# # 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 import json import re import xxhash from fastapi import APIRouter, Depends, Query from api.apps.models.auth_dependencies import get_current_user from api.apps.models.chunk_models import ( ListChunksRequest, SetChunkRequest, SwitchChunksRequest, DeleteChunksRequest, CreateChunkRequest, RetrievalTestRequest, ) from api import settings from api.db import LLMType, ParserType from api.db.services.dialog_service import meta_filter from api.db.services.document_service import DocumentService from api.db.services.knowledgebase_service import KnowledgebaseService from api.db.services.llm_service import LLMBundle from api.db.services.search_service import SearchService from api.db.services.user_service import UserTenantService from api.utils.api_utils import get_data_error_result, get_json_result, server_error_response from rag.app.qa import beAdoc, rmPrefix from rag.app.tag import label_question from rag.nlp import rag_tokenizer, search from rag.prompts.generator import gen_meta_filter, cross_languages, keyword_extraction from rag.settings import PAGERANK_FLD from rag.utils import rmSpace # 创建路由器 router = APIRouter() @router.post('/list') async def list_chunk( request: ListChunksRequest, current_user = Depends(get_current_user) ): """列出文档块""" doc_id = request.doc_id page = request.page or 1 size = request.size or 30 question = request.keywords or "" try: tenant_id = DocumentService.get_tenant_id(doc_id) if not tenant_id: return get_data_error_result(message="Tenant not found!") e, doc = DocumentService.get_by_id(doc_id) if not e: return get_data_error_result(message="Document not found!") kb_ids = KnowledgebaseService.get_kb_ids(tenant_id) query = { "doc_ids": [doc_id], "page": page, "size": size, "question": question, "sort": True } if request.available_int is not None: query["available_int"] = int(request.available_int) sres = settings.retriever.search(query, search.index_name(tenant_id), kb_ids, highlight=["content_ltks"]) res = {"total": sres.total, "chunks": [], "doc": doc.to_dict()} for id in sres.ids: d = { "chunk_id": id, "content_with_weight": rmSpace(sres.highlight[id]) if question and id in sres.highlight else sres.field[ id].get( "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", []), "question_kwd": sres.field[id].get("question_kwd", []), "image_id": sres.field[id].get("img_id", ""), "available_int": int(sres.field[id].get("available_int", 1)), "positions": sres.field[id].get("position_int", []), } assert isinstance(d["positions"], list) assert len(d["positions"]) == 0 or (isinstance(d["positions"][0], list) and len(d["positions"][0]) == 5) 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, message='No chunk found!', code=settings.RetCode.DATA_ERROR) return server_error_response(e) @router.get('/get') async def get( chunk_id: str = Query(..., description="块ID"), current_user = Depends(get_current_user) ): """获取文档块""" try: chunk = None tenants = UserTenantService.query(user_id=current_user.id) if not tenants: return get_data_error_result(message="Tenant not found!") for tenant in tenants: kb_ids = KnowledgebaseService.get_kb_ids(tenant.tenant_id) chunk = settings.docStoreConn.get(chunk_id, search.index_name(tenant.tenant_id), kb_ids) if chunk: break if chunk is None: return server_error_response(Exception("Chunk not found")) k = [] for n in chunk.keys(): if re.search(r"(_vec$|_sm_|_tks|_ltks)", n): k.append(n) for n in k: del chunk[n] return get_json_result(data=chunk) except Exception as e: if str(e).find("NotFoundError") >= 0: return get_json_result(data=False, message='Chunk not found!', code=settings.RetCode.DATA_ERROR) return server_error_response(e) @router.post('/set') async def set( request: SetChunkRequest, current_user = Depends(get_current_user) ): """设置文档块""" d = { "id": request.chunk_id, "content_with_weight": request.content_with_weight} d["content_ltks"] = rag_tokenizer.tokenize(request.content_with_weight) d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"]) if request.important_kwd is not None: if not isinstance(request.important_kwd, list): return get_data_error_result(message="`important_kwd` should be a list") d["important_kwd"] = request.important_kwd d["important_tks"] = rag_tokenizer.tokenize(" ".join(request.important_kwd)) if request.question_kwd is not None: if not isinstance(request.question_kwd, list): return get_data_error_result(message="`question_kwd` should be a list") d["question_kwd"] = request.question_kwd d["question_tks"] = rag_tokenizer.tokenize("\n".join(request.question_kwd)) if request.tag_kwd is not None: d["tag_kwd"] = request.tag_kwd if request.tag_feas is not None: d["tag_feas"] = request.tag_feas if request.available_int is not None: d["available_int"] = request.available_int try: tenant_id = DocumentService.get_tenant_id(request.doc_id) if not tenant_id: return get_data_error_result(message="Tenant not found!") embd_id = DocumentService.get_embd_id(request.doc_id) embd_mdl = LLMBundle(tenant_id, LLMType.EMBEDDING, embd_id) e, doc = DocumentService.get_by_id(request.doc_id) if not e: return get_data_error_result(message="Document not found!") if doc.parser_id == ParserType.QA: arr = [ t for t in re.split( r"[\n\t]", request.content_with_weight) if len(t) > 1] q, a = rmPrefix(arr[0]), rmPrefix("\n".join(arr[1:])) d = beAdoc(d, q, a, not any( [rag_tokenizer.is_chinese(t) for t in q + a])) v, c = embd_mdl.encode([doc.name, request.content_with_weight if not d.get("question_kwd") else "\n".join(d["question_kwd"])]) 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() settings.docStoreConn.update({"id": request.chunk_id}, d, search.index_name(tenant_id), doc.kb_id) return get_json_result(data=True) except Exception as e: return server_error_response(e) @router.post('/switch') async def switch( request: SwitchChunksRequest, current_user = Depends(get_current_user) ): """切换文档块状态""" try: e, doc = DocumentService.get_by_id(request.doc_id) if not e: return get_data_error_result(message="Document not found!") for cid in request.chunk_ids: if not settings.docStoreConn.update({"id": cid}, {"available_int": int(request.available_int)}, search.index_name(DocumentService.get_tenant_id(request.doc_id)), doc.kb_id): return get_data_error_result(message="Index updating failure") return get_json_result(data=True) except Exception as e: return server_error_response(e) @router.post('/rm') async def rm( request: DeleteChunksRequest, current_user = Depends(get_current_user) ): """删除文档块""" from rag.utils.storage_factory import STORAGE_IMPL try: e, doc = DocumentService.get_by_id(request.doc_id) if not e: return get_data_error_result(message="Document not found!") if not settings.docStoreConn.delete({"id": request.chunk_ids}, search.index_name(DocumentService.get_tenant_id(request.doc_id)), doc.kb_id): return get_data_error_result(message="Chunk deleting failure") deleted_chunk_ids = request.chunk_ids chunk_number = len(deleted_chunk_ids) DocumentService.decrement_chunk_num(doc.id, doc.kb_id, 1, chunk_number, 0) for cid in deleted_chunk_ids: if STORAGE_IMPL.obj_exist(doc.kb_id, cid): STORAGE_IMPL.rm(doc.kb_id, cid) return get_json_result(data=True) except Exception as e: return server_error_response(e) @router.post('/create') async def create( request: CreateChunkRequest, current_user = Depends(get_current_user) ): """创建文档块""" chunck_id = xxhash.xxh64((request.content_with_weight + request.doc_id).encode("utf-8")).hexdigest() d = {"id": chunck_id, "content_ltks": rag_tokenizer.tokenize(request.content_with_weight), "content_with_weight": request.content_with_weight} d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"]) d["important_kwd"] = request.important_kwd or [] if not isinstance(d["important_kwd"], list): return get_data_error_result(message="`important_kwd` is required to be a list") d["important_tks"] = rag_tokenizer.tokenize(" ".join(d["important_kwd"])) d["question_kwd"] = request.question_kwd or [] if not isinstance(d["question_kwd"], list): return get_data_error_result(message="`question_kwd` is required to be a list") d["question_tks"] = rag_tokenizer.tokenize("\n".join(d["question_kwd"])) d["create_time"] = str(datetime.datetime.now()).replace("T", " ")[:19] d["create_timestamp_flt"] = datetime.datetime.now().timestamp() if request.tag_feas is not None: d["tag_feas"] = request.tag_feas try: e, doc = DocumentService.get_by_id(request.doc_id) if not e: return get_data_error_result(message="Document not found!") d["kb_id"] = [doc.kb_id] d["docnm_kwd"] = doc.name d["title_tks"] = rag_tokenizer.tokenize(doc.name) d["doc_id"] = doc.id tenant_id = DocumentService.get_tenant_id(request.doc_id) if not tenant_id: return get_data_error_result(message="Tenant not found!") e, kb = KnowledgebaseService.get_by_id(doc.kb_id) if not e: return get_data_error_result(message="Knowledgebase not found!") if kb.pagerank: d[PAGERANK_FLD] = kb.pagerank embd_id = DocumentService.get_embd_id(request.doc_id) embd_mdl = LLMBundle(tenant_id, LLMType.EMBEDDING.value, embd_id) v, c = embd_mdl.encode([doc.name, request.content_with_weight if not d["question_kwd"] else "\n".join(d["question_kwd"])]) v = 0.1 * v[0] + 0.9 * v[1] d["q_%d_vec" % len(v)] = v.tolist() settings.docStoreConn.insert([d], search.index_name(tenant_id), doc.kb_id) DocumentService.increment_chunk_num( doc.id, doc.kb_id, c, 1, 0) return get_json_result(data={"chunk_id": chunck_id}) except Exception as e: return server_error_response(e) @router.post('/retrieval_test') async def retrieval_test( request: RetrievalTestRequest, current_user = Depends(get_current_user) ): """检索测试""" page = request.page or 1 size = request.size or 30 question = request.question kb_ids = request.kb_id if isinstance(kb_ids, str): kb_ids = [kb_ids] if not kb_ids: return get_json_result(data=False, message='Please specify dataset firstly.', code=settings.RetCode.DATA_ERROR) doc_ids = request.doc_ids or [] use_kg = request.use_kg or False top = request.top_k or 1024 langs = request.cross_languages or [] tenant_ids = [] if request.search_id: search_config = SearchService.get_detail(request.search_id).get("search_config", {}) meta_data_filter = search_config.get("meta_data_filter", {}) metas = DocumentService.get_meta_by_kbs(kb_ids) if meta_data_filter.get("method") == "auto": chat_mdl = LLMBundle(current_user.id, LLMType.CHAT, llm_name=search_config.get("chat_id", "")) filters = gen_meta_filter(chat_mdl, metas, question) doc_ids.extend(meta_filter(metas, filters)) if not doc_ids: doc_ids = None elif meta_data_filter.get("method") == "manual": doc_ids.extend(meta_filter(metas, meta_data_filter["manual"])) if not doc_ids: doc_ids = None try: tenants = UserTenantService.query(user_id=current_user.id) for kb_id in kb_ids: for tenant in tenants: if KnowledgebaseService.query( tenant_id=tenant.tenant_id, id=kb_id): tenant_ids.append(tenant.tenant_id) break else: return get_json_result( data=False, message='Only owner of knowledgebase authorized for this operation.', code=settings.RetCode.OPERATING_ERROR) e, kb = KnowledgebaseService.get_by_id(kb_ids[0]) if not e: return get_data_error_result(message="Knowledgebase not found!") if langs: question = cross_languages(kb.tenant_id, None, question, langs) embd_mdl = LLMBundle(kb.tenant_id, LLMType.EMBEDDING.value, llm_name=kb.embd_id) rerank_mdl = None if request.rerank_id: rerank_mdl = LLMBundle(kb.tenant_id, LLMType.RERANK.value, llm_name=request.rerank_id) if request.keyword: chat_mdl = LLMBundle(kb.tenant_id, LLMType.CHAT) question += keyword_extraction(chat_mdl, question) labels = label_question(question, [kb]) ranks = settings.retriever.retrieval(question, embd_mdl, tenant_ids, kb_ids, page, size, float(request.similarity_threshold or 0.0), float(request.vector_similarity_weight or 0.3), top, doc_ids, rerank_mdl=rerank_mdl, highlight=request.highlight or False, rank_feature=labels ) if use_kg: ck = settings.kg_retriever.retrieval(question, tenant_ids, kb_ids, embd_mdl, LLMBundle(kb.tenant_id, LLMType.CHAT)) if ck["content_with_weight"]: ranks["chunks"].insert(0, ck) for c in ranks["chunks"]: c.pop("vector", None) ranks["labels"] = labels return get_json_result(data=ranks) except Exception as e: if str(e).find("not_found") > 0: return get_json_result(data=False, message='No chunk found! Check the chunk status please!', code=settings.RetCode.DATA_ERROR) return server_error_response(e) @router.get('/knowledge_graph') async def knowledge_graph( doc_id: str = Query(..., description="文档ID"), current_user = Depends(get_current_user) ): """获取知识图谱""" tenant_id = DocumentService.get_tenant_id(doc_id) kb_ids = KnowledgebaseService.get_kb_ids(tenant_id) req = { "doc_ids": [doc_id], "knowledge_graph_kwd": ["graph", "mind_map"] } sres = settings.retriever.search(req, search.index_name(tenant_id), kb_ids) obj = {"graph": {}, "mind_map": {}} for id in sres.ids[:2]: ty = sres.field[id]["knowledge_graph_kwd"] try: content_json = json.loads(sres.field[id]["content_with_weight"]) except Exception: continue if ty == 'mind_map': node_dict = {} def repeat_deal(content_json, node_dict): if 'id' in content_json: if content_json['id'] in node_dict: node_name = content_json['id'] content_json['id'] += f"({node_dict[content_json['id']]})" node_dict[node_name] += 1 else: node_dict[content_json['id']] = 1 if 'children' in content_json and content_json['children']: for item in content_json['children']: repeat_deal(item, node_dict) repeat_deal(content_json, node_dict) obj[ty] = content_json return get_json_result(data=obj)