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Merge main into fix/chore-fix

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Yeuoly 2024-09-24 16:38:38 +08:00
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4
api/README.md
View File

@ -65,14 +65,12 @@
8. Start Dify [web](../web) service.
9. Setup your application by visiting `http://localhost:3000`...
10. If you need to debug local async processing, please start the worker service.
10. If you need to handle and debug the async tasks (e.g. dataset importing and documents indexing), please start the worker service.
```bash
poetry run python -m celery -A app.celery worker -P gevent -c 1 --loglevel INFO -Q dataset,generation,mail,ops_trace,app_deletion
```
The started celery app handles the async tasks, e.g. dataset importing and documents indexing.
## Testing
1. Install dependencies for both the backend and the test environment

130
api/commands.py
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@ -28,28 +28,28 @@ from services.account_service import RegisterService, TenantService
@click.command("reset-password", help="Reset the account password.")
@click.option("--email", prompt=True, help="The email address of the account whose password you need to reset")
@click.option("--new-password", prompt=True, help="the new password.")
@click.option("--password-confirm", prompt=True, help="the new password confirm.")
@click.option("--email", prompt=True, help="Account email to reset password for")
@click.option("--new-password", prompt=True, help="New password")
@click.option("--password-confirm", prompt=True, help="Confirm new password")
def reset_password(email, new_password, password_confirm):
"""
Reset password of owner account
Only available in SELF_HOSTED mode
"""
if str(new_password).strip() != str(password_confirm).strip():
click.echo(click.style("sorry. The two passwords do not match.", fg="red"))
click.echo(click.style("Passwords do not match.", fg="red"))
return
account = db.session.query(Account).filter(Account.email == email).one_or_none()
if not account:
click.echo(click.style("sorry. the account: [{}] not exist .".format(email), fg="red"))
click.echo(click.style("Account not found for email: {}".format(email), fg="red"))
return
try:
valid_password(new_password)
except:
click.echo(click.style("sorry. The passwords must match {} ".format(password_pattern), fg="red"))
click.echo(click.style("Invalid password. Must match {}".format(password_pattern), fg="red"))
return
# generate password salt
@ -62,37 +62,37 @@ def reset_password(email, new_password, password_confirm):
account.password = base64_password_hashed
account.password_salt = base64_salt
db.session.commit()
click.echo(click.style("Congratulations! Password has been reset.", fg="green"))
click.echo(click.style("Password reset successfully.", fg="green"))
@click.command("reset-email", help="Reset the account email.")
@click.option("--email", prompt=True, help="The old email address of the account whose email you need to reset")
@click.option("--new-email", prompt=True, help="the new email.")
@click.option("--email-confirm", prompt=True, help="the new email confirm.")
@click.option("--email", prompt=True, help="Current account email")
@click.option("--new-email", prompt=True, help="New email")
@click.option("--email-confirm", prompt=True, help="Confirm new email")
def reset_email(email, new_email, email_confirm):
"""
Replace account email
:return:
"""
if str(new_email).strip() != str(email_confirm).strip():
click.echo(click.style("Sorry, new email and confirm email do not match.", fg="red"))
click.echo(click.style("New emails do not match.", fg="red"))
return
account = db.session.query(Account).filter(Account.email == email).one_or_none()
if not account:
click.echo(click.style("sorry. the account: [{}] not exist .".format(email), fg="red"))
click.echo(click.style("Account not found for email: {}".format(email), fg="red"))
return
try:
email_validate(new_email)
except:
click.echo(click.style("sorry. {} is not a valid email. ".format(email), fg="red"))
click.echo(click.style("Invalid email: {}".format(new_email), fg="red"))
return
account.email = new_email
db.session.commit()
click.echo(click.style("Congratulations!, email has been reset.", fg="green"))
click.echo(click.style("Email updated successfully.", fg="green"))
@click.command(
@ -104,7 +104,7 @@ def reset_email(email, new_email, email_confirm):
)
@click.confirmation_option(
prompt=click.style(
"Are you sure you want to reset encrypt key pair? this operation cannot be rolled back!", fg="red"
"Are you sure you want to reset encrypt key pair? This operation cannot be rolled back!", fg="red"
)
)
def reset_encrypt_key_pair():
@ -114,13 +114,13 @@ def reset_encrypt_key_pair():
Only support SELF_HOSTED mode.
"""
if dify_config.EDITION != "SELF_HOSTED":
click.echo(click.style("Sorry, only support SELF_HOSTED mode.", fg="red"))
click.echo(click.style("This command is only for SELF_HOSTED installations.", fg="red"))
return
tenants = db.session.query(Tenant).all()
for tenant in tenants:
if not tenant:
click.echo(click.style("Sorry, no workspace found. Please enter /install to initialize.", fg="red"))
click.echo(click.style("No workspaces found. Run /install first.", fg="red"))
return
tenant.encrypt_public_key = generate_key_pair(tenant.id)
@ -137,7 +137,7 @@ def reset_encrypt_key_pair():
)
@click.command("vdb-migrate", help="migrate vector db.")
@click.command("vdb-migrate", help="Migrate vector db.")
@click.option("--scope", default="all", prompt=False, help="The scope of vector database to migrate, Default is All.")
def vdb_migrate(scope: str):
if scope in {"knowledge", "all"}:
@ -150,7 +150,7 @@ def migrate_annotation_vector_database():
"""
Migrate annotation datas to target vector database .
"""
click.echo(click.style("Start migrate annotation data.", fg="green"))
click.echo(click.style("Starting annotation data migration.", fg="green"))
create_count = 0
skipped_count = 0
total_count = 0
@ -174,14 +174,14 @@ def migrate_annotation_vector_database():
f"Processing the {total_count} app {app.id}. " + f"{create_count} created, {skipped_count} skipped."
)
try:
click.echo("Create app annotation index: {}".format(app.id))
click.echo("Creating app annotation index: {}".format(app.id))
app_annotation_setting = (
db.session.query(AppAnnotationSetting).filter(AppAnnotationSetting.app_id == app.id).first()
)
if not app_annotation_setting:
skipped_count = skipped_count + 1
click.echo("App annotation setting is disabled: {}".format(app.id))
click.echo("App annotation setting disabled: {}".format(app.id))
continue
# get dataset_collection_binding info
dataset_collection_binding = (
@ -190,7 +190,7 @@ def migrate_annotation_vector_database():
.first()
)
if not dataset_collection_binding:
click.echo("App annotation collection binding is not exist: {}".format(app.id))
click.echo("App annotation collection binding not found: {}".format(app.id))
continue
annotations = db.session.query(MessageAnnotation).filter(MessageAnnotation.app_id == app.id).all()
dataset = Dataset(
@ -211,11 +211,11 @@ def migrate_annotation_vector_database():
documents.append(document)
vector = Vector(dataset, attributes=["doc_id", "annotation_id", "app_id"])
click.echo(f"Start to migrate annotation, app_id: {app.id}.")
click.echo(f"Migrating annotations for app: {app.id}.")
try:
vector.delete()
click.echo(click.style(f"Successfully delete vector index for app: {app.id}.", fg="green"))
click.echo(click.style(f"Deleted vector index for app {app.id}.", fg="green"))
except Exception as e:
click.echo(click.style(f"Failed to delete vector index for app {app.id}.", fg="red"))
raise e
@ -223,12 +223,12 @@ def migrate_annotation_vector_database():
try:
click.echo(
click.style(
f"Start to created vector index with {len(documents)} annotations for app {app.id}.",
f"Creating vector index with {len(documents)} annotations for app {app.id}.",
fg="green",
)
)
vector.create(documents)
click.echo(click.style(f"Successfully created vector index for app {app.id}.", fg="green"))
click.echo(click.style(f"Created vector index for app {app.id}.", fg="green"))
except Exception as e:
click.echo(click.style(f"Failed to created vector index for app {app.id}.", fg="red"))
raise e
@ -237,14 +237,14 @@ def migrate_annotation_vector_database():
except Exception as e:
click.echo(
click.style(
"Create app annotation index error: {} {}".format(e.__class__.__name__, str(e)), fg="red"
"Error creating app annotation index: {} {}".format(e.__class__.__name__, str(e)), fg="red"
)
)
continue
click.echo(
click.style(
f"Congratulations! Create {create_count} app annotation indexes, and skipped {skipped_count} apps.",
f"Migration complete. Created {create_count} app annotation indexes. Skipped {skipped_count} apps.",
fg="green",
)
)
@ -254,7 +254,7 @@ def migrate_knowledge_vector_database():
"""
Migrate vector database datas to target vector database .
"""
click.echo(click.style("Start migrate vector db.", fg="green"))
click.echo(click.style("Starting vector database migration.", fg="green"))
create_count = 0
skipped_count = 0
total_count = 0
@ -278,7 +278,7 @@ def migrate_knowledge_vector_database():
f"Processing the {total_count} dataset {dataset.id}. {create_count} created, {skipped_count} skipped."
)
try:
click.echo("Create dataset vdb index: {}".format(dataset.id))
click.echo("Creating dataset vector database index: {}".format(dataset.id))
if dataset.index_struct_dict:
if dataset.index_struct_dict["type"] == vector_type:
skipped_count = skipped_count + 1
@ -299,7 +299,7 @@ def migrate_knowledge_vector_database():
if dataset_collection_binding:
collection_name = dataset_collection_binding.collection_name
else:
raise ValueError("Dataset Collection Bindings is not exist!")
raise ValueError("Dataset Collection Binding not found")
else:
dataset_id = dataset.id
collection_name = Dataset.gen_collection_name_by_id(dataset_id)
@ -351,14 +351,12 @@ def migrate_knowledge_vector_database():
raise ValueError(f"Vector store {vector_type} is not supported.")
vector = Vector(dataset)
click.echo(f"Start to migrate dataset {dataset.id}.")
click.echo(f"Migrating dataset {dataset.id}.")
try:
vector.delete()
click.echo(
click.style(
f"Successfully delete vector index {collection_name} for dataset {dataset.id}.", fg="green"
)
click.style(f"Deleted vector index {collection_name} for dataset {dataset.id}.", fg="green")
)
except Exception as e:
click.echo(
@ -410,15 +408,13 @@ def migrate_knowledge_vector_database():
try:
click.echo(
click.style(
f"Start to created vector index with {len(documents)} documents of {segments_count}"
f"Creating vector index with {len(documents)} documents of {segments_count}"
f" segments for dataset {dataset.id}.",
fg="green",
)
)
vector.create(documents)
click.echo(
click.style(f"Successfully created vector index for dataset {dataset.id}.", fg="green")
)
click.echo(click.style(f"Created vector index for dataset {dataset.id}.", fg="green"))
except Exception as e:
click.echo(click.style(f"Failed to created vector index for dataset {dataset.id}.", fg="red"))
raise e
@ -429,13 +425,13 @@ def migrate_knowledge_vector_database():
except Exception as e:
db.session.rollback()
click.echo(
click.style("Create dataset index error: {} {}".format(e.__class__.__name__, str(e)), fg="red")
click.style("Error creating dataset index: {} {}".format(e.__class__.__name__, str(e)), fg="red")
)
continue
click.echo(
click.style(
f"Congratulations! Create {create_count} dataset indexes, and skipped {skipped_count} datasets.", fg="green"
f"Migration complete. Created {create_count} dataset indexes. Skipped {skipped_count} datasets.", fg="green"
)
)
@ -445,7 +441,7 @@ def convert_to_agent_apps():
"""
Convert Agent Assistant to Agent App.
"""
click.echo(click.style("Start convert to agent apps.", fg="green"))
click.echo(click.style("Starting convert to agent apps.", fg="green"))
proceeded_app_ids = []
@ -496,23 +492,23 @@ def convert_to_agent_apps():
except Exception as e:
click.echo(click.style("Convert app error: {} {}".format(e.__class__.__name__, str(e)), fg="red"))
click.echo(click.style("Congratulations! Converted {} agent apps.".format(len(proceeded_app_ids)), fg="green"))
click.echo(click.style("Conversion complete. Converted {} agent apps.".format(len(proceeded_app_ids)), fg="green"))
@click.command("add-qdrant-doc-id-index", help="add qdrant doc_id index.")
@click.option("--field", default="metadata.doc_id", prompt=False, help="index field , default is metadata.doc_id.")
@click.command("add-qdrant-doc-id-index", help="Add Qdrant doc_id index.")
@click.option("--field", default="metadata.doc_id", prompt=False, help="Index field , default is metadata.doc_id.")
def add_qdrant_doc_id_index(field: str):
click.echo(click.style("Start add qdrant doc_id index.", fg="green"))
click.echo(click.style("Starting Qdrant doc_id index creation.", fg="green"))
vector_type = dify_config.VECTOR_STORE
if vector_type != "qdrant":
click.echo(click.style("Sorry, only support qdrant vector store.", fg="red"))
click.echo(click.style("This command only supports Qdrant vector store.", fg="red"))
return
create_count = 0
try:
bindings = db.session.query(DatasetCollectionBinding).all()
if not bindings:
click.echo(click.style("Sorry, no dataset collection bindings found.", fg="red"))
click.echo(click.style("No dataset collection bindings found.", fg="red"))
return
import qdrant_client
from qdrant_client.http.exceptions import UnexpectedResponse
@ -522,7 +518,7 @@ def add_qdrant_doc_id_index(field: str):
for binding in bindings:
if dify_config.QDRANT_URL is None:
raise ValueError("Qdrant url is required.")
raise ValueError("Qdrant URL is required.")
qdrant_config = QdrantConfig(
endpoint=dify_config.QDRANT_URL,
api_key=dify_config.QDRANT_API_KEY,
@ -539,41 +535,39 @@ def add_qdrant_doc_id_index(field: str):
except UnexpectedResponse as e:
# Collection does not exist, so return
if e.status_code == 404:
click.echo(
click.style(f"Collection not found, collection_name:{binding.collection_name}.", fg="red")
)
click.echo(click.style(f"Collection not found: {binding.collection_name}.", fg="red"))
continue
# Some other error occurred, so re-raise the exception
else:
click.echo(
click.style(
f"Failed to create qdrant index, collection_name:{binding.collection_name}.", fg="red"
f"Failed to create Qdrant index for collection: {binding.collection_name}.", fg="red"
)
)
except Exception as e:
click.echo(click.style("Failed to create qdrant client.", fg="red"))
click.echo(click.style("Failed to create Qdrant client.", fg="red"))
click.echo(click.style(f"Congratulations! Create {create_count} collection indexes.", fg="green"))
click.echo(click.style(f"Index creation complete. Created {create_count} collection indexes.", fg="green"))
@click.command("create-tenant", help="Create account and tenant.")
@click.option("--email", prompt=True, help="The email address of the tenant account.")
@click.option("--name", prompt=True, help="The workspace name of the tenant account.")
@click.option("--email", prompt=True, help="Tenant account email.")
@click.option("--name", prompt=True, help="Workspace name.")
@click.option("--language", prompt=True, help="Account language, default: en-US.")
def create_tenant(email: str, language: Optional[str] = None, name: Optional[str] = None):
"""
Create tenant account
"""
if not email:
click.echo(click.style("Sorry, email is required.", fg="red"))
click.echo(click.style("Email is required.", fg="red"))
return
# Create account
email = email.strip()
if "@" not in email:
click.echo(click.style("Sorry, invalid email address.", fg="red"))
click.echo(click.style("Invalid email address.", fg="red"))
return
account_name = email.split("@")[0]
@ -593,19 +587,19 @@ def create_tenant(email: str, language: Optional[str] = None, name: Optional[str
click.echo(
click.style(
"Congratulations! Account and tenant created.\nAccount: {}\nPassword: {}".format(email, new_password),
"Account and tenant created.\nAccount: {}\nPassword: {}".format(email, new_password),
fg="green",
)
)
@click.command("upgrade-db", help="upgrade the database")
@click.command("upgrade-db", help="Upgrade the database")
def upgrade_db():
click.echo("Preparing database migration...")
lock = redis_client.lock(name="db_upgrade_lock", timeout=60)
if lock.acquire(blocking=False):
try:
click.echo(click.style("Start database migration.", fg="green"))
click.echo(click.style("Starting database migration.", fg="green"))
# run db migration
import flask_migrate
@ -615,7 +609,7 @@ def upgrade_db():
click.echo(click.style("Database migration successful!", fg="green"))
except Exception as e:
logging.exception(f"Database migration failed, error: {e}")
logging.exception(f"Database migration failed: {e}")
finally:
lock.release()
else:
@ -627,7 +621,7 @@ def fix_app_site_missing():
"""
Fix app related site missing issue.
"""
click.echo(click.style("Start fix app related site missing issue.", fg="green"))
click.echo(click.style("Starting fix for missing app-related sites.", fg="green"))
failed_app_ids = []
while True:
@ -650,22 +644,22 @@ where sites.id is null limit 1000"""
if tenant:
accounts = tenant.get_accounts()
if not accounts:
print("Fix app {} failed.".format(app.id))
print("Fix failed for app {}".format(app.id))
continue
account = accounts[0]
print("Fix app {} related site missing issue.".format(app.id))
print("Fixing missing site for app {}".format(app.id))
app_was_created.send(app, account=account)
except Exception as e:
failed_app_ids.append(app_id)
click.echo(click.style("Fix app {} related site missing issue failed!".format(app_id), fg="red"))
click.echo(click.style("Failed to fix missing site for app {}".format(app_id), fg="red"))
logging.exception(f"Fix app related site missing issue failed, error: {e}")
continue
if not processed_count:
break
click.echo(click.style("Congratulations! Fix app related site missing issue successful!", fg="green"))
click.echo(click.style("Fix for missing app-related sites completed successfully!", fg="green"))
def register_commands(app):

12
api/configs/deploy/__init__.py
View File

@ -4,30 +4,30 @@ from pydantic_settings import BaseSettings
class DeploymentConfig(BaseSettings):
"""
Deployment configs
Configuration settings for application deployment
"""
APPLICATION_NAME: str = Field(
description="application name",
description="Name of the application, used for identification and logging purposes",
default="langgenius/dify",
)
DEBUG: bool = Field(
description="whether to enable debug mode.",
description="Enable debug mode for additional logging and development features",
default=False,
)
TESTING: bool = Field(
description="",
description="Enable testing mode for running automated tests",
default=False,
)
EDITION: str = Field(
description="deployment edition",
description="Deployment edition of the application (e.g., 'SELF_HOSTED', 'CLOUD')",
default="SELF_HOSTED",
)
DEPLOY_ENV: str = Field(
description="deployment environment, default to PRODUCTION.",
description="Deployment environment (e.g., 'PRODUCTION', 'DEVELOPMENT'), default to PRODUCTION",
default="PRODUCTION",
)

6
api/configs/enterprise/__init__.py
View File

@ -4,17 +4,17 @@ from pydantic_settings import BaseSettings
class EnterpriseFeatureConfig(BaseSettings):
"""
Enterprise feature configs.
Configuration for enterprise-level features.
**Before using, please contact business@dify.ai by email to inquire about licensing matters.**
"""
ENTERPRISE_ENABLED: bool = Field(
description="whether to enable enterprise features."
description="Enable or disable enterprise-level features."
"Before using, please contact business@dify.ai by email to inquire about licensing matters.",
default=False,
)
CAN_REPLACE_LOGO: bool = Field(
description="whether to allow replacing enterprise logo.",
description="Allow customization of the enterprise logo.",
default=False,
)

13
api/configs/extra/notion_config.py
View File

@ -6,30 +6,31 @@ from pydantic_settings import BaseSettings
class NotionConfig(BaseSettings):
"""
Notion integration configs
Configuration settings for Notion integration
"""
NOTION_CLIENT_ID: Optional[str] = Field(
description="Notion client ID",
description="Client ID for Notion API authentication. Required for OAuth 2.0 flow.",
default=None,
)
NOTION_CLIENT_SECRET: Optional[str] = Field(
description="Notion client secret key",
description="Client secret for Notion API authentication. Required for OAuth 2.0 flow.",
default=None,
)
NOTION_INTEGRATION_TYPE: Optional[str] = Field(
description="Notion integration type, default to None, available values: internal.",
description="Type of Notion integration."
" Set to 'internal' for internal integrations, or None for public integrations.",
default=None,
)
NOTION_INTERNAL_SECRET: Optional[str] = Field(
description="Notion internal secret key",
description="Secret key for internal Notion integrations. Required when NOTION_INTEGRATION_TYPE is 'internal'.",
default=None,
)
NOTION_INTEGRATION_TOKEN: Optional[str] = Field(
description="Notion integration token",
description="Integration token for Notion API access. Used for direct API calls without OAuth flow.",
default=None,
)

11
api/configs/extra/sentry_config.py
View File

@ -6,20 +6,23 @@ from pydantic_settings import BaseSettings
class SentryConfig(BaseSettings):
"""
Sentry configs
Configuration settings for Sentry error tracking and performance monitoring
"""
SENTRY_DSN: Optional[str] = Field(
description="Sentry DSN",
description="Sentry Data Source Name (DSN)."
" This is the unique identifier of your Sentry project, used to send events to the correct project.",
default=None,
)
SENTRY_TRACES_SAMPLE_RATE: NonNegativeFloat = Field(
description="Sentry trace sample rate",
description="Sample rate for Sentry performance monitoring traces."
" Value between 0.0 and 1.0, where 1.0 means 100% of traces are sent to Sentry.",
default=1.0,
)
SENTRY_PROFILES_SAMPLE_RATE: NonNegativeFloat = Field(
description="Sentry profiles sample rate",
description="Sample rate for Sentry profiling."
" Value between 0.0 and 1.0, where 1.0 means 100% of profiles are sent to Sentry.",
default=1.0,
)

217
api/configs/feature/__init__.py
View File

@ -8,105 +8,104 @@ from configs.feature.hosted_service import HostedServiceConfig
class SecurityConfig(BaseSettings):
"""
Secret Key configs
Security-related configurations for the application
"""
SECRET_KEY: Optional[str] = Field(
description="Your App secret key will be used for securely signing the session cookie"
description="Secret key for secure session cookie signing."
"Make sure you are changing this key for your deployment with a strong key."
"You can generate a strong key using `openssl rand -base64 42`."
"Alternatively you can set it with `SECRET_KEY` environment variable.",
"Generate a strong key using `openssl rand -base64 42` or set via the `SECRET_KEY` environment variable.",
default=None,
)
RESET_PASSWORD_TOKEN_EXPIRY_HOURS: PositiveInt = Field(
description="Expiry time in hours for reset token",
description="Duration in hours for which a password reset token remains valid",
default=24,
)
class AppExecutionConfig(BaseSettings):
"""
App Execution configs
Configuration parameters for application execution
"""
APP_MAX_EXECUTION_TIME: PositiveInt = Field(
description="execution timeout in seconds for app execution",
description="Maximum allowed execution time for the application in seconds",
default=1200,
)
APP_MAX_ACTIVE_REQUESTS: NonNegativeInt = Field(
description="max active request per app, 0 means unlimited",
description="Maximum number of concurrent active requests per app (0 for unlimited)",
default=0,
)
class CodeExecutionSandboxConfig(BaseSettings):
"""
Code Execution Sandbox configs
Configuration for the code execution sandbox environment
"""
CODE_EXECUTION_ENDPOINT: HttpUrl = Field(
description="endpoint URL of code execution service",
description="URL endpoint for the code execution service",
default="http://sandbox:8194",
)
CODE_EXECUTION_API_KEY: str = Field(
description="API key for code execution service",
description="API key for accessing the code execution service",
default="dify-sandbox",
)
CODE_EXECUTION_CONNECT_TIMEOUT: Optional[float] = Field(
description="connect timeout in seconds for code execution request",
description="Connection timeout in seconds for code execution requests",
default=10.0,
)
CODE_EXECUTION_READ_TIMEOUT: Optional[float] = Field(
description="read timeout in seconds for code execution request",
description="Read timeout in seconds for code execution requests",
default=60.0,
)
CODE_EXECUTION_WRITE_TIMEOUT: Optional[float] = Field(
description="write timeout in seconds for code execution request",
description="Write timeout in seconds for code execution request",
default=10.0,
)
CODE_MAX_NUMBER: PositiveInt = Field(
description="max depth for code execution",
description="Maximum allowed numeric value in code execution",
default=9223372036854775807,
)
CODE_MIN_NUMBER: NegativeInt = Field(
description="",
description="Minimum allowed numeric value in code execution",
default=-9223372036854775807,
)
CODE_MAX_DEPTH: PositiveInt = Field(
description="max depth for code execution",
description="Maximum allowed depth for nested structures in code execution",
default=5,
)
CODE_MAX_PRECISION: PositiveInt = Field(
description="max precision digits for float type in code execution",
description="mMaximum number of decimal places for floating-point numbers in code execution",
default=20,
)
CODE_MAX_STRING_LENGTH: PositiveInt = Field(
description="max string length for code execution",
description="Maximum allowed length for strings in code execution",
default=80000,
)
CODE_MAX_STRING_ARRAY_LENGTH: PositiveInt = Field(
description="",
description="Maximum allowed length for string arrays in code execution",
default=30,
)
CODE_MAX_OBJECT_ARRAY_LENGTH: PositiveInt = Field(
description="",
description="Maximum allowed length for object arrays in code execution",
default=30,
)
CODE_MAX_NUMBER_ARRAY_LENGTH: PositiveInt = Field(
description="",
description="Maximum allowed length for numeric arrays in code execution",
default=1000,
)
@ -133,40 +132,39 @@ class PluginConfig(BaseSettings):
class EndpointConfig(BaseSettings):
"""
Module URL configs
Configuration for various application endpoints and URLs
"""
CONSOLE_API_URL: str = Field(
description="The backend URL prefix of the console API."
"used to concatenate the login authorization callback or notion integration callback.",
description="Base URL for the console API,"
"used for login authentication callback or notion integration callbacks",
default="",
)
CONSOLE_WEB_URL: str = Field(
description="The front-end URL prefix of the console web."
"used to concatenate some front-end addresses and for CORS configuration use.",
description="Base URL for the console web interface," "used for frontend references and CORS configuration",
default="",
)
SERVICE_API_URL: str = Field(
description="Service API Url prefix. used to display Service API Base Url to the front-end.",
description="Base URL for the service API, displayed to users for API access",
default="",
)
APP_WEB_URL: str = Field(
description="WebApp Url prefix. used to display WebAPP API Base Url to the front-end.",
description="Base URL for the web application, used for frontend references",
default="",
)
class FileAccessConfig(BaseSettings):
"""
File Access configs
Configuration for file access and handling
"""
FILES_URL: str = Field(
description="File preview or download Url prefix."
" used to display File preview or download Url to the front-end or as Multi-model inputs;"
description="Base URL for file preview or download,"
" used for frontend display and multi-model inputs"
"Url is signed and has expiration time.",
validation_alias=AliasChoices("FILES_URL", "CONSOLE_API_URL"),
alias_priority=1,
@ -174,49 +172,49 @@ class FileAccessConfig(BaseSettings):
)
FILES_ACCESS_TIMEOUT: int = Field(
description="timeout in seconds for file accessing",
description="Expiration time in seconds for file access URLs",
default=300,
)
class FileUploadConfig(BaseSettings):
"""
File Uploading configs
Configuration for file upload limitations
"""
UPLOAD_FILE_SIZE_LIMIT: NonNegativeInt = Field(
description="size limit in Megabytes for uploading files",
description="Maximum allowed file size for uploads in megabytes",
default=15,
)
UPLOAD_FILE_BATCH_LIMIT: NonNegativeInt = Field(
description="batch size limit for uploading files",
description="Maximum number of files allowed in a single upload batch",
default=5,
)
UPLOAD_IMAGE_FILE_SIZE_LIMIT: NonNegativeInt = Field(
description="image file size limit in Megabytes for uploading files",
description="Maximum allowed image file size for uploads in megabytes",
default=10,
)
BATCH_UPLOAD_LIMIT: NonNegativeInt = Field(
description="", # todo: to be clarified
description="Maximum number of files allowed in a batch upload operation",
default=20,
)
class HttpConfig(BaseSettings):
"""
HTTP configs
HTTP-related configurations for the application
"""
API_COMPRESSION_ENABLED: bool = Field(
description="whether to enable HTTP response compression of gzip",
description="Enable or disable gzip compression for HTTP responses",
default=False,
)
inner_CONSOLE_CORS_ALLOW_ORIGINS: str = Field(
description="",
description="Comma-separated list of allowed origins for CORS in the console",
validation_alias=AliasChoices("CONSOLE_CORS_ALLOW_ORIGINS", "CONSOLE_WEB_URL"),
default="",
)
@ -238,359 +236,360 @@ class HttpConfig(BaseSettings):
return self.inner_WEB_API_CORS_ALLOW_ORIGINS.split(",")
HTTP_REQUEST_MAX_CONNECT_TIMEOUT: Annotated[
PositiveInt, Field(ge=10, description="connect timeout in seconds for HTTP request")
PositiveInt, Field(ge=10, description="Maximum connection timeout in seconds for HTTP requests")
] = 10
HTTP_REQUEST_MAX_READ_TIMEOUT: Annotated[
PositiveInt, Field(ge=60, description="read timeout in seconds for HTTP request")
PositiveInt, Field(ge=60, description="Maximum read timeout in seconds for HTTP requests")
] = 60
HTTP_REQUEST_MAX_WRITE_TIMEOUT: Annotated[
PositiveInt, Field(ge=10, description="read timeout in seconds for HTTP request")
PositiveInt, Field(ge=10, description="Maximum write timeout in seconds for HTTP requests")
] = 20
HTTP_REQUEST_NODE_MAX_BINARY_SIZE: PositiveInt = Field(
description="",
description="Maximum allowed size in bytes for binary data in HTTP requests",
default=10 * 1024 * 1024,
)
HTTP_REQUEST_NODE_MAX_TEXT_SIZE: PositiveInt = Field(
description="",
description="Maximum allowed size in bytes for text data in HTTP requests",
default=1 * 1024 * 1024,
)
SSRF_PROXY_HTTP_URL: Optional[str] = Field(
description="HTTP URL for SSRF proxy",
description="Proxy URL for HTTP requests to prevent Server-Side Request Forgery (SSRF)",
default=None,
)
SSRF_PROXY_HTTPS_URL: Optional[str] = Field(
description="HTTPS URL for SSRF proxy",
description="Proxy URL for HTTPS requests to prevent Server-Side Request Forgery (SSRF)",
default=None,
)
class InnerAPIConfig(BaseSettings):
"""
Inner API configs
Configuration for internal API functionality
"""
INNER_API: bool = Field(
description="whether to enable the inner API",
description="Enable or disable the internal API",
default=False,
)
INNER_API_KEY: Optional[str] = Field(
description="The inner API key is used to authenticate the inner API",
description="API key for accessing the internal API",
default=None,
)
class LoggingConfig(BaseSettings):
"""
Logging configs
Configuration for application logging
"""
LOG_LEVEL: str = Field(
description="Log output level, default to INFO. It is recommended to set it to ERROR for production.",
description="Logging level, default to INFO. Set to ERROR for production environments.",
default="INFO",
)
LOG_FILE: Optional[str] = Field(
description="logging output file path",
description="File path for log output.",
default=None,
)
LOG_FORMAT: str = Field(
description="log format",
description="Format string for log messages",
default="%(asctime)s.%(msecs)03d %(levelname)s [%(threadName)s] [%(filename)s:%(lineno)d] - %(message)s",
)
LOG_DATEFORMAT: Optional[str] = Field(
description="log date format",
description="Date format string for log timestamps",
default=None,
)
LOG_TZ: Optional[str] = Field(
description="specify log timezone, eg: America/New_York",
description="Timezone for log timestamps (e.g., 'America/New_York')",
default=None,
)
class ModelLoadBalanceConfig(BaseSettings):
"""
Model load balance configs
Configuration for model load balancing
"""
MODEL_LB_ENABLED: bool = Field(
description="whether to enable model load balancing",
description="Enable or disable load balancing for models",
default=False,
)
class BillingConfig(BaseSettings):
"""
Platform Billing Configurations
Configuration for platform billing features
"""
BILLING_ENABLED: bool = Field(
description="whether to enable billing",
description="Enable or disable billing functionality",
default=False,
)
class UpdateConfig(BaseSettings):
"""
Update configs
Configuration for application update checks
"""
CHECK_UPDATE_URL: str = Field(
description="url for checking updates",
description="URL to check for application updates",
default="https://updates.dify.ai",
)
class WorkflowConfig(BaseSettings):
"""
Workflow feature configs
Configuration for workflow execution
"""
WORKFLOW_MAX_EXECUTION_STEPS: PositiveInt = Field(
description="max execution steps in single workflow execution",
description="Maximum number of steps allowed in a single workflow execution",
default=500,
)
WORKFLOW_MAX_EXECUTION_TIME: PositiveInt = Field(
description="max execution time in seconds in single workflow execution",
description="Maximum execution time in seconds for a single workflow",
default=1200,
)
WORKFLOW_CALL_MAX_DEPTH: PositiveInt = Field(
description="max depth of calling in single workflow execution",
description="Maximum allowed depth for nested workflow calls",
default=5,
)
MAX_VARIABLE_SIZE: PositiveInt = Field(
description="The maximum size in bytes of a variable. default to 5KB.",
description="Maximum size in bytes for a single variable in workflows. Default to 5KB.",
default=5 * 1024,
)
class OAuthConfig(BaseSettings):
"""
oauth configs
Configuration for OAuth authentication
"""
OAUTH_REDIRECT_PATH: str = Field(
description="redirect path for OAuth",
description="Redirect path for OAuth authentication callbacks",
default="/console/api/oauth/authorize",
)
GITHUB_CLIENT_ID: Optional[str] = Field(
description="GitHub client id for OAuth",
description="GitHub OAuth client secret",
default=None,
)
GITHUB_CLIENT_SECRET: Optional[str] = Field(
description="GitHub client secret key for OAuth",
description="GitHub OAuth client secret",
default=None,
)
GOOGLE_CLIENT_ID: Optional[str] = Field(
description="Google client id for OAuth",
description="Google OAuth client ID",
default=None,
)
GOOGLE_CLIENT_SECRET: Optional[str] = Field(
description="Google client secret key for OAuth",
description="Google OAuth client secret",
default=None,
)
class ModerationConfig(BaseSettings):
"""
Moderation in app configs.
Configuration for content moderation
"""
MODERATION_BUFFER_SIZE: PositiveInt = Field(
description="buffer size for moderation",
description="Size of the buffer for content moderation processing",
default=300,
)
class ToolConfig(BaseSettings):
"""
Tool configs
Configuration for tool management
"""
TOOL_ICON_CACHE_MAX_AGE: PositiveInt = Field(
description="max age in seconds for tool icon caching",
description="Maximum age in seconds for caching tool icons",
default=3600,
)
class MailConfig(BaseSettings):
"""
Mail Configurations
Configuration for email services
"""
MAIL_TYPE: Optional[str] = Field(
description="Mail provider type name, default to None, available values are `smtp` and `resend`.",
description="Email service provider type ('smtp' or 'resend'), default to None.",
default=None,
)
MAIL_DEFAULT_SEND_FROM: Optional[str] = Field(
description="default email address for sending from ",
description="Default email address to use as the sender",
default=None,
)
RESEND_API_KEY: Optional[str] = Field(
description="API key for Resend",
description="API key for Resend email service",
default=None,
)
RESEND_API_URL: Optional[str] = Field(
description="API URL for Resend",
description="API URL for Resend email service",
default=None,
)
SMTP_SERVER: Optional[str] = Field(
description="smtp server host",
description="SMTP server hostname",
default=None,
)
SMTP_PORT: Optional[int] = Field(
description="smtp server port",
description="SMTP server port number",
default=465,
)
SMTP_USERNAME: Optional[str] = Field(
description="smtp server username",
description="Username for SMTP authentication",
default=None,
)
SMTP_PASSWORD: Optional[str] = Field(
description="smtp server password",
description="Password for SMTP authentication",
default=None,
)
SMTP_USE_TLS: bool = Field(
description="whether to use TLS connection to smtp server",
description="Enable TLS encryption for SMTP connections",
default=False,
)
SMTP_OPPORTUNISTIC_TLS: bool = Field(
description="whether to use opportunistic TLS connection to smtp server",
description="Enable opportunistic TLS for SMTP connections",
default=False,
)
class RagEtlConfig(BaseSettings):
"""
RAG ETL Configurations.
Configuration for RAG ETL processes
"""
ETL_TYPE: str = Field(
description="RAG ETL type name, default to `dify`, available values are `dify` and `Unstructured`. ",
description="RAG ETL type ('dify' or 'Unstructured'), default to 'dify'",
default="dify",
)
KEYWORD_DATA_SOURCE_TYPE: str = Field(
description="source type for keyword data, default to `database`, available values are `database` .",
description="Data source type for keyword extraction"
" ('database' or other supported types), default to 'database'",
default="database",
)
UNSTRUCTURED_API_URL: Optional[str] = Field(
description="API URL for Unstructured",
description="API URL for Unstructured.io service",
default=None,
)
UNSTRUCTURED_API_KEY: Optional[str] = Field(
description="API key for Unstructured",
description="API key for Unstructured.io service",
default=None,
)
class DataSetConfig(BaseSettings):
"""
Dataset configs
Configuration for dataset management
"""
CLEAN_DAY_SETTING: PositiveInt = Field(
description="interval in days for cleaning up dataset",
description="Interval in days for dataset cleanup operations",
default=30,
)
DATASET_OPERATOR_ENABLED: bool = Field(
description="whether to enable dataset operator",
description="Enable or disable dataset operator functionality",
default=False,
)
class WorkspaceConfig(BaseSettings):
"""
Workspace configs
Configuration for workspace management
"""
INVITE_EXPIRY_HOURS: PositiveInt = Field(
description="workspaces invitation expiration in hours",
description="Expiration time in hours for workspace invitation links",
default=72,
)
class IndexingConfig(BaseSettings):
"""
Indexing configs.
Configuration for indexing operations
"""
INDEXING_MAX_SEGMENTATION_TOKENS_LENGTH: PositiveInt = Field(
description="max segmentation token length for indexing",
description="Maximum token length for text segmentation during indexing",
default=1000,
)
class ImageFormatConfig(BaseSettings):
MULTIMODAL_SEND_IMAGE_FORMAT: str = Field(
description="multi model send image format, support base64, url, default is base64",
description="Format for sending images in multimodal contexts ('base64' or 'url'), default is base64",
default="base64",
)
class CeleryBeatConfig(BaseSettings):
CELERY_BEAT_SCHEDULER_TIME: int = Field(
description="the time of the celery scheduler, default to 1 day",
description="Interval in days for Celery Beat scheduler execution, default to 1 day",
default=1,
)
class PositionConfig(BaseSettings):
POSITION_PROVIDER_PINS: str = Field(
description="The heads of model providers",
description="Comma-separated list of pinned model providers",
default="",
)
POSITION_PROVIDER_INCLUDES: str = Field(
description="The included model providers",
description="Comma-separated list of included model providers",
default="",
)
POSITION_PROVIDER_EXCLUDES: str = Field(
description="The excluded model providers",
description="Comma-separated list of excluded model providers",
default="",
)
POSITION_TOOL_PINS: str = Field(
description="The heads of tools",
description="Comma-separated list of pinned tools",
default="",
)
POSITION_TOOL_INCLUDES: str = Field(
description="The included tools",
description="Comma-separated list of included tools",
default="",
)
POSITION_TOOL_EXCLUDES: str = Field(
description="The excluded tools",
description="Comma-separated list of excluded tools",
default="",
)

66
api/configs/feature/hosted_service/__init__.py
View File

@ -6,31 +6,31 @@ from pydantic_settings import BaseSettings
class HostedOpenAiConfig(BaseSettings):
"""
Hosted OpenAI service config
Configuration for hosted OpenAI service
"""
HOSTED_OPENAI_API_KEY: Optional[str] = Field(
description="",
description="API key for hosted OpenAI service",
default=None,
)
HOSTED_OPENAI_API_BASE: Optional[str] = Field(
description="",
description="Base URL for hosted OpenAI API",
default=None,
)
HOSTED_OPENAI_API_ORGANIZATION: Optional[str] = Field(
description="",
description="Organization ID for hosted OpenAI service",
default=None,
)
HOSTED_OPENAI_TRIAL_ENABLED: bool = Field(
description="",
description="Enable trial access to hosted OpenAI service",
default=False,
)
HOSTED_OPENAI_TRIAL_MODELS: str = Field(
description="",
description="Comma-separated list of available models for trial access",
default="gpt-3.5-turbo,"
"gpt-3.5-turbo-1106,"
"gpt-3.5-turbo-instruct,"
@ -42,17 +42,17 @@ class HostedOpenAiConfig(BaseSettings):
)
HOSTED_OPENAI_QUOTA_LIMIT: NonNegativeInt = Field(
description="",
description="Quota limit for hosted OpenAI service usage",
default=200,
)
HOSTED_OPENAI_PAID_ENABLED: bool = Field(
description="",
description="Enable paid access to hosted OpenAI service",
default=False,
)
HOSTED_OPENAI_PAID_MODELS: str = Field(
description="",
description="Comma-separated list of available models for paid access",
default="gpt-4,"
"gpt-4-turbo-preview,"
"gpt-4-turbo-2024-04-09,"
@ -71,124 +71,122 @@ class HostedOpenAiConfig(BaseSettings):
class HostedAzureOpenAiConfig(BaseSettings):
"""
Hosted OpenAI service config
Configuration for hosted Azure OpenAI service
"""
HOSTED_AZURE_OPENAI_ENABLED: bool = Field(
description="",
description="Enable hosted Azure OpenAI service",
default=False,
)
HOSTED_AZURE_OPENAI_API_KEY: Optional[str] = Field(
description="",
description="API key for hosted Azure OpenAI service",
default=None,
)
HOSTED_AZURE_OPENAI_API_BASE: Optional[str] = Field(
description="",
description="Base URL for hosted Azure OpenAI API",
default=None,
)
HOSTED_AZURE_OPENAI_QUOTA_LIMIT: NonNegativeInt = Field(
description="",
description="Quota limit for hosted Azure OpenAI service usage",
default=200,
)
class HostedAnthropicConfig(BaseSettings):
"""
Hosted Azure OpenAI service config
Configuration for hosted Anthropic service
"""
HOSTED_ANTHROPIC_API_BASE: Optional[str] = Field(
description="",
description="Base URL for hosted Anthropic API",
default=None,
)
HOSTED_ANTHROPIC_API_KEY: Optional[str] = Field(
description="",
description="API key for hosted Anthropic service",
default=None,
)
HOSTED_ANTHROPIC_TRIAL_ENABLED: bool = Field(
description="",
description="Enable trial access to hosted Anthropic service",
default=False,
)
HOSTED_ANTHROPIC_QUOTA_LIMIT: NonNegativeInt = Field(
description="",
description="Quota limit for hosted Anthropic service usage",
default=600000,
)
HOSTED_ANTHROPIC_PAID_ENABLED: bool = Field(
description="",
description="Enable paid access to hosted Anthropic service",
default=False,
)
class HostedMinmaxConfig(BaseSettings):
"""
Hosted Minmax service config
Configuration for hosted Minmax service
"""
HOSTED_MINIMAX_ENABLED: bool = Field(
description="",
description="Enable hosted Minmax service",
default=False,
)
class HostedSparkConfig(BaseSettings):
"""
Hosted Spark service config
Configuration for hosted Spark service
"""
HOSTED_SPARK_ENABLED: bool = Field(
description="",
description="Enable hosted Spark service",
default=False,
)
class HostedZhipuAIConfig(BaseSettings):
"""
Hosted Minmax service config
Configuration for hosted ZhipuAI service
"""
HOSTED_ZHIPUAI_ENABLED: bool = Field(
description="",
description="Enable hosted ZhipuAI service",
default=False,
)
class HostedModerationConfig(BaseSettings):
"""
Hosted Moderation service config
Configuration for hosted Moderation service
"""
HOSTED_MODERATION_ENABLED: bool = Field(
description="",
description="Enable hosted Moderation service",
default=False,
)
HOSTED_MODERATION_PROVIDERS: str = Field(
description="",
description="Comma-separated list of moderation providers",
default="",
)
class HostedFetchAppTemplateConfig(BaseSettings):
"""
Hosted Moderation service config
Configuration for fetching app templates
"""
HOSTED_FETCH_APP_TEMPLATES_MODE: str = Field(
description="the mode for fetching app templates,"
" default to remote,"
" available values: remote, db, builtin",
description="Mode for fetching app templates: remote, db, or builtin" " default to remote,",
default="remote",
)
HOSTED_FETCH_APP_TEMPLATES_REMOTE_DOMAIN: str = Field(
description="the domain for fetching remote app templates",
description="Domain for fetching remote app templates",
default="https://tmpl.dify.ai",
)

49
api/configs/middleware/__init__.py
View File

@ -31,70 +31,71 @@ from configs.middleware.vdb.weaviate_config import WeaviateConfig
class StorageConfig(BaseSettings):
STORAGE_TYPE: str = Field(
description="storage type,"
" default to `local`,"
" available values are `local`, `s3`, `azure-blob`, `aliyun-oss`, `google-storage`.",
description="Type of storage to use."
" Options: 'local', 's3', 'azure-blob', 'aliyun-oss', 'google-storage'. Default is 'local'.",
default="local",
)
STORAGE_LOCAL_PATH: str = Field(
description="local storage path",
description="Path for local storage when STORAGE_TYPE is set to 'local'.",
default="storage",
)
class VectorStoreConfig(BaseSettings):
VECTOR_STORE: Optional[str] = Field(
description="vector store type",
description="Type of vector store to use for efficient similarity search."
" Set to None if not using a vector store.",
default=None,
)
class KeywordStoreConfig(BaseSettings):
KEYWORD_STORE: str = Field(
description="keyword store type",
description="Method for keyword extraction and storage."
" Default is 'jieba', a Chinese text segmentation library.",
default="jieba",
)
class DatabaseConfig:
DB_HOST: str = Field(
description="db host",
description="Hostname or IP address of the database server.",
default="localhost",
)
DB_PORT: PositiveInt = Field(
description="db port",
description="Port number for database connection.",
default=5432,
)
DB_USERNAME: str = Field(
description="db username",
description="Username for database authentication.",
default="postgres",
)
DB_PASSWORD: str = Field(
description="db password",
description="Password for database authentication.",
default="",
)
DB_DATABASE: str = Field(
description="db database",
description="Name of the database to connect to.",
default="dify",
)
DB_CHARSET: str = Field(
description="db charset",
description="Character set for database connection.",
default="",
)
DB_EXTRAS: str = Field(
description="db extras options. Example: keepalives_idle=60&keepalives=1",
description="Additional database connection parameters. Example: 'keepalives_idle=60&keepalives=1'",
default="",
)
SQLALCHEMY_DATABASE_URI_SCHEME: str = Field(
description="db uri scheme",
description="Database URI scheme for SQLAlchemy connection.",
default="postgresql",
)
@ -112,27 +113,27 @@ class DatabaseConfig:
)
SQLALCHEMY_POOL_SIZE: NonNegativeInt = Field(
description="pool size of SqlAlchemy",
description="Maximum number of database connections in the pool.",
default=30,
)
SQLALCHEMY_MAX_OVERFLOW: NonNegativeInt = Field(
description="max overflows for SqlAlchemy",
description="Maximum number of connections that can be created beyond the pool_size.",
default=10,
)
SQLALCHEMY_POOL_RECYCLE: NonNegativeInt = Field(
description="SqlAlchemy pool recycle",
description="Number of seconds after which a connection is automatically recycled.",
default=3600,
)
SQLALCHEMY_POOL_PRE_PING: bool = Field(
description="whether to enable pool pre-ping in SqlAlchemy",
description="If True, enables connection pool pre-ping feature to check connections.",
default=False,
)
SQLALCHEMY_ECHO: bool | str = Field(
description="whether to enable SqlAlchemy echo",
description="If True, SQLAlchemy will log all SQL statements.",
default=False,
)
@ -150,27 +151,27 @@ class DatabaseConfig:
class CeleryConfig(DatabaseConfig):
CELERY_BACKEND: str = Field(
description="Celery backend, available values are `database`, `redis`",
description="Backend for Celery task results. Options: 'database', 'redis'.",
default="database",
)
CELERY_BROKER_URL: Optional[str] = Field(
description="CELERY_BROKER_URL",
description="URL of the message broker for Celery tasks.",
default=None,
)
CELERY_USE_SENTINEL: Optional[bool] = Field(
description="Whether to use Redis Sentinel mode",
description="Whether to use Redis Sentinel for high availability.",
default=False,
)
CELERY_SENTINEL_MASTER_NAME: Optional[str] = Field(
description="Redis Sentinel master name",
description="Name of the Redis Sentinel master.",
default=None,
)
CELERY_SENTINEL_SOCKET_TIMEOUT: Optional[PositiveFloat] = Field(
description="Redis Sentinel socket timeout",
description="Timeout for Redis Sentinel socket operations in seconds.",
default=0.1,
)

26
api/configs/middleware/cache/redis_config.py vendored
View File

@ -6,65 +6,65 @@ from pydantic_settings import BaseSettings
class RedisConfig(BaseSettings):
"""
Redis configs
Configuration settings for Redis connection
"""
REDIS_HOST: str = Field(
description="Redis host",
description="Hostname or IP address of the Redis server",
default="localhost",
)
REDIS_PORT: PositiveInt = Field(
description="Redis port",
description="Port number on which the Redis server is listening",
default=6379,
)
REDIS_USERNAME: Optional[str] = Field(
description="Redis username",
description="Username for Redis authentication (if required)",
default=None,
)
REDIS_PASSWORD: Optional[str] = Field(
description="Redis password",
description="Password for Redis authentication (if required)",
default=None,
)
REDIS_DB: NonNegativeInt = Field(
description="Redis database id, default to 0",
description="Redis database number to use (0-15)",
default=0,
)
REDIS_USE_SSL: bool = Field(
description="whether to use SSL for Redis connection",
description="Enable SSL/TLS for the Redis connection",
default=False,
)
REDIS_USE_SENTINEL: Optional[bool] = Field(
description="Whether to use Redis Sentinel mode",
description="Enable Redis Sentinel mode for high availability",
default=False,
)
REDIS_SENTINELS: Optional[str] = Field(
description="Redis Sentinel nodes",
description="Comma-separated list of Redis Sentinel nodes (host:port)",
default=None,
)
REDIS_SENTINEL_SERVICE_NAME: Optional[str] = Field(
description="Redis Sentinel service name",
description="Name of the Redis Sentinel service to monitor",
default=None,
)
REDIS_SENTINEL_USERNAME: Optional[str] = Field(
description="Redis Sentinel username",
description="Username for Redis Sentinel authentication (if required)",
default=None,
)
REDIS_SENTINEL_PASSWORD: Optional[str] = Field(
description="Redis Sentinel password",
description="Password for Redis Sentinel authentication (if required)",
default=None,
)
REDIS_SENTINEL_SOCKET_TIMEOUT: Optional[PositiveFloat] = Field(
description="Redis Sentinel socket timeout",
description="Socket timeout in seconds for Redis Sentinel connections",
default=0.1,
)

16
api/configs/middleware/storage/aliyun_oss_storage_config.py
View File

@ -6,40 +6,40 @@ from pydantic_settings import BaseSettings
class AliyunOSSStorageConfig(BaseSettings):
"""
Aliyun storage configs
Configuration settings for Aliyun Object Storage Service (OSS)
"""
ALIYUN_OSS_BUCKET_NAME: Optional[str] = Field(
description="Aliyun OSS bucket name",
description="Name of the Aliyun OSS bucket to store and retrieve objects",
default=None,
)
ALIYUN_OSS_ACCESS_KEY: Optional[str] = Field(
description="Aliyun OSS access key",
description="Access key ID for authenticating with Aliyun OSS",
default=None,
)
ALIYUN_OSS_SECRET_KEY: Optional[str] = Field(
description="Aliyun OSS secret key",
description="Secret access key for authenticating with Aliyun OSS",
default=None,
)
ALIYUN_OSS_ENDPOINT: Optional[str] = Field(
description="Aliyun OSS endpoint URL",
description="URL of the Aliyun OSS endpoint for your chosen region",
default=None,
)
ALIYUN_OSS_REGION: Optional[str] = Field(
description="Aliyun OSS region",
description="Aliyun OSS region where your bucket is located (e.g., 'oss-cn-hangzhou')",
default=None,
)
ALIYUN_OSS_AUTH_VERSION: Optional[str] = Field(
description="Aliyun OSS authentication version",
description="Version of the authentication protocol to use with Aliyun OSS (e.g., 'v4')",
default=None,
)
ALIYUN_OSS_PATH: Optional[str] = Field(
description="Aliyun OSS path",
description="Base path within the bucket to store objects (e.g., 'my-app-data/')",
default=None,
)

16
api/configs/middleware/storage/amazon_s3_storage_config.py
View File

@ -6,40 +6,40 @@ from pydantic_settings import BaseSettings
class S3StorageConfig(BaseSettings):
"""
S3 storage configs
Configuration settings for S3-compatible object storage
"""
S3_ENDPOINT: Optional[str] = Field(
description="S3 storage endpoint",
description="URL of the S3-compatible storage endpoint (e.g., 'https://s3.amazonaws.com')",
default=None,
)
S3_REGION: Optional[str] = Field(
description="S3 storage region",
description="Region where the S3 bucket is located (e.g., 'us-east-1')",
default=None,
)
S3_BUCKET_NAME: Optional[str] = Field(
description="S3 storage bucket name",
description="Name of the S3 bucket to store and retrieve objects",
default=None,
)
S3_ACCESS_KEY: Optional[str] = Field(
description="S3 storage access key",
description="Access key ID for authenticating with the S3 service",
default=None,
)
S3_SECRET_KEY: Optional[str] = Field(
description="S3 storage secret key",
description="Secret access key for authenticating with the S3 service",
default=None,
)
S3_ADDRESS_STYLE: str = Field(
description="S3 storage address style",
description="S3 addressing style: 'auto', 'path', or 'virtual'",
default="auto",
)
S3_USE_AWS_MANAGED_IAM: bool = Field(
description="whether to use aws managed IAM for S3",
description="Use AWS managed IAM roles for authentication instead of access/secret keys",
default=False,
)

10
api/configs/middleware/storage/azure_blob_storage_config.py
View File

@ -6,25 +6,25 @@ from pydantic_settings import BaseSettings
class AzureBlobStorageConfig(BaseSettings):
"""
Azure Blob storage configs
Configuration settings for Azure Blob Storage
"""
AZURE_BLOB_ACCOUNT_NAME: Optional[str] = Field(
description="Azure Blob account name",
description="Name of the Azure Storage account (e.g., 'mystorageaccount')",
default=None,
)
AZURE_BLOB_ACCOUNT_KEY: Optional[str] = Field(
description="Azure Blob account key",
description="Access key for authenticating with the Azure Storage account",
default=None,
)
AZURE_BLOB_CONTAINER_NAME: Optional[str] = Field(
description="Azure Blob container name",
description="Name of the Azure Blob container to store and retrieve objects",
default=None,
)
AZURE_BLOB_ACCOUNT_URL: Optional[str] = Field(
description="Azure Blob account URL",
description="URL of the Azure Blob storage endpoint (e.g., 'https://mystorageaccount.blob.core.windows.net')",
default=None,
)

6
api/configs/middleware/storage/google_cloud_storage_config.py
View File

@ -6,15 +6,15 @@ from pydantic_settings import BaseSettings
class GoogleCloudStorageConfig(BaseSettings):
"""
Google Cloud storage configs
Configuration settings for Google Cloud Storage
"""
GOOGLE_STORAGE_BUCKET_NAME: Optional[str] = Field(
description="Google Cloud storage bucket name",
description="Name of the Google Cloud Storage bucket to store and retrieve objects (e.g., 'my-gcs-bucket')",
default=None,
)
GOOGLE_STORAGE_SERVICE_ACCOUNT_JSON_BASE64: Optional[str] = Field(
description="Google Cloud storage service account json base64",
description="Base64-encoded JSON key file for Google Cloud service account authentication",
default=None,
)

10
api/configs/middleware/storage/huawei_obs_storage_config.py
View File

@ -5,25 +5,25 @@ from pydantic import BaseModel, Field
class HuaweiCloudOBSStorageConfig(BaseModel):
"""
Huawei Cloud OBS storage configs
Configuration settings for Huawei Cloud Object Storage Service (OBS)
"""
HUAWEI_OBS_BUCKET_NAME: Optional[str] = Field(
description="Huawei Cloud OBS bucket name",
description="Name of the Huawei Cloud OBS bucket to store and retrieve objects (e.g., 'my-obs-bucket')",
default=None,
)
HUAWEI_OBS_ACCESS_KEY: Optional[str] = Field(
description="Huawei Cloud OBS Access key",
description="Access Key ID for authenticating with Huawei Cloud OBS",
default=None,
)
HUAWEI_OBS_SECRET_KEY: Optional[str] = Field(
description="Huawei Cloud OBS Secret key",
description="Secret Access Key for authenticating with Huawei Cloud OBS",
default=None,
)
HUAWEI_OBS_SERVER: Optional[str] = Field(
description="Huawei Cloud OBS server URL",
description="Endpoint URL for Huawei Cloud OBS (e.g., 'https://obs.cn-north-4.myhuaweicloud.com')",
default=None,
)

12
api/configs/middleware/storage/oci_storage_config.py
View File

@ -6,30 +6,30 @@ from pydantic_settings import BaseSettings
class OCIStorageConfig(BaseSettings):
"""
OCI storage configs
Configuration settings for Oracle Cloud Infrastructure (OCI) Object Storage
"""
OCI_ENDPOINT: Optional[str] = Field(
description="OCI storage endpoint",
description="URL of the OCI Object Storage endpoint (e.g., 'https://objectstorage.us-phoenix-1.oraclecloud.com')",
default=None,
)
OCI_REGION: Optional[str] = Field(
description="OCI storage region",
description="OCI region where the bucket is located (e.g., 'us-phoenix-1')",
default=None,
)
OCI_BUCKET_NAME: Optional[str] = Field(
description="OCI storage bucket name",
description="Name of the OCI Object Storage bucket to store and retrieve objects (e.g., 'my-oci-bucket')",
default=None,
)
OCI_ACCESS_KEY: Optional[str] = Field(
description="OCI storage access key",
description="Access key (also known as API key) for authenticating with OCI Object Storage",
default=None,
)
OCI_SECRET_KEY: Optional[str] = Field(
description="OCI storage secret key",
description="Secret key associated with the access key for authenticating with OCI Object Storage",
default=None,
)

12
api/configs/middleware/storage/tencent_cos_storage_config.py
View File

@ -6,30 +6,30 @@ from pydantic_settings import BaseSettings
class TencentCloudCOSStorageConfig(BaseSettings):
"""
Tencent Cloud COS storage configs
Configuration settings for Tencent Cloud Object Storage (COS)
"""
TENCENT_COS_BUCKET_NAME: Optional[str] = Field(
description="Tencent Cloud COS bucket name",
description="Name of the Tencent Cloud COS bucket to store and retrieve objects",
default=None,
)
TENCENT_COS_REGION: Optional[str] = Field(
description="Tencent Cloud COS region",
description="Tencent Cloud region where the COS bucket is located (e.g., 'ap-guangzhou')",
default=None,
)
TENCENT_COS_SECRET_ID: Optional[str] = Field(
description="Tencent Cloud COS secret id",
description="SecretId for authenticating with Tencent Cloud COS (part of API credentials)",
default=None,
)
TENCENT_COS_SECRET_KEY: Optional[str] = Field(
description="Tencent Cloud COS secret key",
description="SecretKey for authenticating with Tencent Cloud COS (part of API credentials)",
default=None,
)
TENCENT_COS_SCHEME: Optional[str] = Field(
description="Tencent Cloud COS scheme",
description="Protocol scheme for COS requests: 'https' (recommended) or 'http'",
default=None,
)

12
api/configs/middleware/storage/volcengine_tos_storage_config.py
View File

@ -5,30 +5,30 @@ from pydantic import BaseModel, Field
class VolcengineTOSStorageConfig(BaseModel):
"""
Volcengine tos storage configs
Configuration settings for Volcengine Tinder Object Storage (TOS)
"""
VOLCENGINE_TOS_BUCKET_NAME: Optional[str] = Field(
description="Volcengine TOS Bucket Name",
description="Name of the Volcengine TOS bucket to store and retrieve objects (e.g., 'my-tos-bucket')",
default=None,
)
VOLCENGINE_TOS_ACCESS_KEY: Optional[str] = Field(
description="Volcengine TOS Access Key",
description="Access Key ID for authenticating with Volcengine TOS",
default=None,
)
VOLCENGINE_TOS_SECRET_KEY: Optional[str] = Field(
description="Volcengine TOS Secret Key",
description="Secret Access Key for authenticating with Volcengine TOS",
default=None,
)
VOLCENGINE_TOS_ENDPOINT: Optional[str] = Field(
description="Volcengine TOS Endpoint URL",
description="URL of the Volcengine TOS endpoint (e.g., 'https://tos-cn-beijing.volces.com')",
default=None,
)
VOLCENGINE_TOS_REGION: Optional[str] = Field(
description="Volcengine TOS Region",
description="Volcengine region where the TOS bucket is located (e.g., 'cn-beijing')",
default=None,
)

23
api/configs/middleware/vdb/analyticdb_config.py
View File

@ -5,33 +5,38 @@ from pydantic import BaseModel, Field
class AnalyticdbConfig(BaseModel):
"""
Configuration for connecting to AnalyticDB.
Configuration for connecting to Alibaba Cloud AnalyticDB for PostgreSQL.
Refer to the following documentation for details on obtaining credentials:
https://www.alibabacloud.com/help/en/analyticdb-for-postgresql/getting-started/create-an-instance-instances-with-vector-engine-optimization-enabled
"""
ANALYTICDB_KEY_ID: Optional[str] = Field(
default=None, description="The Access Key ID provided by Alibaba Cloud for authentication."
default=None, description="The Access Key ID provided by Alibaba Cloud for API authentication."
)
ANALYTICDB_KEY_SECRET: Optional[str] = Field(
default=None, description="The Secret Access Key corresponding to the Access Key ID for secure access."
default=None, description="The Secret Access Key corresponding to the Access Key ID for secure API access."
)
ANALYTICDB_REGION_ID: Optional[str] = Field(
default=None, description="The region where the AnalyticDB instance is deployed (e.g., 'cn-hangzhou')."
default=None,
description="The region where the AnalyticDB instance is deployed (e.g., 'cn-hangzhou', 'ap-southeast-1').",
)
ANALYTICDB_INSTANCE_ID: Optional[str] = Field(
default=None,
description="The unique identifier of the AnalyticDB instance you want to connect to (e.g., 'gp-ab123456')..",
description="The unique identifier of the AnalyticDB instance you want to connect to.",
)
ANALYTICDB_ACCOUNT: Optional[str] = Field(
default=None, description="The account name used to log in to the AnalyticDB instance."
default=None,
description="The account name used to log in to the AnalyticDB instance"
" (usually the initial account created with the instance).",
)
ANALYTICDB_PASSWORD: Optional[str] = Field(
default=None, description="The password associated with the AnalyticDB account for authentication."
default=None, description="The password associated with the AnalyticDB account for database authentication."
)
ANALYTICDB_NAMESPACE: Optional[str] = Field(
default=None, description="The namespace within AnalyticDB for schema isolation."
default=None, description="The namespace within AnalyticDB for schema isolation (if using namespace feature)."
)
ANALYTICDB_NAMESPACE_PASSWORD: Optional[str] = Field(
default=None, description="The password for accessing the specified namespace within the AnalyticDB instance."
default=None,
description="The password for accessing the specified namespace within the AnalyticDB instance"
" (if namespace feature is enabled).",
)

14
api/configs/middleware/vdb/chroma_config.py
View File

@ -6,35 +6,35 @@ from pydantic_settings import BaseSettings
class ChromaConfig(BaseSettings):
"""
Chroma configs
Configuration settings for Chroma vector database
"""
CHROMA_HOST: Optional[str] = Field(
description="Chroma host",
description="Hostname or IP address of the Chroma server (e.g., 'localhost' or '192.168.1.100')",
default=None,
)
CHROMA_PORT: PositiveInt = Field(
description="Chroma port",
description="Port number on which the Chroma server is listening (default is 8000)",
default=8000,
)
CHROMA_TENANT: Optional[str] = Field(
description="Chroma database",
description="Tenant identifier for multi-tenancy support in Chroma",
default=None,
)
CHROMA_DATABASE: Optional[str] = Field(
description="Chroma database",
description="Name of the Chroma database to connect to",
default=None,
)
CHROMA_AUTH_PROVIDER: Optional[str] = Field(
description="Chroma authentication provider",
description="Authentication provider for Chroma (e.g., 'basic', 'token', or a custom provider)",
default=None,
)
CHROMA_AUTH_CREDENTIALS: Optional[str] = Field(
description="Chroma authentication credentials",
description="Authentication credentials for Chroma (format depends on the auth provider)",
default=None,
)

10
api/configs/middleware/vdb/elasticsearch_config.py
View File

@ -6,25 +6,25 @@ from pydantic_settings import BaseSettings
class ElasticsearchConfig(BaseSettings):
"""
Elasticsearch configs
Configuration settings for Elasticsearch
"""
ELASTICSEARCH_HOST: Optional[str] = Field(
description="Elasticsearch host",
description="Hostname or IP address of the Elasticsearch server (e.g., 'localhost' or '192.168.1.100')",
default="127.0.0.1",
)
ELASTICSEARCH_PORT: PositiveInt = Field(
description="Elasticsearch port",
description="Port number on which the Elasticsearch server is listening (default is 9200)",
default=9200,
)
ELASTICSEARCH_USERNAME: Optional[str] = Field(
description="Elasticsearch username",
description="Username for authenticating with Elasticsearch (default is 'elastic')",
default="elastic",
)
ELASTICSEARCH_PASSWORD: Optional[str] = Field(
description="Elasticsearch password",
description="Password for authenticating with Elasticsearch (default is 'elastic')",
default="elastic",
)

12
api/configs/middleware/vdb/milvus_config.py
View File

@ -6,30 +6,30 @@ from pydantic_settings import BaseSettings
class MilvusConfig(BaseSettings):
"""
Milvus configs
Configuration settings for Milvus vector database
"""
MILVUS_URI: Optional[str] = Field(
description="Milvus uri",
description="URI for connecting to the Milvus server (e.g., 'http://localhost:19530' or 'https://milvus-instance.example.com:19530')",
default="http://127.0.0.1:19530",
)
MILVUS_TOKEN: Optional[str] = Field(
description="Milvus token",
description="Authentication token for Milvus, if token-based authentication is enabled",
default=None,
)
MILVUS_USER: Optional[str] = Field(
description="Milvus user",
description="Username for authenticating with Milvus, if username/password authentication is enabled",
default=None,
)
MILVUS_PASSWORD: Optional[str] = Field(
description="Milvus password",
description="Password for authenticating with Milvus, if username/password authentication is enabled",
default=None,
)
MILVUS_DATABASE: str = Field(
description="Milvus database, default to `default`",
description="Name of the Milvus database to connect to (default is 'default')",
default="default",
)

14
api/configs/middleware/vdb/myscale_config.py
View File

@ -3,35 +3,35 @@ from pydantic import BaseModel, Field, PositiveInt
class MyScaleConfig(BaseModel):
"""
MyScale configs
Configuration settings for MyScale vector database
"""
MYSCALE_HOST: str = Field(
description="MyScale host",
description="Hostname or IP address of the MyScale server (e.g., 'localhost' or 'myscale.example.com')",
default="localhost",
)
MYSCALE_PORT: PositiveInt = Field(
description="MyScale port",
description="Port number on which the MyScale server is listening (default is 8123)",
default=8123,
)
MYSCALE_USER: str = Field(
description="MyScale user",
description="Username for authenticating with MyScale (default is 'default')",
default="default",
)
MYSCALE_PASSWORD: str = Field(
description="MyScale password",
description="Password for authenticating with MyScale (default is an empty string)",
default="",
)
MYSCALE_DATABASE: str = Field(
description="MyScale database name",
description="Name of the MyScale database to connect to (default is 'default')",
default="default",
)
MYSCALE_FTS_PARAMS: str = Field(
description="MyScale fts index parameters",
description="Additional parameters for MyScale Full Text Search index)",
default="",
)

12
api/configs/middleware/vdb/opensearch_config.py
View File

@ -6,30 +6,30 @@ from pydantic_settings import BaseSettings
class OpenSearchConfig(BaseSettings):
"""
OpenSearch configs
Configuration settings for OpenSearch
"""
OPENSEARCH_HOST: Optional[str] = Field(
description="OpenSearch host",
description="Hostname or IP address of the OpenSearch server (e.g., 'localhost' or 'opensearch.example.com')",
default=None,
)
OPENSEARCH_PORT: PositiveInt = Field(
description="OpenSearch port",
description="Port number on which the OpenSearch server is listening (default is 9200)",
default=9200,
)
OPENSEARCH_USER: Optional[str] = Field(
description="OpenSearch user",
description="Username for authenticating with OpenSearch",
default=None,
)
OPENSEARCH_PASSWORD: Optional[str] = Field(
description="OpenSearch password",
description="Password for authenticating with OpenSearch",
default=None,
)
OPENSEARCH_SECURE: bool = Field(
description="whether to use SSL connection for OpenSearch",
description="Whether to use SSL/TLS encrypted connection for OpenSearch (True for HTTPS, False for HTTP)",
default=False,
)

12
api/configs/middleware/vdb/oracle_config.py
View File

@ -6,30 +6,30 @@ from pydantic_settings import BaseSettings
class OracleConfig(BaseSettings):
"""
ORACLE configs
Configuration settings for Oracle database
"""
ORACLE_HOST: Optional[str] = Field(
description="ORACLE host",
description="Hostname or IP address of the Oracle database server (e.g., 'localhost' or 'oracle.example.com')",
default=None,
)
ORACLE_PORT: Optional[PositiveInt] = Field(
description="ORACLE port",
description="Port number on which the Oracle database server is listening (default is 1521)",
default=1521,
)
ORACLE_USER: Optional[str] = Field(
description="ORACLE user",
description="Username for authenticating with the Oracle database",
default=None,
)
ORACLE_PASSWORD: Optional[str] = Field(
description="ORACLE password",
description="Password for authenticating with the Oracle database",
default=None,
)
ORACLE_DATABASE: Optional[str] = Field(
description="ORACLE database",
description="Name of the Oracle database or service to connect to (e.g., 'ORCL' or 'pdborcl')",
default=None,
)

12
api/configs/middleware/vdb/pgvector_config.py
View File

@ -6,30 +6,30 @@ from pydantic_settings import BaseSettings
class PGVectorConfig(BaseSettings):
"""
PGVector configs
Configuration settings for PGVector (PostgreSQL with vector extension)
"""
PGVECTOR_HOST: Optional[str] = Field(
description="PGVector host",
description="Hostname or IP address of the PostgreSQL server with PGVector extension (e.g., 'localhost')",
default=None,
)
PGVECTOR_PORT: Optional[PositiveInt] = Field(
description="PGVector port",
description="Port number on which the PostgreSQL server is listening (default is 5433)",
default=5433,
)
PGVECTOR_USER: Optional[str] = Field(
description="PGVector user",
description="Username for authenticating with the PostgreSQL database",
default=None,
)
PGVECTOR_PASSWORD: Optional[str] = Field(
description="PGVector password",
description="Password for authenticating with the PostgreSQL database",
default=None,
)
PGVECTOR_DATABASE: Optional[str] = Field(
description="PGVector database",
description="Name of the PostgreSQL database to connect to",
default=None,
)

12
api/configs/middleware/vdb/pgvectors_config.py
View File

@ -6,30 +6,30 @@ from pydantic_settings import BaseSettings
class PGVectoRSConfig(BaseSettings):
"""
PGVectoRS configs
Configuration settings for PGVecto.RS (Rust-based vector extension for PostgreSQL)
"""
PGVECTO_RS_HOST: Optional[str] = Field(
description="PGVectoRS host",
description="Hostname or IP address of the PostgreSQL server with PGVecto.RS extension (e.g., 'localhost')",
default=None,
)
PGVECTO_RS_PORT: Optional[PositiveInt] = Field(
description="PGVectoRS port",
description="Port number on which the PostgreSQL server with PGVecto.RS is listening (default is 5431)",
default=5431,
)
PGVECTO_RS_USER: Optional[str] = Field(
description="PGVectoRS user",
description="Username for authenticating with the PostgreSQL database using PGVecto.RS",
default=None,
)
PGVECTO_RS_PASSWORD: Optional[str] = Field(
description="PGVectoRS password",
description="Password for authenticating with the PostgreSQL database using PGVecto.RS",
default=None,
)
PGVECTO_RS_DATABASE: Optional[str] = Field(
description="PGVectoRS database",
description="Name of the PostgreSQL database with PGVecto.RS extension to connect to",
default=None,
)

12
api/configs/middleware/vdb/qdrant_config.py
View File

@ -6,30 +6,30 @@ from pydantic_settings import BaseSettings
class QdrantConfig(BaseSettings):
"""
Qdrant configs
Configuration settings for Qdrant vector database
"""
QDRANT_URL: Optional[str] = Field(
description="Qdrant url",
description="URL of the Qdrant server (e.g., 'http://localhost:6333' or 'https://qdrant.example.com')",
default=None,
)
QDRANT_API_KEY: Optional[str] = Field(
description="Qdrant api key",
description="API key for authenticating with the Qdrant server",
default=None,
)
QDRANT_CLIENT_TIMEOUT: NonNegativeInt = Field(
description="Qdrant client timeout in seconds",
description="Timeout in seconds for Qdrant client operations (default is 20 seconds)",
default=20,
)
QDRANT_GRPC_ENABLED: bool = Field(
description="whether enable grpc support for Qdrant connection",
description="Whether to enable gRPC support for Qdrant connection (True for gRPC, False for HTTP)",
default=False,
)
QDRANT_GRPC_PORT: PositiveInt = Field(
description="Qdrant grpc port",
description="Port number for gRPC connection to Qdrant server (default is 6334)",
default=6334,
)

12
api/configs/middleware/vdb/relyt_config.py
View File

@ -6,30 +6,30 @@ from pydantic_settings import BaseSettings
class RelytConfig(BaseSettings):
"""
Relyt configs
Configuration settings for Relyt database
"""
RELYT_HOST: Optional[str] = Field(
description="Relyt host",
description="Hostname or IP address of the Relyt server (e.g., 'localhost' or 'relyt.example.com')",
default=None,
)
RELYT_PORT: PositiveInt = Field(
description="Relyt port",
description="Port number on which the Relyt server is listening (default is 9200)",
default=9200,
)
RELYT_USER: Optional[str] = Field(
description="Relyt user",
description="Username for authenticating with the Relyt database",
default=None,
)
RELYT_PASSWORD: Optional[str] = Field(
description="Relyt password",
description="Password for authenticating with the Relyt database",
default=None,
)
RELYT_DATABASE: Optional[str] = Field(
description="Relyt database",
description="Name of the Relyt database to connect to (default is 'default')",
default="default",
)

18
api/configs/middleware/vdb/tencent_vector_config.py
View File

@ -6,45 +6,45 @@ from pydantic_settings import BaseSettings
class TencentVectorDBConfig(BaseSettings):
"""
Tencent Vector configs
Configuration settings for Tencent Vector Database
"""
TENCENT_VECTOR_DB_URL: Optional[str] = Field(
description="Tencent Vector URL",
description="URL of the Tencent Vector Database service (e.g., 'https://vectordb.tencentcloudapi.com')",
default=None,
)
TENCENT_VECTOR_DB_API_KEY: Optional[str] = Field(
description="Tencent Vector API key",
description="API key for authenticating with the Tencent Vector Database service",
default=None,
)
TENCENT_VECTOR_DB_TIMEOUT: PositiveInt = Field(
description="Tencent Vector timeout in seconds",
description="Timeout in seconds for Tencent Vector Database operations (default is 30 seconds)",
default=30,
)
TENCENT_VECTOR_DB_USERNAME: Optional[str] = Field(
description="Tencent Vector username",
description="Username for authenticating with the Tencent Vector Database (if required)",
default=None,
)
TENCENT_VECTOR_DB_PASSWORD: Optional[str] = Field(
description="Tencent Vector password",
description="Password for authenticating with the Tencent Vector Database (if required)",
default=None,
)
TENCENT_VECTOR_DB_SHARD: PositiveInt = Field(
description="Tencent Vector sharding number",
description="Number of shards for the Tencent Vector Database (default is 1)",
default=1,
)
TENCENT_VECTOR_DB_REPLICAS: NonNegativeInt = Field(
description="Tencent Vector replicas",
description="Number of replicas for the Tencent Vector Database (default is 2)",
default=2,
)
TENCENT_VECTOR_DB_DATABASE: Optional[str] = Field(
description="Tencent Vector Database",
description="Name of the specific Tencent Vector Database to connect to",
default=None,
)

12
api/configs/middleware/vdb/tidb_vector_config.py
View File

@ -6,30 +6,30 @@ from pydantic_settings import BaseSettings
class TiDBVectorConfig(BaseSettings):
"""
TiDB Vector configs
Configuration settings for TiDB Vector database
"""
TIDB_VECTOR_HOST: Optional[str] = Field(
description="TiDB Vector host",
description="Hostname or IP address of the TiDB Vector server (e.g., 'localhost' or 'tidb.example.com')",
default=None,
)
TIDB_VECTOR_PORT: Optional[PositiveInt] = Field(
description="TiDB Vector port",
description="Port number on which the TiDB Vector server is listening (default is 4000)",
default=4000,
)
TIDB_VECTOR_USER: Optional[str] = Field(
description="TiDB Vector user",
description="Username for authenticating with the TiDB Vector database",
default=None,
)
TIDB_VECTOR_PASSWORD: Optional[str] = Field(
description="TiDB Vector password",
description="Password for authenticating with the TiDB Vector database",
default=None,
)
TIDB_VECTOR_DATABASE: Optional[str] = Field(
description="TiDB Vector database",
description="Name of the TiDB Vector database to connect to",
default=None,
)

10
api/configs/middleware/vdb/weaviate_config.py
View File

@ -6,25 +6,25 @@ from pydantic_settings import BaseSettings
class WeaviateConfig(BaseSettings):
"""
Weaviate configs
Configuration settings for Weaviate vector database
"""
WEAVIATE_ENDPOINT: Optional[str] = Field(
description="Weaviate endpoint URL",
description="URL of the Weaviate server (e.g., 'http://localhost:8080' or 'https://weaviate.example.com')",
default=None,
)
WEAVIATE_API_KEY: Optional[str] = Field(
description="Weaviate API key",
description="API key for authenticating with the Weaviate server",
default=None,
)
WEAVIATE_GRPC_ENABLED: bool = Field(
description="whether to enable gRPC for Weaviate connection",
description="Whether to enable gRPC for Weaviate connection (True for gRPC, False for HTTP)",
default=True,
)
WEAVIATE_BATCH_SIZE: PositiveInt = Field(
description="Weaviate batch size",
description="Number of objects to be processed in a single batch operation (default is 100)",
default=100,
)

2
api/configs/packaging/__init__.py
View File

@ -9,7 +9,7 @@ class PackagingInfo(BaseSettings):
CURRENT_VERSION: str = Field(
description="Dify version",
default="0.8.2",
default="0.8.3",
)
COMMIT_SHA: str = Field(

1
api/constants/__init__.py
View File

@ -1 +1,2 @@
HIDDEN_VALUE = "[__HIDDEN__]"
UUID_NIL = "00000000-0000-0000-0000-000000000000"

1
api/controllers/console/app/completion.py
View File

@ -109,6 +109,7 @@ class ChatMessageApi(Resource):
parser.add_argument("files", type=list, required=False, location="json")
parser.add_argument("model_config", type=dict, required=True, location="json")
parser.add_argument("conversation_id", type=uuid_value, location="json")
parser.add_argument("parent_message_id", type=uuid_value, required=False, location="json")
parser.add_argument("response_mode", type=str, choices=["blocking", "streaming"], location="json")
parser.add_argument("retriever_from", type=str, required=False, default="dev", location="json")
args = parser.parse_args()

2
api/controllers/console/app/message.py
View File

@ -105,8 +105,6 @@ class ChatMessageListApi(Resource):
if rest_count > 0:
has_more = True
history_messages = list(reversed(history_messages))
return InfiniteScrollPagination(data=history_messages, limit=args["limit"], has_more=has_more)

2
api/controllers/console/app/workflow.py
View File

@ -176,6 +176,8 @@ class AdvancedChatDraftWorkflowRunApi(Resource):
parser.add_argument("query", type=str, required=True, location="json", default="")
parser.add_argument("files", type=list, location="json")
parser.add_argument("conversation_id", type=uuid_value, location="json")
parser.add_argument("parent_message_id", type=uuid_value, required=False, location="json")
args = parser.parse_args()
try:

1
api/controllers/console/explore/completion.py
View File

@ -100,6 +100,7 @@ class ChatApi(InstalledAppResource):
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("parent_message_id", type=uuid_value, required=False, location="json")
parser.add_argument("retriever_from", type=str, required=False, default="explore_app", location="json")
args = parser.parse_args()

2
api/controllers/console/explore/message.py
View File

@ -51,7 +51,7 @@ class MessageListApi(InstalledAppResource):
try:
return MessageService.pagination_by_first_id(
app_model, current_user, args["conversation_id"], args["first_id"], args["limit"]
app_model, current_user, args["conversation_id"], args["first_id"], args["limit"], "desc"
)
except services.errors.conversation.ConversationNotExistsError:
raise NotFound("Conversation Not Exists.")

1
api/controllers/service_api/app/message.py
View File

@ -54,6 +54,7 @@ class MessageListApi(Resource):
message_fields = {
"id": fields.String,
"conversation_id": fields.String,
"parent_message_id": fields.String,
"inputs": fields.Raw,
"query": fields.String,
"answer": fields.String(attribute="re_sign_file_url_answer"),

1
api/controllers/web/completion.py
View File

@ -96,6 +96,7 @@ class ChatApi(WebApiResource):
parser.add_argument("files", type=list, required=False, location="json")
parser.add_argument("response_mode", type=str, choices=["blocking", "streaming"], location="json")
parser.add_argument("conversation_id", type=uuid_value, location="json")
parser.add_argument("parent_message_id", type=uuid_value, required=False, location="json")
parser.add_argument("retriever_from", type=str, required=False, default="web_app", location="json")
args = parser.parse_args()

3
api/controllers/web/message.py
View File

@ -57,6 +57,7 @@ class MessageListApi(WebApiResource):
message_fields = {
"id": fields.String,
"conversation_id": fields.String,
"parent_message_id": fields.String,
"inputs": fields.Raw,
"query": fields.String,
"answer": fields.String(attribute="re_sign_file_url_answer"),
@ -89,7 +90,7 @@ class MessageListApi(WebApiResource):
try:
return MessageService.pagination_by_first_id(
app_model, end_user, args["conversation_id"], args["first_id"], args["limit"]
app_model, end_user, args["conversation_id"], args["first_id"], args["limit"], "desc"
)
except services.errors.conversation.ConversationNotExistsError:
raise NotFound("Conversation Not Exists.")

5
api/core/agent/base_agent_runner.py
View File

@ -31,6 +31,7 @@ from core.model_runtime.entities.message_entities import (
)
from core.model_runtime.entities.model_entities import ModelFeature
from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
from core.prompt.utils.extract_thread_messages import extract_thread_messages
from core.tools.__base.tool import Tool
from core.tools.entities.tool_entities import (
ToolParameter,
@ -422,10 +423,12 @@ class BaseAgentRunner(AppRunner):
.filter(
Message.conversation_id == self.message.conversation_id,
)
.order_by(Message.created_at.asc())
.order_by(Message.created_at.desc())
.all()
)
messages = list(reversed(extract_thread_messages(messages)))
for message in messages:
if message.id == self.message.id:
continue

1
api/core/app/apps/advanced_chat/app_generator.py
View File

@ -132,6 +132,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
inputs=conversation.inputs if conversation else self._get_cleaned_inputs(inputs, app_config),
query=query,
files=file_objs,
parent_message_id=args.get("parent_message_id"),
user_id=user.id,
stream=stream,
invoke_from=invoke_from,

1
api/core/app/apps/agent_chat/app_generator.py
View File

@ -137,6 +137,7 @@ class AgentChatAppGenerator(MessageBasedAppGenerator):
inputs=conversation.inputs if conversation else self._get_cleaned_inputs(inputs, app_config),
query=query,
files=file_objs,
parent_message_id=args.get("parent_message_id"),
user_id=user.id,
stream=stream,
invoke_from=invoke_from,

5
api/core/app/apps/base_app_generate_response_converter.py
View File

@ -64,10 +64,10 @@ class AppGenerateResponseConverter(ABC):
:return:
"""
# show_retrieve_source
updated_resources = []
if "retriever_resources" in metadata:
metadata["retriever_resources"] = []
for resource in metadata["retriever_resources"]:
metadata["retriever_resources"].append(
updated_resources.append(
{
"segment_id": resource["segment_id"],
"position": resource["position"],
@ -76,6 +76,7 @@ class AppGenerateResponseConverter(ABC):
"content": resource["content"],
}
)
metadata["retriever_resources"] = updated_resources
# show annotation reply
if "annotation_reply" in metadata:

1
api/core/app/apps/chat/app_generator.py
View File

@ -137,6 +137,7 @@ class ChatAppGenerator(MessageBasedAppGenerator):
inputs=conversation.inputs if conversation else self._get_cleaned_inputs(inputs, app_config),
query=query,
files=file_objs,
parent_message_id=args.get("parent_message_id"),
user_id=user.id,
stream=stream,
invoke_from=invoke_from,

1
api/core/app/apps/message_based_app_generator.py
View File

@ -218,6 +218,7 @@ class MessageBasedAppGenerator(BaseAppGenerator):
answer_tokens=0,
answer_unit_price=0,
answer_price_unit=0,
parent_message_id=getattr(application_generate_entity, "parent_message_id", None),
provider_response_latency=0,
total_price=0,
currency="USD",

3
api/core/app/entities/app_invoke_entities.py
View File

@ -122,6 +122,7 @@ class ChatAppGenerateEntity(EasyUIBasedAppGenerateEntity):
"""
conversation_id: Optional[str] = None
parent_message_id: Optional[str] = None
class CompletionAppGenerateEntity(EasyUIBasedAppGenerateEntity):
@ -138,6 +139,7 @@ class AgentChatAppGenerateEntity(EasyUIBasedAppGenerateEntity):
"""
conversation_id: Optional[str] = None
parent_message_id: Optional[str] = None
class AdvancedChatAppGenerateEntity(AppGenerateEntity):
@ -149,6 +151,7 @@ class AdvancedChatAppGenerateEntity(AppGenerateEntity):
app_config: WorkflowUIBasedAppConfig
conversation_id: Optional[str] = None
parent_message_id: Optional[str] = None
query: str
class SingleIterationRunEntity(BaseModel):

2
api/core/llm_generator/llm_generator.py
View File

@ -47,6 +47,8 @@ class LLMGenerator:
)
answer = response.message.content
cleaned_answer = re.sub(r"^.*(\{.*\}).*$", r"\1", answer, flags=re.DOTALL)
if cleaned_answer is None:
return ""
result_dict = json.loads(cleaned_answer)
answer = result_dict["Your Output"]
name = answer.strip()

1
api/core/llm_generator/prompts.py
View File

@ -65,7 +65,6 @@ SUGGESTED_QUESTIONS_AFTER_ANSWER_INSTRUCTION_PROMPT = (
"Please help me predict the three most likely questions that human would ask, "
"and keeping each question under 20 characters.\n"
"MAKE SURE your output is the SAME language as the Assistant's latest response"
"(if the main response is written in Chinese, then the language of your output must be using Chinese.)!\n"
"The output must be an array in JSON format following the specified schema:\n"
'["question1","question2","question3"]\n'
)

21
api/core/memory/token_buffer_memory.py
View File

@ -11,6 +11,7 @@ from core.model_runtime.entities.message_entities import (
TextPromptMessageContent,
UserPromptMessage,
)
from core.prompt.utils.extract_thread_messages import extract_thread_messages
from extensions.ext_database import db
from models.model import AppMode, Conversation, Message, MessageFile
from models.workflow import WorkflowRun
@ -33,8 +34,17 @@ class TokenBufferMemory:
# fetch limited messages, and return reversed
query = (
db.session.query(Message.id, Message.query, Message.answer, Message.created_at, Message.workflow_run_id)
.filter(Message.conversation_id == self.conversation.id, Message.answer != "")
db.session.query(
Message.id,
Message.query,
Message.answer,
Message.created_at,
Message.workflow_run_id,
Message.parent_message_id,
)
.filter(
Message.conversation_id == self.conversation.id,
)
.order_by(Message.created_at.desc())
)
@ -45,7 +55,12 @@ class TokenBufferMemory:
messages = query.limit(message_limit).all()
messages = list(reversed(messages))
# instead of all messages from the conversation, we only need to extract messages
# that belong to the thread of last message
thread_messages = extract_thread_messages(messages)
thread_messages.pop(0)
messages = list(reversed(thread_messages))
message_file_parser = MessageFileParser(tenant_id=app_record.tenant_id, app_id=app_record.id)
prompt_messages = []
for message in messages:

2
api/core/model_runtime/docs/zh_Hans/predefined_model_scale_out.md
View File

@ -62,7 +62,7 @@ pricing: # 价格信息
建议将所有模型配置都准备完毕后再开始模型代码的实现。
同样，也可以参考 `model_providers` 目录下其他供应商对应模型类型目录下的 YAML 配置信息，完整的 YAML 规则见：[Schema](schema.md#AIModel)。
同样，也可以参考 `model_providers` 目录下其他供应商对应模型类型目录下的 YAML 配置信息，完整的 YAML 规则见：[Schema](schema.md#aimodelentity)。
### 实现模型调用代码

3
api/core/model_runtime/model_providers/_position.yaml
View File

@ -37,3 +37,6 @@
- siliconflow
- perfxcloud
- zhinao
- fireworks
- mixedbread
- nomic

0
api/core/model_runtime/model_providers/fireworks/__init__.py Normal file
View File

3
api/core/model_runtime/model_providers/fireworks/_assets/icon_l_en.svg Normal file
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@ -0,0 +1,3 @@
<svg width="130" role="graphics-symbol" aria-label="Fireworks AI Home" viewBox="0 0 835 130" xmlns="http://www.w3.org/2000/svg">
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52
api/core/model_runtime/model_providers/fireworks/_common.py Normal file
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@ -0,0 +1,52 @@
from collections.abc import Mapping
import openai
from core.model_runtime.errors.invoke import (
InvokeAuthorizationError,
InvokeBadRequestError,
InvokeConnectionError,
InvokeError,
InvokeRateLimitError,
InvokeServerUnavailableError,
)
class _CommonFireworks:
def _to_credential_kwargs(self, credentials: Mapping) -> dict:
"""
Transform credentials to kwargs for model instance
:param credentials:
:return:
"""
credentials_kwargs = {
"api_key": credentials["fireworks_api_key"],
"base_url": "https://api.fireworks.ai/inference/v1",
"max_retries": 1,
}
return credentials_kwargs
@property
def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
"""
Map model invoke error to unified error
The key is the error type thrown to the caller
The value is the error type thrown by the model,
which needs to be converted into a unified error type for the caller.
:return: Invoke error mapping
"""
return {
InvokeConnectionError: [openai.APIConnectionError, openai.APITimeoutError],
InvokeServerUnavailableError: [openai.InternalServerError],
InvokeRateLimitError: [openai.RateLimitError],
InvokeAuthorizationError: [openai.AuthenticationError, openai.PermissionDeniedError],
InvokeBadRequestError: [
openai.BadRequestError,
openai.NotFoundError,
openai.UnprocessableEntityError,
openai.APIError,
],
}

27
api/core/model_runtime/model_providers/fireworks/fireworks.py Normal file
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@ -0,0 +1,27 @@
import logging
from core.model_runtime.entities.model_entities import ModelType
from core.model_runtime.errors.validate import CredentialsValidateFailedError
from core.model_runtime.model_providers.__base.model_provider import ModelProvider
logger = logging.getLogger(__name__)
class FireworksProvider(ModelProvider):
def validate_provider_credentials(self, credentials: dict) -> None:
"""
Validate provider credentials
if validate failed, raise exception
:param credentials: provider credentials, credentials form defined in `provider_credential_schema`.
"""
try:
model_instance = self.get_model_instance(ModelType.LLM)
model_instance.validate_credentials(
model="accounts/fireworks/models/llama-v3p1-8b-instruct", credentials=credentials
)
except CredentialsValidateFailedError as ex:
raise ex
except Exception as ex:
logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
raise ex

29
api/core/model_runtime/model_providers/fireworks/fireworks.yaml Normal file
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provider: fireworks
label:
zh_Hans: Fireworks AI
en_US: Fireworks AI
icon_small:
en_US: icon_s_en.svg
icon_large:
en_US: icon_l_en.svg
background: "#FCFDFF"
help:
title:
en_US: Get your API Key from Fireworks AI
zh_Hans: 从 Fireworks AI 获取 API Key
url:
en_US: https://fireworks.ai/account/api-keys
supported_model_types:
- llm
configurate_methods:
- predefined-model
provider_credential_schema:
credential_form_schemas:
- variable: fireworks_api_key
label:
en_US: API Key
type: secret-input
required: true
placeholder:
zh_Hans: 在此输入您的 API Key
en_US: Enter your API Key

0
api/core/model_runtime/model_providers/fireworks/llm/__init__.py Normal file
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16
api/core/model_runtime/model_providers/fireworks/llm/_position.yaml Normal file
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- llama-v3p1-405b-instruct
- llama-v3p1-70b-instruct
- llama-v3p1-8b-instruct
- llama-v3-70b-instruct
- mixtral-8x22b-instruct
- mixtral-8x7b-instruct
- firefunction-v2
- firefunction-v1
- gemma2-9b-it
- llama-v3-70b-instruct-hf
- llama-v3-8b-instruct
- llama-v3-8b-instruct-hf
- mixtral-8x7b-instruct-hf
- mythomax-l2-13b
- phi-3-vision-128k-instruct
- yi-large

46
api/core/model_runtime/model_providers/fireworks/llm/firefunction-v1.yaml Normal file
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model: accounts/fireworks/models/firefunction-v1
label:
zh_Hans: Firefunction V1
en_US: Firefunction V1
model_type: llm
features:
- agent-thought
- tool-call
model_properties:
mode: chat
context_size: 32768
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: top_k
label:
zh_Hans: 取样数量
en_US: Top k
type: int
help:
zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
en_US: Only sample from the top K options for each subsequent token.
- name: max_tokens
use_template: max_tokens
- name: context_length_exceeded_behavior
default: None
label:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
help:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
type: string
options:
- None
- truncate
- error
- name: response_format
use_template: response_format
pricing:
input: '0.5'
output: '0.5'
unit: '0.000001'
currency: USD

46
api/core/model_runtime/model_providers/fireworks/llm/firefunction-v2.yaml Normal file
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model: accounts/fireworks/models/firefunction-v2
label:
zh_Hans: Firefunction V2
en_US: Firefunction V2
model_type: llm
features:
- agent-thought
- tool-call
model_properties:
mode: chat
context_size: 8192
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: top_k
label:
zh_Hans: 取样数量
en_US: Top k
type: int
help:
zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
en_US: Only sample from the top K options for each subsequent token.
- name: max_tokens
use_template: max_tokens
- name: context_length_exceeded_behavior
default: None
label:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
help:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
type: string
options:
- None
- truncate
- error
- name: response_format
use_template: response_format
pricing:
input: '0.9'
output: '0.9'
unit: '0.000001'
currency: USD

45
api/core/model_runtime/model_providers/fireworks/llm/gemma2-9b-it.yaml Normal file
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model: accounts/fireworks/models/gemma2-9b-it
label:
zh_Hans: Gemma2 9B Instruct
en_US: Gemma2 9B Instruct
model_type: llm
features:
- agent-thought
model_properties:
mode: chat
context_size: 8192
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: top_k
label:
zh_Hans: 取样数量
en_US: Top k
type: int
help:
zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
en_US: Only sample from the top K options for each subsequent token.
- name: max_tokens
use_template: max_tokens
- name: context_length_exceeded_behavior
default: None
label:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
help:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
type: string
options:
- None
- truncate
- error
- name: response_format
use_template: response_format
pricing:
input: '0.2'
output: '0.2'
unit: '0.000001'
currency: USD

46
api/core/model_runtime/model_providers/fireworks/llm/llama-v3-70b-instruct-hf.yaml Normal file
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model: accounts/fireworks/models/llama-v3-70b-instruct-hf
label:
zh_Hans: Llama3 70B Instruct(HF version)
en_US: Llama3 70B Instruct(HF version)
model_type: llm
features:
- agent-thought
- tool-call
model_properties:
mode: chat
context_size: 8192
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: top_k
label:
zh_Hans: 取样数量
en_US: Top k
type: int
help:
zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
en_US: Only sample from the top K options for each subsequent token.
- name: max_tokens
use_template: max_tokens
- name: context_length_exceeded_behavior
default: None
label:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
help:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
type: string
options:
- None
- truncate
- error
- name: response_format
use_template: response_format
pricing:
input: '0.9'
output: '0.9'
unit: '0.000001'
currency: USD

46
api/core/model_runtime/model_providers/fireworks/llm/llama-v3-70b-instruct.yaml Normal file
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model: accounts/fireworks/models/llama-v3-70b-instruct
label:
zh_Hans: Llama3 70B Instruct
en_US: Llama3 70B Instruct
model_type: llm
features:
- agent-thought
- tool-call
model_properties:
mode: chat
context_size: 8192
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: top_k
label:
zh_Hans: 取样数量
en_US: Top k
type: int
help:
zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
en_US: Only sample from the top K options for each subsequent token.
- name: max_tokens
use_template: max_tokens
- name: context_length_exceeded_behavior
default: None
label:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
help:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
type: string
options:
- None
- truncate
- error
- name: response_format
use_template: response_format
pricing:
input: '0.9'
output: '0.9'
unit: '0.000001'
currency: USD

46
api/core/model_runtime/model_providers/fireworks/llm/llama-v3-8b-instruct-hf.yaml Normal file
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model: accounts/fireworks/models/llama-v3-8b-instruct-hf
label:
zh_Hans: Llama3 8B Instruct(HF version)
en_US: Llama3 8B Instruct(HF version)
model_type: llm
features:
- agent-thought
- tool-call
model_properties:
mode: chat
context_size: 8192
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: top_k
label:
zh_Hans: 取样数量
en_US: Top k
type: int
help:
zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
en_US: Only sample from the top K options for each subsequent token.
- name: max_tokens
use_template: max_tokens
- name: context_length_exceeded_behavior
default: None
label:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
help:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
type: string
options:
- None
- truncate
- error
- name: response_format
use_template: response_format
pricing:
input: '0.2'
output: '0.2'
unit: '0.000001'
currency: USD

46
api/core/model_runtime/model_providers/fireworks/llm/llama-v3-8b-instruct.yaml Normal file
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model: accounts/fireworks/models/llama-v3-8b-instruct
label:
zh_Hans: Llama3 8B Instruct
en_US: Llama3 8B Instruct
model_type: llm
features:
- agent-thought
- tool-call
model_properties:
mode: chat
context_size: 8192
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: top_k
label:
zh_Hans: 取样数量
en_US: Top k
type: int
help:
zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
en_US: Only sample from the top K options for each subsequent token.
- name: max_tokens
use_template: max_tokens
- name: context_length_exceeded_behavior
default: None
label:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
help:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
type: string
options:
- None
- truncate
- error
- name: response_format
use_template: response_format
pricing:
input: '0.2'
output: '0.2'
unit: '0.000001'
currency: USD

46
api/core/model_runtime/model_providers/fireworks/llm/llama-v3p1-405b-instruct.yaml Normal file
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model: accounts/fireworks/models/llama-v3p1-405b-instruct
label:
zh_Hans: Llama3.1 405B Instruct
en_US: Llama3.1 405B Instruct
model_type: llm
features:
- agent-thought
- tool-call
model_properties:
mode: chat
context_size: 131072
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: top_k
label:
zh_Hans: 取样数量
en_US: Top k
type: int
help:
zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
en_US: Only sample from the top K options for each subsequent token.
- name: max_tokens
use_template: max_tokens
- name: context_length_exceeded_behavior
default: None
label:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
help:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
type: string
options:
- None
- truncate
- error
- name: response_format
use_template: response_format
pricing:
input: '3'
output: '3'
unit: '0.000001'
currency: USD

46
api/core/model_runtime/model_providers/fireworks/llm/llama-v3p1-70b-instruct.yaml Normal file
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model: accounts/fireworks/models/llama-v3p1-70b-instruct
label:
zh_Hans: Llama3.1 70B Instruct
en_US: Llama3.1 70B Instruct
model_type: llm
features:
- agent-thought
- tool-call
model_properties:
mode: chat
context_size: 131072
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: top_k
label:
zh_Hans: 取样数量
en_US: Top k
type: int
help:
zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
en_US: Only sample from the top K options for each subsequent token.
- name: max_tokens
use_template: max_tokens
- name: context_length_exceeded_behavior
default: None
label:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
help:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
type: string
options:
- None
- truncate
- error
- name: response_format
use_template: response_format
pricing:
input: '0.2'
output: '0.2'
unit: '0.000001'
currency: USD

46
api/core/model_runtime/model_providers/fireworks/llm/llama-v3p1-8b-instruct.yaml Normal file
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model: accounts/fireworks/models/llama-v3p1-8b-instruct
label:
zh_Hans: Llama3.1 8B Instruct
en_US: Llama3.1 8B Instruct
model_type: llm
features:
- agent-thought
- tool-call
model_properties:
mode: chat
context_size: 131072
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: top_k
label:
zh_Hans: 取样数量
en_US: Top k
type: int
help:
zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
en_US: Only sample from the top K options for each subsequent token.
- name: max_tokens
use_template: max_tokens
- name: context_length_exceeded_behavior
default: None
label:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
help:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
type: string
options:
- None
- truncate
- error
- name: response_format
use_template: response_format
pricing:
input: '0.2'
output: '0.2'
unit: '0.000001'
currency: USD

610
api/core/model_runtime/model_providers/fireworks/llm/llm.py Normal file
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import logging
from collections.abc import Generator
from typing import Optional, Union, cast
from openai import OpenAI, Stream
from openai.types.chat import ChatCompletion, ChatCompletionChunk, ChatCompletionMessageToolCall
from openai.types.chat.chat_completion_chunk import ChoiceDeltaFunctionCall, ChoiceDeltaToolCall
from openai.types.chat.chat_completion_message import FunctionCall
from core.model_runtime.callbacks.base_callback import Callback
from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
from core.model_runtime.entities.message_entities import (
AssistantPromptMessage,
ImagePromptMessageContent,
PromptMessage,
PromptMessageContentType,
PromptMessageTool,
SystemPromptMessage,
TextPromptMessageContent,
ToolPromptMessage,
UserPromptMessage,
)
from core.model_runtime.errors.validate import CredentialsValidateFailedError
from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
from core.model_runtime.model_providers.fireworks._common import _CommonFireworks
logger = logging.getLogger(__name__)
FIREWORKS_BLOCK_MODE_PROMPT = """You should always follow the instructions and output a valid {{block}} object.
The structure of the {{block}} object you can found in the instructions, use {"answer": "$your_answer"} as the default structure
if you are not sure about the structure.
<instructions>
{{instructions}}
</instructions>
""" # noqa: E501
class FireworksLargeLanguageModel(_CommonFireworks, LargeLanguageModel):
"""
Model class for Fireworks large language model.
"""
def _invoke(
self,
model: str,
credentials: dict,
prompt_messages: list[PromptMessage],
model_parameters: dict,
tools: Optional[list[PromptMessageTool]] = None,
stop: Optional[list[str]] = None,
stream: bool = True,
user: Optional[str] = None,
) -> Union[LLMResult, Generator]:
"""
Invoke large language model
:param model: model name
:param credentials: model credentials
:param prompt_messages: prompt messages
:param model_parameters: model parameters
:param tools: tools for tool calling
:param stop: stop words
:param stream: is stream response
:param user: unique user id
:return: full response or stream response chunk generator result
"""
return self._chat_generate(
model=model,
credentials=credentials,
prompt_messages=prompt_messages,
model_parameters=model_parameters,
tools=tools,
stop=stop,
stream=stream,
user=user,
)
def _code_block_mode_wrapper(
self,
model: str,
credentials: dict,
prompt_messages: list[PromptMessage],
model_parameters: dict,
tools: Optional[list[PromptMessageTool]] = None,
stop: Optional[list[str]] = None,
stream: bool = True,
user: Optional[str] = None,
callbacks: Optional[list[Callback]] = None,
) -> Union[LLMResult, Generator]:
"""
Code block mode wrapper for invoking large language model
"""
if "response_format" in model_parameters and model_parameters["response_format"] in {"JSON", "XML"}:
stop = stop or []
self._transform_chat_json_prompts(
model=model,
credentials=credentials,
prompt_messages=prompt_messages,
model_parameters=model_parameters,
tools=tools,
stop=stop,
stream=stream,
user=user,
response_format=model_parameters["response_format"],
)
model_parameters.pop("response_format")
return self._invoke(
model=model,
credentials=credentials,
prompt_messages=prompt_messages,
model_parameters=model_parameters,
tools=tools,
stop=stop,
stream=stream,
user=user,
)
def _transform_chat_json_prompts(
self,
model: str,
credentials: dict,
prompt_messages: list[PromptMessage],
model_parameters: dict,
tools: list[PromptMessageTool] | None = None,
stop: list[str] | None = None,
stream: bool = True,
user: str | None = None,
response_format: str = "JSON",
) -> None:
"""
Transform json prompts
"""
if stop is None:
stop = []
if "```\n" not in stop:
stop.append("```\n")
if "\n```" not in stop:
stop.append("\n```")
if len(prompt_messages) > 0 and isinstance(prompt_messages[0], SystemPromptMessage):
prompt_messages[0] = SystemPromptMessage(
content=FIREWORKS_BLOCK_MODE_PROMPT.replace("{{instructions}}", prompt_messages[0].content).replace(
"{{block}}", response_format
)
)
prompt_messages.append(AssistantPromptMessage(content=f"\n```{response_format}\n"))
else:
prompt_messages.insert(
0,
SystemPromptMessage(
content=FIREWORKS_BLOCK_MODE_PROMPT.replace(
"{{instructions}}", f"Please output a valid {response_format} object."
).replace("{{block}}", response_format)
),
)
prompt_messages.append(AssistantPromptMessage(content=f"\n```{response_format}"))
def get_num_tokens(
self,
model: str,
credentials: dict,
prompt_messages: list[PromptMessage],
tools: Optional[list[PromptMessageTool]] = None,
) -> int:
"""
Get number of tokens for given prompt messages
:param model: model name
:param credentials: model credentials
:param prompt_messages: prompt messages
:param tools: tools for tool calling
:return:
"""
return self._num_tokens_from_messages(model, prompt_messages, tools)
def validate_credentials(self, model: str, credentials: dict) -> None:
"""
Validate model credentials
:param model: model name
:param credentials: model credentials
:return:
"""
try:
credentials_kwargs = self._to_credential_kwargs(credentials)
client = OpenAI(**credentials_kwargs)
client.chat.completions.create(
messages=[{"role": "user", "content": "ping"}], model=model, temperature=0, max_tokens=10, stream=False
)
except Exception as e:
raise CredentialsValidateFailedError(str(e))
def _chat_generate(
self,
model: str,
credentials: dict,
prompt_messages: list[PromptMessage],
model_parameters: dict,
tools: Optional[list[PromptMessageTool]] = None,
stop: Optional[list[str]] = None,
stream: bool = True,
user: Optional[str] = None,
) -> Union[LLMResult, Generator]:
credentials_kwargs = self._to_credential_kwargs(credentials)
client = OpenAI(**credentials_kwargs)
extra_model_kwargs = {}
if tools:
extra_model_kwargs["functions"] = [
{"name": tool.name, "description": tool.description, "parameters": tool.parameters} for tool in tools
]
if stop:
extra_model_kwargs["stop"] = stop
if user:
extra_model_kwargs["user"] = user
# chat model
response = client.chat.completions.create(
messages=[self._convert_prompt_message_to_dict(m) for m in prompt_messages],
model=model,
stream=stream,
**model_parameters,
**extra_model_kwargs,
)
if stream:
return self._handle_chat_generate_stream_response(model, credentials, response, prompt_messages, tools)
return self._handle_chat_generate_response(model, credentials, response, prompt_messages, tools)
def _handle_chat_generate_response(
self,
model: str,
credentials: dict,
response: ChatCompletion,
prompt_messages: list[PromptMessage],
tools: Optional[list[PromptMessageTool]] = None,
) -> LLMResult:
"""
Handle llm chat response
:param model: model name
:param credentials: credentials
:param response: response
:param prompt_messages: prompt messages
:param tools: tools for tool calling
:return: llm response
"""
assistant_message = response.choices[0].message
# assistant_message_tool_calls = assistant_message.tool_calls
assistant_message_function_call = assistant_message.function_call
# extract tool calls from response
# tool_calls = self._extract_response_tool_calls(assistant_message_tool_calls)
function_call = self._extract_response_function_call(assistant_message_function_call)
tool_calls = [function_call] if function_call else []
# transform assistant message to prompt message
assistant_prompt_message = AssistantPromptMessage(content=assistant_message.content, tool_calls=tool_calls)
# calculate num tokens
if response.usage:
# transform usage
prompt_tokens = response.usage.prompt_tokens
completion_tokens = response.usage.completion_tokens
else:
# calculate num tokens
prompt_tokens = self._num_tokens_from_messages(model, prompt_messages, tools)
completion_tokens = self._num_tokens_from_messages(model, [assistant_prompt_message])
# transform usage
usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
# transform response
response = LLMResult(
model=response.model,
prompt_messages=prompt_messages,
message=assistant_prompt_message,
usage=usage,
system_fingerprint=response.system_fingerprint,
)
return response
def _handle_chat_generate_stream_response(
self,
model: str,
credentials: dict,
response: Stream[ChatCompletionChunk],
prompt_messages: list[PromptMessage],
tools: Optional[list[PromptMessageTool]] = None,
) -> Generator:
"""
Handle llm chat stream response
:param model: model name
:param response: response
:param prompt_messages: prompt messages
:param tools: tools for tool calling
:return: llm response chunk generator
"""
full_assistant_content = ""
delta_assistant_message_function_call_storage: Optional[ChoiceDeltaFunctionCall] = None
prompt_tokens = 0
completion_tokens = 0
final_tool_calls = []
final_chunk = LLMResultChunk(
model=model,
prompt_messages=prompt_messages,
delta=LLMResultChunkDelta(
index=0,
message=AssistantPromptMessage(content=""),
),
)
for chunk in response:
if len(chunk.choices) == 0:
if chunk.usage:
# calculate num tokens
prompt_tokens = chunk.usage.prompt_tokens
completion_tokens = chunk.usage.completion_tokens
continue
delta = chunk.choices[0]
has_finish_reason = delta.finish_reason is not None
if (
not has_finish_reason
and (delta.delta.content is None or delta.delta.content == "")
and delta.delta.function_call is None
):
continue
# assistant_message_tool_calls = delta.delta.tool_calls
assistant_message_function_call = delta.delta.function_call
# extract tool calls from response
if delta_assistant_message_function_call_storage is not None:
# handle process of stream function call
if assistant_message_function_call:
# message has not ended ever
delta_assistant_message_function_call_storage.arguments += assistant_message_function_call.arguments
continue
else:
# message has ended
assistant_message_function_call = delta_assistant_message_function_call_storage
delta_assistant_message_function_call_storage = None
else:
if assistant_message_function_call:
# start of stream function call
delta_assistant_message_function_call_storage = assistant_message_function_call
if delta_assistant_message_function_call_storage.arguments is None:
delta_assistant_message_function_call_storage.arguments = ""
if not has_finish_reason:
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 []
if tool_calls:
final_tool_calls.extend(tool_calls)
# transform assistant message to prompt message
assistant_prompt_message = AssistantPromptMessage(content=delta.delta.content or "", tool_calls=tool_calls)
full_assistant_content += delta.delta.content or ""
if has_finish_reason:
final_chunk = LLMResultChunk(
model=chunk.model,
prompt_messages=prompt_messages,
system_fingerprint=chunk.system_fingerprint,
delta=LLMResultChunkDelta(
index=delta.index,
message=assistant_prompt_message,
finish_reason=delta.finish_reason,
),
)
else:
yield LLMResultChunk(
model=chunk.model,
prompt_messages=prompt_messages,
system_fingerprint=chunk.system_fingerprint,
delta=LLMResultChunkDelta(
index=delta.index,
message=assistant_prompt_message,
),
)
if not prompt_tokens:
prompt_tokens = self._num_tokens_from_messages(model, prompt_messages, tools)
if not completion_tokens:
full_assistant_prompt_message = AssistantPromptMessage(
content=full_assistant_content, tool_calls=final_tool_calls
)
completion_tokens = self._num_tokens_from_messages(model, [full_assistant_prompt_message])
# transform usage
usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
final_chunk.delta.usage = usage
yield final_chunk
def _extract_response_tool_calls(
self, response_tool_calls: list[ChatCompletionMessageToolCall | ChoiceDeltaToolCall]
) -> list[AssistantPromptMessage.ToolCall]:
"""
Extract tool calls from response
:param response_tool_calls: response tool calls
:return: list of tool calls
"""
tool_calls = []
if response_tool_calls:
for response_tool_call in response_tool_calls:
function = AssistantPromptMessage.ToolCall.ToolCallFunction(
name=response_tool_call.function.name, arguments=response_tool_call.function.arguments
)
tool_call = AssistantPromptMessage.ToolCall(
id=response_tool_call.id, type=response_tool_call.type, function=function
)
tool_calls.append(tool_call)
return tool_calls
def _extract_response_function_call(
self, response_function_call: FunctionCall | ChoiceDeltaFunctionCall
) -> AssistantPromptMessage.ToolCall:
"""
Extract function call from response
:param response_function_call: response function call
:return: tool call
"""
tool_call = None
if response_function_call:
function = AssistantPromptMessage.ToolCall.ToolCallFunction(
name=response_function_call.name, arguments=response_function_call.arguments
)
tool_call = AssistantPromptMessage.ToolCall(
id=response_function_call.name, type="function", function=function
)
return tool_call
def _convert_prompt_message_to_dict(self, message: PromptMessage) -> dict:
"""
Convert PromptMessage to dict for Fireworks API
"""
if isinstance(message, UserPromptMessage):
message = cast(UserPromptMessage, message)
if isinstance(message.content, str):
message_dict = {"role": "user", "content": message.content}
else:
sub_messages = []
for message_content in message.content:
if message_content.type == PromptMessageContentType.TEXT:
message_content = cast(TextPromptMessageContent, message_content)
sub_message_dict = {"type": "text", "text": message_content.data}
sub_messages.append(sub_message_dict)
elif message_content.type == PromptMessageContentType.IMAGE:
message_content = cast(ImagePromptMessageContent, message_content)
sub_message_dict = {
"type": "image_url",
"image_url": {"url": message_content.data, "detail": message_content.detail.value},
}
sub_messages.append(sub_message_dict)
message_dict = {"role": "user", "content": sub_messages}
elif isinstance(message, AssistantPromptMessage):
message = cast(AssistantPromptMessage, message)
message_dict = {"role": "assistant", "content": message.content}
if message.tool_calls:
# message_dict["tool_calls"] = [tool_call.dict() for tool_call in
# message.tool_calls]
function_call = message.tool_calls[0]
message_dict["function_call"] = {
"name": function_call.function.name,
"arguments": function_call.function.arguments,
}
elif isinstance(message, SystemPromptMessage):
message = cast(SystemPromptMessage, message)
message_dict = {"role": "system", "content": message.content}
elif isinstance(message, ToolPromptMessage):
message = cast(ToolPromptMessage, message)
# message_dict = {
# "role": "tool",
# "content": message.content,
# "tool_call_id": message.tool_call_id
# }
message_dict = {"role": "function", "content": message.content, "name": message.tool_call_id}
else:
raise ValueError(f"Got unknown type {message}")
if message.name:
message_dict["name"] = message.name
return message_dict
def _num_tokens_from_messages(
self,
model: str,
messages: list[PromptMessage],
tools: Optional[list[PromptMessageTool]] = None,
credentials: dict = None,
) -> int:
"""
Approximate num tokens with GPT2 tokenizer.
"""
tokens_per_message = 3
tokens_per_name = 1
num_tokens = 0
messages_dict = [self._convert_prompt_message_to_dict(m) for m in messages]
for message in messages_dict:
num_tokens += tokens_per_message
for key, value in message.items():
# Cast str(value) in case the message value is not a string
# This occurs with function messages
# TODO: The current token calculation method for the image type is not implemented,
# which need to download the image and then get the resolution for calculation,
# and will increase the request delay
if isinstance(value, list):
text = ""
for item in value:
if isinstance(item, dict) and item["type"] == "text":
text += item["text"]
value = text
if key == "tool_calls":
for tool_call in value:
for t_key, t_value in tool_call.items():
num_tokens += self._get_num_tokens_by_gpt2(t_key)
if t_key == "function":
for f_key, f_value in t_value.items():
num_tokens += self._get_num_tokens_by_gpt2(f_key)
num_tokens += self._get_num_tokens_by_gpt2(f_value)
else:
num_tokens += self._get_num_tokens_by_gpt2(t_key)
num_tokens += self._get_num_tokens_by_gpt2(t_value)
else:
num_tokens += self._get_num_tokens_by_gpt2(str(value))
if key == "name":
num_tokens += tokens_per_name
# every reply is primed with <im_start>assistant
num_tokens += 3
if tools:
num_tokens += self._num_tokens_for_tools(tools)
return num_tokens
def _num_tokens_for_tools(self, tools: list[PromptMessageTool]) -> int:
"""
Calculate num tokens for tool calling with tiktoken package.
:param tools: tools for tool calling
:return: number of tokens
"""
num_tokens = 0
for tool in tools:
num_tokens += self._get_num_tokens_by_gpt2("type")
num_tokens += self._get_num_tokens_by_gpt2("function")
num_tokens += self._get_num_tokens_by_gpt2("function")
# calculate num tokens for function object
num_tokens += self._get_num_tokens_by_gpt2("name")
num_tokens += self._get_num_tokens_by_gpt2(tool.name)
num_tokens += self._get_num_tokens_by_gpt2("description")
num_tokens += self._get_num_tokens_by_gpt2(tool.description)
parameters = tool.parameters
num_tokens += self._get_num_tokens_by_gpt2("parameters")
if "title" in parameters:
num_tokens += self._get_num_tokens_by_gpt2("title")
num_tokens += self._get_num_tokens_by_gpt2(parameters.get("title"))
num_tokens += self._get_num_tokens_by_gpt2("type")
num_tokens += self._get_num_tokens_by_gpt2(parameters.get("type"))
if "properties" in parameters:
num_tokens += self._get_num_tokens_by_gpt2("properties")
for key, value in parameters.get("properties").items():
num_tokens += self._get_num_tokens_by_gpt2(key)
for field_key, field_value in value.items():
num_tokens += self._get_num_tokens_by_gpt2(field_key)
if field_key == "enum":
for enum_field in field_value:
num_tokens += 3
num_tokens += self._get_num_tokens_by_gpt2(enum_field)
else:
num_tokens += self._get_num_tokens_by_gpt2(field_key)
num_tokens += self._get_num_tokens_by_gpt2(str(field_value))
if "required" in parameters:
num_tokens += self._get_num_tokens_by_gpt2("required")
for required_field in parameters["required"]:
num_tokens += 3
num_tokens += self._get_num_tokens_by_gpt2(required_field)
return num_tokens

46
api/core/model_runtime/model_providers/fireworks/llm/mixtral-8x22b-instruct.yaml Normal file
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@ -0,0 +1,46 @@
model: accounts/fireworks/models/mixtral-8x22b-instruct
label:
zh_Hans: Mixtral MoE 8x22B Instruct
en_US: Mixtral MoE 8x22B Instruct
model_type: llm
features:
- agent-thought
- tool-call
model_properties:
mode: chat
context_size: 65536
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: top_k
label:
zh_Hans: 取样数量
en_US: Top k
type: int
help:
zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
en_US: Only sample from the top K options for each subsequent token.
- name: max_tokens
use_template: max_tokens
- name: context_length_exceeded_behavior
default: None
label:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
help:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
type: string
options:
- None
- truncate
- error
- name: response_format
use_template: response_format
pricing:
input: '1.2'
output: '1.2'
unit: '0.000001'
currency: USD

46
api/core/model_runtime/model_providers/fireworks/llm/mixtral-8x7b-instruct-hf.yaml Normal file
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@ -0,0 +1,46 @@
model: accounts/fireworks/models/mixtral-8x7b-instruct-hf
label:
zh_Hans: Mixtral MoE 8x7B Instruct(HF version)
en_US: Mixtral MoE 8x7B Instruct(HF version)
model_type: llm
features:
- agent-thought
- tool-call
model_properties:
mode: chat
context_size: 32768
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: top_k
label:
zh_Hans: 取样数量
en_US: Top k
type: int
help:
zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
en_US: Only sample from the top K options for each subsequent token.
- name: max_tokens
use_template: max_tokens
- name: context_length_exceeded_behavior
default: None
label:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
help:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
type: string
options:
- None
- truncate
- error
- name: response_format
use_template: response_format
pricing:
input: '0.5'
output: '0.5'
unit: '0.000001'
currency: USD

46
api/core/model_runtime/model_providers/fireworks/llm/mixtral-8x7b-instruct.yaml Normal file
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@ -0,0 +1,46 @@
model: accounts/fireworks/models/mixtral-8x7b-instruct
label:
zh_Hans: Mixtral MoE 8x7B Instruct
en_US: Mixtral MoE 8x7B Instruct
model_type: llm
features:
- agent-thought
- tool-call
model_properties:
mode: chat
context_size: 32768
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: top_k
label:
zh_Hans: 取样数量
en_US: Top k
type: int
help:
zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
en_US: Only sample from the top K options for each subsequent token.
- name: max_tokens
use_template: max_tokens
- name: context_length_exceeded_behavior
default: None
label:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
help:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
type: string
options:
- None
- truncate
- error
- name: response_format
use_template: response_format
pricing:
input: '0.5'
output: '0.5'
unit: '0.000001'
currency: USD

46
api/core/model_runtime/model_providers/fireworks/llm/mythomax-l2-13b.yaml Normal file
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@ -0,0 +1,46 @@
model: accounts/fireworks/models/mythomax-l2-13b
label:
zh_Hans: MythoMax L2 13b
en_US: MythoMax L2 13b
model_type: llm
features:
- agent-thought
- tool-call
model_properties:
mode: chat
context_size: 4096
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: top_k
label:
zh_Hans: 取样数量
en_US: Top k
type: int
help:
zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
en_US: Only sample from the top K options for each subsequent token.
- name: max_tokens
use_template: max_tokens
- name: context_length_exceeded_behavior
default: None
label:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
help:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
type: string
options:
- None
- truncate
- error
- name: response_format
use_template: response_format
pricing:
input: '0.2'
output: '0.2'
unit: '0.000001'
currency: USD

46
api/core/model_runtime/model_providers/fireworks/llm/phi-3-vision-128k-instruct.yaml Normal file
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@ -0,0 +1,46 @@
model: accounts/fireworks/models/phi-3-vision-128k-instruct
label:
zh_Hans: Phi3.5 Vision Instruct
en_US: Phi3.5 Vision Instruct
model_type: llm
features:
- agent-thought
- vision
model_properties:
mode: chat
context_size: 8192
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: top_k
label:
zh_Hans: 取样数量
en_US: Top k
type: int
help:
zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
en_US: Only sample from the top K options for each subsequent token.
- name: max_tokens
use_template: max_tokens
- name: context_length_exceeded_behavior
default: None
label:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
help:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
type: string
options:
- None
- truncate
- error
- name: response_format
use_template: response_format
pricing:
input: '0.2'
output: '0.2'
unit: '0.000001'
currency: USD

45
api/core/model_runtime/model_providers/fireworks/llm/yi-large.yaml Normal file
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@ -0,0 +1,45 @@
model: accounts/yi-01-ai/models/yi-large
label:
zh_Hans: Yi-Large
en_US: Yi-Large
model_type: llm
features:
- agent-thought
model_properties:
mode: chat
context_size: 32768
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: top_k
label:
zh_Hans: 取样数量
en_US: Top k
type: int
help:
zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
en_US: Only sample from the top K options for each subsequent token.
- name: max_tokens
use_template: max_tokens
- name: context_length_exceeded_behavior
default: None
label:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
help:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
type: string
options:
- None
- truncate
- error
- name: response_format
use_template: response_format
pricing:
input: '3'
output: '3'
unit: '0.000001'
currency: USD

39
api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-8b-exp-0827.yaml Normal file
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@ -0,0 +1,39 @@
model: gemini-1.5-flash-8b-exp-0827
label:
en_US: Gemini 1.5 Flash 8B 0827
model_type: llm
features:
- agent-thought
- vision
- tool-call
- stream-tool-call
model_properties:
mode: chat
context_size: 1048576
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: top_k
label:
zh_Hans: 取样数量
en_US: Top k
type: int
help:
zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
en_US: Only sample from the top K options for each subsequent token.
required: false
- name: max_tokens_to_sample
use_template: max_tokens
required: true
default: 8192
min: 1
max: 8192
- name: response_format
use_template: response_format
pricing:
input: '0.00'
output: '0.00'
unit: '0.000001'
currency: USD

39
api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-exp-0827.yaml Normal file
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@ -0,0 +1,39 @@
model: gemini-1.5-flash-exp-0827
label:
en_US: Gemini 1.5 Flash 0827
model_type: llm
features:
- agent-thought
- vision
- tool-call
- stream-tool-call
model_properties:
mode: chat
context_size: 1048576
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: top_k
label:
zh_Hans: 取样数量
en_US: Top k
type: int
help:
zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
en_US: Only sample from the top K options for each subsequent token.
required: false
- name: max_tokens_to_sample
use_template: max_tokens
required: true
default: 8192
min: 1
max: 8192
- name: response_format
use_template: response_format
pricing:
input: '0.00'
output: '0.00'
unit: '0.000001'
currency: USD

39
api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-exp-0801.yaml Normal file
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@ -0,0 +1,39 @@
model: gemini-1.5-pro-exp-0801
label:
en_US: Gemini 1.5 Pro 0801
model_type: llm
features:
- agent-thought
- vision
- tool-call
- stream-tool-call
model_properties:
mode: chat
context_size: 2097152
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: top_k
label:
zh_Hans: 取样数量
en_US: Top k
type: int
help:
zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
en_US: Only sample from the top K options for each subsequent token.
required: false
- name: max_tokens_to_sample
use_template: max_tokens
required: true
default: 8192
min: 1
max: 8192
- name: response_format
use_template: response_format
pricing:
input: '0.00'
output: '0.00'
unit: '0.000001'
currency: USD

39
api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-exp-0827.yaml Normal file
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@ -0,0 +1,39 @@
model: gemini-1.5-pro-exp-0827
label:
en_US: Gemini 1.5 Pro 0827
model_type: llm
features:
- agent-thought
- vision
- tool-call
- stream-tool-call
model_properties:
mode: chat
context_size: 2097152
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: top_k
label:
zh_Hans: 取样数量
en_US: Top k
type: int
help:
zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
en_US: Only sample from the top K options for each subsequent token.
required: false
- name: max_tokens_to_sample
use_template: max_tokens
required: true
default: 8192
min: 1
max: 8192
- name: response_format
use_template: response_format
pricing:
input: '0.00'
output: '0.00'
unit: '0.000001'
currency: USD

2
api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-latest.yaml
View File

@ -9,7 +9,7 @@ features:
- stream-tool-call
model_properties:
mode: chat
context_size: 1048576
context_size: 2097152
parameter_rules:
- name: temperature
use_template: temperature

1
api/core/model_runtime/model_providers/hunyuan/llm/_position.yaml
View File

@ -3,3 +3,4 @@
- hunyuan-standard-256k
- hunyuan-pro
- hunyuan-turbo
- hunyuan-vision

39
api/core/model_runtime/model_providers/hunyuan/llm/hunyuan-vision.yaml Normal file
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@ -0,0 +1,39 @@
model: hunyuan-vision
label:
zh_Hans: hunyuan-vision
en_US: hunyuan-vision
model_type: llm
features:
- agent-thought
- tool-call
- multi-tool-call
- stream-tool-call
- vision
model_properties:
mode: chat
context_size: 8000
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: max_tokens
use_template: max_tokens
default: 1024
min: 1
max: 8000
- name: enable_enhance
label:
zh_Hans: 功能增强
en_US: Enable Enhancement
type: boolean
help:
zh_Hans: 功能增强（如搜索）开关，关闭时将直接由主模型生成回复内容，可以降低响应时延（对于流式输出时的首字时延尤为明显）。但在少数场景里，回复效果可能会下降。
en_US: Allow the model to perform external search to enhance the generation results.
required: false
default: true
pricing:
input: '0.018'
output: '0.018'
unit: '0.001'
currency: RMB

23
api/core/model_runtime/model_providers/hunyuan/llm/llm.py
View File

@ -1,6 +1,7 @@
import json
import logging
from collections.abc import Generator
from typing import cast
from tencentcloud.common import credential
from tencentcloud.common.exception import TencentCloudSDKException
@ -11,9 +12,12 @@ from tencentcloud.hunyuan.v20230901 import hunyuan_client, models
from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
from core.model_runtime.entities.message_entities import (
AssistantPromptMessage,
ImagePromptMessageContent,
PromptMessage,
PromptMessageContentType,
PromptMessageTool,
SystemPromptMessage,
TextPromptMessageContent,
ToolPromptMessage,
UserPromptMessage,
)
@ -143,6 +147,25 @@ class HunyuanLargeLanguageModel(LargeLanguageModel):
tool_execute_result = {"result": message.content}
content = json.dumps(tool_execute_result, ensure_ascii=False)
dict_list.append({"Role": message.role.value, "Content": content, "ToolCallId": message.tool_call_id})
elif isinstance(message, UserPromptMessage):
message = cast(UserPromptMessage, message)
if isinstance(message.content, str):
dict_list.append({"Role": message.role.value, "Content": message.content})
else:
sub_messages = []
for message_content in message.content:
if message_content.type == PromptMessageContentType.TEXT:
message_content = cast(TextPromptMessageContent, message_content)
sub_message_dict = {"Type": "text", "Text": message_content.data}
sub_messages.append(sub_message_dict)
elif message_content.type == PromptMessageContentType.IMAGE:
message_content = cast(ImagePromptMessageContent, message_content)
sub_message_dict = {
"Type": "image_url",
"ImageUrl": {"Url": message_content.data},
}
sub_messages.append(sub_message_dict)
dict_list.append({"Role": message.role.value, "Contents": sub_messages})
else:
dict_list.append({"Role": message.role.value, "Content": message.content})
return dict_list

47
api/core/model_runtime/model_providers/jina/jina.yaml
View File

@ -1,6 +1,6 @@
provider: jina
label:
en_US: Jina
en_US: Jina AI
description:
en_US: Embedding and Rerank Model Supported
icon_small:
@ -11,7 +11,7 @@ background: "#EFFDFD"
help:
title:
en_US: Get your API key from Jina AI
zh_Hans: 从 Jina 获取 API Key
zh_Hans: 从 Jina AI 获取 API Key
url:
en_US: https://jina.ai/
supported_model_types:
@ -67,3 +67,46 @@ model_credential_schema:
required: false
type: text-input
default: '8192'
- variable: task
label:
zh_Hans: 下游任务
en_US: Downstream task
placeholder:
zh_Hans: 选择将使用向量模型的下游任务。模型将返回针对该任务优化的向量。
en_US: Select the downstream task for which the embeddings will be used. The model will return the optimized embeddings for that task.
required: false
type: select
options:
- value: retrieval.query
label:
en_US: retrieval.query
- value: retrieval.passage
label:
en_US: retrieval.passage
- value: separation
label:
en_US: separation
- value: classification
label:
en_US: classification
- value: text-matching
label:
en_US: text-matching
- variable: dimensions
label:
zh_Hans: 输出维度
en_US: Output dimensions
placeholder:
zh_Hans: 输入您的输出维度
en_US: Enter output dimensions
required: false
type: text-input
- variable: late_chunking
label:
zh_Hans: 后期分块
en_US: Late chunking
placeholder:
zh_Hans: 应用后期分块技术来利用模型的长上下文功能来生成上下文块向量化。
en_US: Apply the late chunking technique to leverage the model's long-context capabilities for generating contextual chunk embeddings.
required: false
type: switch

42
api/core/model_runtime/model_providers/jina/text_embedding/text_embedding.py
View File

@ -27,6 +27,38 @@ class JinaTextEmbeddingModel(TextEmbeddingModel):
api_base: str = "https://api.jina.ai/v1"
def _to_payload(self, model: str, texts: list[str], credentials: dict) -> dict:
"""
Parse model credentials
:param model: model name
:param credentials: model credentials
:param texts: texts to embed
:return: parsed credentials
"""
def transform_jina_input_text(model, text):
if model == "jina-clip-v1":
return {"text": text}
return text
data = {"model": model, "input": [transform_jina_input_text(model, text) for text in texts]}
task = credentials.get("task")
dimensions = credentials.get("dimensions")
late_chunking = credentials.get("late_chunking")
if task is not None:
data["task"] = task
if dimensions is not None:
data["dimensions"] = int(dimensions)
if late_chunking is not None:
data["late_chunking"] = late_chunking
return data
def _invoke(
self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
) -> TextEmbeddingResult:
@ -49,15 +81,7 @@ class JinaTextEmbeddingModel(TextEmbeddingModel):
url = base_url + "/embeddings"
headers = {"Authorization": "Bearer " + api_key, "Content-Type": "application/json"}
def transform_jina_input_text(model, text):
if model == "jina-clip-v1":
return {"text": text}
return text
data = {"model": model, "input": [transform_jina_input_text(model, text) for text in texts]}
if model == "jina-embeddings-v3":
data["task"] = "text-matching"
data = self._to_payload(model=model, texts=texts, credentials=credentials)
try:
response = post(url, headers=headers, data=dumps(data))

5
api/core/model_runtime/model_providers/mistralai/llm/_position.yaml
View File

@ -1,3 +1,8 @@
- pixtral-12b-2409
- codestral-latest
- mistral-embed
- open-mistral-nemo
- open-codestral-mamba
- open-mistral-7b
- open-mixtral-8x7b
- open-mixtral-8x22b

51
api/core/model_runtime/model_providers/mistralai/llm/codestral-latest.yaml Normal file
View File

@ -0,0 +1,51 @@
model: codestral-latest
label:
zh_Hans: codestral-latest
en_US: codestral-latest
model_type: llm
features:
- agent-thought
model_properties:
mode: chat
context_size: 32000
parameter_rules:
- name: temperature
use_template: temperature
default: 0.7
min: 0
max: 1
- name: top_p
use_template: top_p
default: 1
min: 0
max: 1
- name: max_tokens
use_template: max_tokens
default: 1024
min: 1
max: 4096
- name: safe_prompt
default: false
type: boolean
help:
en_US: Whether to inject a safety prompt before all conversations.
zh_Hans: 是否开启提示词审查
label:
en_US: SafePrompt
zh_Hans: 提示词审查
- name: random_seed
type: int
help:
en_US: The seed to use for random sampling. If set, different calls will generate deterministic results.
zh_Hans: 当开启随机数种子以后，你可以通过指定一个固定的种子来使得回答结果更加稳定
label:
en_US: RandomSeed
zh_Hans: 随机数种子
default: 0
min: 0
max: 2147483647
pricing:
input: '0.008'
output: '0.024'
unit: '0.001'
currency: USD

51
api/core/model_runtime/model_providers/mistralai/llm/mistral-embed.yaml Normal file
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@ -0,0 +1,51 @@
model: mistral-embed
label:
zh_Hans: mistral-embed
en_US: mistral-embed
model_type: llm
features:
- agent-thought
model_properties:
mode: chat
context_size: 8192
parameter_rules:
- name: temperature
use_template: temperature
default: 0.7
min: 0
max: 1
- name: top_p
use_template: top_p
default: 1
min: 0
max: 1
- name: max_tokens
use_template: max_tokens
default: 1024
min: 1
max: 1024
- name: safe_prompt
default: false
type: boolean
help:
en_US: Whether to inject a safety prompt before all conversations.
zh_Hans: 是否开启提示词审查
label:
en_US: SafePrompt
zh_Hans: 提示词审查
- name: random_seed
type: int
help:
en_US: The seed to use for random sampling. If set, different calls will generate deterministic results.
zh_Hans: 当开启随机数种子以后，你可以通过指定一个固定的种子来使得回答结果更加稳定
label:
en_US: RandomSeed
zh_Hans: 随机数种子
default: 0
min: 0
max: 2147483647
pricing:
input: '0.008'
output: '0.024'
unit: '0.001'
currency: USD

51
api/core/model_runtime/model_providers/mistralai/llm/open-codestral-mamba.yaml Normal file
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@ -0,0 +1,51 @@
model: open-codestral-mamba
label:
zh_Hans: open-codestral-mamba
en_US: open-codestral-mamba
model_type: llm
features:
- agent-thought
model_properties:
mode: chat
context_size: 256000
parameter_rules:
- name: temperature
use_template: temperature
default: 0.7
min: 0
max: 1
- name: top_p
use_template: top_p
default: 1
min: 0
max: 1
- name: max_tokens
use_template: max_tokens
default: 1024
min: 1
max: 16384
- name: safe_prompt
default: false
type: boolean
help:
en_US: Whether to inject a safety prompt before all conversations.
zh_Hans: 是否开启提示词审查
label:
en_US: SafePrompt
zh_Hans: 提示词审查
- name: random_seed
type: int
help:
en_US: The seed to use for random sampling. If set, different calls will generate deterministic results.
zh_Hans: 当开启随机数种子以后，你可以通过指定一个固定的种子来使得回答结果更加稳定
label:
en_US: RandomSeed
zh_Hans: 随机数种子
default: 0
min: 0
max: 2147483647
pricing:
input: '0.008'
output: '0.024'
unit: '0.001'
currency: USD

51
api/core/model_runtime/model_providers/mistralai/llm/open-mistral-nemo.yaml Normal file
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@ -0,0 +1,51 @@
model: open-mistral-nemo
label:
zh_Hans: open-mistral-nemo
en_US: open-mistral-nemo
model_type: llm
features:
- agent-thought
model_properties:
mode: chat
context_size: 128000
parameter_rules:
- name: temperature
use_template: temperature
default: 0.7
min: 0
max: 1
- name: top_p
use_template: top_p
default: 1
min: 0
max: 1
- name: max_tokens
use_template: max_tokens
default: 1024
min: 1
max: 8192
- name: safe_prompt
default: false
type: boolean
help:
en_US: Whether to inject a safety prompt before all conversations.
zh_Hans: 是否开启提示词审查
label:
en_US: SafePrompt
zh_Hans: 提示词审查
- name: random_seed
type: int
help:
en_US: The seed to use for random sampling. If set, different calls will generate deterministic results.
zh_Hans: 当开启随机数种子以后，你可以通过指定一个固定的种子来使得回答结果更加稳定
label:
en_US: RandomSeed
zh_Hans: 随机数种子
default: 0
min: 0
max: 2147483647
pricing:
input: '0.008'
output: '0.024'
unit: '0.001'
currency: USD

51
api/core/model_runtime/model_providers/mistralai/llm/pixtral-12b-2409.yaml Normal file
View File

@ -0,0 +1,51 @@
model: pixtral-12b-2409
label:
zh_Hans: pixtral-12b-2409
en_US: pixtral-12b-2409
model_type: llm
features:
- agent-thought
model_properties:
mode: chat
context_size: 128000
parameter_rules:
- name: temperature
use_template: temperature
default: 0.7
min: 0
max: 1
- name: top_p
use_template: top_p
default: 1
min: 0
max: 1
- name: max_tokens
use_template: max_tokens
default: 1024
min: 1
max: 8192
- name: safe_prompt
default: false
type: boolean
help:
en_US: Whether to inject a safety prompt before all conversations.
zh_Hans: 是否开启提示词审查
label:
en_US: SafePrompt
zh_Hans: 提示词审查
- name: random_seed
type: int
help:
en_US: The seed to use for random sampling. If set, different calls will generate deterministic results.
zh_Hans: 当开启随机数种子以后，你可以通过指定一个固定的种子来使得回答结果更加稳定
label:
en_US: RandomSeed
zh_Hans: 随机数种子
default: 0
min: 0
max: 2147483647
pricing:
input: '0.008'
output: '0.024'
unit: '0.001'
currency: USD

0
api/core/model_runtime/model_providers/mixedbread/__init__.py Normal file
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27
api/core/model_runtime/model_providers/mixedbread/mixedbread.py Normal file
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@ -0,0 +1,27 @@
import logging
from core.model_runtime.entities.model_entities import ModelType
from core.model_runtime.errors.validate import CredentialsValidateFailedError
from core.model_runtime.model_providers.__base.model_provider import ModelProvider
logger = logging.getLogger(__name__)
class MixedBreadProvider(ModelProvider):
def validate_provider_credentials(self, credentials: dict) -> None:
"""
Validate provider credentials
if validate failed, raise exception
:param credentials: provider credentials, credentials form defined in `provider_credential_schema`.
"""
try:
model_instance = self.get_model_instance(ModelType.TEXT_EMBEDDING)
# Use `mxbai-embed-large-v1` model for validate,
model_instance.validate_credentials(model="mxbai-embed-large-v1", credentials=credentials)
except CredentialsValidateFailedError as ex:
raise ex
except Exception as ex:
logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
raise ex

31
api/core/model_runtime/model_providers/mixedbread/mixedbread.yaml Normal file
View File

@ -0,0 +1,31 @@
provider: mixedbread
label:
en_US: MixedBread
description:
en_US: Embedding and Rerank Model Supported
icon_small:
en_US: icon_s_en.png
icon_large:
en_US: icon_l_en.png
background: "#EFFDFD"
help:
title:
en_US: Get your API key from MixedBread AI
zh_Hans: 从 MixedBread 获取 API Key
url:
en_US: https://www.mixedbread.ai/
supported_model_types:
- text-embedding
- rerank
configurate_methods:
- predefined-model
provider_credential_schema:
credential_form_schemas:
- variable: api_key
label:
en_US: API Key
type: secret-input
required: true
placeholder:
zh_Hans: 在此输入您的 API Key
en_US: Enter your API Key

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