diff --git a/api/commands.py b/api/commands.py index b82f7ac3f88f1b..09e69e4886c64c 100644 --- a/api/commands.py +++ b/api/commands.py @@ -15,36 +15,75 @@ from models.account import Tenant from models.dataset import Dataset, DatasetCollectionBinding, DocumentSegment from models.dataset import Document as DatasetDocument -from models.model import Account, App, AppAnnotationSetting, AppMode, Conversation, MessageAnnotation +from models.model import ( + Account, + App, + AppAnnotationSetting, + AppMode, + Conversation, + MessageAnnotation, +) from models.provider import Provider, ProviderModel +from services.account_service import RegisterService -@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.command("register", help="Register a new accout") +@click.option("--email", prompt=True, help="The email address of the account you want to register") +@click.option("--name", prompt=True, help="The name of the account you want to register") +@click.option("--password", prompt=True, help="The password of the account you want to register") +def register(email, name, password): + """ + Register a new accout + """ + try: + email_validate(email) + except: + click.echo(click.style("sorry. {} is not a valid email. ".format(email), fg="red")) + return + + try: + valid_password(password) + except: + click.echo(click.style("sorry. The passwords must match {} ".format(password_pattern), fg="red")) + return + + account = db.session.query(Account).filter(Account.email == email).one_or_none() + + if account: + click.echo(click.style("sorry. the account: [{}] already exists .".format(email), fg="red")) + return + + account = RegisterService.register(email, name, password) + click.echo(click.style("Congratulations!, account has been registered.", fg="green")) + + +@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.") 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("sorry. The two passwords do not match.", fg="red")) return - account = db.session.query(Account). \ - filter(Account.email == email). \ - one_or_none() + 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("sorry. the account: [{}] not exist .".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("sorry. The passwords must match {} ".format(password_pattern), fg="red")) return # generate password salt @@ -57,80 +96,102 @@ 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("Congratulations!, password has been reset.", 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.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.") 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("Sorry, new email and confirm email do not match.", fg="red")) return - account = db.session.query(Account). \ - filter(Account.email == email). \ - one_or_none() + 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("sorry. the account: [{}] not exist .".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("sorry. {} is not a valid email. ".format(email), fg="red")) return account.email = new_email db.session.commit() - click.echo(click.style('Congratulations!, email has been reset.', fg='green')) - - -@click.command('reset-encrypt-key-pair', help='Reset the asymmetric key pair of workspace for encrypt LLM credentials. ' - 'After the reset, all LLM credentials will become invalid, ' - 'requiring re-entry.' - 'Only support SELF_HOSTED mode.') -@click.confirmation_option(prompt=click.style('Are you sure you want to reset encrypt key pair?' - ' this operation cannot be rolled back!', fg='red')) + click.echo(click.style("Congratulations!, email has been reset.", fg="green")) + + +@click.command( + "reset-encrypt-key-pair", + help="Reset the asymmetric key pair of workspace for encrypt LLM credentials. " + "After the reset, all LLM credentials will become invalid, " + "requiring re-entry." + "Only support SELF_HOSTED mode.", +) +@click.confirmation_option( + prompt=click.style( + "Are you sure you want to reset encrypt key pair?" " this operation cannot be rolled back!", + fg="red", + ) +) def reset_encrypt_key_pair(): """ Reset the encrypted key pair of workspace for encrypt LLM credentials. After the reset, all LLM credentials will become invalid, requiring re-entry. Only support SELF_HOSTED mode. """ - if current_app.config['EDITION'] != 'SELF_HOSTED': - click.echo(click.style('Sorry, only support SELF_HOSTED mode.', fg='red')) + if current_app.config["EDITION"] != "SELF_HOSTED": + click.echo(click.style("Sorry, only support SELF_HOSTED mode.", 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( + "Sorry, no workspace found. Please enter /install to initialize.", + fg="red", + ) + ) return tenant.encrypt_public_key = generate_key_pair(tenant.id) - db.session.query(Provider).filter(Provider.provider_type == 'custom', Provider.tenant_id == tenant.id).delete() + db.session.query(Provider).filter(Provider.provider_type == "custom", Provider.tenant_id == tenant.id).delete() db.session.query(ProviderModel).filter(ProviderModel.tenant_id == tenant.id).delete() db.session.commit() - click.echo(click.style('Congratulations! ' - 'the asymmetric key pair of workspace {} has been reset.'.format(tenant.id), fg='green')) - - -@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.') + click.echo( + click.style( + "Congratulations! " "the asymmetric key pair of workspace {} has been reset.".format(tenant.id), + fg="green", + ) + ) + + +@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']: + if scope in ["knowledge", "all"]: migrate_knowledge_vector_database() - if scope in ['annotation', 'all']: + if scope in ["annotation", "all"]: migrate_annotation_vector_database() @@ -138,7 +199,7 @@ 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("Start migrate annotation data.", fg="green")) create_count = 0 skipped_count = 0 total_count = 0 @@ -146,42 +207,48 @@ def migrate_annotation_vector_database(): while True: try: # get apps info - apps = db.session.query(App).filter( - App.status == 'normal' - ).order_by(App.created_at.desc()).paginate(page=page, per_page=50) + apps = ( + db.session.query(App) + .filter(App.status == "normal") + .order_by(App.created_at.desc()) + .paginate(page=page, per_page=50) + ) except NotFound: break page += 1 for app in apps: total_count = total_count + 1 - click.echo(f'Processing the {total_count} app {app.id}. ' - + f'{create_count} created, {skipped_count} skipped.') + click.echo( + 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)) - app_annotation_setting = db.session.query(AppAnnotationSetting).filter( - AppAnnotationSetting.app_id == app.id - ).first() + click.echo("Create 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 is disabled: {}".format(app.id)) continue # get dataset_collection_binding info - dataset_collection_binding = db.session.query(DatasetCollectionBinding).filter( - DatasetCollectionBinding.id == app_annotation_setting.collection_binding_id - ).first() + dataset_collection_binding = ( + db.session.query(DatasetCollectionBinding) + .filter(DatasetCollectionBinding.id == app_annotation_setting.collection_binding_id) + .first() + ) if not dataset_collection_binding: - click.echo('App annotation collection binding is not exist: {}'.format(app.id)) + click.echo("App annotation collection binding is not exist: {}".format(app.id)) continue annotations = db.session.query(MessageAnnotation).filter(MessageAnnotation.app_id == app.id).all() dataset = Dataset( id=app.id, tenant_id=app.tenant_id, - indexing_technique='high_quality', + indexing_technique="high_quality", embedding_model_provider=dataset_collection_binding.provider_name, embedding_model=dataset_collection_binding.model_name, - collection_binding_id=dataset_collection_binding.id + collection_binding_id=dataset_collection_binding.id, ) documents = [] if annotations: @@ -191,101 +258,128 @@ def migrate_annotation_vector_database(): metadata={ "annotation_id": annotation.id, "app_id": app.id, - "doc_id": annotation.id - } + "doc_id": annotation.id, + }, ) documents.append(document) - vector = Vector(dataset, attributes=['doc_id', 'annotation_id', 'app_id']) + vector = Vector(dataset, attributes=["doc_id", "annotation_id", "app_id"]) click.echo(f"Start to migrate annotation, app_id: {app.id}.") try: vector.delete() click.echo( - click.style(f'Successfully delete vector index for app: {app.id}.', - fg='green')) + click.style( + f"Successfully delete 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')) + click.echo(click.style(f"Failed to delete vector index for app {app.id}.", fg="red")) raise e if documents: try: - click.echo(click.style( - f'Start to created vector index with {len(documents)} annotations for app {app.id}.', - fg='green')) + click.echo( + click.style( + f"Start to created 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.style( + f"Successfully 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')) + click.echo( + click.style( + f"Failed to created vector index for app {app.id}.", + fg="red", + ) + ) raise e - click.echo(f'Successfully migrated app annotation {app.id}.') + click.echo(f"Successfully migrated app annotation {app.id}.") create_count += 1 except Exception as e: click.echo( - click.style('Create app annotation index error: {} {}'.format(e.__class__.__name__, str(e)), - fg='red')) + click.style( + "Create app annotation index error: {} {}".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.', - fg='green')) + click.style( + f"Congratulations! Create {create_count} app annotation indexes, and skipped {skipped_count} apps.", + fg="green", + ) + ) 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("Start migrate vector db.", fg="green")) create_count = 0 skipped_count = 0 total_count = 0 config = current_app.config - vector_type = config.get('VECTOR_STORE') + vector_type = config.get("VECTOR_STORE") page = 1 while True: try: - datasets = db.session.query(Dataset).filter(Dataset.indexing_technique == 'high_quality') \ - .order_by(Dataset.created_at.desc()).paginate(page=page, per_page=50) + datasets = ( + db.session.query(Dataset) + .filter(Dataset.indexing_technique == "high_quality") + .order_by(Dataset.created_at.desc()) + .paginate(page=page, per_page=50) + ) except NotFound: break page += 1 for dataset in datasets: total_count = total_count + 1 - click.echo(f'Processing the {total_count} dataset {dataset.id}. ' - + f'{create_count} created, {skipped_count} skipped.') + click.echo( + f"Processing the {total_count} dataset {dataset.id}. " + + f"{create_count} created, {skipped_count} skipped." + ) try: - click.echo('Create dataset vdb index: {}'.format(dataset.id)) + click.echo("Create dataset vdb index: {}".format(dataset.id)) if dataset.index_struct_dict: - if dataset.index_struct_dict['type'] == vector_type: + if dataset.index_struct_dict["type"] == vector_type: skipped_count = skipped_count + 1 continue - collection_name = '' + collection_name = "" if vector_type == "weaviate": dataset_id = dataset.id collection_name = Dataset.gen_collection_name_by_id(dataset_id) index_struct_dict = { - "type": 'weaviate', - "vector_store": {"class_prefix": collection_name} + "type": "weaviate", + "vector_store": {"class_prefix": collection_name}, } dataset.index_struct = json.dumps(index_struct_dict) elif vector_type == "qdrant": if dataset.collection_binding_id: - dataset_collection_binding = db.session.query(DatasetCollectionBinding). \ - filter(DatasetCollectionBinding.id == dataset.collection_binding_id). \ - one_or_none() + dataset_collection_binding = ( + db.session.query(DatasetCollectionBinding) + .filter(DatasetCollectionBinding.id == dataset.collection_binding_id) + .one_or_none() + ) if dataset_collection_binding: collection_name = dataset_collection_binding.collection_name else: - raise ValueError('Dataset Collection Bindings is not exist!') + raise ValueError("Dataset Collection Bindings is not exist!") else: dataset_id = dataset.id collection_name = Dataset.gen_collection_name_by_id(dataset_id) index_struct_dict = { - "type": 'qdrant', - "vector_store": {"class_prefix": collection_name} + "type": "qdrant", + "vector_store": {"class_prefix": collection_name}, } dataset.index_struct = json.dumps(index_struct_dict) @@ -293,7 +387,15 @@ def migrate_knowledge_vector_database(): dataset_id = dataset.id collection_name = Dataset.gen_collection_name_by_id(dataset_id) index_struct_dict = { - "type": 'milvus', + "type": "milvus", + "vector_store": {"class_prefix": collection_name}, + } + dataset.index_struct = json.dumps(index_struct_dict) + elif vector_type == "relyt": + dataset_id = dataset.id + collection_name = Dataset.gen_collection_name_by_id(dataset_id) + index_struct_dict = { + "type": 'relyt', "vector_store": {"class_prefix": collection_name} } dataset.index_struct = json.dumps(index_struct_dict) @@ -314,29 +416,43 @@ def migrate_knowledge_vector_database(): try: vector.delete() click.echo( - click.style(f'Successfully delete vector index {collection_name} for dataset {dataset.id}.', - fg='green')) + click.style( + f"Successfully delete vector index {collection_name} for dataset {dataset.id}.", + fg="green", + ) + ) except Exception as e: click.echo( - click.style(f'Failed to delete vector index {collection_name} for dataset {dataset.id}.', - fg='red')) + click.style( + f"Failed to delete vector index {collection_name} for dataset {dataset.id}.", + fg="red", + ) + ) raise e - dataset_documents = db.session.query(DatasetDocument).filter( - DatasetDocument.dataset_id == dataset.id, - DatasetDocument.indexing_status == 'completed', - DatasetDocument.enabled == True, - DatasetDocument.archived == False, - ).all() + dataset_documents = ( + db.session.query(DatasetDocument) + .filter( + DatasetDocument.dataset_id == dataset.id, + DatasetDocument.indexing_status == "completed", + DatasetDocument.enabled == True, + DatasetDocument.archived == False, + ) + .all() + ) documents = [] segments_count = 0 for dataset_document in dataset_documents: - segments = db.session.query(DocumentSegment).filter( - DocumentSegment.document_id == dataset_document.id, - DocumentSegment.status == 'completed', - DocumentSegment.enabled == True - ).all() + segments = ( + db.session.query(DocumentSegment) + .filter( + DocumentSegment.document_id == dataset_document.id, + DocumentSegment.status == "completed", + DocumentSegment.enabled == True, + ) + .all() + ) for segment in segments: document = Document( @@ -346,7 +462,7 @@ def migrate_knowledge_vector_database(): "doc_hash": segment.index_node_hash, "document_id": segment.document_id, "dataset_id": segment.dataset_id, - } + }, ) documents.append(document) @@ -354,37 +470,55 @@ def migrate_knowledge_vector_database(): if documents: try: - click.echo(click.style( - f'Start to created vector index with {len(documents)} documents of {segments_count} segments for dataset {dataset.id}.', - fg='green')) + click.echo( + click.style( + f"Start to created vector index with {len(documents)} documents of {segments_count} 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.style( + f"Successfully 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')) + click.echo( + click.style( + f"Failed to created vector index for dataset {dataset.id}.", + fg="red", + ) + ) raise e db.session.add(dataset) db.session.commit() - click.echo(f'Successfully migrated dataset {dataset.id}.') + click.echo(f"Successfully migrated dataset {dataset.id}.") create_count += 1 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( + "Create dataset index error: {} {}".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')) + click.style( + f"Congratulations! Create {create_count} dataset indexes, and skipped {skipped_count} datasets.", + fg="green", + ) + ) -@click.command('convert-to-agent-apps', help='Convert Agent Assistant to Agent App.') +@click.command("convert-to-agent-apps", help="Convert Agent Assistant to Agent App.") 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("Start convert to agent apps.", fg="green")) proceeded_app_ids = [] @@ -419,7 +553,7 @@ def convert_to_agent_apps(): break for app in apps: - click.echo('Converting app: {}'.format(app.id)) + click.echo("Converting app: {}".format(app.id)) try: app.mode = AppMode.AGENT_CHAT.value @@ -431,16 +565,25 @@ def convert_to_agent_apps(): ) db.session.commit() - click.echo(click.style('Converted app: {}'.format(app.id), fg='green')) + click.echo(click.style("Converted app: {}".format(app.id), fg="green")) except Exception as e: click.echo( - click.style('Convert app error: {} {}'.format(e.__class__.__name__, - str(e)), fg='red')) + 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( + "Congratulations! Converted {} agent apps.".format(len(proceeded_app_ids)), + fg="green", + ) + ) def register_commands(app): + app.cli.add_command(register) app.cli.add_command(reset_password) app.cli.add_command(reset_email) app.cli.add_command(reset_encrypt_key_pair) diff --git a/api/core/model_runtime/model_providers/_position.yaml b/api/core/model_runtime/model_providers/_position.yaml index c06f1229849393..62cae69dbede4f 100644 --- a/api/core/model_runtime/model_providers/_position.yaml +++ b/api/core/model_runtime/model_providers/_position.yaml @@ -1,3 +1,5 @@ +- modelhub +- openai_api_compatible - openai - anthropic - azure_openai @@ -26,4 +28,3 @@ - yi - openllm - localai -- openai_api_compatible diff --git a/api/core/model_runtime/model_providers/modelhub/__init__.py b/api/core/model_runtime/model_providers/modelhub/__init__.py new file mode 100644 index 00000000000000..e69de29bb2d1d6 diff --git a/api/core/model_runtime/model_providers/modelhub/_assets/icon_l_en.svg b/api/core/model_runtime/model_providers/modelhub/_assets/icon_l_en.svg new file mode 100644 index 00000000000000..f834befb0e3d9d --- /dev/null +++ b/api/core/model_runtime/model_providers/modelhub/_assets/icon_l_en.svg @@ -0,0 +1,4 @@ + + + +
ModelHub
 \ No newline at end of file diff --git a/api/core/model_runtime/model_providers/modelhub/_assets/icon_s_en.svg b/api/core/model_runtime/model_providers/modelhub/_assets/icon_s_en.svg new file mode 100644 index 00000000000000..70686f9b3b58aa --- /dev/null +++ b/api/core/model_runtime/model_providers/modelhub/_assets/icon_s_en.svg @@ -0,0 +1,4 @@ + + + + diff --git a/api/core/model_runtime/model_providers/modelhub/_common.py b/api/core/model_runtime/model_providers/modelhub/_common.py new file mode 100644 index 00000000000000..51950ca3778424 --- /dev/null +++ b/api/core/model_runtime/model_providers/modelhub/_common.py @@ -0,0 +1,44 @@ + +import requests + +from core.model_runtime.errors.invoke import ( + InvokeAuthorizationError, + InvokeBadRequestError, + InvokeConnectionError, + InvokeError, + InvokeRateLimitError, + InvokeServerUnavailableError, +) + + +class _CommonOAI_API_Compat: + @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 { + InvokeAuthorizationError: [ + requests.exceptions.InvalidHeader, # Missing or Invalid API Key + ], + InvokeBadRequestError: [ + requests.exceptions.HTTPError, # Invalid Endpoint URL or model name + requests.exceptions.InvalidURL, # Misconfigured request or other API error + ], + InvokeRateLimitError: [ + requests.exceptions.RetryError # Too many requests sent in a short period of time + ], + InvokeServerUnavailableError: [ + requests.exceptions.ConnectionError, # Engine Overloaded + requests.exceptions.HTTPError # Server Error + ], + InvokeConnectionError: [ + requests.exceptions.ConnectTimeout, # Timeout + requests.exceptions.ReadTimeout # Timeout + ] + } \ No newline at end of file diff --git a/api/core/model_runtime/model_providers/modelhub/llm/Baichuan2-Turbo.yaml b/api/core/model_runtime/model_providers/modelhub/llm/Baichuan2-Turbo.yaml new file mode 100644 index 00000000000000..eeaabff6ea4092 --- /dev/null +++ b/api/core/model_runtime/model_providers/modelhub/llm/Baichuan2-Turbo.yaml @@ -0,0 +1,27 @@ +model: Baichuan2-Turbo +label: + zh_Hans: Baichuan2-Turbo + en_US: Baichuan2-Turbo +model_type: llm +features: + - multi-tool-call + - agent-thought + - stream-tool-call +model_properties: + mode: chat + context_size: 8192 +parameter_rules: + - name: temperature + use_template: temperature + - name: top_p + use_template: top_p + - name: max_tokens + use_template: max_tokens + default: 512 + min: 1 + max: 8192 +pricing: + input: "0.003" + output: "0.004" + unit: "0.001" + currency: USD diff --git a/api/core/model_runtime/model_providers/modelhub/llm/__init__.py b/api/core/model_runtime/model_providers/modelhub/llm/__init__.py new file mode 100644 index 00000000000000..e69de29bb2d1d6 diff --git a/api/core/model_runtime/model_providers/modelhub/llm/_position.yaml b/api/core/model_runtime/model_providers/modelhub/llm/_position.yaml new file mode 100644 index 00000000000000..efa975755e5a72 --- /dev/null +++ b/api/core/model_runtime/model_providers/modelhub/llm/_position.yaml @@ -0,0 +1,4 @@ +- gpt-3.5-turbo +- gpt-4 +- glm-3-turbo +- glm-4 \ No newline at end of file diff --git a/api/core/model_runtime/model_providers/modelhub/llm/glm-3-turbo.yaml b/api/core/model_runtime/model_providers/modelhub/llm/glm-3-turbo.yaml new file mode 100644 index 00000000000000..06b42a14ba845d --- /dev/null +++ b/api/core/model_runtime/model_providers/modelhub/llm/glm-3-turbo.yaml @@ -0,0 +1,21 @@ +model: glm-3-turbo +label: + zh_Hans: glm-3-turbo + en_US: glm-3-turbo +model_type: llm +features: + - multi-tool-call + - agent-thought + - stream-tool-call +model_properties: + mode: chat + context_size: 8192 +parameter_rules: + - name: temperature + use_template: temperature + default: 0.01 +pricing: + input: "0.003" + output: "0.004" + unit: "0.001" + currency: USD diff --git a/api/core/model_runtime/model_providers/modelhub/llm/glm-4.yaml b/api/core/model_runtime/model_providers/modelhub/llm/glm-4.yaml new file mode 100644 index 00000000000000..c23686846a9f10 --- /dev/null +++ b/api/core/model_runtime/model_providers/modelhub/llm/glm-4.yaml @@ -0,0 +1,21 @@ +model: glm-4 +label: + zh_Hans: glm-4 + en_US: glm-4 +model_type: llm +features: + - multi-tool-call + - agent-thought + - stream-tool-call +model_properties: + mode: chat + context_size: 8192 +parameter_rules: + - name: temperature + use_template: temperature + default: 0.01 +pricing: + input: "0.003" + output: "0.004" + unit: "0.001" + currency: USD diff --git a/api/core/model_runtime/model_providers/modelhub/llm/gpt-3.5-turbo.yaml b/api/core/model_runtime/model_providers/modelhub/llm/gpt-3.5-turbo.yaml new file mode 100644 index 00000000000000..72a3b7065dc0fc --- /dev/null +++ b/api/core/model_runtime/model_providers/modelhub/llm/gpt-3.5-turbo.yaml @@ -0,0 +1,33 @@ +model: gpt-3.5-turbo +label: + zh_Hans: gpt-3.5-turbo + en_US: gpt-3.5-turbo +model_type: llm +features: + - multi-tool-call + - agent-thought + - stream-tool-call +model_properties: + mode: chat + context_size: 8192 +parameter_rules: + - name: temperature + use_template: temperature + - name: top_p + use_template: top_p + - name: presence_penalty + use_template: presence_penalty + - name: frequency_penalty + use_template: frequency_penalty + - name: max_tokens + use_template: max_tokens + default: 512 + min: 1 + max: 8192 + - name: response_format + use_template: response_format +pricing: + input: "0.003" + output: "0.004" + unit: "0.001" + currency: USD diff --git a/api/core/model_runtime/model_providers/modelhub/llm/gpt-4.yaml b/api/core/model_runtime/model_providers/modelhub/llm/gpt-4.yaml new file mode 100644 index 00000000000000..415b8dd77bf598 --- /dev/null +++ b/api/core/model_runtime/model_providers/modelhub/llm/gpt-4.yaml @@ -0,0 +1,33 @@ +model: gpt-4 +label: + zh_Hans: gpt-4 + en_US: gpt-4 +model_type: llm +features: + - multi-tool-call + - agent-thought + - stream-tool-call +model_properties: + mode: chat + context_size: 8192 +parameter_rules: + - name: temperature + use_template: temperature + - name: top_p + use_template: top_p + - name: presence_penalty + use_template: presence_penalty + - name: frequency_penalty + use_template: frequency_penalty + - name: max_tokens + use_template: max_tokens + default: 512 + min: 1 + max: 8192 + - name: response_format + use_template: response_format +pricing: + input: "0.003" + output: "0.004" + unit: "0.001" + currency: USD diff --git a/api/core/model_runtime/model_providers/modelhub/llm/llm.py b/api/core/model_runtime/model_providers/modelhub/llm/llm.py new file mode 100644 index 00000000000000..1c559077920ea0 --- /dev/null +++ b/api/core/model_runtime/model_providers/modelhub/llm/llm.py @@ -0,0 +1,782 @@ +import json +import logging +from collections.abc import Generator +from decimal import Decimal +from typing import Optional, Union, cast +from urllib.parse import urljoin + +import requests + +from core.model_runtime.entities.common_entities import I18nObject +from core.model_runtime.entities.llm_entities import LLMMode, LLMResult, LLMResultChunk, LLMResultChunkDelta +from core.model_runtime.entities.message_entities import ( + AssistantPromptMessage, + ImagePromptMessageContent, + PromptMessage, + PromptMessageContent, + PromptMessageContentType, + PromptMessageFunction, + PromptMessageTool, + SystemPromptMessage, + ToolPromptMessage, + UserPromptMessage, +) +from core.model_runtime.entities.model_entities import ( + AIModelEntity, + DefaultParameterName, + FetchFrom, + ModelFeature, + ModelPropertyKey, + ModelType, + ParameterRule, + ParameterType, + PriceConfig, +) +from core.model_runtime.errors.invoke import InvokeError +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.modelhub._common import _CommonOAI_API_Compat +from core.model_runtime.utils import helper + +logger = logging.getLogger(__name__) + + +class ModelHubLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel): + """ + Model class for OpenAI 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 + """ + + # text completion model + return self._generate( + model=model, + credentials=credentials, + prompt_messages=prompt_messages, + model_parameters=model_parameters, + tools=tools, + stop=stop, + stream=stream, + user=user + ) + + 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: + :param credentials: + :param 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 using requests to ensure compatibility with all providers following OpenAI's API standard. + + :param model: model name + :param credentials: model credentials + :return: + """ + try: + headers = { + 'Content-Type': 'application/json' + } + + api_key = credentials.get('api_key') + if api_key: + headers["Authorization"] = f"Bearer {api_key}" + + endpoint_url = credentials['endpoint_url'] + if not endpoint_url.endswith('/'): + endpoint_url += '/' + + # prepare the payload for a simple ping to the model + data = { + 'model': model, + 'max_tokens': 5 + } + + data['messages'] = [ + { + "role": "user", + "content": "ping" + }, + ] + endpoint_url = urljoin(endpoint_url, 'chat/completions') + + # send a post request to validate the credentials + response = requests.post( + endpoint_url, + headers=headers, + json=data, + timeout=(10, 60) + ) + + if response.status_code != 200: + raise CredentialsValidateFailedError( + f'Credentials validation failed with status code {response.status_code}') + + try: + json_result = response.json() + except json.JSONDecodeError as e: + raise CredentialsValidateFailedError('Credentials validation failed: JSON decode error') + + if json_result['object'] == '': + json_result['object'] = 'chat.completion' + + if 'object' not in json_result or json_result['object'] != 'chat.completion': + raise CredentialsValidateFailedError( + 'Credentials validation failed: invalid response object, must be \'chat.completion\'') + except CredentialsValidateFailedError: + raise + except Exception as ex: + raise CredentialsValidateFailedError(f'An error occurred during credentials validation: {str(ex)}') + + def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity: + """ + generate custom model entities from credentials + """ + features = [] + + function_calling_type = credentials.get('function_calling_type', 'no_call') + if function_calling_type in ['function_call']: + features.append(ModelFeature.TOOL_CALL) + elif function_calling_type in ['tool_call']: + features.append(ModelFeature.MULTI_TOOL_CALL) + + stream_function_calling = credentials.get('stream_function_calling', 'supported') + if stream_function_calling == 'supported': + features.append(ModelFeature.STREAM_TOOL_CALL) + + vision_support = credentials.get('vision_support', 'not_support') + if vision_support == 'support': + features.append(ModelFeature.VISION) + + entity = AIModelEntity( + model=model, + label=I18nObject(en_US=model), + model_type=ModelType.LLM, + fetch_from=FetchFrom.CUSTOMIZABLE_MODEL, + features=features, + model_properties={ + ModelPropertyKey.CONTEXT_SIZE: int(credentials.get('context_size', "4096")), + ModelPropertyKey.MODE: credentials.get('mode'), + }, + parameter_rules=[ + ParameterRule( + name=DefaultParameterName.TEMPERATURE.value, + label=I18nObject(en_US="Temperature"), + type=ParameterType.FLOAT, + default=float(credentials.get('temperature', 0.7)), + min=0, + max=2, + precision=2 + ), + ParameterRule( + name=DefaultParameterName.TOP_P.value, + label=I18nObject(en_US="Top P"), + type=ParameterType.FLOAT, + default=float(credentials.get('top_p', 1)), + min=0, + max=1, + precision=2 + ), + ParameterRule( + name=DefaultParameterName.FREQUENCY_PENALTY.value, + label=I18nObject(en_US="Frequency Penalty"), + type=ParameterType.FLOAT, + default=float(credentials.get('frequency_penalty', 0)), + min=-2, + max=2 + ), + ParameterRule( + name=DefaultParameterName.PRESENCE_PENALTY.value, + label=I18nObject(en_US="Presence Penalty"), + type=ParameterType.FLOAT, + default=float(credentials.get('presence_penalty', 0)), + min=-2, + max=2 + ), + ParameterRule( + name=DefaultParameterName.MAX_TOKENS.value, + label=I18nObject(en_US="Max Tokens"), + type=ParameterType.INT, + default=512, + min=1, + max=int(credentials.get('max_tokens_to_sample', 4096)), + ) + ], + pricing=PriceConfig( + input=Decimal(credentials.get('input_price', 0)), + output=Decimal(credentials.get('output_price', 0)), + unit=Decimal(credentials.get('unit', 0)), + currency=credentials.get('currency', "USD") + ), + ) + + entity.model_properties[ModelPropertyKey.MODE] = LLMMode.CHAT.value + + return entity + + # validate_credentials method has been rewritten to use the requests library for compatibility with all providers following OpenAI's API standard. + def _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]: + """ + Invoke llm completion model + + :param model: model name + :param credentials: credentials + :param prompt_messages: prompt messages + :param model_parameters: model parameters + :param stop: stop words + :param stream: is stream response + :param user: unique user id + :return: full response or stream response chunk generator result + """ + headers = { + 'Content-Type': 'application/json', + 'Accept-Charset': 'utf-8', + } + + api_key = credentials.get('api_key') + if api_key: + headers["Authorization"] = f"Bearer {api_key}" + + endpoint_url = credentials["endpoint_url"] + if not endpoint_url.endswith('/'): + endpoint_url += '/' + + data = { + "model": model, + "stream": stream, + **model_parameters + } + + endpoint_url = urljoin(endpoint_url, 'chat/completions') + data['messages'] = [self._convert_prompt_message_to_dict(m) for m in prompt_messages] + + # annotate tools with names, descriptions, etc. + function_calling_type = credentials.get('function_calling_type', 'no_call') + formatted_tools = [] + if tools: + if function_calling_type == 'function_call': + data['functions'] = [{ + "name": tool.name, + "description": tool.description, + "parameters": tool.parameters + } for tool in tools] + elif function_calling_type == 'tool_call': + data["tool_choice"] = "auto" + + for tool in tools: + formatted_tools.append(helper.dump_model(PromptMessageFunction(function=tool))) + + data["tools"] = formatted_tools + + if stop: + data["stop"] = stop + + if user: + data["user"] = user + + response = requests.post( + endpoint_url, + headers=headers, + json=data, + timeout=(10, 60), + stream=stream + ) + + if response.encoding is None or response.encoding == 'ISO-8859-1': + response.encoding = 'utf-8' + + if response.status_code != 200: + raise InvokeError(f"API request failed with status code {response.status_code}: {response.text}") + + if stream: + return self._handle_generate_stream_response(model, credentials, response, prompt_messages) + + return self._handle_generate_response(model, credentials, response, prompt_messages) + + def _handle_generate_stream_response(self, model: str, credentials: dict, response: requests.Response, + prompt_messages: list[PromptMessage]) -> Generator: + """ + Handle llm stream response + + :param model: model name + :param credentials: model credentials + :param response: streamed response + :param prompt_messages: prompt messages + :return: llm response chunk generator + """ + full_assistant_content = '' + chunk_index = 0 + + def create_final_llm_result_chunk(index: int, message: AssistantPromptMessage, finish_reason: str) \ + -> LLMResultChunk: + # calculate num tokens + prompt_tokens = self._num_tokens_from_string(model, prompt_messages[0].content) + completion_tokens = self._num_tokens_from_string(model, full_assistant_content) + + # transform usage + usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens) + + return LLMResultChunk( + model=model, + prompt_messages=prompt_messages, + delta=LLMResultChunkDelta( + index=index, + message=message, + finish_reason=finish_reason, + usage=usage + ) + ) + + # delimiter for stream response, need unicode_escape + import codecs + delimiter = credentials.get("stream_mode_delimiter", "\n\n") + delimiter = codecs.decode(delimiter, "unicode_escape") + + tools_calls: list[AssistantPromptMessage.ToolCall] = [] + + def increase_tool_call(new_tool_calls: list[AssistantPromptMessage.ToolCall]): + def get_tool_call(tool_call_id: str): + if not tool_call_id: + return tools_calls[-1] + + tool_call = next((tool_call for tool_call in tools_calls if tool_call.id == tool_call_id), None) + if tool_call is None: + tool_call = AssistantPromptMessage.ToolCall( + id=tool_call_id, + type="function", + function=AssistantPromptMessage.ToolCall.ToolCallFunction( + name="", + arguments="" + ) + ) + tools_calls.append(tool_call) + + return tool_call + + for new_tool_call in new_tool_calls: + # get tool call + tool_call = get_tool_call(new_tool_call.function.name) + # update tool call + if new_tool_call.id: + tool_call.id = new_tool_call.id + if new_tool_call.type: + tool_call.type = new_tool_call.type + if new_tool_call.function.name: + tool_call.function.name = new_tool_call.function.name + if new_tool_call.function.arguments: + tool_call.function.arguments += new_tool_call.function.arguments + + finish_reason = 'Unknown' + + for chunk in response.iter_lines(decode_unicode=True, delimiter=delimiter): + if chunk: + # ignore sse comments + if chunk.startswith(':'): + continue + decoded_chunk = chunk.strip().lstrip('data: ').lstrip() + + try: + chunk_json = json.loads(decoded_chunk) + # stream ended + except json.JSONDecodeError as e: + yield create_final_llm_result_chunk( + index=chunk_index + 1, + message=AssistantPromptMessage(content=""), + finish_reason="Non-JSON encountered." + ) + break + if not chunk_json or len(chunk_json['choices']) == 0: + continue + + choice = chunk_json['choices'][0] + finish_reason = chunk_json['choices'][0].get('finish_reason') + chunk_index += 1 + + if 'delta' in choice: + delta = choice['delta'] + delta_content = delta.get('content') + + assistant_message_tool_calls = None + + if 'tool_calls' in delta and credentials.get('function_calling_type', 'no_call') == 'tool_call': + assistant_message_tool_calls = delta.get('tool_calls', None) + elif 'function_call' in delta and credentials.get('function_calling_type', 'no_call') == 'function_call': + assistant_message_tool_calls = [{ + 'id': 'tool_call_id', + 'type': 'function', + 'function': delta.get('function_call', {}) + }] + + # assistant_message_function_call = delta.delta.function_call + + # extract tool calls from response + if assistant_message_tool_calls: + tool_calls = self._extract_response_tool_calls(assistant_message_tool_calls) + increase_tool_call(tool_calls) + + if delta_content is None or delta_content == '': + continue + + # transform assistant message to prompt message + assistant_prompt_message = AssistantPromptMessage( + content=delta_content, + ) + + # reset tool calls + tool_calls = [] + full_assistant_content += delta_content + elif 'text' in choice: + choice_text = choice.get('text', '') + if choice_text == '': + continue + + # transform assistant message to prompt message + assistant_prompt_message = AssistantPromptMessage(content=choice_text) + full_assistant_content += choice_text + else: + continue + + yield LLMResultChunk( + model=model, + prompt_messages=prompt_messages, + delta=LLMResultChunkDelta( + index=chunk_index, + message=assistant_prompt_message, + ) + ) + + chunk_index += 1 + + if tools_calls: + yield LLMResultChunk( + model=model, + prompt_messages=prompt_messages, + delta=LLMResultChunkDelta( + index=chunk_index, + message=AssistantPromptMessage( + tool_calls=tools_calls, + content="" + ), + ) + ) + + yield create_final_llm_result_chunk( + index=chunk_index, + message=AssistantPromptMessage(content=""), + finish_reason=finish_reason + ) + + def _handle_generate_response(self, model: str, credentials: dict, response: requests.Response, + prompt_messages: list[PromptMessage]) -> LLMResult: + + response_json = response.json() + + output = response_json['choices'][0] + + response_content = '' + tool_calls = None + function_calling_type = credentials.get('function_calling_type', 'no_call') + response_content = output.get('message', {})['content'] + if function_calling_type == 'tool_call': + tool_calls = output.get('message', {}).get('tool_calls') + elif function_calling_type == 'function_call': + tool_calls = output.get('message', {}).get('function_call') + + + assistant_message = AssistantPromptMessage(content=response_content, tool_calls=[]) + + if tool_calls: + if function_calling_type == 'tool_call': + assistant_message.tool_calls = self._extract_response_tool_calls(tool_calls) + elif function_calling_type == 'function_call': + assistant_message.tool_calls = [self._extract_response_function_call(tool_calls)] + + usage = response_json.get("usage") + if usage: + # transform usage + prompt_tokens = usage["prompt_tokens"] + completion_tokens = usage["completion_tokens"] + else: + # calculate num tokens + prompt_tokens = self._num_tokens_from_string(model, prompt_messages[0].content) + completion_tokens = self._num_tokens_from_string(model, assistant_message.content) + + # transform usage + usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens) + + # transform response + result = LLMResult( + model=response_json["model"], + prompt_messages=prompt_messages, + message=assistant_message, + usage=usage, + ) + + return result + + def _convert_prompt_message_to_dict(self, message: PromptMessage) -> dict: + """ + Convert PromptMessage to dict for OpenAI API format + """ + 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(PromptMessageContent, 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"] = [helper.dump_model(PromptMessageFunction(function=tool_call)) 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_string(self, model: str, text: Union[str, list[PromptMessageContent]], + tools: Optional[list[PromptMessageTool]] = None) -> int: + """ + Approximate num tokens for model with gpt2 tokenizer. + + :param model: model name + :param text: prompt text + :param tools: tools for tool calling + :return: number of tokens + """ + if isinstance(text, str): + full_text = text + else: + full_text = '' + for message_content in text: + if message_content.type == PromptMessageContentType.TEXT: + message_content = cast(PromptMessageContent, message_content) + full_text += message_content.data + + num_tokens = self._get_num_tokens_by_gpt2(full_text) + + if tools: + num_tokens += self._num_tokens_for_tools(tools) + + return num_tokens + + def _num_tokens_from_messages(self, model: str, messages: list[PromptMessage], + tools: Optional[list[PromptMessageTool]] = 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 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 + + def _extract_response_tool_calls(self, + response_tool_calls: list[dict]) \ + -> 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.get("function", {}).get("name", ""), + arguments=response_tool_call.get("function", {}).get("arguments", "") + ) + + tool_call = AssistantPromptMessage.ToolCall( + id=response_tool_call.get("id", ""), + type=response_tool_call.get("type", ""), + function=function + ) + tool_calls.append(tool_call) + + return tool_calls + + def _extract_response_function_call(self, response_function_call) \ + -> 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.get('name', ''), + arguments=response_function_call.get('arguments', '') + ) + + tool_call = AssistantPromptMessage.ToolCall( + id=response_function_call.get('id', ''), + type="function", + function=function + ) + + return tool_call diff --git a/api/core/model_runtime/model_providers/modelhub/modelhub.py b/api/core/model_runtime/model_providers/modelhub/modelhub.py new file mode 100644 index 00000000000000..344c835308eb4a --- /dev/null +++ b/api/core/model_runtime/model_providers/modelhub/modelhub.py @@ -0,0 +1,11 @@ +import logging + +from core.model_runtime.model_providers.__base.model_provider import ModelProvider + +logger = logging.getLogger(__name__) + + +class ModelHubProvider(ModelProvider): + + def validate_provider_credentials(self, credentials: dict) -> None: + pass diff --git a/api/core/model_runtime/model_providers/modelhub/modelhub.yaml b/api/core/model_runtime/model_providers/modelhub/modelhub.yaml new file mode 100644 index 00000000000000..8c13bab976db9b --- /dev/null +++ b/api/core/model_runtime/model_providers/modelhub/modelhub.yaml @@ -0,0 +1,152 @@ +provider: modelhub +label: + en_US: ModelHub +description: + en_US: ModelHub + zh_Hans: ModelHub +icon_small: + en_US: icon_s_en.svg +icon_large: + en_US: icon_l_en.svg +background: "#FFFFFF" +supported_model_types: + - llm + - text-embedding + - rerank +configurate_methods: + - predefined-model + - customizable-model +model_credential_schema: + model: + label: + en_US: Model Name + zh_Hans: 模型名称 + placeholder: + en_US: Enter full model name + zh_Hans: 输入模型全称 + credential_form_schemas: + - variable: api_key + label: + en_US: API Key + type: secret-input + required: false + placeholder: + zh_Hans: 在此输入您的 API Key + en_US: Enter your API Key + - variable: endpoint_url + label: + zh_Hans: API endpoint URL + en_US: API endpoint URL + type: text-input + required: false + default: 'https://modelhub.puyuan.tech/api/v1' + placeholder: + zh_Hans: Base URL, e.g. https://api.openai.com/v1 + en_US: Base URL, e.g. https://api.openai.com/v1 + - variable: context_size + label: + zh_Hans: 模型上下文长度 + en_US: Model context size + required: true + type: text-input + default: '4096' + placeholder: + zh_Hans: 在此输入您的模型上下文长度 + en_US: Enter your Model context size + - variable: max_tokens_to_sample + label: + zh_Hans: 最大 token 上限 + en_US: Upper bound for max tokens + show_on: + - variable: __model_type + value: llm + default: '4096' + type: text-input + - variable: function_calling_type + show_on: + - variable: __model_type + value: llm + label: + en_US: Function calling + type: select + required: false + default: no_call + options: + - value: function_call + label: + en_US: Function Call + zh_Hans: Function Call + - value: tool_call + label: + en_US: Tool Call + zh_Hans: Tool Call + - value: no_call + label: + en_US: Not Support + zh_Hans: 不支持 + - variable: stream_function_calling + show_on: + - variable: __model_type + value: llm + label: + en_US: Stream function calling + type: select + required: false + default: not_supported + options: + - value: supported + label: + en_US: Support + zh_Hans: 支持 + - value: not_supported + label: + en_US: Not Support + zh_Hans: 不支持 + - variable: vision_support + show_on: + - variable: __model_type + value: llm + label: + zh_Hans: Vision 支持 + en_US: Vision Support + type: select + required: false + default: no_support + options: + - value: support + label: + en_US: Support + zh_Hans: 支持 + - value: no_support + label: + en_US: Not Support + zh_Hans: 不支持 + - variable: stream_mode_delimiter + label: + zh_Hans: 流模式返回结果的分隔符 + en_US: Delimiter for streaming results + show_on: + - variable: __model_type + value: llm + default: '\n\n' + type: text-input +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 + - variable: endpoint_url + label: + zh_Hans: endpoint_url + en_US: endpoint_url + type: text-input + required: false + default: 'https://modelhub.puyuan.tech/api/v1' + placeholder: + zh_Hans: 在此输入您的 endpoint_url + en_US: Enter your endpoint_url \ No newline at end of file diff --git a/api/core/model_runtime/model_providers/modelhub/rerank/__init__.py b/api/core/model_runtime/model_providers/modelhub/rerank/__init__.py new file mode 100644 index 00000000000000..e69de29bb2d1d6 diff --git a/api/core/model_runtime/model_providers/modelhub/rerank/bge-reranker-base.yaml b/api/core/model_runtime/model_providers/modelhub/rerank/bge-reranker-base.yaml new file mode 100644 index 00000000000000..62831c8bb9776e --- /dev/null +++ b/api/core/model_runtime/model_providers/modelhub/rerank/bge-reranker-base.yaml @@ -0,0 +1,4 @@ +model: bge-reranker-base +model_type: rerank +model_properties: + context_size: 5120 diff --git a/api/core/model_runtime/model_providers/modelhub/rerank/bge-reranker-v2-m3.yaml b/api/core/model_runtime/model_providers/modelhub/rerank/bge-reranker-v2-m3.yaml new file mode 100644 index 00000000000000..0ee6309ff29e58 --- /dev/null +++ b/api/core/model_runtime/model_providers/modelhub/rerank/bge-reranker-v2-m3.yaml @@ -0,0 +1,4 @@ +model: bge-reranker-v2-m3 +model_type: rerank +model_properties: + context_size: 5120 diff --git a/api/core/model_runtime/model_providers/modelhub/rerank/rerank.py b/api/core/model_runtime/model_providers/modelhub/rerank/rerank.py new file mode 100644 index 00000000000000..2e2623eccfc683 --- /dev/null +++ b/api/core/model_runtime/model_providers/modelhub/rerank/rerank.py @@ -0,0 +1,100 @@ +from typing import Optional + +import numpy as np +from modelhub import ModelhubClient + +from core.model_runtime.entities.rerank_entities import RerankDocument, RerankResult +from core.model_runtime.errors.invoke import ( + InvokeError, +) +from core.model_runtime.errors.validate import CredentialsValidateFailedError +from core.model_runtime.model_providers.__base.rerank_model import RerankModel + + +class CohereRerankModel(RerankModel): + """ + Model class for Cohere rerank model. + """ + + def _invoke( + self, + model: str, + credentials: dict, + query: str, + docs: list[str], + score_threshold: Optional[float] = None, + top_n: Optional[int] = None, + user: Optional[str] = None, + ) -> RerankResult: + """ + Invoke rerank model + + :param model: model name + :param credentials: model credentials + :param query: search query + :param docs: docs for reranking + :param score_threshold: score threshold + :param top_n: top n + :param user: unique user id + :return: rerank result + """ + if len(docs) == 0: + return RerankResult(model=model, docs=docs) + + user_name, user_password = credentials["api_key"].split(":") + client = ModelhubClient( + user_name=user_name, + user_password=user_password, + host=credentials["endpoint_url"].replace("v1", ""), + ) + scores = client.cross_embedding( + [[query, doc] for doc in docs], + model=model, + ).scores + sort_idx = np.argsort(scores)[::-1] + rerank_documents = [] + for i in sort_idx[:top_n] if top_n is not None else sort_idx: + rerank_document = RerankDocument(index=i, text=docs[i], score=scores[i]) + if score_threshold is not None: + if scores[i] >= score_threshold: + rerank_documents.append(rerank_document) + else: + rerank_documents.append(rerank_document) + + return RerankResult(model=model, docs=rerank_documents) + + def validate_credentials(self, model: str, credentials: dict) -> None: + """ + Validate model credentials + + :param model: model name + :param credentials: model credentials + :return: + """ + try: + self.invoke( + model=model, + credentials=credentials, + query="What is the capital of the United States?", + docs=[ + "Carson City is the capital city of the American state of Nevada. At the 2010 United States " + "Census, Carson City had a population of 55,274.", + "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean that " + "are a political division controlled by the United States. Its capital is Saipan.", + ], + score_threshold=0.8, + ) + except Exception as ex: + raise CredentialsValidateFailedError(str(ex)) + + @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 {} diff --git a/api/core/model_runtime/model_providers/modelhub/text_embedding/__init__.py b/api/core/model_runtime/model_providers/modelhub/text_embedding/__init__.py new file mode 100644 index 00000000000000..e69de29bb2d1d6 diff --git a/api/core/model_runtime/model_providers/modelhub/text_embedding/bge-m3.yaml b/api/core/model_runtime/model_providers/modelhub/text_embedding/bge-m3.yaml new file mode 100644 index 00000000000000..13571b282e3ab9 --- /dev/null +++ b/api/core/model_runtime/model_providers/modelhub/text_embedding/bge-m3.yaml @@ -0,0 +1,9 @@ +model: bge-m3 +model_type: text-embedding +model_properties: + context_size: 8191 + max_chunks: 32 +pricing: + input: '0.00013' + unit: '0.001' + currency: USD diff --git a/api/core/model_runtime/model_providers/modelhub/text_embedding/m3e-large.yaml b/api/core/model_runtime/model_providers/modelhub/text_embedding/m3e-large.yaml new file mode 100644 index 00000000000000..005a87f3ef9505 --- /dev/null +++ b/api/core/model_runtime/model_providers/modelhub/text_embedding/m3e-large.yaml @@ -0,0 +1,9 @@ +model: m3e-large +model_type: text-embedding +model_properties: + context_size: 8191 + max_chunks: 32 +pricing: + input: '0.00013' + unit: '0.001' + currency: USD diff --git a/api/core/model_runtime/model_providers/modelhub/text_embedding/text-embedding-3-large.yaml b/api/core/model_runtime/model_providers/modelhub/text_embedding/text-embedding-3-large.yaml new file mode 100644 index 00000000000000..9489170fdea0b4 --- /dev/null +++ b/api/core/model_runtime/model_providers/modelhub/text_embedding/text-embedding-3-large.yaml @@ -0,0 +1,9 @@ +model: text-embedding-3-large +model_type: text-embedding +model_properties: + context_size: 8191 + max_chunks: 32 +pricing: + input: '0.00013' + unit: '0.001' + currency: USD diff --git a/api/core/model_runtime/model_providers/modelhub/text_embedding/text-embedding-3-small.yaml b/api/core/model_runtime/model_providers/modelhub/text_embedding/text-embedding-3-small.yaml new file mode 100644 index 00000000000000..9f46210e55b970 --- /dev/null +++ b/api/core/model_runtime/model_providers/modelhub/text_embedding/text-embedding-3-small.yaml @@ -0,0 +1,9 @@ +model: text-embedding-3-small +model_type: text-embedding +model_properties: + context_size: 8191 + max_chunks: 32 +pricing: + input: '0.00013' + unit: '0.001' + currency: USD diff --git a/api/core/model_runtime/model_providers/modelhub/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/modelhub/text_embedding/text_embedding.py new file mode 100644 index 00000000000000..a1e0e5143fb8c1 --- /dev/null +++ b/api/core/model_runtime/model_providers/modelhub/text_embedding/text_embedding.py @@ -0,0 +1,244 @@ +import json +import time +from decimal import Decimal +from typing import Optional +from urllib.parse import urljoin + +import numpy as np +import requests + +from core.model_runtime.entities.common_entities import I18nObject +from core.model_runtime.entities.model_entities import ( + AIModelEntity, + FetchFrom, + ModelPropertyKey, + ModelType, + PriceConfig, + PriceType, +) +from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult +from core.model_runtime.errors.validate import CredentialsValidateFailedError +from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel +from core.model_runtime.model_providers.modelhub._common import _CommonOAI_API_Compat + + +class ModelHubEmbeddingModel(_CommonOAI_API_Compat, TextEmbeddingModel): + """ + Model class for an OpenAI API-compatible text embedding model. + """ + + def _invoke(self, model: str, credentials: dict, + texts: list[str], user: Optional[str] = None) \ + -> TextEmbeddingResult: + """ + Invoke text embedding model + + :param model: model name + :param credentials: model credentials + :param texts: texts to embed + :param user: unique user id + :return: embeddings result + """ + + # Prepare headers and payload for the request + headers = { + 'Content-Type': 'application/json' + } + + api_key = credentials.get('api_key') + if api_key: + headers["Authorization"] = f"Bearer {api_key}" + + endpoint_url = credentials.get('endpoint_url') + if not endpoint_url.endswith('/'): + endpoint_url += '/' + + endpoint_url = urljoin(endpoint_url, 'embeddings') + + extra_model_kwargs = {} + if user: + extra_model_kwargs['user'] = user + + extra_model_kwargs['encoding_format'] = 'float' + + # get model properties + context_size = self._get_context_size(model, credentials) + max_chunks = self._get_max_chunks(model, credentials) + + inputs = [] + indices = [] + used_tokens = 0 + + for i, text in enumerate(texts): + + # Here token count is only an approximation based on the GPT2 tokenizer + # TODO: Optimize for better token estimation and chunking + num_tokens = self._get_num_tokens_by_gpt2(text) + + if num_tokens >= context_size: + cutoff = int(len(text) * (np.floor(context_size / num_tokens))) + # if num tokens is larger than context length, only use the start + inputs.append(text[0: cutoff]) + else: + inputs.append(text) + indices += [i] + + batched_embeddings = [] + _iter = range(0, len(inputs), max_chunks) + + for i in _iter: + # Prepare the payload for the request + payload = { + 'input': inputs[i: i + max_chunks], + 'model': model, + **extra_model_kwargs + } + + # Make the request to the OpenAI API + response = requests.post( + endpoint_url, + headers=headers, + data=json.dumps(payload), + timeout=(10, 300) + ) + + response.raise_for_status() # Raise an exception for HTTP errors + response_data = response.json() + + # Extract embeddings and used tokens from the response + embeddings_batch = [data['embedding'] for data in response_data['data']] + embedding_used_tokens = response_data['usage']['total_tokens'] + + used_tokens += embedding_used_tokens + batched_embeddings += embeddings_batch + + # calc usage + usage = self._calc_response_usage( + model=model, + credentials=credentials, + tokens=used_tokens + ) + + return TextEmbeddingResult( + embeddings=batched_embeddings, + usage=usage, + model=model + ) + + def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int: + """ + Approximate number of tokens for given messages using GPT2 tokenizer + + :param model: model name + :param credentials: model credentials + :param texts: texts to embed + :return: + """ + return sum(self._get_num_tokens_by_gpt2(text) for text in texts) + + def validate_credentials(self, model: str, credentials: dict) -> None: + """ + Validate model credentials + + :param model: model name + :param credentials: model credentials + :return: + """ + try: + headers = { + 'Content-Type': 'application/json' + } + + api_key = credentials.get('api_key') + + if api_key: + headers["Authorization"] = f"Bearer {api_key}" + + endpoint_url = credentials.get('endpoint_url') + if not endpoint_url.endswith('/'): + endpoint_url += '/' + + endpoint_url = urljoin(endpoint_url, 'embeddings') + + payload = { + 'input': 'ping', + 'model': model + } + + response = requests.post( + url=endpoint_url, + headers=headers, + data=json.dumps(payload), + timeout=(10, 300) + ) + + if response.status_code != 200: + raise CredentialsValidateFailedError( + f'Credentials validation failed with status code {response.status_code}') + + try: + json_result = response.json() + except json.JSONDecodeError as e: + raise CredentialsValidateFailedError('Credentials validation failed: JSON decode error') + + if 'model' not in json_result: + raise CredentialsValidateFailedError( + 'Credentials validation failed: invalid response') + except CredentialsValidateFailedError: + raise + except Exception as ex: + raise CredentialsValidateFailedError(str(ex)) + + def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity: + """ + generate custom model entities from credentials + """ + entity = AIModelEntity( + model=model, + label=I18nObject(en_US=model), + model_type=ModelType.TEXT_EMBEDDING, + fetch_from=FetchFrom.CUSTOMIZABLE_MODEL, + model_properties={ + ModelPropertyKey.CONTEXT_SIZE: int(credentials.get('context_size')), + ModelPropertyKey.MAX_CHUNKS: 1, + }, + parameter_rules=[], + pricing=PriceConfig( + input=Decimal(credentials.get('input_price', 0)), + unit=Decimal(credentials.get('unit', 0)), + currency=credentials.get('currency', "USD") + ) + ) + + return entity + + + def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> EmbeddingUsage: + """ + Calculate response usage + + :param model: model name + :param credentials: model credentials + :param tokens: input tokens + :return: usage + """ + # get input price info + input_price_info = self.get_price( + model=model, + credentials=credentials, + price_type=PriceType.INPUT, + tokens=tokens + ) + + # transform usage + usage = EmbeddingUsage( + tokens=tokens, + total_tokens=tokens, + unit_price=input_price_info.unit_price, + price_unit=input_price_info.unit, + total_price=input_price_info.total_amount, + currency=input_price_info.currency, + latency=time.perf_counter() - self.started_at + ) + + return usage diff --git a/api/requirements.txt b/api/requirements.txt index 5db66f5b354b97..39fdc1d94be4db 100644 --- a/api/requirements.txt +++ b/api/requirements.txt @@ -29,7 +29,7 @@ redis[hiredis]~=5.0.3 openpyxl==3.1.2 chardet~=5.1.0 python-docx~=1.1.0 -pypdfium2~=4.17.0 +pypdfium2 resend~=0.7.0 pyjwt~=2.8.0 anthropic~=0.23.1 diff --git a/docker/docker-compose.middleware.yaml b/docker/docker-compose.middleware.yaml index f85f188bb9b407..9646b5493154d6 100644 --- a/docker/docker-compose.middleware.yaml +++ b/docker/docker-compose.middleware.yaml @@ -29,27 +29,27 @@ services: - "6379:6379" # The Weaviate vector store. - weaviate: - image: semitechnologies/weaviate:1.19.0 - restart: always - volumes: - # Mount the Weaviate data directory to the container. - - ./volumes/weaviate:/var/lib/weaviate - environment: - # The Weaviate configurations - # You can refer to the [Weaviate](https://weaviate.io/developers/weaviate/config-refs/env-vars) documentation for more information. - QUERY_DEFAULTS_LIMIT: 25 - AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: 'false' - PERSISTENCE_DATA_PATH: '/var/lib/weaviate' - DEFAULT_VECTORIZER_MODULE: 'none' - CLUSTER_HOSTNAME: 'node1' - AUTHENTICATION_APIKEY_ENABLED: 'true' - AUTHENTICATION_APIKEY_ALLOWED_KEYS: 'WVF5YThaHlkYwhGUSmCRgsX3tD5ngdN8pkih' - AUTHENTICATION_APIKEY_USERS: 'hello@dify.ai' - AUTHORIZATION_ADMINLIST_ENABLED: 'true' - AUTHORIZATION_ADMINLIST_USERS: 'hello@dify.ai' - ports: - - "8080:8080" + # weaviate: + # image: semitechnologies/weaviate:1.19.0 + # restart: always + # volumes: + # # Mount the Weaviate data directory to the container. + # - ./volumes/weaviate:/var/lib/weaviate + # environment: + # # The Weaviate configurations + # # You can refer to the [Weaviate](https://weaviate.io/developers/weaviate/config-refs/env-vars) documentation for more information. + # QUERY_DEFAULTS_LIMIT: 25 + # AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: 'false' + # PERSISTENCE_DATA_PATH: '/var/lib/weaviate' + # DEFAULT_VECTORIZER_MODULE: 'none' + # CLUSTER_HOSTNAME: 'node1' + # AUTHENTICATION_APIKEY_ENABLED: 'true' + # AUTHENTICATION_APIKEY_ALLOWED_KEYS: 'WVF5YThaHlkYwhGUSmCRgsX3tD5ngdN8pkih' + # AUTHENTICATION_APIKEY_USERS: 'hello@dify.ai' + # AUTHORIZATION_ADMINLIST_ENABLED: 'true' + # AUTHORIZATION_ADMINLIST_USERS: 'hello@dify.ai' + # ports: + # - "8080:8080" # The DifySandbox sandbox: diff --git a/web/package.json b/web/package.json index bfcd93177d6067..10428991614358 100644 --- a/web/package.json +++ b/web/package.json @@ -5,7 +5,7 @@ "scripts": { "dev": "next dev", "build": "next build", - "start": "cp -r .next/static .next/standalone/.next/static && cp -r public .next/standalone/public && cross-env PORT=$npm_config_port HOSTNAME=$npm_config_host node .next/standalone/server.js", + "start": "cp -r .next/static .next/standalone/.next/static && cp -r public .next/standalone/public && cross-env PORT=4544 HOSTNAME=127.0.0.1 node .next/standalone/server.js", "lint": "next lint", "fix": "next lint --fix", "eslint-fix": "eslint --fix", diff --git a/web/yarn.lock b/web/yarn.lock index d57f3cc3e3985f..3a51a5c4a86f2e 100644 --- a/web/yarn.lock +++ b/web/yarn.lock @@ -105,13 +105,6 @@ resolved "https://registry.npmjs.org/@braintree/sanitize-url/-/sanitize-url-6.0.4.tgz" integrity 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