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Code Issues Actions 4 Packages Projects Releases Wiki Activity

Feat/assistant app (#2086)

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Co-authored-by: chenhe <guchenhe@gmail.com>
Co-authored-by: Pascal M <11357019+perzeuss@users.noreply.github.com>
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Yeuoly 2024-01-23 19:58:23 +08:00 committed by GitHub
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175 changed files with 11619 additions and 1235 deletions
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26
.github/workflows/tool-tests.yaml vendored Normal file
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@ -0,0 +1,26 @@
name: Run Tool Pytest
on:
pull_request:
branches:
- main
jobs:
test:
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: '3.10'
cache: 'pip'
cache-dependency-path: ./api/requirements.txt
- name: Install dependencies
run: pip install -r ./api/requirements.txt
- name: Run pytest
run: pytest ./api/tests/integration_tests/tools/test_all_provider.py

2
CONTRIBUTING.md
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@ -91,6 +91,8 @@ To validate your set up, head over to [http://localhost:3000](http://localhost:3
If you are adding a model provider, [this guide](https://github.com/langgenius/dify/blob/main/api/core/model_runtime/README.md) is for you.
If you are adding a tool provider to Agent or Workflow, [this guide](./api/core/tools/README.md) is for you.
To help you quickly navigate where your contribution fits, a brief, annotated outline of Dify's backend & frontend is as follows:
### Backend

2
api/app.py
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@ -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
from models import account, dataset, model, source, task, tool, web, tools
from services.account_service import AccountService
# DO NOT REMOVE ABOVE

63
api/commands.py
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@ -22,7 +22,7 @@ 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
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
@ -775,6 +775,66 @@ def add_annotation_question_field_value():
click.echo(
click.style(f'Congratulations! add annotation question value successful. Deal count {message_annotation_deal_count}', fg='green'))
@click.command('migrate-universal-chat-to-installed-app', help='Migrate universal chat to installed app.')
@click.option("--batch-size", default=500, help="Number of records to migrate in each batch.")
def migrate_universal_chat_to_installed_app(batch_size):
total_records = db.session.query(App).filter(
App.is_universal == True
).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: list[App] = db.session.query(App) \
.filter(App.is_universal == True) \
.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:
# check if the app is already installed
installed_app = db.session.query(InstalledApp).filter(
InstalledApp.app_id == data.id
).first()
if installed_app:
continue
# insert installed app
installed_app = InstalledApp(
app_id=data.id,
tenant_id=data.tenant_id,
position=0,
app_owner_tenant_id=data.tenant_id,
is_pinned=True,
last_used_at=datetime.datetime.utcnow(),
)
db.session.add(installed_app)
db.session.commit()
except Exception as e:
click.secho(f"Error while migrating data: {e}, app_id: {data.id}", fg='red')
continue
click.secho(f"Successfully migrated batch {i + 1}/{num_batches}.", fg='green')
pbar.update(len(data_batch))
def register_commands(app):
app.cli.add_command(reset_password)
@ -791,3 +851,4 @@ def register_commands(app):
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)
app.cli.add_command(migrate_universal_chat_to_installed_app)

2
api/controllers/console/__init__.py
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@ -16,7 +16,5 @@ from .billing import billing
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 universal chat controllers
from .universal_chat import audio, chat, conversation, message, parameter
# Import workspace controllers
from .workspace import account, members, model_providers, models, tool_providers, workspace

27
api/controllers/console/app/app.py
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@ -16,14 +16,15 @@ from events.app_event import app_was_created, app_was_deleted
from extensions.ext_database import db
from fields.app_fields import (app_detail_fields, app_detail_fields_with_site, app_pagination_fields,
template_list_fields)
from flask import current_app
from flask_login import current_user
from flask_restful import Resource, abort, inputs, marshal_with, reqparse
from libs.login import login_required
from models.model import App, AppModelConfig, Site
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:
@ -42,14 +43,30 @@ class AppListApi(Resource):
parser = reqparse.RequestParser()
parser.add_argument('page', type=inputs.int_range(1, 99999), required=False, default=1, location='args')
parser.add_argument('limit', type=inputs.int_range(1, 100), required=False, default=20, location='args')
parser.add_argument('mode', type=str, choices=['chat', 'completion', 'all'], default='all', location='args', required=False)
parser.add_argument('name', type=str, location='args', required=False)
args = parser.parse_args()
filters = [
App.tenant_id == current_user.current_tenant_id,
]
if args['mode'] == 'completion':
filters.append(App.mode == 'completion')
elif args['mode'] == 'chat':
filters.append(App.mode == 'chat')
else:
pass
if 'name' in args and args['name']:
filters.append(App.name.ilike(f'%{args["name"]}%'))
app_models = db.paginate(
db.select(App).where(App.tenant_id == current_user.current_tenant_id,
App.is_universal == False).order_by(App.created_at.desc()),
db.select(App).where(*filters).order_by(App.created_at.desc()),
page=args['page'],
per_page=args['limit'],
error_out=False)
error_out=False
)
return app_models
@ -62,7 +79,7 @@ class AppListApi(Resource):
"""Create app"""
parser = reqparse.RequestParser()
parser.add_argument('name', type=str, required=True, location='json')
parser.add_argument('mode', type=str, choices=['completion', 'chat'], location='json')
parser.add_argument('mode', type=str, choices=['completion', 'chat', 'assistant'], location='json')
parser.add_argument('icon', type=str, location='json')
parser.add_argument('icon_background', type=str, location='json')
parser.add_argument('model_config', type=dict, location='json')

5
api/controllers/console/explore/installed_app.py
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@ -33,8 +33,9 @@ class InstalledAppsListApi(Resource):
'app_owner_tenant_id': installed_app.app_owner_tenant_id,
'is_pinned': installed_app.is_pinned,
'last_used_at': installed_app.last_used_at,
"editable": current_user.role in ["owner", "admin"],
"uninstallable": current_tenant_id == installed_app.app_owner_tenant_id
'editable': current_user.role in ["owner", "admin"],
'uninstallable': current_tenant_id == installed_app.app_owner_tenant_id,
'is_agent': installed_app.is_agent
}
for installed_app in installed_apps
]

6
api/controllers/console/explore/message.py
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@ -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
from flask_restful import marshal_with, reqparse, fields
from flask_restful.inputs import int_range
from libs.helper import uuid_value
from libs.helper import uuid_value, TimestampField
from services.completion_service import CompletionService
from services.errors.app import MoreLikeThisDisabledError
from services.errors.conversation import ConversationNotExistsError
@ -29,7 +29,6 @@ from werkzeug.exceptions import InternalServerError, NotFound
class MessageListApi(InstalledAppResource):
@marshal_with(message_infinite_scroll_pagination_fields)
def get(self, installed_app):
app_model = installed_app.app
@ -51,7 +50,6 @@ class MessageListApi(InstalledAppResource):
except services.errors.message.FirstMessageNotExistsError:
raise NotFound("First Message Not Exists.")
class MessageFeedbackApi(InstalledAppResource):
def post(self, installed_app, message_id):
app_model = installed_app.app

43
api/controllers/console/explore/parameter.py
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@ -1,10 +1,14 @@
# -*- coding:utf-8 -*-
import json
from controllers.console import api
from controllers.console.explore.wraps import InstalledAppResource
from flask import current_app
from flask_restful import fields, marshal_with
from models.model import InstalledApp
from models.model import InstalledApp, AppModelConfig
from models.tools import ApiToolProvider
from extensions.ext_database import db
class AppParameterApi(InstalledAppResource):
"""Resource for app variables."""
@ -58,5 +62,42 @@ class AppParameterApi(InstalledAppResource):
}
}
class ExploreAppMetaApi(InstalledAppResource):
def get(self, installed_app: InstalledApp):
"""Get app meta"""
app_model_config: AppModelConfig = installed_app.app.app_model_config
agent_config = app_model_config.agent_mode_dict or {}
meta = {
'tool_icons': {}
}
# get all tools
tools = agent_config.get('tools', [])
url_prefix = (current_app.config.get("CONSOLE_API_URL")
+ f"/console/api/workspaces/current/tool-provider/builtin/")
for tool in tools:
keys = list(tool.keys())
if len(keys) >= 4:
# current tool standard
provider_type = tool.get('provider_type')
provider_id = tool.get('provider_id')
tool_name = tool.get('tool_name')
if provider_type == 'builtin':
meta['tool_icons'][tool_name] = url_prefix + provider_id + '/icon'
elif provider_type == 'api':
try:
provider: ApiToolProvider = db.session.query(ApiToolProvider).filter(
ApiToolProvider.id == provider_id
)
meta['tool_icons'][tool_name] = json.loads(provider.icon)
except:
meta['tool_icons'][tool_name] = {
"background": "#252525",
"content": "\ud83d\ude01"
}
return meta
api.add_resource(AppParameterApi, '/installed-apps/<uuid:installed_app_id>/parameters', endpoint='installed_app_parameters')
api.add_resource(ExploreAppMetaApi, '/installed-apps/<uuid:installed_app_id>/meta', endpoint='installed_app_meta')

4
api/controllers/console/explore/recommended_app.py
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@ -29,7 +29,8 @@ recommended_app_fields = {
'is_listed': fields.Boolean,
'install_count': fields.Integer,
'installed': fields.Boolean,
'editable': fields.Boolean
'editable': fields.Boolean,
'is_agent': fields.Boolean
}
recommended_app_list_fields = {
@ -82,6 +83,7 @@ class RecommendedAppListApi(Resource):
'install_count': recommended_app.install_count,
'installed': installed,
'editable': current_user.role in ['owner', 'admin'],
"is_agent": app.is_agent
}
recommended_apps_result.append(recommended_app_result)

2
api/controllers/console/universal_chat/audio.py
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@ -60,5 +60,3 @@ class UniversalChatAudioApi(UniversalChatResource):
logging.exception("internal server error.")
raise InternalServerError()
api.add_resource(UniversalChatAudioApi, '/universal-chat/audio-to-text')

120
api/controllers/console/universal_chat/chat.py
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@ -1,120 +0,0 @@
import json
import logging
from typing import Generator, Union
import services
from controllers.console import api
from controllers.console.app.error import (AppUnavailableError, CompletionRequestError, ConversationCompletedError,
ProviderModelCurrentlyNotSupportError, ProviderNotInitializeError,
ProviderQuotaExceededError)
from controllers.console.universal_chat.wraps import UniversalChatResource
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
from flask_login import current_user
from flask_restful import reqparse
from libs.helper import uuid_value
from services.completion_service import CompletionService
from werkzeug.exceptions import InternalServerError, NotFound
class UniversalChatApi(UniversalChatResource):
def post(self, universal_app):
app_model = universal_app
parser = reqparse.RequestParser()
parser.add_argument('query', type=str, required=True, location='json')
parser.add_argument('files', type=list, required=False, location='json')
parser.add_argument('conversation_id', type=uuid_value, location='json')
parser.add_argument('provider', type=str, required=True, location='json')
parser.add_argument('model', type=str, required=True, location='json')
parser.add_argument('tools', type=list, required=True, location='json')
parser.add_argument('retriever_from', type=str, required=False, default='universal_app', location='json')
args = parser.parse_args()
app_model_config = app_model.app_model_config
# update app model config
args['model_config'] = app_model_config.to_dict()
args['model_config']['model']['name'] = args['model']
args['model_config']['model']['provider'] = args['provider']
args['model_config']['agent_mode']['tools'] = args['tools']
if not args['model_config']['agent_mode']['tools']:
args['model_config']['agent_mode']['tools'] = [
{
"current_datetime": {
"enabled": True
}
}
]
else:
args['model_config']['agent_mode']['tools'].append({
"current_datetime": {
"enabled": True
}
})
args['inputs'] = {}
del args['model']
del args['tools']
args['auto_generate_name'] = False
try:
response = CompletionService.completion(
app_model=app_model,
user=current_user,
args=args,
invoke_from=InvokeFrom.EXPLORE,
streaming=True,
is_model_config_override=True,
)
return compact_response(response)
except services.errors.conversation.ConversationNotExistsError:
raise NotFound("Conversation Not Exists.")
except services.errors.conversation.ConversationCompletedError:
raise ConversationCompletedError()
except services.errors.app_model_config.AppModelConfigBrokenError:
logging.exception("App model config broken.")
raise AppUnavailableError()
except ProviderTokenNotInitError:
raise ProviderNotInitializeError()
except QuotaExceededError:
raise ProviderQuotaExceededError()
except ModelCurrentlyNotSupportError:
raise ProviderModelCurrentlyNotSupportError()
except InvokeError as e:
raise CompletionRequestError(e.description)
except ValueError as e:
raise e
except Exception as e:
logging.exception("internal server error.")
raise InternalServerError()
class UniversalChatStopApi(UniversalChatResource):
def post(self, universal_app, task_id):
ApplicationQueueManager.set_stop_flag(task_id, InvokeFrom.EXPLORE, current_user.id)
return {'result': 'success'}, 200
def compact_response(response: Union[dict, Generator]) -> Response:
if isinstance(response, dict):
return Response(response=json.dumps(response), status=200, mimetype='application/json')
else:
def generate() -> Generator:
for chunk in response:
yield chunk
return Response(stream_with_context(generate()), status=200,
mimetype='text/event-stream')
api.add_resource(UniversalChatApi, '/universal-chat/messages')
api.add_resource(UniversalChatStopApi, '/universal-chat/messages/<string:task_id>/stop')

110
api/controllers/console/universal_chat/conversation.py
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@ -1,110 +0,0 @@
# -*- coding:utf-8 -*-
from controllers.console import api
from controllers.console.universal_chat.wraps import UniversalChatResource
from fields.conversation_fields import (conversation_with_model_config_fields,
conversation_with_model_config_infinite_scroll_pagination_fields)
from flask_login import current_user
from flask_restful import fields, marshal_with, reqparse
from flask_restful.inputs import int_range
from libs.helper import TimestampField, uuid_value
from services.conversation_service import ConversationService
from services.errors.conversation import ConversationNotExistsError, LastConversationNotExistsError
from services.web_conversation_service import WebConversationService
from werkzeug.exceptions import NotFound
class UniversalChatConversationListApi(UniversalChatResource):
@marshal_with(conversation_with_model_config_infinite_scroll_pagination_fields)
def get(self, universal_app):
app_model = universal_app
parser = reqparse.RequestParser()
parser.add_argument('last_id', type=uuid_value, location='args')
parser.add_argument('limit', type=int_range(1, 100), required=False, default=20, location='args')
parser.add_argument('pinned', type=str, choices=['true', 'false', None], location='args')
args = parser.parse_args()
pinned = None
if 'pinned' in args and args['pinned'] is not None:
pinned = True if args['pinned'] == 'true' else False
try:
return WebConversationService.pagination_by_last_id(
app_model=app_model,
user=current_user,
last_id=args['last_id'],
limit=args['limit'],
pinned=pinned
)
except LastConversationNotExistsError:
raise NotFound("Last Conversation Not Exists.")
class UniversalChatConversationApi(UniversalChatResource):
def delete(self, universal_app, c_id):
app_model = universal_app
conversation_id = str(c_id)
try:
ConversationService.delete(app_model, conversation_id, current_user)
except ConversationNotExistsError:
raise NotFound("Conversation Not Exists.")
WebConversationService.unpin(app_model, conversation_id, current_user)
return {"result": "success"}, 204
class UniversalChatConversationRenameApi(UniversalChatResource):
@marshal_with(conversation_with_model_config_fields)
def post(self, universal_app, c_id):
app_model = universal_app
conversation_id = str(c_id)
parser = reqparse.RequestParser()
parser.add_argument('name', type=str, required=False, location='json')
parser.add_argument('auto_generate', type=bool, required=False, default=False, location='json')
args = parser.parse_args()
try:
return ConversationService.rename(
app_model,
conversation_id,
current_user,
args['name'],
args['auto_generate']
)
except ConversationNotExistsError:
raise NotFound("Conversation Not Exists.")
class UniversalChatConversationPinApi(UniversalChatResource):
def patch(self, universal_app, c_id):
app_model = universal_app
conversation_id = str(c_id)
try:
WebConversationService.pin(app_model, conversation_id, current_user)
except ConversationNotExistsError:
raise NotFound("Conversation Not Exists.")
return {"result": "success"}
class UniversalChatConversationUnPinApi(UniversalChatResource):
def patch(self, universal_app, c_id):
app_model = universal_app
conversation_id = str(c_id)
WebConversationService.unpin(app_model, conversation_id, current_user)
return {"result": "success"}
api.add_resource(UniversalChatConversationRenameApi, '/universal-chat/conversations/<uuid:c_id>/name')
api.add_resource(UniversalChatConversationListApi, '/universal-chat/conversations')
api.add_resource(UniversalChatConversationApi, '/universal-chat/conversations/<uuid:c_id>')
api.add_resource(UniversalChatConversationPinApi, '/universal-chat/conversations/<uuid:c_id>/pin')
api.add_resource(UniversalChatConversationUnPinApi, '/universal-chat/conversations/<uuid:c_id>/unpin')

145
api/controllers/console/universal_chat/message.py
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@ -1,145 +0,0 @@
# -*- coding:utf-8 -*-
import logging
import services
from controllers.console import api
from controllers.console.app.error import (CompletionRequestError, ProviderModelCurrentlyNotSupportError,
ProviderNotInitializeError, ProviderQuotaExceededError)
from controllers.console.explore.error import AppSuggestedQuestionsAfterAnswerDisabledError
from controllers.console.universal_chat.wraps import UniversalChatResource
from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
from core.model_runtime.errors.invoke import InvokeError
from flask_login import current_user
from flask_restful import fields, marshal_with, reqparse
from flask_restful.inputs import int_range
from libs.helper import TimestampField, uuid_value
from services.errors.conversation import ConversationNotExistsError
from services.errors.message import MessageNotExistsError, SuggestedQuestionsAfterAnswerDisabledError
from services.message_service import MessageService
from werkzeug.exceptions import InternalServerError, NotFound
class UniversalChatMessageListApi(UniversalChatResource):
feedback_fields = {
'rating': fields.String
}
agent_thought_fields = {
'id': fields.String,
'chain_id': fields.String,
'message_id': fields.String,
'position': fields.Integer,
'thought': fields.String,
'tool': fields.String,
'tool_input': fields.String,
'created_at': TimestampField
}
retriever_resource_fields = {
'id': fields.String,
'message_id': fields.String,
'position': fields.Integer,
'dataset_id': fields.String,
'dataset_name': fields.String,
'document_id': fields.String,
'document_name': fields.String,
'data_source_type': fields.String,
'segment_id': fields.String,
'score': fields.Float,
'hit_count': fields.Integer,
'word_count': fields.Integer,
'segment_position': fields.Integer,
'index_node_hash': fields.String,
'content': fields.String,
'created_at': TimestampField
}
message_fields = {
'id': fields.String,
'conversation_id': fields.String,
'inputs': fields.Raw,
'query': fields.String,
'answer': fields.String,
'feedback': fields.Nested(feedback_fields, attribute='user_feedback', allow_null=True),
'retriever_resources': fields.List(fields.Nested(retriever_resource_fields)),
'created_at': TimestampField,
'agent_thoughts': fields.List(fields.Nested(agent_thought_fields))
}
message_infinite_scroll_pagination_fields = {
'limit': fields.Integer,
'has_more': fields.Boolean,
'data': fields.List(fields.Nested(message_fields))
}
@marshal_with(message_infinite_scroll_pagination_fields)
def get(self, universal_app):
app_model = universal_app
parser = reqparse.RequestParser()
parser.add_argument('conversation_id', required=True, type=uuid_value, location='args')
parser.add_argument('first_id', type=uuid_value, location='args')
parser.add_argument('limit', type=int_range(1, 100), required=False, default=20, location='args')
args = parser.parse_args()
try:
return MessageService.pagination_by_first_id(app_model, current_user,
args['conversation_id'], args['first_id'], args['limit'])
except services.errors.conversation.ConversationNotExistsError:
raise NotFound("Conversation Not Exists.")
except services.errors.message.FirstMessageNotExistsError:
raise NotFound("First Message Not Exists.")
class UniversalChatMessageFeedbackApi(UniversalChatResource):
def post(self, universal_app, message_id):
app_model = universal_app
message_id = str(message_id)
parser = reqparse.RequestParser()
parser.add_argument('rating', type=str, choices=['like', 'dislike', None], location='json')
args = parser.parse_args()
try:
MessageService.create_feedback(app_model, message_id, current_user, args['rating'])
except services.errors.message.MessageNotExistsError:
raise NotFound("Message Not Exists.")
return {'result': 'success'}
class UniversalChatMessageSuggestedQuestionApi(UniversalChatResource):
def get(self, universal_app, message_id):
app_model = universal_app
message_id = str(message_id)
try:
questions = MessageService.get_suggested_questions_after_answer(
app_model=app_model,
user=current_user,
message_id=message_id
)
except MessageNotExistsError:
raise NotFound("Message not found")
except ConversationNotExistsError:
raise NotFound("Conversation not found")
except SuggestedQuestionsAfterAnswerDisabledError:
raise AppSuggestedQuestionsAfterAnswerDisabledError()
except ProviderTokenNotInitError:
raise ProviderNotInitializeError()
except QuotaExceededError:
raise ProviderQuotaExceededError()
except ModelCurrentlyNotSupportError:
raise ProviderModelCurrentlyNotSupportError()
except InvokeError as e:
raise CompletionRequestError(e.description)
except Exception:
logging.exception("internal server error.")
raise InternalServerError()
return {'data': questions}
api.add_resource(UniversalChatMessageListApi, '/universal-chat/messages')
api.add_resource(UniversalChatMessageFeedbackApi, '/universal-chat/messages/<uuid:message_id>/feedbacks')
api.add_resource(UniversalChatMessageSuggestedQuestionApi, '/universal-chat/messages/<uuid:message_id>/suggested-questions')

38
api/controllers/console/universal_chat/parameter.py
View File

@ -1,38 +0,0 @@
# -*- coding:utf-8 -*-
import json
from controllers.console import api
from controllers.console.universal_chat.wraps import UniversalChatResource
from flask_restful import fields, marshal_with
from models.model import App
class UniversalChatParameterApi(UniversalChatResource):
"""Resource for app variables."""
parameters_fields = {
'opening_statement': fields.String,
'suggested_questions': fields.Raw,
'suggested_questions_after_answer': fields.Raw,
'speech_to_text': fields.Raw,
'retriever_resource': fields.Raw,
'annotation_reply': fields.Raw
}
@marshal_with(parameters_fields)
def get(self, universal_app: App):
"""Retrieve app parameters."""
app_model = universal_app
app_model_config = app_model.app_model_config
app_model_config.retriever_resource = json.dumps({'enabled': True})
return {
'opening_statement': app_model_config.opening_statement,
'suggested_questions': app_model_config.suggested_questions_list,
'suggested_questions_after_answer': app_model_config.suggested_questions_after_answer_dict,
'speech_to_text': app_model_config.speech_to_text_dict,
'retriever_resource': app_model_config.retriever_resource_dict,
'annotation_reply': app_model_config.annotation_reply_dict,
}
api.add_resource(UniversalChatParameterApi, '/universal-chat/parameters')

86
api/controllers/console/universal_chat/wraps.py
View File

@ -1,86 +0,0 @@
import json
from functools import wraps
from controllers.console.setup import setup_required
from controllers.console.wraps import account_initialization_required
from extensions.ext_database import db
from flask_login import current_user
from flask_restful import Resource
from libs.login import login_required
from models.model import App, AppModelConfig
def universal_chat_app_required(view=None):
def decorator(view):
@wraps(view)
def decorated(*args, **kwargs):
# get universal chat app
universal_app = db.session.query(App).filter(
App.tenant_id == current_user.current_tenant_id,
App.is_universal == True
).first()
if universal_app is None:
# create universal app if not exists
universal_app = App(
tenant_id=current_user.current_tenant_id,
name='Universal Chat',
mode='chat',
is_universal=True,
icon='',
icon_background='',
api_rpm=0,
api_rph=0,
enable_site=False,
enable_api=False,
status='normal'
)
db.session.add(universal_app)
db.session.flush()
app_model_config = AppModelConfig(
provider="",
model_id="",
configs={},
opening_statement='',
suggested_questions=json.dumps([]),
suggested_questions_after_answer=json.dumps({'enabled': True}),
speech_to_text=json.dumps({'enabled': True}),
retriever_resource=json.dumps({'enabled': True}),
more_like_this=None,
sensitive_word_avoidance=None,
model=json.dumps({
"provider": "openai",
"name": "gpt-3.5-turbo-16k",
"completion_params": {
"max_tokens": 800,
"temperature": 0.8,
"top_p": 1,
"presence_penalty": 0,
"frequency_penalty": 0
}
}),
user_input_form=json.dumps([]),
pre_prompt='',
agent_mode=json.dumps({"enabled": True, "strategy": "function_call", "tools": []}),
)
app_model_config.app_id = universal_app.id
db.session.add(app_model_config)
db.session.flush()
universal_app.app_model_config_id = app_model_config.id
db.session.commit()
return view(universal_app, *args, **kwargs)
return decorated
if view:
return decorator(view)
return decorator
class UniversalChatResource(Resource):
# must be reversed if there are multiple decorators
method_decorators = [universal_chat_app_required, account_initialization_required, login_required, setup_required]

339
api/controllers/console/workspace/tool_providers.py
View File

@ -1,136 +1,293 @@
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 core.tool.provider.errors import ToolValidateFailedError
from core.tool.provider.tool_provider_service import ToolProviderService
from extensions.ext_database import db
from flask_login import current_user
from flask_restful import Resource, abort, reqparse
from libs.login import login_required
from models.tool import ToolProvider, ToolProviderName
from werkzeug.exceptions import Forbidden
from services.tools_manage_service import ToolManageService
import io
class ToolProviderListApi(Resource):
@setup_required
@login_required
@account_initialization_required
def get(self):
user_id = current_user.id
tenant_id = current_user.current_tenant_id
tool_credential_dict = {}
for tool_name in ToolProviderName:
tool_credential_dict[tool_name.value] = {
'tool_name': tool_name.value,
'is_enabled': False,
'credentials': None
}
return ToolManageService.list_tool_providers(user_id, tenant_id)
tool_providers = db.session.query(ToolProvider).filter(ToolProvider.tenant_id == tenant_id).all()
class ToolBuiltinProviderListToolsApi(Resource):
@setup_required
@login_required
@account_initialization_required
def get(self, provider):
user_id = current_user.id
tenant_id = current_user.current_tenant_id
for p in tool_providers:
if p.is_enabled:
tool_credential_dict[p.tool_name] = {
'tool_name': p.tool_name,
'is_enabled': p.is_enabled,
'credentials': ToolProviderService(tenant_id, p.tool_name).get_credentials(obfuscated=True)
}
return list(tool_credential_dict.values())
class ToolProviderCredentialsApi(Resource):
return ToolManageService.list_builtin_tool_provider_tools(
user_id,
tenant_id,
provider,
)
class ToolBuiltinProviderDeleteApi(Resource):
@setup_required
@login_required
@account_initialization_required
def post(self, provider):
if provider not in [p.value for p in ToolProviderName]:
abort(404)
# The role of the current user in the ta table must be admin or owner
if current_user.current_tenant.current_role not in ['admin', 'owner']:
raise Forbidden(f'User {current_user.id} is not authorized to update provider token, '
f'current_role is {current_user.current_tenant.current_role}')
parser = reqparse.RequestParser()
parser.add_argument('credentials', type=dict, required=True, nullable=False, location='json')
args = parser.parse_args()
raise Forbidden()
user_id = current_user.id
tenant_id = current_user.current_tenant_id
tool_provider_service = ToolProviderService(tenant_id, provider)
try:
tool_provider_service.credentials_validate(args['credentials'])
except ToolValidateFailedError as ex:
raise ValueError(str(ex))
encrypted_credentials = json.dumps(tool_provider_service.encrypt_credentials(args['credentials']))
tenant = current_user.current_tenant
tool_provider_model = db.session.query(ToolProvider).filter(
ToolProvider.tenant_id == tenant.id,
ToolProvider.tool_name == provider,
).first()
# Only allow updating token for CUSTOM provider type
if tool_provider_model:
tool_provider_model.encrypted_credentials = encrypted_credentials
tool_provider_model.is_enabled = True
else:
tool_provider_model = ToolProvider(
tenant_id=tenant.id,
tool_name=provider,
encrypted_credentials=encrypted_credentials,
is_enabled=True
)
db.session.add(tool_provider_model)
db.session.commit()
return {'result': 'success'}, 201
class ToolProviderCredentialsValidateApi(Resource):
return ToolManageService.delete_builtin_tool_provider(
user_id,
tenant_id,
provider,
)
class ToolBuiltinProviderUpdateApi(Resource):
@setup_required
@login_required
@account_initialization_required
def post(self, provider):
if provider not in [p.value for p in ToolProviderName]:
abort(404)
if current_user.current_tenant.current_role not in ['admin', 'owner']:
raise Forbidden()
user_id = current_user.id
tenant_id = current_user.current_tenant_id
parser = reqparse.RequestParser()
parser.add_argument('credentials', type=dict, required=True, nullable=False, location='json')
args = parser.parse_args()
result = True
error = None
return ToolManageService.update_builtin_tool_provider(
user_id,
tenant_id,
provider,
args['credentials'],
)
class ToolBuiltinProviderIconApi(Resource):
@setup_required
def get(self, provider):
icon_bytes, minetype = ToolManageService.get_builtin_tool_provider_icon(provider)
return send_file(io.BytesIO(icon_bytes), mimetype=minetype)
class ToolApiProviderAddApi(Resource):
@setup_required
@login_required
@account_initialization_required
def post(self):
if current_user.current_tenant.current_role not in ['admin', 'owner']:
raise Forbidden()
user_id = current_user.id
tenant_id = current_user.current_tenant_id
tool_provider_service = ToolProviderService(tenant_id, provider)
parser = reqparse.RequestParser()
parser.add_argument('credentials', type=dict, required=True, nullable=False, location='json')
parser.add_argument('schema_type', type=str, required=True, nullable=False, location='json')
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('icon', type=dict, required=True, nullable=False, location='json')
parser.add_argument('privacy_policy', type=str, required=False, nullable=True, location='json')
try:
tool_provider_service.credentials_validate(args['credentials'])
except ToolValidateFailedError as ex:
result = False
error = str(ex)
args = parser.parse_args()
response = {'result': 'success' if result else 'error'}
return ToolManageService.create_api_tool_provider(
user_id,
tenant_id,
args['provider'],
args['icon'],
args['credentials'],
args['schema_type'],
args['schema'],
args.get('privacy_policy', ''),
)
if not result:
response['error'] = error
class ToolApiProviderGetRemoteSchemaApi(Resource):
@setup_required
@login_required
@account_initialization_required
def get(self):
parser = reqparse.RequestParser()
return response
parser.add_argument('url', type=str, required=True, nullable=False, location='args')
args = parser.parse_args()
return ToolManageService.get_api_tool_provider_remote_schema(
current_user.id,
current_user.current_tenant_id,
args['url'],
)
class ToolApiProviderListToolsApi(Resource):
@setup_required
@login_required
@account_initialization_required
def get(self):
user_id = current_user.id
tenant_id = current_user.current_tenant_id
parser = reqparse.RequestParser()
parser.add_argument('provider', type=str, required=True, nullable=False, location='args')
args = parser.parse_args()
return ToolManageService.list_api_tool_provider_tools(
user_id,
tenant_id,
args['provider'],
)
class ToolApiProviderUpdateApi(Resource):
@setup_required
@login_required
@account_initialization_required
def post(self):
if current_user.current_tenant.current_role not in ['admin', 'owner']:
raise Forbidden()
user_id = current_user.id
tenant_id = current_user.current_tenant_id
parser = reqparse.RequestParser()
parser.add_argument('credentials', type=dict, required=True, nullable=False, location='json')
parser.add_argument('schema_type', type=str, required=True, nullable=False, location='json')
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')
args = parser.parse_args()
return ToolManageService.update_api_tool_provider(
user_id,
tenant_id,
args['provider'],
args['original_provider'],
args['icon'],
args['credentials'],
args['schema_type'],
args['schema'],
args['privacy_policy'],
)
class ToolApiProviderDeleteApi(Resource):
@setup_required
@login_required
@account_initialization_required
def post(self):
if current_user.current_tenant.current_role not in ['admin', 'owner']:
raise Forbidden()
user_id = current_user.id
tenant_id = current_user.current_tenant_id
parser = reqparse.RequestParser()
parser.add_argument('provider', type=str, required=True, nullable=False, location='json')
args = parser.parse_args()
return ToolManageService.delete_api_tool_provider(
user_id,
tenant_id,
args['provider'],
)
class ToolApiProviderGetApi(Resource):
@setup_required
@login_required
@account_initialization_required
def get(self):
user_id = current_user.id
tenant_id = current_user.current_tenant_id
parser = reqparse.RequestParser()
parser.add_argument('provider', type=str, required=True, nullable=False, location='args')
args = parser.parse_args()
return ToolManageService.get_api_tool_provider(
user_id,
tenant_id,
args['provider'],
)
class ToolBuiltinProviderCredentialsSchemaApi(Resource):
@setup_required
@login_required
@account_initialization_required
def get(self, provider):
return ToolManageService.list_builtin_provider_credentials_schema(provider)
class ToolApiProviderSchemaApi(Resource):
@setup_required
@login_required
@account_initialization_required
def post(self):
parser = reqparse.RequestParser()
parser.add_argument('schema', type=str, required=True, nullable=False, location='json')
args = parser.parse_args()
return ToolManageService.parser_api_schema(
schema=args['schema'],
)
class ToolApiProviderPreviousTestApi(Resource):
@setup_required
@login_required
@account_initialization_required
def post(self):
parser = reqparse.RequestParser()
parser.add_argument('tool_name', type=str, required=True, nullable=False, location='json')
parser.add_argument('credentials', type=dict, required=True, nullable=False, location='json')
parser.add_argument('parameters', type=dict, required=True, nullable=False, location='json')
parser.add_argument('schema_type', type=str, required=True, nullable=False, location='json')
parser.add_argument('schema', type=str, required=True, nullable=False, location='json')
args = parser.parse_args()
return ToolManageService.test_api_tool_preview(
current_user.current_tenant_id,
args['tool_name'],
args['credentials'],
args['parameters'],
args['schema_type'],
args['schema'],
)
api.add_resource(ToolProviderListApi, '/workspaces/current/tool-providers')
api.add_resource(ToolProviderCredentialsApi, '/workspaces/current/tool-providers/<provider>/credentials')
api.add_resource(ToolProviderCredentialsValidateApi,
'/workspaces/current/tool-providers/<provider>/credentials-validate')
api.add_resource(ToolBuiltinProviderListToolsApi, '/workspaces/current/tool-provider/builtin/<provider>/tools')
api.add_resource(ToolBuiltinProviderDeleteApi, '/workspaces/current/tool-provider/builtin/<provider>/delete')
api.add_resource(ToolBuiltinProviderUpdateApi, '/workspaces/current/tool-provider/builtin/<provider>/update')
api.add_resource(ToolBuiltinProviderCredentialsSchemaApi, '/workspaces/current/tool-provider/builtin/<provider>/credentials_schema')
api.add_resource(ToolBuiltinProviderIconApi, '/workspaces/current/tool-provider/builtin/<provider>/icon')
api.add_resource(ToolApiProviderAddApi, '/workspaces/current/tool-provider/api/add')
api.add_resource(ToolApiProviderGetRemoteSchemaApi, '/workspaces/current/tool-provider/api/remote')
api.add_resource(ToolApiProviderListToolsApi, '/workspaces/current/tool-provider/api/tools')
api.add_resource(ToolApiProviderUpdateApi, '/workspaces/current/tool-provider/api/update')
api.add_resource(ToolApiProviderDeleteApi, '/workspaces/current/tool-provider/api/delete')
api.add_resource(ToolApiProviderGetApi, '/workspaces/current/tool-provider/api/get')
api.add_resource(ToolApiProviderSchemaApi, '/workspaces/current/tool-provider/api/schema')
api.add_resource(ToolApiProviderPreviousTestApi, '/workspaces/current/tool-provider/api/test/pre')

1
api/controllers/files/__init__.py
View File

@ -7,3 +7,4 @@ api = ExternalApi(bp)
from . import image_preview
from . import tool_files

47
api/controllers/files/tool_files.py Normal file
View File

@ -0,0 +1,47 @@
from controllers.files import api
from flask import Response
from flask_restful import Resource, reqparse
from libs.exception import BaseHTTPException
from werkzeug.exceptions import NotFound, Forbidden
from core.tools.tool_file_manager import ToolFileManager
class ToolFilePreviewApi(Resource):
def get(self, file_id, extension):
file_id = str(file_id)
parser = reqparse.RequestParser()
parser.add_argument('timestamp', type=str, required=True, location='args')
parser.add_argument('nonce', type=str, required=True, location='args')
parser.add_argument('sign', type=str, required=True, location='args')
args = parser.parse_args()
if not ToolFileManager.verify_file(file_id=file_id,
timestamp=args['timestamp'],
nonce=args['nonce'],
sign=args['sign'],
):
raise Forbidden('Invalid request.')
try:
result = ToolFileManager.get_file_generator_by_message_file_id(
file_id,
)
if not result:
raise NotFound(f'file is not found')
generator, mimetype = result
except Exception:
raise UnsupportedFileTypeError()
return Response(generator, mimetype=mimetype)
api.add_resource(ToolFilePreviewApi, '/files/tools/<uuid:file_id>.<string:extension>')
class UnsupportedFileTypeError(BaseHTTPException):
error_code = 'unsupported_file_type'
description = "File type not allowed."
code = 415

44
api/controllers/service_api/app/app.py
View File

@ -3,7 +3,12 @@ from controllers.service_api import api
from controllers.service_api.wraps import AppApiResource
from flask import current_app
from flask_restful import fields, marshal_with
from models.model import App
from models.model import App, AppModelConfig
from models.tools import ApiToolProvider
import json
from extensions.ext_database import db
class AppParameterApi(AppApiResource):
@ -58,5 +63,42 @@ class AppParameterApi(AppApiResource):
}
}
class AppMetaApi(AppApiResource):
def get(self, app_model: App, end_user):
"""Get app meta"""
app_model_config: AppModelConfig = app_model.app_model_config
agent_config = app_model_config.agent_mode_dict or {}
meta = {
'tool_icons': {}
}
# get all tools
tools = agent_config.get('tools', [])
url_prefix = (current_app.config.get("CONSOLE_API_URL")
+ f"/console/api/workspaces/current/tool-provider/builtin/")
for tool in tools:
keys = list(tool.keys())
if len(keys) >= 4:
# current tool standard
provider_type = tool.get('provider_type')
provider_id = tool.get('provider_id')
tool_name = tool.get('tool_name')
if provider_type == 'builtin':
meta['tool_icons'][tool_name] = url_prefix + provider_id + '/icon'
elif provider_type == 'api':
try:
provider: ApiToolProvider = db.session.query(ApiToolProvider).filter(
ApiToolProvider.id == provider_id
)
meta['tool_icons'][tool_name] = json.loads(provider.icon)
except:
meta['tool_icons'][tool_name] = {
"background": "#252525",
"content": "\ud83d\ude01"
}
return meta
api.add_resource(AppParameterApi, '/parameters')
api.add_resource(AppMetaApi, '/meta')

16
api/controllers/service_api/app/message.py
View File

@ -37,6 +37,19 @@ class MessageListApi(AppApiResource):
'created_at': TimestampField
}
agent_thought_fields = {
'id': fields.String,
'chain_id': fields.String,
'message_id': fields.String,
'position': fields.Integer,
'thought': fields.String,
'tool': fields.String,
'tool_input': fields.String,
'created_at': TimestampField,
'observation': fields.String,
'message_files': fields.List(fields.String, attribute='files')
}
message_fields = {
'id': fields.String,
'conversation_id': fields.String,
@ -46,7 +59,8 @@ class MessageListApi(AppApiResource):
'message_files': fields.List(fields.Nested(message_file_fields), attribute='files'),
'feedback': fields.Nested(feedback_fields, attribute='user_feedback', allow_null=True),
'retriever_resources': fields.List(fields.Nested(retriever_resource_fields)),
'created_at': TimestampField
'created_at': TimestampField,
'agent_thoughts': fields.List(fields.Nested(agent_thought_fields))
}
message_infinite_scroll_pagination_fields = {

44
api/controllers/web/app.py
View File

@ -3,7 +3,12 @@ from controllers.web import api
from controllers.web.wraps import WebApiResource
from flask import current_app
from flask_restful import fields, marshal_with
from models.model import App
from models.model import App, AppModelConfig
from models.tools import ApiToolProvider
from extensions.ext_database import db
import json
class AppParameterApi(WebApiResource):
@ -57,5 +62,42 @@ class AppParameterApi(WebApiResource):
}
}
class AppMeta(WebApiResource):
def get(self, app_model: App, end_user):
"""Get app meta"""
app_model_config: AppModelConfig = app_model.app_model_config
agent_config = app_model_config.agent_mode_dict or {}
meta = {
'tool_icons': {}
}
# get all tools
tools = agent_config.get('tools', [])
url_prefix = (current_app.config.get("CONSOLE_API_URL")
+ f"/console/api/workspaces/current/tool-provider/builtin/")
for tool in tools:
keys = list(tool.keys())
if len(keys) >= 4:
# current tool standard
provider_type = tool.get('provider_type')
provider_id = tool.get('provider_id')
tool_name = tool.get('tool_name')
if provider_type == 'builtin':
meta['tool_icons'][tool_name] = url_prefix + provider_id + '/icon'
elif provider_type == 'api':
try:
provider: ApiToolProvider = db.session.query(ApiToolProvider).filter(
ApiToolProvider.id == provider_id
)
meta['tool_icons'][tool_name] = json.loads(provider.icon)
except:
meta['tool_icons'][tool_name] = {
"background": "#252525",
"content": "\ud83d\ude01"
}
return meta
api.add_resource(AppParameterApi, '/parameters')
api.add_resource(AppMeta, '/meta')

4
api/controllers/web/message.py
View File

@ -14,6 +14,7 @@ from core.entities.application_entities import InvokeFrom
from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
from core.model_runtime.errors.invoke import InvokeError
from fields.conversation_fields import message_file_fields
from fields.message_fields import agent_thought_fields
from flask import Response, stream_with_context
from flask_restful import fields, marshal_with, reqparse
from flask_restful.inputs import int_range
@ -59,7 +60,8 @@ class MessageListApi(WebApiResource):
'message_files': fields.List(fields.Nested(message_file_fields), attribute='files'),
'feedback': fields.Nested(feedback_fields, attribute='user_feedback', allow_null=True),
'retriever_resources': fields.List(fields.Nested(retriever_resource_fields)),
'created_at': TimestampField
'created_at': TimestampField,
'agent_thoughts': fields.List(fields.Nested(agent_thought_fields))
}
message_infinite_scroll_pagination_fields = {

4
api/core/agent/agent_executor.py
View File

@ -13,8 +13,8 @@ from core.entities.message_entities import prompt_messages_to_lc_messages
from core.helper import moderation
from core.memory.token_buffer_memory import TokenBufferMemory
from core.model_runtime.errors.invoke import InvokeError
from core.tool.dataset_multi_retriever_tool import DatasetMultiRetrieverTool
from core.tool.dataset_retriever_tool import DatasetRetrieverTool
from core.tools.tool.dataset_retriever.dataset_multi_retriever_tool import DatasetMultiRetrieverTool
from core.tools.tool.dataset_retriever.dataset_retriever_tool import DatasetRetrieverTool
from langchain.agents import AgentExecutor as LCAgentExecutor
from langchain.agents import BaseMultiActionAgent, BaseSingleActionAgent
from langchain.callbacks.manager import Callbacks

251
api/core/app_runner/agent_app_runner.py
View File

@ -1,251 +0,0 @@
import json
import logging
from typing import cast
from core.agent.agent.agent_llm_callback import AgentLLMCallback
from core.app_runner.app_runner import AppRunner
from core.application_queue_manager import ApplicationQueueManager
from core.callback_handler.agent_loop_gather_callback_handler import AgentLoopGatherCallbackHandler
from core.entities.application_entities import ApplicationGenerateEntity, ModelConfigEntity, PromptTemplateEntity
from core.features.agent_runner import AgentRunnerFeature
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.model_providers.__base.large_language_model import LargeLanguageModel
from extensions.ext_database import db
from models.model import App, Conversation, Message, MessageAgentThought, MessageChain
logger = logging.getLogger(__name__)
class AgentApplicationRunner(AppRunner):
"""
Agent Application Runner
"""
def run(self, application_generate_entity: ApplicationGenerateEntity,
queue_manager: ApplicationQueueManager,
conversation: Conversation,
message: Message) -> None:
"""
Run agent application
:param application_generate_entity: application generate entity
:param queue_manager: application queue manager
:param conversation: conversation
:param message: message
:return:
"""
app_record = db.session.query(App).filter(App.id == application_generate_entity.app_id).first()
if not app_record:
raise ValueError(f"App not found")
app_orchestration_config = application_generate_entity.app_orchestration_config_entity
inputs = application_generate_entity.inputs
query = application_generate_entity.query
files = application_generate_entity.files
# Pre-calculate the number of tokens of the prompt messages,
# and return the rest number of tokens by model context token size limit and max token size limit.
# If the rest number of tokens is not enough, raise exception.
# Include: prompt template, inputs, query(optional), files(optional)
# Not Include: memory, external data, dataset context
self.get_pre_calculate_rest_tokens(
app_record=app_record,
model_config=app_orchestration_config.model_config,
prompt_template_entity=app_orchestration_config.prompt_template,
inputs=inputs,
files=files,
query=query
)
memory = None
if application_generate_entity.conversation_id:
# get memory of conversation (read-only)
model_instance = ModelInstance(
provider_model_bundle=app_orchestration_config.model_config.provider_model_bundle,
model=app_orchestration_config.model_config.model
)
memory = TokenBufferMemory(
conversation=conversation,
model_instance=model_instance
)
# reorganize all inputs and template to prompt messages
# Include: prompt template, inputs, query(optional), files(optional)
# memory(optional)
prompt_messages, stop = self.organize_prompt_messages(
app_record=app_record,
model_config=app_orchestration_config.model_config,
prompt_template_entity=app_orchestration_config.prompt_template,
inputs=inputs,
files=files,
query=query,
context=None,
memory=memory
)
# Create MessageChain
message_chain = self._init_message_chain(
message=message,
query=query
)
# add agent callback to record agent thoughts
agent_callback = AgentLoopGatherCallbackHandler(
model_config=app_orchestration_config.model_config,
message=message,
queue_manager=queue_manager,
message_chain=message_chain
)
# init LLM Callback
agent_llm_callback = AgentLLMCallback(
agent_callback=agent_callback
)
agent_runner = AgentRunnerFeature(
tenant_id=application_generate_entity.tenant_id,
app_orchestration_config=app_orchestration_config,
model_config=app_orchestration_config.model_config,
config=app_orchestration_config.agent,
queue_manager=queue_manager,
message=message,
user_id=application_generate_entity.user_id,
agent_llm_callback=agent_llm_callback,
callback=agent_callback,
memory=memory
)
# agent run
result = agent_runner.run(
query=query,
invoke_from=application_generate_entity.invoke_from
)
if result:
self._save_message_chain(
message_chain=message_chain,
output_text=result
)
if (result
and app_orchestration_config.prompt_template.prompt_type == PromptTemplateEntity.PromptType.SIMPLE
and app_orchestration_config.prompt_template.simple_prompt_template
):
# Direct output if agent result exists and has pre prompt
self.direct_output(
queue_manager=queue_manager,
app_orchestration_config=app_orchestration_config,
prompt_messages=prompt_messages,
stream=application_generate_entity.stream,
text=result,
usage=self._get_usage_of_all_agent_thoughts(
model_config=app_orchestration_config.model_config,
message=message
)
)
else:
# As normal LLM run, agent result as context
context = result
# reorganize all inputs and template to prompt messages
# Include: prompt template, inputs, query(optional), files(optional)
# memory(optional), external data, dataset context(optional)
prompt_messages, stop = self.organize_prompt_messages(
app_record=app_record,
model_config=app_orchestration_config.model_config,
prompt_template_entity=app_orchestration_config.prompt_template,
inputs=inputs,
files=files,
query=query,
context=context,
memory=memory
)
# Re-calculate the max tokens if sum(prompt_token + max_tokens) over model token limit
self.recale_llm_max_tokens(
model_config=app_orchestration_config.model_config,
prompt_messages=prompt_messages
)
# Invoke model
model_instance = ModelInstance(
provider_model_bundle=app_orchestration_config.model_config.provider_model_bundle,
model=app_orchestration_config.model_config.model
)
invoke_result = model_instance.invoke_llm(
prompt_messages=prompt_messages,
model_parameters=app_orchestration_config.model_config.parameters,
stop=stop,
stream=application_generate_entity.stream,
user=application_generate_entity.user_id,
)
# handle invoke result
self._handle_invoke_result(
invoke_result=invoke_result,
queue_manager=queue_manager,
stream=application_generate_entity.stream
)
def _init_message_chain(self, message: Message, query: str) -> MessageChain:
"""
Init MessageChain
:param message: message
:param query: query
:return:
"""
message_chain = MessageChain(
message_id=message.id,
type="AgentExecutor",
input=json.dumps({
"input": query
})
)
db.session.add(message_chain)
db.session.commit()
return message_chain
def _save_message_chain(self, message_chain: MessageChain, output_text: str) -> None:
"""
Save MessageChain
:param message_chain: message chain
:param output_text: output text
:return:
"""
message_chain.output = json.dumps({
"output": output_text
})
db.session.commit()
def _get_usage_of_all_agent_thoughts(self, model_config: ModelConfigEntity,
message: Message) -> LLMUsage:
"""
Get usage of all agent thoughts
:param model_config: model config
:param message: message
:return:
"""
agent_thoughts = (db.session.query(MessageAgentThought)
.filter(MessageAgentThought.message_id == message.id).all())
all_message_tokens = 0
all_answer_tokens = 0
for agent_thought in agent_thoughts:
all_message_tokens += agent_thought.message_token
all_answer_tokens += agent_thought.answer_token
model_type_instance = model_config.provider_model_bundle.model_type_instance
model_type_instance = cast(LargeLanguageModel, model_type_instance)
return model_type_instance._calc_response_usage(
model_config.model,
model_config.credentials,
all_message_tokens,
all_answer_tokens
)

128
api/core/app_runner/app_runner.py
View File

@ -2,7 +2,8 @@ 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
from core.entities.application_entities import AppOrchestrationConfigEntity, ModelConfigEntity, \
PromptTemplateEntity, ExternalDataVariableEntity, ApplicationGenerateEntity, InvokeFrom
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
@ -10,9 +11,12 @@ 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
from models.model import App, MessageAnnotation, Message
class AppRunner:
def get_pre_calculate_rest_tokens(self, app_record: App,
@ -199,7 +203,8 @@ class AppRunner:
def _handle_invoke_result(self, invoke_result: Union[LLMResult, Generator],
queue_manager: ApplicationQueueManager,
stream: bool) -> None:
stream: bool,
agent: bool = False) -> None:
"""
Handle invoke result
:param invoke_result: invoke result
@ -210,16 +215,19 @@ class AppRunner:
if not stream:
self._handle_invoke_result_direct(
invoke_result=invoke_result,
queue_manager=queue_manager
queue_manager=queue_manager,
agent=agent
)
else:
self._handle_invoke_result_stream(
invoke_result=invoke_result,
queue_manager=queue_manager
queue_manager=queue_manager,
agent=agent
)
def _handle_invoke_result_direct(self, invoke_result: LLMResult,
queue_manager: ApplicationQueueManager) -> None:
queue_manager: ApplicationQueueManager,
agent: bool) -> None:
"""
Handle invoke result direct
:param invoke_result: invoke result
@ -232,7 +240,8 @@ class AppRunner:
)
def _handle_invoke_result_stream(self, invoke_result: Generator,
queue_manager: ApplicationQueueManager) -> None:
queue_manager: ApplicationQueueManager,
agent: bool) -> None:
"""
Handle invoke result
:param invoke_result: invoke result
@ -244,7 +253,10 @@ class AppRunner:
text = ''
usage = None
for result in invoke_result:
queue_manager.publish_chunk_message(result, PublishFrom.APPLICATION_MANAGER)
if not agent:
queue_manager.publish_chunk_message(result, PublishFrom.APPLICATION_MANAGER)
else:
queue_manager.publish_agent_chunk_message(result, PublishFrom.APPLICATION_MANAGER)
text += result.delta.message.content
@ -271,3 +283,101 @@ class AppRunner:
llm_result=llm_result,
pub_from=PublishFrom.APPLICATION_MANAGER
)
def moderation_for_inputs(self, app_id: str,
tenant_id: str,
app_orchestration_config_entity: AppOrchestrationConfigEntity,
inputs: dict,
query: str) -> Tuple[bool, dict, str]:
"""
Process sensitive_word_avoidance.
:param app_id: app id
:param tenant_id: tenant id
:param app_orchestration_config_entity: app orchestration config entity
:param inputs: inputs
:param query: query
:return:
"""
moderation_feature = ModerationFeature()
return moderation_feature.check(
app_id=app_id,
tenant_id=tenant_id,
app_orchestration_config_entity=app_orchestration_config_entity,
inputs=inputs,
query=query,
)
def check_hosting_moderation(self, application_generate_entity: ApplicationGenerateEntity,
queue_manager: ApplicationQueueManager,
prompt_messages: list[PromptMessage]) -> bool:
"""
Check hosting moderation
:param application_generate_entity: application generate entity
:param queue_manager: queue manager
:param prompt_messages: prompt messages
:return:
"""
hosting_moderation_feature = HostingModerationFeature()
moderation_result = hosting_moderation_feature.check(
application_generate_entity=application_generate_entity,
prompt_messages=prompt_messages
)
if moderation_result:
self.direct_output(
queue_manager=queue_manager,
app_orchestration_config=application_generate_entity.app_orchestration_config_entity,
prompt_messages=prompt_messages,
text="I apologize for any confusion, " \
"but I'm an AI assistant to be helpful, harmless, and honest.",
stream=application_generate_entity.stream
)
return moderation_result
def fill_in_inputs_from_external_data_tools(self, tenant_id: str,
app_id: str,
external_data_tools: list[ExternalDataVariableEntity],
inputs: dict,
query: str) -> dict:
"""
Fill in variable inputs from external data tools if exists.
:param tenant_id: workspace id
:param app_id: app id
:param external_data_tools: external data tools configs
:param inputs: the inputs
:param query: the query
:return: the filled inputs
"""
external_data_fetch_feature = ExternalDataFetchFeature()
return external_data_fetch_feature.fetch(
tenant_id=tenant_id,
app_id=app_id,
external_data_tools=external_data_tools,
inputs=inputs,
query=query
)
def query_app_annotations_to_reply(self, app_record: App,
message: Message,
query: str,
user_id: str,
invoke_from: InvokeFrom) -> Optional[MessageAnnotation]:
"""
Query app annotations to reply
:param app_record: app record
:param message: message
:param query: query
:param user_id: user id
:param invoke_from: invoke from
:return:
"""
annotation_reply_feature = AnnotationReplyFeature()
return annotation_reply_feature.query(
app_record=app_record,
message=message,
query=query,
user_id=user_id,
invoke_from=invoke_from
)

342
api/core/app_runner/assistant_app_runner.py Normal file
View File

@ -0,0 +1,342 @@
import json
import logging
from typing import cast
from core.app_runner.app_runner import AppRunner
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.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.tools import ToolConversationVariables
logger = logging.getLogger(__name__)
class AssistantApplicationRunner(AppRunner):
"""
Assistant Application Runner
"""
def run(self, application_generate_entity: ApplicationGenerateEntity,
queue_manager: ApplicationQueueManager,
conversation: Conversation,
message: Message) -> None:
"""
Run assistant application
:param application_generate_entity: application generate entity
:param queue_manager: application queue manager
:param conversation: conversation
:param message: message
:return:
"""
app_record = db.session.query(App).filter(App.id == application_generate_entity.app_id).first()
if not app_record:
raise ValueError(f"App not found")
app_orchestration_config = application_generate_entity.app_orchestration_config_entity
inputs = application_generate_entity.inputs
query = application_generate_entity.query
files = application_generate_entity.files
# Pre-calculate the number of tokens of the prompt messages,
# and return the rest number of tokens by model context token size limit and max token size limit.
# If the rest number of tokens is not enough, raise exception.
# Include: prompt template, inputs, query(optional), files(optional)
# Not Include: memory, external data, dataset context
self.get_pre_calculate_rest_tokens(
app_record=app_record,
model_config=app_orchestration_config.model_config,
prompt_template_entity=app_orchestration_config.prompt_template,
inputs=inputs,
files=files,
query=query
)
memory = None
if application_generate_entity.conversation_id:
# get memory of conversation (read-only)
model_instance = ModelInstance(
provider_model_bundle=app_orchestration_config.model_config.provider_model_bundle,
model=app_orchestration_config.model_config.model
)
memory = TokenBufferMemory(
conversation=conversation,
model_instance=model_instance
)
# organize all inputs and template to prompt messages
# Include: prompt template, inputs, query(optional), files(optional)
# memory(optional)
prompt_messages, _ = self.organize_prompt_messages(
app_record=app_record,
model_config=app_orchestration_config.model_config,
prompt_template_entity=app_orchestration_config.prompt_template,
inputs=inputs,
files=files,
query=query,
memory=memory
)
# moderation
try:
# process sensitive_word_avoidance
_, inputs, query = self.moderation_for_inputs(
app_id=app_record.id,
tenant_id=application_generate_entity.tenant_id,
app_orchestration_config_entity=app_orchestration_config,
inputs=inputs,
query=query,
)
except ModerationException as e:
self.direct_output(
queue_manager=queue_manager,
app_orchestration_config=app_orchestration_config,
prompt_messages=prompt_messages,
text=str(e),
stream=application_generate_entity.stream
)
return
if query:
# annotation reply
annotation_reply = self.query_app_annotations_to_reply(
app_record=app_record,
message=message,
query=query,
user_id=application_generate_entity.user_id,
invoke_from=application_generate_entity.invoke_from
)
if annotation_reply:
queue_manager.publish_annotation_reply(
message_annotation_id=annotation_reply.id,
pub_from=PublishFrom.APPLICATION_MANAGER
)
self.direct_output(
queue_manager=queue_manager,
app_orchestration_config=app_orchestration_config,
prompt_messages=prompt_messages,
text=annotation_reply.content,
stream=application_generate_entity.stream
)
return
# fill in variable inputs from external data tools if exists
external_data_tools = app_orchestration_config.external_data_variables
if external_data_tools:
inputs = self.fill_in_inputs_from_external_data_tools(
tenant_id=app_record.tenant_id,
app_id=app_record.id,
external_data_tools=external_data_tools,
inputs=inputs,
query=query
)
# reorganize all inputs and template to prompt messages
# Include: prompt template, inputs, query(optional), files(optional)
# memory(optional), external data, dataset context(optional)
prompt_messages, _ = self.organize_prompt_messages(
app_record=app_record,
model_config=app_orchestration_config.model_config,
prompt_template_entity=app_orchestration_config.prompt_template,
inputs=inputs,
files=files,
query=query,
memory=memory
)
# check hosting moderation
hosting_moderation_result = self.check_hosting_moderation(
application_generate_entity=application_generate_entity,
queue_manager=queue_manager,
prompt_messages=prompt_messages
)
if hosting_moderation_result:
return
agent_entity = app_orchestration_config.agent
# 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)
# convert db variables to tool variables
tool_variables = self._convert_db_variables_to_tool_variables(tool_conversation_variables)
message_chain = self._init_message_chain(
message=message,
query=query
)
# init model instance
model_instance = ModelInstance(
provider_model_bundle=app_orchestration_config.model_config.provider_model_bundle,
model=app_orchestration_config.model_config.model
)
prompt_message, _ = self.organize_prompt_messages(
app_record=app_record,
model_config=app_orchestration_config.model_config,
prompt_template_entity=app_orchestration_config.prompt_template,
inputs=inputs,
files=files,
query=query,
memory=memory,
)
# start agent runner
if agent_entity.strategy == AgentEntity.Strategy.CHAIN_OF_THOUGHT:
assistant_cot_runner = AssistantCotApplicationRunner(
tenant_id=application_generate_entity.tenant_id,
application_generate_entity=application_generate_entity,
app_orchestration_config=app_orchestration_config,
model_config=app_orchestration_config.model_config,
config=agent_entity,
queue_manager=queue_manager,
message=message,
user_id=application_generate_entity.user_id,
memory=memory,
prompt_messages=prompt_message,
variables_pool=tool_variables,
db_variables=tool_conversation_variables,
)
invoke_result = assistant_cot_runner.run(
model_instance=model_instance,
conversation=conversation,
message=message,
query=query,
)
elif agent_entity.strategy == AgentEntity.Strategy.FUNCTION_CALLING:
assistant_cot_runner = AssistantFunctionCallApplicationRunner(
tenant_id=application_generate_entity.tenant_id,
application_generate_entity=application_generate_entity,
app_orchestration_config=app_orchestration_config,
model_config=app_orchestration_config.model_config,
config=agent_entity,
queue_manager=queue_manager,
message=message,
user_id=application_generate_entity.user_id,
memory=memory,
prompt_messages=prompt_message,
variables_pool=tool_variables,
db_variables=tool_conversation_variables
)
invoke_result = assistant_cot_runner.run(
model_instance=model_instance,
conversation=conversation,
message=message,
query=query,
)
# handle invoke result
self._handle_invoke_result(
invoke_result=invoke_result,
queue_manager=queue_manager,
stream=application_generate_entity.stream,
agent=True
)
def _load_tool_variables(self, conversation_id: str, user_id: str, tanent_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
).first()
if tool_variables:
# save tool variables to session, so that we can update it later
db.session.add(tool_variables)
else:
# create new tool variables
tool_variables = ToolConversationVariables(
conversation_id=conversation_id,
user_id=user_id,
tenant_id=tanent_id,
variables_str='[]',
)
db.session.add(tool_variables)
db.session.commit()
return tool_variables
def _convert_db_variables_to_tool_variables(self, db_variables: ToolConversationVariables) -> ToolRuntimeVariablePool:
"""
convert db variables to tool variables
"""
return ToolRuntimeVariablePool(**{
'conversation_id': db_variables.conversation_id,
'user_id': db_variables.user_id,
'tenant_id': db_variables.tenant_id,
'pool': db_variables.variables
})
def _init_message_chain(self, message: Message, query: str) -> MessageChain:
"""
Init MessageChain
:param message: message
:param query: query
:return:
"""
message_chain = MessageChain(
message_id=message.id,
type="AgentExecutor",
input=json.dumps({
"input": query
})
)
db.session.add(message_chain)
db.session.commit()
return message_chain
def _save_message_chain(self, message_chain: MessageChain, output_text: str) -> None:
"""
Save MessageChain
:param message_chain: message chain
:param output_text: output text
:return:
"""
message_chain.output = json.dumps({
"output": output_text
})
db.session.commit()
def _get_usage_of_all_agent_thoughts(self, model_config: ModelConfigEntity,
message: Message) -> LLMUsage:
"""
Get usage of all agent thoughts
:param model_config: model config
:param message: message
:return:
"""
agent_thoughts = (db.session.query(MessageAgentThought)
.filter(MessageAgentThought.message_id == message.id).all())
all_message_tokens = 0
all_answer_tokens = 0
for agent_thought in agent_thoughts:
all_message_tokens += agent_thought.message_tokens
all_answer_tokens += agent_thought.answer_tokens
model_type_instance = model_config.provider_model_bundle.model_type_instance
model_type_instance = cast(LargeLanguageModel, model_type_instance)
return model_type_instance._calc_response_usage(
model_config.model,
model_config.credentials,
all_message_tokens,
all_answer_tokens
)

110
api/core/app_runner/basic_app_runner.py
View File

@ -1,23 +1,18 @@
import logging
from typing import Optional, Tuple
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, AppOrchestrationConfigEntity, DatasetEntity,
ExternalDataVariableEntity, InvokeFrom, ModelConfigEntity)
from core.features.annotation_reply import AnnotationReplyFeature
from core.entities.application_entities import (ApplicationGenerateEntity, DatasetEntity,
InvokeFrom, ModelConfigEntity)
from core.features.dataset_retrieval import DatasetRetrievalFeature
from core.features.external_data_fetch import ExternalDataFetchFeature
from core.features.hosting_moderation import HostingModerationFeature
from core.features.moderation import ModerationFeature
from core.memory.token_buffer_memory import TokenBufferMemory
from core.model_manager import ModelInstance
from core.model_runtime.entities.message_entities import PromptMessage
from core.moderation.base import ModerationException
from core.prompt.prompt_transform import AppMode
from extensions.ext_database import db
from models.model import App, Conversation, Message, MessageAnnotation
from models.model import App, Conversation, Message
logger = logging.getLogger(__name__)
@ -213,76 +208,6 @@ class BasicApplicationRunner(AppRunner):
stream=application_generate_entity.stream
)
def moderation_for_inputs(self, app_id: str,
tenant_id: str,
app_orchestration_config_entity: AppOrchestrationConfigEntity,
inputs: dict,
query: str) -> Tuple[bool, dict, str]:
"""
Process sensitive_word_avoidance.
:param app_id: app id
:param tenant_id: tenant id
:param app_orchestration_config_entity: app orchestration config entity
:param inputs: inputs
:param query: query
:return:
"""
moderation_feature = ModerationFeature()
return moderation_feature.check(
app_id=app_id,
tenant_id=tenant_id,
app_orchestration_config_entity=app_orchestration_config_entity,
inputs=inputs,
query=query,
)
def query_app_annotations_to_reply(self, app_record: App,
message: Message,
query: str,
user_id: str,
invoke_from: InvokeFrom) -> Optional[MessageAnnotation]:
"""
Query app annotations to reply
:param app_record: app record
:param message: message
:param query: query
:param user_id: user id
:param invoke_from: invoke from
:return:
"""
annotation_reply_feature = AnnotationReplyFeature()
return annotation_reply_feature.query(
app_record=app_record,
message=message,
query=query,
user_id=user_id,
invoke_from=invoke_from
)
def fill_in_inputs_from_external_data_tools(self, tenant_id: str,
app_id: str,
external_data_tools: list[ExternalDataVariableEntity],
inputs: dict,
query: str) -> dict:
"""
Fill in variable inputs from external data tools if exists.
:param tenant_id: workspace id
:param app_id: app id
:param external_data_tools: external data tools configs
:param inputs: the inputs
:param query: the query
:return: the filled inputs
"""
external_data_fetch_feature = ExternalDataFetchFeature()
return external_data_fetch_feature.fetch(
tenant_id=tenant_id,
app_id=app_id,
external_data_tools=external_data_tools,
inputs=inputs,
query=query
)
def retrieve_dataset_context(self, tenant_id: str,
app_record: App,
queue_manager: ApplicationQueueManager,
@ -335,30 +260,3 @@ class BasicApplicationRunner(AppRunner):
memory=memory
)
def check_hosting_moderation(self, application_generate_entity: ApplicationGenerateEntity,
queue_manager: ApplicationQueueManager,
prompt_messages: list[PromptMessage]) -> bool:
"""
Check hosting moderation
:param application_generate_entity: application generate entity
:param queue_manager: queue manager
:param prompt_messages: prompt messages
:return:
"""
hosting_moderation_feature = HostingModerationFeature()
moderation_result = hosting_moderation_feature.check(
application_generate_entity=application_generate_entity,
prompt_messages=prompt_messages
)
if moderation_result:
self.direct_output(
queue_manager=queue_manager,
app_orchestration_config=application_generate_entity.app_orchestration_config_entity,
prompt_messages=prompt_messages,
text="I apologize for any confusion, " \
"but I'm an AI assistant to be helpful, harmless, and honest.",
stream=application_generate_entity.stream
)
return moderation_result

49
api/core/app_runner/generate_task_pipeline.py
View File

@ -8,7 +8,8 @@ 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)
QueuePingEvent, QueueRetrieverResourcesEvent, QueueStopEvent,
QueueMessageFileEvent, QueueAgentMessageEvent)
from core.errors.error import ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError
from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage
from core.model_runtime.entities.message_entities import (AssistantPromptMessage, ImagePromptMessageContent,
@ -16,11 +17,12 @@ from core.model_runtime.entities.message_entities import (AssistantPromptMessage
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.model_runtime.utils.encoders import jsonable_encoder
from core.prompt.prompt_template import PromptTemplateParser
from events.message_event import message_was_created
from extensions.ext_database import db
from models.model import Conversation, Message, MessageAgentThought
from models.model import Conversation, Message, MessageAgentThought, MessageFile
from pydantic import BaseModel
from services.annotation_service import AppAnnotationService
@ -284,6 +286,7 @@ class GenerateTaskPipeline:
.filter(MessageAgentThought.id == event.agent_thought_id)
.first()
)
db.session.refresh(agent_thought)
if agent_thought:
response = {
@ -293,16 +296,48 @@ class GenerateTaskPipeline:
'message_id': self._message.id,
'position': agent_thought.position,
'thought': agent_thought.thought,
'observation': agent_thought.observation,
'tool': agent_thought.tool,
'tool_input': agent_thought.tool_input,
'created_at': int(self._message.created_at.timestamp())
'created_at': int(self._message.created_at.timestamp()),
'message_files': agent_thought.files
}
if self._conversation.mode == 'chat':
response['conversation_id'] = self._conversation.id
yield self._yield_response(response)
elif isinstance(event, QueueMessageEvent):
elif isinstance(event, QueueMessageFileEvent):
message_file: MessageFile = (
db.session.query(MessageFile)
.filter(MessageFile.id == event.message_file_id)
.first()
)
# get extension
if '.' in message_file.url:
extension = f'.{message_file.url.split(".")[-1]}'
if len(extension) > 10:
extension = '.bin'
else:
extension = '.bin'
# add sign url
url = ToolFileManager.sign_file(file_id=message_file.id, extension=extension)
if message_file:
response = {
'event': 'message_file',
'id': message_file.id,
'type': message_file.type,
'belongs_to': message_file.belongs_to or 'user',
'url': url
}
if self._conversation.mode == 'chat':
response['conversation_id'] = self._conversation.id
yield self._yield_response(response)
elif isinstance(event, (QueueMessageEvent, QueueAgentMessageEvent)):
chunk = event.chunk
delta_text = chunk.delta.message.content
if delta_text is None:
@ -332,7 +367,7 @@ class GenerateTaskPipeline:
self._output_moderation_handler.append_new_token(delta_text)
self._task_state.llm_result.message.content += delta_text
response = self._handle_chunk(delta_text)
response = self._handle_chunk(delta_text, agent=isinstance(event, QueueAgentMessageEvent))
yield self._yield_response(response)
elif isinstance(event, QueueMessageReplaceEvent):
response = {
@ -384,14 +419,14 @@ class GenerateTaskPipeline:
extras=self._application_generate_entity.extras
)
def _handle_chunk(self, text: str) -> dict:
def _handle_chunk(self, text: str, agent: bool = False) -> dict:
"""
Handle completed event.
:param text: text
:return:
"""
response = {
'event': 'message',
'event': 'message' if not agent else 'agent_message',
'id': self._message.id,
'task_id': self._application_generate_entity.task_id,
'message_id': self._message.id,

180
api/core/application_manager.py
View File

@ -4,7 +4,7 @@ import threading
import uuid
from typing import Any, Generator, Optional, Tuple, Union, cast
from core.app_runner.agent_app_runner import AgentApplicationRunner
from core.app_runner.assistant_app_runner import AssistantApplicationRunner
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
@ -13,7 +13,7 @@ from core.entities.application_entities import (AdvancedChatPromptTemplateEntity
ApplicationGenerateEntity, AppOrchestrationConfigEntity, DatasetEntity,
DatasetRetrieveConfigEntity, ExternalDataVariableEntity,
FileUploadEntity, InvokeFrom, ModelConfigEntity, PromptTemplateEntity,
SensitiveWordAvoidanceEntity)
SensitiveWordAvoidanceEntity, AgentPromptEntity)
from core.entities.model_entities import ModelStatus
from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
from core.file.file_obj import FileObj
@ -23,6 +23,7 @@ from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeErr
from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
from core.prompt.prompt_template import PromptTemplateParser
from core.provider_manager import ProviderManager
from core.tools.prompt.template import REACT_PROMPT_TEMPLATES
from extensions.ext_database import db
from flask import Flask, current_app
from models.account import Account
@ -93,6 +94,9 @@ class ApplicationManager:
extras=extras
)
if not stream and application_generate_entity.app_orchestration_config_entity.agent:
raise ValueError("Agent app is not supported in blocking mode.")
# init generate records
(
conversation,
@ -151,7 +155,7 @@ class ApplicationManager:
if application_generate_entity.app_orchestration_config_entity.agent:
# agent app
runner = AgentApplicationRunner()
runner = AssistantApplicationRunner()
runner.run(
application_generate_entity=application_generate_entity,
queue_manager=queue_manager,
@ -354,6 +358,8 @@ class ApplicationManager:
# external data variables
properties['external_data_variables'] = []
# old external_data_tools
external_data_tools = copy_app_model_config_dict.get('external_data_tools', [])
for external_data_tool in external_data_tools:
if 'enabled' not in external_data_tool or not external_data_tool['enabled']:
@ -367,6 +373,19 @@ class ApplicationManager:
)
)
# current external_data_tools
for variable in copy_app_model_config_dict.get('user_input_form', []):
typ = list(variable.keys())[0]
if typ == 'external_data_tool':
val = variable[typ]
properties['external_data_variables'].append(
ExternalDataVariableEntity(
variable=val['variable'],
type=val['type'],
config=val['config']
)
)
# show retrieve source
show_retrieve_source = False
retriever_resource_dict = copy_app_model_config_dict.get('retriever_resource')
@ -376,14 +395,63 @@ class ApplicationManager:
properties['show_retrieve_source'] = show_retrieve_source
dataset_ids = []
if 'datasets' in copy_app_model_config_dict.get('dataset_configs', {}):
datasets = copy_app_model_config_dict.get('dataset_configs', {}).get('datasets', {
'strategy': 'router',
'datasets': []
})
for dataset in datasets.get('datasets', []):
keys = list(dataset.keys())
if len(keys) == 0 or keys[0] != 'dataset':
continue
dataset = dataset['dataset']
if 'enabled' not in dataset or not dataset['enabled']:
continue
dataset_id = dataset.get('id', None)
if dataset_id:
dataset_ids.append(dataset_id)
else:
datasets = {'strategy': 'router', 'datasets': []}
if 'agent_mode' in copy_app_model_config_dict and copy_app_model_config_dict['agent_mode'] \
and 'enabled' in copy_app_model_config_dict['agent_mode'] and copy_app_model_config_dict['agent_mode'][
'enabled']:
agent_dict = copy_app_model_config_dict.get('agent_mode')
agent_strategy = agent_dict.get('strategy', 'router')
if agent_strategy in ['router', 'react_router']:
dataset_ids = []
for tool in agent_dict.get('tools', []):
agent_dict = copy_app_model_config_dict.get('agent_mode', {})
agent_strategy = agent_dict.get('strategy', 'cot')
if agent_strategy == 'function_call':
strategy = AgentEntity.Strategy.FUNCTION_CALLING
elif agent_strategy == 'cot' or agent_strategy == 'react':
strategy = AgentEntity.Strategy.CHAIN_OF_THOUGHT
else:
# old configs, try to detect default strategy
if copy_app_model_config_dict['model']['provider'] == 'openai':
strategy = AgentEntity.Strategy.FUNCTION_CALLING
else:
strategy = AgentEntity.Strategy.CHAIN_OF_THOUGHT
agent_tools = []
for tool in agent_dict.get('tools', []):
keys = tool.keys()
if len(keys) >= 4:
if "enabled" not in tool or not tool["enabled"]:
continue
agent_tool_properties = {
'provider_type': tool['provider_type'],
'provider_id': tool['provider_id'],
'tool_name': tool['tool_name'],
'tool_parameters': tool['tool_parameters'] if 'tool_parameters' in tool else {}
}
agent_tools.append(AgentToolEntity(**agent_tool_properties))
elif len(keys) == 1:
# old standard
key = list(tool.keys())[0]
if key != 'dataset':
@ -397,58 +465,57 @@ class ApplicationManager:
dataset_id = tool_item['id']
dataset_ids.append(dataset_id)
dataset_configs = copy_app_model_config_dict.get('dataset_configs', {'retrieval_model': 'single'})
query_variable = copy_app_model_config_dict.get('dataset_query_variable')
if dataset_configs['retrieval_model'] == 'single':
properties['dataset'] = DatasetEntity(
dataset_ids=dataset_ids,
retrieve_config=DatasetRetrieveConfigEntity(
query_variable=query_variable,
retrieve_strategy=DatasetRetrieveConfigEntity.RetrieveStrategy.value_of(
dataset_configs['retrieval_model']
),
single_strategy=agent_strategy
)
)
else:
properties['dataset'] = DatasetEntity(
dataset_ids=dataset_ids,
retrieve_config=DatasetRetrieveConfigEntity(
query_variable=query_variable,
retrieve_strategy=DatasetRetrieveConfigEntity.RetrieveStrategy.value_of(
dataset_configs['retrieval_model']
),
top_k=dataset_configs.get('top_k'),
score_threshold=dataset_configs.get('score_threshold'),
reranking_model=dataset_configs.get('reranking_model')
)
)
agent_prompt = agent_dict.get('prompt', None) or {}
# check model mode
model_mode = copy_app_model_config_dict.get('model', {}).get('mode', 'completion')
if model_mode == 'completion':
agent_prompt_entity = AgentPromptEntity(
first_prompt=agent_prompt.get('first_prompt', REACT_PROMPT_TEMPLATES['english']['completion']['prompt']),
next_iteration=agent_prompt.get('next_iteration', REACT_PROMPT_TEMPLATES['english']['completion']['agent_scratchpad']),
)
else:
if agent_strategy == 'react':
strategy = AgentEntity.Strategy.CHAIN_OF_THOUGHT
else:
strategy = AgentEntity.Strategy.FUNCTION_CALLING
agent_prompt_entity = AgentPromptEntity(
first_prompt=agent_prompt.get('first_prompt', REACT_PROMPT_TEMPLATES['english']['chat']['prompt']),
next_iteration=agent_prompt.get('next_iteration', REACT_PROMPT_TEMPLATES['english']['chat']['agent_scratchpad']),
)
agent_tools = []
for tool in agent_dict.get('tools', []):
key = list(tool.keys())[0]
tool_item = tool[key]
properties['agent'] = AgentEntity(
provider=properties['model_config'].provider,
model=properties['model_config'].model,
strategy=strategy,
prompt=agent_prompt_entity,
tools=agent_tools,
max_iteration=agent_dict.get('max_iteration', 5)
)
agent_tool_properties = {
"tool_id": key
}
if len(dataset_ids) > 0:
# dataset configs
dataset_configs = copy_app_model_config_dict.get('dataset_configs', {'retrieval_model': 'single'})
query_variable = copy_app_model_config_dict.get('dataset_query_variable')
if "enabled" not in tool_item or not tool_item["enabled"]:
continue
agent_tool_properties["config"] = tool_item
agent_tools.append(AgentToolEntity(**agent_tool_properties))
properties['agent'] = AgentEntity(
provider=properties['model_config'].provider,
model=properties['model_config'].model,
strategy=strategy,
tools=agent_tools
if dataset_configs['retrieval_model'] == 'single':
properties['dataset'] = DatasetEntity(
dataset_ids=dataset_ids,
retrieve_config=DatasetRetrieveConfigEntity(
query_variable=query_variable,
retrieve_strategy=DatasetRetrieveConfigEntity.RetrieveStrategy.value_of(
dataset_configs['retrieval_model']
),
single_strategy=datasets.get('strategy', 'router')
)
)
else:
properties['dataset'] = DatasetEntity(
dataset_ids=dataset_ids,
retrieve_config=DatasetRetrieveConfigEntity(
query_variable=query_variable,
retrieve_strategy=DatasetRetrieveConfigEntity.RetrieveStrategy.value_of(
dataset_configs['retrieval_model']
),
top_k=dataset_configs.get('top_k'),
score_threshold=dataset_configs.get('score_threshold'),
reranking_model=dataset_configs.get('reranking_model')
)
)
# file upload
@ -601,6 +668,7 @@ class ApplicationManager:
message_id=message.id,
type=file.type.value,
transfer_method=file.transfer_method.value,
belongs_to='user',
url=file.url,
upload_file_id=file.upload_file_id,
created_by_role=('account' if account_id else 'end_user'),

27
api/core/application_queue_manager.py
View File

@ -7,10 +7,10 @@ from core.entities.application_entities import InvokeFrom
from core.entities.queue_entities import (AnnotationReplyEvent, AppQueueEvent, QueueAgentThoughtEvent, QueueErrorEvent,
QueueMessage, QueueMessageEndEvent, QueueMessageEvent,
QueueMessageReplaceEvent, QueuePingEvent, QueueRetrieverResourcesEvent,
QueueStopEvent)
QueueStopEvent, QueueMessageFileEvent, QueueAgentMessageEvent)
from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk
from extensions.ext_redis import redis_client
from models.model import MessageAgentThought
from models.model import MessageAgentThought, MessageFile
from sqlalchemy.orm import DeclarativeMeta
@ -96,6 +96,18 @@ class ApplicationQueueManager:
chunk=chunk
), pub_from)
def publish_agent_chunk_message(self, chunk: LLMResultChunk, pub_from: PublishFrom) -> None:
"""
Publish agent chunk message to channel
:param chunk: chunk
:param pub_from: publish from
:return:
"""
self.publish(QueueAgentMessageEvent(
chunk=chunk
), pub_from)
def publish_message_replace(self, text: str, pub_from: PublishFrom) -> None:
"""
Publish message replace
@ -144,6 +156,17 @@ class ApplicationQueueManager:
agent_thought_id=message_agent_thought.id
), pub_from)
def publish_message_file(self, message_file: MessageFile, pub_from: PublishFrom) -> None:
"""
Publish agent thought
:param message_file: message file
:param pub_from: publish from
:return:
"""
self.publish(QueueMessageFileEvent(
message_file_id=message_file.id
), pub_from)
def publish_error(self, e, pub_from: PublishFrom) -> None:
"""
Publish error

74
api/core/callback_handler/agent_tool_callback_handler.py Normal file
View File

@ -0,0 +1,74 @@
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
class DifyAgentCallbackHandler(BaseCallbackHandler, BaseModel):
"""Callback Handler that prints to std out."""
color: Optional[str] = ''
current_loop = 1
def __init__(self, color: Optional[str] = None) -> None:
super().__init__()
"""Initialize callback handler."""
# use a specific color is not specified
self.color = color or 'green'
self.current_loop = 1
def on_tool_start(
self,
tool_name: str,
tool_inputs: Dict[str, Any],
) -> None:
"""Do nothing."""
print_text("\n[on_tool_start] ToolCall:" + tool_name + "\n" + str(tool_inputs) + "\n", color=self.color)
def on_tool_end(
self,
tool_name: str,
tool_inputs: Dict[str, Any],
tool_outputs: str,
) -> None:
"""If not the final action, print out observation."""
print_text("\n[on_tool_end]\n", color=self.color)
print_text("Tool: " + tool_name + "\n", color=self.color)
print_text("Inputs: " + str(tool_inputs) + "\n", color=self.color)
print_text("Outputs: " + str(tool_outputs) + "\n", color=self.color)
print_text("\n")
def on_tool_error(
self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
) -> None:
"""Do nothing."""
print_text("\n[on_tool_error] Error: " + str(error) + "\n", color='red')
def on_agent_start(
self, thought: str
) -> None:
"""Run on agent start."""
if thought:
print_text("\n[on_agent_start] \nCurrent Loop: " + \
str(self.current_loop) + \
"\nThought: " + thought + "\n", color=self.color)
else:
print_text("\n[on_agent_start] \nCurrent Loop: " + str(self.current_loop) + "\n", color=self.color)
def on_agent_finish(
self, color: Optional[str] = None, **kwargs: Any
) -> None:
"""Run on agent end."""
print_text("\n[on_agent_finish]\n Loop: " + str(self.current_loop) + "\n", color=self.color)
self.current_loop += 1
@property
def ignore_agent(self) -> bool:
"""Whether to ignore agent callbacks."""
return not os.environ.get("DEBUG") or os.environ.get("DEBUG").lower() != 'true'
@property
def ignore_chat_model(self) -> bool:
"""Whether to ignore chat model callbacks."""
return not os.environ.get("DEBUG") or os.environ.get("DEBUG").lower() != 'true'

40
api/core/entities/application_entities.py
View File

@ -1,11 +1,12 @@
from enum import Enum
from typing import Any, Optional, cast
from typing import Optional, Any, cast, Literal, Union
from pydantic import BaseModel
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):
@ -153,9 +154,35 @@ class AgentToolEntity(BaseModel):
"""
Agent Tool Entity.
"""
tool_id: str
config: dict[str, Any] = {}
provider_type: Literal["builtin", "api"]
provider_id: str
tool_name: str
tool_parameters: dict[str, Any] = {}
class AgentPromptEntity(BaseModel):
"""
Agent Prompt Entity.
"""
first_prompt: str
next_iteration: str
class AgentScratchpadUnit(BaseModel):
"""
Agent First Prompt Entity.
"""
class Action(BaseModel):
"""
Action Entity.
"""
action_name: str
action_input: Union[dict, str]
agent_response: Optional[str] = None
thought: Optional[str] = None
action_str: Optional[str] = None
observation: Optional[str] = None
action: Optional[Action] = None
class AgentEntity(BaseModel):
"""
@ -171,8 +198,9 @@ class AgentEntity(BaseModel):
provider: str
model: str
strategy: Strategy
tools: list[AgentToolEntity] = []
prompt: Optional[AgentPromptEntity] = None
tools: list[AgentToolEntity] = None
max_iteration: int = 5
class AppOrchestrationConfigEntity(BaseModel):
"""

15
api/core/entities/queue_entities.py
View File

@ -10,11 +10,13 @@ class QueueEvent(Enum):
QueueEvent enum
"""
MESSAGE = "message"
AGENT_MESSAGE = "agent_message"
MESSAGE_REPLACE = "message-replace"
MESSAGE_END = "message-end"
RETRIEVER_RESOURCES = "retriever-resources"
ANNOTATION_REPLY = "annotation-reply"
AGENT_THOUGHT = "agent-thought"
MESSAGE_FILE = "message-file"
ERROR = "error"
PING = "ping"
STOP = "stop"
@ -34,6 +36,13 @@ class QueueMessageEvent(AppQueueEvent):
event = QueueEvent.MESSAGE
chunk: LLMResultChunk
class QueueAgentMessageEvent(AppQueueEvent):
"""
QueueMessageEvent entity
"""
event = QueueEvent.AGENT_MESSAGE
chunk: LLMResultChunk
class QueueMessageReplaceEvent(AppQueueEvent):
"""
@ -74,6 +83,12 @@ class QueueAgentThoughtEvent(AppQueueEvent):
event = QueueEvent.AGENT_THOUGHT
agent_thought_id: str
class QueueMessageFileEvent(AppQueueEvent):
"""
QueueAgentThoughtEvent entity
"""
event = QueueEvent.MESSAGE_FILE
message_file_id: str
class QueueErrorEvent(AppQueueEvent):
"""

146
api/core/features/agent_runner.py
View File

@ -1,30 +1,27 @@
import logging
from typing import List, Optional, cast
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 core.agent.agent.agent_llm_callback import AgentLLMCallback
from core.agent.agent_executor import AgentConfiguration, AgentExecutor, PlanningStrategy
from core.agent.agent_executor import PlanningStrategy, AgentConfiguration, AgentExecutor
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 (AgentEntity, AgentToolEntity, AppOrchestrationConfigEntity, InvokeFrom,
ModelConfigEntity)
from core.entities.application_entities import ModelConfigEntity, InvokeFrom, \
AgentEntity, AgentToolEntity, AppOrchestrationConfigEntity
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.tool.current_datetime_tool import DatetimeTool
from core.tool.dataset_retriever_tool import DatasetRetrieverTool
from core.tool.provider.serpapi_provider import SerpAPIToolProvider
from core.tool.serpapi_wrapper import OptimizedSerpAPIInput, OptimizedSerpAPIWrapper
from core.tool.web_reader_tool import WebReaderTool
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__)
@ -132,55 +129,6 @@ class AgentRunnerFeature:
logger.exception("agent_executor run failed")
return None
def to_tools(self, tool_configs: list[AgentToolEntity],
invoke_from: InvokeFrom,
callbacks: list[BaseCallbackHandler]) \
-> Optional[List[BaseTool]]:
"""
Convert tool configs to tools
:param tool_configs: tool configs
:param invoke_from: invoke from
:param callbacks: callbacks
"""
tools = []
for tool_config in tool_configs:
tool = None
if tool_config.tool_id == "dataset":
tool = self.to_dataset_retriever_tool(
tool_config=tool_config.config,
invoke_from=invoke_from
)
elif tool_config.tool_id == "web_reader":
tool = self.to_web_reader_tool(
tool_config=tool_config.config,
invoke_from=invoke_from
)
elif tool_config.tool_id == "google_search":
tool = self.to_google_search_tool(
tool_config=tool_config.config,
invoke_from=invoke_from
)
elif tool_config.tool_id == "wikipedia":
tool = self.to_wikipedia_tool(
tool_config=tool_config.config,
invoke_from=invoke_from
)
elif tool_config.tool_id == "current_datetime":
tool = self.to_current_datetime_tool(
tool_config=tool_config.config,
invoke_from=invoke_from
)
if tool:
if tool.callbacks is not None:
tool.callbacks.extend(callbacks)
else:
tool.callbacks = callbacks
tools.append(tool)
return tools
def to_dataset_retriever_tool(self, tool_config: dict,
invoke_from: InvokeFrom) \
-> Optional[BaseTool]:
@ -248,77 +196,3 @@ class AgentRunnerFeature:
)
return tool
def to_web_reader_tool(self, tool_config: dict,
invoke_from: InvokeFrom) -> Optional[BaseTool]:
"""
A tool for reading web pages
:param tool_config: tool config
:param invoke_from: invoke from
:return:
"""
model_parameters = {
"temperature": 0,
"max_tokens": 500
}
tool = WebReaderTool(
model_config=self.model_config,
model_parameters=model_parameters,
max_chunk_length=4000,
continue_reading=True
)
return tool
def to_google_search_tool(self, tool_config: dict,
invoke_from: InvokeFrom) -> Optional[BaseTool]:
"""
A tool for performing a Google search and extracting snippets and webpages
:param tool_config: tool config
:param invoke_from: invoke from
:return:
"""
tool_provider = SerpAPIToolProvider(tenant_id=self.tenant_id)
func_kwargs = tool_provider.credentials_to_func_kwargs()
if not func_kwargs:
return None
tool = Tool(
name="google_search",
description="A tool for performing a Google search and extracting snippets and webpages "
"when you need to search for something you don't know or when your information "
"is not up to date. "
"Input should be a search query.",
func=OptimizedSerpAPIWrapper(**func_kwargs).run,
args_schema=OptimizedSerpAPIInput
)
return tool
def to_current_datetime_tool(self, tool_config: dict,
invoke_from: InvokeFrom) -> Optional[BaseTool]:
"""
A tool for getting the current date and time
:param tool_config: tool config
:param invoke_from: invoke from
:return:
"""
return DatetimeTool()
def to_wikipedia_tool(self, tool_config: dict,
invoke_from: InvokeFrom) -> Optional[BaseTool]:
"""
A tool for searching Wikipedia
:param tool_config: tool config
:param invoke_from: invoke from
:return:
"""
class WikipediaInput(BaseModel):
query: str = Field(..., description="search query.")
return WikipediaQueryRun(
name="wikipedia",
api_wrapper=WikipediaAPIWrapper(doc_content_chars_max=4000),
args_schema=WikipediaInput
)

558
api/core/features/assistant_base_runner.py Normal file
View File

@ -0,0 +1,558 @@
import logging
import json
from typing import Optional, List, Tuple, Union
from datetime import datetime
from mimetypes import guess_extension
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.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.file.message_file_parser import FileTransferMethod
logger = logging.getLogger(__name__)
class BaseAssistantApplicationRunner(AppRunner):
def __init__(self, tenant_id: str,
application_generate_entity: ApplicationGenerateEntity,
app_orchestration_config: AppOrchestrationConfigEntity,
model_config: ModelConfigEntity,
config: AgentEntity,
queue_manager: ApplicationQueueManager,
message: Message,
user_id: str,
memory: Optional[TokenBufferMemory] = None,
prompt_messages: Optional[List[PromptMessage]] = None,
variables_pool: Optional[ToolRuntimeVariablePool] = None,
db_variables: Optional[ToolConversationVariables] = None,
) -> None:
"""
Agent runner
:param tenant_id: tenant id
:param app_orchestration_config: app orchestration config
:param model_config: model config
:param config: dataset config
:param queue_manager: queue manager
:param message: message
:param user_id: user id
:param agent_llm_callback: agent llm callback
:param callback: callback
:param memory: memory
"""
self.tenant_id = tenant_id
self.application_generate_entity = application_generate_entity
self.app_orchestration_config = app_orchestration_config
self.model_config = model_config
self.config = config
self.queue_manager = queue_manager
self.message = message
self.user_id = user_id
self.memory = memory
self.history_prompt_messages = prompt_messages
self.variables_pool = variables_pool
self.db_variables_pool = db_variables
# init callback
self.agent_callback = DifyAgentCallbackHandler()
# init dataset tools
hit_callback = DatasetIndexToolCallbackHandler(
queue_manager=queue_manager,
app_id=self.application_generate_entity.app_id,
message_id=message.id,
user_id=user_id,
invoke_from=self.application_generate_entity.invoke_from,
)
self.dataset_tools = DatasetRetrieverTool.get_dataset_tools(
tenant_id=tenant_id,
dataset_ids=app_orchestration_config.dataset.dataset_ids if app_orchestration_config.dataset else [],
retrieve_config=app_orchestration_config.dataset.retrieve_config if app_orchestration_config.dataset else None,
return_resource=app_orchestration_config.show_retrieve_source,
invoke_from=application_generate_entity.invoke_from,
hit_callback=hit_callback
)
# get how many agent thoughts have been created
self.agent_thought_count = db.session.query(MessageAgentThought).filter(
MessageAgentThought.message_id == self.message.id,
).count()
def _repacket_app_orchestration_config(self, app_orchestration_config: AppOrchestrationConfigEntity) -> AppOrchestrationConfigEntity:
"""
Repacket app orchestration config
"""
if app_orchestration_config.prompt_template.simple_prompt_template is None:
app_orchestration_config.prompt_template.simple_prompt_template = ''
return app_orchestration_config
def _convert_tool_response_to_str(self, tool_response: List[ToolInvokeMessage]) -> str:
"""
Handle tool response
"""
result = ''
for response in tool_response:
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."
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."
else:
result += f"tool response: {response.message}."
return result
def _convert_tool_to_prompt_message_tool(self, tool: AgentToolEntity) -> Tuple[PromptMessageTool, Tool]:
"""
convert tool to prompt message tool
"""
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,
agent_callback=self.agent_callback
)
tool_entity.load_variables(self.variables_pool)
message_tool = PromptMessageTool(
name=tool.tool_name,
description=tool_entity.description.llm,
parameters={
"type": "object",
"properties": {},
"required": [],
}
)
runtime_parameters = {}
parameters = tool_entity.parameters or []
user_parameters = tool_entity.get_runtime_parameters() or []
# override parameters
for parameter in user_parameters:
# check if parameter in tool parameters
found = False
for tool_parameter in parameters:
if tool_parameter.name == parameter.name:
found = True
break
if found:
# override parameter
tool_parameter.type = parameter.type
tool_parameter.form = parameter.form
tool_parameter.required = parameter.required
tool_parameter.default = parameter.default
tool_parameter.options = parameter.options
tool_parameter.llm_description = parameter.llm_description
else:
# add new parameter
parameters.append(parameter)
for parameter in parameters:
parameter_type = 'string'
enum = []
if parameter.type == ToolParamter.ToolParameterType.STRING:
parameter_type = 'string'
elif parameter.type == ToolParamter.ToolParameterType.BOOLEAN:
parameter_type = 'boolean'
elif parameter.type == ToolParamter.ToolParameterType.NUMBER:
parameter_type = 'number'
elif parameter.type == ToolParamter.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:
# get tool parameter from form
tool_parameter_config = tool.tool_parameters.get(parameter.name)
if not tool_parameter_config:
# get default value
tool_parameter_config = parameter.default
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:
# check if tool_parameter_config in options
options = list(map(lambda x: x.value, parameter.options))
if tool_parameter_config not in options:
raise ValueError(f"tool parameter {parameter.name} value {tool_parameter_config} not in options {options}")
# convert tool parameter config to correct type
try:
if parameter.type == ToolParamter.ToolParameterType.NUMBER:
# check if tool parameter is integer
if isinstance(tool_parameter_config, int):
tool_parameter_config = tool_parameter_config
elif isinstance(tool_parameter_config, float):
tool_parameter_config = tool_parameter_config
elif isinstance(tool_parameter_config, str):
if '.' in tool_parameter_config:
tool_parameter_config = float(tool_parameter_config)
else:
tool_parameter_config = int(tool_parameter_config)
elif parameter.type == ToolParamter.ToolParameterType.BOOLEAN:
tool_parameter_config = bool(tool_parameter_config)
elif parameter.type not in [ToolParamter.ToolParameterType.SELECT, ToolParamter.ToolParameterType.STRING]:
tool_parameter_config = str(tool_parameter_config)
elif parameter.type == ToolParamter.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")
# save tool parameter to tool entity memory
runtime_parameters[parameter.name] = tool_parameter_config
elif parameter.form == ToolParamter.ToolParameterForm.LLM:
message_tool.parameters['properties'][parameter.name] = {
"type": parameter_type,
"description": parameter.llm_description or '',
}
if len(enum) > 0:
message_tool.parameters['properties'][parameter.name]['enum'] = enum
if parameter.required:
message_tool.parameters['required'].append(parameter.name)
tool_entity.runtime.runtime_parameters.update(runtime_parameters)
return message_tool, tool_entity
def _convert_dataset_retriever_tool_to_prompt_message_tool(self, tool: DatasetRetrieverTool) -> PromptMessageTool:
"""
convert dataset retriever tool to prompt message tool
"""
prompt_tool = PromptMessageTool(
name=tool.identity.name,
description=tool.description.llm,
parameters={
"type": "object",
"properties": {},
"required": [],
}
)
for parameter in tool.get_runtime_parameters():
parameter_type = 'string'
prompt_tool.parameters['properties'][parameter.name] = {
"type": parameter_type,
"description": parameter.llm_description or '',
}
if parameter.required:
if parameter.name not in prompt_tool.parameters['required']:
prompt_tool.parameters['required'].append(parameter.name)
return prompt_tool
def update_prompt_message_tool(self, tool: Tool, prompt_tool: PromptMessageTool) -> PromptMessageTool:
"""
update prompt message tool
"""
# try to get tool runtime parameters
tool_runtime_parameters = tool.get_runtime_parameters() or []
for parameter in tool_runtime_parameters:
parameter_type = 'string'
enum = []
if parameter.type == ToolParamter.ToolParameterType.STRING:
parameter_type = 'string'
elif parameter.type == ToolParamter.ToolParameterType.BOOLEAN:
parameter_type = 'boolean'
elif parameter.type == ToolParamter.ToolParameterType.NUMBER:
parameter_type = 'number'
elif parameter.type == ToolParamter.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:
prompt_tool.parameters['properties'][parameter.name] = {
"type": parameter_type,
"description": parameter.llm_description or '',
}
if len(enum) > 0:
prompt_tool.parameters['properties'][parameter.name]['enum'] = enum
if parameter.required:
if parameter.name not in prompt_tool.parameters['required']:
prompt_tool.parameters['required'].append(parameter.name)
return prompt_tool
def extract_tool_response_binary(self, tool_response: List[ToolInvokeMessage]) -> List[ToolInvokeMessageBinary]:
"""
Extract tool response binary
"""
result = []
for response in tool_response:
if response.type == ToolInvokeMessage.MessageType.IMAGE_LINK or \
response.type == ToolInvokeMessage.MessageType.IMAGE:
result.append(ToolInvokeMessageBinary(
mimetype=response.meta.get('mime_type', 'octet/stream'),
url=response.message,
save_as=response.save_as,
))
elif response.type == ToolInvokeMessage.MessageType.BLOB:
result.append(ToolInvokeMessageBinary(
mimetype=response.meta.get('mime_type', 'octet/stream'),
url=response.message,
save_as=response.save_as,
))
elif response.type == ToolInvokeMessage.MessageType.LINK:
# check if there is a mime type in meta
if response.meta and 'mime_type' in response.meta:
result.append(ToolInvokeMessageBinary(
mimetype=response.meta.get('mime_type', 'octet/stream') if response.meta else 'octet/stream',
url=response.message,
save_as=response.save_as,
))
return result
def create_message_files(self, messages: List[ToolInvokeMessageBinary]) -> List[Tuple[MessageFile, bool]]:
"""
Create message file
:param messages: messages
:return: message files, should save as variable
"""
result = []
for message in messages:
file_type = 'bin'
if 'image' in message.mimetype:
file_type = 'image'
elif 'video' in message.mimetype:
file_type = 'video'
elif 'audio' in message.mimetype:
file_type = 'audio'
elif 'text' in message.mimetype:
file_type = 'text'
elif 'pdf' in message.mimetype:
file_type = 'pdf'
elif 'zip' in message.mimetype:
file_type = 'archive'
# ...
invoke_from = self.application_generate_entity.invoke_from
message_file = MessageFile(
message_id=self.message.id,
type=file_type,
transfer_method=FileTransferMethod.TOOL_FILE.value,
belongs_to='assistant',
url=message.url,
upload_file_id=None,
created_by_role=('account'if invoke_from in [InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER] else 'end_user'),
created_by=self.user_id,
)
db.session.add(message_file)
result.append((
message_file,
message.save_as
))
db.session.commit()
return result
def create_agent_thought(self, message_id: str, message: str,
tool_name: str, tool_input: str, messages_ids: List[str]
) -> MessageAgentThought:
"""
Create agent thought
"""
thought = MessageAgentThought(
message_id=message_id,
message_chain_id=None,
thought='',
tool=tool_name,
tool_input=tool_input,
message=message,
message_token=0,
message_unit_price=0,
message_price_unit=0,
message_files=json.dumps(messages_ids) if messages_ids else '',
answer='',
observation='',
answer_token=0,
answer_unit_price=0,
answer_price_unit=0,
tokens=0,
total_price=0,
position=self.agent_thought_count + 1,
currency='USD',
latency=0,
created_by_role='account',
created_by=self.user_id,
)
db.session.add(thought)
db.session.commit()
self.agent_thought_count += 1
return thought
def save_agent_thought(self,
agent_thought: MessageAgentThought,
tool_name: str,
tool_input: Union[str, dict],
thought: str,
observation: str,
answer: str,
messages_ids: List[str],
llm_usage: LLMUsage = None) -> MessageAgentThought:
"""
Save agent thought
"""
if thought is not None:
agent_thought.thought = thought
if tool_name is not None:
agent_thought.tool = tool_name
if tool_input is not None:
if isinstance(tool_input, dict):
try:
tool_input = json.dumps(tool_input, ensure_ascii=False)
except Exception as e:
tool_input = json.dumps(tool_input)
agent_thought.tool_input = tool_input
if observation is not None:
agent_thought.observation = observation
if answer is not None:
agent_thought.answer = answer
if messages_ids is not None and len(messages_ids) > 0:
agent_thought.message_files = json.dumps(messages_ids)
if llm_usage:
agent_thought.message_token = llm_usage.prompt_tokens
agent_thought.message_price_unit = llm_usage.prompt_price_unit
agent_thought.message_unit_price = llm_usage.prompt_unit_price
agent_thought.answer_token = llm_usage.completion_tokens
agent_thought.answer_price_unit = llm_usage.completion_price_unit
agent_thought.answer_unit_price = llm_usage.completion_unit_price
agent_thought.tokens = llm_usage.total_tokens
agent_thought.total_price = llm_usage.total_price
db.session.commit()
def get_history_prompt_messages(self) -> List[PromptMessage]:
"""
Get history prompt messages
"""
if self.history_prompt_messages is None:
self.history_prompt_messages = db.session.query(PromptMessage).filter(
PromptMessage.message_id == self.message.id,
).order_by(PromptMessage.position.asc()).all()
return self.history_prompt_messages
def transform_tool_invoke_messages(self, messages: List[ToolInvokeMessage]) -> List[ToolInvokeMessage]:
"""
Transform tool message into agent thought
"""
result = []
for message in messages:
if message.type == ToolInvokeMessage.MessageType.TEXT:
result.append(message)
elif message.type == ToolInvokeMessage.MessageType.LINK:
result.append(message)
elif message.type == ToolInvokeMessage.MessageType.IMAGE:
# try to download image
try:
file = ToolFileManager.create_file_by_url(user_id=self.user_id, tenant_id=self.tenant_id,
conversation_id=self.message.conversation_id,
file_url=message.message)
url = f'/files/tools/{file.id}{guess_extension(file.mimetype) or ".png"}'
result.append(ToolInvokeMessage(
type=ToolInvokeMessage.MessageType.IMAGE_LINK,
message=url,
save_as=message.save_as,
meta=message.meta.copy() if message.meta is not None else {},
))
except Exception as e:
logger.exception(e)
result.append(ToolInvokeMessage(
type=ToolInvokeMessage.MessageType.TEXT,
message=f"Failed to download image: {message.message}, you can try to download it yourself.",
meta=message.meta.copy() if message.meta is not None else {},
save_as=message.save_as,
))
elif message.type == ToolInvokeMessage.MessageType.BLOB:
# get mime type and save blob to storage
mimetype = message.meta.get('mime_type', 'octet/stream')
# if message is str, encode it to bytes
if isinstance(message.message, str):
message.message = message.message.encode('utf-8')
file = ToolFileManager.create_file_by_raw(user_id=self.user_id, tenant_id=self.tenant_id,
conversation_id=self.message.conversation_id,
file_binary=message.message,
mimetype=mimetype)
url = f'/files/tools/{file.id}{guess_extension(file.mimetype) or ".bin"}'
# check if file is image
if 'image' in mimetype:
result.append(ToolInvokeMessage(
type=ToolInvokeMessage.MessageType.IMAGE_LINK,
message=url,
save_as=message.save_as,
meta=message.meta.copy() if message.meta is not None else {},
))
else:
result.append(ToolInvokeMessage(
type=ToolInvokeMessage.MessageType.LINK,
message=url,
save_as=message.save_as,
meta=message.meta.copy() if message.meta is not None else {},
))
else:
result.append(message)
return result
def update_db_variables(self, tool_variables: ToolRuntimeVariablePool, db_variables: ToolConversationVariables):
"""
convert tool variables to db variables
"""
db_variables.updated_at = datetime.utcnow()
db_variables.variables_str = json.dumps(jsonable_encoder(tool_variables.pool))
db.session.commit()

578
api/core/features/assistant_cot_runner.py Normal file
View File

@ -0,0 +1,578 @@
import json
import logging
import re
from typing import Literal, Union, Generator, Dict, List
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.features.assistant_base_runner import BaseAssistantApplicationRunner
from models.model import Conversation, Message
logger = logging.getLogger(__name__)
class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
def run(self, model_instance: ModelInstance,
conversation: Conversation,
message: Message,
query: str,
) -> Union[Generator, LLMResult]:
"""
Run Cot agent application
"""
app_orchestration_config = self.app_orchestration_config
self._repacket_app_orchestration_config(app_orchestration_config)
agent_scratchpad: List[AgentScratchpadUnit] = []
# check model mode
if self.app_orchestration_config.model_config.mode == "completion":
# TODO: stop words
if 'Observation' not in app_orchestration_config.model_config.stop:
app_orchestration_config.model_config.stop.append('Observation')
iteration_step = 1
max_iteration_steps = min(self.app_orchestration_config.agent.max_iteration, 5) + 1
prompt_messages = self.history_prompt_messages
# convert tools into ModelRuntime Tool format
prompt_messages_tools: List[PromptMessageTool] = []
tool_instances = {}
for tool in self.app_orchestration_config.agent.tools if self.app_orchestration_config.agent else []:
try:
prompt_tool, tool_entity = self._convert_tool_to_prompt_message_tool(tool)
except Exception:
# api tool may be deleted
continue
# save tool entity
tool_instances[tool.tool_name] = tool_entity
# save prompt tool
prompt_messages_tools.append(prompt_tool)
# convert dataset tools into ModelRuntime Tool format
for dataset_tool in self.dataset_tools:
prompt_tool = self._convert_dataset_retriever_tool_to_prompt_message_tool(dataset_tool)
# save prompt tool
prompt_messages_tools.append(prompt_tool)
# save tool entity
tool_instances[dataset_tool.identity.name] = dataset_tool
function_call_state = True
llm_usage = {
'usage': None
}
final_answer = ''
def increse_usage(final_llm_usage_dict: Dict[str, LLMUsage], usage: LLMUsage):
if not final_llm_usage_dict['usage']:
final_llm_usage_dict['usage'] = usage
else:
llm_usage = final_llm_usage_dict['usage']
llm_usage.prompt_tokens += usage.prompt_tokens
llm_usage.completion_tokens += usage.completion_tokens
llm_usage.prompt_price += usage.prompt_price
llm_usage.completion_price += usage.completion_price
while function_call_state and iteration_step <= max_iteration_steps:
# continue to run until there is not any tool call
function_call_state = False
if iteration_step == max_iteration_steps:
# the last iteration, remove all tools
prompt_messages_tools = []
message_file_ids = []
agent_thought = self.create_agent_thought(
message_id=message.id,
message='',
tool_name='',
tool_input='',
messages_ids=message_file_ids
)
self.queue_manager.publish_agent_thought(agent_thought, PublishFrom.APPLICATION_MANAGER)
# update prompt messages
prompt_messages = self._originze_cot_prompt_messages(
mode=app_orchestration_config.model_config.mode,
prompt_messages=prompt_messages,
tools=prompt_messages_tools,
agent_scratchpad=agent_scratchpad,
agent_prompt_message=app_orchestration_config.agent.prompt,
instruction=app_orchestration_config.prompt_template.simple_prompt_template,
input=query
)
# recale llm max tokens
self.recale_llm_max_tokens(self.model_config, prompt_messages)
# invoke model
llm_result: LLMResult = model_instance.invoke_llm(
prompt_messages=prompt_messages,
model_parameters=app_orchestration_config.model_config.parameters,
tools=[],
stop=app_orchestration_config.model_config.stop,
stream=False,
user=self.user_id,
callbacks=[],
)
# check llm result
if not llm_result:
raise ValueError("failed to invoke llm")
# get scratchpad
scratchpad = self._extract_response_scratchpad(llm_result.message.content)
agent_scratchpad.append(scratchpad)
# get llm usage
if llm_result.usage:
increse_usage(llm_usage, llm_result.usage)
self.save_agent_thought(agent_thought=agent_thought,
tool_name=scratchpad.action.action_name if scratchpad.action else '',
tool_input=scratchpad.action.action_input if scratchpad.action else '',
thought=scratchpad.thought,
observation='',
answer=llm_result.message.content,
messages_ids=[],
llm_usage=llm_result.usage)
if scratchpad.action and scratchpad.action.action_name.lower() != "final answer":
self.queue_manager.publish_agent_thought(agent_thought, PublishFrom.APPLICATION_MANAGER)
# publish agent thought if it's not empty and there is a action
if scratchpad.thought and scratchpad.action:
# check if final answer
if not scratchpad.action.action_name.lower() == "final answer":
yield LLMResultChunk(
model=model_instance.model,
prompt_messages=prompt_messages,
delta=LLMResultChunkDelta(
index=0,
message=AssistantPromptMessage(
content=scratchpad.thought
),
usage=llm_result.usage,
),
system_fingerprint=''
)
if not scratchpad.action:
# failed to extract action, return final answer directly
final_answer = scratchpad.agent_response or ''
else:
if scratchpad.action.action_name.lower() == "final answer":
# action is final answer, return final answer directly
try:
final_answer = scratchpad.action.action_input if \
isinstance(scratchpad.action.action_input, str) else \
json.dumps(scratchpad.action.action_input)
except json.JSONDecodeError:
final_answer = f'{scratchpad.action.action_input}'
else:
function_call_state = True
# action is tool call, invoke tool
tool_call_name = scratchpad.action.action_name
tool_call_args = scratchpad.action.action_input
tool_instance = tool_instances.get(tool_call_name)
if not tool_instance:
logger.error(f"failed to find tool instance: {tool_call_name}")
answer = f"there is not a tool named {tool_call_name}"
self.save_agent_thought(agent_thought=agent_thought,
tool_name='',
tool_input='',
thought=None,
observation=answer,
answer=answer,
messages_ids=[])
self.queue_manager.publish_agent_thought(agent_thought, PublishFrom.APPLICATION_MANAGER)
else:
# invoke tool
error_response = None
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)
)
# transform tool response to llm friendly response
tool_response = self.transform_tool_invoke_messages(tool_response)
# extract binary data from tool invoke message
binary_files = self.extract_tool_response_binary(tool_response)
# create message file
message_files = self.create_message_files(binary_files)
# publish files
for message_file, save_as in message_files:
if save_as:
self.variables_pool.set_file(tool_name=tool_call_name,
value=message_file.id,
name=save_as)
self.queue_manager.publish_message_file(message_file, PublishFrom.APPLICATION_MANAGER)
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"
except (
ToolNotFoundError, ToolNotSupportedError, ToolProviderNotFoundError
) as e:
error_response = f"there is not a tool named {tool_call_name}"
except (
ToolParamterValidationError
) as e:
error_response = f"tool paramters validation error: {e}, please check your tool paramters"
except ToolInvokeError as e:
error_response = f"tool invoke error: {e}"
except Exception as e:
error_response = f"unknown error: {e}"
if error_response:
observation = error_response
logger.error(error_response)
else:
observation = self._convert_tool_response_to_str(tool_response)
# save scratchpad
scratchpad.observation = observation
scratchpad.agent_response = llm_result.message.content
# save agent thought
self.save_agent_thought(
agent_thought=agent_thought,
tool_name=tool_call_name,
tool_input=tool_call_args,
thought=None,
observation=observation,
answer=llm_result.message.content,
messages_ids=message_file_ids,
)
self.queue_manager.publish_agent_thought(agent_thought, PublishFrom.APPLICATION_MANAGER)
# update prompt tool message
for prompt_tool in prompt_messages_tools:
self.update_prompt_message_tool(tool_instances[prompt_tool.name], prompt_tool)
iteration_step += 1
yield LLMResultChunk(
model=model_instance.model,
prompt_messages=prompt_messages,
delta=LLMResultChunkDelta(
index=0,
message=AssistantPromptMessage(
content=final_answer
),
usage=llm_usage['usage']
),
system_fingerprint=''
)
# save agent thought
self.save_agent_thought(
agent_thought=agent_thought,
tool_name='',
tool_input='',
thought=final_answer,
observation='',
answer=final_answer,
messages_ids=[]
)
self.update_db_variables(self.variables_pool, self.db_variables_pool)
# publish end event
self.queue_manager.publish_message_end(LLMResult(
model=model_instance.model,
prompt_messages=prompt_messages,
message=AssistantPromptMessage(
content=final_answer
),
usage=llm_usage['usage'],
system_fingerprint=''
), PublishFrom.APPLICATION_MANAGER)
def _extract_response_scratchpad(self, content: str) -> AgentScratchpadUnit:
"""
extract response from llm response
"""
def extra_quotes() -> AgentScratchpadUnit:
agent_response = content
# try to extract all quotes
pattern = re.compile(r'```(.*?)```', re.DOTALL)
quotes = pattern.findall(content)
# try to extract action from end to start
for i in range(len(quotes) - 1, 0, -1):
"""
1. use json load to parse action
2. use plain text `Action: xxx` to parse action
"""
try:
action = json.loads(quotes[i].replace('```', ''))
action_name = action.get("action")
action_input = action.get("action_input")
agent_thought = agent_response.replace(quotes[i], '')
if action_name and action_input:
return AgentScratchpadUnit(
agent_response=content,
thought=agent_thought,
action_str=quotes[i],
action=AgentScratchpadUnit.Action(
action_name=action_name,
action_input=action_input,
)
)
except:
# try to parse action from plain text
action_name = re.findall(r'action: (.*)', quotes[i], re.IGNORECASE)
action_input = re.findall(r'action input: (.*)', quotes[i], re.IGNORECASE)
# delete action from agent response
agent_thought = agent_response.replace(quotes[i], '')
# remove extra quotes
agent_thought = re.sub(r'```(json)*\n*```', '', agent_thought, flags=re.DOTALL)
# remove Action: xxx from agent thought
agent_thought = re.sub(r'Action:.*', '', agent_thought, flags=re.IGNORECASE)
if action_name and action_input:
return AgentScratchpadUnit(
agent_response=content,
thought=agent_thought,
action_str=quotes[i],
action=AgentScratchpadUnit.Action(
action_name=action_name[0],
action_input=action_input[0],
)
)
def extra_json():
agent_response = content
# try to extract all json
structures, pair_match_stack = [], []
started_at, end_at = 0, 0
for i in range(len(content)):
if content[i] == '{':
pair_match_stack.append(i)
if len(pair_match_stack) == 1:
started_at = i
elif content[i] == '}':
begin = pair_match_stack.pop()
if not pair_match_stack:
end_at = i + 1
structures.append((content[begin:i+1], (started_at, end_at)))
# handle the last character
if pair_match_stack:
end_at = len(content)
structures.append((content[pair_match_stack[0]:], (started_at, end_at)))
for i in range(len(structures), 0, -1):
try:
json_content, (started_at, end_at) = structures[i - 1]
action = json.loads(json_content)
action_name = action.get("action")
action_input = action.get("action_input")
# delete json content from agent response
agent_thought = agent_response[:started_at] + agent_response[end_at:]
# remove extra quotes like ```(json)*\n\n```
agent_thought = re.sub(r'```(json)*\n*```', '', agent_thought, flags=re.DOTALL)
# remove Action: xxx from agent thought
agent_thought = re.sub(r'Action:.*', '', agent_thought, flags=re.IGNORECASE)
if action_name and action_input:
return AgentScratchpadUnit(
agent_response=content,
thought=agent_thought,
action_str=json_content,
action=AgentScratchpadUnit.Action(
action_name=action_name,
action_input=action_input,
)
)
except:
pass
agent_scratchpad = extra_quotes()
if agent_scratchpad:
return agent_scratchpad
agent_scratchpad = extra_json()
if agent_scratchpad:
return agent_scratchpad
return AgentScratchpadUnit(
agent_response=content,
thought=content,
action_str='',
action=None
)
def _check_cot_prompt_messages(self, mode: Literal["completion", "chat"],
agent_prompt_message: AgentPromptEntity,
):
"""
check chain of thought prompt messages, a standard prompt message is like:
Respond to the human as helpfully and accurately as possible.
{{instruction}}
You have access to the following tools:
{{tools}}
Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
Valid action values: "Final Answer" or {{tool_names}}
Provide only ONE action per $JSON_BLOB, as shown:
```
{
"action": $TOOL_NAME,
"action_input": $ACTION_INPUT
}
```
"""
# parse agent prompt message
first_prompt = agent_prompt_message.first_prompt
next_iteration = agent_prompt_message.next_iteration
if not isinstance(first_prompt, str) or not isinstance(next_iteration, str):
raise ValueError(f"first_prompt or next_iteration is required in CoT agent mode")
# check instruction, tools, and tool_names slots
if not first_prompt.find("{{instruction}}") >= 0:
raise ValueError("{{instruction}} is required in first_prompt")
if not first_prompt.find("{{tools}}") >= 0:
raise ValueError("{{tools}} is required in first_prompt")
if not first_prompt.find("{{tool_names}}") >= 0:
raise ValueError("{{tool_names}} is required in first_prompt")
if mode == "completion":
if not first_prompt.find("{{query}}") >= 0:
raise ValueError("{{query}} is required in first_prompt")
if not first_prompt.find("{{agent_scratchpad}}") >= 0:
raise ValueError("{{agent_scratchpad}} is required in first_prompt")
if mode == "completion":
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:
"""
convert agent scratchpad list to str
"""
next_iteration = self.app_orchestration_config.agent.prompt.next_iteration
result = ''
for scratchpad in agent_scratchpad:
result += scratchpad.thought + next_iteration.replace("{{observation}}", scratchpad.observation) + "\n"
return result
def _originze_cot_prompt_messages(self, mode: Literal["completion", "chat"],
prompt_messages: List[PromptMessage],
tools: List[PromptMessageTool],
agent_scratchpad: List[AgentScratchpadUnit],
agent_prompt_message: AgentPromptEntity,
instruction: str,
input: str,
) -> List[PromptMessage]:
"""
originze chain of thought prompt messages, a standard prompt message is like:
Respond to the human as helpfully and accurately as possible.
{{instruction}}
You have access to the following tools:
{{tools}}
Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
Valid action values: "Final Answer" or {{tool_names}}
Provide only ONE action per $JSON_BLOB, as shown:
```
{{{{
"action": $TOOL_NAME,
"action_input": $ACTION_INPUT
}}}}
```
"""
self._check_cot_prompt_messages(mode, agent_prompt_message)
# parse agent prompt message
first_prompt = agent_prompt_message.first_prompt
# parse tools
tools_str = self._jsonify_tool_prompt_messages(tools)
# parse tools name
tool_names = '"' + '","'.join([tool.name for tool in tools]) + '"'
# get system message
system_message = first_prompt.replace("{{instruction}}", instruction) \
.replace("{{tools}}", tools_str) \
.replace("{{tool_names}}", tool_names)
# originze prompt messages
if mode == "chat":
# override system message
overrided = False
prompt_messages = prompt_messages.copy()
for prompt_message in prompt_messages:
if isinstance(prompt_message, SystemPromptMessage):
prompt_message.content = system_message
overrided = True
break
if not overrided:
prompt_messages.insert(0, SystemPromptMessage(
content=system_message,
))
# add assistant message
if len(agent_scratchpad) > 0:
prompt_messages.append(AssistantPromptMessage(
content=agent_scratchpad[-1].thought + "\n" + agent_scratchpad[-1].observation
))
# add user message
if len(agent_scratchpad) > 0:
prompt_messages.append(UserPromptMessage(
content=input,
))
return prompt_messages
elif mode == "completion":
# parse agent scratchpad
agent_scratchpad_str = self._convert_strachpad_list_to_str(agent_scratchpad)
# parse prompt messages
return [UserPromptMessage(
content=first_prompt.replace("{{instruction}}", instruction)
.replace("{{tools}}", tools_str)
.replace("{{tool_names}}", tool_names)
.replace("{{query}}", input)
.replace("{{agent_scratchpad}}", agent_scratchpad_str),
)]
else:
raise ValueError(f"mode {mode} is not supported")
def _jsonify_tool_prompt_messages(self, tools: list[PromptMessageTool]) -> str:
"""
jsonify tool prompt messages
"""
tools = jsonable_encoder(tools)
try:
return json.dumps(tools, ensure_ascii=False)
except json.JSONDecodeError:
return json.dumps(tools)

335
api/core/features/assistant_fc_runner.py Normal file
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@ -0,0 +1,335 @@
import json
import logging
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 models.model import Conversation, Message, MessageAgentThought
logger = logging.getLogger(__name__)
class AssistantFunctionCallApplicationRunner(BaseAssistantApplicationRunner):
def run(self, model_instance: ModelInstance,
conversation: Conversation,
message: Message,
query: str,
) -> Generator[LLMResultChunk, None, None]:
"""
Run FunctionCall agent application
"""
app_orchestration_config = self.app_orchestration_config
prompt_template = self.app_orchestration_config.prompt_template.simple_prompt_template or ''
prompt_messages = self.history_prompt_messages
prompt_messages = self.organize_prompt_messages(
prompt_template=prompt_template,
query=query,
prompt_messages=prompt_messages
)
# convert tools into ModelRuntime Tool format
prompt_messages_tools: List[PromptMessageTool] = []
tool_instances = {}
for tool in self.app_orchestration_config.agent.tools if self.app_orchestration_config.agent else []:
try:
prompt_tool, tool_entity = self._convert_tool_to_prompt_message_tool(tool)
except Exception:
# api tool may be deleted
continue
# save tool entity
tool_instances[tool.tool_name] = tool_entity
# save prompt tool
prompt_messages_tools.append(prompt_tool)
# convert dataset tools into ModelRuntime Tool format
for dataset_tool in self.dataset_tools:
prompt_tool = self._convert_dataset_retriever_tool_to_prompt_message_tool(dataset_tool)
# save prompt tool
prompt_messages_tools.append(prompt_tool)
# save tool entity
tool_instances[dataset_tool.identity.name] = dataset_tool
iteration_step = 1
max_iteration_steps = min(app_orchestration_config.agent.max_iteration, 5) + 1
# continue to run until there is not any tool call
function_call_state = True
agent_thoughts: List[MessageAgentThought] = []
llm_usage = {
'usage': None
}
final_answer = ''
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:
llm_usage = final_llm_usage_dict['usage']
llm_usage.prompt_tokens += usage.prompt_tokens
llm_usage.completion_tokens += usage.completion_tokens
llm_usage.prompt_price += usage.prompt_price
llm_usage.completion_price += usage.completion_price
while function_call_state and iteration_step <= max_iteration_steps:
function_call_state = False
if iteration_step == max_iteration_steps:
# the last iteration, remove all tools
prompt_messages_tools = []
message_file_ids = []
agent_thought = self.create_agent_thought(
message_id=message.id,
message='',
tool_name='',
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(
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,
user=self.user_id,
callbacks=[],
)
tool_calls: List[Tuple[str, str, Dict[str, Any]]] = []
# save full response
response = ''
# save tool call names and inputs
tool_call_names = ''
tool_call_inputs = ''
current_llm_usage = None
for chunk in chunks:
# 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
# save thought
self.save_agent_thought(
agent_thought=agent_thought,
tool_name=tool_call_names,
tool_input=tool_call_inputs,
thought=response,
observation=None,
answer=response,
messages_ids=[],
llm_usage=current_llm_usage
)
self.queue_manager.publish_agent_thought(agent_thought, PublishFrom.APPLICATION_MANAGER)
final_answer += response + '\n'
# call tools
tool_responses = []
for tool_call_id, tool_call_name, tool_call_args in tool_calls:
tool_instance = tool_instances.get(tool_call_name)
if not tool_instance:
logger.error(f"failed to find tool instance: {tool_call_name}")
tool_response = {
"tool_call_id": tool_call_id,
"tool_call_name": tool_call_name,
"tool_response": f"there is not a tool named {tool_call_name}"
}
tool_responses.append(tool_response)
else:
# invoke tool
error_response = None
try:
tool_invoke_message = tool_instance.invoke(
user_id=self.user_id,
tool_paramters=tool_call_args,
)
# transform tool invoke message to get LLM friendly message
tool_invoke_message = self.transform_tool_invoke_messages(tool_invoke_message)
# extract binary data from tool invoke message
binary_files = self.extract_tool_response_binary(tool_invoke_message)
# create message file
message_files = self.create_message_files(binary_files)
# publish files
for message_file, save_as in message_files:
if save_as:
self.variables_pool.set_file(tool_name=tool_call_name, value=message_file.id, name=save_as)
# publish message file
self.queue_manager.publish_message_file(message_file, PublishFrom.APPLICATION_MANAGER)
# add message file ids
message_file_ids.append(message_file.id)
except ToolProviderCredentialValidationError as e:
error_response = f"Plese 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
) as e:
error_response = f"tool paramters validation error: {e}, please check your tool paramters"
except ToolInvokeError as e:
error_response = f"tool invoke error: {e}"
except Exception as e:
error_response = f"unknown error: {e}"
if error_response:
observation = error_response
logger.error(error_response)
tool_response = {
"tool_call_id": tool_call_id,
"tool_call_name": tool_call_name,
"tool_response": error_response
}
tool_responses.append(tool_response)
else:
observation = self._convert_tool_response_to_str(tool_invoke_message)
tool_response = {
"tool_call_id": tool_call_id,
"tool_call_name": tool_call_name,
"tool_response": observation
}
tool_responses.append(tool_response)
prompt_messages = self.organize_prompt_messages(
prompt_template=prompt_template,
query=None,
tool_call_id=tool_call_id,
tool_call_name=tool_call_name,
tool_response=tool_response['tool_response'],
prompt_messages=prompt_messages,
)
if len(tool_responses) > 0:
# save agent thought
self.save_agent_thought(
agent_thought=agent_thought,
tool_name=None,
tool_input=None,
thought=None,
observation=tool_response['tool_response'],
answer=None,
messages_ids=message_file_ids
)
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)
iteration_step += 1
self.update_db_variables(self.variables_pool, self.db_variables_pool)
# publish end event
self.queue_manager.publish_message_end(LLMResult(
model=model_instance.model,
prompt_messages=prompt_messages,
message=AssistantPromptMessage(
content=final_answer,
),
usage=llm_usage['usage'],
system_fingerprint=''
), PublishFrom.APPLICATION_MANAGER)
def check_tool_calls(self, llm_result_chunk: LLMResultChunk) -> bool:
"""
Check if there is any tool call in llm result chunk
"""
if llm_result_chunk.delta.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]]]]:
"""
Extract tool calls from llm result chunk
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_chunk.delta.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,
tool_call_id: str = None, tool_call_name: str = None, tool_response: str = None,
prompt_messages: list[PromptMessage] = None
) -> list[PromptMessage]:
"""
Organize prompt messages
"""
if not prompt_messages:
prompt_messages = [
SystemPromptMessage(content=prompt_template),
UserPromptMessage(content=query),
]
else:
if tool_response:
prompt_messages = prompt_messages.copy()
prompt_messages.append(
ToolPromptMessage(
content=tool_response,
tool_call_id=tool_call_id,
name=tool_call_name,
)
)
return prompt_messages

4
api/core/features/dataset_retrieval.py
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@ -6,8 +6,8 @@ from core.entities.application_entities import DatasetEntity, DatasetRetrieveCon
from core.memory.token_buffer_memory import TokenBufferMemory
from core.model_runtime.entities.model_entities import ModelFeature
from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
from core.tool.dataset_multi_retriever_tool import DatasetMultiRetrieverTool
from core.tool.dataset_retriever_tool import DatasetRetrieverTool
from core.tools.tool.dataset_retriever.dataset_multi_retriever_tool import DatasetMultiRetrieverTool
from core.tools.tool.dataset_retriever.dataset_retriever_tool import DatasetRetrieverTool
from extensions.ext_database import db
from langchain.tools import BaseTool
from models.dataset import Dataset

11
api/core/file/file_obj.py
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@ -22,6 +22,7 @@ class FileType(enum.Enum):
class FileTransferMethod(enum.Enum):
REMOTE_URL = 'remote_url'
LOCAL_FILE = 'local_file'
TOOL_FILE = 'tool_file'
@staticmethod
def value_of(value):
@ -30,6 +31,16 @@ class FileTransferMethod(enum.Enum):
return member
raise ValueError(f"No matching enum found for value '{value}'")
class FileBelongsTo(enum.Enum):
USER = 'user'
ASSISTANT = 'assistant'
@staticmethod
def value_of(value):
for member in FileBelongsTo:
if member.value == value:
return member
raise ValueError(f"No matching enum found for value '{value}'")
class FileObj(BaseModel):
id: Optional[str]

5
api/core/file/message_file_parser.py
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@ -1,7 +1,7 @@
from typing import Dict, List, Optional, Union
import requests
from core.file.file_obj import FileObj, FileTransferMethod, FileType
from core.file.file_obj import FileObj, FileTransferMethod, FileType, FileBelongsTo
from services.file_service import IMAGE_EXTENSIONS
from extensions.ext_database import db
from models.account import Account
@ -128,6 +128,9 @@ class MessageFileParser:
# group by file type and convert file args or message files to FileObj
for file in files:
if file.belongs_to == FileBelongsTo.ASSISTANT.value:
continue
file_obj = self._to_file_obj(file, file_upload_config)
if file_obj.type not in type_file_objs:
continue

8
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@ -0,0 +1,8 @@
tool_file_manager = {
'manager': None
}
class ToolFileParser:
@staticmethod
def get_tool_file_manager() -> 'ToolFileManager':
return tool_file_manager['manager']

20
api/core/model_runtime/model_providers/openai/llm/llm.py
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@ -485,19 +485,37 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
:return: llm response chunk generator
"""
full_assistant_content = ''
delta_assistant_message_function_call_storage: ChoiceDeltaFunctionCall = None
for chunk in response:
if len(chunk.choices) == 0:
continue
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
continue
# tool_calls = self._extract_response_tool_calls(assistant_message_tool_calls)
function_call = self._extract_response_function_call(assistant_message_function_call)
tool_calls = [function_call] if function_call else []

25
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@ -0,0 +1,25 @@
# Tools
This module implements built-in tools used in Agent Assistants and Workflows within Dify. You could define and display your own tools in this module, without modifying the frontend logic. This decoupling allows for easier horizontal scaling of Dify's capabilities.
## Feature Introduction
The tools provided for Agents and Workflows are currently divided into two categories:
- `Built-in Tools` are internally implemented within our product and are hardcoded for use in Agents and Workflows.
- `Api-Based Tools` leverage third-party APIs for implementation. You don't need to code to integrate these -- simply provide interface definitions in formats like `OpenAPI` , `Swagger`, or the `OpenAI-plugin` on the front-end.
### Built-in Tool Providers
![Alt text](docs/zh_Hans/images/index/image.png)
### API Tool Providers
![Alt text](docs/zh_Hans/images/index/image-1.png)
## Tool Integration
To enable developers to build flexible and powerful tools, we provide two guides:
### [Quick Integration 👈🏻](./docs/en_US/tool_scale_out.md)
Quick integration aims at quickly getting you up to speed with tool integration by walking over an example Google Search tool.
### [Advanced Integration 👈🏻](./docs/en_US/advanced_scale_out.md)
Advanced integration will offer a deeper dive into the module interfaces, and explain how to implement more complex capabilities, such as generating images, combining multiple tools, and managing the flow of parameters, images, and files between different tools.

27
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@ -0,0 +1,27 @@
# Tools
该模块提供了各Agent和Workflow中会使用的内置工具的调用、鉴权接口，并为 Dify 提供了统一的工具供应商的信息和凭据表单规则。
- 一方面将工具和业务代码解耦，方便开发者对模型横向扩展，
- 另一方面提供了只需在后端定义供应商和工具，即可在前端页面直接展示，无需修改前端逻辑。
## 功能介绍
对于给Agent和Workflow提供的工具，我们当前将其分为两类：
- `Built-in Tools` 内置工具，即Dify内部实现的工具，通过硬编码的方式提供给Agent和Workflow使用。
- `Api-Based Tools` 基于API的工具，即通过调用第三方API实现的工具，`Api-Based Tool`不需要再额外定义，只需提供`OpenAPI` `Swagger` `OpenAI plugin`等接口文档即可。
### 内置工具供应商
![Alt text](docs/zh_Hans/images/index/image.png)
### API工具供应商
![Alt text](docs/zh_Hans/images/index/image-1.png)
## 工具接入
为了实现更灵活更强大的功能，Tools提供了一系列的接口，帮助开发者快速构建想要的工具，本文作为开发者的入门指南，将会以[快速接入](./docs/zh_Hans/tool_scale_out.md)和[高级接入](./docs/zh_Hans/advanced_scale_out.md)两部分介绍如何接入工具。
### [快速接入 👈🏻](./docs/zh_Hans/tool_scale_out.md)
快速接入可以帮助你在10~20分钟内完成工具的接入，但是这种接入方式只能实现简单的功能，如果你想要实现更复杂的功能，可以参考下面的高级接入。
### [高级接入 👈🏻](./docs/zh_Hans/advanced_scale_out.md)
高级接入将介绍如何实现更复杂的功能配置，包括实现图生图、实现多个工具的组合、实现参数、图片、文件在多个工具之间的流转。

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@ -0,0 +1,266 @@
# Advanced Tool Integration
Before starting with this advanced guide, please make sure you have a basic understanding of the tool integration process in Dify. Check out [Quick Integration](./tool_scale_out.md) for a quick runthrough.
## Tool Interface
We have defined a series of helper methods in the `Tool` class to help developers quickly build more complex tools.
### Message Return
Dify supports various message types such as `text`, `link`, `image`, and `file BLOB`. You can return different types of messages to the LLM and users through the following interfaces.
Please note, some parameters in the following interfaces will be introduced in later sections.
#### Image URL
You only need to pass the URL of the image, and Dify will automatically download the image and return it to the user.
```python
def create_image_message(self, image: str, save_as: str = '') -> ToolInvokeMessage:
"""
create an image message
:param image: the url of the image
:return: the image message
"""
```
#### Link
If you need to return a link, you can use the following interface.
```python
def create_link_message(self, link: str, save_as: str = '') -> ToolInvokeMessage:
"""
create a link message
:param link: the url of the link
:return: the link message
"""
```
#### Text
If you need to return a text message, you can use the following interface.
```python
def create_text_message(self, text: str, save_as: str = '') -> ToolInvokeMessage:
"""
create a text message
:param text: the text of the message
:return: the text message
"""
```
#### File BLOB
If you need to return the raw data of a file, such as images, audio, video, PPT, Word, Excel, etc., you can use the following interface.
- `blob` The raw data of the file, of bytes type
- `meta` The metadata of the file, if you know the type of the file, it is best to pass a `mime_type`, otherwise Dify will use `octet/stream` as the default type
```python
def create_blob_message(self, blob: bytes, meta: dict = None, save_as: str = '') -> ToolInvokeMessage:
"""
create a blob message
:param blob: the blob
:return: the blob message
"""
```
### Shortcut Tools
In large model applications, we have two common needs:
- First, summarize a long text in advance, and then pass the summary content to the LLM to prevent the original text from being too long for the LLM to handle
- The content obtained by the tool is a link, and the web page information needs to be crawled before it can be returned to the LLM
To help developers quickly implement these two needs, we provide the following two shortcut tools.
#### Text Summary Tool
This tool takes in an user_id and the text to be summarized, and returns the summarized text. Dify will use the default model of the current workspace to summarize the long text.
```python
def summary(self, user_id: str, content: str) -> str:
"""
summary the content
:param user_id: the user id
:param content: the content
:return: the summary
"""
```
#### Web Page Crawling Tool
This tool takes in web page link to be crawled and a user_agent (which can be empty), and returns a string containing the information of the web page. The `user_agent` is an optional parameter that can be used to identify the tool. If not passed, Dify will use the default `user_agent`.
```python
def get_url(self, url: str, user_agent: str = None) -> str:
"""
get url
""" the crawled result
```
### Variable Pool
We have introduced a variable pool in `Tool` to store variables, files, etc. generated during the tool's operation. These variables can be used by other tools during the tool's operation.
Next, we will use `DallE3` and `Vectorizer.AI` as examples to introduce how to use the variable pool.
- `DallE3` is an image generation tool that can generate images based on text. Here, we will let `DallE3` generate a logo for a coffee shop
- `Vectorizer.AI` is a vector image conversion tool that can convert images into vector images, so that the images can be infinitely enlarged without distortion. Here, we will convert the PNG icon generated by `DallE3` into a vector image, so that it can be truly used by designers.
#### DallE3
First, we use DallE3. After creating the image, we save the image to the variable pool. The code is as follows:
```python
from typing import Any, Dict, List, Union
from core.tools.entities.tool_entities import ToolInvokeMessage
from core.tools.tool.builtin_tool import BuiltinTool
from base64 import b64decode
from openai import OpenAI
class DallE3Tool(BuiltinTool):
def _invoke(self,
user_id: str,
tool_paramters: Dict[str, Any],
) -> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
invoke tools
"""
client = OpenAI(
api_key=self.runtime.credentials['openai_api_key'],
)
# prompt
prompt = tool_paramters.get('prompt', '')
if not prompt:
return self.create_text_message('Please input prompt')
# call openapi dalle3
response = client.images.generate(
prompt=prompt, model='dall-e-3',
size='1024x1024', n=1, style='vivid', quality='standard',
response_format='b64_json'
)
result = []
for image in response.data:
# Save all images to the variable pool through the save_as parameter. The variable name is self.VARIABLE_KEY.IMAGE.value. If new images are generated later, they will overwrite the previous images.
result.append(self.create_blob_message(blob=b64decode(image.b64_json),
meta={ 'mime_type': 'image/png' },
save_as=self.VARIABLE_KEY.IMAGE.value))
return result
```
Note that we used `self.VARIABLE_KEY.IMAGE.value` as the variable name of the image. In order for developers' tools to cooperate with each other, we defined this `KEY`. You can use it freely, or you can choose not to use this `KEY`. Passing a custom KEY is also acceptable.
#### Vectorizer.AI
Next, we use Vectorizer.AI to convert the PNG icon generated by DallE3 into a vector image. Let's go through the functions we defined here. The code is as follows:
```python
from core.tools.tool.builtin_tool import BuiltinTool
from core.tools.entities.tool_entities import ToolInvokeMessage, ToolParamter
from core.tools.errors import ToolProviderCredentialValidationError
from typing import Any, Dict, List, Union
from httpx import post
from base64 import b64decode
class VectorizerTool(BuiltinTool):
def _invoke(self, user_id: str, tool_paramters: Dict[str, Any]) \
-> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
Tool invocation, the image variable name needs to be passed in from here, so that we can get the image from the variable pool
"""
def get_runtime_parameters(self) -> List[ToolParamter]:
"""
Override the tool parameter list, we can dynamically generate the parameter list based on the actual situation in the current variable pool, so that the LLM can generate the form based on the parameter list
"""
def is_tool_avaliable(self) -> bool:
"""
Whether the current tool is available, if there is no image in the current variable pool, then we don't need to display this tool, just return False here
"""
```
Next, let's implement these three functions
```python
from core.tools.tool.builtin_tool import BuiltinTool
from core.tools.entities.tool_entities import ToolInvokeMessage, ToolParamter
from core.tools.errors import ToolProviderCredentialValidationError
from typing import Any, Dict, List, Union
from httpx import post
from base64 import b64decode
class VectorizerTool(BuiltinTool):
def _invoke(self, user_id: str, tool_paramters: Dict[str, Any]) \
-> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
invoke tools
"""
api_key_name = self.runtime.credentials.get('api_key_name', None)
api_key_value = self.runtime.credentials.get('api_key_value', None)
if not api_key_name or not api_key_value:
raise ToolProviderCredentialValidationError('Please input api key name and value')
# Get image_id, the definition of image_id can be found in get_runtime_parameters
image_id = tool_paramters.get('image_id', '')
if not image_id:
return self.create_text_message('Please input image id')
# Get the image generated by DallE from the variable pool
image_binary = self.get_variable_file(self.VARIABLE_KEY.IMAGE)
if not image_binary:
return self.create_text_message('Image not found, please request user to generate image firstly.')
# Generate vector image
response = post(
'https://vectorizer.ai/api/v1/vectorize',
files={ 'image': image_binary },
data={ 'mode': 'test' },
auth=(api_key_name, api_key_value),
timeout=30
)
if response.status_code != 200:
raise Exception(response.text)
return [
self.create_text_message('the vectorized svg is saved as an image.'),
self.create_blob_message(blob=response.content,
meta={'mime_type': 'image/svg+xml'})
]
def get_runtime_parameters(self) -> List[ToolParamter]:
"""
override the runtime parameters
"""
# Here, we override the tool parameter list, define the image_id, and set its option list to all images in the current variable pool. The configuration here is consistent with the configuration in yaml.
return [
ToolParamter.get_simple_instance(
name='image_id',
llm_description=f'the image id that you want to vectorize, \
and the image id should be specified in \
{[i.name for i in self.list_default_image_variables()]}',
type=ToolParamter.ToolParameterType.SELECT,
required=True,
options=[i.name for i in self.list_default_image_variables()]
)
]
def is_tool_avaliable(self) -> bool:
# Only when there are images in the variable pool, the LLM needs to use this tool
return len(self.list_default_image_variables()) > 0
```
It's worth noting that we didn't actually use `image_id` here. We assumed that there must be an image in the default variable pool when calling this tool, so we directly used `image_binary = self.get_variable_file(self.VARIABLE_KEY.IMAGE)` to get the image. In cases where the model's capabilities are weak, we recommend developers to do the same, which can effectively improve fault tolerance and avoid the model passing incorrect parameters.

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@ -0,0 +1,212 @@
# Quick Tool Integration
Here, we will use GoogleSearch as an example to demonstrate how to quickly integrate a tool.
## 1. Prepare the Tool Provider yaml
### Introduction
This yaml declares a new tool provider, and includes information like the provider's name, icon, author, and other details that are fetched by the frontend for display.
### Example
We need to create a `google` module (folder) under `core/tools/provider/builtin`, and create `google.yaml`. The name must be consistent with the module name.
Subsequently, all operations related to this tool will be carried out under this module.
```yaml
identity: # Basic information of the tool provider
author: Dify # Author
name: google # Name, unique, no duplication with other providers
label: # Label for frontend display
en_US: Google # English label
zh_Hans: Google # Chinese label
description: # Description for frontend display
en_US: Google # English description
zh_Hans: Google # Chinese description
icon: icon.svg # Icon, needs to be placed in the _assets folder of the current module
```
- The `identity` field is mandatory, it contains the basic information of the tool provider, including author, name, label, description, icon, etc.
- The icon needs to be placed in the `_assets` folder of the current module, you can refer to [here](../../provider/builtin/google/_assets/icon.svg).
## 2. Prepare Provider Credentials
Google, as a third-party tool, uses the API provided by SerpApi, which requires an API Key to use. This means that this tool needs a credential to use. For tools like `wikipedia`, there is no need to fill in the credential field, you can refer to [here](../../provider/builtin/wikipedia/wikipedia.yaml).
After configuring the credential field, the effect is as follows:
```yaml
identity:
author: Dify
name: google
label:
en_US: Google
zh_Hans: Google
description:
en_US: Google
zh_Hans: Google
icon: icon.svg
credentails_for_provider: # Credential field
serpapi_api_key: # Credential field name
type: secret-input # Credential field type
required: true # Required or not
label: # Credential field label
en_US: SerpApi API key # English label
zh_Hans: SerpApi API key # Chinese label
placeholder: # Credential field placeholder
en_US: Please input your SerpApi API key # English placeholder
zh_Hans: 请输入你的 SerpApi API key # Chinese placeholder
help: # Credential field help text
en_US: Get your SerpApi API key from SerpApi # English help text
zh_Hans: 从 SerpApi 获取您的 SerpApi API key # Chinese help text
url: https://serpapi.com/manage-api-key # Credential field help link
```
- `type`: Credential field type, currently can be either `secret-input`, `text-input`, or `select` , corresponding to password input box, text input box, and drop-down box, respectively. If set to `secret-input`, it will mask the input content on the frontend, and the backend will encrypt the input content.
## 3. Prepare Tool yaml
A provider can have multiple tools, each tool needs a yaml file to describe, this file contains the basic information, parameters, output, etc. of the tool.
Still taking GoogleSearch as an example, we need to create a `tools` module under the `google` module, and create `tools/google_search.yaml`, the content is as follows.
```yaml
identity: # Basic information of the tool
name: google_search # Tool name, unique, no duplication with other tools
author: Dify # Author
label: # Label for frontend display
en_US: GoogleSearch # English label
zh_Hans: 谷歌搜索 # Chinese label
description: # Description for frontend display
human: # Introduction for frontend display, supports multiple languages
en_US: A tool for performing a Google SERP search and extracting snippets and webpages.Input should be a search query.
zh_Hans: 一个用于执行 Google SERP 搜索并提取片段和网页的工具。输入应该是一个搜索查询。
llm: A tool for performing a Google SERP search and extracting snippets and webpages.Input should be a search query. # Introduction passed to LLM, in order to make LLM better understand this tool, we suggest to write as detailed information about this tool as possible here, so that LLM can understand and use this tool
parameters: # Parameter list
- name: query # Parameter name
type: string # Parameter type
required: true # Required or not
label: # Parameter label
en_US: Query string # English label
zh_Hans: 查询语句 # Chinese label
human_description: # Introduction for frontend display, supports multiple languages
en_US: used for searching
zh_Hans: 用于搜索网页内容
llm_description: key words for searching # Introduction passed to LLM, similarly, in order to make LLM better understand this parameter, we suggest to write as detailed information about this parameter as possible here, so that LLM can understand this parameter
form: llm # Form type, llm means this parameter needs to be inferred by Agent, the frontend will not display this parameter
- name: result_type
type: select # Parameter type
required: true
options: # Drop-down box options
- value: text
label:
en_US: text
zh_Hans: 文本
- value: link
label:
en_US: link
zh_Hans: 链接
default: link
label:
en_US: Result type
zh_Hans: 结果类型
human_description:
en_US: used for selecting the result type, text or link
zh_Hans: 用于选择结果类型，使用文本还是链接进行展示
form: form # Form type, form means this parameter needs to be filled in by the user on the frontend before the conversation starts
```
- The `identity` field is mandatory, it contains the basic information of the tool, including name, author, label, description, etc.
- `parameters` Parameter list
- `name` Parameter name, unique, no duplication with other parameters
- `type` Parameter type, currently supports `string`, `number`, `boolean`, `select` four types, corresponding to string, number, boolean, drop-down box
- `required` Required or not
- In `llm` mode, if the parameter is required, the Agent is required to infer this parameter
- In `form` mode, if the parameter is required, the user is required to fill in this parameter on the frontend before the conversation starts
- `options` Parameter options
- In `llm` mode, Dify will pass all options to LLM, LLM can infer based on these options
- In `form` mode, when `type` is `select`, the frontend will display these options
- `default` Default value
- `label` Parameter label, for frontend display
- `human_description` Introduction for frontend display, supports multiple languages
- `llm_description` Introduction passed to LLM, in order to make LLM better understand this parameter, we suggest to write as detailed information about this parameter as possible here, so that LLM can understand this parameter
- `form` Form type, currently supports `llm`, `form` two types, corresponding to Agent self-inference and frontend filling
## 4. Add Tool Logic
After completing the tool configuration, we can start writing the tool code that defines how it is invoked.
Create `google_search.py` under the `google/tools` module, the content is as follows.
```python
from core.tools.tool.builtin_tool import BuiltinTool
from core.tools.entities.tool_entities import ToolInvokeMessage
from typing import Any, Dict, List, Union
class GoogleSearchTool(BuiltinTool):
def _invoke(self,
user_id: str,
tool_paramters: Dict[str, Any],
) -> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
invoke tools
"""
query = tool_paramters['query']
result_type = tool_paramters['result_type']
api_key = self.runtime.credentials['serpapi_api_key']
# TODO: search with serpapi
result = SerpAPI(api_key).run(query, result_type=result_type)
if result_type == 'text':
return self.create_text_message(text=result)
return self.create_link_message(link=result)
```
### Parameters
The overall logic of the tool is in the `_invoke` method, this method accepts two parameters: `user_id` and `tool_paramters`, which represent the user ID and tool parameters respectively
### Return Data
When the tool returns, you can choose to return one message or multiple messages, here we return one message, using `create_text_message` and `create_link_message` can create a text message or a link message.
## 5. Add Provider Code
Finally, we need to create a provider class under the provider module to implement the provider's credential verification logic. If the credential verification fails, it will throw a `ToolProviderCredentialValidationError` exception.
Create `google.py` under the `google` module, the content is as follows.
```python
from core.tools.entities.tool_entities import ToolInvokeMessage, ToolProviderType
from core.tools.tool.tool import Tool
from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
from core.tools.errors import ToolProviderCredentialValidationError
from core.tools.provider.builtin.google.tools.google_search import GoogleSearchTool
from typing import Any, Dict
class GoogleProvider(BuiltinToolProviderController):
def _validate_credentials(self, credentials: Dict[str, Any]) -> None:
try:
# 1. Here you need to instantiate a GoogleSearchTool with GoogleSearchTool(), it will automatically load the yaml configuration of GoogleSearchTool, but at this time it does not have credential information inside
# 2. Then you need to use the fork_tool_runtime method to pass the current credential information to GoogleSearchTool
# 3. Finally, invoke it, the parameters need to be passed according to the parameter rules configured in the yaml of GoogleSearchTool
GoogleSearchTool().fork_tool_runtime(
meta={
"credentials": credentials,
}
).invoke(
user_id='',
tool_paramters={
"query": "test",
"result_type": "link"
},
)
except Exception as e:
raise ToolProviderCredentialValidationError(str(e))
```
## Completion
After the above steps are completed, we can see this tool on the frontend, and it can be used in the Agent.
Of course, because google_search needs a credential, before using it, you also need to input your credentials on the frontend.
![Alt text](../zh_Hans/images/index/image-2.png)

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# 高级接入Tool
在开始高级接入之前，请确保你已经阅读过[快速接入](./tool_scale_out.md)，并对Dify的工具接入流程有了基本的了解。
## 工具接口
我们在`Tool`类中定义了一系列快捷方法，用于帮助开发者快速构较为复杂的工具
### 消息返回
Dify支持`文本` `链接` `图片` `文件BLOB` 等多种消息类型，你可以通过以下几个接口返回不同类型的消息给LLM和用户。
注意，在下面的接口中的部分参数将在后面的章节中介绍。
#### 图片URL
只需要传递图片的URL即可，Dify会自动下载图片并返回给用户。
```python
def create_image_message(self, image: str, save_as: str = '') -> ToolInvokeMessage:
"""
create an image message
:param image: the url of the image
:return: the image message
"""
```
#### 链接
如果你需要返回一个链接，可以使用以下接口。
```python
def create_link_message(self, link: str, save_as: str = '') -> ToolInvokeMessage:
"""
create a link message
:param link: the url of the link
:return: the link message
"""
```
#### 文本
如果你需要返回一个文本消息，可以使用以下接口。
```python
def create_text_message(self, text: str, save_as: str = '') -> ToolInvokeMessage:
"""
create a text message
:param text: the text of the message
:return: the text message
"""
```
#### 文件BLOB
如果你需要返回文件的原始数据，如图片、音频、视频、PPT、Word、Excel等，可以使用以下接口。
- `blob` 文件的原始数据，bytes类型
- `meta` 文件的元数据，如果你知道该文件的类型，最好传递一个`mime_type`，否则Dify将使用`octet/stream`作为默认类型
```python
def create_blob_message(self, blob: bytes, meta: dict = None, save_as: str = '') -> ToolInvokeMessage:
"""
create a blob message
:param blob: the blob
:return: the blob message
"""
```
### 快捷工具
在大模型应用中，我们有两种常见的需求：
- 先将很长的文本进行提前总结，然后再将总结内容传递给LLM，以防止原文本过长导致LLM无法处理
- 工具获取到的内容是一个链接，需要爬取网页信息后再返回给LLM
为了帮助开发者快速实现这两种需求，我们提供了以下两个快捷工具。
#### 文本总结工具
该工具需要传入user_id和需要进行总结的文本，返回一个总结后的文本，Dify会使用当前工作空间的默认模型对长文本进行总结。
```python
def summary(self, user_id: str, content: str) -> str:
"""
summary the content
:param user_id: the user id
:param content: the content
:return: the summary
"""
```
#### 网页爬取工具
该工具需要传入需要爬取的网页链接和一个user_agent（可为空），返回一个包含该网页信息的字符串，其中`user_agent`是可选参数，可以用来识别工具，如果不传递，Dify将使用默认的`user_agent`。
```python
def get_url(self, url: str, user_agent: str = None) -> str:
"""
get url
""" the crawled result
```
### 变量池
我们在`Tool`中引入了一个变量池，用于存储工具运行过程中产生的变量、文件等，这些变量可以在工具运行过程中被其他工具使用。
下面，我们以`DallE3`和`Vectorizer.AI`为例，介绍如何使用变量池。
- `DallE3`是一个图片生成工具，它可以根据文本生成图片，在这里，我们将让`DallE3`生成一个咖啡厅的Logo
- `Vectorizer.AI`是一个矢量图转换工具，它可以将图片转换为矢量图，使得图片可以无限放大而不失真，在这里，我们将`DallE3`生成的PNG图标转换为矢量图，从而可以真正被设计师使用。
#### DallE3
首先我们使用DallE3，在创建完图片以后，我们将图片保存到变量池中，代码如下
```python
from typing import Any, Dict, List, Union
from core.tools.entities.tool_entities import ToolInvokeMessage
from core.tools.tool.builtin_tool import BuiltinTool
from base64 import b64decode
from openai import OpenAI
class DallE3Tool(BuiltinTool):
def _invoke(self,
user_id: str,
tool_paramters: Dict[str, Any],
) -> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
invoke tools
"""
client = OpenAI(
api_key=self.runtime.credentials['openai_api_key'],
)
# prompt
prompt = tool_paramters.get('prompt', '')
if not prompt:
return self.create_text_message('Please input prompt')
# call openapi dalle3
response = client.images.generate(
prompt=prompt, model='dall-e-3',
size='1024x1024', n=1, style='vivid', quality='standard',
response_format='b64_json'
)
result = []
for image in response.data:
# 将所有图片通过save_as参数保存到变量池中，变量名为self.VARIABLE_KEY.IMAGE.value，如果如果后续有新的图片生成，那么将会覆盖之前的图片
result.append(self.create_blob_message(blob=b64decode(image.b64_json),
meta={ 'mime_type': 'image/png' },
save_as=self.VARIABLE_KEY.IMAGE.value))
return result
```
我们可以注意到这里我们使用了`self.VARIABLE_KEY.IMAGE.value`作为图片的变量名，为了便于开发者们的工具能够互相配合，我们定义了这个`KEY`，大家可以自由使用，也可以不使用这个`KEY`，传递一个自定义的KEY也是可以的。
#### Vectorizer.AI
接下来我们使用Vectorizer.AI，将DallE3生成的PNG图标转换为矢量图，我们先来过一遍我们在这里定义的函数，代码如下
```python
from core.tools.tool.builtin_tool import BuiltinTool
from core.tools.entities.tool_entities import ToolInvokeMessage, ToolParamter
from core.tools.errors import ToolProviderCredentialValidationError
from typing import Any, Dict, List, Union
from httpx import post
from base64 import b64decode
class VectorizerTool(BuiltinTool):
def _invoke(self, user_id: str, tool_paramters: Dict[str, Any]) \
-> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
工具调用，图片变量名需要从这里传递进来，从而我们就可以从变量池中获取到图片
"""
def get_runtime_parameters(self) -> List[ToolParamter]:
"""
重写工具参数列表，我们可以根据当前变量池里的实际情况来动态生成参数列表，从而LLM可以根据参数列表来生成表单
"""
def is_tool_avaliable(self) -> bool:
"""
当前工具是否可用，如果当前变量池中没有图片，那么我们就不需要展示这个工具，这里返回False即可
"""
```
接下来我们来实现这三个函数
```python
from core.tools.tool.builtin_tool import BuiltinTool
from core.tools.entities.tool_entities import ToolInvokeMessage, ToolParamter
from core.tools.errors import ToolProviderCredentialValidationError
from typing import Any, Dict, List, Union
from httpx import post
from base64 import b64decode
class VectorizerTool(BuiltinTool):
def _invoke(self, user_id: str, tool_paramters: Dict[str, Any]) \
-> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
invoke tools
"""
api_key_name = self.runtime.credentials.get('api_key_name', None)
api_key_value = self.runtime.credentials.get('api_key_value', None)
if not api_key_name or not api_key_value:
raise ToolProviderCredentialValidationError('Please input api key name and value')
# 获取image_id，image_id的定义可以在get_runtime_parameters中找到
image_id = tool_paramters.get('image_id', '')
if not image_id:
return self.create_text_message('Please input image id')
# 从变量池中获取到之前DallE生成的图片
image_binary = self.get_variable_file(self.VARIABLE_KEY.IMAGE)
if not image_binary:
return self.create_text_message('Image not found, please request user to generate image firstly.')
# 生成矢量图
response = post(
'https://vectorizer.ai/api/v1/vectorize',
files={ 'image': image_binary },
data={ 'mode': 'test' },
auth=(api_key_name, api_key_value),
timeout=30
)
if response.status_code != 200:
raise Exception(response.text)
return [
self.create_text_message('the vectorized svg is saved as an image.'),
self.create_blob_message(blob=response.content,
meta={'mime_type': 'image/svg+xml'})
]
def get_runtime_parameters(self) -> List[ToolParamter]:
"""
override the runtime parameters
"""
# 这里，我们重写了工具参数列表，定义了image_id，并设置了它的选项列表为当前变量池中的所有图片，这里的配置与yaml中的配置是一致的
return [
ToolParamter.get_simple_instance(
name='image_id',
llm_description=f'the image id that you want to vectorize, \
and the image id should be specified in \
{[i.name for i in self.list_default_image_variables()]}',
type=ToolParamter.ToolParameterType.SELECT,
required=True,
options=[i.name for i in self.list_default_image_variables()]
)
]
def is_tool_avaliable(self) -> bool:
# 只有当变量池中有图片时，LLM才需要使用这个工具
return len(self.list_default_image_variables()) > 0
```
可以注意到的是，我们这里其实并没有使用到`image_id`，我们已经假设了调用这个工具的时候一定有一张图片在默认的变量池中，所以直接使用了`image_binary = self.get_variable_file(self.VARIABLE_KEY.IMAGE)`来获取图片，在模型能力较弱的情况下，我们建议开发者们也这样做，可以有效提升容错率，避免模型传递错误的参数。

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# 快速接入Tool
这里我们以GoogleSearch为例，介绍如何快速接入一个工具。
## 1. 准备工具供应商yaml
### 介绍
这个yaml将包含工具供应商的信息，包括供应商名称、图标、作者等详细信息，以帮助前端灵活展示。
### 示例
我们需要在 `core/tools/provider/builtin`下创建一个`google`模块（文件夹），并创建`google.yaml`，名称必须与模块名称一致。
后续，我们关于这个工具的所有操作都将在这个模块下进行。
```yaml
identity: # 工具供应商的基本信息
author: Dify # 作者
name: google # 名称，唯一，不允许和其他供应商重名
label: # 标签，用于前端展示
en_US: Google # 英文标签
zh_Hans: Google # 中文标签
description: # 描述，用于前端展示
en_US: Google # 英文描述
zh_Hans: Google # 中文描述
icon: icon.svg # 图标，需要放置在当前模块的_assets文件夹下
```
- `identity` 字段是必须的，它包含了工具供应商的基本信息，包括作者、名称、标签、描述、图标等
- 图标需要放置在当前模块的`_assets`文件夹下，可以参考[这里](../../provider/builtin/google/_assets/icon.svg)。
## 2. 准备供应商凭据
Google作为一个第三方工具，使用了SerpApi提供的API，而SerpApi需要一个API Key才能使用，那么就意味着这个工具需要一个凭据才可以使用，而像`wikipedia`这样的工具，就不需要填写凭据字段，可以参考[这里](../../provider/builtin/wikipedia/wikipedia.yaml)。
配置好凭据字段后效果如下：
```yaml
identity:
author: Dify
name: google
label:
en_US: Google
zh_Hans: Google
description:
en_US: Google
zh_Hans: Google
icon: icon.svg
credentails_for_provider: # 凭据字段
serpapi_api_key: # 凭据字段名称
type: secret-input # 凭据字段类型
required: true # 是否必填
label: # 凭据字段标签
en_US: SerpApi API key # 英文标签
zh_Hans: SerpApi API key # 中文标签
placeholder: # 凭据字段占位符
en_US: Please input your SerpApi API key # 英文占位符
zh_Hans: 请输入你的 SerpApi API key # 中文占位符
help: # 凭据字段帮助文本
en_US: Get your SerpApi API key from SerpApi # 英文帮助文本
zh_Hans: 从 SerpApi 获取您的 SerpApi API key # 中文帮助文本
url: https://serpapi.com/manage-api-key # 凭据字段帮助链接
```
- `type`：凭据字段类型，目前支持`secret-input`、`text-input`、`select` 三种类型，分别对应密码输入框、文本输入框、下拉框，如果为`secret-input`，则会在前端隐藏输入内容，并且后端会对输入内容进行加密。
## 3. 准备工具yaml
一个供应商底下可以有多个工具，每个工具都需要一个yaml文件来描述，这个文件包含了工具的基本信息、参数、输出等。
仍然以GoogleSearch为例，我们需要在`google`模块下创建一个`tools`模块，并创建`tools/google_search.yaml`，内容如下。
```yaml
identity: # 工具的基本信息
name: google_search # 工具名称，唯一，不允许和其他工具重名
author: Dify # 作者
label: # 标签，用于前端展示
en_US: GoogleSearch # 英文标签
zh_Hans: 谷歌搜索 # 中文标签
description: # 描述，用于前端展示
human: # 用于前端展示的介绍，支持多语言
en_US: A tool for performing a Google SERP search and extracting snippets and webpages.Input should be a search query.
zh_Hans: 一个用于执行 Google SERP 搜索并提取片段和网页的工具。输入应该是一个搜索查询。
llm: A tool for performing a Google SERP search and extracting snippets and webpages.Input should be a search query. # 传递给LLM的介绍，为了使得LLM更好理解这个工具，我们建议在这里写上关于这个工具尽可能详细的信息，让LLM能够理解并使用这个工具
parameters: # 参数列表
- name: query # 参数名称
type: string # 参数类型
required: true # 是否必填
label: # 参数标签
en_US: Query string # 英文标签
zh_Hans: 查询语句 # 中文标签
human_description: # 用于前端展示的介绍，支持多语言
en_US: used for searching
zh_Hans: 用于搜索网页内容
llm_description: key words for searching # 传递给LLM的介绍，同上，为了使得LLM更好理解这个参数，我们建议在这里写上关于这个参数尽可能详细的信息，让LLM能够理解这个参数
form: llm # 表单类型，llm表示这个参数需要由Agent自行推理出来，前端将不会展示这个参数
- name: result_type
type: select # 参数类型
required: true
options: # 下拉框选项
- value: text
label:
en_US: text
zh_Hans: 文本
- value: link
label:
en_US: link
zh_Hans: 链接
default: link
label:
en_US: Result type
zh_Hans: 结果类型
human_description:
en_US: used for selecting the result type, text or link
zh_Hans: 用于选择结果类型，使用文本还是链接进行展示
form: form # 表单类型，form表示这个参数需要由用户在对话开始前在前端填写
```
- `identity` 字段是必须的，它包含了工具的基本信息，包括名称、作者、标签、描述等
- `parameters` 参数列表
- `name` 参数名称，唯一，不允许和其他参数重名
- `type` 参数类型，目前支持`string`、`number`、`boolean`、`select` 四种类型，分别对应字符串、数字、布尔值、下拉框
- `required` 是否必填
- 在`llm`模式下，如果参数为必填，则会要求Agent必须要推理出这个参数
- 在`form`模式下，如果参数为必填，则会要求用户在对话开始前在前端填写这个参数
- `options` 参数选项
- 在`llm`模式下，Dify会将所有选项传递给LLM，LLM可以根据这些选项进行推理
- 在`form`模式下，`type`为`select`时，前端会展示这些选项
- `default` 默认值
- `label` 参数标签，用于前端展示
- `human_description` 用于前端展示的介绍，支持多语言
- `llm_description` 传递给LLM的介绍，为了使得LLM更好理解这个参数，我们建议在这里写上关于这个参数尽可能详细的信息，让LLM能够理解这个参数
- `form` 表单类型，目前支持`llm`、`form`两种类型，分别对应Agent自行推理和前端填写
## 4. 准备工具代码
当完成工具的配置以后，我们就可以开始编写工具代码了，主要用于实现工具的逻辑。
在`google/tools`模块下创建`google_search.py`，内容如下。
```python
from core.tools.tool.builtin_tool import BuiltinTool
from core.tools.entities.tool_entities import ToolInvokeMessage
from typing import Any, Dict, List, Union
class GoogleSearchTool(BuiltinTool):
def _invoke(self,
user_id: str,
tool_paramters: Dict[str, Any],
) -> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
invoke tools
"""
query = tool_paramters['query']
result_type = tool_paramters['result_type']
api_key = self.runtime.credentials['serpapi_api_key']
# TODO: search with serpapi
result = SerpAPI(api_key).run(query, result_type=result_type)
if result_type == 'text':
return self.create_text_message(text=result)
return self.create_link_message(link=result)
```
### 参数
工具的整体逻辑都在`_invoke`方法中，这个方法接收两个参数：`user_id`和`tool_paramters`，分别表示用户ID和工具参数
### 返回数据
在工具返回时，你可以选择返回一个消息或者多个消息，这里我们返回一个消息，使用`create_text_message`和`create_link_message`可以创建一个文本消息或者一个链接消息。
## 5. 准备供应商代码
最后，我们需要在供应商模块下创建一个供应商类，用于实现供应商的凭据验证逻辑，如果凭据验证失败，将会抛出`ToolProviderCredentialValidationError`异常。
在`google`模块下创建`google.py`，内容如下。
```python
from core.tools.entities.tool_entities import ToolInvokeMessage, ToolProviderType
from core.tools.tool.tool import Tool
from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
from core.tools.errors import ToolProviderCredentialValidationError
from core.tools.provider.builtin.google.tools.google_search import GoogleSearchTool
from typing import Any, Dict
class GoogleProvider(BuiltinToolProviderController):
def _validate_credentials(self, credentials: Dict[str, Any]) -> None:
try:
# 1. 此处需要使用GoogleSearchTool()实例化一个GoogleSearchTool，它会自动加载GoogleSearchTool的yaml配置，但是此时它内部没有凭据信息
# 2. 随后需要使用fork_tool_runtime方法，将当前的凭据信息传递给GoogleSearchTool
# 3. 最后invoke即可，参数需要根据GoogleSearchTool的yaml中配置的参数规则进行传递
GoogleSearchTool().fork_tool_runtime(
meta={
"credentials": credentials,
}
).invoke(
user_id='',
tool_paramters={
"query": "test",
"result_type": "link"
},
)
except Exception as e:
raise ToolProviderCredentialValidationError(str(e))
```
## 完成
当上述步骤完成以后，我们就可以在前端看到这个工具了，并且可以在Agent中使用这个工具。
当然，因为google_search需要一个凭据，在使用之前，还需要在前端配置它的凭据。
![Alt text](images/index/image-2.png)

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from typing import Optional
from pydantic import BaseModel
class I18nObject(BaseModel):
"""
Model class for i18n object.
"""
zh_Hans: Optional[str] = None
en_US: str
def __init__(self, **data):
super().__init__(**data)
if not self.zh_Hans:
self.zh_Hans = self.en_US
def to_dict(self) -> dict:
return {
'zh_Hans': self.zh_Hans,
'en_US': self.en_US,
}

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class DEFAULT_PROVIDERS:
API_BASED = '__api_based'
APP_BASED = '__app_based'

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from pydantic import BaseModel
from typing import Dict, Optional, Any, List
from core.tools.entities.tool_entities import ToolProviderType, ToolParamter
class ApiBasedToolBundle(BaseModel):
"""
This class is used to store the schema information of an api based tool. such as the url, the method, the parameters, etc.
"""
# server_url
server_url: str
# method
method: str
# summary
summary: Optional[str] = None
# operation_id
operation_id: str = None
# parameters
parameters: Optional[List[ToolParamter]] = None
# author
author: str
# icon
icon: Optional[str] = None
# openapi operation
openapi: dict
class AppToolBundle(BaseModel):
"""
This class is used to store the schema information of an tool for an app.
"""
type: ToolProviderType
credential: Optional[Dict[str, Any]] = None
provider_id: str
tool_name: str

305
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from pydantic import BaseModel, Field
from enum import Enum
from typing import Optional, List, Dict, Any, Union, cast
from core.tools.entities.common_entities import I18nObject
class ToolProviderType(Enum):
"""
Enum class for tool provider
"""
BUILT_IN = "built-in"
APP_BASED = "app-based"
API_BASED = "api-based"
@classmethod
def value_of(cls, value: str) -> 'ToolProviderType':
"""
Get value of given mode.
:param value: mode value
:return: mode
"""
for mode in cls:
if mode.value == value:
return mode
raise ValueError(f'invalid mode value {value}')
class ApiProviderSchemaType(Enum):
"""
Enum class for api provider schema type.
"""
OPENAPI = "openapi"
SWAGGER = "swagger"
OPENAI_PLUGIN = "openai_plugin"
OPENAI_ACTIONS = "openai_actions"
@classmethod
def value_of(cls, value: str) -> 'ApiProviderSchemaType':
"""
Get value of given mode.
:param value: mode value
:return: mode
"""
for mode in cls:
if mode.value == value:
return mode
raise ValueError(f'invalid mode value {value}')
class ApiProviderAuthType(Enum):
"""
Enum class for api provider auth type.
"""
NONE = "none"
API_KEY = "api_key"
@classmethod
def value_of(cls, value: str) -> 'ApiProviderAuthType':
"""
Get value of given mode.
:param value: mode value
:return: mode
"""
for mode in cls:
if mode.value == value:
return mode
raise ValueError(f'invalid mode value {value}')
class ToolInvokeMessage(BaseModel):
class MessageType(Enum):
TEXT = "text"
IMAGE = "image"
LINK = "link"
BLOB = "blob"
IMAGE_LINK = "image_link"
type: MessageType = MessageType.TEXT
"""
plain text, image url or link url
"""
message: Union[str, bytes] = None
meta: Dict[str, Any] = None
save_as: str = ''
class ToolInvokeMessageBinary(BaseModel):
mimetype: str = Field(..., description="The mimetype of the binary")
url: str = Field(..., description="The url of the binary")
save_as: str = ''
class ToolParamterOption(BaseModel):
value: str = Field(..., description="The value of the option")
label: I18nObject = Field(..., description="The label of the option")
class ToolParamter(BaseModel):
class ToolParameterType(Enum):
STRING = "string"
NUMBER = "number"
BOOLEAN = "boolean"
SELECT = "select"
class ToolParameterForm(Enum):
SCHEMA = "schema" # should be set while adding tool
FORM = "form" # should be set before invoking tool
LLM = "llm" # will be set by LLM
name: str = Field(..., description="The name of the parameter")
label: I18nObject = Field(..., description="The label presented to the user")
human_description: I18nObject = Field(..., description="The description presented to the user")
type: ToolParameterType = Field(..., description="The type of the parameter")
form: ToolParameterForm = Field(..., description="The form of the parameter, schema/form/llm")
llm_description: Optional[str] = None
required: Optional[bool] = False
default: Optional[str] = None
min: Optional[Union[float, int]] = None
max: Optional[Union[float, int]] = None
options: Optional[List[ToolParamterOption]] = None
@classmethod
def get_simple_instance(cls,
name: str, llm_description: str, type: ToolParameterType,
required: bool, options: Optional[List[str]] = None) -> 'ToolParamter':
"""
get a simple tool parameter
:param name: the name of the parameter
:param llm_description: the description presented to the LLM
:param type: the type of the parameter
:param required: if the parameter is required
:param options: the options of the parameter
"""
# convert options to ToolParamterOption
if options:
options = [ToolParamterOption(value=option, label=I18nObject(en_US=option, zh_Hans=option)) for option in options]
return cls(
name=name,
label=I18nObject(en_US='', zh_Hans=''),
human_description=I18nObject(en_US='', zh_Hans=''),
type=type,
form=cls.ToolParameterForm.LLM,
llm_description=llm_description,
required=required,
options=options,
)
class ToolProviderIdentity(BaseModel):
author: str = Field(..., description="The author of the tool")
name: str = Field(..., description="The name of the tool")
description: I18nObject = Field(..., description="The description of the tool")
icon: str = Field(..., description="The icon of the tool")
label: I18nObject = Field(..., description="The label of the tool")
class ToolDescription(BaseModel):
human: I18nObject = Field(..., description="The description presented to the user")
llm: str = Field(..., description="The description presented to the LLM")
class ToolIdentity(BaseModel):
author: str = Field(..., description="The author of the tool")
name: str = Field(..., description="The name of the tool")
label: I18nObject = Field(..., description="The label of the tool")
class ToolCredentialsOption(BaseModel):
value: str = Field(..., description="The value of the option")
label: I18nObject = Field(..., description="The label of the option")
class ToolProviderCredentials(BaseModel):
class CredentialsType(Enum):
SECRET_INPUT = "secret-input"
TEXT_INPUT = "text-input"
SELECT = "select"
@classmethod
def value_of(cls, value: str) -> "ToolProviderCredentials.CredentialsType":
"""
Get value of given mode.
:param value: mode value
:return: mode
"""
for mode in cls:
if mode.value == value:
return mode
raise ValueError(f'invalid mode value {value}')
@staticmethod
def defaut(value: str) -> str:
return ""
name: str = Field(..., description="The name of the credentials")
type: CredentialsType = Field(..., description="The type of the credentials")
required: bool = False
default: Optional[str] = None
options: Optional[List[ToolCredentialsOption]] = None
label: Optional[I18nObject] = None
help: Optional[I18nObject] = None
url: Optional[str] = None
placeholder: Optional[I18nObject] = None
def to_dict(self) -> dict:
return {
'name': self.name,
'type': self.type.value,
'required': self.required,
'default': self.default,
'options': self.options,
'help': self.help.to_dict() if self.help else None,
'label': self.label.to_dict(),
'url': self.url,
'placeholder': self.placeholder.to_dict() if self.placeholder else None,
}
class ToolRuntimeVariableType(Enum):
TEXT = "text"
IMAGE = "image"
class ToolRuntimeVariable(BaseModel):
type: ToolRuntimeVariableType = Field(..., description="The type of the variable")
name: str = Field(..., description="The name of the variable")
position: int = Field(..., description="The position of the variable")
tool_name: str = Field(..., description="The name of the tool")
class ToolRuntimeTextVariable(ToolRuntimeVariable):
value: str = Field(..., description="The value of the variable")
class ToolRuntimeImageVariable(ToolRuntimeVariable):
value: str = Field(..., description="The path of the image")
class ToolRuntimeVariablePool(BaseModel):
conversation_id: str = Field(..., description="The conversation id")
user_id: str = Field(..., description="The user id")
tenant_id: str = Field(..., description="The tenant id of assistant")
pool: List[ToolRuntimeVariable] = Field(..., description="The pool of variables")
def __init__(self, **data: Any):
pool = data.get('pool', [])
# convert pool into correct type
for index, variable in enumerate(pool):
if variable['type'] == ToolRuntimeVariableType.TEXT.value:
pool[index] = ToolRuntimeTextVariable(**variable)
elif variable['type'] == ToolRuntimeVariableType.IMAGE.value:
pool[index] = ToolRuntimeImageVariable(**variable)
super().__init__(**data)
def dict(self) -> dict:
return {
'conversation_id': self.conversation_id,
'user_id': self.user_id,
'tenant_id': self.tenant_id,
'pool': [variable.dict() for variable in self.pool],
}
def set_text(self, tool_name: str, name: str, value: str) -> None:
"""
set a text variable
"""
for variable in self.pool:
if variable.name == name:
if variable.type == ToolRuntimeVariableType.TEXT:
variable = cast(ToolRuntimeTextVariable, variable)
variable.value = value
return
variable = ToolRuntimeTextVariable(
type=ToolRuntimeVariableType.TEXT,
name=name,
position=len(self.pool),
tool_name=tool_name,
value=value,
)
self.pool.append(variable)
def set_file(self, tool_name: str, value: str, name: str = None) -> None:
"""
set an image variable
:param tool_name: the name of the tool
:param value: the id of the file
"""
# check how many image variables are there
image_variable_count = 0
for variable in self.pool:
if variable.type == ToolRuntimeVariableType.IMAGE:
image_variable_count += 1
if name is None:
name = f"file_{image_variable_count}"
for variable in self.pool:
if variable.name == name:
if variable.type == ToolRuntimeVariableType.IMAGE:
variable = cast(ToolRuntimeImageVariable, variable)
variable.value = value
return
variable = ToolRuntimeImageVariable(
type=ToolRuntimeVariableType.IMAGE,
name=name,
position=len(self.pool),
tool_name=tool_name,
value=value,
)
self.pool.append(variable)

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from pydantic import BaseModel
from enum import Enum
from typing import List, Dict, Optional
from core.tools.entities.common_entities import I18nObject
from core.tools.entities.tool_entities import ToolProviderCredentials
from core.tools.tool.tool import ToolParamter
class UserToolProvider(BaseModel):
class ProviderType(Enum):
BUILTIN = "builtin"
APP = "app"
API = "api"
id: str
author: str
name: str # identifier
description: I18nObject
icon: str
label: I18nObject # label
type: ProviderType
team_credentials: dict = None
is_team_authorization: bool = False
allow_delete: bool = True
def to_dict(self) -> dict:
return {
'id': self.id,
'author': self.author,
'name': self.name,
'description': self.description.to_dict(),
'icon': self.icon,
'label': self.label.to_dict(),
'type': self.type.value,
'team_credentials': self.team_credentials,
'is_team_authorization': self.is_team_authorization,
'allow_delete': self.allow_delete
}
class UserToolProviderCredentials(BaseModel):
credentails: Dict[str, ToolProviderCredentials]
class UserTool(BaseModel):
author: str
name: str # identifier
label: I18nObject # label
description: I18nObject
parameters: Optional[List[ToolParamter]]

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class ToolProviderNotFoundError(ValueError):
pass
class ToolNotFoundError(ValueError):
pass
class ToolParamterValidationError(ValueError):
pass
class ToolProviderCredentialValidationError(ValueError):
pass
class ToolNotSupportedError(ValueError):
pass
class ToolInvokeError(ValueError):
pass
class ToolApiSchemaError(ValueError):
pass

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class InvokeModelError(Exception):
pass

174
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"""
For some reason, model will be used in tools like WebScraperTool, WikipediaSearchTool etc.
Therefore, a model manager is needed to list/invoke/validate models.
"""
from core.model_runtime.entities.message_entities import PromptMessage
from core.model_runtime.entities.llm_entities import LLMResult
from core.model_runtime.entities.model_entities import ModelType
from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel, ModelPropertyKey
from core.model_runtime.errors.invoke import InvokeRateLimitError, InvokeBadRequestError, \
InvokeConnectionError, InvokeAuthorizationError, InvokeServerUnavailableError
from core.model_runtime.utils.encoders import jsonable_encoder
from core.model_manager import ModelManager
from core.tools.model.errors import InvokeModelError
from extensions.ext_database import db
from models.tools import ToolModelInvoke
from typing import List, cast
import json
class ToolModelManager:
@staticmethod
def get_max_llm_context_tokens(
tenant_id: str,
) -> int:
"""
get max llm context tokens of the model
"""
model_manager = ModelManager()
model_instance = model_manager.get_default_model_instance(
tenant_id=tenant_id, model_type=ModelType.LLM,
)
if not model_instance:
raise InvokeModelError(f'Model not found')
llm_model = cast(LargeLanguageModel, model_instance.model_type_instance)
schema = llm_model.get_model_schema(model_instance.model, model_instance.credentials)
if not schema:
raise InvokeModelError(f'No model schema found')
max_tokens = schema.model_properties.get(ModelPropertyKey.CONTEXT_SIZE, None)
if max_tokens is None:
return 2048
return max_tokens
@staticmethod
def calculate_tokens(
tenant_id: str,
prompt_messages: List[PromptMessage]
) -> int:
"""
calculate tokens from prompt messages and model parameters
"""
# get model instance
model_manager = ModelManager()
model_instance = model_manager.get_default_model_instance(
tenant_id=tenant_id, model_type=ModelType.LLM
)
if not model_instance:
raise InvokeModelError(f'Model not found')
llm_model = cast(LargeLanguageModel, model_instance.model_type_instance)
# get tokens
tokens = llm_model.get_num_tokens(model_instance.model, model_instance.credentials, prompt_messages)
return tokens
@staticmethod
def invoke(
user_id: str, tenant_id: str,
tool_type: str, tool_name: str,
prompt_messages: List[PromptMessage]
) -> LLMResult:
"""
invoke model with parameters in user's own context
:param user_id: user id
:param tenant_id: tenant id, the tenant id of the creator of the tool
:param tool_provider: tool provider
:param tool_id: tool id
:param tool_name: tool name
:param provider: model provider
:param model: model name
:param model_parameters: model parameters
:param prompt_messages: prompt messages
:return: AssistantPromptMessage
"""
# get model manager
model_manager = ModelManager()
# get model instance
model_instance = model_manager.get_default_model_instance(
tenant_id=tenant_id, model_type=ModelType.LLM,
)
llm_model = cast(LargeLanguageModel, model_instance.model_type_instance)
# get model credentials
model_credentials = model_instance.credentials
# get prompt tokens
prompt_tokens = llm_model.get_num_tokens(model_instance.model, model_credentials, prompt_messages)
model_parameters = {
'temperature': 0.8,
'top_p': 0.8,
}
# create tool model invoke
tool_model_invoke = ToolModelInvoke(
user_id=user_id,
tenant_id=tenant_id,
provider=model_instance.provider,
tool_type=tool_type,
tool_name=tool_name,
model_parameters=json.dumps(model_parameters),
prompt_messages=json.dumps(jsonable_encoder(prompt_messages)),
model_response='',
prompt_tokens=prompt_tokens,
answer_tokens=0,
answer_unit_price=0,
answer_price_unit=0,
provider_response_latency=0,
total_price=0,
currency='USD',
)
db.session.add(tool_model_invoke)
db.session.commit()
try:
response: LLMResult = llm_model.invoke(
model=model_instance.model,
credentials=model_credentials,
prompt_messages=prompt_messages,
model_parameters=model_parameters,
tools=[], stop=[], stream=False, user=user_id, callbacks=[]
)
except InvokeRateLimitError as e:
raise InvokeModelError(f'Invoke rate limit error: {e}')
except InvokeBadRequestError as e:
raise InvokeModelError(f'Invoke bad request error: {e}')
except InvokeConnectionError as e:
raise InvokeModelError(f'Invoke connection error: {e}')
except InvokeAuthorizationError as e:
raise InvokeModelError(f'Invoke authorization error')
except InvokeServerUnavailableError as e:
raise InvokeModelError(f'Invoke server unavailable error: {e}')
except Exception as e:
raise InvokeModelError(f'Invoke error: {e}')
# update tool model invoke
tool_model_invoke.model_response = response.message.content
if response.usage:
tool_model_invoke.answer_tokens = response.usage.completion_tokens
tool_model_invoke.answer_unit_price = response.usage.completion_unit_price
tool_model_invoke.answer_price_unit = response.usage.completion_price_unit
tool_model_invoke.provider_response_latency = response.usage.latency
tool_model_invoke.total_price = response.usage.total_price
tool_model_invoke.currency = response.usage.currency
db.session.commit()
return response

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ENGLISH_REACT_COMPLETION_PROMPT_TEMPLATES = """Respond to the human as helpfully and accurately as possible.
{{instruction}}
You have access to the following tools:
{{tools}}
Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
Valid "action" values: "Final Answer" or {{tool_names}}
Provide only ONE action per $JSON_BLOB, as shown:
```
{
"action": $TOOL_NAME,
"action_input": $ACTION_INPUT
}
```
Follow this format:
Question: input question to answer
Thought: consider previous and subsequent steps
Action:
```
$JSON_BLOB
```
Observation: action result
... (repeat Thought/Action/Observation N times)
Thought: I know what to respond
Action:
```
{
"action": "Final Answer",
"action_input": "Final response to human"
}
```
Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.
Question: {{query}}
Thought: {{agent_scratchpad}}"""
ENGLISH_REACT_COMPLETION_AGENT_SCRATCHPAD_TEMPLATES = """Observation: {{observation}}
Thought:"""
ENGLISH_REACT_CHAT_PROMPT_TEMPLATES = """Respond to the human as helpfully and accurately as possible.
{{instruction}}
You have access to the following tools:
{{tools}}
Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
Valid "action" values: "Final Answer" or {{tool_names}}
Provide only ONE action per $JSON_BLOB, as shown:
```
{
"action": $TOOL_NAME,
"action_input": $ACTION_INPUT
}
```
Follow this format:
Question: input question to answer
Thought: consider previous and subsequent steps
Action:
```
$JSON_BLOB
```
Observation: action result
... (repeat Thought/Action/Observation N times)
Thought: I know what to respond
Action:
```
{
"action": "Final Answer",
"action_input": "Final response to human"
}
```
Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.
"""
ENGLISH_REACT_CHAT_AGENT_SCRATCHPAD_TEMPLATES = ""
REACT_PROMPT_TEMPLATES = {
'english': {
'chat': {
'prompt': ENGLISH_REACT_CHAT_PROMPT_TEMPLATES,
'agent_scratchpad': ENGLISH_REACT_CHAT_AGENT_SCRATCHPAD_TEMPLATES
},
'completion': {
'prompt': ENGLISH_REACT_COMPLETION_PROMPT_TEMPLATES,
'agent_scratchpad': ENGLISH_REACT_COMPLETION_AGENT_SCRATCHPAD_TEMPLATES
}
}
}

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from typing import Any, Dict, List
from core.tools.entities.tool_entities import ToolProviderType, ApiProviderAuthType, ToolProviderCredentials, ToolCredentialsOption
from core.tools.entities.common_entities import I18nObject
from core.tools.entities.tool_bundle import ApiBasedToolBundle
from core.tools.tool.tool import Tool
from core.tools.tool.api_tool import ApiTool
from core.tools.provider.tool_provider import ToolProviderController
from extensions.ext_database import db
from models.tools import ApiToolProvider
class ApiBasedToolProviderController(ToolProviderController):
@staticmethod
def from_db(db_provider: ApiToolProvider, auth_type: ApiProviderAuthType) -> 'ApiBasedToolProviderController':
credentials_schema = {
'auth_type': ToolProviderCredentials(
name='auth_type',
required=True,
type=ToolProviderCredentials.CredentialsType.SELECT,
options=[
ToolCredentialsOption(value='none', label=I18nObject(en_US='None', zh_Hans='无')),
ToolCredentialsOption(value='api_key', label=I18nObject(en_US='api_key', zh_Hans='api_key'))
],
default='none',
help=I18nObject(
en_US='The auth type of the api provider',
zh_Hans='api provider 的认证类型'
)
)
}
if auth_type == ApiProviderAuthType.API_KEY:
credentials_schema = {
**credentials_schema,
'api_key_header': ToolProviderCredentials(
name='api_key_header',
required=False,
default='api_key',
type=ToolProviderCredentials.CredentialsType.TEXT_INPUT,
help=I18nObject(
en_US='The header name of the api key',
zh_Hans='携带 api key 的 header 名称'
)
),
'api_key_value': ToolProviderCredentials(
name='api_key_value',
required=True,
type=ToolProviderCredentials.CredentialsType.SECRET_INPUT,
help=I18nObject(
en_US='The api key',
zh_Hans='api key的值'
)
)
}
elif auth_type == ApiProviderAuthType.NONE:
pass
else:
raise ValueError(f'invalid auth type {auth_type}')
return ApiBasedToolProviderController(**{
'identity': {
'author': db_provider.user.name if db_provider.user_id and db_provider.user else '',
'name': db_provider.name,
'label': {
'en_US': db_provider.name,
'zh_Hans': db_provider.name
},
'description': {
'en_US': db_provider.description,
'zh_Hans': db_provider.description
},
'icon': db_provider.icon
},
'credentials_schema': credentials_schema
})
@property
def app_type(self) -> ToolProviderType:
return ToolProviderType.API_BASED
def _validate_credentials(self, tool_name: str, credentials: Dict[str, Any]) -> None:
pass
def validate_parameters(self, tool_name: str, tool_parameters: Dict[str, Any]) -> None:
pass
def _parse_tool_bundle(self, tool_bundle: ApiBasedToolBundle) -> ApiTool:
"""
parse tool bundle to tool
:param tool_bundle: the tool bundle
:return: the tool
"""
return ApiTool(**{
'api_bundle': tool_bundle,
'identity' : {
'author': tool_bundle.author,
'name': tool_bundle.operation_id,
'label': {
'en_US': tool_bundle.operation_id,
'zh_Hans': tool_bundle.operation_id
},
'icon': tool_bundle.icon if tool_bundle.icon else ''
},
'description': {
'human': {
'en_US': tool_bundle.summary or '',
'zh_Hans': tool_bundle.summary or ''
},
'llm': tool_bundle.summary or ''
},
'parameters' : tool_bundle.parameters if tool_bundle.parameters else [],
})
def load_bundled_tools(self, tools: List[ApiBasedToolBundle]) -> List[ApiTool]:
"""
load bundled tools
:param tools: the bundled tools
:return: the tools
"""
self.tools = [self._parse_tool_bundle(tool) for tool in tools]
return self.tools
def get_tools(self, user_id: str, tanent_id: str) -> List[ApiTool]:
"""
fetch tools from database
:param user_id: the user id
:param tanent_id: the tanent id
:return: the tools
"""
if self.tools is not None:
return self.tools
tools: List[Tool] = []
# get tanent api providers
db_providers: List[ApiToolProvider] = db.session.query(ApiToolProvider).filter(
ApiToolProvider.tenant_id == tanent_id,
ApiToolProvider.name == self.identity.name
).all()
if db_providers and len(db_providers) != 0:
for db_provider in db_providers:
for tool in db_provider.tools:
assistant_tool = self._parse_tool_bundle(tool)
assistant_tool.is_team_authorization = True
tools.append(assistant_tool)
self.tools = tools
return tools
def get_tool(self, tool_name: str) -> ApiTool:
"""
get tool by name
:param tool_name: the name of the tool
:return: the tool
"""
if self.tools is None:
self.get_tools()
for tool in self.tools:
if tool.identity.name == tool_name:
return tool
raise ValueError(f'tool {tool_name} not found')

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from typing import Any, Dict, List
from core.tools.entities.tool_entities import ToolProviderType, ToolParamter, ToolParamterOption
from core.tools.tool.tool import Tool
from core.tools.entities.common_entities import I18nObject
from core.tools.provider.tool_provider import ToolProviderController
from extensions.ext_database import db
from models.tools import PublishedAppTool
from models.model import App, AppModelConfig
import logging
logger = logging.getLogger(__name__)
class AppBasedToolProviderEntity(ToolProviderController):
@property
def app_type(self) -> ToolProviderType:
return ToolProviderType.APP_BASED
def _validate_credentials(self, tool_name: str, credentials: Dict[str, Any]) -> None:
pass
def validate_parameters(self, tool_name: str, tool_parameters: Dict[str, Any]) -> None:
pass
def get_tools(self, user_id: str) -> List[Tool]:
db_tools: List[PublishedAppTool] = db.session.query(PublishedAppTool).filter(
PublishedAppTool.user_id == user_id,
).all()
if not db_tools or len(db_tools) == 0:
return []
tools: List[Tool] = []
for db_tool in db_tools:
tool = {
'identity': {
'author': db_tool.author,
'name': db_tool.tool_name,
'label': {
'en_US': db_tool.tool_name,
'zh_Hans': db_tool.tool_name
},
'icon': ''
},
'description': {
'human': {
'en_US': db_tool.description_i18n.en_US,
'zh_Hans': db_tool.description_i18n.zh_Hans
},
'llm': db_tool.llm_description
},
'parameters': []
}
# get app from db
app: App = db_tool.app
if not app:
logger.error(f"app {db_tool.app_id} not found")
continue
app_model_config: AppModelConfig = app.app_model_config
user_input_form_list = app_model_config.user_input_form_list
for input_form in user_input_form_list:
# get type
form_type = input_form.keys()[0]
default = input_form[form_type]['default']
required = input_form[form_type]['required']
label = input_form[form_type]['label']
variable_name = input_form[form_type]['variable_name']
options = input_form[form_type].get('options', [])
if form_type == 'paragraph' or form_type == 'text-input':
tool['parameters'].append(ToolParamter(
name=variable_name,
label=I18nObject(
en_US=label,
zh_Hans=label
),
human_description=I18nObject(
en_US=label,
zh_Hans=label
),
llm_description=label,
form=ToolParamter.ToolParameterForm.FORM,
type=ToolParamter.ToolParameterType.STRING,
required=required,
default=default
))
elif form_type == 'select':
tool['parameters'].append(ToolParamter(
name=variable_name,
label=I18nObject(
en_US=label,
zh_Hans=label
),
human_description=I18nObject(
en_US=label,
zh_Hans=label
),
llm_description=label,
form=ToolParamter.ToolParameterForm.FORM,
type=ToolParamter.ToolParameterType.SELECT,
required=required,
default=default,
options=[ToolParamterOption(
value=option,
label=I18nObject(
en_US=option,
zh_Hans=option
)
) for option in options]
))
tools.append(Tool(**tool))
return tools

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api/core/tools/provider/builtin/_positions.py Normal file
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from core.tools.entities.user_entities import UserToolProvider
from typing import List
position = {
'google': 1,
'wikipedia': 2,
'dalle': 3,
'webscraper': 4,
'wolframalpha': 5,
'chart': 6,
'time': 7,
'yahoo': 8,
'stablediffusion': 9,
'vectorizer': 10,
'youtube': 11,
}
class BuiltinToolProviderSort:
@staticmethod
def sort(providers: List[UserToolProvider]) -> List[UserToolProvider]:
def sort_compare(provider: UserToolProvider) -> int:
return position.get(provider.name, 10000)
sorted_providers = sorted(providers, key=sort_compare)
return sorted_providers

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api/core/tools/provider/builtin/chart/chart.py Normal file
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from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
from core.tools.errors import ToolProviderCredentialValidationError
from core.tools.provider.builtin.chart.tools.line import LinearChartTool
import matplotlib.pyplot as plt
# use a business theme
plt.style.use('seaborn-v0_8-darkgrid')
class ChartProvider(BuiltinToolProviderController):
def _validate_credentials(self, credentials: dict) -> None:
try:
LinearChartTool().fork_tool_runtime(
meta={
"credentials": credentials,
}
).invoke(
user_id='',
tool_paramters={
"data": "1,3,5,7,9,2,4,6,8,10",
},
)
except Exception as e:
raise ToolProviderCredentialValidationError(str(e))

11
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identity:
author: Dify
name: chart
label:
en_US: ChartGenerator
zh_Hans: 图表生成
description:
en_US: Chart Generator is a tool for generating statistical charts like bar chart, line chart, pie chart, etc.
zh_Hans: 图表生成是一个用于生成可视化图表的工具，你可以通过它来生成柱状图、折线图、饼图等各类图表
icon: icon.png
credentails_for_provider:

47
api/core/tools/provider/builtin/chart/tools/bar.py Normal file
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from core.tools.tool.builtin_tool import BuiltinTool
from core.tools.entities.tool_entities import ToolInvokeMessage
import matplotlib.pyplot as plt
import io
from typing import Any, Dict, List, Union
class BarChartTool(BuiltinTool):
def _invoke(self, user_id: str, tool_paramters: Dict[str, Any]) \
-> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
data = tool_paramters.get('data', '')
if not data:
return self.create_text_message('Please input data')
data = data.split(';')
# if all data is int, convert to int
if all([i.isdigit() for i in data]):
data = [int(i) for i in data]
else:
data = [float(i) for i in data]
axis = tool_paramters.get('x_axis', None) or None
if axis:
axis = axis.split(';')
if len(axis) != len(data):
axis = None
flg, ax = plt.subplots(figsize=(10, 8))
if axis:
axis = [label[:10] + '...' if len(label) > 10 else label for label in axis]
ax.set_xticklabels(axis, rotation=45, ha='right')
ax.bar(axis, data)
else:
ax.bar(range(len(data)), data)
buf = io.BytesIO()
flg.savefig(buf, format='png')
buf.seek(0)
plt.close(flg)
return [
self.create_text_message('the bar chart is saved as an image.'),
self.create_blob_message(blob=buf.read(),
meta={'mime_type': 'image/png'})
]

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identity:
name: bar_chart
author: Dify
label:
en_US: Bar Chart
zh_Hans: 柱状图
icon: icon.svg
description:
human:
en_US: Bar chart
zh_Hans: 柱状图
llm: generate a bar chart with input data
parameters:
- name: data
type: string
required: true
label:
en_US: data
zh_Hans: 数据
human_description:
en_US: data for generating bar chart
zh_Hans: 用于生成柱状图的数据
llm_description: data for generating bar chart, data should be a string contains a list of numbers like "1;2;3;4;5"
form: llm
- name: x_axis
type: string
required: false
label:
en_US: X Axis
zh_Hans: x 轴
human_description:
en_US: X axis for bar chart
zh_Hans: 柱状图的 x 轴
llm_description: x axis for bar chart, x axis should be a string contains a list of texts like "a;b;c;1;2" in order to match the data
form: llm

49
api/core/tools/provider/builtin/chart/tools/line.py Normal file
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from core.tools.tool.builtin_tool import BuiltinTool
from core.tools.entities.tool_entities import ToolInvokeMessage
import matplotlib.pyplot as plt
import io
from typing import Any, Dict, List, Union
class LinearChartTool(BuiltinTool):
def _invoke(self,
user_id: str,
tool_paramters: Dict[str, Any],
) -> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
data = tool_paramters.get('data', '')
if not data:
return self.create_text_message('Please input data')
data = data.split(';')
axis = tool_paramters.get('x_axis', None) or None
if axis:
axis = axis.split(';')
if len(axis) != len(data):
axis = None
# if all data is int, convert to int
if all([i.isdigit() for i in data]):
data = [int(i) for i in data]
else:
data = [float(i) for i in data]
flg, ax = plt.subplots(figsize=(10, 8))
if axis:
axis = [label[:10] + '...' if len(label) > 10 else label for label in axis]
ax.set_xticklabels(axis, rotation=45, ha='right')
ax.plot(axis, data)
else:
ax.plot(data)
buf = io.BytesIO()
flg.savefig(buf, format='png')
buf.seek(0)
plt.close(flg)
return [
self.create_text_message('the linear chart is saved as an image.'),
self.create_blob_message(blob=buf.read(),
meta={'mime_type': 'image/png'})
]

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api/core/tools/provider/builtin/chart/tools/line.yaml Normal file
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identity:
name: line_chart
author: Dify
label:
en_US: Linear Chart
zh_Hans: 线性图表
icon: icon.svg
description:
human:
en_US: linear chart
zh_Hans: 线性图表
llm: generate a linear chart with input data
parameters:
- name: data
type: string
required: true
label:
en_US: data
zh_Hans: 数据
human_description:
en_US: data for generating linear chart
zh_Hans: 用于生成线性图表的数据
llm_description: data for generating linear chart, data should be a string contains a list of numbers like "1;2;3;4;5"
form: llm
- name: x_axis
type: string
required: false
label:
en_US: X Axis
zh_Hans: x 轴
human_description:
en_US: X axis for linear chart
zh_Hans: 线性图表的 x 轴
llm_description: x axis for linear chart, x axis should be a string contains a list of texts like "a;b;c;1;2" in order to match the data
form: llm

46
api/core/tools/provider/builtin/chart/tools/pie.py Normal file
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from core.tools.tool.builtin_tool import BuiltinTool
from core.tools.entities.tool_entities import ToolInvokeMessage
import matplotlib.pyplot as plt
import io
from typing import Any, Dict, List, Union
class PieChartTool(BuiltinTool):
def _invoke(self,
user_id: str,
tool_paramters: Dict[str, Any],
) -> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
data = tool_paramters.get('data', '')
if not data:
return self.create_text_message('Please input data')
data = data.split(';')
categories = tool_paramters.get('categories', None) or None
# if all data is int, convert to int
if all([i.isdigit() for i in data]):
data = [int(i) for i in data]
else:
data = [float(i) for i in data]
flg, ax = plt.subplots()
if categories:
categories = categories.split(';')
if len(categories) != len(data):
categories = None
if categories:
ax.pie(data, labels=categories)
else:
ax.pie(data)
buf = io.BytesIO()
flg.savefig(buf, format='png')
buf.seek(0)
plt.close(flg)
return [
self.create_text_message('the pie chart is saved as an image.'),
self.create_blob_message(blob=buf.read(),
meta={'mime_type': 'image/png'})
]

35
api/core/tools/provider/builtin/chart/tools/pie.yaml Normal file
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identity:
name: pie_chart
author: Dify
label:
en_US: Pie Chart
zh_Hans: 饼图
icon: icon.svg
description:
human:
en_US: Pie chart
zh_Hans: 饼图
llm: generate a pie chart with input data
parameters:
- name: data
type: string
required: true
label:
en_US: data
zh_Hans: 数据
human_description:
en_US: data for generating pie chart
zh_Hans: 用于生成饼图的数据
llm_description: data for generating pie chart, data should be a string contains a list of numbers like "1;2;3;4;5"
form: llm
- name: categories
type: string
required: true
label:
en_US: Categories
zh_Hans: 分类
human_description:
en_US: Categories for pie chart
zh_Hans: 饼图的分类
llm_description: categories for pie chart, categories should be a string contains a list of texts like "a;b;c;1;2" in order to match the data, each category should be split by ";"
form: llm

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api/core/tools/provider/builtin/dalle/__init__.py Normal file
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api/core/tools/provider/builtin/dalle/dalle.py Normal file
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from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
from core.tools.provider.builtin.dalle.tools.dalle2 import DallE2Tool
from core.tools.errors import ToolProviderCredentialValidationError
from typing import Any, Dict
class DALLEProvider(BuiltinToolProviderController):
def _validate_credentials(self, credentials: Dict[str, Any]) -> None:
try:
DallE2Tool().fork_tool_runtime(
meta={
"credentials": credentials,
}
).invoke(
user_id='',
tool_paramters={
"prompt": "cute girl, blue eyes, white hair, anime style",
"size": "small",
"n": 1
},
)
except Exception as e:
raise ToolProviderCredentialValidationError(str(e))

47
api/core/tools/provider/builtin/dalle/dalle.yaml Normal file
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identity:
author: Dify
name: dalle
label:
en_US: DALL-E
zh_Hans: DALL-E 绘画
description:
en_US: DALL-E art
zh_Hans: DALL-E 绘画
icon: icon.png
credentails_for_provider:
openai_api_key:
type: secret-input
required: true
label:
en_US: OpenAI API key
zh_Hans: OpenAI API key
help:
en_US: Please input your OpenAI API key
zh_Hans: 请输入你的 OpenAI API key
placeholder:
en_US: Please input your OpenAI API key
zh_Hans: 请输入你的 OpenAI API key
openai_organizaion_id:
type: text-input
required: false
label:
en_US: OpenAI organization ID
zh_Hans: OpenAI organization ID
help:
en_US: Please input your OpenAI organization ID
zh_Hans: 请输入你的 OpenAI organization ID
placeholder:
en_US: Please input your OpenAI organization ID
zh_Hans: 请输入你的 OpenAI organization ID
openai_base_url:
type: text-input
required: false
label:
en_US: OpenAI base URL
zh_Hans: OpenAI base URL
help:
en_US: Please input your OpenAI base URL
zh_Hans: 请输入你的 OpenAI base URL
placeholder:
en_US: Please input your OpenAI base URL
zh_Hans: 请输入你的 OpenAI base URL

66
api/core/tools/provider/builtin/dalle/tools/dalle2.py Normal file
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from typing import Any, Dict, List, Union
from core.tools.entities.tool_entities import ToolInvokeMessage
from core.tools.tool.builtin_tool import BuiltinTool
from base64 import b64decode
from os.path import join
from openai import OpenAI
class DallE2Tool(BuiltinTool):
def _invoke(self,
user_id: str,
tool_paramters: Dict[str, Any],
) -> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
invoke tools
"""
openai_organization = self.runtime.credentials.get('openai_organizaion_id', None)
if not openai_organization:
openai_organization = None
openai_base_url = self.runtime.credentials.get('openai_base_url', None)
if not openai_base_url:
openai_base_url = None
else:
openai_base_url = join(openai_base_url, 'v1')
client = OpenAI(
api_key=self.runtime.credentials['openai_api_key'],
base_url=openai_base_url,
organization=openai_organization
)
SIZE_MAPPING = {
'small': '256x256',
'medium': '512x512',
'large': '1024x1024',
}
# prompt
prompt = tool_paramters.get('prompt', '')
if not prompt:
return self.create_text_message('Please input prompt')
# get size
size = SIZE_MAPPING[tool_paramters.get('size', 'large')]
# get n
n = tool_paramters.get('n', 1)
# call openapi dalle2
response = client.images.generate(
prompt=prompt,
model='dall-e-2',
size=size,
n=n,
response_format='b64_json'
)
result = []
for image in response.data:
result.append(self.create_blob_message(blob=b64decode(image.b64_json),
meta={ 'mime_type': 'image/png' },
save_as=self.VARIABLE_KEY.IMAGE.value))
return result

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identity:
name: dalle2
author: Dify
label:
en_US: DALL-E 2
zh_Hans: DALL-E 2 绘画
description:
en_US: DALL-E 2 is a powerful drawing tool that can draw the image you want based on your prompt
zh_Hans: DALL-E 2 是一个强大的绘画工具，它可以根据您的提示词绘制出您想要的图像
description:
human:
en_US: DALL-E is a text to image tool
zh_Hans: DALL-E 是一个文本到图像的工具
llm: DALL-E is a tool used to generate images from text
parameters:
- name: prompt
type: string
required: true
label:
en_US: Prompt
zh_Hans: 提示词
human_description:
en_US: Image prompt, you can check the official documentation of DallE 2
zh_Hans: 图像提示词，您可以查看DallE 2 的官方文档
llm_description: Image prompt of DallE 2, you should describe the image you want to generate as a list of words as possible as detailed
form: llm
- name: size
type: select
required: true
human_description:
en_US: used for selecting the image size
zh_Hans: 用于选择图像大小
label:
en_US: Image size
zh_Hans: 图像大小
form: form
options:
- value: small
label:
en_US: Small(256x256)
zh_Hans: 小(256x256)
- value: medium
label:
en_US: Medium(512x512)
zh_Hans: 中(512x512)
- value: large
label:
en_US: Large(1024x1024)
zh_Hans: 大(1024x1024)
default: large
- name: n
type: number
required: true
human_description:
en_US: used for selecting the number of images
zh_Hans: 用于选择图像数量
label:
en_US: Number of images
zh_Hans: 图像数量
form: form
default: 1
min: 1
max: 10

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api/core/tools/provider/builtin/dalle/tools/dalle3.py Normal file
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from typing import Any, Dict, List, Union
from core.tools.entities.tool_entities import ToolInvokeMessage
from core.tools.tool.builtin_tool import BuiltinTool
from base64 import b64decode
from os.path import join
from openai import OpenAI
class DallE3Tool(BuiltinTool):
def _invoke(self,
user_id: str,
tool_paramters: Dict[str, Any],
) -> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
invoke tools
"""
openai_organization = self.runtime.credentials.get('openai_organizaion_id', None)
if not openai_organization:
openai_organization = None
openai_base_url = self.runtime.credentials.get('openai_base_url', None)
if not openai_base_url:
openai_base_url = None
else:
openai_base_url = join(openai_base_url, 'v1')
client = OpenAI(
api_key=self.runtime.credentials['openai_api_key'],
base_url=openai_base_url,
organization=openai_organization
)
SIZE_MAPPING = {
'square': '1024x1024',
'vertical': '1024x1792',
'horizontal': '1792x1024',
}
# prompt
prompt = tool_paramters.get('prompt', '')
if not prompt:
return self.create_text_message('Please input prompt')
# get size
size = SIZE_MAPPING[tool_paramters.get('size', 'square')]
# get n
n = tool_paramters.get('n', 1)
# get quality
quality = tool_paramters.get('quality', 'standard')
if quality not in ['standard', 'hd']:
return self.create_text_message('Invalid quality')
# get style
style = tool_paramters.get('style', 'vivid')
if style not in ['natural', 'vivid']:
return self.create_text_message('Invalid style')
# call openapi dalle3
response = client.images.generate(
prompt=prompt,
model='dall-e-3',
size=size,
n=n,
style=style,
quality=quality,
response_format='b64_json'
)
result = []
for image in response.data:
result.append(self.create_blob_message(blob=b64decode(image.b64_json),
meta={ 'mime_type': 'image/png' },
save_as=self.VARIABLE_KEY.IMAGE.value))
return result

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identity:
name: dalle3
author: Dify
label:
en_US: DALL-E 3
zh_Hans: DALL-E 3 绘画
description:
en_US: DALL-E 3 is a powerful drawing tool that can draw the image you want based on your prompt, compared to DallE 2, DallE 3 has stronger drawing ability, but it will consume more resources
zh_Hans: DALL-E 3 是一个强大的绘画工具，它可以根据您的提示词绘制出您想要的图像，相比于DallE 2， DallE 3拥有更强的绘画能力，但会消耗更多的资源
description:
human:
en_US: DALL-E is a text to image tool
zh_Hans: DALL-E 是一个文本到图像的工具
llm: DALL-E is a tool used to generate images from text
parameters:
- name: prompt
type: string
required: true
label:
en_US: Prompt
zh_Hans: 提示词
human_description:
en_US: Image prompt, you can check the official documentation of DallE 3
zh_Hans: 图像提示词，您可以查看DallE 3 的官方文档
llm_description: Image prompt of DallE 3, you should describe the image you want to generate as a list of words as possible as detailed
form: llm
- name: size
type: select
required: true
human_description:
en_US: selecting the image size
zh_Hans: 选择图像大小
label:
en_US: Image size
zh_Hans: 图像大小
form: form
options:
- value: square
label:
en_US: Squre(1024x1024)
zh_Hans: 方(1024x1024)
- value: vertical
label:
en_US: Vertical(1024x1792)
zh_Hans: 竖屏(1024x1792)
- value: horizontal
label:
en_US: Horizontal(1792x1024)
zh_Hans: 横屏(1792x1024)
default: square
- name: n
type: number
required: true
human_description:
en_US: selecting the number of images
zh_Hans: 选择图像数量
label:
en_US: Number of images
zh_Hans: 图像数量
form: form
min: 1
max: 1
default: 1
- name: quality
type: select
required: true
human_description:
en_US: selecting the image quality
zh_Hans: 选择图像质量
label:
en_US: Image quality
zh_Hans: 图像质量
form: form
options:
- value: standard
label:
en_US: Standard
zh_Hans: 标准
- value: hd
label:
en_US: HD
zh_Hans: 高清
default: standard
- name: style
type: select
required: true
human_description:
en_US: selecting the image style
zh_Hans: 选择图像风格
label:
en_US: Image style
zh_Hans: 图像风格
form: form
options:
- value: vivid
label:
en_US: Vivid
zh_Hans: 生动
- value: natural
label:
en_US: Natural
zh_Hans: 自然
default: vivid

6
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<svg xmlns="http://www.w3.org/2000/svg" width="24" height="25" viewBox="0 0 24 25" fill="none">
<path d="M22.501 12.7332C22.501 11.8699 22.4296 11.2399 22.2748 10.5865H12.2153V14.4832H18.12C18.001 15.4515 17.3582 16.9099 15.9296 17.8898L15.9096 18.0203L19.0902 20.435L19.3106 20.4565C21.3343 18.6249 22.501 15.9298 22.501 12.7332Z" fill="#4285F4"/>
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23
api/core/tools/provider/builtin/google/google.py Normal file
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from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
from core.tools.errors import ToolProviderCredentialValidationError
from core.tools.provider.builtin.google.tools.google_search import GoogleSearchTool
from typing import Any, Dict, List
class GoogleProvider(BuiltinToolProviderController):
def _validate_credentials(self, credentials: Dict[str, Any]) -> None:
try:
GoogleSearchTool().fork_tool_runtime(
meta={
"credentials": credentials,
}
).invoke(
user_id='',
tool_paramters={
"query": "test",
"result_type": "link"
},
)
except Exception as e:
raise ToolProviderCredentialValidationError(str(e))

24
api/core/tools/provider/builtin/google/google.yaml Normal file
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identity:
author: Dify
name: google
label:
en_US: Google
zh_Hans: Google
description:
en_US: Google
zh_Hans: GoogleSearch
icon: icon.svg
credentails_for_provider:
serpapi_api_key:
type: secret-input
required: true
label:
en_US: SerpApi API key
zh_Hans: SerpApi API key
placeholder:
en_US: Please input your SerpApi API key
zh_Hans: 请输入你的 SerpApi API key
help:
en_US: Get your SerpApi API key from SerpApi
zh_Hans: 从 SerpApi 获取您的 SerpApi API key
url: https://serpapi.com/manage-api-key

163
api/core/tools/provider/builtin/google/tools/google_search.py Normal file
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from core.tools.tool.builtin_tool import BuiltinTool
from core.tools.entities.tool_entities import ToolInvokeMessage
from typing import Any, Dict, List, Union
import os
import sys
from serpapi import GoogleSearch
class HiddenPrints:
"""Context manager to hide prints."""
def __enter__(self) -> None:
"""Open file to pipe stdout to."""
self._original_stdout = sys.stdout
sys.stdout = open(os.devnull, "w")
def __exit__(self, *_: Any) -> None:
"""Close file that stdout was piped to."""
sys.stdout.close()
sys.stdout = self._original_stdout
class SerpAPI:
"""
SerpAPI tool provider.
"""
search_engine: Any #: :meta private:
serpapi_api_key: str = None
def __init__(self, api_key: str) -> None:
"""Initialize SerpAPI tool provider."""
self.serpapi_api_key = api_key
self.search_engine = GoogleSearch
def run(self, query: str, **kwargs: Any) -> str:
"""Run query through SerpAPI and parse result."""
typ = kwargs.get("result_type", "text")
return self._process_response(self.results(query), typ=typ)
def results(self, query: str) -> dict:
"""Run query through SerpAPI and return the raw result."""
params = self.get_params(query)
with HiddenPrints():
search = self.search_engine(params)
res = search.get_dict()
return res
def get_params(self, query: str) -> Dict[str, str]:
"""Get parameters for SerpAPI."""
_params = {
"api_key": self.serpapi_api_key,
"q": query,
}
params = {
"engine": "google",
"google_domain": "google.com",
"gl": "us",
"hl": "en",
**_params
}
return params
@staticmethod
def _process_response(res: dict, typ: str) -> str:
"""Process response from SerpAPI."""
if "error" in res.keys():
raise ValueError(f"Got error from SerpAPI: {res['error']}")
if typ == "text":
if "answer_box" in res.keys() and type(res["answer_box"]) == list:
res["answer_box"] = res["answer_box"][0]
if "answer_box" in res.keys() and "answer" in res["answer_box"].keys():
toret = res["answer_box"]["answer"]
elif "answer_box" in res.keys() and "snippet" in res["answer_box"].keys():
toret = res["answer_box"]["snippet"]
elif (
"answer_box" in res.keys()
and "snippet_highlighted_words" in res["answer_box"].keys()
):
toret = res["answer_box"]["snippet_highlighted_words"][0]
elif (
"sports_results" in res.keys()
and "game_spotlight" in res["sports_results"].keys()
):
toret = res["sports_results"]["game_spotlight"]
elif (
"shopping_results" in res.keys()
and "title" in res["shopping_results"][0].keys()
):
toret = res["shopping_results"][:3]
elif (
"knowledge_graph" in res.keys()
and "description" in res["knowledge_graph"].keys()
):
toret = res["knowledge_graph"]["description"]
elif "snippet" in res["organic_results"][0].keys():
toret = res["organic_results"][0]["snippet"]
elif "link" in res["organic_results"][0].keys():
toret = res["organic_results"][0]["link"]
elif (
"images_results" in res.keys()
and "thumbnail" in res["images_results"][0].keys()
):
thumbnails = [item["thumbnail"] for item in res["images_results"][:10]]
toret = thumbnails
else:
toret = "No good search result found"
elif typ == "link":
if "knowledge_graph" in res.keys() and "title" in res["knowledge_graph"].keys() \
and "description_link" in res["knowledge_graph"].keys():
toret = res["knowledge_graph"]["description_link"]
elif "knowledge_graph" in res.keys() and "see_results_about" in res["knowledge_graph"].keys() \
and len(res["knowledge_graph"]["see_results_about"]) > 0:
see_result_about = res["knowledge_graph"]["see_results_about"]
toret = ""
for item in see_result_about:
if "name" not in item.keys() or "link" not in item.keys():
continue
toret += f"[{item['name']}]({item['link']})\n"
elif "organic_results" in res.keys() and len(res["organic_results"]) > 0:
organic_results = res["organic_results"]
toret = ""
for item in organic_results:
if "title" not in item.keys() or "link" not in item.keys():
continue
toret += f"[{item['title']}]({item['link']})\n"
elif "related_questions" in res.keys() and len(res["related_questions"]) > 0:
related_questions = res["related_questions"]
toret = ""
for item in related_questions:
if "question" not in item.keys() or "link" not in item.keys():
continue
toret += f"[{item['question']}]({item['link']})\n"
elif "related_searches" in res.keys() and len(res["related_searches"]) > 0:
related_searches = res["related_searches"]
toret = ""
for item in related_searches:
if "query" not in item.keys() or "link" not in item.keys():
continue
toret += f"[{item['query']}]({item['link']})\n"
else:
toret = "No good search result found"
return toret
class GoogleSearchTool(BuiltinTool):
def _invoke(self,
user_id: str,
tool_paramters: Dict[str, Any],
) -> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
invoke tools
"""
query = tool_paramters['query']
result_type = tool_paramters['result_type']
api_key = self.runtime.credentials['serpapi_api_key']
result = SerpAPI(api_key).run(query, result_type=result_type)
if result_type == 'text':
return self.create_text_message(text=result)
return self.create_link_message(link=result)

43
api/core/tools/provider/builtin/google/tools/google_search.yaml Normal file
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identity:
name: google_search
author: Dify
label:
en_US: GoogleSearch
zh_Hans: 谷歌搜索
description:
human:
en_US: A tool for performing a Google SERP search and extracting snippets and webpages.Input should be a search query.
zh_Hans: 一个用于执行 Google SERP 搜索并提取片段和网页的工具。输入应该是一个搜索查询。
llm: A tool for performing a Google SERP search and extracting snippets and webpages.Input should be a search query.
parameters:
- name: query
type: string
required: true
label:
en_US: Query string
zh_Hans: 查询语句
human_description:
en_US: used for searching
zh_Hans: 用于搜索网页内容
llm_description: key words for searching
form: llm
- name: result_type
type: select
required: true
options:
- value: text
label:
en_US: text
zh_Hans: 文本
- value: link
label:
en_US: link
zh_Hans: 链接
default: link
label:
en_US: Result type
zh_Hans: 结果类型
human_description:
en_US: used for selecting the result type, text or link
zh_Hans: 用于选择结果类型，使用文本还是链接进行展示
form: form

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26
api/core/tools/provider/builtin/stablediffusion/stablediffusion.py Normal file
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from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
from core.tools.errors import ToolProviderCredentialValidationError
from core.tools.provider.builtin.stablediffusion.tools.stable_diffusion import StableDiffusionTool
from typing import Any, Dict
class StableDiffusionProvider(BuiltinToolProviderController):
def _validate_credentials(self, credentials: Dict[str, Any]) -> None:
try:
StableDiffusionTool().fork_tool_runtime(
meta={
"credentials": credentials,
}
).invoke(
user_id='',
tool_paramters={
"prompt": "cat",
"lora": "",
"steps": 1,
"width": 512,
"height": 512,
},
)
except Exception as e:
raise ToolProviderCredentialValidationError(str(e))

29
api/core/tools/provider/builtin/stablediffusion/stablediffusion.yaml Normal file
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identity:
author: Dify
name: stablediffusion
label:
en_US: Stable Diffusion
zh_Hans: Stable Diffusion
description:
en_US: Stable Diffusion is a tool for generating images which can be deployed locally.
zh_Hans: Stable Diffusion 是一个可以在本地部署的图片生成的工具。
icon: icon.png
credentails_for_provider:
base_url:
type: secret-input
required: true
label:
en_US: Base URL
zh_Hans: StableDiffusion服务器的Base URL
placeholder:
en_US: Please input your StableDiffusion server's Base URL
zh_Hans: 请输入你的 StableDiffusion 服务器的 Base URL
model:
type: text-input
required: true
label:
en_US: Model
zh_Hans: 模型
placeholder:
en_US: Please input your model
zh_Hans: 请输入你的模型名称

244
api/core/tools/provider/builtin/stablediffusion/tools/stable_diffusion.py Normal file
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from core.tools.tool.builtin_tool import BuiltinTool
from core.tools.entities.tool_entities import ToolInvokeMessage, ToolParamter, ToolParamterOption
from core.tools.entities.common_entities import I18nObject
from core.tools.errors import ToolProviderCredentialValidationError
from typing import Any, Dict, List, Union
from httpx import post
from os.path import join
from base64 import b64decode, b64encode
from PIL import Image
import json
import io
from copy import deepcopy
DRAW_TEXT_OPTIONS = {
"prompt": "",
"negative_prompt": "",
"seed": -1,
"subseed": -1,
"subseed_strength": 0,
"seed_resize_from_h": -1,
'sampler_index': 'DPM++ SDE Karras',
"seed_resize_from_w": -1,
"batch_size": 1,
"n_iter": 1,
"steps": 10,
"cfg_scale": 7,
"width": 1024,
"height": 1024,
"restore_faces": False,
"do_not_save_samples": False,
"do_not_save_grid": False,
"eta": 0,
"denoising_strength": 0,
"s_min_uncond": 0,
"s_churn": 0,
"s_tmax": 0,
"s_tmin": 0,
"s_noise": 0,
"override_settings": {},
"override_settings_restore_afterwards": True,
"refiner_switch_at": 0,
"disable_extra_networks": False,
"comments": {},
"enable_hr": False,
"firstphase_width": 0,
"firstphase_height": 0,
"hr_scale": 2,
"hr_second_pass_steps": 0,
"hr_resize_x": 0,
"hr_resize_y": 0,
"hr_prompt": "",
"hr_negative_prompt": "",
"script_args": [],
"send_images": True,
"save_images": False,
"alwayson_scripts": {}
}
class StableDiffusionTool(BuiltinTool):
def _invoke(self, user_id: str, tool_paramters: Dict[str, Any]) \
-> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
invoke tools
"""
# base url
base_url = self.runtime.credentials.get('base_url', None)
if not base_url:
return self.create_text_message('Please input base_url')
model = self.runtime.credentials.get('model', None)
if not model:
return self.create_text_message('Please input model')
# set model
try:
url = join(base_url, 'sdapi/v1/options')
response = post(url, data=json.dumps({
'sd_model_checkpoint': model
}))
if response.status_code != 200:
raise ToolProviderCredentialValidationError('Failed to set model, please tell user to set model')
except Exception as e:
raise ToolProviderCredentialValidationError('Failed to set model, please tell user to set model')
# prompt
prompt = tool_paramters.get('prompt', '')
if not prompt:
return self.create_text_message('Please input prompt')
# get negative prompt
negative_prompt = tool_paramters.get('negative_prompt', '')
# get size
width = tool_paramters.get('width', 1024)
height = tool_paramters.get('height', 1024)
# get steps
steps = tool_paramters.get('steps', 1)
# get lora
lora = tool_paramters.get('lora', '')
# get image id
image_id = tool_paramters.get('image_id', '')
if image_id.strip():
image_variable = self.get_default_image_variable()
if image_variable:
image_binary = self.get_variable_file(image_variable.name)
if not image_binary:
return self.create_text_message('Image not found, please request user to generate image firstly.')
# convert image to RGB
image = Image.open(io.BytesIO(image_binary))
image = image.convert("RGB")
buffer = io.BytesIO()
image.save(buffer, format="PNG")
image_binary = buffer.getvalue()
image.close()
return self.img2img(base_url=base_url,
lora=lora,
image_binary=image_binary,
prompt=prompt,
negative_prompt=negative_prompt,
width=width,
height=height,
steps=steps)
return self.text2img(base_url=base_url,
lora=lora,
prompt=prompt,
negative_prompt=negative_prompt,
width=width,
height=height,
steps=steps)
def img2img(self, base_url: str, lora: str, image_binary: bytes,
prompt: str, negative_prompt: str,
width: int, height: int, steps: int) \
-> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
generate image
"""
draw_options = {
"init_images": [b64encode(image_binary).decode('utf-8')],
"prompt": "",
"negative_prompt": negative_prompt,
"denoising_strength": 0.9,
"width": width,
"height": height,
"cfg_scale": 7,
"sampler_name": "Euler a",
"restore_faces": False,
"steps": steps,
"script_args": ["outpainting mk2"]
}
if lora:
draw_options['prompt'] = f'{lora},{prompt}'
else:
draw_options['prompt'] = prompt
try:
url = join(base_url, 'sdapi/v1/img2img')
response = post(url, data=json.dumps(draw_options), timeout=120)
if response.status_code != 200:
return self.create_text_message('Failed to generate image')
image = response.json()['images'][0]
return self.create_blob_message(blob=b64decode(image),
meta={ 'mime_type': 'image/png' },
save_as=self.VARIABLE_KEY.IMAGE.value)
except Exception as e:
return self.create_text_message('Failed to generate image')
def text2img(self, base_url: str, lora: str, prompt: str, negative_prompt: str, width: int, height: int, steps: int) \
-> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
generate image
"""
# copy draw options
draw_options = deepcopy(DRAW_TEXT_OPTIONS)
if lora:
draw_options['prompt'] = f'{lora},{prompt}'
draw_options['width'] = width
draw_options['height'] = height
draw_options['steps'] = steps
draw_options['negative_prompt'] = negative_prompt
try:
url = join(base_url, 'sdapi/v1/txt2img')
response = post(url, data=json.dumps(draw_options), timeout=120)
if response.status_code != 200:
return self.create_text_message('Failed to generate image')
image = response.json()['images'][0]
return self.create_blob_message(blob=b64decode(image),
meta={ 'mime_type': 'image/png' },
save_as=self.VARIABLE_KEY.IMAGE.value)
except Exception as e:
return self.create_text_message('Failed to generate image')
def get_runtime_parameters(self) -> List[ToolParamter]:
parameters = [
ToolParamter(name='prompt',
label=I18nObject(en_US='Prompt', zh_Hans='Prompt'),
human_description=I18nObject(
en_US='Image prompt, you can check the official documentation of Stable Diffusion',
zh_Hans='图像提示词，您可以查看 Stable Diffusion 的官方文档',
),
type=ToolParamter.ToolParameterType.STRING,
form=ToolParamter.ToolParameterForm.LLM,
llm_description='Image prompt of Stable Diffusion, you should describe the image you want to generate as a list of words as possible as detailed, the prompt must be written in English.',
required=True),
]
if len(self.list_default_image_variables()) != 0:
parameters.append(
ToolParamter(name='image_id',
label=I18nObject(en_US='image_id', zh_Hans='image_id'),
human_description=I18nObject(
en_US='Image id of the image you want to generate based on, if you want to generate image based on the default image, you can leave this field empty.',
zh_Hans='您想要生成的图像的图像 ID，如果您想要基于默认图像生成图像，则可以将此字段留空。',
),
type=ToolParamter.ToolParameterType.STRING,
form=ToolParamter.ToolParameterForm.LLM,
llm_description='Image id of the original image, you can leave this field empty if you want to generate a new image.',
required=True,
options=[ToolParamterOption(
value=i.name,
label=I18nObject(en_US=i.name, zh_Hans=i.name)
) for i in self.list_default_image_variables()])
)
return parameters

77
api/core/tools/provider/builtin/stablediffusion/tools/stable_diffusion.yaml Normal file
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identity:
name: stable_diffusion
author: Dify
label:
en_US: Stable Diffusion WebUI
zh_Hans: Stable Diffusion WebUI
description:
human:
en_US: A tool for generating images which can be deployed locally, you can use stable-diffusion-webui to deploy it.
zh_Hans: 一个可以在本地部署的图片生成的工具，您可以使用 stable-diffusion-webui 来部署它。
llm: draw the image you want based on your prompt.
parameters:
- name: prompt
type: string
required: true
label:
en_US: Prompt
zh_Hans: 提示词
human_description:
en_US: Image prompt, you can check the official documentation of Stable Diffusion
zh_Hans: 图像提示词，您可以查看 Stable Diffusion 的官方文档
llm_description: Image prompt of Stable Diffusion, you should describe the image you want to generate as a list of words as possible as detailed, the prompt must be written in English.
form: llm
- name: lora
type: string
required: false
label:
en_US: Lora
zh_Hans: Lora
human_description:
en_US: Lora
zh_Hans: Lora
form: form
- name: steps
type: number
required: false
label:
en_US: Steps
zh_Hans: Steps
human_description:
en_US: Steps
zh_Hans: Steps
form: form
default: 10
- name: width
type: number
required: false
label:
en_US: Width
zh_Hans: Width
human_description:
en_US: Width
zh_Hans: Width
form: form
default: 1024
- name: height
type: number
required: false
label:
en_US: Height
zh_Hans: Height
human_description:
en_US: Height
zh_Hans: Height
form: form
default: 1024
- name: negative_prompt
type: string
required: false
label:
en_US: Negative prompt
zh_Hans: Negative prompt
human_description:
en_US: Negative prompt
zh_Hans: Negative prompt
form: form
default: bad art, ugly, deformed, watermark, duplicated, discontinuous lines

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<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 16 16" fill="none">
<path fill-rule="evenodd" clip-rule="evenodd" d="M0.666992 8.00008C0.666992 3.94999 3.95024 0.666748 8.00033 0.666748C12.0504 0.666748 15.3337 3.94999 15.3337 8.00008C15.3337 12.0502 12.0504 15.3334 8.00033 15.3334C3.95024 15.3334 0.666992 12.0502 0.666992 8.00008ZM8.66699 4.00008C8.66699 3.63189 8.36852 3.33341 8.00033 3.33341C7.63213 3.33341 7.33366 3.63189 7.33366 4.00008V8.00008C7.33366 8.2526 7.47633 8.48344 7.70218 8.59637L10.3688 9.9297C10.6982 10.0944 11.0986 9.96088 11.2633 9.63156C11.4279 9.30224 11.2945 8.90179 10.9651 8.73713L8.66699 7.58806V4.00008Z" fill="#EC4A0A"/>
</svg>
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16
api/core/tools/provider/builtin/time/time.py Normal file
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from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
from core.tools.errors import ToolProviderCredentialValidationError
from core.tools.provider.builtin.time.tools.current_time import CurrentTimeTool
from typing import Any, Dict
class WikiPediaProvider(BuiltinToolProviderController):
def _validate_credentials(self, credentials: Dict[str, Any]) -> None:
try:
CurrentTimeTool().invoke(
user_id='',
tool_paramters={},
)
except Exception as e:
raise ToolProviderCredentialValidationError(str(e))

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api/core/tools/provider/builtin/time/time.yaml Normal file
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identity:
author: Dify
name: time
label:
en_US: CurrentTime
zh_Hans: 时间
description:
en_US: A tool for getting the current time.
zh_Hans: 一个用于获取当前时间的工具。
icon: icon.svg
credentails_for_provider:

17
api/core/tools/provider/builtin/time/tools/current_time.py Normal file
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from core.tools.entities.tool_entities import ToolInvokeMessage
from core.tools.tool.builtin_tool import BuiltinTool
from typing import Any, Dict, List, Union
from datetime import datetime, timezone
class CurrentTimeTool(BuiltinTool):
def _invoke(self,
user_id: str,
tool_paramters: Dict[str, Any],
) -> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
invoke tools
"""
return self.create_text_message(f'{datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S %Z")}')

12
api/core/tools/provider/builtin/time/tools/current_time.yaml Normal file
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identity:
name: current_time
author: Dify
label:
en_US: Current Time
zh_Hans: 获取当前时间
description:
human:
en_US: A tool for getting the current time.
zh_Hans: 一个用于获取当前时间的工具。
llm: A tool for getting the current time.
parameters:

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api/core/tools/provider/builtin/vectorizer/_assets/icon.png Normal file
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api/core/tools/provider/builtin/vectorizer/tools/test_data.py Normal file
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VECTORIZER_ICON_PNG = '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'

74
api/core/tools/provider/builtin/vectorizer/tools/vectorizer.py Normal file
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from core.tools.tool.builtin_tool import BuiltinTool
from core.tools.entities.tool_entities import ToolInvokeMessage, ToolParamter
from core.tools.provider.builtin.vectorizer.tools.test_data import VECTORIZER_ICON_PNG
from core.tools.errors import ToolProviderCredentialValidationError
from typing import Any, Dict, List, Union
from httpx import post
from base64 import b64decode
class VectorizerTool(BuiltinTool):
def _invoke(self, user_id: str, tool_paramters: Dict[str, Any]) \
-> Union[ToolInvokeMessage, List[ToolInvokeMessage]]:
"""
invoke tools
"""
api_key_name = self.runtime.credentials.get('api_key_name', None)
api_key_value = self.runtime.credentials.get('api_key_value', None)
mode = tool_paramters.get('mode', 'test')
if mode == 'production':
mode = 'preview'
if not api_key_name or not api_key_value:
raise ToolProviderCredentialValidationError('Please input api key name and value')
image_id = tool_paramters.get('image_id', '')
if not image_id:
return self.create_text_message('Please input image id')
if image_id.startswith('__test_'):
image_binary = b64decode(VECTORIZER_ICON_PNG)
else:
image_binary = self.get_variable_file(self.VARIABLE_KEY.IMAGE)
if not image_binary:
return self.create_text_message('Image not found, please request user to generate image firstly.')
response = post(
'https://vectorizer.ai/api/v1/vectorize',
files={
'image': image_binary
},
data={
'mode': mode
} if mode == 'test' else {},
auth=(api_key_name, api_key_value),
timeout=30
)
if response.status_code != 200:
raise Exception(response.text)
return [
self.create_text_message('the vectorized svg is saved as an image.'),
self.create_blob_message(blob=response.content,
meta={'mime_type': 'image/svg+xml'})
]
def get_runtime_parameters(self) -> List[ToolParamter]:
"""
override the runtime parameters
"""
return [
ToolParamter.get_simple_instance(
name='image_id',
llm_description=f'the image id that you want to vectorize, \
and the image id should be specified in \
{[i.name for i in self.list_default_image_variables()]}',
type=ToolParamter.ToolParameterType.SELECT,
required=True,
options=[i.name for i in self.list_default_image_variables()]
)
]
def is_tool_avaliable(self) -> bool:
return len(self.list_default_image_variables()) > 0

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