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| | 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 |
| |
|
| | @classmethod |
| | @DB.connection_context() |
| | 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 |
| |
|
| | @classmethod |
| | @DB.connection_context() |
| | 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 |
| |
|
| | @classmethod |
| | @DB.connection_context() |
| | 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) |
| |
|
| | @classmethod |
| | @DB.connection_context() |
| | 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) |
| |
|
| | @classmethod |
| | @DB.connection_context() |
| | 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()) |
| |
|
| | @classmethod |
| | @DB.connection_context() |
| | 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()) |
| |
|
| | @classmethod |
| | @DB.connection_context() |
| | 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 |
| |
|
| | @classmethod |
| | @DB.connection_context() |
| | 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 |
| |
|
| | @classmethod |
| | @DB.connection_context() |
| | 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 |
| |
|
| | @classmethod |
| | @DB.connection_context() |
| | 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"] |
| |
|
| | @classmethod |
| | @DB.connection_context() |
| | 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"] |
| |
|
| | @classmethod |
| | @DB.connection_context() |
| | 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"] |
| |
|
| | @classmethod |
| | @DB.connection_context() |
| | 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 |
| |
|
| | @classmethod |
| | @DB.connection_context() |
| | 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 |
| |
|
| | @classmethod |
| | @DB.connection_context() |
| | 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"] |
| |
|
| | @classmethod |
| | @DB.connection_context() |
| | 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] |
| |
|
| | @classmethod |
| | @DB.connection_context() |
| | 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"] |
| |
|
| | @classmethod |
| | @DB.connection_context() |
| | 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()) |
| |
|
| | @classmethod |
| | @DB.connection_context() |
| | 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}) |
| |
|
| | @classmethod |
| | @DB.connection_context() |
| | 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) |
| |
|
| | @classmethod |
| | @DB.connection_context() |
| | 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() |
| | }) |
| |
|
| | @classmethod |
| | @DB.connection_context() |
| | 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 |
| | 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") |
| |
|
| | @classmethod |
| | @DB.connection_context() |
| | def get_kb_doc_count(cls, kb_id): |
| | return len(cls.model.select(cls.model.id).where( |
| | cls.model.kb_id == kb_id).dicts()) |
| |
|
| | @classmethod |
| | @DB.connection_context() |
| | 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] |
| |
|