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Unverified Commit cf93d8d6 authored by KVOJJJin's avatar KVOJJJin Committed by GitHub
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Feat: Q&A format segmentation support (#668) 

Co-authored-by: 's avatarjyong <718720800@qq.com>
Co-authored-by: 's avatarStyleZhang <jasonapring2015@outlook.com>
parent aae2fb8a
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Showing with 2039 additions and 275 deletions
api/controllers/console/datasets/datasets.py
View file @ cf93d8d6
......@@ -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
View file @ cf93d8d6
......@@ -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
View file @ cf93d8d6
......@@ -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
View file @ cf93d8d6
......@@ -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
View file @ cf93d8d6
......@@ -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
View file @ cf93d8d6
......@@ -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
View file @ cf93d8d6
......@@ -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
View file @ cf93d8d6
......@@ -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 0 → 100644
View file @ cf93d8d6
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
View file @ cf93d8d6
This diff is collapsed.
api/core/prompt/prompts.py
View file @ cf93d8d6
......@@ -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 0 → 100644
View file @ cf93d8d6
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
View file @ cf93d8d6
......@@ -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 0 → 100644
View file @ cf93d8d6
"""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
View file @ cf93d8d6
......@@ -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
View file @ cf93d8d6
......@@ -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
View file @ cf93d8d6
......@@ -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 0 → 100644
View file @ cf93d8d6
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
View file @ cf93d8d6
......@@ -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 0 → 100644
View file @ cf93d8d6
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 0 → 100644
View file @ cf93d8d6
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 0 → 100644
View file @ cf93d8d6
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 0 → 100644
View file @ cf93d8d6
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 0 → 100644
View file @ cf93d8d6
<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 0 → 100644
View file @ cf93d8d6
<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 0 → 100644
View file @ cf93d8d6
<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 0 → 100644
View file @ cf93d8d6
{
"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 0 → 100644
View file @ cf93d8d6
// 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
View file @ cf93d8d6
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 0 → 100644
View file @ cf93d8d6
{
"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 0 → 100644
View file @ cf93d8d6
// 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 0 → 100644
View file @ cf93d8d6
{
"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 0 → 100644
View file @ cf93d8d6
// 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
View file @ cf93d8d6
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
View file @ cf93d8d6
......@@ -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/preview-item/index.tsx
View file @ cf93d8d6
'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
View file @ cf93d8d6
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/style.module.css
View file @ cf93d8d6
......@@ -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
View file @ cf93d8d6
......@@ -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
View file @ cf93d8d6
......@@ -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 0 → 100644
View file @ cf93d8d6
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
View file @ cf93d8d6
......@@ -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
View file @ cf93d8d6
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
View file @ cf93d8d6
......@@ -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
View file @ cf93d8d6
......@@ -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
View file @ cf93d8d6
......@@ -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
View file @ cf93d8d6
......@@ -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
View file @ cf93d8d6
......@@ -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
View file @ cf93d8d6
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>
......
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