KevinHuSh
remove unused codes, seperate layout detection out as a new api. Add new rag methed 'table' (#55)
407b252
| # | |
| # 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 datetime | |
| from flask import request | |
| from flask_login import login_required, current_user | |
| from elasticsearch_dsl import Q | |
| from rag.app.qa import rmPrefix, beAdoc | |
| from rag.nlp import search, huqie, retrievaler | |
| from rag.utils import ELASTICSEARCH, rmSpace | |
| from api.db import LLMType, ParserType | |
| from api.db.services.knowledgebase_service import KnowledgebaseService | |
| from api.db.services.llm_service import TenantLLMService | |
| from api.db.services.user_service import UserTenantService | |
| from api.utils.api_utils import server_error_response, get_data_error_result, validate_request | |
| from api.db.services.document_service import DocumentService | |
| from api.settings import RetCode | |
| from api.utils.api_utils import get_json_result | |
| import hashlib | |
| import re | |
| def list(): | |
| req = request.json | |
| doc_id = req["doc_id"] | |
| page = int(req.get("page", 1)) | |
| size = int(req.get("size", 30)) | |
| question = req.get("keywords", "") | |
| try: | |
| tenant_id = DocumentService.get_tenant_id(req["doc_id"]) | |
| if not tenant_id: | |
| return get_data_error_result(retmsg="Tenant not found!") | |
| query = { | |
| "doc_ids": [doc_id], "page": page, "size": size, "question": question | |
| } | |
| if "available_int" in req: | |
| query["available_int"] = int(req["available_int"]) | |
| sres = retrievaler.search(query, search.index_name(tenant_id)) | |
| res = {"total": sres.total, "chunks": []} | |
| for id in sres.ids: | |
| d = { | |
| "chunk_id": id, | |
| "content_with_weight": rmSpace(sres.highlight[id]) if question else sres.field[id]["content_with_weight"], | |
| "doc_id": sres.field[id]["doc_id"], | |
| "docnm_kwd": sres.field[id]["docnm_kwd"], | |
| "important_kwd": sres.field[id].get("important_kwd", []), | |
| "img_id": sres.field[id].get("img_id", ""), | |
| "available_int": sres.field[id].get("available_int", 1), | |
| } | |
| res["chunks"].append(d) | |
| return get_json_result(data=res) | |
| except Exception as e: | |
| if str(e).find("not_found") > 0: | |
| return get_json_result(data=False, retmsg=f'Index not found!', | |
| retcode=RetCode.DATA_ERROR) | |
| return server_error_response(e) | |
| def get(): | |
| chunk_id = request.args["chunk_id"] | |
| try: | |
| tenants = UserTenantService.query(user_id=current_user.id) | |
| if not tenants: | |
| return get_data_error_result(retmsg="Tenant not found!") | |
| res = ELASTICSEARCH.get( | |
| chunk_id, search.index_name( | |
| tenants[0].tenant_id)) | |
| if not res.get("found"): | |
| return server_error_response("Chunk not found") | |
| id = res["_id"] | |
| res = res["_source"] | |
| res["chunk_id"] = id | |
| k = [] | |
| for n in res.keys(): | |
| if re.search(r"(_vec$|_sm_|_tks|_ltks)", n): | |
| k.append(n) | |
| for n in k: | |
| del res[n] | |
| return get_json_result(data=res) | |
| except Exception as e: | |
| if str(e).find("NotFoundError") >= 0: | |
| return get_json_result(data=False, retmsg=f'Chunk not found!', | |
| retcode=RetCode.DATA_ERROR) | |
| return server_error_response(e) | |
| def set(): | |
| req = request.json | |
| d = {"id": req["chunk_id"]} | |
| d["content_ltks"] = huqie.qie(req["content_with_weight"]) | |
| d["content_sm_ltks"] = huqie.qieqie(d["content_ltks"]) | |
| d["important_kwd"] = req["important_kwd"] | |
| d["important_tks"] = huqie.qie(" ".join(req["important_kwd"])) | |
| if "available_int" in req: | |
| d["available_int"] = req["available_int"] | |
| try: | |
| tenant_id = DocumentService.get_tenant_id(req["doc_id"]) | |
| if not tenant_id: | |
| return get_data_error_result(retmsg="Tenant not found!") | |
| embd_mdl = TenantLLMService.model_instance( | |
| tenant_id, LLMType.EMBEDDING.value) | |
| e, doc = DocumentService.get_by_id(req["doc_id"]) | |
| if not e: | |
| return get_data_error_result(retmsg="Document not found!") | |
| if doc.parser_id == ParserType.QA: | |
| arr = [t for t in re.split(r"[\n\t]", req["content_with_weight"]) if len(t)>1] | |
| if len(arr) != 2: return get_data_error_result(retmsg="Q&A must be separated by TAB/ENTER key.") | |
| q, a = rmPrefix(arr[0]), rmPrefix[arr[1]] | |
| d = beAdoc(d, arr[0], arr[1], not any([huqie.is_chinese(t) for t in q+a])) | |
| v, c = embd_mdl.encode([doc.name, req["content_with_weight"]]) | |
| v = 0.1 * v[0] + 0.9 * v[1] if doc.parser_id != ParserType.QA else v[1] | |
| d["q_%d_vec" % len(v)] = v.tolist() | |
| ELASTICSEARCH.upsert([d], search.index_name(tenant_id)) | |
| return get_json_result(data=True) | |
| except Exception as e: | |
| return server_error_response(e) | |
| def switch(): | |
| req = request.json | |
| try: | |
| tenant_id = DocumentService.get_tenant_id(req["doc_id"]) | |
| if not tenant_id: | |
| return get_data_error_result(retmsg="Tenant not found!") | |
| if not ELASTICSEARCH.upsert([{"id": i, "available_int": int(req["available_int"])} for i in req["chunk_ids"]], | |
| search.index_name(tenant_id)): | |
| return get_data_error_result(retmsg="Index updating failure") | |
| return get_json_result(data=True) | |
| except Exception as e: | |
| return server_error_response(e) | |
| def rm(): | |
| req = request.json | |
| try: | |
| if not ELASTICSEARCH.deleteByQuery(Q("ids", values=req["chunk_ids"]), search.index_name(current_user.id)): | |
| return get_data_error_result(retmsg="Index updating failure") | |
| return get_json_result(data=True) | |
| except Exception as e: | |
| return server_error_response(e) | |
| def create(): | |
| req = request.json | |
| md5 = hashlib.md5() | |
| md5.update((req["content_with_weight"] + req["doc_id"]).encode("utf-8")) | |
| chunck_id = md5.hexdigest() | |
| d = {"id": chunck_id, "content_ltks": huqie.qie(req["content_with_weight"])} | |
| d["content_sm_ltks"] = huqie.qieqie(d["content_ltks"]) | |
| d["important_kwd"] = req.get("important_kwd", []) | |
| d["important_tks"] = huqie.qie(" ".join(req.get("important_kwd", []))) | |
| d["create_time"] = str(datetime.datetime.now()).replace("T", " ")[:19] | |
| try: | |
| e, doc = DocumentService.get_by_id(req["doc_id"]) | |
| if not e: | |
| return get_data_error_result(retmsg="Document not found!") | |
| d["kb_id"] = [doc.kb_id] | |
| d["docnm_kwd"] = doc.name | |
| d["doc_id"] = doc.id | |
| tenant_id = DocumentService.get_tenant_id(req["doc_id"]) | |
| if not tenant_id: | |
| return get_data_error_result(retmsg="Tenant not found!") | |
| embd_mdl = TenantLLMService.model_instance( | |
| tenant_id, LLMType.EMBEDDING.value) | |
| v, c = embd_mdl.encode([doc.name, req["content_with_weight"]]) | |
| DocumentService.increment_chunk_num(req["doc_id"], doc.kb_id, c, 1, 0) | |
| v = 0.1 * v[0] + 0.9 * v[1] | |
| d["q_%d_vec" % len(v)] = v.tolist() | |
| ELASTICSEARCH.upsert([d], search.index_name(tenant_id)) | |
| return get_json_result(data={"chunk_id": chunck_id}) | |
| except Exception as e: | |
| return server_error_response(e) | |
| def retrieval_test(): | |
| req = request.json | |
| page = int(req.get("page", 1)) | |
| size = int(req.get("size", 30)) | |
| question = req["question"] | |
| kb_id = req["kb_id"] | |
| doc_ids = req.get("doc_ids", []) | |
| similarity_threshold = float(req.get("similarity_threshold", 0.2)) | |
| vector_similarity_weight = float(req.get("vector_similarity_weight", 0.3)) | |
| top = int(req.get("top", 1024)) | |
| try: | |
| e, kb = KnowledgebaseService.get_by_id(kb_id) | |
| if not e: | |
| return get_data_error_result(retmsg="Knowledgebase not found!") | |
| embd_mdl = TenantLLMService.model_instance( | |
| kb.tenant_id, LLMType.EMBEDDING.value) | |
| ranks = retrievaler.retrieval(question, embd_mdl, kb.tenant_id, [kb_id], page, size, similarity_threshold, | |
| vector_similarity_weight, top, doc_ids) | |
| return get_json_result(data=ranks) | |
| except Exception as e: | |
| if str(e).find("not_found") > 0: | |
| return get_json_result(data=False, retmsg=f'Index not found!', | |
| retcode=RetCode.DATA_ERROR) | |
| return server_error_response(e) | |