# # 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 functools import partial import json import os import re from abc import ABC from agent.tools.base import ToolParamBase, ToolBase, ToolMeta from common.constants import LLMType from api.db.services.document_service import DocumentService from api.db.services.dialog_service import meta_filter from api.db.services.knowledgebase_service import KnowledgebaseService from api.db.services.llm_service import LLMBundle from api import settings from common import globals from common.connection_utils import timeout from rag.app.tag import label_question from rag.prompts.generator import cross_languages, kb_prompt, gen_meta_filter class RetrievalParam(ToolParamBase): """ Define the Retrieval component parameters. """ def __init__(self): self.meta:ToolMeta = { "name": "search_my_dateset", "description": "This tool can be utilized for relevant content searching in the datasets.", "parameters": { "query": { "type": "string", "description": "The keywords to search the dataset. The keywords should be the most important words/terms(includes synonyms) from the original request.", "default": "", "required": True } } } super().__init__() self.function_name = "search_my_dateset" self.description = "This tool can be utilized for relevant content searching in the datasets." self.similarity_threshold = 0.2 self.keywords_similarity_weight = 0.5 self.top_n = 8 self.top_k = 1024 self.kb_ids = [] self.kb_vars = [] self.rerank_id = "" self.empty_response = "" self.use_kg = False self.cross_languages = [] self.toc_enhance = False self.meta_data_filter={} 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") def get_input_form(self) -> dict[str, dict]: return { "query": { "name": "Query", "type": "line" } } class Retrieval(ToolBase, ABC): component_name = "Retrieval" @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 12))) def _invoke(self, **kwargs): if not kwargs.get("query"): self.set_output("formalized_content", self._param.empty_response) kb_ids: list[str] = [] for id in self._param.kb_ids: if id.find("@") < 0: kb_ids.append(id) continue kb_nm = self._canvas.get_variable_value(id) # if kb_nm is a list kb_nm_list = kb_nm if isinstance(kb_nm, list) else [kb_nm] for nm_or_id in kb_nm_list: e, kb = KnowledgebaseService.get_by_name(nm_or_id, self._canvas._tenant_id) if not e: e, kb = KnowledgebaseService.get_by_id(nm_or_id) if not e: raise Exception(f"Dataset({nm_or_id}) does not exist.") kb_ids.append(kb.id) filtered_kb_ids: list[str] = list(set([kb_id for kb_id in kb_ids if kb_id])) kbs = KnowledgebaseService.get_by_ids(filtered_kb_ids) if not kbs: raise Exception("No dataset is selected.") embd_nms = list(set([kb.embd_id for kb in kbs])) assert len(embd_nms) == 1, "Knowledge bases use different embedding models." embd_mdl = None if embd_nms: embd_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.EMBEDDING, embd_nms[0]) rerank_mdl = None if self._param.rerank_id: rerank_mdl = LLMBundle(kbs[0].tenant_id, LLMType.RERANK, self._param.rerank_id) vars = self.get_input_elements_from_text(kwargs["query"]) vars = {k:o["value"] for k,o in vars.items()} query = self.string_format(kwargs["query"], vars) doc_ids=[] if self._param.meta_data_filter!={}: metas = DocumentService.get_meta_by_kbs(kb_ids) if self._param.meta_data_filter.get("method") == "auto": chat_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.CHAT) filters = gen_meta_filter(chat_mdl, metas, query) doc_ids.extend(meta_filter(metas, filters)) if not doc_ids: doc_ids = None elif self._param.meta_data_filter.get("method") == "manual": filters=self._param.meta_data_filter["manual"] for flt in filters: pat = re.compile(r"\{* *\{([a-zA-Z:0-9]+@[A-Za-z:0-9_.-]+|sys\.[a-z_]+)\} *\}*") s = flt["value"] out_parts = [] last = 0 for m in pat.finditer(s): out_parts.append(s[last:m.start()]) key = m.group(1) v = self._canvas.get_variable_value(key) if v is None: rep = "" elif isinstance(v, partial): buf = [] for chunk in v(): buf.append(chunk) rep = "".join(buf) elif isinstance(v, str): rep = v else: rep = json.dumps(v, ensure_ascii=False) out_parts.append(rep) last = m.end() out_parts.append(s[last:]) flt["value"] = "".join(out_parts) doc_ids.extend(meta_filter(metas, filters)) if not doc_ids: doc_ids = None if self._param.cross_languages: query = cross_languages(kbs[0].tenant_id, None, query, self._param.cross_languages) if kbs: query = re.sub(r"^user[:：\s]*", "", query, flags=re.IGNORECASE) kbinfos = globals.retriever.retrieval( query, embd_mdl, [kb.tenant_id for kb in kbs], filtered_kb_ids, 1, self._param.top_n, self._param.similarity_threshold, 1 - self._param.keywords_similarity_weight, doc_ids=doc_ids, aggs=False, rerank_mdl=rerank_mdl, rank_feature=label_question(query, kbs), ) if self._param.toc_enhance: chat_mdl = LLMBundle(self._canvas._tenant_id, LLMType.CHAT) cks = globals.retriever.retrieval_by_toc(query, kbinfos["chunks"], [kb.tenant_id for kb in kbs], chat_mdl, self._param.top_n) if cks: kbinfos["chunks"] = cks if self._param.use_kg: ck = settings.kg_retriever.retrieval(query, [kb.tenant_id for kb in kbs], kb_ids, embd_mdl, LLMBundle(self._canvas.get_tenant_id(), LLMType.CHAT)) if ck["content_with_weight"]: kbinfos["chunks"].insert(0, ck) else: kbinfos = {"chunks": [], "doc_aggs": []} if self._param.use_kg and kbs: 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)) if ck["content_with_weight"]: ck["content"] = ck["content_with_weight"] del ck["content_with_weight"] kbinfos["chunks"].insert(0, ck) for ck in kbinfos["chunks"]: if "vector" in ck: del ck["vector"] if "content_ltks" in ck: del ck["content_ltks"] if not kbinfos["chunks"]: self.set_output("formalized_content", self._param.empty_response) return # Format the chunks for JSON output (similar to how other tools do it) json_output = kbinfos["chunks"].copy() self._canvas.add_reference(kbinfos["chunks"], kbinfos["doc_aggs"]) form_cnt = "\n".join(kb_prompt(kbinfos, 200000, True)) # Set both formalized content and JSON output self.set_output("formalized_content", form_cnt) self.set_output("json", json_output) return form_cnt def thoughts(self) -> str: return """ Keywords: {} Looking for the most relevant articles. """.format(self.get_input().get("query", "-_-!"))