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Add task moduel, and pipline the task and every parser (#49)
This commit is contained in:
KevinHuSh
GitHub
parent
af3ef26977
commit
6224edcd1b
@ -22,6 +22,8 @@ from elasticsearch_dsl import Q
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from flask import request
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from flask_login import login_required, current_user
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from api.db.db_models import Task
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from api.db.services.task_service import TaskService
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from rag.nlp import search
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from rag.utils import ELASTICSEARCH
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from api.db.services import duplicate_name
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@ -205,6 +207,26 @@ def rm():
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return server_error_response(e)
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@manager.route('/run', methods=['POST'])
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@login_required
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@validate_request("doc_ids", "run")
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def rm():
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req = request.json
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try:
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for id in req["doc_ids"]:
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DocumentService.update_by_id(id, {"run": str(req["run"])})
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if req["run"] == "2":
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TaskService.filter_delete([Task.doc_id == id])
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tenant_id = DocumentService.get_tenant_id(id)
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if not tenant_id:
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return get_data_error_result(retmsg="Tenant not found!")
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ELASTICSEARCH.deleteByQuery(Q("match", doc_id=id), idxnm=search.index_name(tenant_id))
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return get_json_result(data=True)
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except Exception as e:
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return server_error_response(e)
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@manager.route('/rename', methods=['POST'])
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@login_required
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@validate_request("doc_id", "name", "old_name")
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@ -262,7 +284,7 @@ def change_parser():
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if doc.parser_id.lower() == req["parser_id"].lower():
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return get_json_result(data=True)
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e = DocumentService.update_by_id(doc.id, {"parser_id": req["parser_id"], "progress":0, "progress_msg": ""})
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e = DocumentService.update_by_id(doc.id, {"parser_id": req["parser_id"], "progress":0, "progress_msg": "", "run": 1})
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if not e:
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return get_data_error_result(retmsg="Document not found!")
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e = DocumentService.increment_chunk_num(doc.id, doc.kb_id, doc.token_num*-1, doc.chunk_num*-1, doc.process_duation*-1)
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@ -59,3 +59,14 @@ class ChatStyle(StrEnum):
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PRECISE = 'Precise'
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EVENLY = 'Evenly'
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CUSTOM = 'Custom'
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class ParserType(StrEnum):
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GENERAL = "general"
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PRESENTATION = "presentation"
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LAWS = "laws"
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MANUAL = "manual"
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PAPER = "paper"
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RESUME = ""
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BOOK = ""
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QA = ""
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@ -496,15 +496,27 @@ class Document(DataBaseModel):
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token_num = IntegerField(default=0)
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chunk_num = IntegerField(default=0)
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progress = FloatField(default=0)
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progress_msg = CharField(max_length=255, null=True, help_text="process message", default="")
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progress_msg = CharField(max_length=512, null=True, help_text="process message", default="")
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process_begin_at = DateTimeField(null=True)
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process_duation = FloatField(default=0)
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run = CharField(max_length=1, null=True, help_text="start to run processing or cancel.(1: run it; 2: cancel)", default="0")
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status = CharField(max_length=1, null=True, help_text="is it validate(0: wasted，1: validate)", default="1")
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class Meta:
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db_table = "document"
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class Task(DataBaseModel):
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id = CharField(max_length=32, primary_key=True)
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doc_id = CharField(max_length=32, null=False, index=True)
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from_page = IntegerField(default=0)
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to_page = IntegerField(default=-1)
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begin_at = DateTimeField(null=True)
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process_duation = FloatField(default=0)
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progress = FloatField(default=0)
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progress_msg = CharField(max_length=255, null=True, help_text="process message", default="")
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class Dialog(DataBaseModel):
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id = CharField(max_length=32, primary_key=True)
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tenant_id = CharField(max_length=32, null=False)
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@ -553,72 +565,6 @@ class Conversation(DataBaseModel):
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"""
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class Job(DataBaseModel):
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# multi-party common configuration
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f_user_id = CharField(max_length=25, null=True)
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f_job_id = CharField(max_length=25, index=True)
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f_name = CharField(max_length=500, null=True, default='')
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f_description = TextField(null=True, default='')
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f_tag = CharField(max_length=50, null=True, default='')
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f_dsl = JSONField()
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f_runtime_conf = JSONField()
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f_runtime_conf_on_party = JSONField()
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f_train_runtime_conf = JSONField(null=True)
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f_roles = JSONField()
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f_initiator_role = CharField(max_length=50)
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f_initiator_party_id = CharField(max_length=50)
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f_status = CharField(max_length=50)
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f_status_code = IntegerField(null=True)
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f_user = JSONField()
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# this party configuration
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f_role = CharField(max_length=50, index=True)
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f_party_id = CharField(max_length=10, index=True)
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f_is_initiator = BooleanField(null=True, default=False)
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f_progress = IntegerField(null=True, default=0)
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f_ready_signal = BooleanField(default=False)
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f_ready_time = BigIntegerField(null=True)
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f_cancel_signal = BooleanField(default=False)
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f_cancel_time = BigIntegerField(null=True)
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f_rerun_signal = BooleanField(default=False)
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f_end_scheduling_updates = IntegerField(null=True, default=0)
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f_engine_name = CharField(max_length=50, null=True)
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f_engine_type = CharField(max_length=10, null=True)
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f_cores = IntegerField(default=0)
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f_memory = IntegerField(default=0) # MB
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f_remaining_cores = IntegerField(default=0)
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f_remaining_memory = IntegerField(default=0) # MB
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f_resource_in_use = BooleanField(default=False)
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f_apply_resource_time = BigIntegerField(null=True)
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f_return_resource_time = BigIntegerField(null=True)
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f_inheritance_info = JSONField(null=True)
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f_inheritance_status = CharField(max_length=50, null=True)
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f_start_time = BigIntegerField(null=True)
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f_start_date = DateTimeField(null=True)
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f_end_time = BigIntegerField(null=True)
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f_end_date = DateTimeField(null=True)
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f_elapsed = BigIntegerField(null=True)
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class Meta:
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db_table = "t_job"
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primary_key = CompositeKey('f_job_id', 'f_role', 'f_party_id')
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class PipelineComponentMeta(DataBaseModel):
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f_model_id = CharField(max_length=100, index=True)
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f_model_version = CharField(max_length=100, index=True)
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f_role = CharField(max_length=50, index=True)
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f_party_id = CharField(max_length=10, index=True)
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f_component_name = CharField(max_length=100, index=True)
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f_component_module_name = CharField(max_length=100)
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f_model_alias = CharField(max_length=100, index=True)
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f_model_proto_index = JSONField(null=True)
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f_run_parameters = JSONField(null=True)
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f_archive_sha256 = CharField(max_length=100, null=True)
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f_archive_from_ip = CharField(max_length=100, null=True)
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class Meta:
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db_table = 't_pipeline_component_meta'
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@ -32,19 +32,19 @@ LOGGER = getLogger()
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def bulk_insert_into_db(model, data_source, replace_on_conflict=False):
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DB.create_tables([model])
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current_time = current_timestamp()
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current_date = timestamp_to_date(current_time)
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for data in data_source:
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if 'f_create_time' not in data:
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data['f_create_time'] = current_time
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data['f_create_date'] = timestamp_to_date(data['f_create_time'])
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data['f_update_time'] = current_time
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data['f_update_date'] = current_date
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current_time = current_timestamp()
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current_date = timestamp_to_date(current_time)
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if 'create_time' not in data:
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data['create_time'] = current_time
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data['create_date'] = timestamp_to_date(data['create_time'])
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data['update_time'] = current_time
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data['update_date'] = current_date
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preserve = tuple(data_source[0].keys() - {'f_create_time', 'f_create_date'})
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preserve = tuple(data_source[0].keys() - {'create_time', 'create_date'})
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batch_size = 50 if RuntimeConfig.USE_LOCAL_DATABASE else 1000
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batch_size = 1000
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for i in range(0, len(data_source), batch_size):
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with DB.atomic():
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@ -70,6 +70,7 @@ class CommonService:
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@DB.connection_context()
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def insert_many(cls, data_list, batch_size=100):
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with DB.atomic():
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for d in data_list: d["create_time"] = datetime_format(datetime.now())
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for i in range(0, len(data_list), batch_size):
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cls.model.insert_many(data_list[i:i + batch_size]).execute()
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@ -61,8 +61,8 @@ class DocumentService(CommonService):
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@classmethod
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@DB.connection_context()
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def get_newly_uploaded(cls, tm, mod, comm, items_per_page=64):
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fields = [cls.model.id, cls.model.kb_id, cls.model.parser_id, cls.model.name, cls.model.location, cls.model.size, Knowledgebase.tenant_id, Tenant.embd_id, Tenant.img2txt_id, cls.model.update_time]
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def get_newly_uploaded(cls, tm, mod=0, comm=1, items_per_page=64):
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fields = [cls.model.id, cls.model.kb_id, cls.model.parser_id, 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]
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docs = cls.model.select(*fields) \
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.join(Knowledgebase, on=(cls.model.kb_id == Knowledgebase.id)) \
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.join(Tenant, on=(Knowledgebase.tenant_id == Tenant.id))\
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@ -76,6 +76,18 @@ class DocumentService(CommonService):
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.paginate(1, items_per_page)
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return list(docs.dicts())
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@classmethod
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@DB.connection_context()
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def get_unfinished_docs(cls):
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fields = [cls.model.id, cls.model.process_begin_at]
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docs = cls.model.select(*fields) \
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.where(
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cls.model.status == StatusEnum.VALID.value,
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~(cls.model.type == FileType.VIRTUAL.value),
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cls.model.progress < 1,
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cls.model.progress > 0)
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return list(docs.dicts())
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@classmethod
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@DB.connection_context()
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def increment_chunk_num(cls, doc_id, kb_id, token_num, chunk_num, duation):
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53
api/db/services/task_service.py
Normal file
53
api/db/services/task_service.py
Normal file
@ -0,0 +1,53 @@
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#
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# Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from peewee import Expression
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from api.db.db_models import DB
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from api.db import StatusEnum, FileType
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from api.db.db_models import Task, Document, Knowledgebase, Tenant
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from api.db.services.common_service import CommonService
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class TaskService(CommonService):
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model = Task
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@classmethod
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@DB.connection_context()
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def get_tasks(cls, tm, mod=0, comm=1, items_per_page=64):
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fields = [cls.model.id, cls.model.doc_id, cls.model.from_page,cls.model.to_page, Document.kb_id, Document.parser_id, Document.name, Document.type, Document.location, Document.size, Knowledgebase.tenant_id, Tenant.embd_id, Tenant.img2txt_id, Tenant.asr_id, cls.model.update_time]
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docs = cls.model.select(*fields) \
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.join(Document, on=(cls.model.doc_id == Document.id)) \
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.join(Knowledgebase, on=(Document.kb_id == Knowledgebase.id)) \
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.join(Tenant, on=(Knowledgebase.tenant_id == Tenant.id))\
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.where(
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Document.status == StatusEnum.VALID.value,
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~(Document.type == FileType.VIRTUAL.value),
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cls.model.progress == 0,
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cls.model.update_time >= tm,
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(Expression(cls.model.create_time, "%%", comm) == mod))\
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.order_by(cls.model.update_time.asc())\
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.paginate(1, items_per_page)
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return list(docs.dicts())
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@classmethod
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@DB.connection_context()
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def do_cancel(cls, id):
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try:
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cls.model.get_by_id(id)
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return False
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except Exception as e:
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pass
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return True
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@ -67,4 +67,6 @@ def tokenize(d, t, eng):
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d["content_ltks"] = " ".join([stemmer.stem(w) for w in word_tokenize(t)])
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else:
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d["content_ltks"] = huqie.qie(t)
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d["content_sm_ltks"] = huqie.qieqie(d["content_ltks"])
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d["content_sm_ltks"] = huqie.qieqie(d["content_ltks"])
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@ -32,14 +32,12 @@ class Pdf(HuParser):
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zoomin,
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from_page,
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to_page)
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callback__((min(to_page, self.total_page) - from_page) / self.total_page / 2,
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"Page {}~{}: OCR finished".format(from_page, min(to_page, self.total_page)), callback)
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callback__(0.1, "OCR finished", callback)
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from timeit import default_timer as timer
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start = timer()
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self._layouts_paddle(zoomin)
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callback__((min(to_page, self.total_page) - from_page) / self.total_page / 2,
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"Page {}~{}: Layout analysis finished".format(from_page, min(to_page, self.total_page)), callback)
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callback__(0.77, "Layout analysis finished", callback)
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print("paddle layouts:", timer()-start)
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bxs = self.sort_Y_firstly(self.boxes, np.median(self.mean_height) / 3)
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# is it English
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@ -77,8 +75,7 @@ class Pdf(HuParser):
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b["x1"] = max(b["x1"], b_["x1"])
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bxs.pop(i + 1)
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callback__((min(to_page, self.total_page) - from_page) / self.total_page / 2,
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"Page {}~{}: Text extraction finished".format(from_page, min(to_page, self.total_page)), callback)
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callback__(0.8, "Text extraction finished", callback)
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return [b["text"] + self._line_tag(b, zoomin) for b in bxs]
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@ -92,14 +89,17 @@ def chunk(filename, binary=None, from_page=0, to_page=100000, callback=None):
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pdf_parser = None
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sections = []
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if re.search(r"\.docx?$", filename, re.IGNORECASE):
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callback__(0.1, "Start to parse.", callback)
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for txt in Docx()(filename, binary):
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sections.append(txt)
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if re.search(r"\.pdf$", filename, re.IGNORECASE):
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callback__(0.8, "Finish parsing.", callback)
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elif re.search(r"\.pdf$", filename, re.IGNORECASE):
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pdf_parser = Pdf()
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for txt in pdf_parser(filename if not binary else binary,
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from_page=from_page, to_page=to_page, callback=callback):
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sections.append(txt)
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if re.search(r"\.txt$", filename, re.IGNORECASE):
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elif re.search(r"\.txt$", filename, re.IGNORECASE):
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callback__(0.1, "Start to parse.", callback)
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txt = ""
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if binary:txt = binary.decode("utf-8")
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else:
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@ -110,6 +110,8 @@ def chunk(filename, binary=None, from_page=0, to_page=100000, callback=None):
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txt += l
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sections = txt.split("\n")
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sections = [l for l in sections if l]
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callback__(0.8, "Finish parsing.", callback)
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else: raise NotImplementedError("file type not supported yet(docx, pdf, txt supported)")
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# is it English
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eng = is_english(sections)
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@ -1,12 +1,8 @@
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import copy
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import re
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from collections import Counter
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from rag.app import callback__, bullets_category, BULLET_PATTERN, is_english, tokenize
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from rag.nlp import huqie, stemmer
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from rag.parser.docx_parser import HuDocxParser
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from rag.app import callback__, tokenize
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from rag.nlp import huqie
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from rag.parser.pdf_parser import HuParser
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from nltk.tokenize import word_tokenize
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import numpy as np
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from rag.utils import num_tokens_from_string
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@ -18,24 +14,19 @@ class Pdf(HuParser):
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zoomin,
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from_page,
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to_page)
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callback__((min(to_page, self.total_page) - from_page) / self.total_page / 4,
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"Page {}~{}: OCR finished".format(from_page, min(to_page, self.total_page)), callback)
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callback__(0.2, "OCR finished.", callback)
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from timeit import default_timer as timer
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start = timer()
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self._layouts_paddle(zoomin)
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callback__((min(to_page, self.total_page) - from_page) / self.total_page / 4,
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"Page {}~{}: Layout analysis finished".format(from_page, min(to_page, self.total_page)), callback)
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callback__(0.5, "Layout analysis finished.", callback)
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print("paddle layouts:", timer() - start)
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self._table_transformer_job(zoomin)
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callback__((min(to_page, self.total_page) - from_page) / self.total_page / 4,
|
||||
"Page {}~{}: Table analysis finished".format(from_page, min(to_page, self.total_page)), callback)
|
||||
callback__(0.7, "Table analysis finished.", callback)
|
||||
self._text_merge()
|
||||
column_width = np.median([b["x1"] - b["x0"] for b in self.boxes])
|
||||
self._concat_downward(concat_between_pages=False)
|
||||
self._filter_forpages()
|
||||
callback__((min(to_page, self.total_page) - from_page) / self.total_page / 4,
|
||||
"Page {}~{}: Text merging finished".format(from_page, min(to_page, self.total_page)), callback)
|
||||
callback__(0.77, "Text merging finished", callback)
|
||||
tbls = self._extract_table_figure(True, zoomin, False)
|
||||
|
||||
# clean mess
|
||||
@ -71,6 +62,7 @@ class Pdf(HuParser):
|
||||
b_["top"] = b["top"]
|
||||
self.boxes.pop(i)
|
||||
|
||||
callback__(0.8, "Parsing finished", callback)
|
||||
for b in self.boxes: print(b["text"], b.get("layoutno"))
|
||||
|
||||
print(tbls)
|
||||
@ -85,6 +77,7 @@ def chunk(filename, binary=None, from_page=0, to_page=100000, callback=None):
|
||||
pdf_parser = Pdf()
|
||||
cks, tbls = pdf_parser(filename if not binary else binary,
|
||||
from_page=from_page, to_page=to_page, callback=callback)
|
||||
else: raise NotImplementedError("file type not supported yet(pdf supported)")
|
||||
doc = {
|
||||
"docnm_kwd": filename
|
||||
}
|
||||
|
||||
@ -18,24 +18,20 @@ class Pdf(HuParser):
|
||||
zoomin,
|
||||
from_page,
|
||||
to_page)
|
||||
callback__((min(to_page, self.total_page) - from_page) / self.total_page / 4,
|
||||
"Page {}~{}: OCR finished".format(from_page, min(to_page, self.total_page)), callback)
|
||||
callback__(0.2, "OCR finished.", callback)
|
||||
|
||||
from timeit import default_timer as timer
|
||||
start = timer()
|
||||
self._layouts_paddle(zoomin)
|
||||
callback__((min(to_page, self.total_page) - from_page) / self.total_page / 4,
|
||||
"Page {}~{}: Layout analysis finished".format(from_page, min(to_page, self.total_page)), callback)
|
||||
callback__(0.47, "Layout analysis finished", callback)
|
||||
print("paddle layouts:", timer() - start)
|
||||
self._table_transformer_job(zoomin)
|
||||
callback__((min(to_page, self.total_page) - from_page) / self.total_page / 4,
|
||||
"Page {}~{}: Table analysis finished".format(from_page, min(to_page, self.total_page)), callback)
|
||||
callback__(0.68, "Table analysis finished", callback)
|
||||
self._text_merge()
|
||||
column_width = np.median([b["x1"] - b["x0"] for b in self.boxes])
|
||||
self._concat_downward(concat_between_pages=False)
|
||||
self._filter_forpages()
|
||||
callback__((min(to_page, self.total_page) - from_page) / self.total_page / 4,
|
||||
"Page {}~{}: Text merging finished".format(from_page, min(to_page, self.total_page)), callback)
|
||||
callback__(0.75, "Text merging finished.", callback)
|
||||
tbls = self._extract_table_figure(True, zoomin, False)
|
||||
|
||||
# clean mess
|
||||
@ -105,6 +101,7 @@ class Pdf(HuParser):
|
||||
break
|
||||
if not abstr: i = 0
|
||||
|
||||
callback__(0.8, "Page {}~{}: Text merging finished".format(from_page, min(to_page, self.total_page)), callback)
|
||||
for b in self.boxes: print(b["text"], b.get("layoutno"))
|
||||
print(tbls)
|
||||
|
||||
@ -126,6 +123,7 @@ def chunk(filename, binary=None, from_page=0, to_page=100000, callback=None):
|
||||
pdf_parser = Pdf()
|
||||
paper = pdf_parser(filename if not binary else binary,
|
||||
from_page=from_page, to_page=to_page, callback=callback)
|
||||
else: raise NotImplementedError("file type not supported yet(pdf supported)")
|
||||
doc = {
|
||||
"docnm_kwd": paper["title"] if paper["title"] else filename,
|
||||
"authors_tks": paper["authors"]
|
||||
|
||||
@ -42,10 +42,8 @@ class Ppt(object):
|
||||
txt = self.__extract(shape)
|
||||
if txt: texts.append(txt)
|
||||
txts.append("\n".join(texts))
|
||||
callback__((i+1)/self.total_page/2, "", callback)
|
||||
|
||||
callback__((min(to_page, self.total_page) - from_page) / self.total_page,
|
||||
"Page {}~{}: Text extraction finished".format(from_page, min(to_page, self.total_page)), callback)
|
||||
callback__(0.5, "Text extraction finished.", callback)
|
||||
import aspose.slides as slides
|
||||
import aspose.pydrawing as drawing
|
||||
imgs = []
|
||||
@ -55,8 +53,7 @@ class Ppt(object):
|
||||
slide.get_thumbnail(0.5, 0.5).save(buffered, drawing.imaging.ImageFormat.jpeg)
|
||||
imgs.append(buffered.getvalue())
|
||||
assert len(imgs) == len(txts), "Slides text and image do not match: {} vs. {}".format(len(imgs), len(txts))
|
||||
callback__((min(to_page, self.total_page) - from_page) / self.total_page,
|
||||
"Page {}~{}: Image extraction finished".format(from_page, min(to_page, self.total_page)), callback)
|
||||
callback__(0.9, "Image extraction finished", callback)
|
||||
self.is_english = is_english(txts)
|
||||
return [(txts[i], imgs[i]) for i in range(len(txts))]
|
||||
|
||||
@ -73,7 +70,7 @@ class Pdf(HuParser):
|
||||
|
||||
def __call__(self, filename, binary=None, from_page=0, to_page=100000, zoomin=3, callback=None):
|
||||
self.__images__(filename if not binary else binary, zoomin, from_page, to_page)
|
||||
callback__((min(to_page, self.total_page)-from_page) / self.total_page, "Page {}~{}: OCR finished".format(from_page, min(to_page, self.total_page)), callback)
|
||||
callback__(0.8, "Page {}~{}: OCR finished".format(from_page, min(to_page, self.total_page)), callback)
|
||||
assert len(self.boxes) == len(self.page_images), "{} vs. {}".format(len(self.boxes), len(self.page_images))
|
||||
res = []
|
||||
#################### More precisely ###################
|
||||
@ -92,6 +89,7 @@ class Pdf(HuParser):
|
||||
for i in range(len(self.boxes)):
|
||||
lines = "\n".join([b["text"] for b in self.boxes[i] if not self.__garbage(b["text"])])
|
||||
res.append((lines, self.page_images[i]))
|
||||
callback__(0.9, "Page {}~{}: Parsing finished".format(from_page, min(to_page, self.total_page)), callback)
|
||||
return res
|
||||
|
||||
|
||||
@ -104,13 +102,13 @@ def chunk(filename, binary=None, from_page=0, to_page=100000, callback=None):
|
||||
res = []
|
||||
if re.search(r"\.pptx?$", filename, re.IGNORECASE):
|
||||
ppt_parser = Ppt()
|
||||
for txt,img in ppt_parser(filename if not binary else binary, from_page, to_page, callback):
|
||||
for txt,img in ppt_parser(filename if not binary else binary, from_page, 1000000, callback):
|
||||
d = copy.deepcopy(doc)
|
||||
d["image"] = img
|
||||
tokenize(d, txt, ppt_parser.is_english)
|
||||
res.append(d)
|
||||
return res
|
||||
if re.search(r"\.pdf$", filename, re.IGNORECASE):
|
||||
elif re.search(r"\.pdf$", filename, re.IGNORECASE):
|
||||
pdf_parser = Pdf()
|
||||
for txt,img in pdf_parser(filename if not binary else binary, from_page=from_page, to_page=to_page, callback=callback):
|
||||
d = copy.deepcopy(doc)
|
||||
@ -118,7 +116,8 @@ def chunk(filename, binary=None, from_page=0, to_page=100000, callback=None):
|
||||
tokenize(d, txt, pdf_parser.is_english)
|
||||
res.append(d)
|
||||
return res
|
||||
callback__(-1, "This kind of presentation document did not support yet!", callback)
|
||||
|
||||
raise NotImplementedError("file type not supported yet(pptx, pdf supported)")
|
||||
|
||||
|
||||
if __name__== "__main__":
|
||||
|
||||
@ -1559,6 +1559,15 @@ class HuParser:
|
||||
|
||||
return "\n\n".join(res)
|
||||
|
||||
@staticmethod
|
||||
def total_page_number(fnm, binary=None):
|
||||
try:
|
||||
pdf = pdfplumber.open(fnm) if not binary else pdfplumber.open(BytesIO(binary))
|
||||
return len(pdf.pages)
|
||||
except Exception as e:
|
||||
pdf = fitz.open(fnm) if not binary else fitz.open(stream=fnm, filetype="pdf")
|
||||
return len(pdf)
|
||||
|
||||
def __images__(self, fnm, zoomin=3, page_from=0, page_to=299):
|
||||
self.lefted_chars = []
|
||||
self.mean_height = []
|
||||
|
||||
130
rag/svr/task_broker.py
Normal file
130
rag/svr/task_broker.py
Normal file
@ -0,0 +1,130 @@
|
||||
#
|
||||
# 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 os
|
||||
import time
|
||||
import random
|
||||
from timeit import default_timer as timer
|
||||
from api.db.db_models import Task
|
||||
from api.db.db_utils import bulk_insert_into_db
|
||||
from api.db.services.task_service import TaskService
|
||||
from rag.parser.pdf_parser import HuParser
|
||||
from rag.settings import cron_logger
|
||||
from rag.utils import MINIO
|
||||
from rag.utils import findMaxTm
|
||||
import pandas as pd
|
||||
from api.db import FileType
|
||||
from api.db.services.document_service import DocumentService
|
||||
from api.settings import database_logger
|
||||
from api.utils import get_format_time, get_uuid
|
||||
from api.utils.file_utils import get_project_base_directory
|
||||
|
||||
|
||||
def collect(tm):
|
||||
docs = DocumentService.get_newly_uploaded(tm)
|
||||
if len(docs) == 0:
|
||||
return pd.DataFrame()
|
||||
docs = pd.DataFrame(docs)
|
||||
mtm = docs["update_time"].max()
|
||||
cron_logger.info("TOTAL:{}, To:{}".format(len(docs), mtm))
|
||||
return docs
|
||||
|
||||
|
||||
def set_dispatching(docid):
|
||||
try:
|
||||
DocumentService.update_by_id(
|
||||
docid, {"progress": random.randint(0, 3) / 100.,
|
||||
"progress_msg": "Task dispatched...",
|
||||
"process_begin_at": get_format_time()
|
||||
})
|
||||
except Exception as e:
|
||||
cron_logger.error("set_dispatching:({}), {}".format(docid, str(e)))
|
||||
|
||||
|
||||
def dispatch():
|
||||
tm_fnm = os.path.join(get_project_base_directory(), "rag/res", f"broker.tm")
|
||||
tm = findMaxTm(tm_fnm)
|
||||
rows = collect(tm)
|
||||
if len(rows) == 0:
|
||||
return
|
||||
|
||||
tmf = open(tm_fnm, "a+")
|
||||
for _, r in rows.iterrows():
|
||||
try:
|
||||
tsks = TaskService.query(doc_id=r["id"])
|
||||
if tsks:
|
||||
for t in tsks:
|
||||
TaskService.delete_by_id(t.id)
|
||||
except Exception as e:
|
||||
cron_logger.error("delete task exception:" + str(e))
|
||||
|
||||
def new_task():
|
||||
nonlocal r
|
||||
return {
|
||||
"id": get_uuid(),
|
||||
"doc_id": r["id"]
|
||||
}
|
||||
|
||||
tsks = []
|
||||
if r["type"] == FileType.PDF.value:
|
||||
pages = HuParser.total_page_number(r["name"], MINIO.get(r["kb_id"], r["location"]))
|
||||
for p in range(0, pages, 10):
|
||||
task = new_task()
|
||||
task["from_page"] = p
|
||||
task["to_page"] = min(p + 10, pages)
|
||||
tsks.append(task)
|
||||
else:
|
||||
tsks.append(new_task())
|
||||
print(tsks)
|
||||
bulk_insert_into_db(Task, tsks, True)
|
||||
set_dispatching(r["id"])
|
||||
tmf.write(str(r["update_time"]) + "\n")
|
||||
tmf.close()
|
||||
|
||||
|
||||
def update_progress():
|
||||
docs = DocumentService.get_unfinished_docs()
|
||||
for d in docs:
|
||||
try:
|
||||
tsks = TaskService.query(doc_id=d["id"], order_by=Task.create_time)
|
||||
if not tsks:continue
|
||||
msg = []
|
||||
prg = 0
|
||||
finished = True
|
||||
bad = 0
|
||||
for t in tsks:
|
||||
if 0 <= t.progress < 1: finished = False
|
||||
prg += t.progress if t.progress >= 0 else 0
|
||||
msg.append(t.progress_msg)
|
||||
if t.progress == -1: bad += 1
|
||||
prg /= len(tsks)
|
||||
if finished and bad: prg = -1
|
||||
msg = "\n".join(msg)
|
||||
DocumentService.update_by_id(d["id"], {"progress": prg, "progress_msg": msg, "process_duation": timer()-d["process_begin_at"].timestamp()})
|
||||
except Exception as e:
|
||||
cron_logger.error("fetch task exception:" + str(e))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
peewee_logger = logging.getLogger('peewee')
|
||||
peewee_logger.propagate = False
|
||||
peewee_logger.addHandler(database_logger.handlers[0])
|
||||
peewee_logger.setLevel(database_logger.level)
|
||||
|
||||
while True:
|
||||
dispatch()
|
||||
time.sleep(3)
|
||||
update_progress()
|
||||
@ -19,49 +19,59 @@ import logging
|
||||
import os
|
||||
import hashlib
|
||||
import copy
|
||||
import time
|
||||
import random
|
||||
import re
|
||||
import sys
|
||||
from functools import partial
|
||||
from timeit import default_timer as timer
|
||||
|
||||
from api.db.services.task_service import TaskService
|
||||
from rag.llm import EmbeddingModel, CvModel
|
||||
from rag.settings import cron_logger, DOC_MAXIMUM_SIZE
|
||||
from rag.utils import ELASTICSEARCH
|
||||
from rag.utils import MINIO
|
||||
from rag.utils import rmSpace, findMaxTm
|
||||
|
||||
from rag.nlp import huchunk, huqie, search
|
||||
from rag.nlp import search
|
||||
from io import BytesIO
|
||||
import pandas as pd
|
||||
from elasticsearch_dsl import Q
|
||||
from PIL import Image
|
||||
from rag.parser import (
|
||||
PdfParser,
|
||||
DocxParser,
|
||||
ExcelParser
|
||||
)
|
||||
from rag.nlp.huchunk import (
|
||||
PdfChunker,
|
||||
DocxChunker,
|
||||
ExcelChunker,
|
||||
PptChunker,
|
||||
TextChunker
|
||||
)
|
||||
from api.db import LLMType
|
||||
|
||||
from rag.app import laws, paper, presentation, manual
|
||||
|
||||
from api.db import LLMType, ParserType
|
||||
from api.db.services.document_service import DocumentService
|
||||
from api.db.services.llm_service import TenantLLMService, LLMBundle
|
||||
from api.db.services.llm_service import LLMBundle
|
||||
from api.settings import database_logger
|
||||
from api.utils import get_format_time
|
||||
from api.utils.file_utils import get_project_base_directory
|
||||
|
||||
BATCH_SIZE = 64
|
||||
|
||||
PDF = PdfChunker(PdfParser())
|
||||
DOC = DocxChunker(DocxParser())
|
||||
EXC = ExcelChunker(ExcelParser())
|
||||
PPT = PptChunker()
|
||||
FACTORY = {
|
||||
ParserType.GENERAL.value: laws,
|
||||
ParserType.PAPER.value: paper,
|
||||
ParserType.PRESENTATION.value: presentation,
|
||||
ParserType.MANUAL.value: manual,
|
||||
ParserType.LAWS.value: laws,
|
||||
}
|
||||
|
||||
|
||||
def set_progress(task_id, from_page, to_page, prog=None, msg="Processing..."):
|
||||
cancel = TaskService.do_cancel(task_id)
|
||||
if cancel:
|
||||
msg = "Canceled."
|
||||
prog = -1
|
||||
|
||||
if to_page > 0: msg = f"Page({from_page}~{to_page}): " + msg
|
||||
d = {"progress_msg": msg}
|
||||
if prog is not None: d["progress"] = prog
|
||||
try:
|
||||
TaskService.update_by_id(task_id, d)
|
||||
except Exception as e:
|
||||
cron_logger.error("set_progress:({}), {}".format(task_id, str(e)))
|
||||
|
||||
if cancel:sys.exit()
|
||||
|
||||
|
||||
"""
|
||||
def chuck_doc(name, binary, tenant_id, cvmdl=None):
|
||||
suff = os.path.split(name)[-1].lower().split(".")[-1]
|
||||
if suff.find("pdf") >= 0:
|
||||
@ -81,27 +91,17 @@ def chuck_doc(name, binary, tenant_id, cvmdl=None):
|
||||
return field
|
||||
|
||||
return TextChunker()(binary)
|
||||
"""
|
||||
|
||||
|
||||
def collect(comm, mod, tm):
|
||||
docs = DocumentService.get_newly_uploaded(tm, mod, comm)
|
||||
if len(docs) == 0:
|
||||
tasks = TaskService.get_tasks(tm, mod, comm)
|
||||
if len(tasks) == 0:
|
||||
return pd.DataFrame()
|
||||
docs = pd.DataFrame(docs)
|
||||
mtm = docs["update_time"].max()
|
||||
cron_logger.info("TOTAL:{}, To:{}".format(len(docs), mtm))
|
||||
return docs
|
||||
|
||||
|
||||
def set_progress(docid, prog, msg="Processing...", begin=False):
|
||||
d = {"progress": prog, "progress_msg": msg}
|
||||
if begin:
|
||||
d["process_begin_at"] = get_format_time()
|
||||
try:
|
||||
DocumentService.update_by_id(
|
||||
docid, {"progress": prog, "progress_msg": msg})
|
||||
except Exception as e:
|
||||
cron_logger.error("set_progress:({}), {}".format(docid, str(e)))
|
||||
tasks = pd.DataFrame(tasks)
|
||||
mtm = tasks["update_time"].max()
|
||||
cron_logger.info("TOTAL:{}, To:{}".format(len(tasks), mtm))
|
||||
return tasks
|
||||
|
||||
|
||||
def build(row, cvmdl):
|
||||
@ -110,97 +110,50 @@ def build(row, cvmdl):
|
||||
(int(DOC_MAXIMUM_SIZE / 1024 / 1024)))
|
||||
return []
|
||||
|
||||
# res = ELASTICSEARCH.search(Q("term", doc_id=row["id"]))
|
||||
# if ELASTICSEARCH.getTotal(res) > 0:
|
||||
# ELASTICSEARCH.updateScriptByQuery(Q("term", doc_id=row["id"]),
|
||||
# scripts="""
|
||||
# if(!ctx._source.kb_id.contains('%s'))
|
||||
# ctx._source.kb_id.add('%s');
|
||||
# """ % (str(row["kb_id"]), str(row["kb_id"])),
|
||||
# idxnm=search.index_name(row["tenant_id"])
|
||||
# )
|
||||
# set_progress(row["id"], 1, "Done")
|
||||
# return []
|
||||
|
||||
random.seed(time.time())
|
||||
set_progress(row["id"], random.randint(0, 20) /
|
||||
100., "Finished preparing! Start to slice file!", True)
|
||||
callback = partial(set_progress, row["id"], row["from_page"], row["to_page"])
|
||||
chunker = FACTORY[row["parser_id"]]
|
||||
try:
|
||||
cron_logger.info("Chunkking {}/{}".format(row["location"], row["name"]))
|
||||
obj = chuck_doc(row["name"], MINIO.get(row["kb_id"], row["location"]), row["tenant_id"], cvmdl)
|
||||
cks = chunker.chunk(row["name"], MINIO.get(row["kb_id"], row["location"]), row["from_page"], row["to_page"],
|
||||
callback)
|
||||
except Exception as e:
|
||||
if re.search("(No such file|not found)", str(e)):
|
||||
set_progress(
|
||||
row["id"], -1, "Can not find file <%s>" %
|
||||
row["doc_name"])
|
||||
callback(-1, "Can not find file <%s>" % row["doc_name"])
|
||||
else:
|
||||
set_progress(
|
||||
row["id"], -1, f"Internal server error: %s" %
|
||||
str(e).replace(
|
||||
"'", ""))
|
||||
callback(-1, f"Internal server error: %s" % str(e).replace("'", ""))
|
||||
|
||||
cron_logger.warn("Chunkking {}/{}: {}".format(row["location"], row["name"], str(e)))
|
||||
|
||||
return []
|
||||
|
||||
if not obj.text_chunks and not obj.table_chunks:
|
||||
set_progress(
|
||||
row["id"],
|
||||
1,
|
||||
"Nothing added! Mostly, file type unsupported yet.")
|
||||
return []
|
||||
callback(msg="Finished slicing files. Start to embedding the content.")
|
||||
|
||||
set_progress(row["id"], random.randint(20, 60) / 100.,
|
||||
"Finished slicing files. Start to embedding the content.")
|
||||
|
||||
doc = {
|
||||
"doc_id": row["id"],
|
||||
"kb_id": [str(row["kb_id"])],
|
||||
"docnm_kwd": os.path.split(row["location"])[-1],
|
||||
"title_tks": huqie.qie(row["name"])
|
||||
}
|
||||
doc["title_sm_tks"] = huqie.qieqie(doc["title_tks"])
|
||||
output_buffer = BytesIO()
|
||||
docs = []
|
||||
for txt, img in obj.text_chunks:
|
||||
doc = {
|
||||
"doc_id": row["doc_id"],
|
||||
"kb_id": [str(row["kb_id"])]
|
||||
}
|
||||
for ck in cks:
|
||||
d = copy.deepcopy(doc)
|
||||
d.update(ck)
|
||||
md5 = hashlib.md5()
|
||||
md5.update((txt + str(d["doc_id"])).encode("utf-8"))
|
||||
md5.update((ck["content_with_weight"] + str(d["doc_id"])).encode("utf-8"))
|
||||
d["_id"] = md5.hexdigest()
|
||||
d["content_ltks"] = huqie.qie(txt)
|
||||
d["content_sm_ltks"] = huqie.qieqie(d["content_ltks"])
|
||||
if not img:
|
||||
d["create_time"] = str(datetime.datetime.now()).replace("T", " ")[:19]
|
||||
if not d.get("image"):
|
||||
docs.append(d)
|
||||
continue
|
||||
|
||||
if isinstance(img, bytes):
|
||||
output_buffer = BytesIO(img)
|
||||
output_buffer = BytesIO()
|
||||
if isinstance(d["image"], bytes):
|
||||
output_buffer = BytesIO(d["image"])
|
||||
else:
|
||||
img.save(output_buffer, format='JPEG')
|
||||
d["image"].save(output_buffer, format='JPEG')
|
||||
|
||||
MINIO.put(row["kb_id"], d["_id"], output_buffer.getvalue())
|
||||
d["img_id"] = "{}-{}".format(row["kb_id"], d["_id"])
|
||||
d["create_time"] = str(datetime.datetime.now()).replace("T", " ")[:19]
|
||||
docs.append(d)
|
||||
|
||||
for arr, img in obj.table_chunks:
|
||||
for i, txt in enumerate(arr):
|
||||
d = copy.deepcopy(doc)
|
||||
d["content_ltks"] = huqie.qie(txt)
|
||||
md5 = hashlib.md5()
|
||||
md5.update((txt + str(d["doc_id"])).encode("utf-8"))
|
||||
d["_id"] = md5.hexdigest()
|
||||
if not img:
|
||||
docs.append(d)
|
||||
continue
|
||||
img.save(output_buffer, format='JPEG')
|
||||
MINIO.put(row["kb_id"], d["_id"], output_buffer.getvalue())
|
||||
d["img_id"] = "{}-{}".format(row["kb_id"], d["_id"])
|
||||
d["create_time"] = str(datetime.datetime.now()).replace("T", " ")[:19]
|
||||
docs.append(d)
|
||||
set_progress(row["id"], random.randint(60, 70) /
|
||||
100., "Continue embedding the content.")
|
||||
|
||||
return docs
|
||||
|
||||
|
||||
@ -213,7 +166,7 @@ def init_kb(row):
|
||||
|
||||
|
||||
def embedding(docs, mdl):
|
||||
tts, cnts = [rmSpace(d["title_tks"]) for d in docs], [rmSpace(d["content_ltks"]) for d in docs]
|
||||
tts, cnts = [d["docnm_kwd"] for d in docs], [d["content_with_weight"] for d in docs]
|
||||
tk_count = 0
|
||||
tts, c = mdl.encode(tts)
|
||||
tk_count += c
|
||||
@ -223,7 +176,7 @@ def embedding(docs, mdl):
|
||||
assert len(vects) == len(docs)
|
||||
for i, d in enumerate(docs):
|
||||
v = vects[i].tolist()
|
||||
d["q_%d_vec"%len(v)] = v
|
||||
d["q_%d_vec" % len(v)] = v
|
||||
return tk_count
|
||||
|
||||
|
||||
@ -239,11 +192,12 @@ def main(comm, mod):
|
||||
try:
|
||||
embd_mdl = LLMBundle(r["tenant_id"], LLMType.EMBEDDING)
|
||||
cv_mdl = LLMBundle(r["tenant_id"], LLMType.IMAGE2TEXT)
|
||||
#TODO: sequence2text model
|
||||
# TODO: sequence2text model
|
||||
except Exception as e:
|
||||
set_progress(r["id"], -1, str(e))
|
||||
continue
|
||||
|
||||
callback = partial(set_progress, r["id"], r["from_page"], r["to_page"])
|
||||
st_tm = timer()
|
||||
cks = build(r, cv_mdl)
|
||||
if not cks:
|
||||
@ -254,21 +208,20 @@ def main(comm, mod):
|
||||
try:
|
||||
tk_count = embedding(cks, embd_mdl)
|
||||
except Exception as e:
|
||||
set_progress(r["id"], -1, "Embedding error:{}".format(str(e)))
|
||||
callback(-1, "Embedding error:{}".format(str(e)))
|
||||
cron_logger.error(str(e))
|
||||
continue
|
||||
|
||||
set_progress(r["id"], random.randint(70, 95) / 100.,
|
||||
"Finished embedding! Start to build index!")
|
||||
callback(msg="Finished embedding! Start to build index!")
|
||||
init_kb(r)
|
||||
chunk_count = len(set([c["_id"] for c in cks]))
|
||||
callback(1., "Done!")
|
||||
es_r = ELASTICSEARCH.bulk(cks, search.index_name(r["tenant_id"]))
|
||||
if es_r:
|
||||
set_progress(r["id"], -1, "Index failure!")
|
||||
callback(-1, "Index failure!")
|
||||
cron_logger.error(str(es_r))
|
||||
else:
|
||||
set_progress(r["id"], 1., "Done!")
|
||||
DocumentService.increment_chunk_num(r["id"], r["kb_id"], tk_count, chunk_count, timer()-st_tm)
|
||||
DocumentService.increment_chunk_num(r["doc_id"], r["kb_id"], tk_count, chunk_count, 0)
|
||||
cron_logger.info("Chunk doc({}), token({}), chunks({})".format(r["id"], tk_count, len(cks)))
|
||||
|
||||
tmf.write(str(r["update_time"]) + "\n")
|
||||
@ -282,5 +235,6 @@ if __name__ == "__main__":
|
||||
peewee_logger.setLevel(database_logger.level)
|
||||
|
||||
from mpi4py import MPI
|
||||
|
||||
comm = MPI.COMM_WORLD
|
||||
main(comm.Get_size(), comm.Get_rank())
|
||||
Loading…
x
Reference in New Issue
Block a user