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ac465ba2a6
…e retrieval component. ### What problem does this PR solve? issue: #10861 change: add variables to the metadata filtering function of the knowledge retrieval component ### Type of change - [x] New Feature (non-breaking change which adds functionality)
237 lines
9.4 KiB
Python
237 lines
9.4 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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from functools import partial
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import json
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import os
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import re
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from abc import ABC
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from agent.tools.base import ToolParamBase, ToolBase, ToolMeta
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from api.db import LLMType
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from api.db.services.document_service import DocumentService
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from api.db.services.dialog_service import meta_filter
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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 api.utils.api_utils import timeout
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from rag.app.tag import label_question
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from rag.prompts.generator import cross_languages, kb_prompt, gen_meta_filter
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class RetrievalParam(ToolParamBase):
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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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self.meta:ToolMeta = {
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"name": "search_my_dateset",
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"description": "This tool can be utilized for relevant content searching in the datasets.",
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"parameters": {
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"query": {
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"type": "string",
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"description": "The keywords to search the dataset. The keywords should be the most important words/terms(includes synonyms) from the original request.",
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"default": "",
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"required": True
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}
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}
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}
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super().__init__()
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self.function_name = "search_my_dateset"
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self.description = "This tool can be utilized for relevant content searching in the datasets."
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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.kb_vars = []
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self.rerank_id = ""
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self.empty_response = ""
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self.use_kg = False
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self.cross_languages = []
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self.toc_enhance = False
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self.meta_data_filter={}
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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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def get_input_form(self) -> dict[str, dict]:
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return {
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"query": {
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"name": "Query",
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"type": "line"
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}
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}
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class Retrieval(ToolBase, ABC):
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component_name = "Retrieval"
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@timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 12)))
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def _invoke(self, **kwargs):
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if not kwargs.get("query"):
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self.set_output("formalized_content", self._param.empty_response)
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kb_ids: list[str] = []
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for id in self._param.kb_ids:
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if id.find("@") < 0:
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kb_ids.append(id)
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continue
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kb_nm = self._canvas.get_variable_value(id)
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# if kb_nm is a list
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kb_nm_list = kb_nm if isinstance(kb_nm, list) else [kb_nm]
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for nm_or_id in kb_nm_list:
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e, kb = KnowledgebaseService.get_by_name(nm_or_id,
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self._canvas._tenant_id)
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if not e:
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e, kb = KnowledgebaseService.get_by_id(nm_or_id)
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if not e:
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raise Exception(f"Dataset({nm_or_id}) does not exist.")
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kb_ids.append(kb.id)
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filtered_kb_ids: list[str] = list(set([kb_id for kb_id in kb_ids if kb_id]))
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kbs = KnowledgebaseService.get_by_ids(filtered_kb_ids)
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if not kbs:
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raise Exception("No dataset is selected.")
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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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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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vars = self.get_input_elements_from_text(kwargs["query"])
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vars = {k:o["value"] for k,o in vars.items()}
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query = self.string_format(kwargs["query"], vars)
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doc_ids=[]
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if self._param.meta_data_filter!={}:
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metas = DocumentService.get_meta_by_kbs(kb_ids)
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if self._param.meta_data_filter.get("method") == "auto":
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chat_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.CHAT)
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filters = gen_meta_filter(chat_mdl, metas, query)
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doc_ids.extend(meta_filter(metas, filters))
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if not doc_ids:
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doc_ids = None
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elif self._param.meta_data_filter.get("method") == "manual":
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filters=self._param.meta_data_filter["manual"]
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for flt in filters:
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pat = re.compile(r"\{* *\{([a-zA-Z:0-9]+@[A-Za-z:0-9_.-]+|sys\.[a-z_]+)\} *\}*")
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s = flt["value"]
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out_parts = []
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last = 0
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for m in pat.finditer(s):
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out_parts.append(s[last:m.start()])
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key = m.group(1)
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v = self._canvas.get_variable_value(key)
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if v is None:
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rep = ""
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elif isinstance(v, partial):
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buf = []
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for chunk in v():
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buf.append(chunk)
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rep = "".join(buf)
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elif isinstance(v, str):
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rep = v
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else:
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rep = json.dumps(v, ensure_ascii=False)
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out_parts.append(rep)
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last = m.end()
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out_parts.append(s[last:])
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flt["value"] = "".join(out_parts)
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doc_ids.extend(meta_filter(metas, filters))
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if not doc_ids:
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doc_ids = None
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if self._param.cross_languages:
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query = cross_languages(kbs[0].tenant_id, None, query, self._param.cross_languages)
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if kbs:
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query = re.sub(r"^user[:：\s]*", "", query, flags=re.IGNORECASE)
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kbinfos = settings.retriever.retrieval(
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query,
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embd_mdl,
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[kb.tenant_id for kb in kbs],
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filtered_kb_ids,
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1,
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self._param.top_n,
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self._param.similarity_threshold,
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1 - self._param.keywords_similarity_weight,
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doc_ids=doc_ids,
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aggs=False,
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rerank_mdl=rerank_mdl,
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rank_feature=label_question(query, kbs),
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)
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if self._param.toc_enhance:
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chat_mdl = LLMBundle(self._canvas._tenant_id, LLMType.CHAT)
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cks = settings.retriever.retrieval_by_toc(query, kbinfos["chunks"], [kb.tenant_id for kb in kbs], chat_mdl, self._param.top_n)
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if cks:
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kbinfos["chunks"] = cks
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if self._param.use_kg:
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ck = settings.kg_retriever.retrieval(query,
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[kb.tenant_id for kb in kbs],
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kb_ids,
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embd_mdl,
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LLMBundle(self._canvas.get_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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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_retriever.retrieval(query, [kb.tenant_id for kb in kbs], filtered_kb_ids, embd_mdl, LLMBundle(kbs[0].tenant_id, LLMType.CHAT))
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if ck["content_with_weight"]:
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ck["content"] = ck["content_with_weight"]
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del ck["content_with_weight"]
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kbinfos["chunks"].insert(0, ck)
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for ck in kbinfos["chunks"]:
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if "vector" in ck:
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del ck["vector"]
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if "content_ltks" in ck:
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del ck["content_ltks"]
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if not kbinfos["chunks"]:
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self.set_output("formalized_content", self._param.empty_response)
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return
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# Format the chunks for JSON output (similar to how other tools do it)
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json_output = kbinfos["chunks"].copy()
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self._canvas.add_reference(kbinfos["chunks"], kbinfos["doc_aggs"])
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form_cnt = "\n".join(kb_prompt(kbinfos, 200000, True))
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# Set both formalized content and JSON output
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self.set_output("formalized_content", form_cnt)
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self.set_output("json", json_output)
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return form_cnt
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def thoughts(self) -> str:
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return """
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Keywords: {}
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Looking for the most relevant articles.
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""".format(self.get_input().get("query", "-_-!"))
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