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ad4e59edb2
### What problem does this PR solve? Actually fix #6241 Hello, I ran into the same problem as #6241. When I'm testing my agent flow in the web ui using `Run` button with a file input, the retrieval component always gave an empty output. In the code I found that: `web/src/pages/flow/debug-content/index.tsx`: ```tsx const onOk = useCallback(async () => { const values = await form.validateFields(); const nextValues = Object.entries(values).map(([key, value]) => { const item = parameters[Number(key)]; let nextValue = value; if (Array.isArray(value)) { nextValue = ``; value.forEach((x) => { nextValue += x?.originFileObj instanceof File ? `${x.name}\n${x.response?.data}\n----\n` // Here, the file content always ends in '\n' : `${x.url}\n${x.result}\n----\n`; }); } return { ...item, value: nextValue }; }); ok(nextValues); }, [form, ok, parameters]); ``` while in the `agent/component/retrieval.py`: ```python def _run(self, history, **kwargs): query = self.get_input() query = str(query["content"][0]) if "content" in query else "" lines = query.split('\n') # inputs are split to ['xxx','yyy','----',''] query = lines[-1] if lines else "" # Here we always get '', thus no result kbs = KnowledgebaseService.get_by_ids(self._param.kb_ids) if not kbs: return Retrieval.be_output("") ``` so the code will never got correct result. I'm not sure why the input needs such a split here, so I just removed the splitting, and it works well on my side. ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) - [ ] New Feature (non-breaking change which adds functionality) - [ ] Documentation Update - [ ] Refactoring - [ ] Performance Improvement - [ ] Other (please describe):
112 lines
4.2 KiB
Python
112 lines
4.2 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 logging
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from abc import ABC
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import pandas as pd
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from api.db import LLMType
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from api.db.services.knowledgebase_service import KnowledgebaseService
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from api.db.services.llm_service import LLMBundle
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from api import settings
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from agent.component.base import ComponentBase, ComponentParamBase
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from rag.app.tag import label_question
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from rag.utils.tavily_conn import Tavily
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class RetrievalParam(ComponentParamBase):
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"""
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Define the Retrieval component parameters.
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"""
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def __init__(self):
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super().__init__()
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self.similarity_threshold = 0.2
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self.keywords_similarity_weight = 0.5
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self.top_n = 8
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self.top_k = 1024
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self.kb_ids = []
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self.rerank_id = ""
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self.empty_response = ""
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self.tavily_api_key = ""
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self.use_kg = False
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def check(self):
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self.check_decimal_float(self.similarity_threshold, "[Retrieval] Similarity threshold")
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self.check_decimal_float(self.keywords_similarity_weight, "[Retrieval] Keyword similarity weight")
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self.check_positive_number(self.top_n, "[Retrieval] Top N")
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class Retrieval(ComponentBase, ABC):
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component_name = "Retrieval"
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def _run(self, history, **kwargs):
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query = self.get_input()
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query = str(query["content"][0]) if "content" in query else ""
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kbs = KnowledgebaseService.get_by_ids(self._param.kb_ids)
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if not kbs:
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return Retrieval.be_output("")
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embd_nms = list(set([kb.embd_id for kb in kbs]))
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assert len(embd_nms) == 1, "Knowledge bases use different embedding models."
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embd_mdl = None
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if embd_nms:
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embd_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.EMBEDDING, embd_nms[0])
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self._canvas.set_embedding_model(embd_nms[0])
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rerank_mdl = None
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if self._param.rerank_id:
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rerank_mdl = LLMBundle(kbs[0].tenant_id, LLMType.RERANK, self._param.rerank_id)
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if kbs:
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kbinfos = settings.retrievaler.retrieval(query, embd_mdl, kbs[0].tenant_id, self._param.kb_ids,
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1, self._param.top_n,
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self._param.similarity_threshold, 1 - self._param.keywords_similarity_weight,
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aggs=False, rerank_mdl=rerank_mdl,
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rank_feature=label_question(query, kbs))
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else:
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kbinfos = {"chunks": [], "doc_aggs": []}
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if self._param.use_kg and kbs:
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ck = settings.kg_retrievaler.retrieval(query,
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[kbs[0].tenant_id],
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self._param.kb_ids,
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embd_mdl,
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LLMBundle(kbs[0].tenant_id, LLMType.CHAT))
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if ck["content_with_weight"]:
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kbinfos["chunks"].insert(0, ck)
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if self._param.tavily_api_key:
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tav = Tavily(self._param.tavily_api_key)
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tav_res = tav.retrieve_chunks(query)
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kbinfos["chunks"].extend(tav_res["chunks"])
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kbinfos["doc_aggs"].extend(tav_res["doc_aggs"])
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if not kbinfos["chunks"]:
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df = Retrieval.be_output("")
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if self._param.empty_response and self._param.empty_response.strip():
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df["empty_response"] = self._param.empty_response
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return df
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df = pd.DataFrame(kbinfos["chunks"])
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df["content"] = df["content_with_weight"]
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del df["content_with_weight"]
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logging.debug("{} {}".format(query, df))
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return df.dropna()
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