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24.4 kB
| # | |
| # 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 logging | |
| import xxhash | |
| import json | |
| import random | |
| import re | |
| from concurrent.futures import ThreadPoolExecutor | |
| from copy import deepcopy | |
| from datetime import datetime | |
| from io import BytesIO | |
| from peewee import fn | |
| from api.db.db_utils import bulk_insert_into_db | |
| from api import settings | |
| from api.utils import current_timestamp, get_format_time, get_uuid | |
| from graphrag.general.mind_map_extractor import MindMapExtractor | |
| from rag.settings import SVR_QUEUE_NAME | |
| from rag.utils.storage_factory import STORAGE_IMPL | |
| from rag.nlp import search, rag_tokenizer | |
| from api.db import FileType, TaskStatus, ParserType, LLMType | |
| from api.db.db_models import DB, Knowledgebase, Tenant, Task, UserTenant | |
| from api.db.db_models import Document | |
| from api.db.services.common_service import CommonService | |
| from api.db.services.knowledgebase_service import KnowledgebaseService | |
| from api.db import StatusEnum | |
| from rag.utils.redis_conn import REDIS_CONN | |
| class DocumentService(CommonService): | |
| model = Document | |
| def get_list(cls, kb_id, page_number, items_per_page, | |
| orderby, desc, keywords, id, name): | |
| docs = cls.model.select().where(cls.model.kb_id == kb_id) | |
| if id: | |
| docs = docs.where( | |
| cls.model.id == id) | |
| if name: | |
| docs = docs.where( | |
| cls.model.name == name | |
| ) | |
| if keywords: | |
| docs = docs.where( | |
| fn.LOWER(cls.model.name).contains(keywords.lower()) | |
| ) | |
| if desc: | |
| docs = docs.order_by(cls.model.getter_by(orderby).desc()) | |
| else: | |
| docs = docs.order_by(cls.model.getter_by(orderby).asc()) | |
| count = docs.count() | |
| docs = docs.paginate(page_number, items_per_page) | |
| return list(docs.dicts()), count | |
| def get_by_kb_id(cls, kb_id, page_number, items_per_page, | |
| orderby, desc, keywords): | |
| if keywords: | |
| docs = cls.model.select().where( | |
| (cls.model.kb_id == kb_id), | |
| (fn.LOWER(cls.model.name).contains(keywords.lower())) | |
| ) | |
| else: | |
| docs = cls.model.select().where(cls.model.kb_id == kb_id) | |
| count = docs.count() | |
| if desc: | |
| docs = docs.order_by(cls.model.getter_by(orderby).desc()) | |
| else: | |
| docs = docs.order_by(cls.model.getter_by(orderby).asc()) | |
| docs = docs.paginate(page_number, items_per_page) | |
| return list(docs.dicts()), count | |
| def insert(cls, doc): | |
| if not cls.save(**doc): | |
| raise RuntimeError("Database error (Document)!") | |
| e, kb = KnowledgebaseService.get_by_id(doc["kb_id"]) | |
| if not KnowledgebaseService.update_by_id( | |
| kb.id, {"doc_num": kb.doc_num + 1}): | |
| raise RuntimeError("Database error (Knowledgebase)!") | |
| return Document(**doc) | |
| def remove_document(cls, doc, tenant_id): | |
| cls.clear_chunk_num(doc.id) | |
| try: | |
| settings.docStoreConn.delete({"doc_id": doc.id}, search.index_name(tenant_id), doc.kb_id) | |
| settings.docStoreConn.update({"kb_id": doc.kb_id, "knowledge_graph_kwd": ["entity", "relation", "graph", "community_report"], "source_id": doc.id}, | |
| {"remove": {"source_id": doc.id}}, | |
| search.index_name(tenant_id), doc.kb_id) | |
| settings.docStoreConn.update({"kb_id": doc.kb_id, "knowledge_graph_kwd": ["graph"]}, | |
| {"removed_kwd": "Y"}, | |
| search.index_name(tenant_id), doc.kb_id) | |
| settings.docStoreConn.delete({"kb_id": doc.kb_id, "knowledge_graph_kwd": ["entity", "relation", "graph", "community_report"], "must_not": {"exists": "source_id"}}, | |
| search.index_name(tenant_id), doc.kb_id) | |
| except Exception: | |
| pass | |
| return cls.delete_by_id(doc.id) | |
| def get_newly_uploaded(cls): | |
| fields = [ | |
| cls.model.id, | |
| cls.model.kb_id, | |
| cls.model.parser_id, | |
| cls.model.parser_config, | |
| cls.model.name, | |
| cls.model.type, | |
| cls.model.location, | |
| cls.model.size, | |
| Knowledgebase.tenant_id, | |
| Tenant.embd_id, | |
| Tenant.img2txt_id, | |
| Tenant.asr_id, | |
| cls.model.update_time] | |
| docs = cls.model.select(*fields) \ | |
| .join(Knowledgebase, on=(cls.model.kb_id == Knowledgebase.id)) \ | |
| .join(Tenant, on=(Knowledgebase.tenant_id == Tenant.id)) \ | |
| .where( | |
| cls.model.status == StatusEnum.VALID.value, | |
| ~(cls.model.type == FileType.VIRTUAL.value), | |
| cls.model.progress == 0, | |
| cls.model.update_time >= current_timestamp() - 1000 * 600, | |
| cls.model.run == TaskStatus.RUNNING.value) \ | |
| .order_by(cls.model.update_time.asc()) | |
| return list(docs.dicts()) | |
| def get_unfinished_docs(cls): | |
| fields = [cls.model.id, cls.model.process_begin_at, cls.model.parser_config, cls.model.progress_msg, | |
| cls.model.run, cls.model.parser_id] | |
| docs = cls.model.select(*fields) \ | |
| .where( | |
| cls.model.status == StatusEnum.VALID.value, | |
| ~(cls.model.type == FileType.VIRTUAL.value), | |
| cls.model.progress < 1, | |
| cls.model.progress > 0) | |
| return list(docs.dicts()) | |
| def increment_chunk_num(cls, doc_id, kb_id, token_num, chunk_num, duation): | |
| num = cls.model.update(token_num=cls.model.token_num + token_num, | |
| chunk_num=cls.model.chunk_num + chunk_num, | |
| process_duation=cls.model.process_duation + duation).where( | |
| cls.model.id == doc_id).execute() | |
| if num == 0: | |
| raise LookupError( | |
| "Document not found which is supposed to be there") | |
| num = Knowledgebase.update( | |
| token_num=Knowledgebase.token_num + | |
| token_num, | |
| chunk_num=Knowledgebase.chunk_num + | |
| chunk_num).where( | |
| Knowledgebase.id == kb_id).execute() | |
| return num | |
| def decrement_chunk_num(cls, doc_id, kb_id, token_num, chunk_num, duation): | |
| num = cls.model.update(token_num=cls.model.token_num - token_num, | |
| chunk_num=cls.model.chunk_num - chunk_num, | |
| process_duation=cls.model.process_duation + duation).where( | |
| cls.model.id == doc_id).execute() | |
| if num == 0: | |
| raise LookupError( | |
| "Document not found which is supposed to be there") | |
| num = Knowledgebase.update( | |
| token_num=Knowledgebase.token_num - | |
| token_num, | |
| chunk_num=Knowledgebase.chunk_num - | |
| chunk_num | |
| ).where( | |
| Knowledgebase.id == kb_id).execute() | |
| return num | |
| def clear_chunk_num(cls, doc_id): | |
| doc = cls.model.get_by_id(doc_id) | |
| assert doc, "Can't fine document in database." | |
| num = Knowledgebase.update( | |
| token_num=Knowledgebase.token_num - | |
| doc.token_num, | |
| chunk_num=Knowledgebase.chunk_num - | |
| doc.chunk_num, | |
| doc_num=Knowledgebase.doc_num - 1 | |
| ).where( | |
| Knowledgebase.id == doc.kb_id).execute() | |
| return num | |
| def get_tenant_id(cls, doc_id): | |
| docs = cls.model.select( | |
| Knowledgebase.tenant_id).join( | |
| Knowledgebase, on=( | |
| Knowledgebase.id == cls.model.kb_id)).where( | |
| cls.model.id == doc_id, Knowledgebase.status == StatusEnum.VALID.value) | |
| docs = docs.dicts() | |
| if not docs: | |
| return | |
| return docs[0]["tenant_id"] | |
| def get_knowledgebase_id(cls, doc_id): | |
| docs = cls.model.select(cls.model.kb_id).where(cls.model.id == doc_id) | |
| docs = docs.dicts() | |
| if not docs: | |
| return | |
| return docs[0]["kb_id"] | |
| def get_tenant_id_by_name(cls, name): | |
| docs = cls.model.select( | |
| Knowledgebase.tenant_id).join( | |
| Knowledgebase, on=( | |
| Knowledgebase.id == cls.model.kb_id)).where( | |
| cls.model.name == name, Knowledgebase.status == StatusEnum.VALID.value) | |
| docs = docs.dicts() | |
| if not docs: | |
| return | |
| return docs[0]["tenant_id"] | |
| def accessible(cls, doc_id, user_id): | |
| docs = cls.model.select( | |
| cls.model.id).join( | |
| Knowledgebase, on=( | |
| Knowledgebase.id == cls.model.kb_id) | |
| ).join(UserTenant, on=(UserTenant.tenant_id == Knowledgebase.tenant_id) | |
| ).where(cls.model.id == doc_id, UserTenant.user_id == user_id).paginate(0, 1) | |
| docs = docs.dicts() | |
| if not docs: | |
| return False | |
| return True | |
| def accessible4deletion(cls, doc_id, user_id): | |
| docs = cls.model.select( | |
| cls.model.id).join( | |
| Knowledgebase, on=( | |
| Knowledgebase.id == cls.model.kb_id) | |
| ).where(cls.model.id == doc_id, Knowledgebase.created_by == user_id).paginate(0, 1) | |
| docs = docs.dicts() | |
| if not docs: | |
| return False | |
| return True | |
| def get_embd_id(cls, doc_id): | |
| docs = cls.model.select( | |
| Knowledgebase.embd_id).join( | |
| Knowledgebase, on=( | |
| Knowledgebase.id == cls.model.kb_id)).where( | |
| cls.model.id == doc_id, Knowledgebase.status == StatusEnum.VALID.value) | |
| docs = docs.dicts() | |
| if not docs: | |
| return | |
| return docs[0]["embd_id"] | |
| def get_chunking_config(cls, doc_id): | |
| configs = ( | |
| cls.model.select( | |
| cls.model.id, | |
| cls.model.kb_id, | |
| cls.model.parser_id, | |
| cls.model.parser_config, | |
| Knowledgebase.language, | |
| Knowledgebase.embd_id, | |
| Tenant.id.alias("tenant_id"), | |
| Tenant.img2txt_id, | |
| Tenant.asr_id, | |
| Tenant.llm_id, | |
| ) | |
| .join(Knowledgebase, on=(cls.model.kb_id == Knowledgebase.id)) | |
| .join(Tenant, on=(Knowledgebase.tenant_id == Tenant.id)) | |
| .where(cls.model.id == doc_id) | |
| ) | |
| configs = configs.dicts() | |
| if not configs: | |
| return None | |
| return configs[0] | |
| def get_doc_id_by_doc_name(cls, doc_name): | |
| fields = [cls.model.id] | |
| doc_id = cls.model.select(*fields) \ | |
| .where(cls.model.name == doc_name) | |
| doc_id = doc_id.dicts() | |
| if not doc_id: | |
| return | |
| return doc_id[0]["id"] | |
| def get_thumbnails(cls, docids): | |
| fields = [cls.model.id, cls.model.kb_id, cls.model.thumbnail] | |
| return list(cls.model.select( | |
| *fields).where(cls.model.id.in_(docids)).dicts()) | |
| def update_parser_config(cls, id, config): | |
| e, d = cls.get_by_id(id) | |
| if not e: | |
| raise LookupError(f"Document({id}) not found.") | |
| def dfs_update(old, new): | |
| for k, v in new.items(): | |
| if k not in old: | |
| old[k] = v | |
| continue | |
| if isinstance(v, dict): | |
| assert isinstance(old[k], dict) | |
| dfs_update(old[k], v) | |
| else: | |
| old[k] = v | |
| dfs_update(d.parser_config, config) | |
| if not config.get("raptor") and d.parser_config.get("raptor"): | |
| del d.parser_config["raptor"] | |
| cls.update_by_id(id, {"parser_config": d.parser_config}) | |
| def get_doc_count(cls, tenant_id): | |
| docs = cls.model.select(cls.model.id).join(Knowledgebase, | |
| on=(Knowledgebase.id == cls.model.kb_id)).where( | |
| Knowledgebase.tenant_id == tenant_id) | |
| return len(docs) | |
| def begin2parse(cls, docid): | |
| cls.update_by_id( | |
| docid, {"progress": random.random() * 1 / 100., | |
| "progress_msg": "Task is queued...", | |
| "process_begin_at": get_format_time() | |
| }) | |
| def update_progress(cls): | |
| MSG = { | |
| "raptor": "Start RAPTOR (Recursive Abstractive Processing for Tree-Organized Retrieval).", | |
| "graphrag": "Entities extraction progress", | |
| "graph_resolution": "Start Graph Resolution", | |
| "graph_community": "Start Graph Community Reports Generation" | |
| } | |
| docs = cls.get_unfinished_docs() | |
| for d in docs: | |
| try: | |
| tsks = Task.query(doc_id=d["id"], order_by=Task.create_time) | |
| if not tsks: | |
| continue | |
| msg = [] | |
| prg = 0 | |
| finished = True | |
| bad = 0 | |
| e, doc = DocumentService.get_by_id(d["id"]) | |
| status = doc.run # TaskStatus.RUNNING.value | |
| for t in tsks: | |
| if 0 <= t.progress < 1: | |
| finished = False | |
| prg += t.progress if t.progress >= 0 else 0 | |
| if t.progress_msg not in msg: | |
| msg.append(t.progress_msg) | |
| if t.progress == -1: | |
| bad += 1 | |
| prg /= len(tsks) | |
| if finished and bad: | |
| prg = -1 | |
| status = TaskStatus.FAIL.value | |
| elif finished: | |
| m = "\n".join(sorted(msg)) | |
| if d["parser_config"].get("raptor", {}).get("use_raptor") and m.find(MSG["raptor"]) < 0: | |
| queue_raptor_o_graphrag_tasks(d, "raptor", MSG["raptor"]) | |
| prg = 0.98 * len(tsks) / (len(tsks) + 1) | |
| elif d["parser_config"].get("graphrag", {}).get("use_graphrag") and m.find(MSG["graphrag"]) < 0: | |
| queue_raptor_o_graphrag_tasks(d, "graphrag", MSG["graphrag"]) | |
| prg = 0.98 * len(tsks) / (len(tsks) + 1) | |
| elif d["parser_config"].get("graphrag", {}).get("use_graphrag") \ | |
| and d["parser_config"].get("graphrag", {}).get("resolution") \ | |
| and m.find(MSG["graph_resolution"]) < 0: | |
| queue_raptor_o_graphrag_tasks(d, "graph_resolution", MSG["graph_resolution"]) | |
| prg = 0.98 * len(tsks) / (len(tsks) + 1) | |
| elif d["parser_config"].get("graphrag", {}).get("use_graphrag") \ | |
| and d["parser_config"].get("graphrag", {}).get("community") \ | |
| and m.find(MSG["graph_community"]) < 0: | |
| queue_raptor_o_graphrag_tasks(d, "graph_community", MSG["graph_community"]) | |
| prg = 0.98 * len(tsks) / (len(tsks) + 1) | |
| else: | |
| status = TaskStatus.DONE.value | |
| msg = "\n".join(sorted(msg)) | |
| info = { | |
| "process_duation": datetime.timestamp( | |
| datetime.now()) - | |
| d["process_begin_at"].timestamp(), | |
| "run": status} | |
| if prg != 0: | |
| info["progress"] = prg | |
| if msg: | |
| info["progress_msg"] = msg | |
| cls.update_by_id(d["id"], info) | |
| except Exception as e: | |
| if str(e).find("'0'") < 0: | |
| logging.exception("fetch task exception") | |
| def get_kb_doc_count(cls, kb_id): | |
| return len(cls.model.select(cls.model.id).where( | |
| cls.model.kb_id == kb_id).dicts()) | |
| def do_cancel(cls, doc_id): | |
| try: | |
| _, doc = DocumentService.get_by_id(doc_id) | |
| return doc.run == TaskStatus.CANCEL.value or doc.progress < 0 | |
| except Exception: | |
| pass | |
| return False | |
| def queue_raptor_o_graphrag_tasks(doc, ty, msg): | |
| chunking_config = DocumentService.get_chunking_config(doc["id"]) | |
| hasher = xxhash.xxh64() | |
| for field in sorted(chunking_config.keys()): | |
| hasher.update(str(chunking_config[field]).encode("utf-8")) | |
| def new_task(): | |
| nonlocal doc | |
| return { | |
| "id": get_uuid(), | |
| "doc_id": doc["id"], | |
| "from_page": 100000000, | |
| "to_page": 100000000, | |
| "progress_msg": datetime.now().strftime("%H:%M:%S") + " " + msg | |
| } | |
| task = new_task() | |
| for field in ["doc_id", "from_page", "to_page"]: | |
| hasher.update(str(task.get(field, "")).encode("utf-8")) | |
| hasher.update(ty.encode("utf-8")) | |
| task["digest"] = hasher.hexdigest() | |
| bulk_insert_into_db(Task, [task], True) | |
| task["task_type"] = ty | |
| assert REDIS_CONN.queue_product(SVR_QUEUE_NAME, message=task), "Can't access Redis. Please check the Redis' status." | |
| def doc_upload_and_parse(conversation_id, file_objs, user_id): | |
| from rag.app import presentation, picture, naive, audio, email | |
| from api.db.services.dialog_service import DialogService | |
| from api.db.services.file_service import FileService | |
| from api.db.services.llm_service import LLMBundle | |
| from api.db.services.user_service import TenantService | |
| from api.db.services.api_service import API4ConversationService | |
| from api.db.services.conversation_service import ConversationService | |
| e, conv = ConversationService.get_by_id(conversation_id) | |
| if not e: | |
| e, conv = API4ConversationService.get_by_id(conversation_id) | |
| assert e, "Conversation not found!" | |
| e, dia = DialogService.get_by_id(conv.dialog_id) | |
| kb_id = dia.kb_ids[0] | |
| e, kb = KnowledgebaseService.get_by_id(kb_id) | |
| if not e: | |
| raise LookupError("Can't find this knowledgebase!") | |
| embd_mdl = LLMBundle(kb.tenant_id, LLMType.EMBEDDING, llm_name=kb.embd_id, lang=kb.language) | |
| err, files = FileService.upload_document(kb, file_objs, user_id) | |
| assert not err, "\n".join(err) | |
| def dummy(prog=None, msg=""): | |
| pass | |
| FACTORY = { | |
| ParserType.PRESENTATION.value: presentation, | |
| ParserType.PICTURE.value: picture, | |
| ParserType.AUDIO.value: audio, | |
| ParserType.EMAIL.value: email | |
| } | |
| parser_config = {"chunk_token_num": 4096, "delimiter": "\n!?;。;!?", "layout_recognize": "Plain Text"} | |
| exe = ThreadPoolExecutor(max_workers=12) | |
| threads = [] | |
| doc_nm = {} | |
| for d, blob in files: | |
| doc_nm[d["id"]] = d["name"] | |
| for d, blob in files: | |
| kwargs = { | |
| "callback": dummy, | |
| "parser_config": parser_config, | |
| "from_page": 0, | |
| "to_page": 100000, | |
| "tenant_id": kb.tenant_id, | |
| "lang": kb.language | |
| } | |
| threads.append(exe.submit(FACTORY.get(d["parser_id"], naive).chunk, d["name"], blob, **kwargs)) | |
| for (docinfo, _), th in zip(files, threads): | |
| docs = [] | |
| doc = { | |
| "doc_id": docinfo["id"], | |
| "kb_id": [kb.id] | |
| } | |
| for ck in th.result(): | |
| d = deepcopy(doc) | |
| d.update(ck) | |
| d["id"] = xxhash.xxh64((ck["content_with_weight"] + str(d["doc_id"])).encode("utf-8")).hexdigest() | |
| d["create_time"] = str(datetime.now()).replace("T", " ")[:19] | |
| d["create_timestamp_flt"] = datetime.now().timestamp() | |
| if not d.get("image"): | |
| docs.append(d) | |
| continue | |
| output_buffer = BytesIO() | |
| if isinstance(d["image"], bytes): | |
| output_buffer = BytesIO(d["image"]) | |
| else: | |
| d["image"].save(output_buffer, format='JPEG') | |
| STORAGE_IMPL.put(kb.id, d["id"], output_buffer.getvalue()) | |
| d["img_id"] = "{}-{}".format(kb.id, d["id"]) | |
| d.pop("image", None) | |
| docs.append(d) | |
| parser_ids = {d["id"]: d["parser_id"] for d, _ in files} | |
| docids = [d["id"] for d, _ in files] | |
| chunk_counts = {id: 0 for id in docids} | |
| token_counts = {id: 0 for id in docids} | |
| es_bulk_size = 64 | |
| def embedding(doc_id, cnts, batch_size=16): | |
| nonlocal embd_mdl, chunk_counts, token_counts | |
| vects = [] | |
| for i in range(0, len(cnts), batch_size): | |
| vts, c = embd_mdl.encode(cnts[i: i + batch_size]) | |
| vects.extend(vts.tolist()) | |
| chunk_counts[doc_id] += len(cnts[i:i + batch_size]) | |
| token_counts[doc_id] += c | |
| return vects | |
| idxnm = search.index_name(kb.tenant_id) | |
| try_create_idx = True | |
| _, tenant = TenantService.get_by_id(kb.tenant_id) | |
| llm_bdl = LLMBundle(kb.tenant_id, LLMType.CHAT, tenant.llm_id) | |
| for doc_id in docids: | |
| cks = [c for c in docs if c["doc_id"] == doc_id] | |
| if parser_ids[doc_id] != ParserType.PICTURE.value: | |
| mindmap = MindMapExtractor(llm_bdl) | |
| try: | |
| mind_map = json.dumps(mindmap([c["content_with_weight"] for c in docs if c["doc_id"] == doc_id]).output, | |
| ensure_ascii=False, indent=2) | |
| if len(mind_map) < 32: | |
| raise Exception("Few content: " + mind_map) | |
| cks.append({ | |
| "id": get_uuid(), | |
| "doc_id": doc_id, | |
| "kb_id": [kb.id], | |
| "docnm_kwd": doc_nm[doc_id], | |
| "title_tks": rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", doc_nm[doc_id])), | |
| "content_ltks": rag_tokenizer.tokenize("summary summarize 总结 概况 file 文件 概括"), | |
| "content_with_weight": mind_map, | |
| "knowledge_graph_kwd": "mind_map" | |
| }) | |
| except Exception as e: | |
| logging.exception("Mind map generation error") | |
| vects = embedding(doc_id, [c["content_with_weight"] for c in cks]) | |
| assert len(cks) == len(vects) | |
| for i, d in enumerate(cks): | |
| v = vects[i] | |
| d["q_%d_vec" % len(v)] = v | |
| for b in range(0, len(cks), es_bulk_size): | |
| if try_create_idx: | |
| if not settings.docStoreConn.indexExist(idxnm, kb_id): | |
| settings.docStoreConn.createIdx(idxnm, kb_id, len(vects[0])) | |
| try_create_idx = False | |
| settings.docStoreConn.insert(cks[b:b + es_bulk_size], idxnm, kb_id) | |
| DocumentService.increment_chunk_num( | |
| doc_id, kb.id, token_counts[doc_id], chunk_counts[doc_id], 0) | |
| return [d["id"] for d, _ in files] | |