Feat: Q&A format segmentation support (#668)

Co-authored-by: jyong <718720800@qq.com>
Co-authored-by: StyleZhang <jasonapring2015@outlook.com>

api/controllers/console/datasets/datasets.py

@@ -220,6 +220,7 @@ class DatasetIndexingEstimateApi(Resource):
         parser = reqparse.RequestParser()
         parser.add_argument('info_list', type=dict, required=True, nullable=True, location='json')
         parser.add_argument('process_rule', type=dict, required=True, nullable=True, location='json')
+        parser.add_argument('doc_form', type=str, default='text_model', required=False, nullable=False, location='json')
         args = parser.parse_args()
         # validate args
         DocumentService.estimate_args_validate(args)
@@ -234,12 +235,12 @@ class DatasetIndexingEstimateApi(Resource):
                 raise NotFound("File not found.")
 
             indexing_runner = IndexingRunner()
-            response = indexing_runner.file_indexing_estimate(file_details, args['process_rule'])
+            response = indexing_runner.file_indexing_estimate(file_details, args['process_rule'], args['doc_form'])
         elif args['info_list']['data_source_type'] == 'notion_import':
 
             indexing_runner = IndexingRunner()
             response = indexing_runner.notion_indexing_estimate(args['info_list']['notion_info_list'],
-                                                                args['process_rule'])
+                                                                args['process_rule'], args['doc_form'])
         else:
             raise ValueError('Data source type not support')
         return response, 200

api/controllers/console/datasets/datasets_document.py

@@ -60,6 +60,7 @@ document_fields = {
     'display_status': fields.String,
     'word_count': fields.Integer,
     'hit_count': fields.Integer,
+    'doc_form': fields.String,
 }
 
 document_with_segments_fields = {
@@ -86,6 +87,7 @@ document_with_segments_fields = {
     'total_segments': fields.Integer
 }
 
+
 class DocumentResource(Resource):
     def get_document(self, dataset_id: str, document_id: str) -> Document:
         dataset = DatasetService.get_dataset(dataset_id)
@@ -269,6 +271,7 @@ class DatasetDocumentListApi(Resource):
         parser.add_argument('process_rule', type=dict, required=False, location='json')
         parser.add_argument('duplicate', type=bool, nullable=False, location='json')
         parser.add_argument('original_document_id', type=str, required=False, location='json')
+        parser.add_argument('doc_form', type=str, default='text_model', required=False, nullable=False, location='json')
         args = parser.parse_args()
 
         if not dataset.indexing_technique and not args['indexing_technique']:
@@ -313,6 +316,7 @@ class DatasetInitApi(Resource):
                             nullable=False, location='json')
         parser.add_argument('data_source', type=dict, required=True, nullable=True, location='json')
         parser.add_argument('process_rule', type=dict, required=True, nullable=True, location='json')
+        parser.add_argument('doc_form', type=str, default='text_model', required=False, nullable=False, location='json')
         args = parser.parse_args()
 
         # validate args
@@ -488,6 +492,8 @@ class DocumentBatchIndexingStatusApi(DocumentResource):
                                                           DocumentSegment.status != 're_segment').count()
             document.completed_segments = completed_segments
             document.total_segments = total_segments
+            if document.is_paused:
+                document.indexing_status = 'paused'
             documents_status.append(marshal(document, self.document_status_fields))
         data = {
             'data': documents_status
@@ -583,7 +589,8 @@ class DocumentDetailApi(DocumentResource):
                 'segment_count': document.segment_count,
                 'average_segment_length': document.average_segment_length,
                 'hit_count': document.hit_count,
-                'display_status': document.display_status
+                'display_status': document.display_status,
+                'doc_form': document.doc_form
             }
         else:
             process_rules = DatasetService.get_process_rules(dataset_id)
@@ -614,7 +621,8 @@ class DocumentDetailApi(DocumentResource):
                 'segment_count': document.segment_count,
                 'average_segment_length': document.average_segment_length,
                 'hit_count': document.hit_count,
-                'display_status': document.display_status
+                'display_status': document.display_status,
+                'doc_form': document.doc_form
             }
 
         return response, 200

api/controllers/console/datasets/datasets_segments.py

@@ -15,8 +15,8 @@ from extensions.ext_redis import redis_client
 from models.dataset import DocumentSegment
 
 from libs.helper import TimestampField
-from services.dataset_service import DatasetService, DocumentService
-from tasks.add_segment_to_index_task import add_segment_to_index_task
+from services.dataset_service import DatasetService, DocumentService, SegmentService
+from tasks.enable_segment_to_index_task import enable_segment_to_index_task
 from tasks.remove_segment_from_index_task import remove_segment_from_index_task
 
 segment_fields = {
@@ -24,6 +24,7 @@ segment_fields = {
     'position': fields.Integer,
     'document_id': fields.String,
     'content': fields.String,
+    'answer': fields.String,
     'word_count': fields.Integer,
     'tokens': fields.Integer,
     'keywords': fields.List(fields.String),
@@ -125,6 +126,7 @@ class DatasetDocumentSegmentListApi(Resource):
 
         return {
             'data': marshal(segments, segment_fields),
+            'doc_form': document.doc_form,
             'has_more': has_more,
             'limit': limit,
             'total': total
@@ -180,7 +182,7 @@ class DatasetDocumentSegmentApi(Resource):
             # Set cache to prevent indexing the same segment multiple times
             redis_client.setex(indexing_cache_key, 600, 1)
 
-            add_segment_to_index_task.delay(segment.id)
+            enable_segment_to_index_task.delay(segment.id)
 
             return {'result': 'success'}, 200
         elif action == "disable":
@@ -202,7 +204,92 @@ class DatasetDocumentSegmentApi(Resource):
             raise InvalidActionError()
 
 
+class DatasetDocumentSegmentAddApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self, dataset_id, document_id):
+        # check dataset
+        dataset_id = str(dataset_id)
+        dataset = DatasetService.get_dataset(dataset_id)
+        if not dataset:
+            raise NotFound('Dataset not found.')
+        # check document
+        document_id = str(document_id)
+        document = DocumentService.get_document(dataset_id, document_id)
+        if not document:
+            raise NotFound('Document not found.')
+        # The role of the current user in the ta table must be admin or owner
+        if current_user.current_tenant.current_role not in ['admin', 'owner']:
+            raise Forbidden()
+        try:
+            DatasetService.check_dataset_permission(dataset, current_user)
+        except services.errors.account.NoPermissionError as e:
+            raise Forbidden(str(e))
+        # validate args
+        parser = reqparse.RequestParser()
+        parser.add_argument('content', type=str, required=True, nullable=False, location='json')
+        parser.add_argument('answer', type=str, required=False, nullable=True, location='json')
+        parser.add_argument('keywords', type=list, required=False, nullable=True, location='json')
+        args = parser.parse_args()
+        SegmentService.segment_create_args_validate(args, document)
+        segment = SegmentService.create_segment(args, document)
+        return {
+            'data': marshal(segment, segment_fields),
+            'doc_form': document.doc_form
+        }, 200
+
+
+class DatasetDocumentSegmentUpdateApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def patch(self, dataset_id, document_id, segment_id):
+        # check dataset
+        dataset_id = str(dataset_id)
+        dataset = DatasetService.get_dataset(dataset_id)
+        if not dataset:
+            raise NotFound('Dataset not found.')
+        # check document
+        document_id = str(document_id)
+        document = DocumentService.get_document(dataset_id, document_id)
+        if not document:
+            raise NotFound('Document not found.')
+        # check segment
+        segment_id = str(segment_id)
+        segment = DocumentSegment.query.filter(
+            DocumentSegment.id == str(segment_id),
+            DocumentSegment.tenant_id == current_user.current_tenant_id
+        ).first()
+        if not segment:
+            raise NotFound('Segment not found.')
+        # The role of the current user in the ta table must be admin or owner
+        if current_user.current_tenant.current_role not in ['admin', 'owner']:
+            raise Forbidden()
+        try:
+            DatasetService.check_dataset_permission(dataset, current_user)
+        except services.errors.account.NoPermissionError as e:
+            raise Forbidden(str(e))
+        # validate args
+        parser = reqparse.RequestParser()
+        parser.add_argument('content', type=str, required=True, nullable=False, location='json')
+        parser.add_argument('answer', type=str, required=False, nullable=True, location='json')
+        parser.add_argument('keywords', type=list, required=False, nullable=True, location='json')
+        args = parser.parse_args()
+        SegmentService.segment_create_args_validate(args, document)
+        segment = SegmentService.update_segment(args, segment, document)
+        return {
+            'data': marshal(segment, segment_fields),
+            'doc_form': document.doc_form
+        }, 200
+
+
 api.add_resource(DatasetDocumentSegmentListApi,
                  '/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/segments')
 api.add_resource(DatasetDocumentSegmentApi,
                  '/datasets/<uuid:dataset_id>/segments/<uuid:segment_id>/<string:action>')
+api.add_resource(DatasetDocumentSegmentAddApi,
+                 '/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/segment')
+api.add_resource(DatasetDocumentSegmentUpdateApi,
+                 '/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/segments/<uuid:segment_id>')
+

api/controllers/console/datasets/hit_testing.py

@@ -28,6 +28,7 @@ segment_fields = {
     'position': fields.Integer,
     'document_id': fields.String,
     'content': fields.String,
+    'answer': fields.String,
     'word_count': fields.Integer,
     'tokens': fields.Integer,
     'keywords': fields.List(fields.String),

api/core/data_loader/loader/excel.py

@@ -39,7 +39,7 @@ class ExcelLoader(BaseLoader):
                     row_dict = dict(zip(keys, list(map(str, row))))
                     row_dict = {k: v for k, v in row_dict.items() if v}
                     item = ''.join(f'{k}:{v}\n' for k, v in row_dict.items())
-                    document = Document(page_content=item)
+                    document = Document(page_content=item, metadata={'source': self._file_path})
                     data.append(document)
 
         return data

api/core/docstore/dataset_docstore.py

@@ -68,7 +68,7 @@ class DatesetDocumentStore:
         self, docs: Sequence[Document], allow_update: bool = True
     ) -> None:
         max_position = db.session.query(func.max(DocumentSegment.position)).filter(
-            DocumentSegment.document == self._document_id
+            DocumentSegment.document_id == self._document_id
         ).scalar()
 
         if max_position is None:
@@ -105,9 +105,14 @@ class DatesetDocumentStore:
                     tokens=tokens,
                     created_by=self._user_id,
                 )
+                if 'answer' in doc.metadata and doc.metadata['answer']:
+                    segment_document.answer = doc.metadata.pop('answer', '')
+
                 db.session.add(segment_document)
             else:
                 segment_document.content = doc.page_content
+                if 'answer' in doc.metadata and doc.metadata['answer']:
+                    segment_document.answer = doc.metadata.pop('answer', '')
                 segment_document.index_node_hash = doc.metadata['doc_hash']
                 segment_document.word_count = len(doc.page_content)
                 segment_document.tokens = tokens

api/core/generator/llm_generator.py

@@ -2,7 +2,7 @@ import logging
 
 from langchain import PromptTemplate
 from langchain.chat_models.base import BaseChatModel
-from langchain.schema import HumanMessage, OutputParserException, BaseMessage
+from langchain.schema import HumanMessage, OutputParserException, BaseMessage, SystemMessage
 
 from core.constant import llm_constant
 from core.llm.llm_builder import LLMBuilder
@@ -12,8 +12,8 @@ from core.prompt.output_parser.rule_config_generator import RuleConfigGeneratorO
 
 from core.prompt.output_parser.suggested_questions_after_answer import SuggestedQuestionsAfterAnswerOutputParser
 from core.prompt.prompt_template import JinjaPromptTemplate, OutLinePromptTemplate
-from core.prompt.prompts import CONVERSATION_TITLE_PROMPT, CONVERSATION_SUMMARY_PROMPT, INTRODUCTION_GENERATE_PROMPT
-
+from core.prompt.prompts import CONVERSATION_TITLE_PROMPT, CONVERSATION_SUMMARY_PROMPT, INTRODUCTION_GENERATE_PROMPT, \
+    GENERATOR_QA_PROMPT
 
 # gpt-3.5-turbo works not well
 generate_base_model = 'text-davinci-003'
@@ -31,7 +31,8 @@ class LLMGenerator:
         llm: StreamableOpenAI = LLMBuilder.to_llm(
             tenant_id=tenant_id,
             model_name='gpt-3.5-turbo',
-            max_tokens=50
+            max_tokens=50,
+            timeout=600
         )
 
         if isinstance(llm, BaseChatModel):
@@ -185,3 +186,27 @@ class LLMGenerator:
             }
 
         return rule_config
+
+    @classmethod
+    async def generate_qa_document(cls, llm: StreamableOpenAI, query):
+        prompt = GENERATOR_QA_PROMPT
+
+
+        if isinstance(llm, BaseChatModel):
+            prompt = [SystemMessage(content=prompt), HumanMessage(content=query)]
+
+        response = llm.generate([prompt])
+        answer = response.generations[0][0].text
+        return answer.strip()
+
+    @classmethod
+    def generate_qa_document_sync(cls, llm: StreamableOpenAI, query):
+        prompt = GENERATOR_QA_PROMPT
+
+
+        if isinstance(llm, BaseChatModel):
+            prompt = [SystemMessage(content=prompt), HumanMessage(content=query)]
+
+        response = llm.generate([prompt])
+        answer = response.generations[0][0].text
+        return answer.strip()

api/core/index/keyword_table_index/keyword_table_index.py

@@ -205,6 +205,16 @@ class KeywordTableIndex(BaseIndex):
             document_segment.keywords = keywords
             db.session.commit()
 
+    def create_segment_keywords(self, node_id: str, keywords: List[str]):
+        keyword_table = self._get_dataset_keyword_table()
+        self._update_segment_keywords(node_id, keywords)
+        keyword_table = self._add_text_to_keyword_table(keyword_table, node_id, keywords)
+        self._save_dataset_keyword_table(keyword_table)
+
+    def update_segment_keywords_index(self, node_id: str, keywords: List[str]):
+        keyword_table = self._get_dataset_keyword_table()
+        keyword_table = self._add_text_to_keyword_table(keyword_table, node_id, keywords)
+        self._save_dataset_keyword_table(keyword_table)
 
 class KeywordTableRetriever(BaseRetriever, BaseModel):
     index: KeywordTableIndex

api/core/index/vector_index/test-embedding.py

+import numpy as np
+import sklearn.decomposition
+import pickle
+import time
+
+
+# Apply 'Algorithm 1' to the ada-002 embeddings to make them isotropic, taken from the paper:
+# ALL-BUT-THE-TOP: SIMPLE AND EFFECTIVE POST- PROCESSING FOR WORD REPRESENTATIONS
+# Jiaqi Mu, Pramod Viswanath
+
+# This uses Principal Component Analysis (PCA) to 'evenly distribute' the embedding vectors (make them isotropic)
+# For more information on PCA, see https://jamesmccaffrey.wordpress.com/2021/07/16/computing-pca-using-numpy-without-scikit/
+
+
+# get the file pointer of the pickle containing the embeddings
+fp = open('/path/to/your/data/Embedding-Latest.pkl', 'rb')
+
+
+# the embedding data here is a dict consisting of key / value pairs
+# the key is the hash of the message (SHA3-256), the value is the embedding from ada-002 (array of dimension 1536)
+# the hash can be used to lookup the orignal text in a database
+E = pickle.load(fp) # load the data into memory
+
+# seperate the keys (hashes) and values (embeddings) into seperate vectors
+K = list(E.keys()) # vector of all the hash values
+X = np.array(list(E.values())) # vector of all the embeddings, converted to numpy arrays
+
+
+# list the total number of embeddings
+# this can be truncated if there are too many embeddings to do PCA on
+print(f"Total number of embeddings: {len(X)}")
+
+# get dimension of embeddings, used later
+Dim = len(X[0])
+
+# flash out the first few embeddings
+print("First two embeddings are: ")
+print(X[0])
+print(f"First embedding length: {len(X[0])}")
+print(X[1])
+print(f"Second embedding length: {len(X[1])}")
+
+
+# compute the mean of all the embeddings, and flash the result
+mu = np.mean(X, axis=0) # same as mu in paper
+print(f"Mean embedding vector: {mu}")
+print(f"Mean embedding vector length: {len(mu)}")
+
+
+# subtract the mean vector from each embedding vector ... vectorized in numpy
+X_tilde = X - mu # same as v_tilde(w) in paper
+
+
+
+# do the heavy lifting of extracting the principal components
+# note that this is a function of the embeddings you currently have here, and this set may grow over time
+# therefore the PCA basis vectors may change over time, and your final isotropic embeddings may drift over time
+# but the drift should stabilize after you have extracted enough embedding data to characterize the nature of the embedding engine
+print(f"Performing PCA on the normalized embeddings ...")
+pca = sklearn.decomposition.PCA()  # new object
+TICK = time.time() # start timer
+pca.fit(X_tilde) # do the heavy lifting!
+TOCK = time.time() # end timer
+DELTA = TOCK - TICK
+
+print(f"PCA finished in {DELTA} seconds ...")
+
+# dimensional reduction stage (the only hyperparameter)
+# pick max dimension of PCA components to express embddings
+# in general this is some integer less than or equal to the dimension of your embeddings
+# it could be set as a high percentile, say 95th percentile of pca.explained_variance_ratio_
+# but just hardcoding a constant here
+D = 15 # hyperparameter on dimension (out of 1536 for ada-002), paper recommeds D = Dim/100
+
+
+# form the set of v_prime(w), which is the final embedding
+# this could be vectorized in numpy to speed it up, but coding it directly here in a double for-loop to avoid errors and to be transparent
+E_prime = dict() # output dict of the new embeddings
+N = len(X_tilde)
+N10 = round(N/10)
+U = pca.components_ # set of PCA basis vectors, sorted by most significant to least significant
+print(f"Shape of full set of PCA componenents {U.shape}")
+U = U[0:D,:] # take the top D dimensions (or take them all if D is the size of the embedding vector)
+print(f"Shape of downselected PCA componenents {U.shape}")
+for ii in range(N):
+    v_tilde = X_tilde[ii]
+    v = X[ii]
+    v_projection = np.zeros(Dim) # start to build the projection
+    # project the original embedding onto the PCA basis vectors, use only first D dimensions
+    for jj in range(D):
+        u_jj = U[jj,:] # vector
+        v_jj = np.dot(u_jj,v) # scaler
+        v_projection += v_jj*u_jj # vector
+    v_prime = v_tilde - v_projection # final embedding vector
+    v_prime = v_prime/np.linalg.norm(v_prime) # create unit vector
+    E_prime[K[ii]] = v_prime
+
+    if (ii%N10 == 0) or (ii == N-1):
+        print(f"Finished with {ii+1} embeddings out of {N} ({round(100*ii/N)}% done)")
+
+
+# save as new pickle
+print("Saving new pickle ...")
+embeddingName = '/path/to/your/data/Embedding-Latest-Isotropic.pkl'
+with open(embeddingName, 'wb') as f:  # Python 3: open(..., 'wb')
+    pickle.dump([E_prime,mu,U], f)
+    print(embeddingName)
+
+print("Done!")
+
+# When working with live data with a new embedding from ada-002, be sure to tranform it first with this function before comparing it
+#
+def projectEmbedding(v,mu,U):
+    v = np.array(v)
+    v_tilde = v - mu
+    v_projection = np.zeros(len(v)) # start to build the projection
+    # project the original embedding onto the PCA basis vectors, use only first D dimensions
+    for u in U:
+        v_jj = np.dot(u,v) # scaler
+        v_projection += v_jj*u # vector
+    v_prime = v_tilde - v_projection # final embedding vector
+    v_prime = v_prime/np.linalg.norm(v_prime) # create unit vector
+    return v_prime
\ No newline at end of file

api/core/indexing_runner.py

+import asyncio
+import concurrent
 import datetime
 import json
 import logging
 import re
+import threading
 import time
 import uuid
+from multiprocessing import Process
 from typing import Optional, List, cast
 
-from flask import current_app
+import openai
+from billiard.pool import Pool
+from flask import current_app, Flask
 from flask_login import current_user
+from gevent.threadpool import ThreadPoolExecutor
 from langchain.embeddings import OpenAIEmbeddings
 from langchain.schema import Document
 from langchain.text_splitter import RecursiveCharacterTextSplitter, TextSplitter
@@ -16,11 +23,13 @@ from core.data_loader.file_extractor import FileExtractor
 from core.data_loader.loader.notion import NotionLoader
 from core.docstore.dataset_docstore import DatesetDocumentStore
 from core.embedding.cached_embedding import CacheEmbedding
+from core.generator.llm_generator import LLMGenerator
 from core.index.index import IndexBuilder
 from core.index.keyword_table_index.keyword_table_index import KeywordTableIndex, KeywordTableConfig
 from core.index.vector_index.vector_index import VectorIndex
 from core.llm.error import ProviderTokenNotInitError
 from core.llm.llm_builder import LLMBuilder
+from core.llm.streamable_open_ai import StreamableOpenAI
 from core.spiltter.fixed_text_splitter import FixedRecursiveCharacterTextSplitter
 from core.llm.token_calculator import TokenCalculator
 from extensions.ext_database import db
@@ -70,7 +79,13 @@ class IndexingRunner:
                     dataset_document=dataset_document,
                     processing_rule=processing_rule
                 )
-
+                # new_documents = []
+                # for document in documents:
+                #     response = LLMGenerator.generate_qa_document(dataset.tenant_id, document.page_content)
+                #     document_qa_list = self.format_split_text(response)
+                #     for result in document_qa_list:
+                #         document = Document(page_content=result['question'], metadata={'source': result['answer']})
+                #         new_documents.append(document)
                 # build index
                 self._build_index(
                     dataset=dataset,
@@ -91,6 +106,22 @@ class IndexingRunner:
                 dataset_document.stopped_at = datetime.datetime.utcnow()
                 db.session.commit()
 
+    def format_split_text(self, text):
+        regex = r"Q\d+:\s*(.*?)\s*A\d+:\s*([\s\S]*?)(?=Q|$)"
+        matches = re.findall(regex, text, re.MULTILINE)
+
+        result = []
+        for match in matches:
+            q = match[0]
+            a = match[1]
+            if q and a:
+                result.append({
+                    "question": q,
+                    "answer": re.sub(r"\n\s*", "\n", a.strip())
+                })
+
+        return result
+
     def run_in_splitting_status(self, dataset_document: DatasetDocument):
         """Run the indexing process when the index_status is splitting."""
         try:
@@ -205,7 +236,8 @@ class IndexingRunner:
             dataset_document.stopped_at = datetime.datetime.utcnow()
             db.session.commit()
 
-    def file_indexing_estimate(self, file_details: List[UploadFile], tmp_processing_rule: dict) -> dict:
+    def file_indexing_estimate(self, file_details: List[UploadFile], tmp_processing_rule: dict,
+                               doc_form: str = None) -> dict:
         """
         Estimate the indexing for the document.
         """
@@ -225,7 +257,7 @@ class IndexingRunner:
             splitter = self._get_splitter(processing_rule)
 
             # split to documents
-            documents = self._split_to_documents(
+            documents = self._split_to_documents_for_estimate(
                 text_docs=text_docs,
                 splitter=splitter,
                 processing_rule=processing_rule
@@ -237,7 +269,25 @@ class IndexingRunner:
 
                 tokens += TokenCalculator.get_num_tokens(self.embedding_model_name,
                                                          self.filter_string(document.page_content))
-
+        if doc_form and doc_form == 'qa_model':
+            if len(preview_texts) > 0:
+                # qa model document
+                llm: StreamableOpenAI = LLMBuilder.to_llm(
+                    tenant_id=current_user.current_tenant_id,
+                    model_name='gpt-3.5-turbo',
+                    max_tokens=2000
+                )
+                response = LLMGenerator.generate_qa_document_sync(llm, preview_texts[0])
+                document_qa_list = self.format_split_text(response)
+                return {
+                    "total_segments": total_segments * 20,
+                    "tokens": total_segments * 2000,
+                    "total_price": '{:f}'.format(
+                        TokenCalculator.get_token_price('gpt-3.5-turbo', total_segments * 2000, 'completion')),
+                    "currency": TokenCalculator.get_currency(self.embedding_model_name),
+                    "qa_preview": document_qa_list,
+                    "preview": preview_texts
+                }
         return {
             "total_segments": total_segments,
             "tokens": tokens,
@@ -246,7 +296,7 @@ class IndexingRunner:
             "preview": preview_texts
         }
 
-    def notion_indexing_estimate(self, notion_info_list: list, tmp_processing_rule: dict) -> dict:
+    def notion_indexing_estimate(self, notion_info_list: list, tmp_processing_rule: dict, doc_form: str = None) -> dict:
         """
         Estimate the indexing for the document.
         """
@@ -285,7 +335,7 @@ class IndexingRunner:
                 splitter = self._get_splitter(processing_rule)
 
                 # split to documents
-                documents = self._split_to_documents(
+                documents = self._split_to_documents_for_estimate(
                     text_docs=documents,
                     splitter=splitter,
                     processing_rule=processing_rule
@@ -296,7 +346,25 @@ class IndexingRunner:
                         preview_texts.append(document.page_content)
 
                     tokens += TokenCalculator.get_num_tokens(self.embedding_model_name, document.page_content)
-
+        if doc_form and doc_form == 'qa_model':
+            if len(preview_texts) > 0:
+                # qa model document
+                llm: StreamableOpenAI = LLMBuilder.to_llm(
+                    tenant_id=current_user.current_tenant_id,
+                    model_name='gpt-3.5-turbo',
+                    max_tokens=2000
+                )
+                response = LLMGenerator.generate_qa_document_sync(llm, preview_texts[0])
+                document_qa_list = self.format_split_text(response)
+                return {
+                    "total_segments": total_segments * 20,
+                    "tokens": total_segments * 2000,
+                    "total_price": '{:f}'.format(
+                        TokenCalculator.get_token_price('gpt-3.5-turbo', total_segments * 2000, 'completion')),
+                    "currency": TokenCalculator.get_currency(self.embedding_model_name),
+                    "qa_preview": document_qa_list,
+                    "preview": preview_texts
+                }
         return {
             "total_segments": total_segments,
             "tokens": tokens,
@@ -391,7 +459,9 @@ class IndexingRunner:
         documents = self._split_to_documents(
             text_docs=text_docs,
             splitter=splitter,
-            processing_rule=processing_rule
+            processing_rule=processing_rule,
+            tenant_id=dataset.tenant_id,
+            document_form=dataset_document.doc_form
         )
 
         # save node to document segment
@@ -428,7 +498,64 @@ class IndexingRunner:
         return documents
 
     def _split_to_documents(self, text_docs: List[Document], splitter: TextSplitter,
-                            processing_rule: DatasetProcessRule) -> List[Document]:
+                            processing_rule: DatasetProcessRule, tenant_id: str, document_form: str) -> List[Document]:
+        """
+        Split the text documents into nodes.
+        """
+        all_documents = []
+        for text_doc in text_docs:
+            # document clean
+            document_text = self._document_clean(text_doc.page_content, processing_rule)
+            text_doc.page_content = document_text
+
+            # parse document to nodes
+            documents = splitter.split_documents([text_doc])
+            split_documents = []
+            llm: StreamableOpenAI = LLMBuilder.to_llm(
+                tenant_id=tenant_id,
+                model_name='gpt-3.5-turbo',
+                max_tokens=2000
+            )
+            self.format_document(llm, documents, split_documents, document_form)
+            all_documents.extend(split_documents)
+
+        return all_documents
+
+    def format_document(self, llm: StreamableOpenAI, documents: List[Document], split_documents: List, document_form: str):
+        for document_node in documents:
+            format_documents = []
+            if document_node.page_content is None or not document_node.page_content.strip():
+                return format_documents
+            if document_form == 'text_model':
+                # text model document
+                doc_id = str(uuid.uuid4())
+                hash = helper.generate_text_hash(document_node.page_content)
+
+                document_node.metadata['doc_id'] = doc_id
+                document_node.metadata['doc_hash'] = hash
+
+                format_documents.append(document_node)
+            elif document_form == 'qa_model':
+                try:
+                    # qa model document
+                    response = LLMGenerator.generate_qa_document_sync(llm, document_node.page_content)
+                    document_qa_list = self.format_split_text(response)
+                    qa_documents = []
+                    for result in document_qa_list:
+                        qa_document = Document(page_content=result['question'], metadata=document_node.metadata.copy())
+                        doc_id = str(uuid.uuid4())
+                        hash = helper.generate_text_hash(result['question'])
+                        qa_document.metadata['answer'] = result['answer']
+                        qa_document.metadata['doc_id'] = doc_id
+                        qa_document.metadata['doc_hash'] = hash
+                        qa_documents.append(qa_document)
+                    format_documents.extend(qa_documents)
+                except Exception:
+                    continue
+            split_documents.extend(format_documents)
+
+    def _split_to_documents_for_estimate(self, text_docs: List[Document], splitter: TextSplitter,
+                                         processing_rule: DatasetProcessRule) -> List[Document]:
         """
         Split the text documents into nodes.
         """
@@ -445,7 +572,6 @@ class IndexingRunner:
             for document in documents:
                 if document.page_content is None or not document.page_content.strip():
                     continue
-
                 doc_id = str(uuid.uuid4())
                 hash = helper.generate_text_hash(document.page_content)
 
@@ -487,6 +613,23 @@ class IndexingRunner:
 
         return text
 
+    def format_split_text(self, text):
+        regex = r"Q\d+:\s*(.*?)\s*A\d+:\s*([\s\S]*?)(?=Q|$)"  # 匹配Q和A的正则表达式
+        matches = re.findall(regex, text, re.MULTILINE)  # 获取所有匹配到的结果
+
+        result = []  # 存储最终的结果
+        for match in matches:
+            q = match[0]
+            a = match[1]
+            if q and a:
+                # 如果Q和A都存在，就将其添加到结果中
+                result.append({
+                    "question": q,
+                    "answer": re.sub(r"\n\s*", "\n", a.strip())
+                })
+
+        return result
+
     def _build_index(self, dataset: Dataset, dataset_document: DatasetDocument, documents: List[Document]) -> None:
         """
         Build the index for the document.

api/core/prompt/prompts.py

@@ -43,6 +43,16 @@ SUGGESTED_QUESTIONS_AFTER_ANSWER_INSTRUCTION_PROMPT = (
     "[\"question1\",\"question2\",\"question3\"]\n"
 )
 
+GENERATOR_QA_PROMPT = (
+    "Please respond according to the language of the user's input text. If the text is in language [A], you must also reply in language [A].\n"
+    'Step 1: Understand and summarize the main content of this text.\n'
+    'Step 2: What key information or concepts are mentioned in this text?\n'
+    'Step 3: Decompose or combine multiple pieces of information and concepts.\n'
+    'Step 4: Generate 20 questions and answers based on these key information and concepts.'
+    'The questions should be clear and detailed, and the answers should be detailed and complete.\n'
+    "Answer in the following format: Q1:\nA1:\nQ2:\nA2:...\n"
+)
+
 RULE_CONFIG_GENERATE_TEMPLATE = """Given MY INTENDED AUDIENCES and HOPING TO SOLVE using a language model, please select \
 the model prompt that best suits the input. 
 You will be provided with the prompt, variables, and an opening statement. 

api/core/tool/dataset_index_tool.py

+from flask import current_app
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.tools import BaseTool
+
+from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCallbackHandler
+from core.embedding.cached_embedding import CacheEmbedding
+from core.index.keyword_table_index.keyword_table_index import KeywordTableIndex, KeywordTableConfig
+from core.index.vector_index.vector_index import VectorIndex
+from core.llm.llm_builder import LLMBuilder
+from models.dataset import Dataset, DocumentSegment
+
+
+class DatasetTool(BaseTool):
+    """Tool for querying a Dataset."""
+
+    dataset: Dataset
+    k: int = 2
+
+    def _run(self, tool_input: str) -> str:
+        if self.dataset.indexing_technique == "economy":
+            # use keyword table query
+            kw_table_index = KeywordTableIndex(
+                dataset=self.dataset,
+                config=KeywordTableConfig(
+                    max_keywords_per_chunk=5
+                )
+            )
+
+            documents = kw_table_index.search(tool_input, search_kwargs={'k': self.k})
+            return str("\n".join([document.page_content for document in documents]))
+        else:
+            model_credentials = LLMBuilder.get_model_credentials(
+                tenant_id=self.dataset.tenant_id,
+                model_provider=LLMBuilder.get_default_provider(self.dataset.tenant_id, 'text-embedding-ada-002'),
+                model_name='text-embedding-ada-002'
+            )
+
+            embeddings = CacheEmbedding(OpenAIEmbeddings(
+                **model_credentials
+            ))
+
+            vector_index = VectorIndex(
+                dataset=self.dataset,
+                config=current_app.config,
+                embeddings=embeddings
+            )
+
+            documents = vector_index.search(
+                tool_input,
+                search_type='similarity',
+                search_kwargs={
+                    'k': self.k
+                }
+            )
+
+            hit_callback = DatasetIndexToolCallbackHandler(self.dataset.id)
+            hit_callback.on_tool_end(documents)
+            document_context_list = []
+            index_node_ids = [document.metadata['doc_id'] for document in documents]
+            segments = DocumentSegment.query.filter(DocumentSegment.completed_at.isnot(None),
+                                                    DocumentSegment.status == 'completed',
+                                                    DocumentSegment.enabled == True,
+                                                    DocumentSegment.index_node_id.in_(index_node_ids)
+                                                    ).all()
+
+            if segments:
+                for segment in segments:
+                    if segment.answer:
+                        document_context_list.append(segment.answer)
+                    else:
+                        document_context_list.append(segment.content)
+
+            return str("\n".join(document_context_list))
+
+    async def _arun(self, tool_input: str) -> str:
+        model_credentials = LLMBuilder.get_model_credentials(
+            tenant_id=self.dataset.tenant_id,
+            model_provider=LLMBuilder.get_default_provider(self.dataset.tenant_id, 'text-embedding-ada-002'),
+            model_name='text-embedding-ada-002'
+        )
+
+        embeddings = CacheEmbedding(OpenAIEmbeddings(
+            **model_credentials
+        ))
+
+        vector_index = VectorIndex(
+            dataset=self.dataset,
+            config=current_app.config,
+            embeddings=embeddings
+        )
+
+        documents = await vector_index.asearch(
+            tool_input,
+            search_type='similarity',
+            search_kwargs={
+                'k': 10
+            }
+        )
+
+        hit_callback = DatasetIndexToolCallbackHandler(self.dataset.id)
+        hit_callback.on_tool_end(documents)
+        return str("\n".join([document.page_content for document in documents]))

api/core/tool/dataset_retriever_tool.py

@@ -12,7 +12,7 @@ from core.index.keyword_table_index.keyword_table_index import KeywordTableIndex
 from core.index.vector_index.vector_index import VectorIndex
 from core.llm.llm_builder import LLMBuilder
 from extensions.ext_database import db
-from models.dataset import Dataset
+from models.dataset import Dataset, DocumentSegment
 
 
 class DatasetRetrieverToolInput(BaseModel):
@@ -69,6 +69,7 @@ class DatasetRetrieverTool(BaseTool):
             )
 
             documents = kw_table_index.search(query, search_kwargs={'k': self.k})
+            return str("\n".join([document.page_content for document in documents]))
         else:
             model_credentials = LLMBuilder.get_model_credentials(
                 tenant_id=dataset.tenant_id,
@@ -99,8 +100,22 @@ class DatasetRetrieverTool(BaseTool):
 
             hit_callback = DatasetIndexToolCallbackHandler(dataset.id)
             hit_callback.on_tool_end(documents)
-
-        return str("\n".join([document.page_content for document in documents]))
+            document_context_list = []
+            index_node_ids = [document.metadata['doc_id'] for document in documents]
+            segments = DocumentSegment.query.filter(DocumentSegment.completed_at.isnot(None),
+                                                    DocumentSegment.status == 'completed',
+                                                    DocumentSegment.enabled == True,
+                                                    DocumentSegment.index_node_id.in_(index_node_ids)
+                                                    ).all()
+
+            if segments:
+                for segment in segments:
+                    if segment.answer:
+                        document_context_list.append(f'question:{segment.content} \nanswer:{segment.answer}')
+                    else:
+                        document_context_list.append(segment.content)
+
+            return str("\n".join(document_context_list))
 
     async def _arun(self, tool_input: str) -> str:
         raise NotImplementedError()

api/migrations/versions/8d2d099ceb74_add_qa_model_support.py

+"""add_qa_model_support
+
+Revision ID: 8d2d099ceb74
+Revises: a5b56fb053ef
+Create Date: 2023-07-18 15:25:15.293438
+
+"""
+from alembic import op
+import sqlalchemy as sa
+from sqlalchemy.dialects import postgresql
+
+# revision identifiers, used by Alembic.
+revision = '8d2d099ceb74'
+down_revision = '7ce5a52e4eee'
+branch_labels = None
+depends_on = None
+
+
+def upgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    with op.batch_alter_table('document_segments', schema=None) as batch_op:
+        batch_op.add_column(sa.Column('answer', sa.Text(), nullable=True))
+        batch_op.add_column(sa.Column('updated_by', postgresql.UUID(), nullable=True))
+        batch_op.add_column(sa.Column('updated_at', sa.DateTime(), server_default=sa.text('CURRENT_TIMESTAMP(0)'), nullable=False))
+
+    with op.batch_alter_table('documents', schema=None) as batch_op:
+        batch_op.add_column(sa.Column('doc_form', sa.String(length=255), server_default=sa.text("'text_model'::character varying"), nullable=False))
+
+    # ### end Alembic commands ###
+
+
+def downgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    with op.batch_alter_table('documents', schema=None) as batch_op:
+        batch_op.drop_column('doc_form')
+
+    with op.batch_alter_table('document_segments', schema=None) as batch_op:
+        batch_op.drop_column('updated_at')
+        batch_op.drop_column('updated_by')
+        batch_op.drop_column('answer')
+
+    # ### end Alembic commands ###

api/models/dataset.py

@@ -206,6 +206,8 @@ class Document(db.Model):
                            server_default=db.text('CURRENT_TIMESTAMP(0)'))
     doc_type = db.Column(db.String(40), nullable=True)
     doc_metadata = db.Column(db.JSON, nullable=True)
+    doc_form = db.Column(db.String(
+        255), nullable=False, server_default=db.text("'text_model'::character varying"))
 
     DATA_SOURCES = ['upload_file', 'notion_import']
 
@@ -308,6 +310,7 @@ class DocumentSegment(db.Model):
     document_id = db.Column(UUID, nullable=False)
     position = db.Column(db.Integer, nullable=False)
     content = db.Column(db.Text, nullable=False)
+    answer = db.Column(db.Text, nullable=True)
     word_count = db.Column(db.Integer, nullable=False)
     tokens = db.Column(db.Integer, nullable=False)
 
@@ -327,6 +330,9 @@ class DocumentSegment(db.Model):
     created_by = db.Column(UUID, nullable=False)
     created_at = db.Column(db.DateTime, nullable=False,
                            server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    updated_by = db.Column(UUID, nullable=True)
+    updated_at = db.Column(db.DateTime, nullable=False,
+                           server_default=db.text('CURRENT_TIMESTAMP(0)'))
     indexing_at = db.Column(db.DateTime, nullable=True)
     completed_at = db.Column(db.DateTime, nullable=True)
     error = db.Column(db.Text, nullable=True)
@@ -442,4 +448,4 @@ class Embedding(db.Model):
         self.embedding = pickle.dumps(embedding_data, protocol=pickle.HIGHEST_PROTOCOL)
 
     def get_embedding(self) -> list[float]:
-        return pickle.loads(self.embedding)
+        return pickle.loads(self.embedding)
\ No newline at end of file

api/services/completion_service.py

@@ -201,6 +201,7 @@ class CompletionService:
                     conversation = db.session.query(Conversation).filter_by(id=conversation.id).first()
 
                 # run
+
                 Completion.generate(
                     task_id=generate_task_id,
                     app=app_model,

api/services/dataset_service.py

@@ -3,16 +3,20 @@ import logging
 import datetime
 import time
 import random
+import uuid
 from typing import Optional, List
 
 from flask import current_app
+from sqlalchemy import func
 
+from core.llm.token_calculator import TokenCalculator
 from extensions.ext_redis import redis_client
 from flask_login import current_user
 
 from events.dataset_event import dataset_was_deleted
 from events.document_event import document_was_deleted
 from extensions.ext_database import db
+from libs import helper
 from models.account import Account
 from models.dataset import Dataset, Document, DatasetQuery, DatasetProcessRule, AppDatasetJoin, DocumentSegment
 from models.model import UploadFile
@@ -25,6 +29,10 @@ from tasks.clean_notion_document_task import clean_notion_document_task
 from tasks.deal_dataset_vector_index_task import deal_dataset_vector_index_task
 from tasks.document_indexing_task import document_indexing_task
 from tasks.document_indexing_update_task import document_indexing_update_task
+from tasks.create_segment_to_index_task import create_segment_to_index_task
+from tasks.update_segment_index_task import update_segment_index_task
+from tasks.update_segment_keyword_index_task\
+    import update_segment_keyword_index_task
 
 
 class DatasetService:
@@ -308,6 +316,7 @@ class DocumentService:
         ).all()
 
         return documents
+
     @staticmethod
     def get_document_file_detail(file_id: str):
         file_detail = db.session.query(UploadFile). \
@@ -440,6 +449,7 @@ class DocumentService:
                     }
                     document = DocumentService.save_document(dataset, dataset_process_rule.id,
                                                              document_data["data_source"]["type"],
+                                                             document_data["doc_form"],
                                                              data_source_info, created_from, position,
                                                              account, file_name, batch)
                     db.session.add(document)
@@ -484,6 +494,7 @@ class DocumentService:
                             }
                             document = DocumentService.save_document(dataset, dataset_process_rule.id,
                                                                      document_data["data_source"]["type"],
+                                                                     document_data["doc_form"],
                                                                      data_source_info, created_from, position,
                                                                      account, page['page_name'], batch)
                             # if page['type'] == 'database':
@@ -514,8 +525,9 @@ class DocumentService:
         return documents, batch
 
     @staticmethod
-    def save_document(dataset: Dataset, process_rule_id: str, data_source_type: str, data_source_info: dict,
-                      created_from: str, position: int, account: Account, name: str, batch: str):
+    def save_document(dataset: Dataset, process_rule_id: str, data_source_type: str, document_form: str,
+                      data_source_info: dict, created_from: str, position: int, account: Account, name: str,
+                      batch: str):
         document = Document(
             tenant_id=dataset.tenant_id,
             dataset_id=dataset.id,
@@ -527,6 +539,7 @@ class DocumentService:
             name=name,
             created_from=created_from,
             created_by=account.id,
+            doc_form=document_form
         )
         return document
 
@@ -618,6 +631,7 @@ class DocumentService:
         document.splitting_completed_at = None
         document.updated_at = datetime.datetime.utcnow()
         document.created_from = created_from
+        document.doc_form = document_data['doc_form']
         db.session.add(document)
         db.session.commit()
         # update document segment
@@ -667,7 +681,7 @@ class DocumentService:
             DocumentService.data_source_args_validate(args)
             DocumentService.process_rule_args_validate(args)
         else:
-            if ('data_source' not in args and not args['data_source'])\
+            if ('data_source' not in args and not args['data_source']) \
                     and ('process_rule' not in args and not args['process_rule']):
                 raise ValueError("Data source or Process rule is required")
             else:
@@ -694,10 +708,12 @@ class DocumentService:
             raise ValueError("Data source info is required")
 
         if args['data_source']['type'] == 'upload_file':
-            if 'file_info_list' not in args['data_source']['info_list'] or not args['data_source']['info_list']['file_info_list']:
+            if 'file_info_list' not in args['data_source']['info_list'] or not args['data_source']['info_list'][
+                'file_info_list']:
                 raise ValueError("File source info is required")
         if args['data_source']['type'] == 'notion_import':
-            if 'notion_info_list' not in args['data_source']['info_list'] or not args['data_source']['info_list']['notion_info_list']:
+            if 'notion_info_list' not in args['data_source']['info_list'] or not args['data_source']['info_list'][
+                'notion_info_list']:
                 raise ValueError("Notion source info is required")
 
     @classmethod
@@ -843,3 +859,80 @@ class DocumentService:
 
             if not isinstance(args['process_rule']['rules']['segmentation']['max_tokens'], int):
                 raise ValueError("Process rule segmentation max_tokens is invalid")
+
+
+class SegmentService:
+    @classmethod
+    def segment_create_args_validate(cls, args: dict, document: Document):
+        if document.doc_form == 'qa_model':
+            if 'answer' not in args or not args['answer']:
+                raise ValueError("Answer is required")
+
+    @classmethod
+    def create_segment(cls, args: dict, document: Document):
+        content = args['content']
+        doc_id = str(uuid.uuid4())
+        segment_hash = helper.generate_text_hash(content)
+        # calc embedding use tokens
+        tokens = TokenCalculator.get_num_tokens('text-embedding-ada-002', content)
+        max_position = db.session.query(func.max(DocumentSegment.position)).filter(
+            DocumentSegment.document_id == document.id
+        ).scalar()
+        segment_document = DocumentSegment(
+            tenant_id=current_user.current_tenant_id,
+            dataset_id=document.dataset_id,
+            document_id=document.id,
+            index_node_id=doc_id,
+            index_node_hash=segment_hash,
+            position=max_position + 1 if max_position else 1,
+            content=content,
+            word_count=len(content),
+            tokens=tokens,
+            created_by=current_user.id
+        )
+        if document.doc_form == 'qa_model':
+            segment_document.answer = args['answer']
+
+        db.session.add(segment_document)
+        db.session.commit()
+        indexing_cache_key = 'segment_{}_indexing'.format(segment_document.id)
+        redis_client.setex(indexing_cache_key, 600, 1)
+        create_segment_to_index_task.delay(segment_document.id, args['keywords'])
+        return segment_document
+
+    @classmethod
+    def update_segment(cls, args: dict, segment: DocumentSegment, document: Document):
+        indexing_cache_key = 'segment_{}_indexing'.format(segment.id)
+        cache_result = redis_client.get(indexing_cache_key)
+        if cache_result is not None:
+            raise ValueError("Segment is indexing, please try again later")
+        content = args['content']
+        if segment.content == content:
+            if document.doc_form == 'qa_model':
+                segment.answer = args['answer']
+            if args['keywords']:
+                segment.keywords = args['keywords']
+            db.session.add(segment)
+            db.session.commit()
+            # update segment index task
+            redis_client.setex(indexing_cache_key, 600, 1)
+            update_segment_keyword_index_task.delay(segment.id)
+        else:
+            segment_hash = helper.generate_text_hash(content)
+            # calc embedding use tokens
+            tokens = TokenCalculator.get_num_tokens('text-embedding-ada-002', content)
+            segment.content = content
+            segment.index_node_hash = segment_hash
+            segment.word_count = len(content)
+            segment.tokens = tokens
+            segment.status = 'updating'
+            segment.updated_by = current_user.id
+            segment.updated_at = datetime.datetime.utcnow()
+            if document.doc_form == 'qa_model':
+                segment.answer = args['answer']
+            db.session.add(segment)
+            db.session.commit()
+            # update segment index task
+            redis_client.setex(indexing_cache_key, 600, 1)
+            update_segment_index_task.delay(segment.id, args['keywords'])
+        return segment

api/tasks/create_segment_to_index_task.py

+import datetime
+import logging
+import time
+from typing import Optional, List
+
+import click
+from celery import shared_task
+from langchain.schema import Document
+from werkzeug.exceptions import NotFound
+
+from core.index.index import IndexBuilder
+from extensions.ext_database import db
+from extensions.ext_redis import redis_client
+from models.dataset import DocumentSegment
+
+
+@shared_task
+def create_segment_to_index_task(segment_id: str, keywords: Optional[List[str]] = None):
+    """
+    Async create segment to index
+    :param segment_id:
+    :param keywords:
+    Usage: create_segment_to_index_task.delay(segment_id)
+    """
+    logging.info(click.style('Start create segment to index: {}'.format(segment_id), fg='green'))
+    start_at = time.perf_counter()
+
+    segment = db.session.query(DocumentSegment).filter(DocumentSegment.id == segment_id).first()
+    if not segment:
+        raise NotFound('Segment not found')
+
+    if segment.status != 'waiting':
+        return
+
+    indexing_cache_key = 'segment_{}_indexing'.format(segment.id)
+
+    try:
+        # update segment status to indexing
+        update_params = {
+            DocumentSegment.status: "indexing",
+            DocumentSegment.indexing_at: datetime.datetime.utcnow()
+        }
+        DocumentSegment.query.filter_by(id=segment.id).update(update_params)
+        db.session.commit()
+        document = Document(
+            page_content=segment.content,
+            metadata={
+                "doc_id": segment.index_node_id,
+                "doc_hash": segment.index_node_hash,
+                "document_id": segment.document_id,
+                "dataset_id": segment.dataset_id,
+            }
+        )
+
+        dataset = segment.dataset
+
+        if not dataset:
+            logging.info(click.style('Segment {} has no dataset, pass.'.format(segment.id), fg='cyan'))
+            return
+
+        dataset_document = segment.document
+
+        if not dataset_document:
+            logging.info(click.style('Segment {} has no document, pass.'.format(segment.id), fg='cyan'))
+            return
+
+        if not dataset_document.enabled or dataset_document.archived or dataset_document.indexing_status != 'completed':
+            logging.info(click.style('Segment {} document status is invalid, pass.'.format(segment.id), fg='cyan'))
+            return
+
+        # save vector index
+        index = IndexBuilder.get_index(dataset, 'high_quality')
+        if index:
+            index.add_texts([document], duplicate_check=True)
+
+        # save keyword index
+        index = IndexBuilder.get_index(dataset, 'economy')
+        if index:
+            if keywords and len(keywords) > 0:
+                index.create_segment_keywords(segment.index_node_id, keywords)
+            else:
+                index.add_texts([document])
+
+        # update segment to completed
+        update_params = {
+            DocumentSegment.status: "completed",
+            DocumentSegment.completed_at: datetime.datetime.utcnow()
+        }
+        DocumentSegment.query.filter_by(id=segment.id).update(update_params)
+        db.session.commit()
+
+        end_at = time.perf_counter()
+        logging.info(click.style('Segment created to index: {} latency: {}'.format(segment.id, end_at - start_at), fg='green'))
+    except Exception as e:
+        logging.exception("create segment to index failed")
+        segment.enabled = False
+        segment.disabled_at = datetime.datetime.utcnow()
+        segment.status = 'error'
+        segment.error = str(e)
+        db.session.commit()
+    finally:
+        redis_client.delete(indexing_cache_key)

api/tasks/add_segment_to_index_task.py → api/tasks/enable_segment_to_index_task.py

@@ -14,14 +14,14 @@ from models.dataset import DocumentSegment
 
 
 @shared_task
-def add_segment_to_index_task(segment_id: str):
+def enable_segment_to_index_task(segment_id: str):
     """
-    Async Add segment to index
+    Async enable segment to index
     :param segment_id:
 
-    Usage: add_segment_to_index.delay(segment_id)
+    Usage: enable_segment_to_index_task.delay(segment_id)
     """
-    logging.info(click.style('Start add segment to index: {}'.format(segment_id), fg='green'))
+    logging.info(click.style('Start enable segment to index: {}'.format(segment_id), fg='green'))
     start_at = time.perf_counter()
 
     segment = db.session.query(DocumentSegment).filter(DocumentSegment.id == segment_id).first()
@@ -71,9 +71,9 @@ def add_segment_to_index_task(segment_id: str):
             index.add_texts([document])
 
         end_at = time.perf_counter()
-        logging.info(click.style('Segment added to index: {} latency: {}'.format(segment.id, end_at - start_at), fg='green'))
+        logging.info(click.style('Segment enabled to index: {} latency: {}'.format(segment.id, end_at - start_at), fg='green'))
     except Exception as e:
-        logging.exception("add segment to index failed")
+        logging.exception("enable segment to index failed")
         segment.enabled = False
         segment.disabled_at = datetime.datetime.utcnow()
         segment.status = 'error'

api/tasks/generate_test_task.py

+import logging
+import time
+
+import click
+import requests
+from celery import shared_task
+
+from core.generator.llm_generator import LLMGenerator
+
+
+@shared_task
+def generate_test_task():
+    logging.info(click.style('Start generate test', fg='green'))
+    start_at = time.perf_counter()
+
+    try:
+        #res = requests.post('https://api.openai.com/v1/chat/completions')
+        answer = LLMGenerator.generate_conversation_name('84b2202c-c359-46b7-a810-bce50feaa4d1', 'avb', 'ccc')
+        print(f'answer: {answer}')
+
+        end_at = time.perf_counter()
+        logging.info(click.style('Conversation test, latency: {}'.format(end_at - start_at), fg='green'))
+    except Exception:
+        logging.exception("generate test failed")

api/tasks/update_segment_index_task.py

+import datetime
+import logging
+import time
+from typing import List, Optional
+
+import click
+from celery import shared_task
+from langchain.schema import Document
+from werkzeug.exceptions import NotFound
+
+from core.index.index import IndexBuilder
+from extensions.ext_database import db
+from extensions.ext_redis import redis_client
+from models.dataset import DocumentSegment
+
+
+@shared_task
+def update_segment_index_task(segment_id: str, keywords: Optional[List[str]] = None):
+    """
+    Async update segment index
+    :param segment_id:
+    :param keywords:
+    Usage: update_segment_index_task.delay(segment_id)
+    """
+    logging.info(click.style('Start update segment index: {}'.format(segment_id), fg='green'))
+    start_at = time.perf_counter()
+
+    segment = db.session.query(DocumentSegment).filter(DocumentSegment.id == segment_id).first()
+    if not segment:
+        raise NotFound('Segment not found')
+
+    if segment.status != 'updating':
+        return
+
+    indexing_cache_key = 'segment_{}_indexing'.format(segment.id)
+
+    try:
+        dataset = segment.dataset
+
+        if not dataset:
+            logging.info(click.style('Segment {} has no dataset, pass.'.format(segment.id), fg='cyan'))
+            return
+
+        dataset_document = segment.document
+
+        if not dataset_document:
+            logging.info(click.style('Segment {} has no document, pass.'.format(segment.id), fg='cyan'))
+            return
+
+        if not dataset_document.enabled or dataset_document.archived or dataset_document.indexing_status != 'completed':
+            logging.info(click.style('Segment {} document status is invalid, pass.'.format(segment.id), fg='cyan'))
+            return
+
+        # update segment status to indexing
+        update_params = {
+            DocumentSegment.status: "indexing",
+            DocumentSegment.indexing_at: datetime.datetime.utcnow()
+        }
+        DocumentSegment.query.filter_by(id=segment.id).update(update_params)
+        db.session.commit()
+
+        vector_index = IndexBuilder.get_index(dataset, 'high_quality')
+        kw_index = IndexBuilder.get_index(dataset, 'economy')
+
+        # delete from vector index
+        if vector_index:
+            vector_index.delete_by_ids([segment.index_node_id])
+
+        # delete from keyword index
+        kw_index.delete_by_ids([segment.index_node_id])
+
+        # add new index
+        document = Document(
+            page_content=segment.content,
+            metadata={
+                "doc_id": segment.index_node_id,
+                "doc_hash": segment.index_node_hash,
+                "document_id": segment.document_id,
+                "dataset_id": segment.dataset_id,
+            }
+        )
+
+        # save vector index
+        index = IndexBuilder.get_index(dataset, 'high_quality')
+        if index:
+            index.add_texts([document], duplicate_check=True)
+
+        # save keyword index
+        index = IndexBuilder.get_index(dataset, 'economy')
+        if index:
+            if keywords and len(keywords) > 0:
+                index.create_segment_keywords(segment.index_node_id, keywords)
+            else:
+                index.add_texts([document])
+
+        # update segment to completed
+        update_params = {
+            DocumentSegment.status: "completed",
+            DocumentSegment.completed_at: datetime.datetime.utcnow()
+        }
+        DocumentSegment.query.filter_by(id=segment.id).update(update_params)
+        db.session.commit()
+
+        end_at = time.perf_counter()
+        logging.info(click.style('Segment update index: {} latency: {}'.format(segment.id, end_at - start_at), fg='green'))
+    except Exception as e:
+        logging.exception("update segment index failed")
+        segment.enabled = False
+        segment.disabled_at = datetime.datetime.utcnow()
+        segment.status = 'error'
+        segment.error = str(e)
+        db.session.commit()
+    finally:
+        redis_client.delete(indexing_cache_key)

api/tasks/update_segment_keyword_index_task.py

+import datetime
+import logging
+import time
+from typing import List, Optional
+
+import click
+from celery import shared_task
+from langchain.schema import Document
+from werkzeug.exceptions import NotFound
+
+from core.index.index import IndexBuilder
+from extensions.ext_database import db
+from extensions.ext_redis import redis_client
+from models.dataset import DocumentSegment
+
+
+@shared_task
+def update_segment_keyword_index_task(segment_id: str):
+    """
+    Async update segment index
+    :param segment_id:
+    Usage: update_segment_keyword_index_task.delay(segment_id)
+    """
+    logging.info(click.style('Start update segment keyword index: {}'.format(segment_id), fg='green'))
+    start_at = time.perf_counter()
+
+    segment = db.session.query(DocumentSegment).filter(DocumentSegment.id == segment_id).first()
+    if not segment:
+        raise NotFound('Segment not found')
+
+    indexing_cache_key = 'segment_{}_indexing'.format(segment.id)
+
+    try:
+        dataset = segment.dataset
+
+        if not dataset:
+            logging.info(click.style('Segment {} has no dataset, pass.'.format(segment.id), fg='cyan'))
+            return
+
+        dataset_document = segment.document
+
+        if not dataset_document:
+            logging.info(click.style('Segment {} has no document, pass.'.format(segment.id), fg='cyan'))
+            return
+
+        if not dataset_document.enabled or dataset_document.archived or dataset_document.indexing_status != 'completed':
+            logging.info(click.style('Segment {} document status is invalid, pass.'.format(segment.id), fg='cyan'))
+            return
+
+        kw_index = IndexBuilder.get_index(dataset, 'economy')
+
+        # delete from keyword index
+        kw_index.delete_by_ids([segment.index_node_id])
+
+        # add new index
+        document = Document(
+            page_content=segment.content,
+            metadata={
+                "doc_id": segment.index_node_id,
+                "doc_hash": segment.index_node_hash,
+                "document_id": segment.document_id,
+                "dataset_id": segment.dataset_id,
+            }
+        )
+
+        # save keyword index
+        index = IndexBuilder.get_index(dataset, 'economy')
+        if index:
+            index.update_segment_keywords_index(segment.index_node_id, segment.keywords)
+
+        end_at = time.perf_counter()
+        logging.info(click.style('Segment update index: {} latency: {}'.format(segment.id, end_at - start_at), fg='green'))
+    except Exception as e:
+        logging.exception("update segment index failed")
+        segment.enabled = False
+        segment.disabled_at = datetime.datetime.utcnow()
+        segment.status = 'error'
+        segment.error = str(e)
+        db.session.commit()
+    finally:
+        redis_client.delete(indexing_cache_key)

web/app/components/base/auto-height-textarea/common.tsx

+import { forwardRef, useEffect, useRef } from 'react'
+import cn from 'classnames'
+
+type AutoHeightTextareaProps =
+  & React.DetailedHTMLProps<React.TextareaHTMLAttributes<HTMLTextAreaElement>, HTMLTextAreaElement>
+  & { outerClassName?: string }
+
+const AutoHeightTextarea = forwardRef<HTMLTextAreaElement, AutoHeightTextareaProps>(
+  (
+    {
+      outerClassName,
+      value,
+      className,
+      placeholder,
+      autoFocus,
+      disabled,
+      ...rest
+    },
+    outRef,
+  ) => {
+    const innerRef = useRef<HTMLTextAreaElement>(null)
+    const ref = outRef || innerRef
+
+    useEffect(() => {
+      if (autoFocus && !disabled && value) {
+        if (typeof ref !== 'function') {
+          ref.current?.setSelectionRange(`${value}`.length, `${value}`.length)
+          ref.current?.focus()
+        }
+      }
+    }, [autoFocus, disabled, ref])
+    return (
+      <div className={outerClassName}>
+        <div className='relative'>
+          <div className={cn(className, 'invisible whitespace-pre-wrap break-all')}>
+            {!value ? placeholder : `${value}`.replace(/\n$/, '\n ')}
+          </div>
+          <textarea
+            ref={ref}
+            placeholder={placeholder}
+            className={cn(className, 'disabled:bg-transparent absolute inset-0 outline-none border-none appearance-none resize-none')}
+            value={value}
+            disabled={disabled}
+            {...rest}
+          />
+        </div>
+      </div>
+    )
+  },
+)
+
+export default AutoHeightTextarea

web/app/components/base/icons/assets/public/common/message-chat-square.svg

+<svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path fill-rule="evenodd" clip-rule="evenodd" d="M8.77438 6.6665H12.5591C12.9105 6.66649 13.2137 6.66648 13.4634 6.68688C13.727 6.70842 13.9891 6.75596 14.2414 6.88449C14.6177 7.07624 14.9237 7.3822 15.1154 7.75852C15.244 8.01078 15.2915 8.27292 15.313 8.53649C15.3334 8.7862 15.3334 9.08938 15.3334 9.44082V11.2974C15.3334 11.5898 15.3334 11.8421 15.3192 12.0509C15.3042 12.2708 15.2712 12.4908 15.1812 12.7081C14.9782 13.1981 14.5888 13.5875 14.0988 13.7905C13.8815 13.8805 13.6616 13.9135 13.4417 13.9285C13.4068 13.9308 13.3707 13.9328 13.3334 13.9345V14.6665C13.3334 14.9147 13.1955 15.1424 12.9756 15.2573C12.7556 15.3723 12.49 15.3556 12.2862 15.2139L10.8353 14.2051C10.6118 14.0498 10.5666 14.0214 10.5238 14.0021C10.4746 13.9798 10.4228 13.9635 10.3696 13.9537C10.3235 13.9452 10.2702 13.9427 9.99803 13.9427H8.7744C8.42296 13.9427 8.11978 13.9427 7.87006 13.9223C7.6065 13.9008 7.34435 13.8532 7.0921 13.7247C6.71578 13.533 6.40981 13.227 6.21807 12.8507C6.08954 12.5984 6.04199 12.3363 6.02046 12.0727C6.00006 11.823 6.00007 11.5198 6.00008 11.1684V9.44081C6.00007 9.08938 6.00006 8.7862 6.02046 8.53649C6.04199 8.27292 6.08954 8.01078 6.21807 7.75852C6.40981 7.3822 6.71578 7.07624 7.0921 6.88449C7.34435 6.75596 7.6065 6.70842 7.87006 6.68688C8.11978 6.66648 8.42295 6.66649 8.77438 6.6665Z" fill="#444CE7"/>
+<path d="M9.4943 0.666504H4.5059C3.96926 0.666496 3.52635 0.666489 3.16555 0.695967C2.79082 0.726584 2.44635 0.792293 2.12279 0.957154C1.62103 1.21282 1.21308 1.62076 0.957417 2.12253C0.792557 2.44609 0.726847 2.79056 0.69623 3.16529C0.666752 3.52608 0.666759 3.96899 0.666768 4.50564L0.666758 7.6804C0.666669 7.97482 0.666603 8.19298 0.694924 8.38632C0.86568 9.55207 1.78121 10.4676 2.94695 10.6383C2.99461 10.6453 3.02432 10.6632 3.03714 10.6739L3.03714 11.7257C3.03711 11.9075 3.03708 12.0858 3.04976 12.2291C3.06103 12.3565 3.09053 12.6202 3.27795 12.8388C3.48686 13.0825 3.80005 13.2111 4.11993 13.1845C4.40689 13.1607 4.61323 12.9938 4.71072 12.9111C4.73849 12.8875 4.76726 12.8618 4.7968 12.8344C4.73509 12.594 4.70707 12.3709 4.69157 12.1813C4.66659 11.8756 4.66668 11.5224 4.66676 11.1966V9.41261C4.66668 9.08685 4.66659 8.73364 4.69157 8.42793C4.71984 8.08191 4.78981 7.62476 5.03008 7.15322C5.34965 6.52601 5.85959 6.01608 6.4868 5.6965C6.95834 5.45624 7.41549 5.38627 7.7615 5.358C8.06722 5.33302 8.42041 5.3331 8.74617 5.33318H12.5873C12.8311 5.33312 13.0903 5.33306 13.3334 5.3435V4.50562C13.3334 3.96898 13.3334 3.52608 13.304 3.16529C13.2734 2.79056 13.2076 2.44609 13.0428 2.12253C12.7871 1.62076 12.3792 1.21282 11.8774 0.957154C11.5539 0.792293 11.2094 0.726584 10.8347 0.695967C10.4739 0.666489 10.0309 0.666496 9.4943 0.666504Z" fill="#444CE7"/>
+</svg>

web/app/components/base/icons/assets/vender/line/general/edit-03.svg

+<svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg">
+<g id="Icon">
+<path id="Icon_2" d="M7.99998 13.3332H14M2 13.3332H3.11636C3.44248 13.3332 3.60554 13.3332 3.75899 13.2963C3.89504 13.2637 4.0251 13.2098 4.1444 13.1367C4.27895 13.0542 4.39425 12.9389 4.62486 12.7083L13 4.33316C13.5523 3.78087 13.5523 2.88544 13 2.33316C12.4477 1.78087 11.5523 1.78087 11 2.33316L2.62484 10.7083C2.39424 10.9389 2.27894 11.0542 2.19648 11.1888C2.12338 11.3081 2.0695 11.4381 2.03684 11.5742C2 11.7276 2 11.8907 2 12.2168V13.3332Z" stroke="#667085" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/>
+</g>
+</svg>

web/app/components/base/icons/assets/vender/line/general/hash-02.svg

+<svg width="12" height="12" viewBox="0 0 12 12" fill="none" xmlns="http://www.w3.org/2000/svg">
+<g id="hash-02">
+<path id="Icon" d="M4.74999 1.5L3.24999 10.5M8.74998 1.5L7.24998 10.5M10.25 4H1.75M9.75 8H1.25" stroke="#98A2B3" stroke-linecap="round" stroke-linejoin="round"/>
+</g>
+</svg>

web/app/components/base/icons/src/public/common/MessageChatSquare.json

+{
+	"icon": {
+		"type": "element",
+		"isRootNode": true,
+		"name": "svg",
+		"attributes": {
+			"width": "16",
+			"height": "16",
+			"viewBox": "0 0 16 16",
+			"fill": "none",
+			"xmlns": "http://www.w3.org/2000/svg"
+		},
+		"children": [
+			{
+				"type": "element",
+				"name": "path",
+				"attributes": {
+					"fill-rule": "evenodd",
+					"clip-rule": "evenodd",
+					"d": "M8.77438 6.6665H12.5591C12.9105 6.66649 13.2137 6.66648 13.4634 6.68688C13.727 6.70842 13.9891 6.75596 14.2414 6.88449C14.6177 7.07624 14.9237 7.3822 15.1154 7.75852C15.244 8.01078 15.2915 8.27292 15.313 8.53649C15.3334 8.7862 15.3334 9.08938 15.3334 9.44082V11.2974C15.3334 11.5898 15.3334 11.8421 15.3192 12.0509C15.3042 12.2708 15.2712 12.4908 15.1812 12.7081C14.9782 13.1981 14.5888 13.5875 14.0988 13.7905C13.8815 13.8805 13.6616 13.9135 13.4417 13.9285C13.4068 13.9308 13.3707 13.9328 13.3334 13.9345V14.6665C13.3334 14.9147 13.1955 15.1424 12.9756 15.2573C12.7556 15.3723 12.49 15.3556 12.2862 15.2139L10.8353 14.2051C10.6118 14.0498 10.5666 14.0214 10.5238 14.0021C10.4746 13.9798 10.4228 13.9635 10.3696 13.9537C10.3235 13.9452 10.2702 13.9427 9.99803 13.9427H8.7744C8.42296 13.9427 8.11978 13.9427 7.87006 13.9223C7.6065 13.9008 7.34435 13.8532 7.0921 13.7247C6.71578 13.533 6.40981 13.227 6.21807 12.8507C6.08954 12.5984 6.04199 12.3363 6.02046 12.0727C6.00006 11.823 6.00007 11.5198 6.00008 11.1684V9.44081C6.00007 9.08938 6.00006 8.7862 6.02046 8.53649C6.04199 8.27292 6.08954 8.01078 6.21807 7.75852C6.40981 7.3822 6.71578 7.07624 7.0921 6.88449C7.34435 6.75596 7.6065 6.70842 7.87006 6.68688C8.11978 6.66648 8.42295 6.66649 8.77438 6.6665Z",
+					"fill": "#444CE7"
+				},
+				"children": []
+			},
+			{
+				"type": "element",
+				"name": "path",
+				"attributes": {
+					"d": "M9.4943 0.666504H4.5059C3.96926 0.666496 3.52635 0.666489 3.16555 0.695967C2.79082 0.726584 2.44635 0.792293 2.12279 0.957154C1.62103 1.21282 1.21308 1.62076 0.957417 2.12253C0.792557 2.44609 0.726847 2.79056 0.69623 3.16529C0.666752 3.52608 0.666759 3.96899 0.666768 4.50564L0.666758 7.6804C0.666669 7.97482 0.666603 8.19298 0.694924 8.38632C0.86568 9.55207 1.78121 10.4676 2.94695 10.6383C2.99461 10.6453 3.02432 10.6632 3.03714 10.6739L3.03714 11.7257C3.03711 11.9075 3.03708 12.0858 3.04976 12.2291C3.06103 12.3565 3.09053 12.6202 3.27795 12.8388C3.48686 13.0825 3.80005 13.2111 4.11993 13.1845C4.40689 13.1607 4.61323 12.9938 4.71072 12.9111C4.73849 12.8875 4.76726 12.8618 4.7968 12.8344C4.73509 12.594 4.70707 12.3709 4.69157 12.1813C4.66659 11.8756 4.66668 11.5224 4.66676 11.1966V9.41261C4.66668 9.08685 4.66659 8.73364 4.69157 8.42793C4.71984 8.08191 4.78981 7.62476 5.03008 7.15322C5.34965 6.52601 5.85959 6.01608 6.4868 5.6965C6.95834 5.45624 7.41549 5.38627 7.7615 5.358C8.06722 5.33302 8.42041 5.3331 8.74617 5.33318H12.5873C12.8311 5.33312 13.0903 5.33306 13.3334 5.3435V4.50562C13.3334 3.96898 13.3334 3.52608 13.304 3.16529C13.2734 2.79056 13.2076 2.44609 13.0428 2.12253C12.7871 1.62076 12.3792 1.21282 11.8774 0.957154C11.5539 0.792293 11.2094 0.726584 10.8347 0.695967C10.4739 0.666489 10.0309 0.666496 9.4943 0.666504Z",
+					"fill": "#444CE7"
+				},
+				"children": []
+			}
+		]
+	},
+	"name": "MessageChatSquare"
+}
\ No newline at end of file

web/app/components/base/icons/src/public/common/MessageChatSquare.tsx

+// GENERATE BY script
+// DON NOT EDIT IT MANUALLY
+
+import * as React from 'react'
+import data from './MessageChatSquare.json'
+import IconBase from '@/app/components/base/icons/IconBase'
+import type { IconBaseProps, IconData } from '@/app/components/base/icons/IconBase'
+
+const Icon = React.forwardRef<React.MutableRefObject<SVGElement>, Omit<IconBaseProps, 'data'>>((
+  props,
+  ref,
+) => <IconBase {...props} ref={ref} data={data as IconData} />)
+
+export default Icon

web/app/components/base/icons/src/public/common/index.ts

 export { default as Dify } from './Dify'
 export { default as Github } from './Github'
+export { default as MessageChatSquare } from './MessageChatSquare'

web/app/components/base/icons/src/vender/line/general/Edit03.json

+{
+	"icon": {
+		"type": "element",
+		"isRootNode": true,
+		"name": "svg",
+		"attributes": {
+			"width": "16",
+			"height": "16",
+			"viewBox": "0 0 16 16",
+			"fill": "none",
+			"xmlns": "http://www.w3.org/2000/svg"
+		},
+		"children": [
+			{
+				"type": "element",
+				"name": "g",
+				"attributes": {
+					"id": "Icon"
+				},
+				"children": [
+					{
+						"type": "element",
+						"name": "path",
+						"attributes": {
+							"id": "Icon_2",
+							"d": "M7.99998 13.3332H14M2 13.3332H3.11636C3.44248 13.3332 3.60554 13.3332 3.75899 13.2963C3.89504 13.2637 4.0251 13.2098 4.1444 13.1367C4.27895 13.0542 4.39425 12.9389 4.62486 12.7083L13 4.33316C13.5523 3.78087 13.5523 2.88544 13 2.33316C12.4477 1.78087 11.5523 1.78087 11 2.33316L2.62484 10.7083C2.39424 10.9389 2.27894 11.0542 2.19648 11.1888C2.12338 11.3081 2.0695 11.4381 2.03684 11.5742C2 11.7276 2 11.8907 2 12.2168V13.3332Z",
+							"stroke": "currentColor",
+							"stroke-width": "1.5",
+							"stroke-linecap": "round",
+							"stroke-linejoin": "round"
+						},
+						"children": []
+					}
+				]
+			}
+		]
+	},
+	"name": "Edit03"
+}
\ No newline at end of file

web/app/components/base/icons/src/vender/line/general/Edit03.tsx

+// GENERATE BY script
+// DON NOT EDIT IT MANUALLY
+
+import * as React from 'react'
+import data from './Edit03.json'
+import IconBase from '@/app/components/base/icons/IconBase'
+import type { IconBaseProps, IconData } from '@/app/components/base/icons/IconBase'
+
+const Icon = React.forwardRef<React.MutableRefObject<SVGElement>, Omit<IconBaseProps, 'data'>>((
+  props,
+  ref,
+) => <IconBase {...props} ref={ref} data={data as IconData} />)
+
+export default Icon

web/app/components/base/icons/src/vender/line/general/Hash02.json

+{
+	"icon": {
+		"type": "element",
+		"isRootNode": true,
+		"name": "svg",
+		"attributes": {
+			"width": "12",
+			"height": "12",
+			"viewBox": "0 0 12 12",
+			"fill": "none",
+			"xmlns": "http://www.w3.org/2000/svg"
+		},
+		"children": [
+			{
+				"type": "element",
+				"name": "g",
+				"attributes": {
+					"id": "hash-02"
+				},
+				"children": [
+					{
+						"type": "element",
+						"name": "path",
+						"attributes": {
+							"id": "Icon",
+							"d": "M4.74999 1.5L3.24999 10.5M8.74998 1.5L7.24998 10.5M10.25 4H1.75M9.75 8H1.25",
+							"stroke": "currentColor",
+							"stroke-linecap": "round",
+							"stroke-linejoin": "round"
+						},
+						"children": []
+					}
+				]
+			}
+		]
+	},
+	"name": "Hash02"
+}
\ No newline at end of file

web/app/components/base/icons/src/vender/line/general/Hash02.tsx

+// GENERATE BY script
+// DON NOT EDIT IT MANUALLY
+
+import * as React from 'react'
+import data from './Hash02.json'
+import IconBase from '@/app/components/base/icons/IconBase'
+import type { IconBaseProps, IconData } from '@/app/components/base/icons/IconBase'
+
+const Icon = React.forwardRef<React.MutableRefObject<SVGElement>, Omit<IconBaseProps, 'data'>>((
+  props,
+  ref,
+) => <IconBase {...props} ref={ref} data={data as IconData} />)
+
+export default Icon

web/app/components/base/icons/src/vender/line/general/index.ts

 export { default as Check } from './Check'
+export { default as Edit03 } from './Edit03'
+export { default as Hash02 } from './Hash02'
 export { default as LinkExternal02 } from './LinkExternal02'
 export { default as Loading02 } from './Loading02'
 export { default as LogOut01 } from './LogOut01'

web/app/components/datasets/create/step-two/index.module.css

@@ -291,7 +291,7 @@
 }
 
 .source {
-  @apply flex justify-between items-center mt-8 px-6 py-4 rounded-xl bg-gray-50;
+  @apply flex justify-between items-center mt-8 px-6 py-4 rounded-xl bg-gray-50 border border-gray-100;
 }
 
 .source .divider {

web/app/components/datasets/create/step-two/index.tsx

@@ -7,7 +7,7 @@ import { XMarkIcon } from '@heroicons/react/20/solid'
 import cn from 'classnames'
 import Link from 'next/link'
 import { groupBy } from 'lodash-es'
-import PreviewItem from './preview-item'
+import PreviewItem, { PreviewType } from './preview-item'
 import s from './index.module.css'
 import type { CreateDocumentReq, File, FullDocumentDetail, FileIndexingEstimateResponse as IndexingEstimateResponse, NotionInfo, PreProcessingRule, Rules, createDocumentResponse } from '@/models/datasets'
 import {
@@ -24,6 +24,8 @@ import { formatNumber } from '@/utils/format'
 import type { DataSourceNotionPage } from '@/models/common'
 import { DataSourceType } from '@/models/datasets'
 import NotionIcon from '@/app/components/base/notion-icon'
+import Switch from '@/app/components/base/switch'
+import { MessageChatSquare } from '@/app/components/base/icons/src/public/common'
 import { useDatasetDetailContext } from '@/context/dataset-detail'
 
 type Page = DataSourceNotionPage & { workspace_id: string }
@@ -53,6 +55,10 @@ enum IndexingType {
   QUALIFIED = 'high_quality',
   ECONOMICAL = 'economy',
 }
+enum DocForm {
+  TEXT = 'text_model',
+  QA = 'qa_model',
+}
 
 const StepTwo = ({
   isSetting,
@@ -88,6 +94,10 @@ const StepTwo = ({
       ? IndexingType.QUALIFIED
       : IndexingType.ECONOMICAL,
   )
+  const [docForm, setDocForm] = useState<DocForm | string>(
+    datasetId && documentDetail ? documentDetail.doc_form : DocForm.TEXT,
+  )
+  const [previewSwitched, setPreviewSwitched] = useState(false)
   const [showPreview, { setTrue: setShowPreview, setFalse: hidePreview }] = useBoolean()
   const [customFileIndexingEstimate, setCustomFileIndexingEstimate] = useState<IndexingEstimateResponse | null>(null)
   const [automaticFileIndexingEstimate, setAutomaticFileIndexingEstimate] = useState<IndexingEstimateResponse | null>(null)
@@ -145,9 +155,9 @@ const StepTwo = ({
     }
   }
 
-  const fetchFileIndexingEstimate = async () => {
+  const fetchFileIndexingEstimate = async (docForm = DocForm.TEXT) => {
     // eslint-disable-next-line @typescript-eslint/no-use-before-define
-    const res = await didFetchFileIndexingEstimate(getFileIndexingEstimateParams())
+    const res = await didFetchFileIndexingEstimate(getFileIndexingEstimateParams(docForm))
     if (segmentationType === SegmentType.CUSTOM)
       setCustomFileIndexingEstimate(res)
 
@@ -155,10 +165,11 @@ const StepTwo = ({
       setAutomaticFileIndexingEstimate(res)
   }
 
-  const confirmChangeCustomConfig = async () => {
+  const confirmChangeCustomConfig = () => {
     setCustomFileIndexingEstimate(null)
     setShowPreview()
-    await fetchFileIndexingEstimate()
+    fetchFileIndexingEstimate()
+    setPreviewSwitched(false)
   }
 
   const getIndexing_technique = () => indexingType || indexType
@@ -205,7 +216,7 @@ const StepTwo = ({
     }) as NotionInfo[]
   }
 
-  const getFileIndexingEstimateParams = () => {
+  const getFileIndexingEstimateParams = (docForm: DocForm) => {
     let params
     if (dataSourceType === DataSourceType.FILE) {
       params = {
@@ -217,6 +228,7 @@ const StepTwo = ({
         },
         indexing_technique: getIndexing_technique(),
         process_rule: getProcessRule(),
+        doc_form: docForm,
       }
     }
     if (dataSourceType === DataSourceType.NOTION) {
@@ -227,6 +239,7 @@ const StepTwo = ({
         },
         indexing_technique: getIndexing_technique(),
         process_rule: getProcessRule(),
+        doc_form: docForm,
       }
     }
     return params
@@ -237,6 +250,7 @@ const StepTwo = ({
     if (isSetting) {
       params = {
         original_document_id: documentDetail?.id,
+        doc_form: docForm,
         process_rule: getProcessRule(),
       } as CreateDocumentReq
     }
@@ -250,6 +264,7 @@ const StepTwo = ({
         },
         indexing_technique: getIndexing_technique(),
         process_rule: getProcessRule(),
+        doc_form: docForm,
       } as CreateDocumentReq
       if (dataSourceType === DataSourceType.FILE) {
         params.data_source.info_list.file_info_list = {
@@ -325,6 +340,29 @@ const StepTwo = ({
     }
   }
 
+  const handleSwitch = (state: boolean) => {
+    if (state)
+      setDocForm(DocForm.QA)
+    else
+      setDocForm(DocForm.TEXT)
+  }
+
+  const changeToEconomicalType = () => {
+    if (!hasSetIndexType) {
+      setIndexType(IndexingType.ECONOMICAL)
+      setDocForm(DocForm.TEXT)
+    }
+  }
+
+  const previewSwitch = async () => {
+    setPreviewSwitched(true)
+    if (segmentationType === SegmentType.AUTO)
+      setAutomaticFileIndexingEstimate(null)
+    else
+      setCustomFileIndexingEstimate(null)
+    await fetchFileIndexingEstimate(DocForm.QA)
+  }
+
   useEffect(() => {
     // fetch rules
     if (!isSetting) {
@@ -352,6 +390,11 @@ const StepTwo = ({
     }
   }, [showPreview])
 
+  useEffect(() => {
+    if (indexingType === IndexingType.ECONOMICAL && docForm === DocForm.QA)
+      setDocForm(DocForm.TEXT)
+  }, [indexingType, docForm])
+
   useEffect(() => {
     // get indexing type by props
     if (indexingType)
@@ -366,10 +409,12 @@ const StepTwo = ({
       setAutomaticFileIndexingEstimate(null)
       setShowPreview()
       fetchFileIndexingEstimate()
+      setPreviewSwitched(false)
     }
     else {
       hidePreview()
       setCustomFileIndexingEstimate(null)
+      setPreviewSwitched(false)
     }
   }, [segmentationType, indexType])
 
@@ -508,7 +553,7 @@ const StepTwo = ({
                     hasSetIndexType && s.disabled,
                     hasSetIndexType && '!w-full',
                   )}
-                  onClick={() => !hasSetIndexType && setIndexType(IndexingType.ECONOMICAL)}
+                  onClick={changeToEconomicalType}
                 >
                   <span className={cn(s.typeIcon, s.economical)} />
                   {!hasSetIndexType && <span className={cn(s.radio)} />}
@@ -527,6 +572,24 @@ const StepTwo = ({
                 <Link className='text-[#155EEF]' href={`/datasets/${datasetId}/settings`}>{t('datasetCreation.stepTwo.datasetSettingLink')}</Link>
               </div>
             )}
+            {indexType === IndexingType.QUALIFIED && (
+              <div className='flex justify-between items-center mt-3 px-5 py-4 rounded-xl bg-gray-50 border border-gray-100'>
+                <div className='flex justify-center items-center w-8 h-8 rounded-lg bg-indigo-50'>
+                  <MessageChatSquare className='w-4 h-4' />
+                </div>
+                <div className='grow mx-3'>
+                  <div className='mb-[2px] text-md font-medium text-gray-900'>{t('datasetCreation.stepTwo.QATitle')}</div>
+                  <div className='text-[13px] leading-[18px] text-gray-500'>{t('datasetCreation.stepTwo.QATip')}</div>
+                </div>
+                <div className='shrink-0'>
+                  <Switch
+                    defaultValue={docForm === DocForm.QA}
+                    onChange={handleSwitch}
+                    size='md'
+                  />
+                </div>
+              </div>
+            )}
             <div className={s.source}>
               <div className={s.sourceContent}>
                 {dataSourceType === DataSourceType.FILE && (
@@ -602,23 +665,50 @@ const StepTwo = ({
       {(showPreview)
         ? (
           <div ref={previewScrollRef} className={cn(s.previewWrap, 'relativeh-full overflow-y-scroll border-l border-[#F2F4F7]')}>
-            <div className={cn(s.previewHeader, previewScrolled && `${s.fixed} pb-3`, ' flex items-center justify-between px-8')}>
-              <span>{t('datasetCreation.stepTwo.previewTitle')}</span>
-              <div className='flex items-center justify-center w-6 h-6 cursor-pointer' onClick={hidePreview}>
-                <XMarkIcon className='h-4 w-4'></XMarkIcon>
+            <div className={cn(s.previewHeader, previewScrolled && `${s.fixed} pb-3`)}>
+              <div className='flex items-center justify-between px-8'>
+                <div className='grow flex items-center'>
+                  <div>{t('datasetCreation.stepTwo.previewTitle')}</div>
+                  {docForm === DocForm.QA && !previewSwitched && (
+                    <Button className='ml-2 !h-[26px] !py-[3px] !px-2 !text-xs !font-medium !text-primary-600' onClick={previewSwitch}>{t('datasetCreation.stepTwo.previewButton')}</Button>
+                  )}
+                </div>
+                <div className='flex items-center justify-center w-6 h-6 cursor-pointer' onClick={hidePreview}>
+                  <XMarkIcon className='h-4 w-4'></XMarkIcon>
+                </div>
               </div>
+              {docForm === DocForm.QA && !previewSwitched && (
+                <div className='px-8 pr-12 text-xs text-gray-500'>
+                  <span>{t('datasetCreation.stepTwo.previewSwitchTipStart')}</span>
+                  <span className='text-amber-600'>{t('datasetCreation.stepTwo.previewSwitchTipEnd')}</span>
+                </div>
+              )}
             </div>
             <div className='my-4 px-8 space-y-4'>
-              {fileIndexingEstimate?.preview
-                ? (
-                  <>
-                    {fileIndexingEstimate?.preview.map((item, index) => (
-                      <PreviewItem key={item} content={item} index={index + 1} />
-                    ))}
-                  </>
-                )
-                : <div className='flex items-center justify-center h-[200px]'><Loading type='area'></Loading></div>
-              }
+              {previewSwitched && docForm === DocForm.QA && fileIndexingEstimate?.qa_preview && (
+                <>
+                  {fileIndexingEstimate?.qa_preview.map((item, index) => (
+                    <PreviewItem type={PreviewType.QA} key={item.question} qa={item} index={index + 1} />
+                  ))}
+                </>
+              )}
+              {(docForm === DocForm.TEXT || !previewSwitched) && fileIndexingEstimate?.preview && (
+                <>
+                  {fileIndexingEstimate?.preview.map((item, index) => (
+                    <PreviewItem type={PreviewType.TEXT} key={item} content={item} index={index + 1} />
+                  ))}
+                </>
+              )}
+              {previewSwitched && docForm === DocForm.QA && !fileIndexingEstimate?.qa_preview && (
+                <div className='flex items-center justify-center h-[200px]'>
+                  <Loading type='area' />
+                </div>
+              )}
+              {!previewSwitched && !fileIndexingEstimate?.preview && (
+                <div className='flex items-center justify-center h-[200px]'>
+                  <Loading type='area' />
+                </div>
+              )}
             </div>
           </div>
         )

web/app/components/datasets/create/step-two/preview-item/index.tsx

 'use client'
-import React, { FC } from 'react'
+import type { FC } from 'react'
+import React from 'react'
 import { useTranslation } from 'react-i18next'
 
-export interface IPreviewItemProps {
+export type IPreviewItemProps = {
+  type: string
   index: number
-  content: string
+  content?: string
+  qa?: {
+    answer: string
+    question: string
+  }
+}
+
+export enum PreviewType {
+  TEXT = 'text',
+  QA = 'QA',
 }
 
 const sharpIcon = (
@@ -21,12 +32,16 @@ const textIcon = (
 )
 
 const PreviewItem: FC<IPreviewItemProps> = ({
+  type = PreviewType.TEXT,
   index,
   content,
+  qa,
 }) => {
   const { t } = useTranslation()
-  const charNums = (content || '').length
-  const formatedIndex = (() => (index + '').padStart(3, '0'))()
+  const charNums = type === PreviewType.TEXT
+    ? (content || '').length
+    : (qa?.answer || '').length + (qa?.question || '').length
+  const formatedIndex = (() => String(index).padStart(3, '0'))()
 
   return (
     <div className='p-4 rounded-xl bg-gray-50'>
@@ -41,7 +56,21 @@ const PreviewItem: FC<IPreviewItemProps> = ({
         </div>
       </div>
       <div className='mt-2 max-h-[120px] line-clamp-6 overflow-hidden text-sm text-gray-800'>
-        <div style={{ whiteSpace: 'pre-line'}}>{content}</div>
+        {type === PreviewType.TEXT && (
+          <div style={{ whiteSpace: 'pre-line' }}>{content}</div>
+        )}
+        {type === PreviewType.QA && (
+          <div style={{ whiteSpace: 'pre-line' }}>
+            <div className='flex'>
+              <div className='shrink-0 mr-2 text-medium text-gray-400'>Q</div>
+              <div style={{ whiteSpace: 'pre-line' }}>{qa?.question}</div>
+            </div>
+            <div className='flex'>
+              <div className='shrink-0 mr-2 text-medium text-gray-400'>A</div>
+              <div style={{ whiteSpace: 'pre-line' }}>{qa?.answer}</div>
+            </div>
+          </div>
+        )}
       </div>
     </div>
   )

web/app/components/datasets/documents/detail/completed/InfiniteVirtualList.tsx

-import React, { FC, CSSProperties } from "react";
-import { FixedSizeList as List } from "react-window";
-import InfiniteLoader from "react-window-infinite-loader";
-import type { SegmentDetailModel } from "@/models/datasets";
-import SegmentCard from "./SegmentCard";
-import s from "./style.module.css";
+import type { CSSProperties, FC } from 'react'
+import React from 'react'
+import { FixedSizeList as List } from 'react-window'
+import InfiniteLoader from 'react-window-infinite-loader'
+import SegmentCard from './SegmentCard'
+import s from './style.module.css'
+import type { SegmentDetailModel } from '@/models/datasets'
 
 type IInfiniteVirtualListProps = {
-  hasNextPage?: boolean; // Are there more items to load? (This information comes from the most recent API request.)
-  isNextPageLoading: boolean; // Are we currently loading a page of items? (This may be an in-flight flag in your Redux store for example.)
-  items: Array<SegmentDetailModel[]>; // Array of items loaded so far.
-  loadNextPage: () => Promise<any>; // Callback function responsible for loading the next page of items.
-  onClick: (detail: SegmentDetailModel) => void;
-  onChangeSwitch: (segId: string, enabled: boolean) => Promise<void>;
-};
+  hasNextPage?: boolean // Are there more items to load? (This information comes from the most recent API request.)
+  isNextPageLoading: boolean // Are we currently loading a page of items? (This may be an in-flight flag in your Redux store for example.)
+  items: Array<SegmentDetailModel[]> // Array of items loaded so far.
+  loadNextPage: () => Promise<any> // Callback function responsible for loading the next page of items.
+  onClick: (detail: SegmentDetailModel) => void
+  onChangeSwitch: (segId: string, enabled: boolean) => Promise<void>
+}
 
 const InfiniteVirtualList: FC<IInfiniteVirtualListProps> = ({
   hasNextPage,
@@ -23,28 +24,29 @@ const InfiniteVirtualList: FC<IInfiniteVirtualListProps> = ({
   onChangeSwitch,
 }) => {
   // If there are more items to be loaded then add an extra row to hold a loading indicator.
-  const itemCount = hasNextPage ? items.length + 1 : items.length;
+  const itemCount = hasNextPage ? items.length + 1 : items.length
 
   // Only load 1 page of items at a time.
   // Pass an empty callback to InfiniteLoader in case it asks us to load more than once.
-  const loadMoreItems = isNextPageLoading ? () => { } : loadNextPage;
+  const loadMoreItems = isNextPageLoading ? () => { } : loadNextPage
 
   // Every row is loaded except for our loading indicator row.
-  const isItemLoaded = (index: number) => !hasNextPage || index < items.length;
+  const isItemLoaded = (index: number) => !hasNextPage || index < items.length
 
   // Render an item or a loading indicator.
   const Item = ({ index, style }: { index: number; style: CSSProperties }) => {
-    let content;
+    let content
     if (!isItemLoaded(index)) {
       content = (
         <>
-          {[1, 2, 3].map((v) => (
+          {[1, 2, 3].map(v => (
             <SegmentCard loading={true} detail={{ position: v } as any} />
           ))}
         </>
-      );
-    } else {
-      content = items[index].map((segItem) => (
+      )
+    }
+    else {
+      content = items[index].map(segItem => (
         <SegmentCard
           key={segItem.id}
           detail={segItem}
@@ -52,15 +54,15 @@ const InfiniteVirtualList: FC<IInfiniteVirtualListProps> = ({
           onChangeSwitch={onChangeSwitch}
           loading={false}
         />
-      ));
+      ))
     }
 
     return (
       <div style={style} className={s.cardWrapper}>
         {content}
       </div>
-    );
-  };
+    )
+  }
 
   return (
     <InfiniteLoader
@@ -73,7 +75,7 @@ const InfiniteVirtualList: FC<IInfiniteVirtualListProps> = ({
           ref={ref}
           className="List"
           height={800}
-          width={"100%"}
+          width={'100%'}
           itemSize={200}
           itemCount={itemCount}
           onItemsRendered={onItemsRendered}
@@ -82,6 +84,6 @@ const InfiniteVirtualList: FC<IInfiniteVirtualListProps> = ({
         </List>
       )}
     </InfiniteLoader>
-  );
-};
-export default InfiniteVirtualList;
+  )
+}
+export default InfiniteVirtualList

web/app/components/datasets/documents/detail/completed/SegmentCard.tsx

-import type { FC } from "react";
-import React from "react";
-import cn from "classnames";
-import { ArrowUpRightIcon } from "@heroicons/react/24/outline";
-import Switch from "@/app/components/base/switch";
-import Divider from "@/app/components/base/divider";
-import Indicator from "@/app/components/header/indicator";
-import { formatNumber } from "@/utils/format";
-import type { SegmentDetailModel } from "@/models/datasets";
-import { StatusItem } from "../../list";
-import s from "./style.module.css";
-import { SegmentIndexTag } from "./index";
+import type { FC } from 'react'
+import React from 'react'
+import cn from 'classnames'
+import { ArrowUpRightIcon } from '@heroicons/react/24/outline'
+import { useTranslation } from 'react-i18next'
+import { StatusItem } from '../../list'
 import { DocumentTitle } from '../index'
-import { useTranslation } from "react-i18next";
+import s from './style.module.css'
+import { SegmentIndexTag } from './index'
+import Switch from '@/app/components/base/switch'
+import Divider from '@/app/components/base/divider'
+import Indicator from '@/app/components/header/indicator'
+import { formatNumber } from '@/utils/format'
+import type { SegmentDetailModel } from '@/models/datasets'
 
 const ProgressBar: FC<{ percent: number; loading: boolean }> = ({ percent, loading }) => {
   return (
@@ -30,14 +30,14 @@ const ProgressBar: FC<{ percent: number; loading: boolean }> = ({ percent, loadi
 export type UsageScene = 'doc' | 'hitTesting'
 
 type ISegmentCardProps = {
-  loading: boolean;
-  detail?: SegmentDetailModel & { document: { name: string } };
+  loading: boolean
+  detail?: SegmentDetailModel & { document: { name: string } }
   score?: number
-  onClick?: () => void;
-  onChangeSwitch?: (segId: string, enabled: boolean) => Promise<void>;
+  onClick?: () => void
+  onChangeSwitch?: (segId: string, enabled: boolean) => Promise<void>
   scene?: UsageScene
-  className?: string;
-};
+  className?: string
+}
 
 const SegmentCard: FC<ISegmentCardProps> = ({
   detail = {},
@@ -46,7 +46,7 @@ const SegmentCard: FC<ISegmentCardProps> = ({
   onChangeSwitch,
   loading = true,
   scene = 'doc',
-  className = ''
+  className = '',
 }) => {
   const { t } = useTranslation()
   const {
@@ -57,110 +57,138 @@ const SegmentCard: FC<ISegmentCardProps> = ({
     word_count,
     hit_count,
     index_node_hash,
-  } = detail as any;
+    answer,
+  } = detail as any
   const isDocScene = scene === 'doc'
 
+  const renderContent = () => {
+    if (answer) {
+      return (
+        <>
+          <div className='flex mb-2'>
+            <div className='mr-2 text-[13px] font-semibold text-gray-400'>Q</div>
+            <div className='text-[13px]'>{content}</div>
+          </div>
+          <div className='flex'>
+            <div className='mr-2 text-[13px] font-semibold text-gray-400'>A</div>
+            <div className='text-[13px]'>{answer}</div>
+          </div>
+        </>
+      )
+    }
+
+    return content
+  }
+
   return (
     <div
       className={cn(
         s.segWrapper,
-        isDocScene && !enabled ? "bg-gray-25" : "",
-        "group",
-        !loading ? "pb-4" : "",
+        (isDocScene && !enabled) ? 'bg-gray-25' : '',
+        'group',
+        !loading ? 'pb-4' : '',
         className,
       )}
       onClick={() => onClick?.()}
     >
       <div className={s.segTitleWrapper}>
-        {isDocScene ? <>
-          <SegmentIndexTag positionId={position} className={cn("w-fit group-hover:opacity-100", isDocScene && !enabled ? 'opacity-50' : '')} />
-          <div className={s.segStatusWrapper}>
-            {loading ? (
-              <Indicator
-                color="gray"
-                className="bg-gray-200 border-gray-300 shadow-none"
-              />
-            ) : (
-              <>
-                <StatusItem status={enabled ? "enabled" : "disabled"} reverse textCls="text-gray-500 text-xs" />
-                <div className="hidden group-hover:inline-flex items-center">
-                  <Divider type="vertical" className="!h-2" />
-                  <div
-                    onClick={(e: React.MouseEvent<HTMLDivElement, MouseEvent>) =>
-                      e.stopPropagation()
-                    }
-                    className="inline-flex items-center"
-                  >
-                    <Switch
-                      size='md'
-                      defaultValue={enabled}
-                      onChange={async (val) => {
-                        await onChangeSwitch?.(id, val)
-                      }}
-                    />
-                  </div>
-                </div>
-              </>
-            )}
-          </div>
-        </> : <div className={s.hitTitleWrapper}>
-          <div className={cn(s.commonIcon, s.targetIcon, loading ? '!bg-gray-300' : '', '!w-3.5 !h-3.5')} />
-          <ProgressBar percent={score ?? 0} loading={loading} />
-        </div>}
-      </div>
-      {loading ? (
-        <div className={cn(s.cardLoadingWrapper, s.cardLoadingIcon)}>
-          <div className={cn(s.cardLoadingBg)} />
-        </div>
-      ) : (
-        isDocScene ? <>
-          <div
-            className={cn(
-              s.segContent,
-              enabled ? "" : "opacity-50",
-              "group-hover:text-transparent group-hover:bg-clip-text group-hover:bg-gradient-to-b"
-            )}
-          >
-            {content}
-          </div>
-          <div className={cn('group-hover:flex', s.segData)}>
-            <div className="flex items-center mr-6">
-              <div className={cn(s.commonIcon, s.typeSquareIcon)}></div>
-              <div className={s.segDataText}>{formatNumber(word_count)}</div>
-            </div>
-            <div className="flex items-center mr-6">
-              <div className={cn(s.commonIcon, s.targetIcon)} />
-              <div className={s.segDataText}>{formatNumber(hit_count)}</div>
+        {isDocScene
+          ? <>
+            <SegmentIndexTag positionId={position} className={cn('w-fit group-hover:opacity-100', (isDocScene && !enabled) ? 'opacity-50' : '')} />
+            <div className={s.segStatusWrapper}>
+              {loading
+                ? (
+                  <Indicator
+                    color="gray"
+                    className="bg-gray-200 border-gray-300 shadow-none"
+                  />
+                )
+                : (
+                  <>
+                    <StatusItem status={enabled ? 'enabled' : 'disabled'} reverse textCls="text-gray-500 text-xs" />
+                    <div className="hidden group-hover:inline-flex items-center">
+                      <Divider type="vertical" className="!h-2" />
+                      <div
+                        onClick={(e: React.MouseEvent<HTMLDivElement, MouseEvent>) =>
+                          e.stopPropagation()
+                        }
+                        className="inline-flex items-center"
+                      >
+                        <Switch
+                          size='md'
+                          defaultValue={enabled}
+                          onChange={async (val) => {
+                            await onChangeSwitch?.(id, val)
+                          }}
+                        />
+                      </div>
+                    </div>
+                  </>
+                )}
             </div>
-            <div className="flex items-center">
-              <div className={cn(s.commonIcon, s.bezierCurveIcon)} />
-              <div className={s.segDataText}>{index_node_hash}</div>
-            </div>
-          </div>
-        </> : <>
-          <div className="h-[140px] overflow-hidden text-ellipsis text-sm font-normal text-gray-800">
-            {content}
+          </>
+          : <div className={s.hitTitleWrapper}>
+            <div className={cn(s.commonIcon, s.targetIcon, loading ? '!bg-gray-300' : '', '!w-3.5 !h-3.5')} />
+            <ProgressBar percent={score ?? 0} loading={loading} />
+          </div>}
+      </div>
+      {loading
+        ? (
+          <div className={cn(s.cardLoadingWrapper, s.cardLoadingIcon)}>
+            <div className={cn(s.cardLoadingBg)} />
           </div>
-          <div className={cn("w-full bg-gray-50 group-hover:bg-white")}>
-            <Divider />
-            <div className="relative flex items-center w-full">
-              <DocumentTitle
-                name={detail?.document?.name || ''}
-                extension={(detail?.document?.name || '').split('.').pop() || 'txt'}
-                wrapperCls='w-full'
-                iconCls="!h-4 !w-4 !bg-contain"
-                textCls="text-xs text-gray-700 !font-normal overflow-hidden whitespace-nowrap text-ellipsis"
-              />
-              <div className={cn(s.chartLinkText, 'group-hover:inline-flex')}>
-                {t('datasetHitTesting.viewChart')}
-                <ArrowUpRightIcon className="w-3 h-3 ml-1 stroke-current stroke-2" />
+        )
+        : (
+          isDocScene
+            ? <>
+              <div
+                className={cn(
+                  s.segContent,
+                  enabled ? '' : 'opacity-50',
+                  'group-hover:text-transparent group-hover:bg-clip-text group-hover:bg-gradient-to-b',
+                )}
+              >
+                {renderContent()}
               </div>
-            </div>
-          </div>
-        </>
-      )}
+              <div className={cn('group-hover:flex', s.segData)}>
+                <div className="flex items-center mr-6">
+                  <div className={cn(s.commonIcon, s.typeSquareIcon)}></div>
+                  <div className={s.segDataText}>{formatNumber(word_count)}</div>
+                </div>
+                <div className="flex items-center mr-6">
+                  <div className={cn(s.commonIcon, s.targetIcon)} />
+                  <div className={s.segDataText}>{formatNumber(hit_count)}</div>
+                </div>
+                <div className="flex items-center">
+                  <div className={cn(s.commonIcon, s.bezierCurveIcon)} />
+                  <div className={s.segDataText}>{index_node_hash}</div>
+                </div>
+              </div>
+            </>
+            : <>
+              <div className="h-[140px] overflow-hidden text-ellipsis text-sm font-normal text-gray-800">
+                {renderContent()}
+              </div>
+              <div className={cn('w-full bg-gray-50 group-hover:bg-white')}>
+                <Divider />
+                <div className="relative flex items-center w-full">
+                  <DocumentTitle
+                    name={detail?.document?.name || ''}
+                    extension={(detail?.document?.name || '').split('.').pop() || 'txt'}
+                    wrapperCls='w-full'
+                    iconCls="!h-4 !w-4 !bg-contain"
+                    textCls="text-xs text-gray-700 !font-normal overflow-hidden whitespace-nowrap text-ellipsis"
+                  />
+                  <div className={cn(s.chartLinkText, 'group-hover:inline-flex')}>
+                    {t('datasetHitTesting.viewChart')}
+                    <ArrowUpRightIcon className="w-3 h-3 ml-1 stroke-current stroke-2" />
+                  </div>
+                </div>
+              </div>
+            </>
+        )}
     </div>
-  );
-};
+  )
+}
 
-export default SegmentCard;
+export default SegmentCard

web/app/components/datasets/documents/detail/completed/index.tsx

 'use client'
 import type { FC } from 'react'
-import React, { memo, useState, useEffect, useMemo } from 'react'
+import React, { memo, useEffect, useMemo, useState } from 'react'
 import { HashtagIcon } from '@heroicons/react/24/solid'
 import { useTranslation } from 'react-i18next'
 import { useContext } from 'use-context-selector'
-import { omitBy, isNil, debounce } from 'lodash-es'
-import { formatNumber } from '@/utils/format'
+import { debounce, isNil, omitBy } from 'lodash-es'
+import cn from 'classnames'
 import { StatusItem } from '../../list'
 import { DocumentContext } from '../index'
 import s from './style.module.css'
+import InfiniteVirtualList from './InfiniteVirtualList'
+import { formatNumber } from '@/utils/format'
 import Modal from '@/app/components/base/modal'
 import Switch from '@/app/components/base/switch'
 import Divider from '@/app/components/base/divider'
 import Input from '@/app/components/base/input'
-import Loading from '@/app/components/base/loading'
 import { ToastContext } from '@/app/components/base/toast'
-import { SimpleSelect, Item } from '@/app/components/base/select'
-import { disableSegment, enableSegment, fetchSegments } from '@/service/datasets'
-import type { SegmentDetailModel, SegmentsResponse, SegmentsQuery } from '@/models/datasets'
+import type { Item } from '@/app/components/base/select'
+import { SimpleSelect } from '@/app/components/base/select'
+import { disableSegment, enableSegment, fetchSegments, updateSegment } from '@/service/datasets'
+import type { SegmentDetailModel, SegmentUpdator, SegmentsQuery, SegmentsResponse } from '@/models/datasets'
 import { asyncRunSafe } from '@/utils'
 import type { CommonResponse } from '@/models/common'
-import InfiniteVirtualList from "./InfiniteVirtualList";
-import cn from 'classnames'
+import { Edit03, XClose } from '@/app/components/base/icons/src/vender/line/general'
+import AutoHeightTextarea from '@/app/components/base/auto-height-textarea/common'
+import Button from '@/app/components/base/button'
+import NewSegmentModal from '@/app/components/datasets/documents/detail/new-segment-modal'
 
 export const SegmentIndexTag: FC<{ positionId: string | number; className?: string }> = ({ positionId, className }) => {
   const localPositionId = useMemo(() => {
@@ -41,19 +45,105 @@ export const SegmentIndexTag: FC<{ positionId: string | number; className?: stri
 type ISegmentDetailProps = {
   segInfo?: Partial<SegmentDetailModel> & { id: string }
   onChangeSwitch?: (segId: string, enabled: boolean) => Promise<void>
+  onUpdate: (segmentId: string, q: string, a: string) => void
+  onCancel: () => void
 }
 /**
  * Show all the contents of the segment
  */
 export const SegmentDetail: FC<ISegmentDetailProps> = memo(({
   segInfo,
-  onChangeSwitch }) => {
+  onChangeSwitch,
+  onUpdate,
+  onCancel,
+}) => {
   const { t } = useTranslation()
+  const [isEditing, setIsEditing] = useState(false)
+  const [question, setQuestion] = useState(segInfo?.content || '')
+  const [answer, setAnswer] = useState(segInfo?.answer || '')
+
+  const handleCancel = () => {
+    setIsEditing(false)
+    setQuestion(segInfo?.content || '')
+    setAnswer(segInfo?.answer || '')
+  }
+  const handleSave = () => {
+    onUpdate(segInfo?.id || '', question, answer)
+  }
+
+  const renderContent = () => {
+    if (segInfo?.answer) {
+      return (
+        <>
+          <div className='mb-1 text-xs font-medium text-gray-500'>QUESTION</div>
+          <AutoHeightTextarea
+            outerClassName='mb-4'
+            className='leading-6 text-md text-gray-800'
+            value={question}
+            placeholder={t('datasetDocuments.segment.questionPlaceholder') || ''}
+            onChange={e => setQuestion(e.target.value)}
+            disabled={!isEditing}
+          />
+          <div className='mb-1 text-xs font-medium text-gray-500'>ANSWER</div>
+          <AutoHeightTextarea
+            outerClassName='mb-4'
+            className='leading-6 text-md text-gray-800'
+            value={answer}
+            placeholder={t('datasetDocuments.segment.answerPlaceholder') || ''}
+            onChange={e => setAnswer(e.target.value)}
+            disabled={!isEditing}
+            autoFocus
+          />
+        </>
+      )
+    }
+
+    return (
+      <AutoHeightTextarea
+        className='leading-6 text-md text-gray-800'
+        value={question}
+        placeholder={t('datasetDocuments.segment.contentPlaceholder') || ''}
+        onChange={e => setQuestion(e.target.value)}
+        disabled={!isEditing}
+        autoFocus
+      />
+    )
+  }
 
   return (
-    <div className={'flex flex-col'}>
-      <SegmentIndexTag positionId={segInfo?.position || ''} className='w-fit mb-6' />
-      <div className={s.segModalContent}>{segInfo?.content}</div>
+    <div className={'flex flex-col relative'}>
+      <div className='absolute right-0 top-0 flex items-center h-7'>
+        {
+          isEditing
+            ? (
+              <>
+                <Button
+                  className='mr-2 !h-7 !px-3 !py-[5px] text-xs font-medium text-gray-700 !rounded-md'
+                  onClick={handleCancel}>
+                  {t('common.operation.cancel')}
+                </Button>
+                <Button
+                  type='primary'
+                  className='!h-7 !px-3 !py-[5px] text-xs font-medium !rounded-md'
+                  onClick={handleSave}>
+                  {t('common.operation.save')}
+                </Button>
+              </>
+            )
+            : (
+              <div className='group relative flex justify-center items-center w-6 h-6 hover:bg-gray-100 rounded-md cursor-pointer'>
+                <div className={cn(s.editTip, 'hidden items-center absolute -top-10 px-3 h-[34px] bg-white rounded-lg whitespace-nowrap text-xs font-semibold text-gray-700 group-hover:flex')}>{t('common.operation.edit')}</div>
+                <Edit03 className='w-4 h-4 text-gray-500' onClick={() => setIsEditing(true)} />
+              </div>
+            )
+        }
+        <div className='mx-3 w-[1px] h-3 bg-gray-200' />
+        <div className='flex justify-center items-center w-6 h-6 cursor-pointer' onClick={onCancel}>
+          <XClose className='w-4 h-4 text-gray-500' />
+        </div>
+      </div>
+      <SegmentIndexTag positionId={segInfo?.position || ''} className='w-fit mt-[2px] mb-6' />
+      <div className={s.segModalContent}>{renderContent()}</div>
       <div className={s.keywordTitle}>{t('datasetDocuments.segment.keywords')}</div>
       <div className={s.keywordWrapper}>
         {!segInfo?.keywords?.length
@@ -74,7 +164,7 @@ export const SegmentDetail: FC<ISegmentDetailProps> = memo(({
           <Switch
             size='md'
             defaultValue={segInfo?.enabled}
-            onChange={async val => {
+            onChange={async (val) => {
               await onChangeSwitch?.(segInfo?.id || '', val)
             }}
           />
@@ -94,16 +184,18 @@ export const splitArray = (arr: any[], size = 3) => {
 }
 
 type ICompletedProps = {
+  showNewSegmentModal: boolean
+  onNewSegmentModalChange: (state: boolean) => void
   // data: Array<{}> // all/part segments
 }
 /**
  * Embedding done, show list of all segments
  * Support search and filter
  */
-const Completed: FC<ICompletedProps> = () => {
+const Completed: FC<ICompletedProps> = ({ showNewSegmentModal, onNewSegmentModalChange }) => {
   const { t } = useTranslation()
   const { notify } = useContext(ToastContext)
-  const { datasetId = '', documentId = '' } = useContext(DocumentContext)
+  const { datasetId = '', documentId = '', docForm } = useContext(DocumentContext)
   // the current segment id and whether to show the modal
   const [currSegment, setCurrSegment] = useState<{ segInfo?: SegmentDetailModel; showModal: boolean }>({ showModal: false })
 
@@ -115,37 +207,45 @@ const Completed: FC<ICompletedProps> = () => {
   const [loading, setLoading] = useState(false)
   const [total, setTotal] = useState<number | undefined>()
 
-  useEffect(() => {
-    if (lastSegmentsRes !== undefined) {
-      getSegments(false)
-    }
-  }, [selectedStatus, searchValue])
-
   const onChangeStatus = ({ value }: Item) => {
     setSelectedStatus(value === 'all' ? 'all' : !!value)
   }
 
   const getSegments = async (needLastId?: boolean) => {
-    const finalLastId = lastSegmentsRes?.data?.[lastSegmentsRes.data.length - 1]?.id || '';
+    const finalLastId = lastSegmentsRes?.data?.[lastSegmentsRes.data.length - 1]?.id || ''
     setLoading(true)
     const [e, res] = await asyncRunSafe<SegmentsResponse>(fetchSegments({
       datasetId,
       documentId,
       params: omitBy({
         last_id: !needLastId ? undefined : finalLastId,
-        limit: 9,
+        limit: 12,
         keyword: searchValue,
         enabled: selectedStatus === 'all' ? 'all' : !!selectedStatus,
-      }, isNil) as SegmentsQuery
+      }, isNil) as SegmentsQuery,
     }) as Promise<SegmentsResponse>)
     if (!e) {
       setAllSegments([...(!needLastId ? [] : allSegments), ...splitArray(res.data || [])])
       setLastSegmentsRes(res)
-      if (!lastSegmentsRes) { setTotal(res?.total || 0) }
+      if (!lastSegmentsRes || !needLastId)
+        setTotal(res?.total || 0)
     }
     setLoading(false)
   }
 
+  const resetList = () => {
+    setLastSegmentsRes(undefined)
+    setAllSegments([])
+    setLoading(false)
+    setTotal(undefined)
+    getSegments(false)
+  }
+
+  useEffect(() => {
+    if (lastSegmentsRes !== undefined)
+      getSegments(false)
+  }, [selectedStatus, searchValue])
+
   const onClickCard = (detail: SegmentDetailModel) => {
     setCurrSegment({ segInfo: detail, showModal: true })
   }
@@ -161,17 +261,53 @@ const Completed: FC<ICompletedProps> = () => {
       notify({ type: 'success', message: t('common.actionMsg.modifiedSuccessfully') })
       for (const item of allSegments) {
         for (const seg of item) {
-          if (seg.id === segId) {
+          if (seg.id === segId)
             seg.enabled = enabled
-          }
         }
       }
       setAllSegments([...allSegments])
-    } else {
+    }
+    else {
       notify({ type: 'error', message: t('common.actionMsg.modificationFailed') })
     }
   }
 
+  const handleUpdateSegment = async (segmentId: string, question: string, answer: string) => {
+    const params: SegmentUpdator = { content: '' }
+    if (docForm === 'qa_model') {
+      if (!question.trim())
+        return notify({ type: 'error', message: t('datasetDocuments.segment.questionEmpty') })
+      if (!answer.trim())
+        return notify({ type: 'error', message: t('datasetDocuments.segment.answerEmpty') })
+
+      params.content = question
+      params.answer = answer
+    }
+    else {
+      if (!question.trim())
+        return notify({ type: 'error', message: t('datasetDocuments.segment.contentEmpty') })
+
+      params.content = question
+    }
+
+    const res = await updateSegment({ datasetId, documentId, segmentId, body: params })
+    notify({ type: 'success', message: t('common.actionMsg.modifiedSuccessfully') })
+    onCloseModal()
+    for (const item of allSegments) {
+      for (const seg of item) {
+        if (seg.id === segmentId) {
+          seg.answer = res.data.answer
+          seg.content = res.data.content
+          seg.word_count = res.data.word_count
+          seg.hit_count = res.data.hit_count
+          seg.index_node_hash = res.data.index_node_hash
+          seg.enabled = res.data.enabled
+        }
+      }
+    }
+    setAllSegments([...allSegments])
+  }
+
   return (
     <>
       <div className={s.docSearchWrapper}>
@@ -196,9 +332,20 @@ const Completed: FC<ICompletedProps> = () => {
         onChangeSwitch={onChangeSwitch}
         onClick={onClickCard}
       />
-      <Modal isShow={currSegment.showModal} onClose={onCloseModal} className='!max-w-[640px]' closable>
-        <SegmentDetail segInfo={currSegment.segInfo ?? { id: '' }} onChangeSwitch={onChangeSwitch} />
+      <Modal isShow={currSegment.showModal} onClose={() => {}} className='!max-w-[640px] !overflow-visible'>
+        <SegmentDetail
+          segInfo={currSegment.segInfo ?? { id: '' }}
+          onChangeSwitch={onChangeSwitch}
+          onUpdate={handleUpdateSegment}
+          onCancel={onCloseModal}
+        />
       </Modal>
+      <NewSegmentModal
+        isShow={showNewSegmentModal}
+        docForm={docForm}
+        onCancel={() => onNewSegmentModalChange(false)}
+        onSave={resetList}
+      />
     </>
   )
 }

web/app/components/datasets/documents/detail/completed/style.module.css

@@ -129,3 +129,6 @@
   border-radius: 5px;
   @apply h-3.5 w-3.5 bg-[#EAECF0];
 }
+.editTip {
+  box-shadow: 0px 4px 6px -2px rgba(16, 24, 40, 0.03), 0px 12px 16px -4px rgba(16, 24, 40, 0.08);
+}

web/app/components/datasets/documents/detail/embedding/index.tsx

@@ -30,6 +30,7 @@ type Props = {
   datasetId?: string
   documentId?: string
   indexingType?: string
+  detailUpdate: VoidFunction
 }
 
 const StopIcon: FC<{ className?: string }> = ({ className }) => {
@@ -108,7 +109,7 @@ const RuleDetail: FC<{ sourceData?: ProcessRuleResponse; docName?: string }> = (
   </div>
 }
 
-const EmbeddingDetail: FC<Props> = ({ detail, stopPosition = 'top', datasetId: dstId, documentId: docId, indexingType }) => {
+const EmbeddingDetail: FC<Props> = ({ detail, stopPosition = 'top', datasetId: dstId, documentId: docId, indexingType, detailUpdate }) => {
   const onTop = stopPosition === 'top'
   const { t } = useTranslation()
   const { notify } = useContext(ToastContext)
@@ -145,6 +146,7 @@ const EmbeddingDetail: FC<Props> = ({ detail, stopPosition = 'top', datasetId: d
       const indexingStatusDetail = getIndexingStatusDetail()
       if (indexingStatusDetail?.indexing_status === 'completed') {
         stopQueryStatus()
+        detailUpdate()
         return
       }
       fetchIndexingStatus()

web/app/components/datasets/documents/detail/index.tsx

@@ -27,7 +27,7 @@ export const BackCircleBtn: FC<{ onClick: () => void }> = ({ onClick }) => {
   )
 }
 
-export const DocumentContext = createContext<{ datasetId?: string; documentId?: string }>({})
+export const DocumentContext = createContext<{ datasetId?: string; documentId?: string; docForm: string }>({ docForm: '' })
 
 type DocumentTitleProps = {
   extension?: string
@@ -54,6 +54,7 @@ const DocumentDetail: FC<Props> = ({ datasetId, documentId }) => {
   const { t } = useTranslation()
   const router = useRouter()
   const [showMetadata, setShowMetadata] = useState(true)
+  const [showNewSegmentModal, setShowNewSegmentModal] = useState(false)
 
   const { data: documentDetail, error, mutate: detailMutate } = useSWR({
     action: 'fetchDocumentDetail',
@@ -87,7 +88,7 @@ const DocumentDetail: FC<Props> = ({ datasetId, documentId }) => {
   }
 
   return (
-    <DocumentContext.Provider value={{ datasetId, documentId }}>
+    <DocumentContext.Provider value={{ datasetId, documentId, docForm: documentDetail?.doc_form || '' }}>
       <div className='flex flex-col h-full'>
         <div className='flex h-16 border-b-gray-100 border-b items-center p-4'>
           <BackCircleBtn onClick={backToPrev} />
@@ -100,10 +101,12 @@ const DocumentDetail: FC<Props> = ({ datasetId, documentId }) => {
               enabled: documentDetail?.enabled || false,
               archived: documentDetail?.archived || false,
               id: documentId,
+              doc_form: documentDetail?.doc_form || '',
             }}
             datasetId={datasetId}
             onUpdate={handleOperate}
             className='!w-[216px]'
+            showNewSegmentModal={() => setShowNewSegmentModal(true)}
           />
           <button
             className={cn(style.layoutRightIcon, showMetadata ? style.iconShow : style.iconClose)}
@@ -114,7 +117,13 @@ const DocumentDetail: FC<Props> = ({ datasetId, documentId }) => {
           {isDetailLoading
             ? <Loading type='app' />
             : <div className={`box-border h-full w-full overflow-y-scroll ${embedding ? 'py-12 px-16' : 'pb-[30px] pt-3 px-6'}`}>
-              {embedding ? <Embedding detail={documentDetail} /> : <Completed />}
+              {embedding
+                ? <Embedding detail={documentDetail} detailUpdate={detailMutate} />
+                : <Completed
+                  showNewSegmentModal={showNewSegmentModal}
+                  onNewSegmentModalChange={setShowNewSegmentModal}
+                />
+              }
             </div>
           }
           {showMetadata && <Metadata

web/app/components/datasets/documents/detail/new-segment-modal.tsx

+import { memo, useState } from 'react'
+import type { FC } from 'react'
+import { useTranslation } from 'react-i18next'
+import { useContext } from 'use-context-selector'
+import { useParams } from 'next/navigation'
+import Modal from '@/app/components/base/modal'
+import Button from '@/app/components/base/button'
+import AutoHeightTextarea from '@/app/components/base/auto-height-textarea/common'
+import { Hash02, XClose } from '@/app/components/base/icons/src/vender/line/general'
+import { ToastContext } from '@/app/components/base/toast'
+import type { SegmentUpdator } from '@/models/datasets'
+import { addSegment } from '@/service/datasets'
+
+type NewSegmentModalProps = {
+  isShow: boolean
+  onCancel: () => void
+  docForm: string
+  onSave: () => void
+}
+
+const NewSegmentModal: FC<NewSegmentModalProps> = memo(({
+  isShow,
+  onCancel,
+  docForm,
+  onSave,
+}) => {
+  const { t } = useTranslation()
+  const { notify } = useContext(ToastContext)
+  const [question, setQuestion] = useState('')
+  const [answer, setAnswer] = useState('')
+  const { datasetId, documentId } = useParams()
+
+  const handleCancel = () => {
+    setQuestion('')
+    setAnswer('')
+    onCancel()
+  }
+
+  const handleSave = async () => {
+    const params: SegmentUpdator = { content: '' }
+    if (docForm === 'qa_model') {
+      if (!question.trim())
+        return notify({ type: 'error', message: t('datasetDocuments.segment.questionEmpty') })
+      if (!answer.trim())
+        return notify({ type: 'error', message: t('datasetDocuments.segment.answerEmpty') })
+
+      params.content = question
+      params.answer = answer
+    }
+    else {
+      if (!question.trim())
+        return notify({ type: 'error', message: t('datasetDocuments.segment.contentEmpty') })
+
+      params.content = question
+    }
+
+    await addSegment({ datasetId, documentId, body: params })
+    notify({ type: 'success', message: t('common.actionMsg.modifiedSuccessfully') })
+    handleCancel()
+    onSave()
+  }
+
+  const renderContent = () => {
+    if (docForm === 'qa_model') {
+      return (
+        <>
+          <div className='mb-1 text-xs font-medium text-gray-500'>QUESTION</div>
+          <AutoHeightTextarea
+            outerClassName='mb-4'
+            className='leading-6 text-md text-gray-800'
+            value={question}
+            placeholder={t('datasetDocuments.segment.questionPlaceholder') || ''}
+            onChange={e => setQuestion(e.target.value)}
+            autoFocus
+          />
+          <div className='mb-1 text-xs font-medium text-gray-500'>ANSWER</div>
+          <AutoHeightTextarea
+            outerClassName='mb-4'
+            className='leading-6 text-md text-gray-800'
+            value={answer}
+            placeholder={t('datasetDocuments.segment.answerPlaceholder') || ''}
+            onChange={e => setAnswer(e.target.value)}
+          />
+        </>
+      )
+    }
+
+    return (
+      <AutoHeightTextarea
+        className='leading-6 text-md text-gray-800'
+        value={question}
+        placeholder={t('datasetDocuments.segment.contentPlaceholder') || ''}
+        onChange={e => setQuestion(e.target.value)}
+        autoFocus
+      />
+    )
+  }
+
+  return (
+    <Modal isShow={isShow} onClose={() => {}} className='pt-8 px-8 pb-6 !max-w-[640px] !rounded-xl'>
+      <div className={'flex flex-col relative'}>
+        <div className='absolute right-0 -top-0.5 flex items-center h-6'>
+          <div className='flex justify-center items-center w-6 h-6 cursor-pointer' onClick={handleCancel}>
+            <XClose className='w-4 h-4 text-gray-500' />
+          </div>
+        </div>
+        <div className='mb-[14px]'>
+          <span className='inline-flex items-center px-1.5 h-5 border border-gray-200 rounded-md'>
+            <Hash02 className='mr-0.5 w-3 h-3 text-gray-400' />
+            <span className='text-[11px] font-medium text-gray-500 italic'>
+              {
+                docForm === 'qa_model'
+                  ? t('datasetDocuments.segment.newQaSegment')
+                  : t('datasetDocuments.segment.newTextSegment')
+              }
+            </span>
+          </span>
+        </div>
+        <div className='mb-4 py-1.5 h-[420px] overflow-auto'>{renderContent()}</div>
+        <div className='mb-2 text-xs font-medium text-gray-500'>{t('datasetDocuments.segment.keywords')}</div>
+        <div className='mb-8'></div>
+        <div className='flex justify-end'>
+          <Button
+            className='mr-2 !h-9 !px-4 !py-2 text-sm font-medium text-gray-700 !rounded-lg'
+            onClick={handleCancel}>
+            {t('common.operation.cancel')}
+          </Button>
+          <Button
+            type='primary'
+            className='!h-9 !px-4 !py-2 text-sm font-medium !rounded-lg'
+            onClick={handleSave}>
+            {t('common.operation.save')}
+          </Button>
+        </div>
+      </div>
+    </Modal>
+  )
+})
+
+export default NewSegmentModal

web/app/components/datasets/documents/list.tsx

@@ -27,6 +27,7 @@ import NotionIcon from '@/app/components/base/notion-icon'
 import ProgressBar from '@/app/components/base/progress-bar'
 import { DataSourceType, type DocumentDisplayStatus, type SimpleDocumentDetail } from '@/models/datasets'
 import type { CommonResponse } from '@/models/common'
+import { FilePlus02 } from '@/app/components/base/icons/src/vender/line/files'
 
 export const SettingsIcon: FC<{ className?: string }> = ({ className }) => {
   return <svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg" className={className ?? ''}>
@@ -94,12 +95,14 @@ export const OperationAction: FC<{
     archived: boolean
     id: string
     data_source_type: string
+    doc_form: string
   }
   datasetId: string
   onUpdate: (operationName?: string) => void
   scene?: 'list' | 'detail'
   className?: string
-}> = ({ datasetId, detail, onUpdate, scene = 'list', className = '' }) => {
+  showNewSegmentModal?: () => void
+}> = ({ datasetId, detail, onUpdate, scene = 'list', className = '', showNewSegmentModal }) => {
   const { id, enabled = false, archived = false, data_source_type } = detail || {}
   const [showModal, setShowModal] = useState(false)
   const { notify } = useContext(ToastContext)
@@ -185,6 +188,14 @@ export const OperationAction: FC<{
                 <SettingsIcon />
                 <span className={s.actionName}>{t('datasetDocuments.list.action.settings')}</span>
               </div>
+              {
+                !isListScene && (
+                  <div className={s.actionItem} onClick={showNewSegmentModal}>
+                    <FilePlus02 className='w-4 h-4 text-gray-500' />
+                    <span className={s.actionName}>{t('datasetDocuments.list.action.add')}</span>
+                  </div>
+                )
+              }
               {
                 data_source_type === 'notion_import' && (
                   <div className={s.actionItem} onClick={() => onOperate('sync')}>
@@ -339,7 +350,7 @@ const DocumentList: FC<IDocumentListProps> = ({ documents = [], datasetId, onUpd
               <td>
                 <OperationAction
                   datasetId={datasetId}
-                  detail={pick(doc, ['enabled', 'archived', 'id', 'data_source_type'])}
+                  detail={pick(doc, ['enabled', 'archived', 'id', 'data_source_type', 'doc_form'])}
                   onUpdate={onUpdate}
                 />
               </td>

web/app/components/datasets/hit-testing/hit-detail.tsx

-import React, { FC } from "react";
-import cn from "classnames";
-import { SegmentDetailModel } from "@/models/datasets";
-import { useTranslation } from "react-i18next";
-import Divider from "@/app/components/base/divider";
-import { SegmentIndexTag } from "../documents/detail/completed";
-import s from "../documents/detail/completed/style.module.css";
-import ReactECharts from "echarts-for-react";
+import type { FC } from 'react'
+import React from 'react'
+import cn from 'classnames'
+import { useTranslation } from 'react-i18next'
+import ReactECharts from 'echarts-for-react'
+import { SegmentIndexTag } from '../documents/detail/completed'
+import s from '../documents/detail/completed/style.module.css'
+import type { SegmentDetailModel } from '@/models/datasets'
+import Divider from '@/app/components/base/divider'
 
 type IScatterChartProps = {
   data: Array<number[]>
@@ -19,8 +20,8 @@ const ScatterChart: FC<IScatterChartProps> = ({ data, curr }) => {
     tooltip: {
       trigger: 'item',
       axisPointer: {
-        type: 'cross'
-      }
+        type: 'cross',
+      },
     },
     series: [
       {
@@ -32,49 +33,64 @@ const ScatterChart: FC<IScatterChartProps> = ({ data, curr }) => {
         type: 'scatter',
         symbolSize: 5,
         data,
-      }
-    ]
-  };
+      },
+    ],
+  }
   return (
     <ReactECharts option={option} style={{ height: 380, width: 430 }} />
   )
 }
 
 type IHitDetailProps = {
-  segInfo?: Partial<SegmentDetailModel> & { id: string };
-  vectorInfo?: { curr: Array<number[]>; points: Array<number[]> };
-};
+  segInfo?: Partial<SegmentDetailModel> & { id: string }
+  vectorInfo?: { curr: Array<number[]>; points: Array<number[]> }
+}
 
 const HitDetail: FC<IHitDetailProps> = ({ segInfo, vectorInfo }) => {
-  const { t } = useTranslation();
+  const { t } = useTranslation()
+
+  const renderContent = () => {
+    if (segInfo?.answer) {
+      return (
+        <>
+          <div className='mt-2 mb-1 text-xs font-medium text-gray-500'>QUESTION</div>
+          <div className='mb-4 text-md text-gray-800'>{segInfo.content}</div>
+          <div className='mb-1 text-xs font-medium text-gray-500'>ANSWER</div>
+          <div className='text-md text-gray-800'>{segInfo.answer}</div>
+        </>
+      )
+    }
+
+    return segInfo?.content
+  }
 
   return (
-    <div className={"flex flex-row"}>
+    <div className={'flex flex-row'}>
       <div className="flex-1 bg-gray-25 p-6">
         <div className="flex items-center">
           <SegmentIndexTag
-            positionId={segInfo?.position || ""}
+            positionId={segInfo?.position || ''}
             className="w-fit mr-6"
           />
           <div className={cn(s.commonIcon, s.typeSquareIcon)} />
-          <span className={cn("mr-6", s.numberInfo)}>
-            {segInfo?.word_count} {t("datasetDocuments.segment.characters")}
+          <span className={cn('mr-6', s.numberInfo)}>
+            {segInfo?.word_count} {t('datasetDocuments.segment.characters')}
           </span>
           <div className={cn(s.commonIcon, s.targetIcon)} />
           <span className={s.numberInfo}>
-            {segInfo?.hit_count} {t("datasetDocuments.segment.hitCount")}
+            {segInfo?.hit_count} {t('datasetDocuments.segment.hitCount')}
           </span>
         </div>
         <Divider />
-        <div className={s.segModalContent}>{segInfo?.content}</div>
+        <div className={s.segModalContent}>{renderContent()}</div>
         <div className={s.keywordTitle}>
-          {t("datasetDocuments.segment.keywords")}
+          {t('datasetDocuments.segment.keywords')}
         </div>
         <div className={s.keywordWrapper}>
           {!segInfo?.keywords?.length
-            ? "-"
+            ? '-'
             : segInfo?.keywords?.map((word: any) => {
-              return <div className={s.keyword}>{word}</div>;
+              return <div className={s.keyword}>{word}</div>
             })}
         </div>
       </div>
@@ -82,18 +98,18 @@ const HitDetail: FC<IHitDetailProps> = ({ segInfo, vectorInfo }) => {
         <div className="flex items-center">
           <div className={cn(s.commonIcon, s.bezierCurveIcon)} />
           <span className={s.numberInfo}>
-            {t("datasetDocuments.segment.vectorHash")}
+            {t('datasetDocuments.segment.vectorHash')}
           </span>
         </div>
         <div
-          className={cn(s.numberInfo, "w-[400px] truncate text-gray-700 mt-1")}
+          className={cn(s.numberInfo, 'w-[400px] truncate text-gray-700 mt-1')}
         >
           {segInfo?.index_node_hash}
         </div>
         <ScatterChart data={vectorInfo?.points || []} curr={vectorInfo?.curr || []} />
       </div>
     </div>
-  );
-};
+  )
+}
 
-export default HitDetail;
+export default HitDetail

web/i18n/lang/dataset-creation.en.ts

@@ -73,6 +73,8 @@ const translation = {
     click: 'Go to settings',
     economical: 'Economical',
     economicalTip: 'Use offline vector engines, keyword indexes, etc. to reduce accuracy without spending tokens',
+    QATitle: 'Segmenting in Question & Answer format',
+    QATip: 'Enable this option will consume more tokens',
     emstimateCost: 'Estimation',
     emstimateSegment: 'Estimated segments',
     segmentCount: 'segments',
@@ -92,6 +94,9 @@ const translation = {
     sideTipP3: 'Cleaning removes unnecessary characters and formats, making datasets cleaner and easier to parse.',
     sideTipP4: 'Proper segmentation and cleaning improve model performance, providing more accurate and valuable results.',
     previewTitle: 'Preview',
+    previewButton: 'Switching to Q&A format',
+    previewSwitchTipStart: 'The current segment preview is in text format, switching to a question-and-answer format preview will',
+    previewSwitchTipEnd: ' consume additional tokens',
     characters: 'characters',
     indexSettedTip: 'To change the index method, please go to the ',
     datasetSettingLink: 'dataset settings.',

web/i18n/lang/dataset-creation.zh.ts

@@ -73,6 +73,8 @@ const translation = {
     click: '前往设置',
     economical: '经济',
     economicalTip: '使用离线的向量引擎、关键词索引等方式，降低了准确度但无需花费 Token',
+    QATitle: '采用 Q&A 分段模式',
+    QATip: '开启后将会消耗额外的 token',
     emstimateCost: '执行嵌入预估消耗',
     emstimateSegment: '预估分段数',
     segmentCount: '段',
@@ -92,6 +94,9 @@ const translation = {
     sideTipP3: '清洗则是对文本进行预处理，删除不必要的字符、符号或格式，使数据集更加干净、整洁，便于模型解析。',
     sideTipP4: '通过对数据集进行适当的分段和清洗，可以提高模型在实际应用中的表现，从而为用户提供更准确、更有价值的结果。',
     previewTitle: '分段预览',
+    previewButton: '切换至 Q&A 形式',
+    previewSwitchTipStart: '当前分段预览是文本模式，切换到 Q&A 模式将会',
+    previewSwitchTipEnd: '消耗额外的 token',
     characters: '字符',
     indexSettedTip: '要更改索引方法，请转到',
     datasetSettingLink: '数据集设置。',

web/i18n/lang/dataset-documents.en.ts

@@ -17,6 +17,7 @@ const translation = {
     action: {
       uploadFile: 'Upload new file',
       settings: 'Segment settings',
+      add: 'Add new segment',
       archive: 'Archive',
       delete: 'Delete',
       enableWarning: 'Archived file cannot be enabled',
@@ -310,6 +311,14 @@ const translation = {
     characters: 'characters',
     hitCount: 'hit count',
     vectorHash: 'Vector hash: ',
+    questionPlaceholder: 'add question here',
+    questionEmpty: 'Question can not be empty',
+    answerPlaceholder: 'add answer here',
+    answerEmpty: 'Answer can not be empty',
+    contentPlaceholder: 'add content here',
+    contentEmpty: 'Content can not be empty',
+    newTextSegment: 'New Text Segment',
+    newQaSegment: 'New Q&A Segment',
   },
 }
 

web/i18n/lang/dataset-documents.zh.ts

@@ -17,6 +17,7 @@ const translation = {
     action: {
       uploadFile: '上传新文件',
       settings: '分段设置',
+      add: '添加新分段',
       archive: '归档',
       delete: '删除',
       enableWarning: '归档的文件无法启用',
@@ -309,6 +310,14 @@ const translation = {
     characters: '字符',
     hitCount: '命中次数',
     vectorHash: '向量哈希：',
+    questionPlaceholder: '在这里添加问题',
+    questionEmpty: '问题不能为空',
+    answerPlaceholder: '在这里添加答案',
+    answerEmpty: '答案不能为空',
+    contentPlaceholder: '在这里添加内容',
+    contentEmpty: '内容不能为空',
+    newTextSegment: '新文本分段',
+    newQaSegment: '新问答分段',
   },
 }
 

web/models/datasets.ts

@@ -42,12 +42,18 @@ export type DataSetListResponse = {
   total: number
 }
 
+export type QA = {
+  question: string
+  answer: string
+}
+
 export type IndexingEstimateResponse = {
   tokens: number
   total_price: number
   currency: string
   total_segments: number
   preview: string[]
+  qa_preview?: QA[]
 }
 
 export type FileIndexingEstimateResponse = {
@@ -148,6 +154,7 @@ export type InitialDocumentDetail = {
   display_status: DocumentDisplayStatus
   completed_segments?: number
   total_segments?: number
+  doc_form: 'text_model' | 'qa_model'
 }
 
 export type SimpleDocumentDetail = InitialDocumentDetail & {
@@ -171,6 +178,7 @@ export type DocumentListResponse = {
 export type CreateDocumentReq = {
   original_document_id?: string
   indexing_technique?: string
+  doc_form: 'text_model' | 'qa_model'
   data_source: DataSource
   process_rule: ProcessRule
 }
@@ -293,6 +301,7 @@ export type SegmentDetailModel = {
   completed_at: number
   error: string | null
   stopped_at: number
+  answer?: string
 }
 
 export type SegmentsResponse = {
@@ -370,3 +379,8 @@ export type RelatedAppResponse = {
   data: Array<RelatedApp>
   total: number
 }
+
+export type SegmentUpdator = {
+  content: string
+  answer?: string
+}

web/service/datasets.ts

 import type { Fetcher } from 'swr'
 import qs from 'qs'
 import { del, get, patch, post, put } from './base'
-import type { CreateDocumentReq, DataSet, DataSetListResponse, DocumentDetailResponse, DocumentListResponse, FileIndexingEstimateResponse, HitTestingRecordsResponse, HitTestingResponse, IndexingEstimateResponse, IndexingStatusBatchResponse, IndexingStatusResponse, ProcessRuleResponse, RelatedAppResponse, SegmentsQuery, SegmentsResponse, createDocumentResponse } from '@/models/datasets'
+import type {
+  CreateDocumentReq,
+  DataSet,
+  DataSetListResponse,
+  DocumentDetailResponse,
+  DocumentListResponse,
+  FileIndexingEstimateResponse,
+  HitTestingRecordsResponse,
+  HitTestingResponse,
+  IndexingEstimateResponse,
+  IndexingStatusBatchResponse,
+  IndexingStatusResponse,
+  ProcessRuleResponse,
+  RelatedAppResponse,
+  SegmentDetailModel,
+  SegmentUpdator,
+  SegmentsQuery,
+  SegmentsResponse,
+  createDocumentResponse,
+} from '@/models/datasets'
 import type { CommonResponse, DataSourceNotionWorkspace } from '@/models/common'
 
 // apis for documents in a dataset
@@ -137,6 +156,14 @@ export const disableSegment: Fetcher<CommonResponse, { datasetId: string; segmen
   return patch(`/datasets/${datasetId}/segments/${segmentId}/disable`) as Promise<CommonResponse>
 }
 
+export const updateSegment: Fetcher<{ data: SegmentDetailModel; doc_form: string }, { datasetId: string; documentId: string; segmentId: string; body: SegmentUpdator }> = ({ datasetId, documentId, segmentId, body }) => {
+  return patch(`/datasets/${datasetId}/documents/${documentId}/segments/${segmentId}`, { body }) as Promise<{ data: SegmentDetailModel; doc_form: string }>
+}
+
+export const addSegment: Fetcher<{ data: SegmentDetailModel; doc_form: string }, { datasetId: string; documentId: string; body: SegmentUpdator }> = ({ datasetId, documentId, body }) => {
+  return post(`/datasets/${datasetId}/documents/${documentId}/segment`, { body }) as Promise<{ data: SegmentDetailModel; doc_form: string }>
+}
+
 // hit testing
 export const hitTesting: Fetcher<HitTestingResponse, { datasetId: string; queryText: string }> = ({ datasetId, queryText }) => {
   return post(`/datasets/${datasetId}/hit-testing`, { body: { query: queryText } }) as Promise<HitTestingResponse>