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from flask import request, jsonify
from db import LLMType, ParserType
from db.services.knowledgebase_service import KnowledgebaseService
from db.services.llm_service import LLMBundle
from settings import retrievaler, kg_retrievaler, RetCode
from utils.api_utils import validate_request, build_error_result, apikey_required
@manager.route('/dify/retrieval', methods=['POST'])
@apikey_required
@validate_request("knowledge_id", "query")
def retrieval(tenant_id):
req = request.json
question = req["query"]
kb_id = req["knowledge_id"]
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(error_msg="Knowledgebase not found!", retcode=RetCode.NOT_FOUND)
if kb.tenant_id != tenant_id:
return build_error_result(error_msg="Knowledgebase not found!", retcode=RetCode.NOT_FOUND)
embd_mdl = LLMBundle(kb.tenant_id, LLMType.EMBEDDING.value, llm_name=kb.embd_id)
retr = retrievaler if kb.parser_id != ParserType.KG else kg_retrievaler
ranks = retr.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
)
records = []
for c in ranks["chunks"]:
if "vector" in c:
del c["vector"]
records.append({
"content": c["content_ltks"],
"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(
error_msg=f'No chunk found! Check the chunk status please!',
retcode=RetCode.NOT_FOUND
)
return build_error_result(error_msg=str(e), retcode=RetCode.SERVER_ERROR)
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