bump version to 0.5.3 (#2306)

Co-authored-by: luowei <glpat-EjySCyNjWiLqAED-YmwM>
Co-authored-by: crazywoola <427733928@qq.com>
Co-authored-by: crazywoola <100913391+crazywoola@users.noreply.github.com>

api/app.py

@@ -30,7 +30,7 @@ from flask import Flask, Response, request
 from flask_cors import CORS
 from libs.passport import PassportService
 # DO NOT REMOVE BELOW
-from models import account, dataset, model, source, task, tool, web, tools
+from models import account, dataset, model, source, task, tool, tools, web
 from services.account_service import AccountService
 
 # DO NOT REMOVE ABOVE

api/commands.py

@@ -1,32 +1,20 @@
 import base64
-import datetime
 import json
-import math
-import random
 import secrets
-import string
-import threading
-import time
-import uuid
 
 import click
-import qdrant_client
-from constants.languages import user_input_form_template
 from core.embedding.cached_embedding import CacheEmbedding
-from core.index.index import IndexBuilder
 from core.model_manager import ModelManager
 from core.model_runtime.entities.model_entities import ModelType
 from extensions.ext_database import db
-from flask import Flask, current_app
+from flask import current_app
 from libs.helper import email as email_validate
 from libs.password import hash_password, password_pattern, valid_password
 from libs.rsa import generate_key_pair
-from models.account import InvitationCode, Tenant, TenantAccountJoin
-from models.dataset import Dataset, DatasetCollectionBinding, DatasetQuery, Document
-from models.model import Account, App, AppModelConfig, Message, MessageAnnotation, InstalledApp
-from models.provider import Provider, ProviderModel, ProviderQuotaType, ProviderType
-from qdrant_client.http.models import TextIndexParams, TextIndexType, TokenizerType
-from tqdm import tqdm
+from models.account import Tenant
+from models.dataset import Dataset
+from models.model import Account
+from models.provider import Provider, ProviderModel
 from werkzeug.exceptions import NotFound
 
 
@@ -35,15 +23,22 @@ from werkzeug.exceptions import NotFound
 @click.option('--new-password', prompt=True, help='the new password.')
 @click.option('--password-confirm', prompt=True, help='the new password confirm.')
 def reset_password(email, new_password, password_confirm):
+    """
+    Reset password of owner account
+    Only available in SELF_HOSTED mode
+    """
     if str(new_password).strip() != str(password_confirm).strip():
         click.echo(click.style('sorry. The two passwords do not match.', fg='red'))
         return
+
     account = db.session.query(Account). \
         filter(Account.email == email). \
         one_or_none()
+
     if not account:
         click.echo(click.style('sorry. the account: [{}] not exist .'.format(email), fg='red'))
         return
+
     try:
         valid_password(new_password)
     except:
@@ -69,15 +64,22 @@ def reset_password(email, new_password, password_confirm):
 @click.option('--new-email', prompt=True, help='the new email.')
 @click.option('--email-confirm', prompt=True, help='the new email confirm.')
 def reset_email(email, new_email, email_confirm):
+    """
+    Replace account email
+    :return:
+    """
     if str(new_email).strip() != str(email_confirm).strip():
         click.echo(click.style('Sorry, new email and confirm email do not match.', fg='red'))
         return
+
     account = db.session.query(Account). \
         filter(Account.email == email). \
         one_or_none()
+
     if not account:
         click.echo(click.style('sorry. the account: [{}] not exist .'.format(email), fg='red'))
         return
+
     try:
         email_validate(new_email)
     except:
@@ -97,6 +99,11 @@ def reset_email(email, new_email, email_confirm):
 @click.confirmation_option(prompt=click.style('Are you sure you want to reset encrypt key pair?'
                                               ' this operation cannot be rolled back!', fg='red'))
 def reset_encrypt_key_pair():
+    """
+    Reset the encrypted key pair of workspace for encrypt LLM credentials.
+    After the reset, all LLM credentials will become invalid, requiring re-entry.
+    Only support SELF_HOSTED mode.
+    """
     if current_app.config['EDITION'] != 'SELF_HOSTED':
         click.echo(click.style('Sorry, only support SELF_HOSTED mode.', fg='red'))
         return
@@ -116,201 +123,11 @@ def reset_encrypt_key_pair():
                            'the asymmetric key pair of workspace {} has been reset.'.format(tenant.id), fg='green'))
 
 
-@click.command('generate-invitation-codes', help='Generate invitation codes.')
-@click.option('--batch', help='The batch of invitation codes.')
-@click.option('--count', prompt=True, help='Invitation codes count.')
-def generate_invitation_codes(batch, count):
-    if not batch:
-        now = datetime.datetime.now()
-        batch = now.strftime('%Y%m%d%H%M%S')
-
-    if not count or int(count) <= 0:
-        click.echo(click.style('sorry. the count must be greater than 0.', fg='red'))
-        return
-
-    count = int(count)
-
-    click.echo('Start generate {} invitation codes for batch {}.'.format(count, batch))
-
-    codes = ''
-    for i in range(count):
-        code = generate_invitation_code()
-        invitation_code = InvitationCode(
-            code=code,
-            batch=batch
-        )
-        db.session.add(invitation_code)
-        click.echo(code)
-
-        codes += code + "\n"
-    db.session.commit()
-
-    filename = 'storage/invitation-codes-{}.txt'.format(batch)
-
-    with open(filename, 'w') as f:
-        f.write(codes)
-
-    click.echo(click.style(
-        'Congratulations! Generated {} invitation codes for batch {} and saved to the file \'{}\''.format(count, batch,
-                                                                                                          filename),
-        fg='green'))
-
-
-def generate_invitation_code():
-    code = generate_upper_string()
-    while db.session.query(InvitationCode).filter(InvitationCode.code == code).count() > 0:
-        code = generate_upper_string()
-
-    return code
-
-
-def generate_upper_string():
-    letters_digits = string.ascii_uppercase + string.digits
-    result = ""
-    for i in range(8):
-        result += random.choice(letters_digits)
-
-    return result
-
-
-@click.command('recreate-all-dataset-indexes', help='Recreate all dataset indexes.')
-def recreate_all_dataset_indexes():
-    click.echo(click.style('Start recreate all dataset indexes.', fg='green'))
-    recreate_count = 0
-
-    page = 1
-    while True:
-        try:
-            datasets = db.session.query(Dataset).filter(Dataset.indexing_technique == 'high_quality') \
-                .order_by(Dataset.created_at.desc()).paginate(page=page, per_page=50)
-        except NotFound:
-            break
-
-        page += 1
-        for dataset in datasets:
-            try:
-                click.echo('Recreating dataset index: {}'.format(dataset.id))
-                index = IndexBuilder.get_index(dataset, 'high_quality')
-                if index and index._is_origin():
-                    index.recreate_dataset(dataset)
-                    recreate_count += 1
-                else:
-                    click.echo('passed.')
-            except Exception as e:
-                click.echo(
-                    click.style('Recreate dataset index error: {} {}'.format(e.__class__.__name__, str(e)), fg='red'))
-                continue
-
-    click.echo(click.style('Congratulations! Recreate {} dataset indexes.'.format(recreate_count), fg='green'))
-
-
-@click.command('clean-unused-dataset-indexes', help='Clean unused dataset indexes.')
-def clean_unused_dataset_indexes():
-    click.echo(click.style('Start clean unused dataset indexes.', fg='green'))
-    clean_days = int(current_app.config.get('CLEAN_DAY_SETTING'))
-    start_at = time.perf_counter()
-    thirty_days_ago = datetime.datetime.now() - datetime.timedelta(days=clean_days)
-    page = 1
-    while True:
-        try:
-            datasets = db.session.query(Dataset).filter(Dataset.created_at < thirty_days_ago) \
-                .order_by(Dataset.created_at.desc()).paginate(page=page, per_page=50)
-        except NotFound:
-            break
-        page += 1
-        for dataset in datasets:
-            dataset_query = db.session.query(DatasetQuery).filter(
-                DatasetQuery.created_at > thirty_days_ago,
-                DatasetQuery.dataset_id == dataset.id
-            ).all()
-            if not dataset_query or len(dataset_query) == 0:
-                documents = db.session.query(Document).filter(
-                    Document.dataset_id == dataset.id,
-                    Document.indexing_status == 'completed',
-                    Document.enabled == True,
-                    Document.archived == False,
-                    Document.updated_at > thirty_days_ago
-                ).all()
-                if not documents or len(documents) == 0:
-                    try:
-                        # remove index
-                        vector_index = IndexBuilder.get_index(dataset, 'high_quality')
-                        kw_index = IndexBuilder.get_index(dataset, 'economy')
-                        # delete from vector index
-                        if vector_index:
-                            if dataset.collection_binding_id:
-                                vector_index.delete_by_group_id(dataset.id)
-                            else:
-                                if dataset.collection_binding_id:
-                                    vector_index.delete_by_group_id(dataset.id)
-                                else:
-                                    vector_index.delete()
-                        kw_index.delete()
-                        # update document
-                        update_params = {
-                            Document.enabled: False
-                        }
-
-                        Document.query.filter_by(dataset_id=dataset.id).update(update_params)
-                        db.session.commit()
-                        click.echo(click.style('Cleaned unused dataset {} from db success!'.format(dataset.id),
-                                               fg='green'))
-                    except Exception as e:
-                        click.echo(
-                            click.style('clean dataset index error: {} {}'.format(e.__class__.__name__, str(e)),
-                                        fg='red'))
-    end_at = time.perf_counter()
-    click.echo(click.style('Cleaned unused dataset from db success latency: {}'.format(end_at - start_at), fg='green'))
-
-
-@click.command('sync-anthropic-hosted-providers', help='Sync anthropic hosted providers.')
-def sync_anthropic_hosted_providers():
-    if not hosted_model_providers.anthropic:
-        click.echo(click.style('Anthropic hosted provider is not configured.', fg='red'))
-        return
-
-    click.echo(click.style('Start sync anthropic hosted providers.', fg='green'))
-    count = 0
-
-    new_quota_limit = hosted_model_providers.anthropic.quota_limit
-
-    page = 1
-    while True:
-        try:
-            providers = db.session.query(Provider).filter(
-                Provider.provider_name == 'anthropic',
-                Provider.provider_type == ProviderType.SYSTEM.value,
-                Provider.quota_type == ProviderQuotaType.TRIAL.value,
-                Provider.quota_limit != new_quota_limit
-            ).order_by(Provider.created_at.desc()).paginate(page=page, per_page=100)
-        except NotFound:
-            break
-
-        page += 1
-        for provider in providers:
-            try:
-                click.echo('Syncing tenant anthropic hosted provider: {}, origin: limit {}, used {}'
-                           .format(provider.tenant_id, provider.quota_limit, provider.quota_used))
-                original_quota_limit = provider.quota_limit
-                division = math.ceil(new_quota_limit / 1000)
-
-                provider.quota_limit = new_quota_limit if original_quota_limit == 1000 \
-                    else original_quota_limit * division
-                provider.quota_used = division * provider.quota_used
-                db.session.commit()
-
-                count += 1
-            except Exception as e:
-                click.echo(click.style(
-                    'Sync tenant anthropic hosted provider error: {} {}'.format(e.__class__.__name__, str(e)),
-                    fg='red'))
-                continue
-
-    click.echo(click.style('Congratulations! Synced {} anthropic hosted providers.'.format(count), fg='green'))
-
-
 @click.command('create-qdrant-indexes', help='Create qdrant indexes.')
 def create_qdrant_indexes():
+    """
+    Migrate other vector database datas to Qdrant.
+    """
     click.echo(click.style('Start create qdrant indexes.', fg='green'))
     create_count = 0
 
@@ -339,26 +156,7 @@ def create_qdrant_indexes():
 
                             )
                         except Exception:
-                            try:
-                                embedding_model = model_manager.get_default_model_instance(
-                                    tenant_id=dataset.tenant_id,
-                                    model_type=ModelType.TEXT_EMBEDDING,
-                                )
-                                dataset.embedding_model = embedding_model.model
-                                dataset.embedding_model_provider = embedding_model.provider
-                            except Exception:
-
-                                provider = Provider(
-                                    id='provider_id',
-                                    tenant_id=dataset.tenant_id,
-                                    provider_name='openai',
-                                    provider_type=ProviderType.SYSTEM.value,
-                                    encrypted_config=json.dumps({'openai_api_key': 'TEST'}),
-                                    is_valid=True,
-                                )
-                                model_provider = OpenAIProvider(provider=provider)
-                                embedding_model = OpenAIEmbedding(name="text-embedding-ada-002",
-                                                                  model_provider=model_provider)
+                            continue
                         embeddings = CacheEmbedding(embedding_model)
 
                         from core.index.vector_index.qdrant_vector_index import QdrantConfig, QdrantVectorIndex
@@ -393,380 +191,8 @@ def create_qdrant_indexes():
     click.echo(click.style('Congratulations! Create {} dataset indexes.'.format(create_count), fg='green'))
 
 
-@click.command('update-qdrant-indexes', help='Update qdrant indexes.')
-def update_qdrant_indexes():
-    click.echo(click.style('Start Update qdrant indexes.', fg='green'))
-    create_count = 0
-
-    page = 1
-    while True:
-        try:
-            datasets = db.session.query(Dataset).filter(Dataset.indexing_technique == 'high_quality') \
-                .order_by(Dataset.created_at.desc()).paginate(page=page, per_page=50)
-        except NotFound:
-            break
-
-        page += 1
-        for dataset in datasets:
-            if dataset.index_struct_dict:
-                if dataset.index_struct_dict['type'] != 'qdrant':
-                    try:
-                        click.echo('Update dataset qdrant index: {}'.format(dataset.id))
-                        try:
-                            embedding_model = ModelFactory.get_embedding_model(
-                                tenant_id=dataset.tenant_id,
-                                model_provider_name=dataset.embedding_model_provider,
-                                model_name=dataset.embedding_model
-                            )
-                        except Exception:
-                            provider = Provider(
-                                id='provider_id',
-                                tenant_id=dataset.tenant_id,
-                                provider_name='openai',
-                                provider_type=ProviderType.CUSTOM.value,
-                                encrypted_config=json.dumps({'openai_api_key': 'TEST'}),
-                                is_valid=True,
-                            )
-                            model_provider = OpenAIProvider(provider=provider)
-                            embedding_model = OpenAIEmbedding(name="text-embedding-ada-002",
-                                                              model_provider=model_provider)
-                        embeddings = CacheEmbedding(embedding_model)
-
-                        from core.index.vector_index.qdrant_vector_index import QdrantConfig, QdrantVectorIndex
-
-                        index = QdrantVectorIndex(
-                            dataset=dataset,
-                            config=QdrantConfig(
-                                endpoint=current_app.config.get('QDRANT_URL'),
-                                api_key=current_app.config.get('QDRANT_API_KEY'),
-                                root_path=current_app.root_path
-                            ),
-                            embeddings=embeddings
-                        )
-                        if index:
-                            index.update_qdrant_dataset(dataset)
-                            create_count += 1
-                        else:
-                            click.echo('passed.')
-                    except Exception as e:
-                        click.echo(
-                            click.style('Create dataset index error: {} {}'.format(e.__class__.__name__, str(e)),
-                                        fg='red'))
-                        continue
-
-    click.echo(click.style('Congratulations! Update {} dataset indexes.'.format(create_count), fg='green'))
-
-
-@click.command('normalization-collections', help='restore all collections in one')
-def normalization_collections():
-    click.echo(click.style('Start normalization collections.', fg='green'))
-    normalization_count = []
-    page = 1
-    while True:
-        try:
-            datasets = db.session.query(Dataset).filter(Dataset.indexing_technique == 'high_quality') \
-                .order_by(Dataset.created_at.desc()).paginate(page=page, per_page=100)
-        except NotFound:
-            break
-        datasets_result = datasets.items
-        page += 1
-        for i in range(0, len(datasets_result), 5):
-            threads = []
-            sub_datasets = datasets_result[i:i + 5]
-            for dataset in sub_datasets:
-                document_format_thread = threading.Thread(target=deal_dataset_vector, kwargs={
-                    'flask_app': current_app._get_current_object(),
-                    'dataset': dataset,
-                    'normalization_count': normalization_count
-                })
-                threads.append(document_format_thread)
-                document_format_thread.start()
-            for thread in threads:
-                thread.join()
-
-    click.echo(click.style('Congratulations! restore {} dataset indexes.'.format(len(normalization_count)), fg='green'))
-
-
-@click.command('add-qdrant-full-text-index', help='add qdrant full text index')
-def add_qdrant_full_text_index():
-    click.echo(click.style('Start add full text index.', fg='green'))
-    binds = db.session.query(DatasetCollectionBinding).all()
-    if binds and current_app.config['VECTOR_STORE'] == 'qdrant':
-        qdrant_url = current_app.config['QDRANT_URL']
-        qdrant_api_key = current_app.config['QDRANT_API_KEY']
-        client = qdrant_client.QdrantClient(
-            qdrant_url,
-            api_key=qdrant_api_key,  # For Qdrant Cloud, None for local instance
-        )
-        for bind in binds:
-            try:
-                text_index_params = TextIndexParams(
-                    type=TextIndexType.TEXT,
-                    tokenizer=TokenizerType.MULTILINGUAL,
-                    min_token_len=2,
-                    max_token_len=20,
-                    lowercase=True
-                )
-                client.create_payload_index(bind.collection_name, 'page_content',
-                                            field_schema=text_index_params)
-            except Exception as e:
-                click.echo(
-                    click.style('Create full text index error: {} {}'.format(e.__class__.__name__, str(e)),
-                                fg='red'))
-            click.echo(
-                click.style(
-                    'Congratulations! add collection {} full text index successful.'.format(bind.collection_name),
-                    fg='green'))
-
-
-def deal_dataset_vector(flask_app: Flask, dataset: Dataset, normalization_count: list):
-    with flask_app.app_context():
-        try:
-            click.echo('restore dataset index: {}'.format(dataset.id))
-            try:
-                embedding_model = ModelFactory.get_embedding_model(
-                    tenant_id=dataset.tenant_id,
-                    model_provider_name=dataset.embedding_model_provider,
-                    model_name=dataset.embedding_model
-                )
-            except Exception:
-                provider = Provider(
-                    id='provider_id',
-                    tenant_id=dataset.tenant_id,
-                    provider_name='openai',
-                    provider_type=ProviderType.CUSTOM.value,
-                    encrypted_config=json.dumps({'openai_api_key': 'TEST'}),
-                    is_valid=True,
-                )
-                model_provider = OpenAIProvider(provider=provider)
-                embedding_model = OpenAIEmbedding(name="text-embedding-ada-002",
-                                                  model_provider=model_provider)
-            embeddings = CacheEmbedding(embedding_model)
-            dataset_collection_binding = db.session.query(DatasetCollectionBinding). \
-                filter(DatasetCollectionBinding.provider_name == embedding_model.model_provider.provider_name,
-                       DatasetCollectionBinding.model_name == embedding_model.name). \
-                order_by(DatasetCollectionBinding.created_at). \
-                first()
-
-            if not dataset_collection_binding:
-                dataset_collection_binding = DatasetCollectionBinding(
-                    provider_name=embedding_model.model_provider.provider_name,
-                    model_name=embedding_model.name,
-                    collection_name="Vector_index_" + str(uuid.uuid4()).replace("-", "_") + '_Node'
-                )
-                db.session.add(dataset_collection_binding)
-                db.session.commit()
-
-            from core.index.vector_index.qdrant_vector_index import QdrantConfig, QdrantVectorIndex
-
-            index = QdrantVectorIndex(
-                dataset=dataset,
-                config=QdrantConfig(
-                    endpoint=current_app.config.get('QDRANT_URL'),
-                    api_key=current_app.config.get('QDRANT_API_KEY'),
-                    root_path=current_app.root_path
-                ),
-                embeddings=embeddings
-            )
-            if index:
-                # index.delete_by_group_id(dataset.id)
-                index.restore_dataset_in_one(dataset, dataset_collection_binding)
-            else:
-                click.echo('passed.')
-            normalization_count.append(1)
-        except Exception as e:
-            click.echo(
-                click.style('Create dataset index error: {} {}'.format(e.__class__.__name__, str(e)),
-                            fg='red'))
-
-
-@click.command('update_app_model_configs', help='Migrate data to support paragraph variable.')
-@click.option("--batch-size", default=500, help="Number of records to migrate in each batch.")
-def update_app_model_configs(batch_size):
-    pre_prompt_template = '{{default_input}}'
-
-    click.secho("Start migrate old data that the text generator can support paragraph variable.", fg='green')
-
-    total_records = db.session.query(AppModelConfig) \
-        .join(App, App.app_model_config_id == AppModelConfig.id) \
-        .filter(App.mode == 'completion') \
-        .count()
-
-    if total_records == 0:
-        click.secho("No data to migrate.", fg='green')
-        return
-
-    num_batches = (total_records + batch_size - 1) // batch_size
-
-    with tqdm(total=total_records, desc="Migrating Data") as pbar:
-        for i in range(num_batches):
-            offset = i * batch_size
-            limit = min(batch_size, total_records - offset)
-
-            click.secho(f"Fetching batch {i + 1}/{num_batches} from source database...", fg='green')
-
-            data_batch = db.session.query(AppModelConfig) \
-                .join(App, App.app_model_config_id == AppModelConfig.id) \
-                .filter(App.mode == 'completion') \
-                .order_by(App.created_at) \
-                .offset(offset).limit(limit).all()
-
-            if not data_batch:
-                click.secho("No more data to migrate.", fg='green')
-                break
-
-            try:
-                click.secho(f"Migrating {len(data_batch)} records...", fg='green')
-                for data in data_batch:
-                    # click.secho(f"Migrating data {data.id}, pre_prompt: {data.pre_prompt}, user_input_form: {data.user_input_form}", fg='green')
-
-                    if data.pre_prompt is None:
-                        data.pre_prompt = pre_prompt_template
-                    else:
-                        if pre_prompt_template in data.pre_prompt:
-                            continue
-                        data.pre_prompt += pre_prompt_template
-
-                    app_data = db.session.query(App) \
-                        .filter(App.id == data.app_id) \
-                        .one()
-
-                    account_data = db.session.query(Account) \
-                        .join(TenantAccountJoin, Account.id == TenantAccountJoin.account_id) \
-                        .filter(TenantAccountJoin.role == 'owner') \
-                        .filter(TenantAccountJoin.tenant_id == app_data.tenant_id) \
-                        .one_or_none()
-
-                    if not account_data:
-                        continue
-
-                    if data.user_input_form is None or data.user_input_form == 'null':
-                        data.user_input_form = json.dumps(user_input_form_template[account_data.interface_language])
-                    else:
-                        raw_json_data = json.loads(data.user_input_form)
-                        raw_json_data.append(user_input_form_template[account_data.interface_language][0])
-                        data.user_input_form = json.dumps(raw_json_data)
-
-                    # click.secho(f"Updated data {data.id}, pre_prompt: {data.pre_prompt}, user_input_form: {data.user_input_form}", fg='green')
-
-                db.session.commit()
-
-            except Exception as e:
-                click.secho(f"Error while migrating data: {e}, app_id: {data.app_id}, app_model_config_id: {data.id}",
-                            fg='red')
-                continue
-
-            click.secho(f"Successfully migrated batch {i + 1}/{num_batches}.", fg='green')
-
-            pbar.update(len(data_batch))
-
-
-@click.command('migrate_default_input_to_dataset_query_variable')
-@click.option("--batch-size", default=500, help="Number of records to migrate in each batch.")
-def migrate_default_input_to_dataset_query_variable(batch_size):
-    click.secho("Starting...", fg='green')
-
-    total_records = db.session.query(AppModelConfig) \
-        .join(App, App.app_model_config_id == AppModelConfig.id) \
-        .filter(App.mode == 'completion') \
-        .filter(AppModelConfig.dataset_query_variable == None) \
-        .count()
-
-    if total_records == 0:
-        click.secho("No data to migrate.", fg='green')
-        return
-
-    num_batches = (total_records + batch_size - 1) // batch_size
-
-    with tqdm(total=total_records, desc="Migrating Data") as pbar:
-        for i in range(num_batches):
-            offset = i * batch_size
-            limit = min(batch_size, total_records - offset)
-
-            click.secho(f"Fetching batch {i + 1}/{num_batches} from source database...", fg='green')
-
-            data_batch = db.session.query(AppModelConfig) \
-                .join(App, App.app_model_config_id == AppModelConfig.id) \
-                .filter(App.mode == 'completion') \
-                .filter(AppModelConfig.dataset_query_variable == None) \
-                .order_by(App.created_at) \
-                .offset(offset).limit(limit).all()
-
-            if not data_batch:
-                click.secho("No more data to migrate.", fg='green')
-                break
-
-            try:
-                click.secho(f"Migrating {len(data_batch)} records...", fg='green')
-                for data in data_batch:
-                    config = AppModelConfig.to_dict(data)
-
-                    tools = config["agent_mode"]["tools"]
-                    dataset_exists = "dataset" in str(tools)
-                    if not dataset_exists:
-                        continue
-
-                    user_input_form = config.get("user_input_form", [])
-                    for form in user_input_form:
-                        paragraph = form.get('paragraph')
-                        if paragraph \
-                                and paragraph.get('variable') == 'query':
-                            data.dataset_query_variable = 'query'
-                            break
-
-                        if paragraph \
-                                and paragraph.get('variable') == 'default_input':
-                            data.dataset_query_variable = 'default_input'
-                            break
-
-                db.session.commit()
-
-            except Exception as e:
-                click.secho(f"Error while migrating data: {e}, app_id: {data.app_id}, app_model_config_id: {data.id}",
-                            fg='red')
-                continue
-
-            click.secho(f"Successfully migrated batch {i + 1}/{num_batches}.", fg='green')
-
-            pbar.update(len(data_batch))
-
-
-@click.command('add-annotation-question-field-value', help='add annotation question value')
-def add_annotation_question_field_value():
-    click.echo(click.style('Start add annotation question value.', fg='green'))
-    message_annotations = db.session.query(MessageAnnotation).all()
-    message_annotation_deal_count = 0
-    if message_annotations:
-        for message_annotation in message_annotations:
-            try:
-                if message_annotation.message_id and not message_annotation.question:
-                    message = db.session.query(Message).filter(
-                        Message.id == message_annotation.message_id
-                    ).first()
-                    message_annotation.question = message.query
-                    db.session.add(message_annotation)
-                    db.session.commit()
-                    message_annotation_deal_count += 1
-            except Exception as e:
-                click.echo(
-                    click.style('Add annotation question value error: {} {}'.format(e.__class__.__name__, str(e)),
-                                fg='red'))
-            click.echo(
-                click.style(f'Congratulations! add annotation question value successful. Deal count {message_annotation_deal_count}', fg='green'))
-
-
 def register_commands(app):
     app.cli.add_command(reset_password)
     app.cli.add_command(reset_email)
-    app.cli.add_command(generate_invitation_codes)
     app.cli.add_command(reset_encrypt_key_pair)
-    app.cli.add_command(recreate_all_dataset_indexes)
-    app.cli.add_command(sync_anthropic_hosted_providers)
-    app.cli.add_command(clean_unused_dataset_indexes)
     app.cli.add_command(create_qdrant_indexes)
-    app.cli.add_command(update_qdrant_indexes)
-    app.cli.add_command(update_app_model_configs)
-    app.cli.add_command(normalization_collections)
-    app.cli.add_command(migrate_default_input_to_dataset_query_variable)
-    app.cli.add_command(add_qdrant_full_text_index)
-    app.cli.add_command(add_annotation_question_field_value)

api/config.py

@@ -40,17 +40,11 @@ DEFAULTS = {
     'HOSTED_OPENAI_QUOTA_LIMIT': 200,
     'HOSTED_OPENAI_TRIAL_ENABLED': 'False',
     'HOSTED_OPENAI_PAID_ENABLED': 'False',
-    'HOSTED_OPENAI_PAID_INCREASE_QUOTA': 1,
-    'HOSTED_OPENAI_PAID_MIN_QUANTITY': 1,
-    'HOSTED_OPENAI_PAID_MAX_QUANTITY': 1,
     'HOSTED_AZURE_OPENAI_ENABLED': 'False',
     'HOSTED_AZURE_OPENAI_QUOTA_LIMIT': 200,
     'HOSTED_ANTHROPIC_QUOTA_LIMIT': 600000,
     'HOSTED_ANTHROPIC_TRIAL_ENABLED': 'False',
     'HOSTED_ANTHROPIC_PAID_ENABLED': 'False',
-    'HOSTED_ANTHROPIC_PAID_INCREASE_QUOTA': 1,
-    'HOSTED_ANTHROPIC_PAID_MIN_QUANTITY': 1,
-    'HOSTED_ANTHROPIC_PAID_MAX_QUANTITY': 1,
     'HOSTED_MODERATION_ENABLED': 'False',
     'HOSTED_MODERATION_PROVIDERS': '',
     'CLEAN_DAY_SETTING': 30,
@@ -93,7 +87,7 @@ class Config:
         # ------------------------
         # General Configurations.
         # ------------------------
-        self.CURRENT_VERSION = "0.5.2"
+        self.CURRENT_VERSION = "0.5.3"
         self.COMMIT_SHA = get_env('COMMIT_SHA')
         self.EDITION = "SELF_HOSTED"
         self.DEPLOY_ENV = get_env('DEPLOY_ENV')
@@ -262,10 +256,6 @@ class Config:
         self.HOSTED_OPENAI_TRIAL_ENABLED = get_bool_env('HOSTED_OPENAI_TRIAL_ENABLED')
         self.HOSTED_OPENAI_QUOTA_LIMIT = int(get_env('HOSTED_OPENAI_QUOTA_LIMIT'))
         self.HOSTED_OPENAI_PAID_ENABLED = get_bool_env('HOSTED_OPENAI_PAID_ENABLED')
-        self.HOSTED_OPENAI_PAID_STRIPE_PRICE_ID = get_env('HOSTED_OPENAI_PAID_STRIPE_PRICE_ID')
-        self.HOSTED_OPENAI_PAID_INCREASE_QUOTA = int(get_env('HOSTED_OPENAI_PAID_INCREASE_QUOTA'))
-        self.HOSTED_OPENAI_PAID_MIN_QUANTITY = int(get_env('HOSTED_OPENAI_PAID_MIN_QUANTITY'))
-        self.HOSTED_OPENAI_PAID_MAX_QUANTITY = int(get_env('HOSTED_OPENAI_PAID_MAX_QUANTITY'))
 
         self.HOSTED_AZURE_OPENAI_ENABLED = get_bool_env('HOSTED_AZURE_OPENAI_ENABLED')
         self.HOSTED_AZURE_OPENAI_API_KEY = get_env('HOSTED_AZURE_OPENAI_API_KEY')
@@ -277,10 +267,6 @@ class Config:
         self.HOSTED_ANTHROPIC_TRIAL_ENABLED = get_bool_env('HOSTED_ANTHROPIC_TRIAL_ENABLED')
         self.HOSTED_ANTHROPIC_QUOTA_LIMIT = int(get_env('HOSTED_ANTHROPIC_QUOTA_LIMIT'))
         self.HOSTED_ANTHROPIC_PAID_ENABLED = get_bool_env('HOSTED_ANTHROPIC_PAID_ENABLED')
-        self.HOSTED_ANTHROPIC_PAID_STRIPE_PRICE_ID = get_env('HOSTED_ANTHROPIC_PAID_STRIPE_PRICE_ID')
-        self.HOSTED_ANTHROPIC_PAID_INCREASE_QUOTA = int(get_env('HOSTED_ANTHROPIC_PAID_INCREASE_QUOTA'))
-        self.HOSTED_ANTHROPIC_PAID_MIN_QUANTITY = int(get_env('HOSTED_ANTHROPIC_PAID_MIN_QUANTITY'))
-        self.HOSTED_ANTHROPIC_PAID_MAX_QUANTITY = int(get_env('HOSTED_ANTHROPIC_PAID_MAX_QUANTITY'))
 
         self.HOSTED_MINIMAX_ENABLED = get_bool_env('HOSTED_MINIMAX_ENABLED')
         self.HOSTED_SPARK_ENABLED = get_bool_env('HOSTED_SPARK_ENABLED')

api/constants/languages.py

@@ -1,5 +1,6 @@
 
 import json
+
 from models.model import AppModelConfig
 
 languages = ['en-US', 'zh-Hans', 'pt-BR', 'es-ES', 'fr-FR', 'de-DE', 'ja-JP', 'ko-KR', 'ru-RU', 'it-IT']

api/controllers/console/__init__.py

@@ -11,13 +11,11 @@ from .app import (advanced_prompt_template, annotation, app, audio, completion,
                   model_config, site, statistic)
 # Import auth controllers
 from .auth import activate, data_source_oauth, login, oauth
+# Import billing controllers
+from .billing import billing
 # Import datasets controllers
 from .datasets import data_source, datasets, datasets_document, datasets_segments, file, hit_testing
 # Import explore controllers
 from .explore import audio, completion, conversation, installed_app, message, parameter, recommended_app, saved_message
 # Import workspace controllers
 from .workspace import account, members, model_providers, models, tool_providers, workspace
-# Import billing controllers
-from .billing import billing
-# Import operation controllers
-from .operation import operation

api/controllers/console/admin.py

@@ -1,12 +1,12 @@
 import os
 from functools import wraps
 
+from constants.languages import supported_language
 from controllers.console import api
 from controllers.console.wraps import only_edition_cloud
 from extensions.ext_database import db
 from flask import request
 from flask_restful import Resource, reqparse
-from constants.languages import supported_language
 from models.model import App, InstalledApp, RecommendedApp
 from werkzeug.exceptions import NotFound, Unauthorized
 

api/controllers/console/app/app.py

@@ -3,8 +3,8 @@ import json
 import logging
 from datetime import datetime
 
-from constants.model_template import model_templates
 from constants.languages import demo_model_templates, languages
+from constants.model_template import model_templates
 from controllers.console import api
 from controllers.console.app.error import AppNotFoundError, ProviderNotInitializeError
 from controllers.console.setup import setup_required
@@ -26,6 +26,7 @@ from models.tools import ApiToolProvider
 from services.app_model_config_service import AppModelConfigService
 from werkzeug.exceptions import Forbidden
 
+
 def _get_app(app_id, tenant_id):
     app = db.session.query(App).filter(App.id == app_id, App.tenant_id == tenant_id).first()
     if not app:
@@ -107,20 +108,33 @@ class AppListApi(Resource):
             # validate config
             model_config_dict = args['model_config']
 
-            # get model provider
-            model_manager = ModelManager()
-            model_instance = model_manager.get_default_model_instance(
-                tenant_id=current_user.current_tenant_id,
-                model_type=ModelType.LLM
+            # Get provider configurations
+            provider_manager = ProviderManager()
+            provider_configurations = provider_manager.get_configurations(current_user.current_tenant_id)
+
+            # get available models from provider_configurations
+            available_models = provider_configurations.get_models(
+                model_type=ModelType.LLM,
+                only_active=True
             )
 
-            if not model_instance:
-                raise ProviderNotInitializeError(
-                    f"No Default System Reasoning Model available. Please configure "
-                    f"in the Settings -> Model Provider.")
-            else:
-                model_config_dict["model"]["provider"] = model_instance.provider
-                model_config_dict["model"]["name"] = model_instance.model
+            # check if model is available
+            available_models_names = [f'{model.provider.provider}.{model.model}' for model in available_models]
+            provider_model = f"{model_config_dict['model']['provider']}.{model_config_dict['model']['name']}"
+            if provider_model not in available_models_names:
+                model_manager = ModelManager()
+                model_instance = model_manager.get_default_model_instance(
+                    tenant_id=current_user.current_tenant_id,
+                    model_type=ModelType.LLM
+                )
+
+                if not model_instance:
+                    raise ProviderNotInitializeError(
+                        f"No Default System Reasoning Model available. Please configure "
+                        f"in the Settings -> Model Provider.")
+                else:
+                    model_config_dict["model"]["provider"] = model_instance.provider
+                    model_config_dict["model"]["name"] = model_instance.model
 
             model_configuration = AppModelConfigService.validate_configuration(
                 tenant_id=current_user.current_tenant_id,

api/controllers/console/app/site.py

@@ -1,4 +1,5 @@
 # -*- coding:utf-8 -*-
+from constants.languages import supported_language
 from controllers.console import api
 from controllers.console.app import _get_app
 from controllers.console.setup import setup_required
@@ -7,7 +8,6 @@ from extensions.ext_database import db
 from fields.app_fields import app_site_fields
 from flask_login import current_user
 from flask_restful import Resource, marshal_with, reqparse
-from constants.languages import supported_language
 from libs.login import login_required
 from models.model import Site
 from werkzeug.exceptions import Forbidden, NotFound

api/controllers/console/auth/activate.py

@@ -2,12 +2,12 @@ import base64
 import secrets
 from datetime import datetime
 
+from constants.languages import supported_language
 from controllers.console import api
 from controllers.console.error import AlreadyActivateError
 from extensions.ext_database import db
 from flask_restful import Resource, reqparse
 from libs.helper import email, str_len, timezone
-from constants.languages import supported_language
 from libs.password import hash_password, valid_password
 from models.account import AccountStatus, Tenant
 from services.account_service import RegisterService

api/controllers/console/billing/billing.py

@@ -20,7 +20,7 @@ class Subscription(Resource):
         parser.add_argument('interval', type=str, required=True, location='args', choices=['month', 'year'])
         args = parser.parse_args()
 
-        BillingService.is_tenant_owner(current_user)
+        BillingService.is_tenant_owner_or_admin(current_user)
 
         return BillingService.get_subscription(args['plan'],
                                                args['interval'],
@@ -35,8 +35,8 @@ class Invoices(Resource):
     @account_initialization_required
     @only_edition_cloud
     def get(self):
-        BillingService.is_tenant_owner(current_user)
-        return BillingService.get_invoices(current_user.email)
+        BillingService.is_tenant_owner_or_admin(current_user)
+        return BillingService.get_invoices(current_user.email, current_user.current_tenant_id)
 
 
 api.add_resource(Subscription, '/billing/subscription')

api/controllers/console/datasets/file.py

@@ -9,7 +9,7 @@ from flask import current_app, request
 from flask_login import current_user
 from flask_restful import Resource, marshal_with
 from libs.login import login_required
-from services.file_service import FileService, ALLOWED_EXTENSIONS, UNSTRUSTURED_ALLOWED_EXTENSIONS
+from services.file_service import ALLOWED_EXTENSIONS, UNSTRUSTURED_ALLOWED_EXTENSIONS, FileService
 
 PREVIEW_WORDS_LIMIT = 3000
 

api/controllers/console/error.py

@@ -13,6 +13,16 @@ class NotSetupError(BaseHTTPException):
                   "Please proceed with the initialization and installation process first."
     code = 401
 
+class NotInitValidateError(BaseHTTPException):
+    error_code = 'not_init_validated'
+    description = "Init validation has not been completed yet. " \
+                  "Please proceed with the init validation process first."
+    code = 401
+
+class InitValidateFailedError(BaseHTTPException):
+    error_code = 'init_validate_failed'
+    description = "Init validation failed. Please check the password and try again."
+    code = 401
 
 class AccountNotLinkTenantError(BaseHTTPException):
     error_code = 'account_not_link_tenant'

api/controllers/console/explore/message.py

@@ -17,9 +17,9 @@ from core.model_runtime.errors.invoke import InvokeError
 from fields.message_fields import message_infinite_scroll_pagination_fields
 from flask import Response, stream_with_context
 from flask_login import current_user
-from flask_restful import marshal_with, reqparse, fields
+from flask_restful import fields, marshal_with, reqparse
 from flask_restful.inputs import int_range
-from libs.helper import uuid_value, TimestampField
+from libs.helper import TimestampField, uuid_value
 from services.completion_service import CompletionService
 from services.errors.app import MoreLikeThisDisabledError
 from services.errors.conversation import ConversationNotExistsError

api/controllers/console/explore/parameter.py

@@ -3,12 +3,12 @@ import json
 
 from controllers.console import api
 from controllers.console.explore.wraps import InstalledAppResource
+from extensions.ext_database import db
 from flask import current_app
 from flask_restful import fields, marshal_with
-from models.model import InstalledApp, AppModelConfig
+from models.model import AppModelConfig, InstalledApp
 from models.tools import ApiToolProvider
 
-from extensions.ext_database import db
 
 class AppParameterApi(InstalledAppResource):
     """Resource for app variables."""

api/controllers/console/explore/recommended_app.py

@@ -1,4 +1,5 @@
 # -*- coding:utf-8 -*-
+from constants.languages import languages
 from controllers.console import api
 from controllers.console.app.error import AppNotFoundError
 from controllers.console.wraps import account_initialization_required
@@ -9,7 +10,6 @@ from libs.login import login_required
 from models.model import App, InstalledApp, RecommendedApp
 from services.account_service import TenantService
 from sqlalchemy import and_
-from constants.languages import languages
 
 app_fields = {
     'id': fields.String,

api/controllers/console/feature.py

@@ -3,10 +3,12 @@ from flask_restful import Resource
 from services.feature_service import FeatureService
 
 from . import api
+from .wraps import cloud_utm_record
 
 
 class FeatureApi(Resource):
 
+    @cloud_utm_record
     def get(self):
         return FeatureService.get_features(current_user.current_tenant_id).dict()
 

api/controllers/console/init_validate.py

@@ -0,0 +1,48 @@
+import os
+
+from flask import current_app, session
+from flask_restful import Resource, reqparse
+from libs.helper import str_len
+from models.model import DifySetup
+from services.account_service import TenantService
+
+from . import api
+from .error import AlreadySetupError, InitValidateFailedError
+from .wraps import only_edition_self_hosted
+
+
+class InitValidateAPI(Resource):
+
+    def get(self):
+        init_status = get_init_validate_status()
+        if init_status:
+            return { 'status': 'finished' }
+        return {'status': 'not_started' }
+
+    @only_edition_self_hosted
+    def post(self):
+        # is tenant created
+        tenant_count = TenantService.get_tenant_count()
+        if tenant_count > 0:
+            raise AlreadySetupError()
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('password', type=str_len(30),
+                            required=True, location='json')
+        input_password = parser.parse_args()['password']
+
+        if input_password != os.environ.get('INIT_PASSWORD'):
+            session['is_init_validated'] = False
+            raise InitValidateFailedError()
+            
+        session['is_init_validated'] = True
+        return {'result': 'success'}, 201
+
+def get_init_validate_status():
+    if current_app.config['EDITION'] == 'SELF_HOSTED':
+        if os.environ.get('INIT_PASSWORD'):
+            return session.get('is_init_validated') or DifySetup.query.first()
+    
+    return True
+
+api.add_resource(InitValidateAPI, '/init')

api/controllers/console/operation/operation.py

@@ -1,30 +0,0 @@
-from flask_login import current_user
-from flask_restful import Resource, reqparse
-
-from controllers.console import api
-from controllers.console.setup import setup_required
-from controllers.console.wraps import account_initialization_required, only_edition_cloud
-from libs.login import login_required
-from services.operation_service import OperationService
-
-
-class TenantUtm(Resource):
-
-    @setup_required
-    @login_required
-    @account_initialization_required
-    @only_edition_cloud
-    def post(self):
-
-        parser = reqparse.RequestParser()
-        parser.add_argument('utm_source', type=str, required=True)
-        parser.add_argument('utm_medium', type=str, required=True)
-        parser.add_argument('utm_campaign', type=str, required=False, default='')
-        parser.add_argument('utm_content', type=str, required=False, default='')
-        parser.add_argument('utm_term', type=str, required=False, default='')
-        args = parser.parse_args()
-
-        return OperationService.record_utm(current_user.current_tenant_id, args)
-
-
-api.add_resource(TenantUtm, '/operation/utm')

api/controllers/console/setup.py

@@ -10,7 +10,8 @@ from models.model import DifySetup
 from services.account_service import AccountService, RegisterService, TenantService
 
 from . import api
-from .error import AlreadySetupError, NotSetupError
+from .error import AlreadySetupError, NotInitValidateError, NotSetupError
+from .init_validate import get_init_validate_status
 from .wraps import only_edition_self_hosted
 
 
@@ -24,7 +25,7 @@ class SetupApi(Resource):
                     'step': 'finished',
                     'setup_at': setup_status.setup_at.isoformat()
                 }
-            return {'step': 'not_start'}
+            return {'step': 'not_started'}
         return {'step': 'finished'}
 
     @only_edition_self_hosted
@@ -37,6 +38,9 @@ class SetupApi(Resource):
         tenant_count = TenantService.get_tenant_count()
         if tenant_count > 0:
             raise AlreadySetupError()
+    
+        if not get_init_validate_status():
+            raise NotInitValidateError()
 
         parser = reqparse.RequestParser()
         parser.add_argument('email', type=email,
@@ -71,7 +75,10 @@ def setup_required(view):
     @wraps(view)
     def decorated(*args, **kwargs):
         # check setup
-        if not get_setup_status():
+        if not get_init_validate_status():
+            raise NotInitValidateError()
+        
+        elif not get_setup_status():
             raise NotSetupError()
 
         return view(*args, **kwargs)

api/controllers/console/workspace/account.py

@@ -2,6 +2,7 @@
 from datetime import datetime
 
 import pytz
+from constants.languages import supported_language
 from controllers.console import api
 from controllers.console.setup import setup_required
 from controllers.console.workspace.error import (AccountAlreadyInitedError, CurrentPasswordIncorrectError,
@@ -12,7 +13,6 @@ from flask import current_app, request
 from flask_login import current_user
 from flask_restful import Resource, fields, marshal_with, reqparse
 from libs.helper import TimestampField, timezone
-from constants.languages import supported_language
 from libs.login import login_required
 from models.account import AccountIntegrate, InvitationCode
 from services.account_service import AccountService

api/controllers/console/workspace/members.py

@@ -1,13 +1,12 @@
 # -*- coding:utf-8 -*-
-from flask import current_app
-from flask_login import current_user
-from flask_restful import Resource, abort, fields, marshal_with, reqparse
-
 import services
 from controllers.console import api
 from controllers.console.setup import setup_required
 from controllers.console.wraps import account_initialization_required, cloud_edition_billing_resource_check
 from extensions.ext_database import db
+from flask import current_app
+from flask_login import current_user
+from flask_restful import Resource, abort, fields, marshal_with, reqparse
 from libs.helper import TimestampField
 from libs.login import login_required
 from models.account import Account
@@ -52,10 +51,12 @@ class MemberInviteEmailApi(Resource):
         parser = reqparse.RequestParser()
         parser.add_argument('emails', type=str, required=True, location='json', action='append')
         parser.add_argument('role', type=str, required=True, default='admin', location='json')
+        parser.add_argument('language', type=str, required=False, location='json')
         args = parser.parse_args()
 
         invitee_emails = args['emails']
         invitee_role = args['role']
+        interface_language = args['language']
         if invitee_role not in ['admin', 'normal']:
             return {'code': 'invalid-role', 'message': 'Invalid role'}, 400
 
@@ -64,8 +65,7 @@ class MemberInviteEmailApi(Resource):
         console_web_url = current_app.config.get("CONSOLE_WEB_URL")
         for invitee_email in invitee_emails:
             try:
-                token = RegisterService.invite_new_member(inviter.current_tenant, invitee_email, role=invitee_role,
-                                                          inviter=inviter)
+                token = RegisterService.invite_new_member(inviter.current_tenant, invitee_email, interface_language, role=invitee_role, inviter=inviter)
                 invitation_results.append({
                     'status': 'success',
                     'email': invitee_email,

api/controllers/console/workspace/model_providers.py

@@ -186,10 +186,11 @@ class ModelProviderPaymentCheckoutUrlApi(Resource):
     def get(self, provider: str):
         if provider != 'anthropic':
             raise ValueError(f'provider name {provider} is invalid')
-
+        BillingService.is_tenant_owner_or_admin(current_user)
         data = BillingService.get_model_provider_payment_link(provider_name=provider,
                                                               tenant_id=current_user.current_tenant_id,
-                                                              account_id=current_user.id)
+                                                              account_id=current_user.id,
+                                                              prefilled_email=current_user.email)
         return data
 
 

api/controllers/console/workspace/tool_providers.py

@@ -1,18 +1,16 @@
+import io
 import json
 
-from libs.login import login_required
-from flask_login import current_user
-from flask_restful import Resource, reqparse
-from flask import send_file
-from werkzeug.exceptions import Forbidden
-
 from controllers.console import api
 from controllers.console.setup import setup_required
 from controllers.console.wraps import account_initialization_required
-
+from flask import send_file
+from flask_login import current_user
+from flask_restful import Resource, reqparse
+from libs.login import login_required
 from services.tools_manage_service import ToolManageService
+from werkzeug.exceptions import Forbidden
 
-import io
 
 class ToolProviderListApi(Resource):
     @setup_required
@@ -171,8 +169,8 @@ class ToolApiProviderUpdateApi(Resource):
         parser.add_argument('schema', type=str, required=True, nullable=False, location='json')
         parser.add_argument('provider', type=str, required=True, nullable=False, location='json')
         parser.add_argument('original_provider', type=str, required=True, nullable=False, location='json')
-        parser.add_argument('icon', type=str, required=True, nullable=False, location='json')
-        parser.add_argument('privacy_policy', type=str, required=True, nullable=False, location='json')
+        parser.add_argument('icon', type=dict, required=True, nullable=False, location='json')
+        parser.add_argument('privacy_policy', type=str, required=True, nullable=True, location='json')
 
         args = parser.parse_args()
 

api/controllers/console/wraps.py

@@ -1,10 +1,12 @@
 # -*- coding:utf-8 -*-
+import json
 from functools import wraps
 
 from controllers.console.workspace.error import AccountNotInitializedError
-from flask import abort, current_app
+from flask import abort, current_app, request
 from flask_login import current_user
 from services.feature_service import FeatureService
+from services.operation_service import OperationService
 
 
 def account_initialization_required(view):
@@ -73,3 +75,20 @@ def cloud_edition_billing_resource_check(resource: str,
         return decorated
     return interceptor
 
+
+def cloud_utm_record(view):
+    @wraps(view)
+    def decorated(*args, **kwargs):
+        try:
+            features = FeatureService.get_features(current_user.current_tenant_id)
+
+            if features.billing.enabled:
+                utm_info = request.cookies.get('utm_info')
+
+                if utm_info:
+                    utm_info = json.loads(utm_info)
+                    OperationService.record_utm(current_user.current_tenant_id, utm_info)
+        except Exception as e:
+            pass
+        return view(*args, **kwargs)
+    return decorated

api/controllers/files/__init__.py

@@ -6,5 +6,4 @@ bp = Blueprint('files', __name__)
 api = ExternalApi(bp)
 
 
-from . import image_preview
-from . import tool_files
+from . import image_preview, tool_files

api/controllers/files/tool_files.py

@@ -1,10 +1,10 @@
 from controllers.files import api
+from core.tools.tool_file_manager import ToolFileManager
 from flask import Response
 from flask_restful import Resource, reqparse
 from libs.exception import BaseHTTPException
-from werkzeug.exceptions import NotFound, Forbidden
+from werkzeug.exceptions import Forbidden, NotFound
 
-from core.tools.tool_file_manager import ToolFileManager
 
 class ToolFilePreviewApi(Resource):
     def get(self, file_id, extension):

api/controllers/service_api/app/app.py

@@ -1,15 +1,14 @@
 # -*- coding:utf-8 -*-
+import json
+
 from controllers.service_api import api
 from controllers.service_api.wraps import AppApiResource
+from extensions.ext_database import db
 from flask import current_app
 from flask_restful import fields, marshal_with
 from models.model import App, AppModelConfig
 from models.tools import ApiToolProvider
 
-import json
-
-from extensions.ext_database import db
-
 
 class AppParameterApi(AppApiResource):
     """Resource for app variables."""

api/controllers/service_api/app/completion.py

@@ -13,7 +13,7 @@ from core.application_queue_manager import ApplicationQueueManager
 from core.entities.application_entities import InvokeFrom
 from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
 from core.model_runtime.errors.invoke import InvokeError
-from flask import Response, stream_with_context, request
+from flask import Response, stream_with_context
 from flask_restful import reqparse
 from libs.helper import uuid_value
 from services.completion_service import CompletionService
@@ -75,18 +75,22 @@ class CompletionApi(AppApiResource):
 
 
 class CompletionStopApi(AppApiResource):
-    def post(self, app_model, _, task_id):
+    def post(self, app_model, end_user, task_id):
         if app_model.mode != 'completion':
             raise AppUnavailableError()
 
-        parser = reqparse.RequestParser()
-        parser.add_argument('user', required=True, nullable=False, type=str, location='json')
+        if end_user is None:
+            parser = reqparse.RequestParser()
+            parser.add_argument('user', required=True, nullable=False, type=str, location='json')
+            args = parser.parse_args()
 
-        args = parser.parse_args()
+            user = args.get('user')
+            if user is not None:
+                end_user = create_or_update_end_user_for_user_id(app_model, user)
+            else:
+                raise ValueError("arg user muse be input.")
 
-        end_user_id = args.get('user')
-
-        ApplicationQueueManager.set_stop_flag(task_id, InvokeFrom.SERVICE_API, end_user_id)
+        ApplicationQueueManager.set_stop_flag(task_id, InvokeFrom.SERVICE_API, end_user.id)
 
         return {'result': 'success'}, 200
 
@@ -146,13 +150,22 @@ class ChatApi(AppApiResource):
 
 
 class ChatStopApi(AppApiResource):
-    def post(self, app_model, _, task_id):
+    def post(self, app_model, end_user, task_id):
         if app_model.mode != 'chat':
             raise NotChatAppError()
 
-        end_user_id = request.get_json().get('user')
+        if end_user is None:
+            parser = reqparse.RequestParser()
+            parser.add_argument('user', required=True, nullable=False, type=str, location='json')
+            args = parser.parse_args()
 
-        ApplicationQueueManager.set_stop_flag(task_id, InvokeFrom.SERVICE_API, end_user_id)
+            user = args.get('user')
+            if user is not None:
+                end_user = create_or_update_end_user_for_user_id(app_model, user)
+            else:
+                raise ValueError("arg user muse be input.")
+
+        ApplicationQueueManager.set_stop_flag(task_id, InvokeFrom.SERVICE_API, end_user.id)
 
         return {'result': 'success'}, 200
 

api/controllers/service_api/dataset/dataset.py

@@ -1,4 +1,3 @@
-from models.dataset import Dataset
 import services.dataset_service
 from controllers.service_api import api
 from controllers.service_api.dataset.error import DatasetNameDuplicateError
@@ -9,6 +8,7 @@ from fields.dataset_fields import dataset_detail_fields
 from flask import request
 from flask_restful import marshal, reqparse
 from libs.login import current_user
+from models.dataset import Dataset
 from services.dataset_service import DatasetService
 
 

api/controllers/service_api/index.py

@@ -1,8 +1,7 @@
+from controllers.service_api import api
 from flask import current_app
 from flask_restful import Resource
 
-from controllers.service_api import api
-
 
 class IndexApi(Resource):
     def get(self):

api/controllers/web/app.py

@@ -1,15 +1,14 @@
 # -*- coding:utf-8 -*-
+import json
+
 from controllers.web import api
 from controllers.web.wraps import WebApiResource
+from extensions.ext_database import db
 from flask import current_app
 from flask_restful import fields, marshal_with
 from models.model import App, AppModelConfig
 from models.tools import ApiToolProvider
 
-from extensions.ext_database import db
-
-import json
-
 
 class AppParameterApi(WebApiResource):
     """Resource for app variables."""

api/core/app_runner/app_runner.py

@@ -2,8 +2,13 @@ import time
 from typing import Generator, List, Optional, Tuple, Union, cast
 
 from core.application_queue_manager import ApplicationQueueManager, PublishFrom
-from core.entities.application_entities import AppOrchestrationConfigEntity, ModelConfigEntity, \
-    PromptTemplateEntity, ExternalDataVariableEntity, ApplicationGenerateEntity, InvokeFrom
+from core.entities.application_entities import (ApplicationGenerateEntity, AppOrchestrationConfigEntity,
+                                                ExternalDataVariableEntity, InvokeFrom, ModelConfigEntity,
+                                                PromptTemplateEntity)
+from core.features.annotation_reply import AnnotationReplyFeature
+from core.features.external_data_fetch import ExternalDataFetchFeature
+from core.features.hosting_moderation import HostingModerationFeature
+from core.features.moderation import ModerationFeature
 from core.file.file_obj import FileObj
 from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage
@@ -11,12 +16,9 @@ from core.model_runtime.entities.message_entities import AssistantPromptMessage,
 from core.model_runtime.entities.model_entities import ModelPropertyKey
 from core.model_runtime.errors.invoke import InvokeBadRequestError
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
-from core.features.hosting_moderation import HostingModerationFeature
-from core.features.moderation import ModerationFeature
-from core.features.external_data_fetch import ExternalDataFetchFeature
-from core.features.annotation_reply import AnnotationReplyFeature
 from core.prompt.prompt_transform import PromptTransform
-from models.model import App, MessageAnnotation, Message
+from models.model import App, Message, MessageAnnotation
+
 
 class AppRunner:
     def get_pre_calculate_rest_tokens(self, app_record: App,

api/core/app_runner/assistant_app_runner.py

@@ -3,19 +3,19 @@ import logging
 from typing import cast
 
 from core.app_runner.app_runner import AppRunner
+from core.application_queue_manager import ApplicationQueueManager, PublishFrom
+from core.entities.application_entities import AgentEntity, ApplicationGenerateEntity, ModelConfigEntity
 from core.features.assistant_cot_runner import AssistantCotApplicationRunner
 from core.features.assistant_fc_runner import AssistantFunctionCallApplicationRunner
-from core.entities.application_entities import ApplicationGenerateEntity, ModelConfigEntity, \
-    AgentEntity
-from core.application_queue_manager import ApplicationQueueManager, PublishFrom
 from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance
 from core.model_runtime.entities.llm_entities import LLMUsage
+from core.model_runtime.entities.model_entities import ModelFeature
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 from core.moderation.base import ModerationException
 from core.tools.entities.tool_entities import ToolRuntimeVariablePool
 from extensions.ext_database import db
-from models.model import Conversation, Message, App, MessageChain, MessageAgentThought
+from models.model import App, Conversation, Message, MessageAgentThought, MessageChain
 from models.tools import ToolConversationVariables
 
 logger = logging.getLogger(__name__)
@@ -169,7 +169,7 @@ class AssistantApplicationRunner(AppRunner):
         # load tool variables
         tool_conversation_variables = self._load_tool_variables(conversation_id=conversation.id,
                                                    user_id=application_generate_entity.user_id,
-                                                   tanent_id=application_generate_entity.tenant_id)
+                                                   tenant_id=application_generate_entity.tenant_id)
 
         # convert db variables to tool variables
         tool_variables = self._convert_db_variables_to_tool_variables(tool_conversation_variables)
@@ -194,6 +194,13 @@ class AssistantApplicationRunner(AppRunner):
             memory=memory,
         )
 
+        # change function call strategy based on LLM model
+        llm_model = cast(LargeLanguageModel, model_instance.model_type_instance)
+        model_schema = llm_model.get_model_schema(model_instance.model, model_instance.credentials)
+
+        if set([ModelFeature.MULTI_TOOL_CALL, ModelFeature.TOOL_CALL]).intersection(model_schema.features or []):
+            agent_entity.strategy = AgentEntity.Strategy.FUNCTION_CALLING
+
         # start agent runner
         if agent_entity.strategy == AgentEntity.Strategy.CHAIN_OF_THOUGHT:
             assistant_cot_runner = AssistantCotApplicationRunner(
@@ -209,9 +216,9 @@ class AssistantApplicationRunner(AppRunner):
                 prompt_messages=prompt_message,
                 variables_pool=tool_variables,
                 db_variables=tool_conversation_variables,
+                model_instance=model_instance
             )
             invoke_result = assistant_cot_runner.run(
-                model_instance=model_instance,
                 conversation=conversation,
                 message=message,
                 query=query,
@@ -229,10 +236,10 @@ class AssistantApplicationRunner(AppRunner):
                 memory=memory,
                 prompt_messages=prompt_message,
                 variables_pool=tool_variables,
-                db_variables=tool_conversation_variables
+                db_variables=tool_conversation_variables,
+                model_instance=model_instance
             )
             invoke_result = assistant_fc_runner.run(
-                model_instance=model_instance,
                 conversation=conversation,
                 message=message,
                 query=query,
@@ -246,13 +253,13 @@ class AssistantApplicationRunner(AppRunner):
             agent=True
         )
 
-    def _load_tool_variables(self, conversation_id: str, user_id: str, tanent_id: str) -> ToolConversationVariables:
+    def _load_tool_variables(self, conversation_id: str, user_id: str, tenant_id: str) -> ToolConversationVariables:
         """
         load tool variables from database
         """
         tool_variables: ToolConversationVariables = db.session.query(ToolConversationVariables).filter(
             ToolConversationVariables.conversation_id == conversation_id,
-            ToolConversationVariables.tenant_id == tanent_id
+            ToolConversationVariables.tenant_id == tenant_id
         ).first()
 
         if tool_variables:
@@ -263,7 +270,7 @@ class AssistantApplicationRunner(AppRunner):
             tool_variables = ToolConversationVariables(
                 conversation_id=conversation_id,
                 user_id=user_id,
-                tenant_id=tanent_id,
+                tenant_id=tenant_id,
                 variables_str='[]',
             )
             db.session.add(tool_variables)

api/core/app_runner/basic_app_runner.py

@@ -4,8 +4,7 @@ from typing import Optional
 from core.app_runner.app_runner import AppRunner
 from core.application_queue_manager import ApplicationQueueManager, PublishFrom
 from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCallbackHandler
-from core.entities.application_entities import (ApplicationGenerateEntity, DatasetEntity,
-                                                 InvokeFrom, ModelConfigEntity)
+from core.entities.application_entities import ApplicationGenerateEntity, DatasetEntity, InvokeFrom, ModelConfigEntity
 from core.features.dataset_retrieval import DatasetRetrievalFeature
 from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance

api/core/app_runner/generate_task_pipeline.py

@@ -6,21 +6,21 @@ from typing import Generator, Optional, Union, cast
 from core.app_runner.moderation_handler import ModerationRule, OutputModerationHandler
 from core.application_queue_manager import ApplicationQueueManager, PublishFrom
 from core.entities.application_entities import ApplicationGenerateEntity, InvokeFrom
-from core.entities.queue_entities import (AnnotationReplyEvent, QueueAgentThoughtEvent, QueueErrorEvent,
-                                          QueueMessageEndEvent, QueueMessageEvent, QueueMessageReplaceEvent,
-                                          QueuePingEvent, QueueRetrieverResourcesEvent, QueueStopEvent,
-                                          QueueMessageFileEvent, QueueAgentMessageEvent)
-from core.errors.error import ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError
+from core.entities.queue_entities import (AnnotationReplyEvent, QueueAgentMessageEvent, QueueAgentThoughtEvent,
+                                          QueueErrorEvent, QueueMessageEndEvent, QueueMessageEvent,
+                                          QueueMessageFileEvent, QueueMessageReplaceEvent, QueuePingEvent,
+                                          QueueRetrieverResourcesEvent, QueueStopEvent)
+from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage
 from core.model_runtime.entities.message_entities import (AssistantPromptMessage, ImagePromptMessageContent,
                                                           PromptMessage, PromptMessageContentType, PromptMessageRole,
                                                           TextPromptMessageContent)
 from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeError
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
-from core.tools.tool_file_manager import ToolFileManager
-from core.tools.tool_manager import ToolManager
 from core.model_runtime.utils.encoders import jsonable_encoder
 from core.prompt.prompt_template import PromptTemplateParser
+from core.tools.tool_file_manager import ToolFileManager
+from core.tools.tool_manager import ToolManager
 from events.message_event import message_was_created
 from extensions.ext_database import db
 from models.model import Conversation, Message, MessageAgentThought, MessageFile

api/core/application_manager.py

@@ -9,11 +9,12 @@ from core.app_runner.basic_app_runner import BasicApplicationRunner
 from core.app_runner.generate_task_pipeline import GenerateTaskPipeline
 from core.application_queue_manager import ApplicationQueueManager, ConversationTaskStoppedException, PublishFrom
 from core.entities.application_entities import (AdvancedChatPromptTemplateEntity,
-                                                AdvancedCompletionPromptTemplateEntity, AgentEntity, AgentToolEntity,
-                                                ApplicationGenerateEntity, AppOrchestrationConfigEntity, DatasetEntity,
+                                                AdvancedCompletionPromptTemplateEntity, AgentEntity, AgentPromptEntity,
+                                                AgentToolEntity, ApplicationGenerateEntity,
+                                                AppOrchestrationConfigEntity, DatasetEntity,
                                                 DatasetRetrieveConfigEntity, ExternalDataVariableEntity,
                                                 FileUploadEntity, InvokeFrom, ModelConfigEntity, PromptTemplateEntity,
-                                                SensitiveWordAvoidanceEntity, AgentPromptEntity)
+                                                SensitiveWordAvoidanceEntity)
 from core.entities.model_entities import ModelStatus
 from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
 from core.file.file_obj import FileObj

api/core/application_queue_manager.py

@@ -4,10 +4,10 @@ from enum import Enum
 from typing import Any, Generator
 
 from core.entities.application_entities import InvokeFrom
-from core.entities.queue_entities import (AnnotationReplyEvent, AppQueueEvent, QueueAgentThoughtEvent, QueueErrorEvent,
-                                          QueueMessage, QueueMessageEndEvent, QueueMessageEvent,
-                                          QueueMessageReplaceEvent, QueuePingEvent, QueueRetrieverResourcesEvent,
-                                          QueueStopEvent, QueueMessageFileEvent, QueueAgentMessageEvent)
+from core.entities.queue_entities import (AnnotationReplyEvent, AppQueueEvent, QueueAgentMessageEvent,
+                                          QueueAgentThoughtEvent, QueueErrorEvent, QueueMessage, QueueMessageEndEvent,
+                                          QueueMessageEvent, QueueMessageFileEvent, QueueMessageReplaceEvent,
+                                          QueuePingEvent, QueueRetrieverResourcesEvent, QueueStopEvent)
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk
 from extensions.ext_redis import redis_client
 from models.model import MessageAgentThought, MessageFile

api/core/callback_handler/agent_tool_callback_handler.py

@@ -1,9 +1,10 @@
 import os
 from typing import Any, Dict, Optional, Union
-from pydantic import BaseModel
 
 from langchain.callbacks.base import BaseCallbackHandler
 from langchain.input import print_text
+from pydantic import BaseModel
+
 
 class DifyAgentCallbackHandler(BaseCallbackHandler, BaseModel):
     """Callback Handler that prints to std out."""

api/core/data_loader/loader/unstructured/unstructured_ppt.py

@@ -1,5 +1,6 @@
 import logging
 from typing import List
+
 from langchain.document_loaders.base import BaseLoader
 from langchain.schema import Document
 

api/core/embedding/cached_embedding.py

@@ -8,9 +8,8 @@ from core.model_manager import ModelInstance
 from core.model_runtime.entities.model_entities import ModelPropertyKey
 from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
 from extensions.ext_database import db
-from langchain.embeddings.base import Embeddings
-
 from extensions.ext_redis import redis_client
+from langchain.embeddings.base import Embeddings
 from libs import helper
 from models.dataset import Embedding
 from sqlalchemy.exc import IntegrityError

api/core/entities/application_entities.py

@@ -1,12 +1,11 @@
 from enum import Enum
-from typing import Optional, Any, cast, Literal, Union
-
-from pydantic import BaseModel
+from typing import Any, Literal, Optional, Union, cast
 
 from core.entities.provider_configuration import ProviderModelBundle
 from core.file.file_obj import FileObj
 from core.model_runtime.entities.message_entities import PromptMessageRole
 from core.model_runtime.entities.model_entities import AIModelEntity
+from pydantic import BaseModel
 
 
 class ModelConfigEntity(BaseModel):

api/core/entities/provider_entities.py

@@ -9,6 +9,7 @@ from pydantic import BaseModel
 class QuotaUnit(Enum):
     TIMES = 'times'
     TOKENS = 'tokens'
+    CREDITS = 'credits'
 
 
 class SystemConfigurationStatus(Enum):

api/core/features/agent_runner.py

@@ -1,27 +1,26 @@
 import logging
-from typing import cast, Optional, List
-
-from langchain import WikipediaAPIWrapper
-from langchain.callbacks.base import BaseCallbackHandler
-from langchain.tools import BaseTool, WikipediaQueryRun, Tool
-from pydantic import BaseModel, Field
+from typing import List, Optional, cast
 
 from core.agent.agent.agent_llm_callback import AgentLLMCallback
-from core.agent.agent_executor import PlanningStrategy, AgentConfiguration, AgentExecutor
+from core.agent.agent_executor import AgentConfiguration, AgentExecutor, PlanningStrategy
 from core.application_queue_manager import ApplicationQueueManager
 from core.callback_handler.agent_loop_gather_callback_handler import AgentLoopGatherCallbackHandler
 from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCallbackHandler
 from core.callback_handler.std_out_callback_handler import DifyStdOutCallbackHandler
-from core.entities.application_entities import ModelConfigEntity, InvokeFrom, \
-    AgentEntity, AgentToolEntity, AppOrchestrationConfigEntity
+from core.entities.application_entities import (AgentEntity, AgentToolEntity, AppOrchestrationConfigEntity, InvokeFrom,
+                                                ModelConfigEntity)
 from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_runtime.entities.model_entities import ModelFeature, ModelType
 from core.model_runtime.model_providers import model_provider_factory
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 from core.tools.tool.dataset_retriever.dataset_retriever_tool import DatasetRetrieverTool
 from extensions.ext_database import db
+from langchain import WikipediaAPIWrapper
+from langchain.callbacks.base import BaseCallbackHandler
+from langchain.tools import BaseTool, Tool, WikipediaQueryRun
 from models.dataset import Dataset
 from models.model import Message
+from pydantic import BaseModel, Field
 
 logger = logging.getLogger(__name__)
 

api/core/features/assistant_base_runner.py

@@ -1,34 +1,32 @@
-import logging
 import json
-
-from typing import Optional, List, Tuple, Union
+import logging
 from datetime import datetime
 from mimetypes import guess_extension
+from typing import List, Optional, Tuple, Union, cast
 
 from core.app_runner.app_runner import AppRunner
-from extensions.ext_database import db
-
-from models.model import MessageAgentThought, Message, MessageFile
-from models.tools import ToolConversationVariables
-
-from core.tools.entities.tool_entities import ToolInvokeMessage, ToolInvokeMessageBinary, \
-    ToolRuntimeVariablePool, ToolParamter
-from core.tools.tool.tool import Tool
-from core.tools.tool_manager import ToolManager
-from core.tools.tool_file_manager import ToolFileManager
-from core.tools.tool.dataset_retriever_tool import DatasetRetrieverTool
-from core.app_runner.app_runner import AppRunner
+from core.application_queue_manager import ApplicationQueueManager
 from core.callback_handler.agent_tool_callback_handler import DifyAgentCallbackHandler
 from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCallbackHandler
-from core.entities.application_entities import ModelConfigEntity, AgentEntity, AgentToolEntity
-from core.application_queue_manager import ApplicationQueueManager
-from core.memory.token_buffer_memory import TokenBufferMemory
-from core.entities.application_entities import ModelConfigEntity, \
-    AgentEntity, AppOrchestrationConfigEntity, ApplicationGenerateEntity, InvokeFrom
-from core.model_runtime.entities.message_entities import PromptMessage, PromptMessageTool
-from core.model_runtime.entities.llm_entities import LLMUsage
-from core.model_runtime.utils.encoders import jsonable_encoder
+from core.entities.application_entities import (AgentEntity, AgentToolEntity, ApplicationGenerateEntity,
+                                                AppOrchestrationConfigEntity, InvokeFrom, ModelConfigEntity)
 from core.file.message_file_parser import FileTransferMethod
+from core.memory.token_buffer_memory import TokenBufferMemory
+from core.model_manager import ModelInstance
+from core.model_runtime.entities.llm_entities import LLMUsage
+from core.model_runtime.entities.message_entities import PromptMessage, PromptMessageTool
+from core.model_runtime.entities.model_entities import ModelFeature
+from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
+from core.model_runtime.utils.encoders import jsonable_encoder
+from core.tools.entities.tool_entities import (ToolInvokeMessage, ToolInvokeMessageBinary, ToolParameter,
+                                               ToolRuntimeVariablePool)
+from core.tools.tool.dataset_retriever_tool import DatasetRetrieverTool
+from core.tools.tool.tool import Tool
+from core.tools.tool_file_manager import ToolFileManager
+from core.tools.tool_manager import ToolManager
+from extensions.ext_database import db
+from models.model import Message, MessageAgentThought, MessageFile
+from models.tools import ToolConversationVariables
 
 logger = logging.getLogger(__name__)
 
@@ -45,6 +43,7 @@ class BaseAssistantApplicationRunner(AppRunner):
                  prompt_messages: Optional[List[PromptMessage]] = None,
                  variables_pool: Optional[ToolRuntimeVariablePool] = None,
                  db_variables: Optional[ToolConversationVariables] = None,
+                 model_instance: ModelInstance = None
                  ) -> None:
         """
         Agent runner
@@ -71,6 +70,7 @@ class BaseAssistantApplicationRunner(AppRunner):
         self.history_prompt_messages = prompt_messages
         self.variables_pool = variables_pool
         self.db_variables_pool = db_variables
+        self.model_instance = model_instance
 
         # init callback
         self.agent_callback = DifyAgentCallbackHandler()
@@ -95,9 +95,17 @@ class BaseAssistantApplicationRunner(AppRunner):
             MessageAgentThought.message_id == self.message.id,
         ).count()
 
-    def _repacket_app_orchestration_config(self, app_orchestration_config: AppOrchestrationConfigEntity) -> AppOrchestrationConfigEntity:
+        # check if model supports stream tool call
+        llm_model = cast(LargeLanguageModel, model_instance.model_type_instance)
+        model_schema = llm_model.get_model_schema(model_instance.model, model_instance.credentials)
+        if model_schema and ModelFeature.STREAM_TOOL_CALL in (model_schema.features or []):
+            self.stream_tool_call = True
+        else:
+            self.stream_tool_call = False
+
+    def _repack_app_orchestration_config(self, app_orchestration_config: AppOrchestrationConfigEntity) -> AppOrchestrationConfigEntity:
         """
-        Repacket app orchestration config
+        Repack app orchestration config
         """
         if app_orchestration_config.prompt_template.simple_prompt_template is None:
             app_orchestration_config.prompt_template.simple_prompt_template = ''
@@ -113,7 +121,7 @@ class BaseAssistantApplicationRunner(AppRunner):
             if response.type == ToolInvokeMessage.MessageType.TEXT:
                 result += response.message
             elif response.type == ToolInvokeMessage.MessageType.LINK:
-                result += f"result link: {response.message}. please dirct user to check it."
+                result += f"result link: {response.message}. please tell user to check it."
             elif response.type == ToolInvokeMessage.MessageType.IMAGE_LINK or \
                  response.type == ToolInvokeMessage.MessageType.IMAGE:
                 result += f"image has been created and sent to user already, you should tell user to check it now."
@@ -128,7 +136,7 @@ class BaseAssistantApplicationRunner(AppRunner):
         """
         tool_entity = ToolManager.get_tool_runtime(
             provider_type=tool.provider_type, provider_name=tool.provider_id, tool_name=tool.tool_name, 
-            tanent_id=self.application_generate_entity.tenant_id,
+            tenant_id=self.application_generate_entity.tenant_id,
             agent_callback=self.agent_callback
         )
         tool_entity.load_variables(self.variables_pool)
@@ -172,20 +180,20 @@ class BaseAssistantApplicationRunner(AppRunner):
         for parameter in parameters:
             parameter_type = 'string'
             enum = []
-            if parameter.type == ToolParamter.ToolParameterType.STRING:
+            if parameter.type == ToolParameter.ToolParameterType.STRING:
                 parameter_type = 'string'
-            elif parameter.type == ToolParamter.ToolParameterType.BOOLEAN:
+            elif parameter.type == ToolParameter.ToolParameterType.BOOLEAN:
                 parameter_type = 'boolean'
-            elif parameter.type == ToolParamter.ToolParameterType.NUMBER:
+            elif parameter.type == ToolParameter.ToolParameterType.NUMBER:
                 parameter_type = 'number'
-            elif parameter.type == ToolParamter.ToolParameterType.SELECT:
+            elif parameter.type == ToolParameter.ToolParameterType.SELECT:
                 for option in parameter.options:
                     enum.append(option.value)
                 parameter_type = 'string'
             else:
                 raise ValueError(f"parameter type {parameter.type} is not supported")
             
-            if parameter.form == ToolParamter.ToolParameterForm.FORM:
+            if parameter.form == ToolParameter.ToolParameterForm.FORM:
                 # get tool parameter from form
                 tool_parameter_config = tool.tool_parameters.get(parameter.name)
                 if not tool_parameter_config:
@@ -194,7 +202,7 @@ class BaseAssistantApplicationRunner(AppRunner):
                     if not tool_parameter_config and parameter.required:
                         raise ValueError(f"tool parameter {parameter.name} not found in tool config")
                     
-                if parameter.type == ToolParamter.ToolParameterType.SELECT:
+                if parameter.type == ToolParameter.ToolParameterType.SELECT:
                     # check if tool_parameter_config in options
                     options = list(map(lambda x: x.value, parameter.options))
                     if tool_parameter_config not in options:
@@ -202,7 +210,7 @@ class BaseAssistantApplicationRunner(AppRunner):
                     
                 # convert tool parameter config to correct type
                 try:
-                    if parameter.type == ToolParamter.ToolParameterType.NUMBER:
+                    if parameter.type == ToolParameter.ToolParameterType.NUMBER:
                         # check if tool parameter is integer
                         if isinstance(tool_parameter_config, int):
                             tool_parameter_config = tool_parameter_config
@@ -213,11 +221,11 @@ class BaseAssistantApplicationRunner(AppRunner):
                                 tool_parameter_config = float(tool_parameter_config)
                             else:
                                 tool_parameter_config = int(tool_parameter_config)
-                    elif parameter.type == ToolParamter.ToolParameterType.BOOLEAN:
+                    elif parameter.type == ToolParameter.ToolParameterType.BOOLEAN:
                         tool_parameter_config = bool(tool_parameter_config)
-                    elif parameter.type not in [ToolParamter.ToolParameterType.SELECT, ToolParamter.ToolParameterType.STRING]:
+                    elif parameter.type not in [ToolParameter.ToolParameterType.SELECT, ToolParameter.ToolParameterType.STRING]:
                         tool_parameter_config = str(tool_parameter_config)
-                    elif parameter.type == ToolParamter.ToolParameterType:
+                    elif parameter.type == ToolParameter.ToolParameterType:
                         tool_parameter_config = str(tool_parameter_config)
                 except Exception as e:
                     raise ValueError(f"tool parameter {parameter.name} value {tool_parameter_config} is not correct type")
@@ -225,7 +233,7 @@ class BaseAssistantApplicationRunner(AppRunner):
                 # save tool parameter to tool entity memory
                 runtime_parameters[parameter.name] = tool_parameter_config
             
-            elif parameter.form == ToolParamter.ToolParameterForm.LLM:
+            elif parameter.form == ToolParameter.ToolParameterForm.LLM:
                 message_tool.parameters['properties'][parameter.name] = {
                     "type": parameter_type,
                     "description": parameter.llm_description or '',
@@ -279,20 +287,20 @@ class BaseAssistantApplicationRunner(AppRunner):
         for parameter in tool_runtime_parameters:
             parameter_type = 'string'
             enum = []
-            if parameter.type == ToolParamter.ToolParameterType.STRING:
+            if parameter.type == ToolParameter.ToolParameterType.STRING:
                 parameter_type = 'string'
-            elif parameter.type == ToolParamter.ToolParameterType.BOOLEAN:
+            elif parameter.type == ToolParameter.ToolParameterType.BOOLEAN:
                 parameter_type = 'boolean'
-            elif parameter.type == ToolParamter.ToolParameterType.NUMBER:
+            elif parameter.type == ToolParameter.ToolParameterType.NUMBER:
                 parameter_type = 'number'
-            elif parameter.type == ToolParamter.ToolParameterType.SELECT:
+            elif parameter.type == ToolParameter.ToolParameterType.SELECT:
                 for option in parameter.options:
                     enum.append(option.value)
                 parameter_type = 'string'
             else:
                 raise ValueError(f"parameter type {parameter.type} is not supported")
         
-            if parameter.form == ToolParamter.ToolParameterForm.LLM:
+            if parameter.form == ToolParameter.ToolParameterForm.LLM:
                 prompt_tool.parameters['properties'][parameter.name] = {
                     "type": parameter_type,
                     "description": parameter.llm_description or '',

api/core/features/assistant_cot_runner.py

@@ -1,27 +1,23 @@
 import json
 import logging
 import re
-from typing import Literal, Union, Generator, Dict, List
+from typing import Dict, Generator, List, Literal, Union
 
-from core.entities.application_entities import AgentPromptEntity, AgentScratchpadUnit
 from core.application_queue_manager import PublishFrom
-from core.model_runtime.utils.encoders import jsonable_encoder
-from core.model_runtime.entities.message_entities import PromptMessageTool, PromptMessage, \
-    UserPromptMessage, SystemPromptMessage, AssistantPromptMessage
-from core.model_runtime.entities.llm_entities import LLMResult, LLMUsage, LLMResultChunk, LLMResultChunkDelta
-from core.model_manager import ModelInstance
-
-from core.tools.errors import ToolInvokeError, ToolNotFoundError, \
-    ToolNotSupportedError, ToolProviderNotFoundError, ToolParamterValidationError, \
-          ToolProviderCredentialValidationError
-
+from core.entities.application_entities import AgentPromptEntity, AgentScratchpadUnit
 from core.features.assistant_base_runner import BaseAssistantApplicationRunner
-
+from core.model_manager import ModelInstance
+from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage
+from core.model_runtime.entities.message_entities import (AssistantPromptMessage, PromptMessage, PromptMessageTool,
+                                                          SystemPromptMessage, UserPromptMessage)
+from core.model_runtime.utils.encoders import jsonable_encoder
+from core.tools.errors import (ToolInvokeError, ToolNotFoundError, ToolNotSupportedError, ToolParameterValidationError,
+                               ToolProviderCredentialValidationError, ToolProviderNotFoundError)
 from models.model import Conversation, Message
 
+
 class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
-    def run(self, model_instance: ModelInstance,
-        conversation: Conversation,
+    def run(self, conversation: Conversation,
         message: Message,
         query: str,
     ) -> Union[Generator, LLMResult]:
@@ -29,7 +25,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
         Run Cot agent application
         """
         app_orchestration_config = self.app_orchestration_config
-        self._repacket_app_orchestration_config(app_orchestration_config)
+        self._repack_app_orchestration_config(app_orchestration_config)
 
         agent_scratchpad: List[AgentScratchpadUnit] = []
 
@@ -72,7 +68,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
         }
         final_answer = ''
 
-        def increse_usage(final_llm_usage_dict: Dict[str, LLMUsage], usage: LLMUsage):
+        def increase_usage(final_llm_usage_dict: Dict[str, LLMUsage], usage: LLMUsage):
             if not final_llm_usage_dict['usage']:
                 final_llm_usage_dict['usage'] = usage
             else:
@@ -82,6 +78,8 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
                 llm_usage.prompt_price += usage.prompt_price
                 llm_usage.completion_price += usage.completion_price
 
+        model_instance = self.model_instance
+
         while function_call_state and iteration_step <= max_iteration_steps:
             # continue to run until there is not any tool call
             function_call_state = False
@@ -104,7 +102,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
                 self.queue_manager.publish_agent_thought(agent_thought, PublishFrom.APPLICATION_MANAGER)
 
             # update prompt messages
-            prompt_messages = self._originze_cot_prompt_messages(
+            prompt_messages = self._organize_cot_prompt_messages(
                 mode=app_orchestration_config.model_config.mode,
                 prompt_messages=prompt_messages,
                 tools=prompt_messages_tools,
@@ -137,7 +135,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
                         
             # get llm usage
             if llm_result.usage:
-                increse_usage(llm_usage, llm_result.usage)
+                increase_usage(llm_usage, llm_result.usage)
             
             # publish agent thought if it's first iteration
             if iteration_step == 1:
@@ -207,7 +205,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
                         try:
                             tool_response = tool_instance.invoke(
                                 user_id=self.user_id, 
-                                tool_paramters=tool_call_args if isinstance(tool_call_args, dict) else json.loads(tool_call_args)
+                                tool_parameters=tool_call_args if isinstance(tool_call_args, dict) else json.loads(tool_call_args)
                             )
                             # transform tool response to llm friendly response
                             tool_response = self.transform_tool_invoke_messages(tool_response)
@@ -225,15 +223,15 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
 
                             message_file_ids = [message_file.id for message_file, _ in message_files]
                         except ToolProviderCredentialValidationError as e:
-                            error_response = f"Plese check your tool provider credentials"
+                            error_response = f"Please check your tool provider credentials"
                         except (
                             ToolNotFoundError, ToolNotSupportedError, ToolProviderNotFoundError
                         ) as e:
                             error_response = f"there is not a tool named {tool_call_name}"
                         except (
-                            ToolParamterValidationError
+                            ToolParameterValidationError
                         ) as e:
-                            error_response = f"tool paramters validation error: {e}, please check your tool paramters"
+                            error_response = f"tool parameters validation error: {e}, please check your tool parameters"
                         except ToolInvokeError as e:
                             error_response = f"tool invoke error: {e}"
                         except Exception as e:
@@ -390,7 +388,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
                     # remove Action: xxx from agent thought
                     agent_thought = re.sub(r'Action:.*', '', agent_thought, flags=re.IGNORECASE)
 
-                    if action_name and action_input:
+                    if action_name and action_input is not None:
                         return AgentScratchpadUnit(
                             agent_response=content,
                             thought=agent_thought,
@@ -468,7 +466,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
             if not next_iteration.find("{{observation}}") >= 0:
                 raise ValueError("{{observation}} is required in next_iteration")
             
-    def _convert_strachpad_list_to_str(self, agent_scratchpad: List[AgentScratchpadUnit]) -> str:
+    def _convert_scratchpad_list_to_str(self, agent_scratchpad: List[AgentScratchpadUnit]) -> str:
         """
             convert agent scratchpad list to str
         """
@@ -480,7 +478,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
 
         return result
     
-    def _originze_cot_prompt_messages(self, mode: Literal["completion", "chat"],
+    def _organize_cot_prompt_messages(self, mode: Literal["completion", "chat"],
                                       prompt_messages: List[PromptMessage],
                                       tools: List[PromptMessageTool], 
                                       agent_scratchpad: List[AgentScratchpadUnit],
@@ -489,7 +487,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
                                       input: str,
         ) -> List[PromptMessage]:
         """
-            originze chain of thought prompt messages, a standard prompt message is like:
+            organize chain of thought prompt messages, a standard prompt message is like:
                 Respond to the human as helpfully and accurately as possible. 
 
                 {{instruction}}
@@ -527,7 +525,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
                                      .replace("{{tools}}", tools_str) \
                                      .replace("{{tool_names}}", tool_names)
 
-        # originze prompt messages
+        # organize prompt messages
         if mode == "chat":
             # override system message
             overrided = False
@@ -558,7 +556,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
             return prompt_messages
         elif mode == "completion":
             # parse agent scratchpad
-            agent_scratchpad_str = self._convert_strachpad_list_to_str(agent_scratchpad)
+            agent_scratchpad_str = self._convert_scratchpad_list_to_str(agent_scratchpad)
             # parse prompt messages
             return [UserPromptMessage(
                 content=first_prompt.replace("{{instruction}}", instruction)

api/core/features/assistant_fc_runner.py

@@ -1,27 +1,21 @@
 import json
 import logging
+from typing import Any, Dict, Generator, List, Tuple, Union
 
-from typing import Union, Generator, Dict, Any, Tuple, List
-
-from core.model_runtime.entities.message_entities import PromptMessage, UserPromptMessage,\
-      SystemPromptMessage, AssistantPromptMessage, ToolPromptMessage, PromptMessageTool
-from core.model_runtime.entities.llm_entities import LLMResultChunk, LLMResult, LLMUsage
-from core.model_manager import ModelInstance
 from core.application_queue_manager import PublishFrom
-
-from core.tools.errors import ToolInvokeError, ToolNotFoundError, \
-    ToolNotSupportedError, ToolProviderNotFoundError, ToolParamterValidationError, \
-          ToolProviderCredentialValidationError
-
 from core.features.assistant_base_runner import BaseAssistantApplicationRunner
-
+from core.model_manager import ModelInstance
+from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage
+from core.model_runtime.entities.message_entities import (AssistantPromptMessage, PromptMessage, PromptMessageTool,
+                                                          SystemPromptMessage, ToolPromptMessage, UserPromptMessage)
+from core.tools.errors import (ToolInvokeError, ToolNotFoundError, ToolNotSupportedError, ToolParameterValidationError,
+                               ToolProviderCredentialValidationError, ToolProviderNotFoundError)
 from models.model import Conversation, Message, MessageAgentThought
 
 logger = logging.getLogger(__name__)
 
 class AssistantFunctionCallApplicationRunner(BaseAssistantApplicationRunner):
-    def run(self, model_instance: ModelInstance,
-                conversation: Conversation,
+    def run(self, conversation: Conversation,
                 message: Message,
                 query: str,
     ) -> Generator[LLMResultChunk, None, None]:
@@ -81,6 +75,8 @@ class AssistantFunctionCallApplicationRunner(BaseAssistantApplicationRunner):
                 llm_usage.prompt_price += usage.prompt_price
                 llm_usage.completion_price += usage.completion_price
 
+        model_instance = self.model_instance
+
         while function_call_state and iteration_step <= max_iteration_steps:
             function_call_state = False
 
@@ -96,17 +92,16 @@ class AssistantFunctionCallApplicationRunner(BaseAssistantApplicationRunner):
                 tool_input='',
                 messages_ids=message_file_ids
             )
-            self.queue_manager.publish_agent_thought(agent_thought, PublishFrom.APPLICATION_MANAGER)
 
             # recale llm max tokens
             self.recale_llm_max_tokens(self.model_config, prompt_messages)
             # invoke model
-            chunks: Generator[LLMResultChunk, None, None] = model_instance.invoke_llm(
+            chunks: Union[Generator[LLMResultChunk, None, None], LLMResult] = model_instance.invoke_llm(
                 prompt_messages=prompt_messages,
                 model_parameters=app_orchestration_config.model_config.parameters,
                 tools=prompt_messages_tools,
                 stop=app_orchestration_config.model_config.stop,
-                stream=True,
+                stream=self.stream_tool_call,
                 user=self.user_id,
                 callbacks=[],
             )
@@ -122,11 +117,45 @@ class AssistantFunctionCallApplicationRunner(BaseAssistantApplicationRunner):
 
             current_llm_usage = None
 
-            for chunk in chunks:
+            if self.stream_tool_call:
+                is_first_chunk = True
+                for chunk in chunks:
+                    if is_first_chunk:
+                        self.queue_manager.publish_agent_thought(agent_thought, PublishFrom.APPLICATION_MANAGER)
+                        is_first_chunk = False
+                    # check if there is any tool call
+                    if self.check_tool_calls(chunk):
+                        function_call_state = True
+                        tool_calls.extend(self.extract_tool_calls(chunk))
+                        tool_call_names = ';'.join([tool_call[1] for tool_call in tool_calls])
+                        try:
+                            tool_call_inputs = json.dumps({
+                                tool_call[1]: tool_call[2] for tool_call in tool_calls
+                            }, ensure_ascii=False)
+                        except json.JSONDecodeError as e:
+                            # ensure ascii to avoid encoding error
+                            tool_call_inputs = json.dumps({
+                                tool_call[1]: tool_call[2] for tool_call in tool_calls
+                            })
+
+                    if chunk.delta.message and chunk.delta.message.content:
+                        if isinstance(chunk.delta.message.content, list):
+                            for content in chunk.delta.message.content:
+                                response += content.data
+                        else:
+                            response += chunk.delta.message.content
+
+                    if chunk.delta.usage:
+                        increase_usage(llm_usage, chunk.delta.usage)
+                        current_llm_usage = chunk.delta.usage
+
+                    yield chunk
+            else:
+                result: LLMResult = chunks
                 # check if there is any tool call
-                if self.check_tool_calls(chunk):
+                if self.check_blocking_tool_calls(result):
                     function_call_state = True
-                    tool_calls.extend(self.extract_tool_calls(chunk))
+                    tool_calls.extend(self.extract_blocking_tool_calls(result))
                     tool_call_names = ';'.join([tool_call[1] for tool_call in tool_calls])
                     try:
                         tool_call_inputs = json.dumps({
@@ -138,18 +167,46 @@ class AssistantFunctionCallApplicationRunner(BaseAssistantApplicationRunner):
                             tool_call[1]: tool_call[2] for tool_call in tool_calls
                         })
 
-                if chunk.delta.message and chunk.delta.message.content:
-                    if isinstance(chunk.delta.message.content, list):
-                        for content in chunk.delta.message.content:
+                if result.usage:
+                    increase_usage(llm_usage, result.usage)
+                    current_llm_usage = result.usage
+
+                if result.message and result.message.content:
+                    if isinstance(result.message.content, list):
+                        for content in result.message.content:
                             response += content.data
                     else:
-                        response += chunk.delta.message.content
+                        response += result.message.content
 
-                if chunk.delta.usage:
-                    increase_usage(llm_usage, chunk.delta.usage)
-                    current_llm_usage = chunk.delta.usage
+                if not result.message.content:
+                    result.message.content = ''
 
-                yield chunk
+                self.queue_manager.publish_agent_thought(agent_thought, PublishFrom.APPLICATION_MANAGER)
+                
+                yield LLMResultChunk(
+                    model=model_instance.model,
+                    prompt_messages=result.prompt_messages,
+                    system_fingerprint=result.system_fingerprint,
+                    delta=LLMResultChunkDelta(
+                        index=0,
+                        message=result.message,
+                        usage=result.usage,
+                    )
+                )
+
+            if tool_calls:
+                prompt_messages.append(AssistantPromptMessage(
+                    content='',
+                    name='',
+                    tool_calls=[AssistantPromptMessage.ToolCall(
+                        id=tool_call[0],
+                        type='function',
+                        function=AssistantPromptMessage.ToolCall.ToolCallFunction(
+                            name=tool_call[1],
+                            arguments=json.dumps(tool_call[2], ensure_ascii=False)
+                        )
+                    ) for tool_call in tool_calls]
+                ))
 
             # save thought
             self.save_agent_thought(
@@ -167,6 +224,12 @@ class AssistantFunctionCallApplicationRunner(BaseAssistantApplicationRunner):
             
             final_answer += response + '\n'
 
+            # update prompt messages
+            if response.strip():
+                prompt_messages.append(AssistantPromptMessage(
+                    content=response,
+                ))
+            
             # call tools
             tool_responses = []
             for tool_call_id, tool_call_name, tool_call_args in tool_calls:
@@ -184,7 +247,7 @@ class AssistantFunctionCallApplicationRunner(BaseAssistantApplicationRunner):
                     try:
                         tool_invoke_message = tool_instance.invoke(
                             user_id=self.user_id, 
-                            tool_paramters=tool_call_args, 
+                            tool_parameters=tool_call_args, 
                         )
                         # transform tool invoke message to get LLM friendly message
                         tool_invoke_message = self.transform_tool_invoke_messages(tool_invoke_message)
@@ -203,15 +266,15 @@ class AssistantFunctionCallApplicationRunner(BaseAssistantApplicationRunner):
                             message_file_ids.append(message_file.id)
                             
                     except ToolProviderCredentialValidationError as e:
-                        error_response = f"Plese check your tool provider credentials"
+                        error_response = f"Please check your tool provider credentials"
                     except (
                         ToolNotFoundError, ToolNotSupportedError, ToolProviderNotFoundError
                     ) as e:
                         error_response = f"there is not a tool named {tool_call_name}"
                     except (
-                        ToolParamterValidationError
+                        ToolParameterValidationError
                     ) as e:
-                        error_response = f"tool paramters validation error: {e}, please check your tool paramters"
+                        error_response = f"tool parameters validation error: {e}, please check your tool parameters"
                     except ToolInvokeError as e:
                         error_response = f"tool invoke error: {e}"
                     except Exception as e:
@@ -256,12 +319,6 @@ class AssistantFunctionCallApplicationRunner(BaseAssistantApplicationRunner):
                 )
                 self.queue_manager.publish_agent_thought(agent_thought, PublishFrom.APPLICATION_MANAGER)
 
-            # update prompt messages
-            if response.strip():
-                prompt_messages.append(AssistantPromptMessage(
-                    content=response,
-                ))
-
             # update prompt tool
             for prompt_tool in prompt_messages_tools:
                 self.update_prompt_message_tool(tool_instances[prompt_tool.name], prompt_tool)
@@ -287,6 +344,14 @@ class AssistantFunctionCallApplicationRunner(BaseAssistantApplicationRunner):
         if llm_result_chunk.delta.message.tool_calls:
             return True
         return False
+    
+    def check_blocking_tool_calls(self, llm_result: LLMResult) -> bool:
+        """
+        Check if there is any blocking tool call in llm result
+        """
+        if llm_result.message.tool_calls:
+            return True
+        return False
 
     def extract_tool_calls(self, llm_result_chunk: LLMResultChunk) -> Union[None, List[Tuple[str, str, Dict[str, Any]]]]:
         """
@@ -304,6 +369,23 @@ class AssistantFunctionCallApplicationRunner(BaseAssistantApplicationRunner):
             ))
 
         return tool_calls
+    
+    def extract_blocking_tool_calls(self, llm_result: LLMResult) -> Union[None, List[Tuple[str, str, Dict[str, Any]]]]:
+        """
+        Extract blocking tool calls from llm result
+
+        Returns:
+            List[Tuple[str, str, Dict[str, Any]]]: [(tool_call_id, tool_call_name, tool_call_args)]
+        """
+        tool_calls = []
+        for prompt_message in llm_result.message.tool_calls:
+            tool_calls.append((
+                prompt_message.id,
+                prompt_message.function.name,
+                json.loads(prompt_message.function.arguments),
+            ))
+
+        return tool_calls
 
     def organize_prompt_messages(self, prompt_template: str,
                                  query: str = None, 

api/core/file/message_file_parser.py

@@ -1,11 +1,11 @@
 from typing import Dict, List, Optional, Union
 
 import requests
-from core.file.file_obj import FileObj, FileTransferMethod, FileType, FileBelongsTo
-from services.file_service import IMAGE_EXTENSIONS
+from core.file.file_obj import FileBelongsTo, FileObj, FileTransferMethod, FileType
 from extensions.ext_database import db
 from models.account import Account
 from models.model import AppModelConfig, EndUser, MessageFile, UploadFile
+from services.file_service import IMAGE_EXTENSIONS
 
 
 class MessageFileParser:

api/core/hosting_configuration.py

@@ -2,7 +2,7 @@ from typing import Optional
 
 from core.entities.provider_entities import QuotaUnit, RestrictModel
 from core.model_runtime.entities.model_entities import ModelType
-from flask import Flask, Config
+from flask import Config, Flask
 from models.provider import ProviderQuotaType
 from pydantic import BaseModel
 
@@ -20,10 +20,6 @@ class TrialHostingQuota(HostingQuota):
 
 class PaidHostingQuota(HostingQuota):
     quota_type: ProviderQuotaType = ProviderQuotaType.PAID
-    stripe_price_id: str = None
-    increase_quota: int = 1
-    min_quantity: int = 20
-    max_quantity: int = 100
 
 
 class FreeHostingQuota(HostingQuota):
@@ -102,7 +98,7 @@ class HostingConfiguration:
         )
 
     def init_openai(self, app_config: Config) -> HostingProvider:
-        quota_unit = QuotaUnit.TIMES
+        quota_unit = QuotaUnit.CREDITS
         quotas = []
 
         if app_config.get("HOSTED_OPENAI_TRIAL_ENABLED"):
@@ -114,6 +110,8 @@ class HostingConfiguration:
                     RestrictModel(model="gpt-3.5-turbo-1106", model_type=ModelType.LLM),
                     RestrictModel(model="gpt-3.5-turbo-instruct", model_type=ModelType.LLM),
                     RestrictModel(model="gpt-3.5-turbo-16k", model_type=ModelType.LLM),
+                    RestrictModel(model="gpt-3.5-turbo-16k-0613", model_type=ModelType.LLM),
+                    RestrictModel(model="gpt-3.5-turbo-0613", model_type=ModelType.LLM),
                     RestrictModel(model="text-davinci-003", model_type=ModelType.LLM),
                     RestrictModel(model="whisper-1", model_type=ModelType.SPEECH2TEXT),
                 ]
@@ -122,10 +120,20 @@ class HostingConfiguration:
 
         if app_config.get("HOSTED_OPENAI_PAID_ENABLED"):
             paid_quota = PaidHostingQuota(
-                stripe_price_id=app_config.get("HOSTED_OPENAI_PAID_STRIPE_PRICE_ID"),
-                increase_quota=int(app_config.get("HOSTED_OPENAI_PAID_INCREASE_QUOTA", "1")),
-                min_quantity=int(app_config.get("HOSTED_OPENAI_PAID_MIN_QUANTITY", "1")),
-                max_quantity=int(app_config.get("HOSTED_OPENAI_PAID_MAX_QUANTITY", "1"))
+                restrict_models=[
+                    RestrictModel(model="gpt-4", model_type=ModelType.LLM),
+                    RestrictModel(model="gpt-4-turbo-preview", model_type=ModelType.LLM),
+                    RestrictModel(model="gpt-4-32k", model_type=ModelType.LLM),
+                    RestrictModel(model="gpt-4-1106-preview", model_type=ModelType.LLM),
+                    RestrictModel(model="gpt-3.5-turbo", model_type=ModelType.LLM),
+                    RestrictModel(model="gpt-3.5-turbo-16k", model_type=ModelType.LLM),
+                    RestrictModel(model="gpt-3.5-turbo-16k-0613", model_type=ModelType.LLM),
+                    RestrictModel(model="gpt-3.5-turbo-1106", model_type=ModelType.LLM),
+                    RestrictModel(model="gpt-4-0125-preview", model_type=ModelType.LLM),
+                    RestrictModel(model="gpt-3.5-turbo-0613", model_type=ModelType.LLM),
+                    RestrictModel(model="gpt-3.5-turbo-instruct", model_type=ModelType.LLM),
+                    RestrictModel(model="text-davinci-003", model_type=ModelType.LLM),
+                ]
             )
             quotas.append(paid_quota)
 
@@ -164,12 +172,7 @@ class HostingConfiguration:
             quotas.append(trial_quota)
 
         if app_config.get("HOSTED_ANTHROPIC_PAID_ENABLED"):
-            paid_quota = PaidHostingQuota(
-                stripe_price_id=app_config.get("HOSTED_ANTHROPIC_PAID_STRIPE_PRICE_ID"),
-                increase_quota=int(app_config.get("HOSTED_ANTHROPIC_PAID_INCREASE_QUOTA", "1000000")),
-                min_quantity=int(app_config.get("HOSTED_ANTHROPIC_PAID_MIN_QUANTITY", "20")),
-                max_quantity=int(app_config.get("HOSTED_ANTHROPIC_PAID_MAX_QUANTITY", "100"))
-            )
+            paid_quota = PaidHostingQuota()
             quotas.append(paid_quota)
 
         if len(quotas) > 0:

api/core/indexing_runner.py

@@ -13,7 +13,7 @@ from core.docstore.dataset_docstore import DatasetDocumentStore
 from core.errors.error import ProviderTokenNotInitError
 from core.generator.llm_generator import LLMGenerator
 from core.index.index import IndexBuilder
-from core.model_manager import ModelManager, ModelInstance
+from core.model_manager import ModelInstance, ModelManager
 from core.model_runtime.entities.model_entities import ModelType, PriceType
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel

api/core/model_manager.py

@@ -12,8 +12,8 @@ from core.model_runtime.model_providers.__base.large_language_model import Large
 from core.model_runtime.model_providers.__base.moderation_model import ModerationModel
 from core.model_runtime.model_providers.__base.rerank_model import RerankModel
 from core.model_runtime.model_providers.__base.speech2text_model import Speech2TextModel
-from core.model_runtime.model_providers.__base.tts_model import TTSModel
 from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
+from core.model_runtime.model_providers.__base.tts_model import TTSModel
 from core.provider_manager import ProviderManager
 
 

api/core/model_runtime/entities/model_entities.py

@@ -78,6 +78,7 @@ class ModelFeature(Enum):
     MULTI_TOOL_CALL = "multi-tool-call"
     AGENT_THOUGHT = "agent-thought"
     VISION = "vision"
+    STREAM_TOOL_CALL = "stream-tool-call"
 
 
 class DefaultParameterName(Enum):

api/core/model_runtime/model_providers/__base/tts_model.py

@@ -1,13 +1,12 @@
-import uuid
 import hashlib
 import subprocess
+import uuid
 from abc import abstractmethod
 from typing import Optional
 
+from core.model_runtime.entities.model_entities import ModelPropertyKey, ModelType
 from core.model_runtime.errors.invoke import InvokeBadRequestError
-from core.model_runtime.entities.model_entities import ModelType
 from core.model_runtime.model_providers.__base.ai_model import AIModel
-from core.model_runtime.entities.model_entities import ModelPropertyKey
 
 
 class TTSModel(AIModel):

api/core/model_runtime/model_providers/azure_openai/_constant.py

@@ -36,6 +36,7 @@ LLM_BASE_MODELS = [
             features=[
                 ModelFeature.AGENT_THOUGHT,
                 ModelFeature.MULTI_TOOL_CALL,
+                ModelFeature.STREAM_TOOL_CALL,
             ],
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_properties={
@@ -80,6 +81,7 @@ LLM_BASE_MODELS = [
             features=[
                 ModelFeature.AGENT_THOUGHT,
                 ModelFeature.MULTI_TOOL_CALL,
+                ModelFeature.STREAM_TOOL_CALL,
             ],
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_properties={
@@ -124,6 +126,7 @@ LLM_BASE_MODELS = [
             features=[
                 ModelFeature.AGENT_THOUGHT,
                 ModelFeature.MULTI_TOOL_CALL,
+                ModelFeature.STREAM_TOOL_CALL,
             ],
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_properties={
@@ -198,6 +201,7 @@ LLM_BASE_MODELS = [
             features=[
                 ModelFeature.AGENT_THOUGHT,
                 ModelFeature.MULTI_TOOL_CALL,
+                ModelFeature.STREAM_TOOL_CALL,
             ],
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_properties={
@@ -272,6 +276,7 @@ LLM_BASE_MODELS = [
             features=[
                 ModelFeature.AGENT_THOUGHT,
                 ModelFeature.MULTI_TOOL_CALL,
+                ModelFeature.STREAM_TOOL_CALL,
             ],
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_properties={

api/core/model_runtime/model_providers/azure_openai/llm/llm.py

@@ -324,6 +324,7 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
                                               tools: Optional[list[PromptMessageTool]] = None) -> Generator:
         index = 0
         full_assistant_content = ''
+        delta_assistant_message_function_call_storage: ChoiceDeltaFunctionCall = None
         real_model = model
         system_fingerprint = None
         completion = ''
@@ -333,12 +334,32 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
 
             delta = chunk.choices[0]
 
-            if delta.finish_reason is None and (delta.delta.content is None or delta.delta.content == ''):
+            if delta.finish_reason is None and (delta.delta.content is None or delta.delta.content == '') and \
+                delta.delta.function_call is None:
                 continue
-
+            
             # assistant_message_tool_calls = delta.delta.tool_calls
             assistant_message_function_call = delta.delta.function_call
 
+            # extract tool calls from response
+            if delta_assistant_message_function_call_storage is not None:
+                # handle process of stream function call
+                if assistant_message_function_call:
+                    # message has not ended ever
+                    delta_assistant_message_function_call_storage.arguments += assistant_message_function_call.arguments
+                    continue
+                else:
+                    # message has ended
+                    assistant_message_function_call = delta_assistant_message_function_call_storage
+                    delta_assistant_message_function_call_storage = None
+            else:
+                if assistant_message_function_call:
+                    # start of stream function call
+                    delta_assistant_message_function_call_storage = assistant_message_function_call
+                    if delta_assistant_message_function_call_storage.arguments is None:
+                        delta_assistant_message_function_call_storage.arguments = ''
+                    continue
+
             # extract tool calls from response
             # tool_calls = self._extract_response_tool_calls(assistant_message_tool_calls)
             function_call = self._extract_response_function_call(assistant_message_function_call)
@@ -489,7 +510,7 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
         else:
             raise ValueError(f"Got unknown type {message}")
 
-        if message.name is not None:
+        if message.name:
             message_dict["name"] = message.name
 
         return message_dict
@@ -586,7 +607,6 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
         num_tokens = 0
         for tool in tools:
             num_tokens += len(encoding.encode('type'))
-            num_tokens += len(encoding.encode(tool.get("type")))
             num_tokens += len(encoding.encode('function'))
 
             # calculate num tokens for function object

api/core/model_runtime/model_providers/bedrock/llm/anthropic.claude-v1.yaml

@@ -8,9 +8,9 @@ model_properties:
 parameter_rules:
   - name: temperature
     use_template: temperature
-  - name: topP
+  - name: top_p
     use_template: top_p
-  - name: topK
+  - name: top_k
     label:
       zh_Hans: 取样数量
       en_US: Top K

api/core/model_runtime/model_providers/bedrock/llm/anthropic.claude-v2.1.yaml

@@ -8,9 +8,9 @@ model_properties:
 parameter_rules:
   - name: temperature
     use_template: temperature
-  - name: topP
+  - name: top_p
     use_template: top_p
-  - name: topK
+  - name: top_k
     label:
       zh_Hans: 取样数量
       en_US: Top K

api/core/model_runtime/model_providers/bedrock/llm/anthropic.claude-v2.yaml

@@ -8,9 +8,9 @@ model_properties:
 parameter_rules:
   - name: temperature
     use_template: temperature
-  - name: topP
+  - name: top_p
     use_template: top_p
-  - name: topK
+  - name: top_k
     label:
       zh_Hans: 取样数量
       en_US: Top K

api/core/model_runtime/model_providers/bedrock/llm/llm.py

@@ -1,15 +1,14 @@
+import json
 import logging
 from typing import Generator, List, Optional, Union
 
 import boto3
-from botocore.exceptions import ClientError, EndpointConnectionError, NoRegionError, ServiceNotInRegionError, UnknownServiceError
 from botocore.config import Config
-import json
-
+from botocore.exceptions import (ClientError, EndpointConnectionError, NoRegionError, ServiceNotInRegionError,
+                                 UnknownServiceError)
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
-
-from core.model_runtime.entities.message_entities import (AssistantPromptMessage, PromptMessage,
-                                                          PromptMessageTool, SystemPromptMessage, UserPromptMessage)
+from core.model_runtime.entities.message_entities import (AssistantPromptMessage, PromptMessage, PromptMessageTool,
+                                                          SystemPromptMessage, UserPromptMessage)
 from core.model_runtime.errors.invoke import (InvokeAuthorizationError, InvokeBadRequestError, InvokeConnectionError,
                                               InvokeError, InvokeRateLimitError, InvokeServerUnavailableError)
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
@@ -250,9 +249,12 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
             invoke = runtime_client.invoke_model
 
         try:
+            body_jsonstr=json.dumps(payload)
             response = invoke(
-                body=json.dumps(payload),
                 modelId=model,
+                contentType="application/json",
+                accept= "*/*",
+                body=body_jsonstr
             )
         except ClientError as ex:
             error_code = ex.response['Error']['Code']
@@ -385,7 +387,6 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
             if not chunk:
                 exception_name = next(iter(event))
                 full_ex_msg = f"{exception_name}: {event[exception_name]['message']}"
-
                 raise self._map_client_to_invoke_error(exception_name, full_ex_msg)
 
             payload = json.loads(chunk.get('bytes').decode())
@@ -396,7 +397,7 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
                 finish_reason = payload.get("completion_reason")
  
             elif model_prefix == "anthropic":
-                content_delta = payload
+                content_delta = payload.get("completion")
                 finish_reason = payload.get("stop_reason")
 
             elif model_prefix == "cohere":
@@ -410,12 +411,12 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
             else:
                 raise ValueError(f"Got unknown model prefix {model_prefix} when handling stream response")
 
-            index += 1
-           
+            # transform assistant message to prompt message
             assistant_prompt_message = AssistantPromptMessage(
                 content = content_delta if content_delta else '',
             )
-  
+            index += 1
+           
             if not finish_reason:
                 yield LLMResultChunk(
                     model=model,

api/core/model_runtime/model_providers/chatglm/llm/llm.py

@@ -5,7 +5,8 @@ from typing import Generator, List, Optional, cast
 
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
 from core.model_runtime.entities.message_entities import (AssistantPromptMessage, PromptMessage, PromptMessageFunction,
-                                                          PromptMessageTool, SystemPromptMessage, UserPromptMessage)
+                                                          PromptMessageTool, SystemPromptMessage, ToolPromptMessage,
+                                                          UserPromptMessage)
 from core.model_runtime.errors.invoke import (InvokeAuthorizationError, InvokeBadRequestError, InvokeConnectionError,
                                               InvokeError, InvokeRateLimitError, InvokeServerUnavailableError)
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
@@ -194,6 +195,10 @@ class ChatGLMLargeLanguageModel(LargeLanguageModel):
         elif isinstance(message, SystemPromptMessage):
             message = cast(SystemPromptMessage, message)
             message_dict = {"role": "system", "content": message.content}
+        elif isinstance(message, ToolPromptMessage):
+            # check if last message is user message
+            message = cast(ToolPromptMessage, message)
+            message_dict = {"role": "function", "content": message.content}
         else:
             raise ValueError(f"Unknown message type {type(message)}")
         

api/core/model_runtime/model_providers/cohere/llm/llm.py

@@ -1,19 +1,18 @@
 import logging
-from typing import Generator, List, Optional, Union, cast, Tuple
+from typing import Generator, List, Optional, Tuple, Union, cast
 
 import cohere
 from cohere.responses import Chat, Generations
-from cohere.responses.chat import StreamingChat, StreamTextGeneration, StreamEnd
-from cohere.responses.generation import StreamingText, StreamingGenerations
-
+from cohere.responses.chat import StreamEnd, StreamingChat, StreamTextGeneration
+from cohere.responses.generation import StreamingGenerations, StreamingText
 from core.model_runtime.entities.llm_entities import LLMMode, LLMResult, LLMResultChunk, LLMResultChunkDelta
 from core.model_runtime.entities.message_entities import (AssistantPromptMessage, PromptMessage,
-                                                          PromptMessageContentType, SystemPromptMessage,
-                                                          TextPromptMessageContent, UserPromptMessage,
-                                                          PromptMessageTool)
+                                                          PromptMessageContentType, PromptMessageTool,
+                                                          SystemPromptMessage, TextPromptMessageContent,
+                                                          UserPromptMessage)
 from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, I18nObject, ModelType
-from core.model_runtime.errors.invoke import InvokeConnectionError, InvokeServerUnavailableError, InvokeError, \
-    InvokeRateLimitError, InvokeAuthorizationError, InvokeBadRequestError
+from core.model_runtime.errors.invoke import (InvokeAuthorizationError, InvokeBadRequestError, InvokeConnectionError,
+                                              InvokeError, InvokeRateLimitError, InvokeServerUnavailableError)
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 

api/core/model_runtime/model_providers/cohere/text_embedding/text_embedding.py

@@ -4,11 +4,10 @@ from typing import Optional, Tuple
 import cohere
 import numpy as np
 from cohere.responses import Tokens
-
 from core.model_runtime.entities.model_entities import PriceType
 from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
-from core.model_runtime.errors.invoke import InvokeConnectionError, InvokeServerUnavailableError, InvokeRateLimitError, \
-    InvokeAuthorizationError, InvokeBadRequestError, InvokeError
+from core.model_runtime.errors.invoke import (InvokeAuthorizationError, InvokeBadRequestError, InvokeConnectionError,
+                                              InvokeError, InvokeRateLimitError, InvokeServerUnavailableError)
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
 from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
 

api/core/model_runtime/model_providers/minimax/llm/abab5.5-chat.yaml

@@ -4,6 +4,8 @@ label:
 model_type: llm
 features:
   - agent-thought
+  - tool-call
+  - stream-tool-call
 model_properties:
   mode: chat
   context_size: 16384

api/core/model_runtime/model_providers/minimax/llm/abab6-chat.yaml

@@ -4,6 +4,8 @@ label:
 model_type: llm
 features:
   - agent-thought
+  - tool-call
+  - stream-tool-call
 model_properties:
   mode: chat
   context_size: 32768

api/core/model_runtime/model_providers/minimax/llm/chat_completion.py

@@ -16,7 +16,7 @@ class MinimaxChatCompletion(object):
     """
     def generate(self, model: str, api_key: str, group_id: str, 
                  prompt_messages: List[MinimaxMessage], model_parameters: dict,
-                 tools: Dict[str, Any], stop: List[str] | None, stream: bool, user: str) \
+                 tools: List[Dict[str, Any]], stop: List[str] | None, stream: bool, user: str) \
         -> Union[MinimaxMessage, Generator[MinimaxMessage, None, None]]:
         """
             generate chat completion
@@ -162,7 +162,6 @@ class MinimaxChatCompletion(object):
                 continue
 
             for choice in choices:
-                print(choice)
                 message = choice['delta']
                 yield MinimaxMessage(
                     content=message,

api/core/model_runtime/model_providers/minimax/llm/chat_completion_pro.py

@@ -17,7 +17,7 @@ class MinimaxChatCompletionPro(object):
     """
     def generate(self, model: str, api_key: str, group_id: str, 
                  prompt_messages: List[MinimaxMessage], model_parameters: dict,
-                 tools: Dict[str, Any], stop: List[str] | None, stream: bool, user: str) \
+                 tools: List[Dict[str, Any]], stop: List[str] | None, stream: bool, user: str) \
         -> Union[MinimaxMessage, Generator[MinimaxMessage, None, None]]:
         """
             generate chat completion
@@ -82,6 +82,10 @@ class MinimaxChatCompletionPro(object):
             **extra_kwargs
         }
 
+        if tools:
+            body['functions'] = tools
+            body['function_call'] = { 'type': 'auto' }
+
         try:
             response = post(
                 url=url, data=dumps(body), headers=headers, stream=stream, timeout=(10, 300))
@@ -135,6 +139,7 @@ class MinimaxChatCompletionPro(object):
         """
             handle stream chat generate response
         """
+        function_call_storage = None
         for line in response.iter_lines():
             if not line:
                 continue
@@ -148,7 +153,7 @@ class MinimaxChatCompletionPro(object):
                 msg = data['base_resp']['status_msg']
                 self._handle_error(code, msg)
 
-            if data['reply']:
+            if data['reply'] or 'usage' in data and data['usage']:
                 total_tokens = data['usage']['total_tokens']
                 message =  MinimaxMessage(
                     role=MinimaxMessage.Role.ASSISTANT.value,
@@ -160,6 +165,12 @@ class MinimaxChatCompletionPro(object):
                     'total_tokens': total_tokens
                 }
                 message.stop_reason = data['choices'][0]['finish_reason']
+
+                if function_call_storage:
+                    function_call_message = MinimaxMessage(content='', role=MinimaxMessage.Role.ASSISTANT.value)
+                    function_call_message.function_call = function_call_storage
+                    yield function_call_message
+
                 yield message
                 return
 
@@ -168,11 +179,28 @@ class MinimaxChatCompletionPro(object):
                 continue
 
             for choice in choices:
-                message = choice['messages'][0]['text']
-                if not message:
-                    continue
+                message = choice['messages'][0]
+
+                if 'function_call' in message:
+                    if not function_call_storage:
+                        function_call_storage = message['function_call']
+                        if 'arguments' not in function_call_storage or not function_call_storage['arguments']:
+                            function_call_storage['arguments'] = ''
+                            continue
+                    else:
+                        function_call_storage['arguments'] += message['function_call']['arguments']
+                        continue
+                else:
+                    if function_call_storage:
+                        message['function_call'] = function_call_storage
+                        function_call_storage = None
                 
-                yield MinimaxMessage(
-                    content=message,
-                    role=MinimaxMessage.Role.ASSISTANT.value
-                )
+                minimax_message = MinimaxMessage(content='', role=MinimaxMessage.Role.ASSISTANT.value)
+
+                if 'function_call' in message:
+                    minimax_message.function_call = message['function_call']
+
+                if 'text' in message:
+                    minimax_message.content = message['text']
+
+                yield minimax_message

api/core/model_runtime/model_providers/minimax/llm/llm.py

@@ -2,7 +2,7 @@ from typing import Generator, List
 
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
 from core.model_runtime.entities.message_entities import (AssistantPromptMessage, PromptMessage, PromptMessageTool,
-                                                          SystemPromptMessage, UserPromptMessage)
+                                                          SystemPromptMessage, ToolPromptMessage, UserPromptMessage)
 from core.model_runtime.errors.invoke import (InvokeAuthorizationError, InvokeBadRequestError, InvokeConnectionError,
                                               InvokeError, InvokeRateLimitError, InvokeServerUnavailableError)
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
@@ -84,6 +84,13 @@ class MinimaxLargeLanguageModel(LargeLanguageModel):
         """
         client: MinimaxChatCompletionPro = self.model_apis[model]()
 
+        if tools:
+            tools = [{
+                "name": tool.name,
+                "description": tool.description,
+                "parameters": tool.parameters
+            } for tool in tools]
+
         response = client.generate(
             model=model,
             api_key=credentials['minimax_api_key'],
@@ -109,7 +116,19 @@ class MinimaxLargeLanguageModel(LargeLanguageModel):
         elif isinstance(prompt_message, UserPromptMessage):
             return MinimaxMessage(role=MinimaxMessage.Role.USER.value, content=prompt_message.content)
         elif isinstance(prompt_message, AssistantPromptMessage):
+            if prompt_message.tool_calls:
+                message = MinimaxMessage(
+                    role=MinimaxMessage.Role.ASSISTANT.value,
+                    content=''
+                )
+                message.function_call={
+                    'name': prompt_message.tool_calls[0].function.name,
+                    'arguments': prompt_message.tool_calls[0].function.arguments
+                }
+                return message
             return MinimaxMessage(role=MinimaxMessage.Role.ASSISTANT.value, content=prompt_message.content)
+        elif isinstance(prompt_message, ToolPromptMessage):
+            return MinimaxMessage(role=MinimaxMessage.Role.FUNCTION.value, content=prompt_message.content)
         else:
             raise NotImplementedError(f'Prompt message type {type(prompt_message)} is not supported')
 
@@ -151,6 +170,28 @@ class MinimaxLargeLanguageModel(LargeLanguageModel):
                         finish_reason=message.stop_reason if message.stop_reason else None,
                     ),
                 )
+            elif message.function_call:
+                if 'name' not in message.function_call or 'arguments' not in message.function_call:
+                    continue
+
+                yield LLMResultChunk(
+                    model=model,
+                    prompt_messages=prompt_messages,
+                    delta=LLMResultChunkDelta(
+                        index=0,
+                        message=AssistantPromptMessage(
+                            content='',
+                            tool_calls=[AssistantPromptMessage.ToolCall(
+                                id='',
+                                type='function',
+                                function=AssistantPromptMessage.ToolCall.ToolCallFunction(
+                                    name=message.function_call['name'],
+                                    arguments=message.function_call['arguments']
+                                )
+                            )]
+                        ),
+                    ),
+                )
             else:
                 yield LLMResultChunk(
                     model=model,

api/core/model_runtime/model_providers/minimax/llm/types.py

@@ -7,13 +7,23 @@ class MinimaxMessage:
         USER = 'USER'
         ASSISTANT = 'BOT'
         SYSTEM = 'SYSTEM'
+        FUNCTION = 'FUNCTION'
 
     role: str = Role.USER.value
     content: str
     usage: Dict[str, int] = None
     stop_reason: str = ''
+    function_call: Dict[str, Any] = None
 
     def to_dict(self) -> Dict[str, Any]:
+        if self.function_call and self.role == MinimaxMessage.Role.ASSISTANT.value:
+            return {
+                'sender_type': 'BOT',
+                'sender_name': '专家',
+                'text': '',
+                'function_call': self.function_call
+            }
+        
         return {
             'sender_type': self.role,
             'sender_name': '我' if self.role == 'USER' else '专家',

api/core/model_runtime/model_providers/ollama/llm/llm.py

@@ -2,21 +2,20 @@ import json
 import logging
 import re
 from decimal import Decimal
-from typing import Optional, Generator, Union, List, cast
+from typing import Generator, List, Optional, Union, cast
 from urllib.parse import urljoin
 
 import requests
-
-from core.model_runtime.entities.message_entities import PromptMessageTool, PromptMessage, AssistantPromptMessage, \
-    UserPromptMessage, PromptMessageContentType, ImagePromptMessageContent, \
-    TextPromptMessageContent, SystemPromptMessage
-from core.model_runtime.entities.model_entities import I18nObject, ModelType, \
-    PriceConfig, AIModelEntity, FetchFrom, ModelPropertyKey, ParameterRule, ParameterType, DefaultParameterName, \
-    ModelFeature
-from core.model_runtime.entities.llm_entities import LLMMode, LLMResult, \
-    LLMResultChunk, LLMResultChunkDelta
-from core.model_runtime.errors.invoke import InvokeError, InvokeAuthorizationError, InvokeBadRequestError, \
-    InvokeRateLimitError, InvokeServerUnavailableError, InvokeConnectionError
+from core.model_runtime.entities.llm_entities import LLMMode, LLMResult, LLMResultChunk, LLMResultChunkDelta
+from core.model_runtime.entities.message_entities import (AssistantPromptMessage, ImagePromptMessageContent,
+                                                          PromptMessage, PromptMessageContentType, PromptMessageTool,
+                                                          SystemPromptMessage, TextPromptMessageContent,
+                                                          UserPromptMessage)
+from core.model_runtime.entities.model_entities import (AIModelEntity, DefaultParameterName, FetchFrom, I18nObject,
+                                                        ModelFeature, ModelPropertyKey, ModelType, ParameterRule,
+                                                        ParameterType, PriceConfig)
+from core.model_runtime.errors.invoke import (InvokeAuthorizationError, InvokeBadRequestError, InvokeConnectionError,
+                                              InvokeError, InvokeRateLimitError, InvokeServerUnavailableError)
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 

api/core/model_runtime/model_providers/ollama/ollama.yaml

@@ -11,7 +11,7 @@ help:
     en_US: How to integrate with Ollama
     zh_Hans: 如何集成 Ollama
   url:
-    en_US: https://docs.dify.ai/advanced/model-configuration/ollama
+    en_US: https://docs.dify.ai/tutorials/model-configuration/ollama
 supported_model_types:
   - llm
   - text-embedding

api/core/model_runtime/model_providers/ollama/text_embedding/text_embedding.py

@@ -1,19 +1,18 @@
+import json
 import logging
 import time
 from decimal import Decimal
 from typing import Optional
 from urllib.parse import urljoin
-import requests
-import json
 
 import numpy as np
-
+import requests
 from core.model_runtime.entities.common_entities import I18nObject
-from core.model_runtime.entities.model_entities import PriceType, ModelPropertyKey, ModelType, AIModelEntity, FetchFrom, \
-    PriceConfig
-from core.model_runtime.entities.text_embedding_entities import TextEmbeddingResult, EmbeddingUsage
-from core.model_runtime.errors.invoke import InvokeError, InvokeAuthorizationError, InvokeBadRequestError, \
-    InvokeRateLimitError, InvokeServerUnavailableError, InvokeConnectionError
+from core.model_runtime.entities.model_entities import (AIModelEntity, FetchFrom, ModelPropertyKey, ModelType,
+                                                        PriceConfig, PriceType)
+from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
+from core.model_runtime.errors.invoke import (InvokeAuthorizationError, InvokeBadRequestError, InvokeConnectionError,
+                                              InvokeError, InvokeRateLimitError, InvokeServerUnavailableError)
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
 from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
 

api/core/model_runtime/model_providers/openai/llm/gpt-3.5-turbo-0613.yaml

@@ -6,6 +6,7 @@ model_type: llm
 features:
   - multi-tool-call
   - agent-thought
+  - stream-tool-call
 model_properties:
   mode: chat
   context_size: 4096

api/core/model_runtime/model_providers/openai/llm/gpt-3.5-turbo-1106.yaml

@@ -6,6 +6,7 @@ model_type: llm
 features:
   - multi-tool-call
   - agent-thought
+  - stream-tool-call
 model_properties:
   mode: chat
   context_size: 16385

api/core/model_runtime/model_providers/openai/llm/gpt-3.5-turbo-16k-0613.yaml

@@ -6,6 +6,7 @@ model_type: llm
 features:
   - multi-tool-call
   - agent-thought
+  - stream-tool-call
 model_properties:
   mode: chat
   context_size: 16385

api/core/model_runtime/model_providers/openai/llm/gpt-3.5-turbo-16k.yaml

@@ -6,6 +6,7 @@ model_type: llm
 features:
   - multi-tool-call
   - agent-thought
+  - stream-tool-call
 model_properties:
   mode: chat
   context_size: 16385

api/core/model_runtime/model_providers/openai/llm/gpt-3.5-turbo.yaml

@@ -6,6 +6,7 @@ model_type: llm
 features:
   - multi-tool-call
   - agent-thought
+  - stream-tool-call
 model_properties:
   mode: chat
   context_size: 4096

api/core/model_runtime/model_providers/openai/llm/gpt-4-0125-preview.yaml

@@ -6,6 +6,7 @@ model_type: llm
 features:
   - multi-tool-call
   - agent-thought
+  - stream-tool-call
 model_properties:
   mode: chat
   context_size: 128000

api/core/model_runtime/model_providers/openai/llm/gpt-4-1106-preview.yaml

@@ -6,6 +6,7 @@ model_type: llm
 features:
   - multi-tool-call
   - agent-thought
+  - stream-tool-call
 model_properties:
   mode: chat
   context_size: 128000

api/core/model_runtime/model_providers/openai/llm/gpt-4-32k.yaml

@@ -6,6 +6,7 @@ model_type: llm
 features:
   - multi-tool-call
   - agent-thought
+  - stream-tool-call
 model_properties:
   mode: chat
   context_size: 32768

api/core/model_runtime/model_providers/openai/llm/gpt-4-turbo-preview.yaml

@@ -6,6 +6,7 @@ model_type: llm
 features:
   - multi-tool-call
   - agent-thought
+  - stream-tool-call
 model_properties:
   mode: chat
   context_size: 128000

api/core/model_runtime/model_providers/openai/llm/gpt-4.yaml

@@ -6,6 +6,7 @@ model_type: llm
 features:
   - multi-tool-call
   - agent-thought
+  - stream-tool-call
 model_properties:
   mode: chat
   context_size: 8192

api/core/model_runtime/model_providers/openai/llm/llm.py

@@ -671,7 +671,7 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
         else:
             raise ValueError(f"Got unknown type {message}")
 
-        if message.name is not None:
+        if message.name:
             message_dict["name"] = message.name
 
         return message_dict

api/core/model_runtime/model_providers/openai/llm/text-davinci-003.yaml

@@ -26,3 +26,4 @@ pricing:
   output: '0.002'
   unit: '0.001'
   currency: USD
+deprecated: true

api/core/model_runtime/model_providers/openai/tts/tts.py

@@ -1,16 +1,15 @@
+import concurrent.futures
+from functools import reduce
 from io import BytesIO
 from typing import Optional
-from functools import reduce
-from pydub import AudioSegment
 
-from core.model_runtime.errors.validate import CredentialsValidateFailedError
 from core.model_runtime.errors.invoke import InvokeBadRequestError
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
 from core.model_runtime.model_providers.__base.tts_model import TTSModel
 from core.model_runtime.model_providers.openai._common import _CommonOpenAI
-
 from flask import Response, stream_with_context
 from openai import OpenAI
-import concurrent.futures
+from pydub import AudioSegment
 
 
 class OpenAIText2SpeechModel(_CommonOpenAI, TTSModel):

api/core/model_runtime/model_providers/openai_api_compatible/llm/llm.py

@@ -372,15 +372,9 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
 
                 if 'delta' in choice:
                     delta = choice['delta']
-                    if delta.get('content') is None or delta.get('content') == '':
-                        if finish_reason is not None:
-                            yield create_final_llm_result_chunk(
-                                index=chunk_index,
-                                message=AssistantPromptMessage(content=choice.get('text', '')),
-                                finish_reason=finish_reason
-                            )
-                        else:
-                            continue
+                    delta_content = delta.get('content')
+                    if delta_content is None or delta_content == '':
+                        continue
 
                     assistant_message_tool_calls = delta.get('tool_calls', None)
                     # assistant_message_function_call = delta.delta.function_call
@@ -393,11 +387,11 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
 
                     # transform assistant message to prompt message
                     assistant_prompt_message = AssistantPromptMessage(
-                        content=delta.get('content', ''),
+                        content=delta_content,
                         tool_calls=tool_calls if assistant_message_tool_calls else []
                     )
 
-                    full_assistant_content += delta.get('content', '')
+                    full_assistant_content += delta_content
                 elif 'text' in choice:
                     choice_text = choice.get('text', '')
                     if choice_text == '':

api/core/model_runtime/model_providers/openllm/llm/openllm_generate.py

@@ -41,7 +41,7 @@ class OpenLLMGenerate(object):
         if not server_url:
             raise InvalidAuthenticationError('Invalid server URL')
 
-        defautl_llm_config = {
+        default_llm_config = {
             "max_new_tokens": 128,
             "min_length": 0,
             "early_stopping": False,
@@ -75,19 +75,19 @@ class OpenLLMGenerate(object):
         }
 
         if 'max_tokens' in model_parameters and type(model_parameters['max_tokens']) == int:
-            defautl_llm_config['max_new_tokens'] = model_parameters['max_tokens']
+            default_llm_config['max_new_tokens'] = model_parameters['max_tokens']
 
         if 'temperature' in model_parameters and type(model_parameters['temperature']) == float:
-            defautl_llm_config['temperature'] = model_parameters['temperature']
+            default_llm_config['temperature'] = model_parameters['temperature']
 
         if 'top_p' in model_parameters and type(model_parameters['top_p']) == float:
-            defautl_llm_config['top_p'] = model_parameters['top_p']
+            default_llm_config['top_p'] = model_parameters['top_p']
 
         if 'top_k' in model_parameters and type(model_parameters['top_k']) == int:
-            defautl_llm_config['top_k'] = model_parameters['top_k']
+            default_llm_config['top_k'] = model_parameters['top_k']
 
         if 'use_cache' in model_parameters and type(model_parameters['use_cache']) == bool:
-            defautl_llm_config['use_cache'] = model_parameters['use_cache']
+            default_llm_config['use_cache'] = model_parameters['use_cache']
 
         headers = {
             'Content-Type': 'application/json',
@@ -104,7 +104,7 @@ class OpenLLMGenerate(object):
         data = {
             'stop': stop if stop else [],
             'prompt': '\n'.join([message.content for message in prompt_messages]),
-            'llm_config': defautl_llm_config,
+            'llm_config': default_llm_config,
         }
 
         try:

api/core/model_runtime/model_providers/spark/llm/_client.py

@@ -35,6 +35,10 @@ class SparkLLMClient:
             'spark-3': {
                 'version': 'v3.1',
                 'chat_domain': 'generalv3'
+            },
+            'spark-3.5': {
+                'version': 'v3.5',
+                'chat_domain': 'generalv3.5'
             }
         }
 

api/core/model_runtime/model_providers/spark/llm/_position.yaml

@@ -0,0 +1,4 @@
+- spark-3.5
+- spark-3
+- spark-1.5
+- spark-2

api/core/model_runtime/model_providers/spark/llm/spark-3.5.yaml

@@ -0,0 +1,33 @@
+model: spark-3.5
+label:
+  en_US: Spark V3.5
+model_type: llm
+model_properties:
+  mode: chat
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    default: 0.5
+    help:
+      zh_Hans: 核采样阈值。用于决定结果随机性，取值越高随机性越强即相同的问题得到的不同答案的可能性越高。
+      en_US: Kernel sampling threshold. Used to determine the randomness of the results. The higher the value, the stronger the randomness, that is, the higher the possibility of getting different answers to the same question.
+  - name: max_tokens
+    use_template: max_tokens
+    default: 2048
+    min: 1
+    max: 8192
+    help:
+      zh_Hans: 模型回答的tokens的最大长度。
+      en_US: 模型回答的tokens的最大长度。
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    default: 4
+    min: 1
+    max: 6
+    help:
+      zh_Hans: 从 k 个候选中随机选择⼀个（⾮等概率）。
+      en_US: Randomly select one from k candidates (non-equal probability).
+    required: false

api/core/model_runtime/model_providers/spark/spark.py

@@ -16,26 +16,5 @@ class SparkProvider(ModelProvider):
 
         :param credentials: provider credentials, credentials form defined in `provider_credential_schema`.
         """
-        try:
-            model_instance = self.get_model_instance(ModelType.LLM)
-
-            model_instance.validate_credentials(
-                model='spark-1.5',
-                credentials=credentials
-            )
-        except CredentialsValidateFailedError as ex:
-            try:
-                model_instance = self.get_model_instance(ModelType.LLM)
-
-                model_instance.validate_credentials(
-                    model='spark-3',
-                    credentials=credentials
-                )
-            except CredentialsValidateFailedError as ex:
-                raise ex
-            except Exception as ex:
-                logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
-                raise ex
-        except Exception as ex:
-            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
-            raise ex
+        # ignore credentials validation because every model has their own spark quota pool
+        pass

api/core/model_runtime/model_providers/tongyi/llm/llm.py

@@ -1,14 +1,13 @@
 from typing import Generator, List, Optional, Union
 
-from dashscope import get_tokenizer
-
-from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMMode
+from core.model_runtime.entities.llm_entities import LLMMode, LLMResult, LLMResultChunk, LLMResultChunkDelta
 from core.model_runtime.entities.message_entities import (AssistantPromptMessage, PromptMessage, PromptMessageTool,
                                                           SystemPromptMessage, UserPromptMessage)
 from core.model_runtime.errors.invoke import (InvokeAuthorizationError, InvokeBadRequestError, InvokeConnectionError,
                                               InvokeError, InvokeRateLimitError, InvokeServerUnavailableError)
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
+from dashscope import get_tokenizer
 from dashscope.api_entities.dashscope_response import DashScopeAPIResponse
 from dashscope.common.error import (AuthenticationError, InvalidParameter, RequestFailure, ServiceUnavailableError,
                                     UnsupportedHTTPMethod, UnsupportedModel)
@@ -168,7 +167,7 @@ class TongyiLargeLanguageModel(LargeLanguageModel):
 
         return result
 
-    def _handle_generate_stream_response(self, model: str, credentials: dict, responses: list[Generator],
+    def _handle_generate_stream_response(self, model: str, credentials: dict, responses: Generator,
                                          prompt_messages: list[PromptMessage]) -> Generator:
         """
         Handle llm stream response
@@ -182,7 +181,7 @@ class TongyiLargeLanguageModel(LargeLanguageModel):
         for index, response in enumerate(responses):
             resp_finish_reason = response.output.finish_reason
             resp_content = response.output.text
-            useage = response.usage
+            usage = response.usage
 
             if resp_finish_reason is None and (resp_content is None or resp_content == ''):
                 continue
@@ -194,7 +193,7 @@ class TongyiLargeLanguageModel(LargeLanguageModel):
 
             if resp_finish_reason is not None:
                 # transform usage
-                usage = self._calc_response_usage(model, credentials, useage.input_tokens, useage.output_tokens)
+                usage = self._calc_response_usage(model, credentials, usage.input_tokens, usage.output_tokens)
 
                 yield LLMResultChunk(
                     model=model,

api/core/model_runtime/model_providers/tongyi/tts/tts.py

@@ -1,16 +1,15 @@
+import concurrent.futures
+from functools import reduce
 from io import BytesIO
 from typing import Optional
-from functools import reduce
-from pydub import AudioSegment
-
-from core.model_runtime.errors.validate import CredentialsValidateFailedError
-from core.model_runtime.errors.invoke import InvokeBadRequestError
-from core.model_runtime.model_providers.__base.tts_model import TTSModel
-from core.model_runtime.model_providers.tongyi._common import _CommonTongyi
 
 import dashscope
+from core.model_runtime.errors.invoke import InvokeBadRequestError
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.tts_model import TTSModel
+from core.model_runtime.model_providers.tongyi._common import _CommonTongyi
 from flask import Response, stream_with_context
-import concurrent.futures
+from pydub import AudioSegment
 
 
 class TongyiText2SpeechModel(_CommonTongyi, TTSModel):

api/core/model_runtime/model_providers/xinference/llm/llm.py

@@ -3,15 +3,15 @@ from typing import Generator, Iterator, List, Optional, Union, cast
 from core.model_runtime.entities.common_entities import I18nObject
 from core.model_runtime.entities.llm_entities import LLMMode, LLMResult, LLMResultChunk, LLMResultChunkDelta
 from core.model_runtime.entities.message_entities import (AssistantPromptMessage, PromptMessage, PromptMessageTool,
-                                                          SystemPromptMessage, UserPromptMessage)
-from core.model_runtime.entities.model_entities import (AIModelEntity, FetchFrom, ModelPropertyKey, ModelType,
-                                                        ParameterRule, ParameterType)
+                                                          SystemPromptMessage, ToolPromptMessage, UserPromptMessage)
+from core.model_runtime.entities.model_entities import (AIModelEntity, FetchFrom, ModelFeature, ModelPropertyKey,
+                                                        ModelType, ParameterRule, ParameterType)
 from core.model_runtime.errors.invoke import (InvokeAuthorizationError, InvokeBadRequestError, InvokeConnectionError,
                                               InvokeError, InvokeRateLimitError, InvokeServerUnavailableError)
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
-from core.model_runtime.model_providers.xinference.llm.xinference_helper import (XinferenceHelper,
-                                                                                 XinferenceModelExtraParameter)
+from core.model_runtime.model_providers.xinference.xinference_helper import (XinferenceHelper,
+                                                                             XinferenceModelExtraParameter)
 from core.model_runtime.utils import helper
 from openai import (APIConnectionError, APITimeoutError, AuthenticationError, ConflictError, InternalServerError,
                     NotFoundError, OpenAI, PermissionDeniedError, RateLimitError, Stream, UnprocessableEntityError)
@@ -33,6 +33,12 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
 
             see `core.model_runtime.model_providers.__base.large_language_model.LargeLanguageModel._invoke`
         """
+        if 'temperature' in model_parameters:
+            if model_parameters['temperature'] < 0.01:
+                model_parameters['temperature'] = 0.01
+            elif model_parameters['temperature'] > 1.0:
+                model_parameters['temperature'] = 0.99
+
         return self._generate(
             model=model, credentials=credentials, prompt_messages=prompt_messages, model_parameters=model_parameters,
             tools=tools, stop=stop, stream=stream, user=user,
@@ -65,6 +71,12 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
                     credentials['completion_type'] = 'completion'
                 else:
                     raise ValueError(f'xinference model ability {extra_param.model_ability} is not supported')
+                
+            if extra_param.support_function_call:
+                credentials['support_function_call'] = True
+
+            if extra_param.context_length:
+                credentials['context_length'] = extra_param.context_length
 
         except RuntimeError as e:
             raise CredentialsValidateFailedError(f'Xinference credentials validate failed: {e}')
@@ -220,6 +232,9 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
         elif isinstance(message, SystemPromptMessage):
             message = cast(SystemPromptMessage, message)
             message_dict = {"role": "system", "content": message.content}
+        elif isinstance(message, ToolPromptMessage):
+            message = cast(ToolPromptMessage, message)
+            message_dict = {"tool_call_id": message.tool_call_id, "role": "tool", "content": message.content}
         else:
             raise ValueError(f"Unknown message type {type(message)}")
         
@@ -237,7 +252,7 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
                 label=I18nObject(
                     zh_Hans='温度',
                     en_US='Temperature'
-                )
+                ),
             ),
             ParameterRule(
                 name='top_p',
@@ -282,6 +297,9 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
                 completion_type = LLMMode.COMPLETION.value
             else:
                 raise ValueError(f'xinference model ability {extra_args.model_ability} is not supported')
+            
+        support_function_call = credentials.get('support_function_call', False)
+        context_length = credentials.get('context_length', 2048)
 
         entity = AIModelEntity(
             model=model,
@@ -290,8 +308,12 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
             ),
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.LLM,
+            features=[
+                ModelFeature.TOOL_CALL
+            ] if support_function_call else [],
             model_properties={ 
                 ModelPropertyKey.MODE: completion_type,
+                ModelPropertyKey.CONTEXT_SIZE: context_length
             },
             parameter_rules=rules
         )
@@ -310,6 +332,12 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
             
             extra_model_kwargs can be got by `XinferenceHelper.get_xinference_extra_parameter`
         """
+        if 'server_url' not in credentials:
+            raise CredentialsValidateFailedError('server_url is required in credentials')
+        
+        if credentials['server_url'].endswith('/'):
+            credentials['server_url'] = credentials['server_url'][:-1]
+
         client = OpenAI(
             base_url=f'{credentials["server_url"]}/v1',
             api_key='abc',

api/core/model_runtime/model_providers/xinference/text_embedding/text_embedding.py

@@ -2,12 +2,13 @@ import time
 from typing import Optional
 
 from core.model_runtime.entities.common_entities import I18nObject
-from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelType, PriceType
+from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelPropertyKey, ModelType, PriceType
 from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
 from core.model_runtime.errors.invoke import (InvokeAuthorizationError, InvokeBadRequestError, InvokeConnectionError,
                                               InvokeError, InvokeRateLimitError, InvokeServerUnavailableError)
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
 from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
+from core.model_runtime.model_providers.xinference.xinference_helper import XinferenceHelper
 from xinference_client.client.restful.restful_client import Client, RESTfulEmbeddingModelHandle, RESTfulModelHandle
 
 
@@ -35,7 +36,10 @@ class XinferenceTextEmbeddingModel(TextEmbeddingModel):
         """
         server_url = credentials['server_url']
         model_uid = credentials['model_uid']
-        
+
+        if server_url.endswith('/'):
+            server_url = server_url[:-1]
+
         client = Client(base_url=server_url)
         
         try:
@@ -102,8 +106,15 @@ class XinferenceTextEmbeddingModel(TextEmbeddingModel):
         :return:
         """
         try:
+            server_url = credentials['server_url']
+            model_uid = credentials['model_uid']
+            extra_args = XinferenceHelper.get_xinference_extra_parameter(server_url=server_url, model_uid=model_uid)
+
+            if extra_args.max_tokens:
+                credentials['max_tokens'] = extra_args.max_tokens
+
             self._invoke(model=model, credentials=credentials, texts=['ping'])
-        except InvokeAuthorizationError:
+        except (InvokeAuthorizationError, RuntimeError):
             raise CredentialsValidateFailedError('Invalid api key')
 
     @property
@@ -160,6 +171,7 @@ class XinferenceTextEmbeddingModel(TextEmbeddingModel):
         """
             used to define customizable model schema
         """
+        
         entity = AIModelEntity(
             model=model,
             label=I18nObject(
@@ -167,7 +179,10 @@ class XinferenceTextEmbeddingModel(TextEmbeddingModel):
             ),
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.TEXT_EMBEDDING,
-            model_properties={},
+            model_properties={
+                ModelPropertyKey.MAX_CHUNKS: 1,
+                ModelPropertyKey.CONTEXT_SIZE: 'max_tokens' in credentials and credentials['max_tokens'] or 512,
+            },
             parameter_rules=[]
         )
 

api/core/model_runtime/model_providers/xinference/xinference_helper.py

@@ -1,3 +1,4 @@
+from os import path
 from threading import Lock
 from time import time
 from typing import List
@@ -12,11 +13,18 @@ class XinferenceModelExtraParameter(object):
     model_format: str
     model_handle_type: str
     model_ability: List[str]
+    max_tokens: int = 512
+    context_length: int = 2048
+    support_function_call: bool = False
 
-    def __init__(self, model_format: str, model_handle_type: str, model_ability: List[str]) -> None:
+    def __init__(self, model_format: str, model_handle_type: str, model_ability: List[str], 
+                 support_function_call: bool, max_tokens: int, context_length: int) -> None:
         self.model_format = model_format
         self.model_handle_type = model_handle_type
         self.model_ability = model_ability
+        self.support_function_call = support_function_call
+        self.max_tokens = max_tokens
+        self.context_length = context_length
 
 cache = {}
 cache_lock = Lock()
@@ -49,9 +57,9 @@ class XinferenceHelper:
             get xinference model extra parameter like model_format and model_handle_type
         """
 
-        url = f'{server_url}/v1/models/{model_uid}'
+        url = path.join(server_url, 'v1/models', model_uid)
 
-        # this methid is surrounded by a lock, and default requests may hang forever, so we just set a Adapter with max_retries=3
+        # this method is surrounded by a lock, and default requests may hang forever, so we just set a Adapter with max_retries=3
         session = Session()
         session.mount('http://', HTTPAdapter(max_retries=3))
         session.mount('https://', HTTPAdapter(max_retries=3))
@@ -66,10 +74,12 @@ class XinferenceHelper:
         
         response_json = response.json()
 
-        model_format = response_json['model_format']
-        model_ability = response_json['model_ability']
+        model_format = response_json.get('model_format', 'ggmlv3')
+        model_ability = response_json.get('model_ability', [])
 
-        if model_format == 'ggmlv3' and 'chatglm' in response_json['model_name']:
+        if response_json.get('model_type') == 'embedding':
+            model_handle_type = 'embedding'
+        elif model_format == 'ggmlv3' and 'chatglm' in response_json['model_name']:
             model_handle_type = 'chatglm'
         elif 'generate' in model_ability:
             model_handle_type = 'generate'
@@ -78,8 +88,16 @@ class XinferenceHelper:
         else:
             raise NotImplementedError(f'xinference model handle type {model_handle_type} is not supported')
         
+        support_function_call = 'tools' in model_ability
+        max_tokens = response_json.get('max_tokens', 512)
+
+        context_length = response_json.get('context_length', 2048)
+        
         return XinferenceModelExtraParameter(
             model_format=model_format,
             model_handle_type=model_handle_type,
-            model_ability=model_ability
+            model_ability=model_ability,
+            support_function_call=support_function_call,
+            max_tokens=max_tokens,
+            context_length=context_length
         )

api/core/model_runtime/model_providers/zhipuai/llm/glm_3_turbo.yaml

@@ -2,6 +2,10 @@ model: glm-3-turbo
 label:
   en_US: glm-3-turbo
 model_type: llm
+features:
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
 model_properties:
   mode: chat
 parameter_rules:
@@ -28,15 +32,3 @@ parameter_rules:
       zh_Hans: SSE接口调用时，用于控制每次返回内容方式是增量还是全量，不提供此参数时默认为增量返回，true 为增量返回，false 为全量返回。
       en_US: When the SSE interface is called, it is used to control whether the content is returned incrementally or in full. If this parameter is not provided, the default is incremental return. true means incremental return, false means full return.
     required: false
-  - name: return_type
-    label:
-      zh_Hans: 回复类型
-      en_US: Return Type
-    type: string
-    help:
-      zh_Hans: 用于控制每次返回内容的类型，空或者没有此字段时默认按照 json_string 返回，json_string 返回标准的 JSON 字符串，text 返回原始的文本内容。
-      en_US: Used to control the type of content returned each time. When it is empty or does not have this field, it will be returned as json_string by default. json_string returns a standard JSON string, and text returns the original text content.
-    required: false
-    options:
-      - text
-      - json_string

api/core/model_runtime/model_providers/zhipuai/llm/glm_4.yaml

@@ -2,6 +2,10 @@ model: glm-4
 label:
   en_US: glm-4
 model_type: llm
+features:
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
 model_properties:
   mode: chat
 parameter_rules:
@@ -28,15 +32,3 @@ parameter_rules:
       zh_Hans: SSE接口调用时，用于控制每次返回内容方式是增量还是全量，不提供此参数时默认为增量返回，true 为增量返回，false 为全量返回。
       en_US: When the SSE interface is called, it is used to control whether the content is returned incrementally or in full. If this parameter is not provided, the default is incremental return. true means incremental return, false means full return.
     required: false
-  - name: return_type
-    label:
-      zh_Hans: 回复类型
-      en_US: Return Type
-    type: string
-    help:
-      zh_Hans: 用于控制每次返回内容的类型，空或者没有此字段时默认按照 json_string 返回，json_string 返回标准的 JSON 字符串，text 返回原始的文本内容。
-      en_US: Used to control the type of content returned each time. When it is empty or does not have this field, it will be returned as json_string by default. json_string returns a standard JSON string, and text returns the original text content.
-    required: false
-    options:
-      - text
-      - json_string

api/core/model_runtime/model_providers/zhipuai/llm/glm_4v.yaml

@@ -30,15 +30,3 @@ parameter_rules:
       zh_Hans: SSE接口调用时，用于控制每次返回内容方式是增量还是全量，不提供此参数时默认为增量返回，true 为增量返回，false 为全量返回。
       en_US: When the SSE interface is called, it is used to control whether the content is returned incrementally or in full. If this parameter is not provided, the default is incremental return. true means incremental return, false means full return.
     required: false
-  - name: return_type
-    label:
-      zh_Hans: 回复类型
-      en_US: Return Type
-    type: string
-    help:
-      zh_Hans: 用于控制每次返回内容的类型，空或者没有此字段时默认按照 json_string 返回，json_string 返回标准的 JSON 字符串，text 返回原始的文本内容。
-      en_US: Used to control the type of content returned each time. When it is empty or does not have this field, it will be returned as json_string by default. json_string returns a standard JSON string, and text returns the original text content.
-    required: false
-    options:
-      - text
-      - json_string

api/core/model_runtime/model_providers/zhipuai/llm/llm.py

@@ -2,16 +2,19 @@ import json
 from typing import Any, Dict, Generator, List, Optional, Union
 
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
-from core.model_runtime.entities.message_entities import (AssistantPromptMessage, PromptMessage, PromptMessageRole,
-                                                          PromptMessageTool, SystemPromptMessage, UserPromptMessage, ToolPromptMessage,
-                                                          TextPromptMessageContent, ImagePromptMessageContent, PromptMessageContentType)
+from core.model_runtime.entities.message_entities import (AssistantPromptMessage, ImagePromptMessageContent,
+                                                          PromptMessage, PromptMessageContentType, PromptMessageRole,
+                                                          PromptMessageTool, SystemPromptMessage,
+                                                          TextPromptMessageContent, ToolPromptMessage,
+                                                          UserPromptMessage)
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
-from core.model_runtime.utils import helper
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 from core.model_runtime.model_providers.zhipuai._common import _CommonZhipuaiAI
 from core.model_runtime.model_providers.zhipuai.zhipuai_sdk._client import ZhipuAI
-from core.model_runtime.model_providers.zhipuai.zhipuai_sdk.types.chat.chat_completion_chunk import ChatCompletionChunk
 from core.model_runtime.model_providers.zhipuai.zhipuai_sdk.types.chat.chat_completion import Completion
+from core.model_runtime.model_providers.zhipuai.zhipuai_sdk.types.chat.chat_completion_chunk import ChatCompletionChunk
+from core.model_runtime.utils import helper
+
 
 class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
 
@@ -194,6 +197,27 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
                             'content': prompt_message.content,
                             'tool_call_id': prompt_message.tool_call_id
                         })
+                    elif isinstance(prompt_message, AssistantPromptMessage):
+                        if prompt_message.tool_calls:
+                            params['messages'].append({
+                                'role': 'assistant',
+                                'content': prompt_message.content,
+                                'tool_calls': [
+                                    {
+                                        'id': tool_call.id,
+                                        'type': tool_call.type,
+                                        'function': {
+                                            'name': tool_call.function.name,
+                                            'arguments': tool_call.function.arguments
+                                        }
+                                    } for tool_call in prompt_message.tool_calls
+                                ]
+                            })
+                        else:
+                            params['messages'].append({
+                                'role': 'assistant',
+                                'content': prompt_message.content
+                            })
                     else:
                         params['messages'].append({
                             'role': prompt_message.role.value,

api/core/model_runtime/model_providers/zhipuai/text_embedding/text_embedding.py

@@ -5,8 +5,8 @@ from core.model_runtime.entities.model_entities import PriceType
 from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
 from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
-from core.model_runtime.model_providers.zhipuai.zhipuai_sdk._client import ZhipuAI
 from core.model_runtime.model_providers.zhipuai._common import _CommonZhipuaiAI
+from core.model_runtime.model_providers.zhipuai.zhipuai_sdk._client import ZhipuAI
 from langchain.schema.language_model import _get_token_ids_default_method
 
 

api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/__init__.py

@@ -1,17 +1,6 @@
 
-from ._client import ZhipuAI
-
-from .core._errors import (
-    ZhipuAIError,
-    APIStatusError,
-    APIRequestFailedError,
-    APIAuthenticationError,
-    APIReachLimitError,
-    APIInternalError,
-    APIServerFlowExceedError,
-    APIResponseError,
-    APIResponseValidationError,
-    APITimeoutError,
-)
-
 from .__version__ import __version__
+from ._client import ZhipuAI
+from .core._errors import (APIAuthenticationError, APIInternalError, APIReachLimitError, APIRequestFailedError,
+                           APIResponseError, APIResponseValidationError, APIServerFlowExceedError, APIStatusError,
+                           APITimeoutError, ZhipuAIError)