ragflow/rag/svr/task_executor.py

#
#  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.

# from beartype import BeartypeConf
# from beartype.claw import beartype_all  # <-- you didn't sign up for this
# beartype_all(conf=BeartypeConf(violation_type=UserWarning))    # <-- emit warnings from all code
import random
import sys

from api.utils.log_utils import initRootLogger, get_project_base_directory
from graphrag.general.index import run_graphrag
from graphrag.utils import get_llm_cache, set_llm_cache, get_tags_from_cache, set_tags_to_cache
from rag.prompts import keyword_extraction, question_proposal, content_tagging

CONSUMER_NO = "0" if len(sys.argv) < 2 else sys.argv[1]
CONSUMER_NAME = "task_executor_" + CONSUMER_NO
initRootLogger(CONSUMER_NAME)

import logging
import os
from datetime import datetime
import json
import xxhash
import copy
import re
from functools import partial
from io import BytesIO
from multiprocessing.context import TimeoutError
from timeit import default_timer as timer
import tracemalloc
import signal
import trio
import exceptiongroup

import numpy as np
from peewee import DoesNotExist

from api.db import LLMType, ParserType, TaskStatus
from api.db.services.document_service import DocumentService
from api.db.services.llm_service import LLMBundle
from api.db.services.task_service import TaskService
from api.db.services.file2document_service import File2DocumentService
from api import settings
from api.versions import get_ragflow_version
from api.db.db_models import close_connection
from rag.app import laws, paper, presentation, manual, qa, table, book, resume, picture, naive, one, audio, \
    email, tag
from rag.nlp import search, rag_tokenizer
from rag.raptor import RecursiveAbstractiveProcessing4TreeOrganizedRetrieval as Raptor
from rag.settings import DOC_MAXIMUM_SIZE, SVR_QUEUE_NAME, print_rag_settings, TAG_FLD, PAGERANK_FLD
from rag.utils import num_tokens_from_string
from rag.utils.redis_conn import REDIS_CONN
from rag.utils.storage_factory import STORAGE_IMPL
from graphrag.utils import chat_limiter

BATCH_SIZE = 64

FACTORY = {
    "general": naive,
    ParserType.NAIVE.value: naive,
    ParserType.PAPER.value: paper,
    ParserType.BOOK.value: book,
    ParserType.PRESENTATION.value: presentation,
    ParserType.MANUAL.value: manual,
    ParserType.LAWS.value: laws,
    ParserType.QA.value: qa,
    ParserType.TABLE.value: table,
    ParserType.RESUME.value: resume,
    ParserType.PICTURE.value: picture,
    ParserType.ONE.value: one,
    ParserType.AUDIO.value: audio,
    ParserType.EMAIL.value: email,
    ParserType.KG.value: naive,
    ParserType.TAG.value: tag
}

UNACKED_ITERATOR = None
CONSUMER_NAME = "task_consumer_" + CONSUMER_NO
BOOT_AT = datetime.now().astimezone().isoformat(timespec="milliseconds")
PENDING_TASKS = 0
LAG_TASKS = 0
DONE_TASKS = 0
FAILED_TASKS = 0

CURRENT_TASKS = {}

MAX_CONCURRENT_TASKS = int(os.environ.get('MAX_CONCURRENT_TASKS', "5"))
MAX_CONCURRENT_CHUNK_BUILDERS = int(os.environ.get('MAX_CONCURRENT_CHUNK_BUILDERS', "1"))
task_limiter = trio.CapacityLimiter(MAX_CONCURRENT_TASKS)
chunk_limiter = trio.CapacityLimiter(MAX_CONCURRENT_CHUNK_BUILDERS)

# SIGUSR1 handler: start tracemalloc and take snapshot
def start_tracemalloc_and_snapshot(signum, frame):
    if not tracemalloc.is_tracing():
        logging.info("start tracemalloc")
        tracemalloc.start()
    else:
        logging.info("tracemalloc is already running")

    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    snapshot_file = f"snapshot_{timestamp}.trace"
    snapshot_file = os.path.abspath(os.path.join(get_project_base_directory(), "logs", f"{os.getpid()}_snapshot_{timestamp}.trace"))

    snapshot = tracemalloc.take_snapshot()
    snapshot.dump(snapshot_file)
    current, peak = tracemalloc.get_traced_memory()
    if sys.platform == "win32":
        import  psutil
        process = psutil.Process()
        max_rss = process.memory_info().rss / 1024
    else:
        import resource
        max_rss = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
    logging.info(f"taken snapshot {snapshot_file}. max RSS={max_rss / 1000:.2f} MB, current memory usage: {current / 10**6:.2f} MB, Peak memory usage: {peak / 10**6:.2f} MB")

# SIGUSR2 handler: stop tracemalloc
def stop_tracemalloc(signum, frame):
    if tracemalloc.is_tracing():
        logging.info("stop tracemalloc")
        tracemalloc.stop()
    else:
        logging.info("tracemalloc not running")

class TaskCanceledException(Exception):
    def __init__(self, msg):
        self.msg = msg


def set_progress(task_id, from_page=0, to_page=-1, prog=None, msg="Processing..."):
    if prog is not None and prog < 0:
        msg = "[ERROR]" + msg
    cancel = TaskService.do_cancel(task_id)

    if cancel:
        msg += " [Canceled]"
        prog = -1

    if to_page > 0:
        if msg:
            if from_page < to_page:
                msg = f"Page({from_page + 1}~{to_page + 1}): " + msg
    if msg:
        msg = datetime.now().strftime("%H:%M:%S") + " " + msg
    d = {"progress_msg": msg}
    if prog is not None:
        d["progress"] = prog

    logging.info(f"set_progress({task_id}), progress: {prog}, progress_msg: {msg}")
    TaskService.update_progress(task_id, d)

    close_connection()
    if cancel:
        raise TaskCanceledException(msg)

async def collect():
    global CONSUMER_NAME, DONE_TASKS, FAILED_TASKS
    global UNACKED_ITERATOR
    try:
        if not UNACKED_ITERATOR:
            UNACKED_ITERATOR = REDIS_CONN.get_unacked_iterator(SVR_QUEUE_NAME, "rag_flow_svr_task_broker", CONSUMER_NAME)
        try:
            redis_msg = next(UNACKED_ITERATOR)
        except StopIteration:
            redis_msg = REDIS_CONN.queue_consumer(SVR_QUEUE_NAME, "rag_flow_svr_task_broker", CONSUMER_NAME)
        if not redis_msg:
            await trio.sleep(1)
            return None, None
    except Exception:
        logging.exception("collect got exception")
        return None, None

    msg = redis_msg.get_message()
    if not msg:
        logging.error(f"collect got empty message of {redis_msg.get_msg_id()}")
        redis_msg.ack()
        return None, None

    canceled = False
    task = TaskService.get_task(msg["id"])
    if task:
        _, doc = DocumentService.get_by_id(task["doc_id"])
        canceled = doc.run == TaskStatus.CANCEL.value or doc.progress < 0
    if not task or canceled:
        state = "is unknown" if not task else "has been cancelled"
        FAILED_TASKS += 1
        logging.warning(f"collect task {msg['id']} {state}")
        redis_msg.ack()
        return None, None
    task["task_type"] = msg.get("task_type", "")
    return redis_msg, task


async def get_storage_binary(bucket, name):
    return await trio.to_thread.run_sync(lambda: STORAGE_IMPL.get(bucket, name))


async def build_chunks(task, progress_callback):
    if task["size"] > DOC_MAXIMUM_SIZE:
        set_progress(task["id"], prog=-1, msg="File size exceeds( <= %dMb )" %
                                              (int(DOC_MAXIMUM_SIZE / 1024 / 1024)))
        return []

    chunker = FACTORY[task["parser_id"].lower()]
    try:
        st = timer()
        bucket, name = File2DocumentService.get_storage_address(doc_id=task["doc_id"])
        binary = await get_storage_binary(bucket, name)
        logging.info("From minio({}) {}/{}".format(timer() - st, task["location"], task["name"]))
    except TimeoutError:
        progress_callback(-1, "Internal server error: Fetch file from minio timeout. Could you try it again.")
        logging.exception(
            "Minio {}/{} got timeout: Fetch file from minio timeout.".format(task["location"], task["name"]))
        raise
    except Exception as e:
        if re.search("(No such file|not found)", str(e)):
            progress_callback(-1, "Can not find file <%s> from minio. Could you try it again?" % task["name"])
        else:
            progress_callback(-1, "Get file from minio: %s" % str(e).replace("'", ""))
        logging.exception("Chunking {}/{} got exception".format(task["location"], task["name"]))
        raise

    try:
        async with chunk_limiter:
            cks = await trio.to_thread.run_sync(lambda: chunker.chunk(task["name"], binary=binary, from_page=task["from_page"],
                                to_page=task["to_page"], lang=task["language"], callback=progress_callback,
                                kb_id=task["kb_id"], parser_config=task["parser_config"], tenant_id=task["tenant_id"]))
        logging.info("Chunking({}) {}/{} done".format(timer() - st, task["location"], task["name"]))
    except TaskCanceledException:
        raise
    except Exception as e:
        progress_callback(-1, "Internal server error while chunking: %s" % str(e).replace("'", ""))
        logging.exception("Chunking {}/{} got exception".format(task["location"], task["name"]))
        raise

    docs = []
    doc = {
        "doc_id": task["doc_id"],
        "kb_id": str(task["kb_id"])
    }
    if task["pagerank"]:
        doc[PAGERANK_FLD] = int(task["pagerank"])
    el = 0
    for ck in cks:
        d = copy.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"):
            _ = d.pop("image", None)
            d["img_id"] = ""
            docs.append(d)
            continue

        try:
            output_buffer = BytesIO()
            if isinstance(d["image"], bytes):
                output_buffer = BytesIO(d["image"])
            else:
                d["image"].save(output_buffer, format='JPEG')

            st = timer()
            await trio.to_thread.run_sync(lambda: STORAGE_IMPL.put(task["kb_id"], d["id"], output_buffer.getvalue()))
            el += timer() - st
        except Exception:
            logging.exception(
                "Saving image of chunk {}/{}/{} got exception".format(task["location"], task["name"], d["id"]))
            raise

        d["img_id"] = "{}-{}".format(task["kb_id"], d["id"])
        del d["image"]
        docs.append(d)
    logging.info("MINIO PUT({}):{}".format(task["name"], el))

    if task["parser_config"].get("auto_keywords", 0):
        st = timer()
        progress_callback(msg="Start to generate keywords for every chunk ...")
        chat_mdl = LLMBundle(task["tenant_id"], LLMType.CHAT, llm_name=task["llm_id"], lang=task["language"])

        async def doc_keyword_extraction(chat_mdl, d, topn):
            cached = get_llm_cache(chat_mdl.llm_name, d["content_with_weight"], "keywords", {"topn": topn})
            if not cached:
                async with chat_limiter:
                    cached = await trio.to_thread.run_sync(lambda: keyword_extraction(chat_mdl, d["content_with_weight"], topn))
                set_llm_cache(chat_mdl.llm_name, d["content_with_weight"], cached, "keywords", {"topn": topn})
            if cached:
                d["important_kwd"] = cached.split(",")
                d["important_tks"] = rag_tokenizer.tokenize(" ".join(d["important_kwd"]))
            return
        async with trio.open_nursery() as nursery:
            for d in docs:
                nursery.start_soon(lambda: doc_keyword_extraction(chat_mdl, d, task["parser_config"]["auto_keywords"]))
        progress_callback(msg="Keywords generation {} chunks completed in {:.2f}s".format(len(docs), timer() - st))

    if task["parser_config"].get("auto_questions", 0):
        st = timer()
        progress_callback(msg="Start to generate questions for every chunk ...")
        chat_mdl = LLMBundle(task["tenant_id"], LLMType.CHAT, llm_name=task["llm_id"], lang=task["language"])

        async def doc_question_proposal(chat_mdl, d, topn):
            cached = get_llm_cache(chat_mdl.llm_name, d["content_with_weight"], "question", {"topn": topn})
            if not cached:
                async with chat_limiter:
                    cached = await trio.to_thread.run_sync(lambda: question_proposal(chat_mdl, d["content_with_weight"], topn))
                set_llm_cache(chat_mdl.llm_name, d["content_with_weight"], cached, "question", {"topn": topn})
            if cached:
                d["question_kwd"] = cached.split("\n")
                d["question_tks"] = rag_tokenizer.tokenize("\n".join(d["question_kwd"]))
        async with trio.open_nursery() as nursery:
            for d in docs:
                nursery.start_soon(lambda: doc_question_proposal(chat_mdl, d, task["parser_config"]["auto_questions"]))
        progress_callback(msg="Question generation {} chunks completed in {:.2f}s".format(len(docs), timer() - st))

    if task["kb_parser_config"].get("tag_kb_ids", []):
        progress_callback(msg="Start to tag for every chunk ...")
        kb_ids = task["kb_parser_config"]["tag_kb_ids"]
        tenant_id = task["tenant_id"]
        topn_tags = task["kb_parser_config"].get("topn_tags", 3)
        S = 1000
        st = timer()
        examples = []
        all_tags = get_tags_from_cache(kb_ids)
        if not all_tags:
            all_tags = settings.retrievaler.all_tags_in_portion(tenant_id, kb_ids, S)
            set_tags_to_cache(kb_ids, all_tags)
        else:
            all_tags = json.loads(all_tags)

        chat_mdl = LLMBundle(task["tenant_id"], LLMType.CHAT, llm_name=task["llm_id"], lang=task["language"])

        docs_to_tag = []
        for d in docs:
            if settings.retrievaler.tag_content(tenant_id, kb_ids, d, all_tags, topn_tags=topn_tags, S=S):
                examples.append({"content": d["content_with_weight"], TAG_FLD: d[TAG_FLD]})
            else:
                docs_to_tag.append(d)

        async def doc_content_tagging(chat_mdl, d, topn_tags):
            cached = get_llm_cache(chat_mdl.llm_name, d["content_with_weight"], all_tags, {"topn": topn_tags})
            if not cached:
                picked_examples = random.choices(examples, k=2) if len(examples)>2 else examples
                async with chat_limiter:
                    cached = await trio.to_thread.run_sync(lambda: content_tagging(chat_mdl, d["content_with_weight"], all_tags, picked_examples, topn=topn_tags))
                if cached:
                    cached = json.dumps(cached)
            if cached:
                set_llm_cache(chat_mdl.llm_name, d["content_with_weight"], cached, all_tags, {"topn": topn_tags})
                d[TAG_FLD] = json.loads(cached)
        async with trio.open_nursery() as nursery:
            for d in docs_to_tag:
                nursery.start_soon(lambda: doc_content_tagging(chat_mdl, d, topn_tags))
        progress_callback(msg="Tagging {} chunks completed in {:.2f}s".format(len(docs), timer() - st))

    return docs


def init_kb(row, vector_size: int):
    idxnm = search.index_name(row["tenant_id"])
    return settings.docStoreConn.createIdx(idxnm, row.get("kb_id", ""), vector_size)


async def embedding(docs, mdl, parser_config=None, callback=None):
    if parser_config is None:
        parser_config = {}
    batch_size = 16
    tts, cnts = [], []
    for d in docs:
        tts.append(d.get("docnm_kwd", "Title"))
        c = "\n".join(d.get("question_kwd", []))
        if not c:
            c = d["content_with_weight"]
        c = re.sub(r"</?(table|td|caption|tr|th)( [^<>]{0,12})?>", " ", c)
        if not c:
            c = "None"
        cnts.append(c)

    tk_count = 0
    if len(tts) == len(cnts):
        vts, c = await trio.to_thread.run_sync(lambda: mdl.encode(tts[0: 1]))
        tts = np.concatenate([vts for _ in range(len(tts))], axis=0)
        tk_count += c

    cnts_ = np.array([])
    for i in range(0, len(cnts), batch_size):
        vts, c = await trio.to_thread.run_sync(lambda: mdl.encode(cnts[i: i + batch_size]))
        if len(cnts_) == 0:
            cnts_ = vts
        else:
            cnts_ = np.concatenate((cnts_, vts), axis=0)
        tk_count += c
        callback(prog=0.7 + 0.2 * (i + 1) / len(cnts), msg="")
    cnts = cnts_

    title_w = float(parser_config.get("filename_embd_weight", 0.1))
    vects = (title_w * tts + (1 - title_w) *
             cnts) if len(tts) == len(cnts) else cnts

    assert len(vects) == len(docs)
    vector_size = 0
    for i, d in enumerate(docs):
        v = vects[i].tolist()
        vector_size = len(v)
        d["q_%d_vec" % len(v)] = v
    return tk_count, vector_size


async def run_raptor(row, chat_mdl, embd_mdl, vector_size, callback=None):
    chunks = []
    vctr_nm = "q_%d_vec"%vector_size
    for d in settings.retrievaler.chunk_list(row["doc_id"], row["tenant_id"], [str(row["kb_id"])],
                                             fields=["content_with_weight", vctr_nm]):
        chunks.append((d["content_with_weight"], np.array(d[vctr_nm])))

    raptor = Raptor(
        row["parser_config"]["raptor"].get("max_cluster", 64),
        chat_mdl,
        embd_mdl,
        row["parser_config"]["raptor"]["prompt"],
        row["parser_config"]["raptor"]["max_token"],
        row["parser_config"]["raptor"]["threshold"]
    )
    original_length = len(chunks)
    chunks = await raptor(chunks, row["parser_config"]["raptor"]["random_seed"], callback)
    doc = {
        "doc_id": row["doc_id"],
        "kb_id": [str(row["kb_id"])],
        "docnm_kwd": row["name"],
        "title_tks": rag_tokenizer.tokenize(row["name"])
    }
    if row["pagerank"]:
        doc[PAGERANK_FLD] = int(row["pagerank"])
    res = []
    tk_count = 0
    for content, vctr in chunks[original_length:]:
        d = copy.deepcopy(doc)
        d["id"] = xxhash.xxh64((content + str(d["doc_id"])).encode("utf-8")).hexdigest()
        d["create_time"] = str(datetime.now()).replace("T", " ")[:19]
        d["create_timestamp_flt"] = datetime.now().timestamp()
        d[vctr_nm] = vctr.tolist()
        d["content_with_weight"] = content
        d["content_ltks"] = rag_tokenizer.tokenize(content)
        d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"])
        res.append(d)
        tk_count += num_tokens_from_string(content)
    return res, tk_count


async def do_handle_task(task):
    task_id = task["id"]
    task_from_page = task["from_page"]
    task_to_page = task["to_page"]
    task_tenant_id = task["tenant_id"]
    task_embedding_id = task["embd_id"]
    task_language = task["language"]
    task_llm_id = task["llm_id"]
    task_dataset_id = task["kb_id"]
    task_doc_id = task["doc_id"]
    task_document_name = task["name"]
    task_parser_config = task["parser_config"]
    task_start_ts = timer()

    # prepare the progress callback function
    progress_callback = partial(set_progress, task_id, task_from_page, task_to_page)

    # FIXME: workaround, Infinity doesn't support table parsing method, this check is to notify user
    lower_case_doc_engine = settings.DOC_ENGINE.lower()
    if lower_case_doc_engine == 'infinity' and task['parser_id'].lower() == 'table':
        error_message = "Table parsing method is not supported by Infinity, please use other parsing methods or use Elasticsearch as the document engine."
        progress_callback(-1, msg=error_message)
        raise Exception(error_message)

    task_canceled = TaskService.do_cancel(task_id)
    if task_canceled:
        progress_callback(-1, msg="Task has been canceled.")
        return

    try:
        # bind embedding model
        embedding_model = LLMBundle(task_tenant_id, LLMType.EMBEDDING, llm_name=task_embedding_id, lang=task_language)
        vts, _ = embedding_model.encode(["ok"])
        vector_size = len(vts[0])
    except Exception as e:
        error_message = f'Fail to bind embedding model: {str(e)}'
        progress_callback(-1, msg=error_message)
        logging.exception(error_message)
        raise

    init_kb(task, vector_size)

    # Either using RAPTOR or Standard chunking methods
    if task.get("task_type", "") == "raptor":
        # bind LLM for raptor
        chat_model = LLMBundle(task_tenant_id, LLMType.CHAT, llm_name=task_llm_id, lang=task_language)
        # run RAPTOR
        chunks, token_count = await run_raptor(task, chat_model, embedding_model, vector_size, progress_callback)
    # Either using graphrag or Standard chunking methods
    elif task.get("task_type", "") == "graphrag":
        graphrag_conf = task_parser_config.get("graphrag", {})
        if not graphrag_conf.get("use_graphrag", False):
            return
        start_ts = timer()
        chat_model = LLMBundle(task_tenant_id, LLMType.CHAT, llm_name=task_llm_id, lang=task_language)
        with_resolution = graphrag_conf.get("resolution", False)
        with_community = graphrag_conf.get("community", False)
        await run_graphrag(task, task_language, with_resolution, with_community, chat_model, embedding_model, progress_callback)
        progress_callback(prog=1.0, msg="Knowledge Graph done ({:.2f}s)".format(timer() - start_ts))
        return
    else:
        # Standard chunking methods
        start_ts = timer()
        chunks = await build_chunks(task, progress_callback)
        logging.info("Build document {}: {:.2f}s".format(task_document_name, timer() - start_ts))
        if chunks is None:
            return
        if not chunks:
            progress_callback(1., msg=f"No chunk built from {task_document_name}")
            return
        # TODO: exception handler
        ## set_progress(task["did"], -1, "ERROR: ")
        progress_callback(msg="Generate {} chunks".format(len(chunks)))
        start_ts = timer()
        try:
            token_count, vector_size = await embedding(chunks, embedding_model, task_parser_config, progress_callback)
        except Exception as e:
            error_message = "Generate embedding error:{}".format(str(e))
            progress_callback(-1, error_message)
            logging.exception(error_message)
            token_count = 0
            raise
        progress_message = "Embedding chunks ({:.2f}s)".format(timer() - start_ts)
        logging.info(progress_message)
        progress_callback(msg=progress_message)

    chunk_count = len(set([chunk["id"] for chunk in chunks]))
    start_ts = timer()
    doc_store_result = ""
    es_bulk_size = 4
    for b in range(0, len(chunks), es_bulk_size):
        doc_store_result = await trio.to_thread.run_sync(lambda: settings.docStoreConn.insert(chunks[b:b + es_bulk_size], search.index_name(task_tenant_id), task_dataset_id))
        if b % 128 == 0:
            progress_callback(prog=0.8 + 0.1 * (b + 1) / len(chunks), msg="")
        if doc_store_result:
            error_message = f"Insert chunk error: {doc_store_result}, please check log file and Elasticsearch/Infinity status!"
            progress_callback(-1, msg=error_message)
            raise Exception(error_message)
        chunk_ids = [chunk["id"] for chunk in chunks[:b + es_bulk_size]]
        chunk_ids_str = " ".join(chunk_ids)
        try:
            TaskService.update_chunk_ids(task["id"], chunk_ids_str)
        except DoesNotExist:
            logging.warning(f"do_handle_task update_chunk_ids failed since task {task['id']} is unknown.")
            doc_store_result = await trio.to_thread.run_sync(lambda: settings.docStoreConn.delete({"id": chunk_ids}, search.index_name(task_tenant_id), task_dataset_id))
            return
    logging.info("Indexing doc({}), page({}-{}), chunks({}), elapsed: {:.2f}".format(task_document_name, task_from_page,
                                                                                     task_to_page, len(chunks),
                                                                                     timer() - start_ts))

    DocumentService.increment_chunk_num(task_doc_id, task_dataset_id, token_count, chunk_count, 0)

    time_cost = timer() - start_ts
    task_time_cost = timer() - task_start_ts
    progress_callback(prog=1.0, msg="Indexing done ({:.2f}s). Task done ({:.2f}s)".format(time_cost, task_time_cost))
    logging.info(
        "Chunk doc({}), page({}-{}), chunks({}), token({}), elapsed:{:.2f}".format(task_document_name, task_from_page,
                                                                                   task_to_page, len(chunks),
                                                                                   token_count, task_time_cost))


async def handle_task():
    global DONE_TASKS, FAILED_TASKS
    redis_msg, task = await collect()
    if not task:
        return
    try:
        logging.info(f"handle_task begin for task {json.dumps(task)}")
        CURRENT_TASKS[task["id"]] = copy.deepcopy(task)
        await do_handle_task(task)
        DONE_TASKS += 1
        CURRENT_TASKS.pop(task["id"], None)
        logging.info(f"handle_task done for task {json.dumps(task)}")
    except Exception as e:
        FAILED_TASKS += 1
        CURRENT_TASKS.pop(task["id"], None)
        try:
            err_msg = str(e)
            while isinstance(e, exceptiongroup.ExceptionGroup):
                e = e.exceptions[0]
                err_msg += ' -- ' + str(e)
            set_progress(task["id"], prog=-1, msg=f"[Exception]: {err_msg}")
        except Exception:
            pass
        logging.exception(f"handle_task got exception for task {json.dumps(task)}")
    redis_msg.ack()


async def report_status():
    global CONSUMER_NAME, BOOT_AT, PENDING_TASKS, LAG_TASKS, DONE_TASKS, FAILED_TASKS
    REDIS_CONN.sadd("TASKEXE", CONSUMER_NAME)
    while True:
        try:
            now = datetime.now()
            group_info = REDIS_CONN.queue_info(SVR_QUEUE_NAME, "rag_flow_svr_task_broker")
            if group_info is not None:
                PENDING_TASKS = int(group_info.get("pending", 0))
                LAG_TASKS = int(group_info.get("lag", 0))

            current = copy.deepcopy(CURRENT_TASKS)
            heartbeat = json.dumps({
                "name": CONSUMER_NAME,
                "now": now.astimezone().isoformat(timespec="milliseconds"),
                "boot_at": BOOT_AT,
                "pending": PENDING_TASKS,
                "lag": LAG_TASKS,
                "done": DONE_TASKS,
                "failed": FAILED_TASKS,
                "current": current,
            })
            REDIS_CONN.zadd(CONSUMER_NAME, heartbeat, now.timestamp())
            logging.info(f"{CONSUMER_NAME} reported heartbeat: {heartbeat}")

            expired = REDIS_CONN.zcount(CONSUMER_NAME, 0, now.timestamp() - 60 * 30)
            if expired > 0:
                REDIS_CONN.zpopmin(CONSUMER_NAME, expired)
        except Exception:
            logging.exception("report_status got exception")
        await trio.sleep(30)


async def main():
    logging.info(r"""
  ______           __      ______                     __            
 /_  __/___ ______/ /__   / ____/  _____  _______  __/ /_____  _____
  / / / __ `/ ___/ //_/  / __/ | |/_/ _ \/ ___/ / / / __/ __ \/ ___/
 / / / /_/ (__  ) ,<    / /____>  </  __/ /__/ /_/ / /_/ /_/ / /    
/_/  \__,_/____/_/|_|  /_____/_/|_|\___/\___/\__,_/\__/\____/_/                               
    """)
    logging.info(f'TaskExecutor: RAGFlow version: {get_ragflow_version()}')
    settings.init_settings()
    print_rag_settings()
    if sys.platform != "win32":
        signal.signal(signal.SIGUSR1, start_tracemalloc_and_snapshot)
        signal.signal(signal.SIGUSR2, stop_tracemalloc)
    TRACE_MALLOC_ENABLED = int(os.environ.get('TRACE_MALLOC_ENABLED', "0"))
    if TRACE_MALLOC_ENABLED:
        start_tracemalloc_and_snapshot(None, None)

    async with trio.open_nursery() as nursery:
        nursery.start_soon(report_status)
        while True:
            async with task_limiter:
                nursery.start_soon(handle_task)
    logging.error("BUG!!! You should not reach here!!!")

if __name__ == "__main__":
    trio.run(main)