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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.
#
import logging
from abc import ABC
import pandas as pd
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 agent.component.base import ComponentBase, ComponentParamBase
from rag.app.tag import label_question
from rag.utils.tavily_conn import Tavily
class RetrievalParam(ComponentParamBase):
"""
Define the Retrieval component parameters.
"""
def __init__(self):
super().__init__()
self.similarity_threshold = 0.2
self.keywords_similarity_weight = 0.5
self.top_n = 8
self.top_k = 1024
self.kb_ids = []
self.rerank_id = ""
self.empty_response = ""
self.tavily_api_key = ""
self.use_kg = False
def check(self):
self.check_decimal_float(self.similarity_threshold, "[Retrieval] Similarity threshold")
self.check_decimal_float(self.keywords_similarity_weight, "[Retrieval] Keyword similarity weight")
self.check_positive_number(self.top_n, "[Retrieval] Top N")
class Retrieval(ComponentBase, ABC):
component_name = "Retrieval"
def _run(self, history, **kwargs):
query = self.get_input()
query = str(query["content"][0]) if "content" in query else ""
lines = query.split('\n')
user_queries = [line.split("USER:", 1)[1] for line in lines if line.startswith("USER:")]
query = user_queries[-1] if user_queries else ""
kbs = KnowledgebaseService.get_by_ids(self._param.kb_ids)
if not kbs:
return Retrieval.be_output("")
embd_nms = list(set([kb.embd_id for kb in kbs]))
assert len(embd_nms) == 1, "Knowledge bases use different embedding models."
embd_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.EMBEDDING, embd_nms[0])
self._canvas.set_embedding_model(embd_nms[0])
rerank_mdl = None
if self._param.rerank_id:
rerank_mdl = LLMBundle(kbs[0].tenant_id, LLMType.RERANK, self._param.rerank_id)
kbinfos = settings.retrievaler.retrieval(query, embd_mdl, kbs[0].tenant_id, self._param.kb_ids,
1, self._param.top_n,
self._param.similarity_threshold, 1 - self._param.keywords_similarity_weight,
aggs=False, rerank_mdl=rerank_mdl,
rank_feature=label_question(query, kbs))
if self._param.use_kg:
ck = settings.kg_retrievaler.retrieval(query,
[kbs[0].tenant_id],
self._param.kb_ids,
embd_mdl,
LLMBundle(kbs[0].tenant_id, LLMType.CHAT))
if ck["content_with_weight"]:
kbinfos["chunks"].insert(0, ck)
if self._param.tavily_api_key:
tav = Tavily(self._param.tavily_api_key)
tav_res = tav.retrieve_chunks(query)
kbinfos["chunks"].extend(tav_res["chunks"])
kbinfos["doc_aggs"].extend(tav_res["doc_aggs"])
if not kbinfos["chunks"]:
df = Retrieval.be_output("")
if self._param.empty_response and self._param.empty_response.strip():
df["empty_response"] = self._param.empty_response
return df
df = pd.DataFrame(kbinfos["chunks"])
df["content"] = df["content_with_weight"]
del df["content_with_weight"]
logging.debug("{} {}".format(query, df))
return df
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