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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, jsonify
from api.db import LLMType
from api.db.services.knowledgebase_service import KnowledgebaseService
from api.db.services.llm_service import LLMBundle
from api import settings
from api.utils.api_utils import validate_request, build_error_result, apikey_required
from rag.app.tag import label_question
@manager.route('/dify/retrieval', methods=['POST']) # noqa: F821
@apikey_required
@validate_request("knowledge_id", "query")
def retrieval(tenant_id):
req = request.json
question = req["query"]
kb_id = req["knowledge_id"]
use_kg = req.get("use_kg", False)
retrieval_setting = req.get("retrieval_setting", {})
similarity_threshold = float(retrieval_setting.get("score_threshold", 0.0))
top = int(retrieval_setting.get("top_k", 1024))
try:
e, kb = KnowledgebaseService.get_by_id(kb_id)
if not e:
return build_error_result(message="Knowledgebase not found!", code=settings.RetCode.NOT_FOUND)
if kb.tenant_id != tenant_id:
return build_error_result(message="Knowledgebase not found!", code=settings.RetCode.NOT_FOUND)
embd_mdl = LLMBundle(kb.tenant_id, LLMType.EMBEDDING.value, llm_name=kb.embd_id)
ranks = settings.retrievaler.retrieval(
question,
embd_mdl,
kb.tenant_id,
[kb_id],
page=1,
page_size=top,
similarity_threshold=similarity_threshold,
vector_similarity_weight=0.3,
top=top,
rank_feature=label_question(question, [kb])
)
if use_kg:
ck = settings.kg_retrievaler.retrieval(question,
[tenant_id],
[kb_id],
embd_mdl,
LLMBundle(kb.tenant_id, LLMType.CHAT))
if ck["content_with_weight"]:
ranks["chunks"].insert(0, ck)
records = []
for c in ranks["chunks"]:
c.pop("vector", None)
records.append({
"content": c["content_with_weight"],
"score": c["similarity"],
"title": c["docnm_kwd"],
"metadata": {}
})
return jsonify({"records": records})
except Exception as e:
if str(e).find("not_found") > 0:
return build_error_result(
message='No chunk found! Check the chunk status please!',
code=settings.RetCode.NOT_FOUND
)
return build_error_result(message=str(e), code=settings.RetCode.SERVER_ERROR)
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