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.
#
import datetime
import json
import logging
import os
import hashlib
import copy
import re
import sys
import time
import traceback
from functools import partial

from api.db.services.file2document_service import File2DocumentService
from api.settings import retrievaler
from rag.raptor import RecursiveAbstractiveProcessing4TreeOrganizedRetrieval as Raptor
from rag.utils.minio_conn import MINIO
from api.db.db_models import close_connection
from rag.settings import database_logger, SVR_QUEUE_NAME
from rag.settings import cron_logger, DOC_MAXIMUM_SIZE
from multiprocessing import Pool
import numpy as np
from elasticsearch_dsl import Q, Search
from multiprocessing.context import TimeoutError
from api.db.services.task_service import TaskService
from rag.utils.es_conn import ELASTICSEARCH
from timeit import default_timer as timer
from rag.utils import rmSpace, findMaxTm, num_tokens_from_string

from rag.nlp import search, rag_tokenizer
from io import BytesIO
import pandas as pd

from rag.app import laws, paper, presentation, manual, qa, table, book, resume, picture, naive, one, audio, knowledge_graph, email

from api.db import LLMType, ParserType
from api.db.services.document_service import DocumentService
from api.db.services.llm_service import LLMBundle
from api.utils.file_utils import get_project_base_directory
from rag.utils.redis_conn import REDIS_CONN

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: knowledge_graph
}


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:
            msg = f"Page({from_page + 1}~{to_page + 1}): " + msg
    d = {"progress_msg": msg}
    if prog is not None:
        d["progress"] = prog
    try:
        TaskService.update_progress(task_id, d)
    except Exception as e:
        cron_logger.error("set_progress:({}), {}".format(task_id, str(e)))

    close_connection()
    if cancel:
        sys.exit()


def collect():
    try:
        payload = REDIS_CONN.queue_consumer(SVR_QUEUE_NAME, "rag_flow_svr_task_broker", "rag_flow_svr_task_consumer")
        if not payload:
            time.sleep(1)
            return pd.DataFrame()
    except Exception as e:
        cron_logger.error("Get task event from queue exception:" + str(e))
        return pd.DataFrame()

    msg = payload.get_message()
    payload.ack()
    if not msg: return pd.DataFrame()

    if TaskService.do_cancel(msg["id"]):
        cron_logger.info("Task {} has been canceled.".format(msg["id"]))
        return pd.DataFrame()
    tasks = TaskService.get_tasks(msg["id"])
    assert tasks, "{} empty task!".format(msg["id"])
    tasks = pd.DataFrame(tasks)
    if msg.get("type", "") == "raptor":
        tasks["task_type"] = "raptor"
    return tasks


def get_minio_binary(bucket, name):
    return MINIO.get(bucket, name)


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

    callback = partial(
        set_progress,
        row["id"],
        row["from_page"],
        row["to_page"])
    chunker = FACTORY[row["parser_id"].lower()]
    try:
        st = timer()
        bucket, name = File2DocumentService.get_minio_address(doc_id=row["doc_id"])
        binary = get_minio_binary(bucket, name)
        cron_logger.info(
            "From minio({}) {}/{}".format(timer() - st, row["location"], row["name"]))
    except TimeoutError as e:
        callback(-1, f"Internal server error: Fetch file from minio timeout. Could you try it again.")
        cron_logger.error(
            "Minio {}/{}: Fetch file from minio timeout.".format(row["location"], row["name"]))
        return
    except Exception as e:
        if re.search("(No such file|not found)", str(e)):
            callback(-1, "Can not find file <%s> from minio. Could you try it again?" % row["name"])
        else:
            callback(-1, f"Get file from minio: %s" %
                     str(e).replace("'", ""))
        traceback.print_exc()
        return

    try:
        cks = chunker.chunk(row["name"], binary=binary, from_page=row["from_page"],
                            to_page=row["to_page"], lang=row["language"], callback=callback,
                            kb_id=row["kb_id"], parser_config=row["parser_config"], tenant_id=row["tenant_id"])
        cron_logger.info(
            "Chunking({}) {}/{}".format(timer() - st, row["location"], row["name"]))
    except Exception as e:
        callback(-1, f"Internal server error while chunking: %s" %
                     str(e).replace("'", ""))
        cron_logger.error(
            "Chunking {}/{}: {}".format(row["location"], row["name"], str(e)))
        traceback.print_exc()
        return

    docs = []
    doc = {
        "doc_id": row["doc_id"],
        "kb_id": [str(row["kb_id"])]
    }
    el = 0
    for ck in cks:
        d = copy.deepcopy(doc)
        d.update(ck)
        md5 = hashlib.md5()
        md5.update((ck["content_with_weight"] +
                    str(d["doc_id"])).encode("utf-8"))
        d["_id"] = md5.hexdigest()
        d["create_time"] = str(datetime.datetime.now()).replace("T", " ")[:19]
        d["create_timestamp_flt"] = datetime.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')

        st = timer()
        MINIO.put(row["kb_id"], d["_id"], output_buffer.getvalue())
        el += timer() - st
        d["img_id"] = "{}-{}".format(row["kb_id"], d["_id"])
        del d["image"]
        docs.append(d)
    cron_logger.info("MINIO PUT({}):{}".format(row["name"], el))

    return docs


def init_kb(row):
    idxnm = search.index_name(row["tenant_id"])
    if ELASTICSEARCH.indexExist(idxnm):
        return
    return ELASTICSEARCH.createIdx(idxnm, json.load(
        open(os.path.join(get_project_base_directory(), "conf", "mapping.json"), "r")))


def embedding(docs, mdl, parser_config={}, callback=None):
    batch_size = 32
    tts, cnts = [rmSpace(d["title_tks"]) for d in docs if d.get("title_tks")], [
        re.sub(r"</?(table|td|caption|tr|th)( [^<>]{0,12})?>", " ", d["content_with_weight"]) for d in docs]
    tk_count = 0
    if len(tts) == len(cnts):
        tts_ = np.array([])
        for i in range(0, len(tts), batch_size):
            vts, c = mdl.encode(tts[i: i + batch_size])
            if len(tts_) == 0:
                tts_ = vts
            else:
                tts_ = np.concatenate((tts_, vts), axis=0)
            tk_count += c
            callback(prog=0.6 + 0.1 * (i + 1) / len(tts), msg="")
        tts = tts_

    cnts_ = np.array([])
    for i in range(0, len(cnts), batch_size):
        vts, c = 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)
    for i, d in enumerate(docs):
        v = vects[i].tolist()
        d["q_%d_vec" % len(v)] = v
    return tk_count


def run_raptor(row, chat_mdl, embd_mdl, callback=None):
    vts, _ = embd_mdl.encode(["ok"])
    vctr_nm = "q_%d_vec"%len(vts[0])
    chunks = []
    for d in retrievaler.chunk_list(row["doc_id"], row["tenant_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)
    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"])
    }
    res = []
    tk_count = 0
    for content, vctr in chunks[original_length:]:
        d = copy.deepcopy(doc)
        md5 = hashlib.md5()
        md5.update((content + str(d["doc_id"])).encode("utf-8"))
        d["_id"] = md5.hexdigest()
        d["create_time"] = str(datetime.datetime.now()).replace("T", " ")[:19]
        d["create_timestamp_flt"] = datetime.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


def main():
    rows = collect()
    if len(rows) == 0:
        return

    for _, r in rows.iterrows():
        callback = partial(set_progress, r["id"], r["from_page"], r["to_page"])
        try:
            embd_mdl = LLMBundle(r["tenant_id"], LLMType.EMBEDDING, llm_name=r["embd_id"], lang=r["language"])
        except Exception as e:
            callback(-1, msg=str(e))
            cron_logger.error(str(e))
            continue

        if r.get("task_type", "") == "raptor":
            try:
                chat_mdl = LLMBundle(r["tenant_id"], LLMType.CHAT, llm_name=r["llm_id"], lang=r["language"])
                cks, tk_count = run_raptor(r, chat_mdl, embd_mdl, callback)
            except Exception as e:
                callback(-1, msg=str(e))
                cron_logger.error(str(e))
                continue
        else:
            st = timer()
            cks = build(r)
            cron_logger.info("Build chunks({}): {}".format(r["name"], timer() - st))
            if cks is None:
                continue
            if not cks:
                callback(1., "No chunk! Done!")
                continue
            # TODO: exception handler
            ## set_progress(r["did"], -1, "ERROR: ")
            callback(
                msg="Finished slicing files(%d). Start to embedding the content." %
                    len(cks))
            st = timer()
            try:
                tk_count = embedding(cks, embd_mdl, r["parser_config"], callback)
            except Exception as e:
                callback(-1, "Embedding error:{}".format(str(e)))
                cron_logger.error(str(e))
                tk_count = 0
            cron_logger.info("Embedding elapsed({}): {:.2f}".format(r["name"], timer() - st))
            callback(msg="Finished embedding({:.2f})! Start to build index!".format(timer() - st))

        init_kb(r)
        chunk_count = len(set([c["_id"] for c in cks]))
        st = timer()
        es_r = ""
        es_bulk_size = 16
        for b in range(0, len(cks), es_bulk_size):
            es_r = ELASTICSEARCH.bulk(cks[b:b + es_bulk_size], search.index_name(r["tenant_id"]))
            if b % 128 == 0:
                callback(prog=0.8 + 0.1 * (b + 1) / len(cks), msg="")

        cron_logger.info("Indexing elapsed({}): {:.2f}".format(r["name"], timer() - st))
        if es_r:
            callback(-1, "Index failure!")
            ELASTICSEARCH.deleteByQuery(
                Q("match", doc_id=r["doc_id"]), idxnm=search.index_name(r["tenant_id"]))
            cron_logger.error(str(es_r))
        else:
            if TaskService.do_cancel(r["id"]):
                ELASTICSEARCH.deleteByQuery(
                    Q("match", doc_id=r["doc_id"]), idxnm=search.index_name(r["tenant_id"]))
                continue
            callback(1., "Done!")
            DocumentService.increment_chunk_num(
                r["doc_id"], r["kb_id"], tk_count, chunk_count, 0)
            cron_logger.info(
                "Chunk doc({}), token({}), chunks({}), elapsed:{:.2f}".format(
                    r["id"], tk_count, len(cks), timer() - st))


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:
        main()