("knowledgebases")
if not DialogService.update_by_id(assistant_id, req):
return get_data_error_result(retmsg="Assistant not found!")
return get_json_result(data=True)
@manager.route('/delete', methods=['DELETE'])
@token_required
def delete(tenant_id):
req = request.args
if "id" not in req:
return get_data_error_result(retmsg="id is required")
id = req['id']
if not DialogService.query(tenant_id=tenant_id, id=id, status=StatusEnum.VALID.value):
return get_json_result(data=False, retmsg='you do not own the assistant.', retcode=RetCode.OPERATING_ERROR)
temp_dict = {"status": StatusEnum.INVALID.value}
DialogService.update_by_id(req["id"], temp_dict)
return get_json_result(data=True)
@manager.route('/get', methods=['GET'])
@token_required
def get(tenant_id):
req = request.args
if "id" in req:
id = req["id"]
ass = DialogService.query(tenant_id=tenant_id, id=id, status=StatusEnum.VALID.value)
if not ass:
return get_json_result(data=False, retmsg='You do not own the assistant.', retcode=RetCode.OPERATING_ERROR)
if "name" in req:
name = req["name"]
if ass[0].name != name:
return get_json_result(data=False, retmsg='name does not match id.', retcode=RetCode.OPERATING_ERROR)
res = ass[0].to_json()
else:
if "name" in req:
name = req["name"]
ass = DialogService.query(name=name, tenant_id=tenant_id, status=StatusEnum.VALID.value)
if not ass:
return get_json_result(data=False, retmsg='You do not own the assistant.',
retcode=RetCode.OPERATING_ERROR)
res = ass[0].to_json()
else:
return get_data_error_result(retmsg="At least one of `id` or `name` must be provided.")
renamed_dict = {}
key_mapping = {"parameters": "variables",
"prologue": "opener",
"quote": "show_quote",
"system": "prompt",
"rerank_id": "rerank_model",
"vector_similarity_weight": "keywords_similarity_weight"}
key_list = ["similarity_threshold", "vector_similarity_weight", "top_n", "rerank_id"]
for key, value in res["prompt_config"].items():
new_key = key_mapping.get(key, key)
renamed_dict[new_key] = value
res["prompt"] = renamed_dict
del res["prompt_config"]
new_dict = {"similarity_threshold": res["similarity_threshold"],
"keywords_similarity_weight": res["vector_similarity_weight"],
"top_n": res["top_n"],
"rerank_model": res['rerank_id']}
res["prompt"].update(new_dict)
for key in key_list:
del res[key]
res["llm"] = res.pop("llm_setting")
res["llm"]["model_name"] = res.pop("llm_id")
kb_list = []
for kb_id in res["kb_ids"]:
kb = KnowledgebaseService.query(id=kb_id)
kb_list.append(kb[0].to_json())
del res["kb_ids"]
res["knowledgebases"] = kb_list
res["avatar"] = res.pop("icon")
return get_json_result(data=res)
@manager.route('/list', methods=['GET'])
@token_required
def list_assistants(tenant_id):
assts = DialogService.query(
tenant_id=tenant_id,
status=StatusEnum.VALID.value,
reverse=True,
order_by=DialogService.model.create_time)
assts = [d.to_dict() for d in assts]
list_assts = []
renamed_dict = {}
key_mapping = {"parameters": "variables",
"prologue": "opener",
"quote": "show_quote",
"system": "prompt",
"rerank_id": "rerank_model",
"vector_similarity_weight": "keywords_similarity_weight"}
key_list = ["similarity_threshold", "vector_similarity_weight", "top_n", "rerank_id"]
for res in assts:
for key, value in res["prompt_config"].items():
new_key = key_mapping.get(key, key)
renamed_dict[new_key] = value
res["prompt"] = renamed_dict
del res["prompt_config"]
new_dict = {"similarity_threshold": res["similarity_threshold"],
"keywords_similarity_weight": res["vector_similarity_weight"],
"top_n": res["top_n"],
"rerank_model": res['rerank_id']}
res["prompt"].update(new_dict)
for key in key_list:
del res[key]
res["llm"] = res.pop("llm_setting")
res["llm"]["model_name"] = res.pop("llm_id")
kb_list = []
for kb_id in res["kb_ids"]:
kb = KnowledgebaseService.query(id=kb_id)
kb_list.append(kb[0].to_json())
del res["kb_ids"]
res["knowledgebases"] = kb_list
res["avatar"] = res.pop("icon")
list_assts.append(res)
return get_json_result(data=list_assts)
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