ragflow/api/apps/chunk_app.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

from flask import request
from flask_login import login_required, current_user
from elasticsearch_dsl import Q

from rag.app.qa import rmPrefix, beAdoc
from rag.nlp import search, rag_tokenizer
from rag.utils.es_conn import ELASTICSEARCH
from rag.utils import rmSpace
from api.db import LLMType, ParserType
from api.db.services.knowledgebase_service import KnowledgebaseService
from api.db.services.llm_service import TenantLLMService
from api.db.services.user_service import UserTenantService
from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
from api.db.services.document_service import DocumentService
from api.settings import RetCode, retrievaler
from api.utils.api_utils import get_json_result
import hashlib
import re


@manager.route('/list', methods=['POST'])
@login_required
@validate_request("doc_id")
def list():
    req = request.json
    doc_id = req["doc_id"]
    page = int(req.get("page", 1))
    size = int(req.get("size", 30))
    question = req.get("keywords", "")
    try:
        tenant_id = DocumentService.get_tenant_id(req["doc_id"])
        if not tenant_id:
            return get_data_error_result(retmsg="Tenant not found!")
        e, doc = DocumentService.get_by_id(doc_id)
        if not e:
            return get_data_error_result(retmsg="Document not found!")
        query = {
            "doc_ids": [doc_id], "page": page, "size": size, "question": question, "sort": True
        }
        if "available_int" in req:
            query["available_int"] = int(req["available_int"])
        sres = retrievaler.search(query, search.index_name(tenant_id))
        res = {"total": sres.total, "chunks": [], "doc": doc.to_dict()}
        for id in sres.ids:
            d = {
                "chunk_id": id,
                "content_with_weight": rmSpace(sres.highlight[id]) if question and id in  sres.highlight else sres.field[id].get(
                    "content_with_weight", ""),
                "doc_id": sres.field[id]["doc_id"],
                "docnm_kwd": sres.field[id]["docnm_kwd"],
                "important_kwd": sres.field[id].get("important_kwd", []),
                "img_id": sres.field[id].get("img_id", ""),
                "available_int": sres.field[id].get("available_int", 1),
                "positions": sres.field[id].get("position_int", "").split("\t")
            }
            if len(d["positions"]) % 5 == 0:
                poss = []
                for i in range(0, len(d["positions"]), 5):
                    poss.append([float(d["positions"][i]), float(d["positions"][i + 1]), float(d["positions"][i + 2]),
                                 float(d["positions"][i + 3]), float(d["positions"][i + 4])])
                d["positions"] = poss
            res["chunks"].append(d)
        return get_json_result(data=res)
    except Exception as e:
        if str(e).find("not_found") > 0:
            return get_json_result(data=False, retmsg=f'No chunk found!',
                                   retcode=RetCode.DATA_ERROR)
        return server_error_response(e)


@manager.route('/get', methods=['GET'])
@login_required
def get():
    chunk_id = request.args["chunk_id"]
    try:
        tenants = UserTenantService.query(user_id=current_user.id)
        if not tenants:
            return get_data_error_result(retmsg="Tenant not found!")
        res = ELASTICSEARCH.get(
            chunk_id, search.index_name(
                tenants[0].tenant_id))
        if not res.get("found"):
            return server_error_response("Chunk not found")
        id = res["_id"]
        res = res["_source"]
        res["chunk_id"] = id
        k = []
        for n in res.keys():
            if re.search(r"(_vec$|_sm_|_tks|_ltks)", n):
                k.append(n)
        for n in k:
            del res[n]

        return get_json_result(data=res)
    except Exception as e:
        if str(e).find("NotFoundError") >= 0:
            return get_json_result(data=False, retmsg=f'Chunk not found!',
                                   retcode=RetCode.DATA_ERROR)
        return server_error_response(e)


@manager.route('/set', methods=['POST'])
@login_required
@validate_request("doc_id", "chunk_id", "content_with_weight",
                  "important_kwd")
def set():
    req = request.json
    d = {
        "id": req["chunk_id"],
        "content_with_weight": req["content_with_weight"]}
    d["content_ltks"] = rag_tokenizer.tokenize(req["content_with_weight"])
    d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"])
    d["important_kwd"] = req["important_kwd"]
    d["important_tks"] = rag_tokenizer.tokenize(" ".join(req["important_kwd"]))
    if "available_int" in req:
        d["available_int"] = req["available_int"]

    try:
        tenant_id = DocumentService.get_tenant_id(req["doc_id"])
        if not tenant_id:
            return get_data_error_result(retmsg="Tenant not found!")
        embd_mdl = TenantLLMService.model_instance(
            tenant_id, LLMType.EMBEDDING.value)
        e, doc = DocumentService.get_by_id(req["doc_id"])
        if not e:
            return get_data_error_result(retmsg="Document not found!")

        if doc.parser_id == ParserType.QA:
            arr = [
                t for t in re.split(
                    r"[\n\t]",
                    req["content_with_weight"]) if len(t) > 1]
            if len(arr) != 2:
                return get_data_error_result(
                    retmsg="Q&A must be separated by TAB/ENTER key.")
            q, a = rmPrefix(arr[0]), rmPrefix[arr[1]]
            d = beAdoc(d, arr[0], arr[1], not any(
                [rag_tokenizer.is_chinese(t) for t in q + a]))

        v, c = embd_mdl.encode([doc.name, req["content_with_weight"]])
        v = 0.1 * v[0] + 0.9 * v[1] if doc.parser_id != ParserType.QA else v[1]
        d["q_%d_vec" % len(v)] = v.tolist()
        ELASTICSEARCH.upsert([d], search.index_name(tenant_id))
        return get_json_result(data=True)
    except Exception as e:
        return server_error_response(e)


@manager.route('/switch', methods=['POST'])
@login_required
@validate_request("chunk_ids", "available_int", "doc_id")
def switch():
    req = request.json
    try:
        tenant_id = DocumentService.get_tenant_id(req["doc_id"])
        if not tenant_id:
            return get_data_error_result(retmsg="Tenant not found!")
        if not ELASTICSEARCH.upsert([{"id": i, "available_int": int(req["available_int"])} for i in req["chunk_ids"]],
                                    search.index_name(tenant_id)):
            return get_data_error_result(retmsg="Index updating failure")
        return get_json_result(data=True)
    except Exception as e:
        return server_error_response(e)


@manager.route('/rm', methods=['POST'])
@login_required
@validate_request("chunk_ids")
def rm():
    req = request.json
    try:
        if not ELASTICSEARCH.deleteByQuery(
                Q("ids", values=req["chunk_ids"]), search.index_name(current_user.id)):
            return get_data_error_result(retmsg="Index updating failure")
        return get_json_result(data=True)
    except Exception as e:
        return server_error_response(e)


@manager.route('/create', methods=['POST'])
@login_required
@validate_request("doc_id", "content_with_weight")
def create():
    req = request.json
    md5 = hashlib.md5()
    md5.update((req["content_with_weight"] + req["doc_id"]).encode("utf-8"))
    chunck_id = md5.hexdigest()
    d = {"id": chunck_id, "content_ltks": rag_tokenizer.tokenize(req["content_with_weight"]),
         "content_with_weight": req["content_with_weight"]}
    d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"])
    d["important_kwd"] = req.get("important_kwd", [])
    d["important_tks"] = rag_tokenizer.tokenize(" ".join(req.get("important_kwd", [])))
    d["create_time"] = str(datetime.datetime.now()).replace("T", " ")[:19]
    d["create_timestamp_flt"] = datetime.datetime.now().timestamp()

    try:
        e, doc = DocumentService.get_by_id(req["doc_id"])
        if not e:
            return get_data_error_result(retmsg="Document not found!")
        d["kb_id"] = [doc.kb_id]
        d["docnm_kwd"] = doc.name
        d["doc_id"] = doc.id

        tenant_id = DocumentService.get_tenant_id(req["doc_id"])
        if not tenant_id:
            return get_data_error_result(retmsg="Tenant not found!")

        embd_mdl = TenantLLMService.model_instance(
            tenant_id, LLMType.EMBEDDING.value)
        v, c = embd_mdl.encode([doc.name, req["content_with_weight"]])
        DocumentService.increment_chunk_num(req["doc_id"], doc.kb_id, c, 1, 0)
        v = 0.1 * v[0] + 0.9 * v[1]
        d["q_%d_vec" % len(v)] = v.tolist()
        ELASTICSEARCH.upsert([d], search.index_name(tenant_id))
        return get_json_result(data={"chunk_id": chunck_id})
    except Exception as e:
        return server_error_response(e)


@manager.route('/retrieval_test', methods=['POST'])
@login_required
@validate_request("kb_id", "question")
def retrieval_test():
    req = request.json
    page = int(req.get("page", 1))
    size = int(req.get("size", 30))
    question = req["question"]
    kb_id = req["kb_id"]
    doc_ids = req.get("doc_ids", [])
    similarity_threshold = float(req.get("similarity_threshold", 0.2))
    vector_similarity_weight = float(req.get("vector_similarity_weight", 0.3))
    top = int(req.get("top_k", 1024))
    try:
        e, kb = KnowledgebaseService.get_by_id(kb_id)
        if not e:
            return get_data_error_result(retmsg="Knowledgebase not found!")

        embd_mdl = TenantLLMService.model_instance(
            kb.tenant_id, LLMType.EMBEDDING.value, llm_name=kb.embd_id)
        ranks = retrievaler.retrieval(question, embd_mdl, kb.tenant_id, [kb_id], page, size, similarity_threshold,
                                      vector_similarity_weight, top, doc_ids)
        for c in ranks["chunks"]:
            if "vector" in c:
                del c["vector"]

        return get_json_result(data=ranks)
    except Exception as e:
        if str(e).find("not_found") > 0:
            return get_json_result(data=False, retmsg=f'No chunk found! Check the chunk status please!',
                                   retcode=RetCode.DATA_ERROR)
        return server_error_response(e)