diff --git a/.gitattributes b/.gitattributes index 4d5551fd9d0e2ccc6c5b8ec8a399d0846eda32f2..9c14c8705a550e864cd87de5e76b228faab9cbf1 100644 --- a/.gitattributes +++ b/.gitattributes @@ -26,3 +26,9 @@ api/core/model_runtime/docs/zh_Hans/images/index/image-20231210144229650.png fil api/core/model_runtime/docs/zh_Hans/images/index/image-20231210144814617.png filter=lfs diff=lfs merge=lfs -text api/core/model_runtime/docs/zh_Hans/images/index/image-20231210165243632.png filter=lfs diff=lfs merge=lfs -text api/core/model_runtime/docs/zh_Hans/images/index/image.png filter=lfs diff=lfs merge=lfs -text +api/core/model_runtime/model_providers/__base/audio.mp3 filter=lfs diff=lfs merge=lfs -text +api/core/model_runtime/model_providers/gpustack/_assets/icon_l_en.png filter=lfs diff=lfs merge=lfs -text +api/core/model_runtime/model_providers/leptonai/_assets/icon_l_en.png filter=lfs diff=lfs merge=lfs -text +api/core/model_runtime/model_providers/mixedbread/_assets/icon_l_en.png filter=lfs diff=lfs merge=lfs -text +api/core/model_runtime/model_providers/nvidia_nim/_assets/icon_l_en.png filter=lfs diff=lfs merge=lfs -text +api/core/model_runtime/model_providers/nvidia/_assets/icon_l_en.png filter=lfs diff=lfs merge=lfs -text diff --git a/api/core/model_runtime/README.md b/api/core/model_runtime/README.md new file mode 100644 index 0000000000000000000000000000000000000000..b5de7ad412067d56c6134d6baff8155ed82f2021 --- /dev/null +++ b/api/core/model_runtime/README.md @@ -0,0 +1,70 @@ +# Model Runtime + +This module provides the interface for invoking and authenticating various models, and offers Dify a unified information and credentials form rule for model providers. + +- On one hand, it decouples models from upstream and downstream processes, facilitating horizontal expansion for developers, +- On the other hand, it allows for direct display of providers and models in the frontend interface by simply defining them in the backend, eliminating the need to modify frontend logic. + +## Features + +- Supports capability invocation for 5 types of models + + - `LLM` - LLM text completion, dialogue, pre-computed tokens capability + - `Text Embedding Model` - Text Embedding, pre-computed tokens capability + - `Rerank Model` - Segment Rerank capability + - `Speech-to-text Model` - Speech to text capability + - `Text-to-speech Model` - Text to speech capability + - `Moderation` - Moderation capability + +- Model provider display + + ![image-20231210143654461](./docs/en_US/images/index/image-20231210143654461.png) + + Displays a list of all supported providers, including provider names, icons, supported model types list, predefined model list, configuration method, and credentials form rules, etc. For detailed rule design, see: [Schema](./docs/en_US/schema.md). + +- Selectable model list display + + ![image-20231210144229650](./docs/en_US/images/index/image-20231210144229650.png) + + After configuring provider/model credentials, the dropdown (application orchestration interface/default model) allows viewing of the available LLM list. Greyed out items represent predefined model lists from providers without configured credentials, facilitating user review of supported models. + + In addition, this list also returns configurable parameter information and rules for LLM, as shown below: + + ![image-20231210144814617](./docs/en_US/images/index/image-20231210144814617.png) + + These parameters are all defined in the backend, allowing different settings for various parameters supported by different models, as detailed in: [Schema](./docs/en_US/schema.md#ParameterRule). + +- Provider/model credential authentication + + ![image-20231210151548521](./docs/en_US/images/index/image-20231210151548521.png) + + ![image-20231210151628992](./docs/en_US/images/index/image-20231210151628992.png) + + The provider list returns configuration information for the credentials form, which can be authenticated through Runtime's interface. The first image above is a provider credential DEMO, and the second is a model credential DEMO. + +## Structure + +![](./docs/en_US/images/index/image-20231210165243632.png) + +Model Runtime is divided into three layers: + +- The outermost layer is the factory method + + It provides methods for obtaining all providers, all model lists, getting provider instances, and authenticating provider/model credentials. + +- The second layer is the provider layer + + It provides the current provider's model list, model instance obtaining, provider credential authentication, and provider configuration rule information, **allowing horizontal expansion** to support different providers. + +- The bottom layer is the model layer + + It offers direct invocation of various model types, predefined model configuration information, getting predefined/remote model lists, model credential authentication methods. Different models provide additional special methods, like LLM's pre-computed tokens method, cost information obtaining method, etc., **allowing horizontal expansion** for different models under the same provider (within supported model types). + + + +## Next Steps + +- Add new provider configuration: [Link](./docs/en_US/provider_scale_out.md) +- Add new models for existing providers: [Link](./docs/en_US/provider_scale_out.md#AddModel) +- View YAML configuration rules: [Link](./docs/en_US/schema.md) +- Implement interface methods: [Link](./docs/en_US/interfaces.md) diff --git a/api/core/model_runtime/README_CN.md b/api/core/model_runtime/README_CN.md new file mode 100644 index 0000000000000000000000000000000000000000..3664fa2ca37924f022c81c56d60bdcdda9a63529 --- /dev/null +++ b/api/core/model_runtime/README_CN.md @@ -0,0 +1,89 @@ +# Model Runtime + +该模块提供了各模型的调用、鉴权接口，并为 Dify 提供了统一的模型供应商的信息和凭据表单规则。 + +- 一方面将模型和上下游解耦，方便开发者对模型横向扩展， +- 另一方面提供了只需在后端定义供应商和模型，即可在前端页面直接展示，无需修改前端逻辑。 + +## 功能介绍 + +- 支持 5 种模型类型的能力调用 + + - `LLM` - LLM 文本补全、对话，预计算 tokens 能力 + - `Text Embedidng Model` - 文本 Embedding ，预计算 tokens 能力 + - `Rerank Model` - 分段 Rerank 能力 + - `Speech-to-text Model` - 语音转文本能力 + - `Text-to-speech Model` - 文本转语音能力 + - `Moderation` - Moderation 能力 + +- 模型供应商展示 + + ![image-20231210143654461](./docs/zh_Hans/images/index/image-20231210143654461.png) + +​ 展示所有已支持的供应商列表，除了返回供应商名称、图标之外，还提供了支持的模型类型列表，预定义模型列表、配置方式以及配置凭据的表单规则等等，规则设计详见：[Schema](./docs/zh_Hans/schema.md)。 + +- 可选择的模型列表展示 + + ![image-20231210144229650](./docs/zh_Hans/images/index/image-20231210144229650.png) + +​ 配置供应商/模型凭据后，可在此下拉（应用编排界面/默认模型）查看可用的 LLM 列表，其中灰色的为未配置凭据供应商的预定义模型列表，方便用户查看已支持的模型。 + +​ 除此之外，该列表还返回了 LLM 可配置的参数信息和规则，如下图： + +​ ![image-20231210144814617](./docs/zh_Hans/images/index/image-20231210144814617.png) + +​ 这里的参数均为后端定义，相比之前只有 5 种固定参数，这里可为不同模型设置所支持的各种参数，详见：[Schema](./docs/zh_Hans/schema.md#ParameterRule)。 + +- 供应商/模型凭据鉴权 + + ![image-20231210151548521](./docs/zh_Hans/images/index/image-20231210151548521.png) + +![image-20231210151628992](./docs/zh_Hans/images/index/image-20231210151628992.png) + +​ 供应商列表返回了凭据表单的配置信息，可通过 Runtime 提供的接口对凭据进行鉴权，上图 1 为供应商凭据 DEMO，上图 2 为模型凭据 DEMO。 + +## 结构 + +![](./docs/zh_Hans/images/index/image-20231210165243632.png) + +Model Runtime 分三层： + +- 最外层为工厂方法 + + 提供获取所有供应商、所有模型列表、获取供应商实例、供应商/模型凭据鉴权方法。 + +- 第二层为供应商层 + + 提供获取当前供应商模型列表、获取模型实例、供应商凭据鉴权、供应商配置规则信息，**可横向扩展**以支持不同的供应商。 + + 对于供应商/模型凭据，有两种情况 + - 如OpenAI这类中心化供应商，需要定义如**api_key**这类的鉴权凭据 + - 如[**Xinference**](https://github.com/xorbitsai/inference)这类本地部署的供应商，需要定义如**server_url**这类的地址凭据，有时候还需要定义**model_uid**之类的模型类型凭据，就像下面这样，当在供应商层定义了这些凭据后，就可以在前端页面上直接展示，无需修改前端逻辑。 + ![Alt text](docs/zh_Hans/images/index/image.png) + + 当配置好凭据后，就可以通过DifyRuntime的外部接口直接获取到对应供应商所需要的**Schema**（凭据表单规则），从而在可以在不修改前端逻辑的情况下，提供新的供应商/模型的支持。 + +- 最底层为模型层 + + 提供各种模型类型的直接调用、预定义模型配置信息、获取预定义/远程模型列表、模型凭据鉴权方法，不同模型额外提供了特殊方法，如 LLM 提供预计算 tokens 方法、获取费用信息方法等，**可横向扩展**同供应商下不同的模型（支持的模型类型下）。 + + 在这里我们需要先区分模型参数与模型凭据。 + + - 模型参数(**在本层定义**)：这是一类经常需要变动，随时调整的参数，如 LLM 的 **max_tokens**、**temperature** 等，这些参数是由用户在前端页面上进行调整的，因此需要在后端定义参数的规则，以便前端页面进行展示和调整。在DifyRuntime中，他们的参数名一般为**model_parameters: dict[str, any]**。 + + - 模型凭据(**在供应商层定义**)：这是一类不经常变动，一般在配置好后就不会再变动的参数，如 **api_key**、**server_url** 等。在DifyRuntime中，他们的参数名一般为**credentials: dict[str, any]**，Provider层的credentials会直接被传递到这一层，不需要再单独定义。 + +## 下一步 + +### [增加新的供应商配置 👈🏻](./docs/zh_Hans/provider_scale_out.md) +当添加后，这里将会出现一个新的供应商 + +![Alt text](docs/zh_Hans/images/index/image-1.png) + +### [为已存在的供应商新增模型 👈🏻](./docs/zh_Hans/provider_scale_out.md#增加模型) +当添加后，对应供应商的模型列表中将会出现一个新的预定义模型供用户选择，如GPT-3.5 GPT-4 ChatGLM3-6b等，而对于支持自定义模型的供应商，则不需要新增模型。 + +![Alt text](docs/zh_Hans/images/index/image-2.png) + +### [接口的具体实现 👈🏻](./docs/zh_Hans/interfaces.md) +你可以在这里找到你想要查看的接口的具体实现，以及接口的参数和返回值的具体含义。 diff --git a/api/core/model_runtime/__init__.py b/api/core/model_runtime/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/api/core/model_runtime/model_providers/__base/__init__.py b/api/core/model_runtime/model_providers/__base/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/api/core/model_runtime/model_providers/__base/ai_model.py b/api/core/model_runtime/model_providers/__base/ai_model.py new file mode 100644 index 0000000000000000000000000000000000000000..4cac66ac4ab54a9cb8a9e599f24e6b3a2a36ed11 --- /dev/null +++ b/api/core/model_runtime/model_providers/__base/ai_model.py @@ -0,0 +1,334 @@ +import decimal +import os +from abc import ABC, abstractmethod +from typing import Optional + +from pydantic import ConfigDict + +from core.helper.position_helper import get_position_map, sort_by_position_map +from core.model_runtime.entities.common_entities import I18nObject +from core.model_runtime.entities.defaults import PARAMETER_RULE_TEMPLATE +from core.model_runtime.entities.model_entities import ( + AIModelEntity, + DefaultParameterName, + FetchFrom, + ModelType, + PriceConfig, + PriceInfo, + PriceType, +) +from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeError +from core.model_runtime.model_providers.__base.tokenizers.gpt2_tokenzier import GPT2Tokenizer +from core.tools.utils.yaml_utils import load_yaml_file + + +class AIModel(ABC): + """ + Base class for all models. + """ + + model_type: ModelType + model_schemas: Optional[list[AIModelEntity]] = None + started_at: float = 0 + + # pydantic configs + model_config = ConfigDict(protected_namespaces=()) + + @abstractmethod + def validate_credentials(self, model: str, credentials: dict) -> None: + """ + Validate model credentials + + :param model: model name + :param credentials: model credentials + :return: + """ + raise NotImplementedError + + @property + @abstractmethod + 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 + """ + raise NotImplementedError + + def _transform_invoke_error(self, error: Exception) -> InvokeError: + """ + Transform invoke error to unified error + + :param error: model invoke error + :return: unified error + """ + provider_name = self.__class__.__module__.split(".")[-3] + + for invoke_error, model_errors in self._invoke_error_mapping.items(): + if isinstance(error, tuple(model_errors)): + if invoke_error == InvokeAuthorizationError: + return invoke_error( + description=( + f"[{provider_name}] Incorrect model credentials provided, please check and try again." + ) + ) + + return invoke_error(description=f"[{provider_name}] {invoke_error.description}, {str(error)}") + + return InvokeError(description=f"[{provider_name}] Error: {str(error)}") + + def get_price(self, model: str, credentials: dict, price_type: PriceType, tokens: int) -> PriceInfo: + """ + Get price for given model and tokens + + :param model: model name + :param credentials: model credentials + :param price_type: price type + :param tokens: number of tokens + :return: price info + """ + # get model schema + model_schema = self.get_model_schema(model, credentials) + + # get price info from predefined model schema + price_config: Optional[PriceConfig] = None + if model_schema and model_schema.pricing: + price_config = model_schema.pricing + + # get unit price + unit_price = None + if price_config: + if price_type == PriceType.INPUT: + unit_price = price_config.input + elif price_type == PriceType.OUTPUT and price_config.output is not None: + unit_price = price_config.output + + if unit_price is None: + return PriceInfo( + unit_price=decimal.Decimal("0.0"), + unit=decimal.Decimal("0.0"), + total_amount=decimal.Decimal("0.0"), + currency="USD", + ) + + # calculate total amount + if not price_config: + raise ValueError(f"Price config not found for model {model}") + total_amount = tokens * unit_price * price_config.unit + total_amount = total_amount.quantize(decimal.Decimal("0.0000001"), rounding=decimal.ROUND_HALF_UP) + + return PriceInfo( + unit_price=unit_price, + unit=price_config.unit, + total_amount=total_amount, + currency=price_config.currency, + ) + + def predefined_models(self) -> list[AIModelEntity]: + """ + Get all predefined models for given provider. + + :return: + """ + if self.model_schemas: + return self.model_schemas + + model_schemas = [] + + # get module name + model_type = self.__class__.__module__.split(".")[-1] + + # get provider name + provider_name = self.__class__.__module__.split(".")[-3] + + # get the path of current classes + current_path = os.path.abspath(__file__) + # get parent path of the current path + provider_model_type_path = os.path.join( + os.path.dirname(os.path.dirname(current_path)), provider_name, model_type + ) + + # get all yaml files path under provider_model_type_path that do not start with __ + model_schema_yaml_paths = [ + os.path.join(provider_model_type_path, model_schema_yaml) + for model_schema_yaml in os.listdir(provider_model_type_path) + if not model_schema_yaml.startswith("__") + and not model_schema_yaml.startswith("_") + and os.path.isfile(os.path.join(provider_model_type_path, model_schema_yaml)) + and model_schema_yaml.endswith(".yaml") + ] + + # get _position.yaml file path + position_map = get_position_map(provider_model_type_path) + + # traverse all model_schema_yaml_paths + for model_schema_yaml_path in model_schema_yaml_paths: + # read yaml data from yaml file + yaml_data = load_yaml_file(model_schema_yaml_path) + + new_parameter_rules = [] + for parameter_rule in yaml_data.get("parameter_rules", []): + if "use_template" in parameter_rule: + try: + default_parameter_name = DefaultParameterName.value_of(parameter_rule["use_template"]) + default_parameter_rule = self._get_default_parameter_rule_variable_map(default_parameter_name) + copy_default_parameter_rule = default_parameter_rule.copy() + copy_default_parameter_rule.update(parameter_rule) + parameter_rule = copy_default_parameter_rule + except ValueError: + pass + + if "label" not in parameter_rule: + parameter_rule["label"] = {"zh_Hans": parameter_rule["name"], "en_US": parameter_rule["name"]} + + new_parameter_rules.append(parameter_rule) + + yaml_data["parameter_rules"] = new_parameter_rules + + if "label" not in yaml_data: + yaml_data["label"] = {"zh_Hans": yaml_data["model"], "en_US": yaml_data["model"]} + + yaml_data["fetch_from"] = FetchFrom.PREDEFINED_MODEL.value + + try: + # yaml_data to entity + model_schema = AIModelEntity(**yaml_data) + except Exception as e: + model_schema_yaml_file_name = os.path.basename(model_schema_yaml_path).rstrip(".yaml") + raise Exception( + f"Invalid model schema for {provider_name}.{model_type}.{model_schema_yaml_file_name}: {str(e)}" + ) + + # cache model schema + model_schemas.append(model_schema) + + # resort model schemas by position + model_schemas = sort_by_position_map(position_map, model_schemas, lambda x: x.model) + + # cache model schemas + self.model_schemas = model_schemas + + return model_schemas + + def get_model_schema(self, model: str, credentials: Optional[dict] = None) -> Optional[AIModelEntity]: + """ + Get model schema by model name and credentials + + :param model: model name + :param credentials: model credentials + :return: model schema + """ + # Try to get model schema from predefined models + for predefined_model in self.predefined_models(): + if model == predefined_model.model: + return predefined_model + + # Try to get model schema from credentials + if credentials: + model_schema = self.get_customizable_model_schema_from_credentials(model, credentials) + if model_schema: + return model_schema + + return None + + def get_customizable_model_schema_from_credentials(self, model: str, credentials: dict) -> Optional[AIModelEntity]: + """ + Get customizable model schema from credentials + + :param model: model name + :param credentials: model credentials + :return: model schema + """ + return self._get_customizable_model_schema(model, credentials) + + def _get_customizable_model_schema(self, model: str, credentials: dict) -> Optional[AIModelEntity]: + """ + Get customizable model schema and fill in the template + """ + schema = self.get_customizable_model_schema(model, credentials) + + if not schema: + return None + + # fill in the template + new_parameter_rules = [] + for parameter_rule in schema.parameter_rules: + if parameter_rule.use_template: + try: + default_parameter_name = DefaultParameterName.value_of(parameter_rule.use_template) + default_parameter_rule = self._get_default_parameter_rule_variable_map(default_parameter_name) + if not parameter_rule.max and "max" in default_parameter_rule: + parameter_rule.max = default_parameter_rule["max"] + if not parameter_rule.min and "min" in default_parameter_rule: + parameter_rule.min = default_parameter_rule["min"] + if not parameter_rule.default and "default" in default_parameter_rule: + parameter_rule.default = default_parameter_rule["default"] + if not parameter_rule.precision and "precision" in default_parameter_rule: + parameter_rule.precision = default_parameter_rule["precision"] + if not parameter_rule.required and "required" in default_parameter_rule: + parameter_rule.required = default_parameter_rule["required"] + if not parameter_rule.help and "help" in default_parameter_rule: + parameter_rule.help = I18nObject( + en_US=default_parameter_rule["help"]["en_US"], + ) + if ( + parameter_rule.help + and not parameter_rule.help.en_US + and ("help" in default_parameter_rule and "en_US" in default_parameter_rule["help"]) + ): + parameter_rule.help.en_US = default_parameter_rule["help"]["en_US"] + if ( + parameter_rule.help + and not parameter_rule.help.zh_Hans + and ("help" in default_parameter_rule and "zh_Hans" in default_parameter_rule["help"]) + ): + parameter_rule.help.zh_Hans = default_parameter_rule["help"].get( + "zh_Hans", default_parameter_rule["help"]["en_US"] + ) + except ValueError: + pass + + new_parameter_rules.append(parameter_rule) + + schema.parameter_rules = new_parameter_rules + + return schema + + def get_customizable_model_schema(self, model: str, credentials: dict) -> Optional[AIModelEntity]: + """ + Get customizable model schema + + :param model: model name + :param credentials: model credentials + :return: model schema + """ + return None + + def _get_default_parameter_rule_variable_map(self, name: DefaultParameterName) -> dict: + """ + Get default parameter rule for given name + + :param name: parameter name + :return: parameter rule + """ + default_parameter_rule = PARAMETER_RULE_TEMPLATE.get(name) + + if not default_parameter_rule: + raise Exception(f"Invalid model parameter rule name {name}") + + return default_parameter_rule + + def _get_num_tokens_by_gpt2(self, text: str) -> int: + """ + Get number of tokens for given prompt messages by gpt2 + Some provider models do not provide an interface for obtaining the number of tokens. + Here, the gpt2 tokenizer is used to calculate the number of tokens. + This method can be executed offline, and the gpt2 tokenizer has been cached in the project. + + :param text: plain text of prompt. You need to convert the original message to plain text + :return: number of tokens + """ + return GPT2Tokenizer.get_num_tokens(text) diff --git a/api/core/model_runtime/model_providers/__base/audio.mp3 b/api/core/model_runtime/model_providers/__base/audio.mp3 new file mode 100644 index 0000000000000000000000000000000000000000..6796e9e373b07d3da686fa8cf514534cb6716ec4 --- /dev/null +++ b/api/core/model_runtime/model_providers/__base/audio.mp3 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:29b714073410fefc10ecb80526b5c7c33df73b0830ff0e7778d5065a6cfcae3e +size 218880 diff --git a/api/core/model_runtime/model_providers/__base/large_language_model.py b/api/core/model_runtime/model_providers/__base/large_language_model.py new file mode 100644 index 0000000000000000000000000000000000000000..710e168e4fb58b2c3350217c0a097d4cf1cb9700 --- /dev/null +++ b/api/core/model_runtime/model_providers/__base/large_language_model.py @@ -0,0 +1,904 @@ +import logging +import re +import time +from abc import abstractmethod +from collections.abc import Generator, Sequence +from typing import Optional, Union + +from pydantic import ConfigDict + +from configs import dify_config +from core.model_runtime.callbacks.base_callback import Callback +from core.model_runtime.callbacks.logging_callback import LoggingCallback +from core.model_runtime.entities.llm_entities import LLMMode, LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage +from core.model_runtime.entities.message_entities import ( + AssistantPromptMessage, + PromptMessage, + PromptMessageContentType, + PromptMessageTool, + SystemPromptMessage, + UserPromptMessage, +) +from core.model_runtime.entities.model_entities import ( + ModelPropertyKey, + ModelType, + ParameterRule, + ParameterType, + PriceType, +) +from core.model_runtime.model_providers.__base.ai_model import AIModel + +logger = logging.getLogger(__name__) + + +class LargeLanguageModel(AIModel): + """ + Model class for large language model. + """ + + model_type: ModelType = ModelType.LLM + + # pydantic configs + model_config = ConfigDict(protected_namespaces=()) + + def invoke( + self, + model: str, + credentials: dict, + prompt_messages: list[PromptMessage], + model_parameters: Optional[dict] = None, + 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]: + """ + 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 + :param callbacks: callbacks + :return: full response or stream response chunk generator result + """ + # validate and filter model parameters + if model_parameters is None: + model_parameters = {} + + model_parameters = self._validate_and_filter_model_parameters(model, model_parameters, credentials) + + self.started_at = time.perf_counter() + + callbacks = callbacks or [] + + if dify_config.DEBUG: + callbacks.append(LoggingCallback()) + + # trigger before invoke callbacks + self._trigger_before_invoke_callbacks( + model=model, + credentials=credentials, + prompt_messages=prompt_messages, + model_parameters=model_parameters, + tools=tools, + stop=stop, + stream=stream, + user=user, + callbacks=callbacks, + ) + + try: + if "response_format" in model_parameters and model_parameters["response_format"] in {"JSON", "XML"}: + result = self._code_block_mode_wrapper( + model=model, + credentials=credentials, + prompt_messages=prompt_messages, + model_parameters=model_parameters, + tools=tools, + stop=stop, + stream=stream, + user=user, + callbacks=callbacks, + ) + else: + result = self._invoke( + model=model, + credentials=credentials, + prompt_messages=prompt_messages, + model_parameters=model_parameters, + tools=tools, + stop=stop, + stream=stream, + user=user, + ) + except Exception as e: + self._trigger_invoke_error_callbacks( + model=model, + ex=e, + credentials=credentials, + prompt_messages=prompt_messages, + model_parameters=model_parameters, + tools=tools, + stop=stop, + stream=stream, + user=user, + callbacks=callbacks, + ) + + raise self._transform_invoke_error(e) + + if stream and isinstance(result, Generator): + return self._invoke_result_generator( + model=model, + result=result, + credentials=credentials, + prompt_messages=prompt_messages, + model_parameters=model_parameters, + tools=tools, + stop=stop, + stream=stream, + user=user, + callbacks=callbacks, + ) + elif isinstance(result, LLMResult): + self._trigger_after_invoke_callbacks( + model=model, + result=result, + credentials=credentials, + prompt_messages=prompt_messages, + model_parameters=model_parameters, + tools=tools, + stop=stop, + stream=stream, + user=user, + callbacks=callbacks, + ) + + return result + + def _code_block_mode_wrapper( + self, + model: str, + credentials: dict, + prompt_messages: list[PromptMessage], + model_parameters: dict, + tools: Optional[list[PromptMessageTool]] = None, + stop: Optional[Sequence[str]] = None, + stream: bool = True, + user: Optional[str] = None, + callbacks: Optional[list[Callback]] = None, + ) -> Union[LLMResult, Generator]: + """ + Code block mode wrapper, ensure the response is a code block with output markdown quote + + :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 + :param callbacks: callbacks + :return: full response or stream response chunk generator result + """ + + block_prompts = """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}} + +""" # noqa: E501 + + code_block = model_parameters.get("response_format", "") + if not code_block: + return self._invoke( + model=model, + credentials=credentials, + prompt_messages=prompt_messages, + model_parameters=model_parameters, + tools=tools, + stop=stop, + stream=stream, + user=user, + ) + + model_parameters.pop("response_format") + stop = list(stop) if stop is not None else [] + stop.extend(["\n```", "```\n"]) + block_prompts = block_prompts.replace("{{block}}", code_block) + + # check if there is a system message + if len(prompt_messages) > 0 and isinstance(prompt_messages[0], SystemPromptMessage): + # override the system message + prompt_messages[0] = SystemPromptMessage( + content=block_prompts.replace("{{instructions}}", str(prompt_messages[0].content)) + ) + else: + # insert the system message + prompt_messages.insert( + 0, + SystemPromptMessage( + content=block_prompts.replace("{{instructions}}", f"Please output a valid {code_block} object.") + ), + ) + + if len(prompt_messages) > 0 and isinstance(prompt_messages[-1], UserPromptMessage): + # add ```JSON\n to the last text message + if isinstance(prompt_messages[-1].content, str): + prompt_messages[-1].content += f"\n```{code_block}\n" + elif isinstance(prompt_messages[-1].content, list): + for i in range(len(prompt_messages[-1].content) - 1, -1, -1): + if prompt_messages[-1].content[i].type == PromptMessageContentType.TEXT: + prompt_messages[-1].content[i].data += f"\n```{code_block}\n" + break + else: + # append a user message + prompt_messages.append(UserPromptMessage(content=f"```{code_block}\n")) + + response = self._invoke( + model=model, + credentials=credentials, + prompt_messages=prompt_messages, + model_parameters=model_parameters, + tools=tools, + stop=stop, + stream=stream, + user=user, + ) + + if isinstance(response, Generator): + first_chunk = next(response) + + def new_generator(): + yield first_chunk + yield from response + + if first_chunk.delta.message.content and first_chunk.delta.message.content.startswith("`"): + return self._code_block_mode_stream_processor_with_backtick( + model=model, prompt_messages=prompt_messages, input_generator=new_generator() + ) + else: + return self._code_block_mode_stream_processor( + model=model, prompt_messages=prompt_messages, input_generator=new_generator() + ) + + return response + + def _code_block_mode_stream_processor( + self, model: str, prompt_messages: list[PromptMessage], input_generator: Generator[LLMResultChunk, None, None] + ) -> Generator[LLMResultChunk, None, None]: + """ + Code block mode stream processor, ensure the response is a code block with output markdown quote + + :param model: model name + :param prompt_messages: prompt messages + :param input_generator: input generator + :return: output generator + """ + state = "normal" + backtick_count = 0 + for piece in input_generator: + if piece.delta.message.content: + content = piece.delta.message.content + piece.delta.message.content = "" + yield piece + content_piece = content + else: + yield piece + continue + new_piece: str = "" + for char in content_piece: + char = str(char) + if state == "normal": + if char == "`": + state = "in_backticks" + backtick_count = 1 + else: + new_piece += char + elif state == "in_backticks": + if char == "`": + backtick_count += 1 + if backtick_count == 3: + state = "skip_content" + backtick_count = 0 + else: + new_piece += "`" * backtick_count + char + state = "normal" + backtick_count = 0 + elif state == "skip_content": + if char.isspace(): + state = "normal" + + if new_piece: + yield LLMResultChunk( + model=model, + prompt_messages=prompt_messages, + delta=LLMResultChunkDelta( + index=0, + message=AssistantPromptMessage(content=new_piece, tool_calls=[]), + ), + ) + + def _code_block_mode_stream_processor_with_backtick( + self, model: str, prompt_messages: list, input_generator: Generator[LLMResultChunk, None, None] + ) -> Generator[LLMResultChunk, None, None]: + """ + Code block mode stream processor, ensure the response is a code block with output markdown quote. + This version skips the language identifier that follows the opening triple backticks. + + :param model: model name + :param prompt_messages: prompt messages + :param input_generator: input generator + :return: output generator + """ + state = "search_start" + backtick_count = 0 + + for piece in input_generator: + if piece.delta.message.content: + content = piece.delta.message.content + # Reset content to ensure we're only processing and yielding the relevant parts + piece.delta.message.content = "" + # Yield a piece with cleared content before processing it to maintain the generator structure + yield piece + content_piece = content + else: + # Yield pieces without content directly + yield piece + continue + + if state == "done": + continue + + new_piece: str = "" + for char in content_piece: + if state == "search_start": + if char == "`": + backtick_count += 1 + if backtick_count == 3: + state = "skip_language" + backtick_count = 0 + else: + backtick_count = 0 + elif state == "skip_language": + # Skip everything until the first newline, marking the end of the language identifier + if char == "\n": + state = "in_code_block" + elif state == "in_code_block": + if char == "`": + backtick_count += 1 + if backtick_count == 3: + state = "done" + break + else: + if backtick_count > 0: + # If backticks were counted but we're still collecting content, it was a false start + new_piece += "`" * backtick_count + backtick_count = 0 + new_piece += str(char) + + elif state == "done": + break + + if new_piece: + # Only yield content collected within the code block + yield LLMResultChunk( + model=model, + prompt_messages=prompt_messages, + delta=LLMResultChunkDelta( + index=0, + message=AssistantPromptMessage(content=new_piece, tool_calls=[]), + ), + ) + + def _wrap_thinking_by_reasoning_content(self, delta: dict, is_reasoning: bool) -> tuple[str, bool]: + """ + If the reasoning response is from delta.get("reasoning_content"), we wrap + it with HTML think tag. + + :param delta: delta dictionary from LLM streaming response + :param is_reasoning: is reasoning + :return: tuple of (processed_content, is_reasoning) + """ + + content = delta.get("content") or "" + reasoning_content = delta.get("reasoning_content") + + if reasoning_content: + if not is_reasoning: + content = "\n" + reasoning_content + is_reasoning = True + else: + content = reasoning_content + elif is_reasoning and content: + # do not end reasoning when content is empty + # there may be more reasoning_content later that follows previous reasoning closely + content = "\n" + content + is_reasoning = False + return content, is_reasoning + + def _invoke_result_generator( + self, + model: str, + result: Generator, + credentials: dict, + prompt_messages: list[PromptMessage], + model_parameters: dict, + tools: Optional[list[PromptMessageTool]] = None, + stop: Optional[Sequence[str]] = None, + stream: bool = True, + user: Optional[str] = None, + callbacks: Optional[list[Callback]] = None, + ) -> Generator: + """ + Invoke result generator + + :param result: result generator + :return: result generator + """ + callbacks = callbacks or [] + prompt_message = AssistantPromptMessage(content="") + usage = None + system_fingerprint = None + real_model = model + + try: + for chunk in result: + yield chunk + + self._trigger_new_chunk_callbacks( + chunk=chunk, + model=model, + credentials=credentials, + prompt_messages=prompt_messages, + model_parameters=model_parameters, + tools=tools, + stop=stop, + stream=stream, + user=user, + callbacks=callbacks, + ) + + prompt_message.content += chunk.delta.message.content + real_model = chunk.model + if chunk.delta.usage: + usage = chunk.delta.usage + + if chunk.system_fingerprint: + system_fingerprint = chunk.system_fingerprint + except Exception as e: + raise self._transform_invoke_error(e) + + self._trigger_after_invoke_callbacks( + model=model, + result=LLMResult( + model=real_model, + prompt_messages=prompt_messages, + message=prompt_message, + usage=usage or LLMUsage.empty_usage(), + system_fingerprint=system_fingerprint, + ), + credentials=credentials, + prompt_messages=prompt_messages, + model_parameters=model_parameters, + tools=tools, + stop=stop, + stream=stream, + user=user, + callbacks=callbacks, + ) + + @abstractmethod + def _invoke( + self, + model: str, + credentials: dict, + prompt_messages: list[PromptMessage], + model_parameters: dict, + tools: Optional[list[PromptMessageTool]] = None, + stop: Optional[Sequence[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 + """ + raise NotImplementedError + + @abstractmethod + 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: + """ + raise NotImplementedError + + def enforce_stop_tokens(self, text: str, stop: list[str]) -> str: + """Cut off the text as soon as any stop words occur.""" + return re.split("|".join(stop), text, maxsplit=1)[0] + + def get_parameter_rules(self, model: str, credentials: dict) -> list[ParameterRule]: + """ + Get parameter rules + + :param model: model name + :param credentials: model credentials + :return: parameter rules + """ + model_schema = self.get_model_schema(model, credentials) + if model_schema: + return model_schema.parameter_rules + + return [] + + def get_model_mode(self, model: str, credentials: Optional[dict] = None) -> LLMMode: + """ + Get model mode + + :param model: model name + :param credentials: model credentials + :return: model mode + """ + model_schema = self.get_model_schema(model, credentials) + + mode = LLMMode.CHAT + if model_schema and model_schema.model_properties.get(ModelPropertyKey.MODE): + mode = LLMMode.value_of(model_schema.model_properties[ModelPropertyKey.MODE]) + + return mode + + def _calc_response_usage( + self, model: str, credentials: dict, prompt_tokens: int, completion_tokens: int + ) -> LLMUsage: + """ + Calculate response usage + + :param model: model name + :param credentials: model credentials + :param prompt_tokens: prompt tokens + :param completion_tokens: completion tokens + :return: usage + """ + # get prompt price info + prompt_price_info = self.get_price( + model=model, + credentials=credentials, + price_type=PriceType.INPUT, + tokens=prompt_tokens, + ) + + # get completion price info + completion_price_info = self.get_price( + model=model, credentials=credentials, price_type=PriceType.OUTPUT, tokens=completion_tokens + ) + + # transform usage + usage = LLMUsage( + prompt_tokens=prompt_tokens, + prompt_unit_price=prompt_price_info.unit_price, + prompt_price_unit=prompt_price_info.unit, + prompt_price=prompt_price_info.total_amount, + completion_tokens=completion_tokens, + completion_unit_price=completion_price_info.unit_price, + completion_price_unit=completion_price_info.unit, + completion_price=completion_price_info.total_amount, + total_tokens=prompt_tokens + completion_tokens, + total_price=prompt_price_info.total_amount + completion_price_info.total_amount, + currency=prompt_price_info.currency, + latency=time.perf_counter() - self.started_at, + ) + + return usage + + def _trigger_before_invoke_callbacks( + self, + model: str, + credentials: dict, + prompt_messages: list[PromptMessage], + model_parameters: dict, + tools: Optional[list[PromptMessageTool]] = None, + stop: Optional[Sequence[str]] = None, + stream: bool = True, + user: Optional[str] = None, + callbacks: Optional[list[Callback]] = None, + ) -> None: + """ + Trigger before invoke callbacks + + :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 + :param callbacks: callbacks + """ + if callbacks: + for callback in callbacks: + try: + callback.on_before_invoke( + llm_instance=self, + model=model, + credentials=credentials, + prompt_messages=prompt_messages, + model_parameters=model_parameters, + tools=tools, + stop=stop, + stream=stream, + user=user, + ) + except Exception as e: + if callback.raise_error: + raise e + else: + logger.warning(f"Callback {callback.__class__.__name__} on_before_invoke failed with error {e}") + + def _trigger_new_chunk_callbacks( + self, + chunk: LLMResultChunk, + model: str, + credentials: dict, + prompt_messages: list[PromptMessage], + model_parameters: dict, + tools: Optional[list[PromptMessageTool]] = None, + stop: Optional[Sequence[str]] = None, + stream: bool = True, + user: Optional[str] = None, + callbacks: Optional[list[Callback]] = None, + ) -> None: + """ + Trigger new chunk callbacks + + :param chunk: chunk + :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 + """ + if callbacks: + for callback in callbacks: + try: + callback.on_new_chunk( + llm_instance=self, + chunk=chunk, + model=model, + credentials=credentials, + prompt_messages=prompt_messages, + model_parameters=model_parameters, + tools=tools, + stop=stop, + stream=stream, + user=user, + ) + except Exception as e: + if callback.raise_error: + raise e + else: + logger.warning(f"Callback {callback.__class__.__name__} on_new_chunk failed with error {e}") + + def _trigger_after_invoke_callbacks( + self, + model: str, + result: LLMResult, + credentials: dict, + prompt_messages: list[PromptMessage], + model_parameters: dict, + tools: Optional[list[PromptMessageTool]] = None, + stop: Optional[Sequence[str]] = None, + stream: bool = True, + user: Optional[str] = None, + callbacks: Optional[list[Callback]] = None, + ) -> None: + """ + Trigger after invoke callbacks + + :param model: model name + :param result: result + :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 + :param callbacks: callbacks + """ + if callbacks: + for callback in callbacks: + try: + callback.on_after_invoke( + llm_instance=self, + result=result, + model=model, + credentials=credentials, + prompt_messages=prompt_messages, + model_parameters=model_parameters, + tools=tools, + stop=stop, + stream=stream, + user=user, + ) + except Exception as e: + if callback.raise_error: + raise e + else: + logger.warning(f"Callback {callback.__class__.__name__} on_after_invoke failed with error {e}") + + def _trigger_invoke_error_callbacks( + self, + model: str, + ex: Exception, + credentials: dict, + prompt_messages: list[PromptMessage], + model_parameters: dict, + tools: Optional[list[PromptMessageTool]] = None, + stop: Optional[Sequence[str]] = None, + stream: bool = True, + user: Optional[str] = None, + callbacks: Optional[list[Callback]] = None, + ) -> None: + """ + Trigger invoke error callbacks + + :param model: model name + :param ex: exception + :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 + :param callbacks: callbacks + """ + if callbacks: + for callback in callbacks: + try: + callback.on_invoke_error( + llm_instance=self, + ex=ex, + model=model, + credentials=credentials, + prompt_messages=prompt_messages, + model_parameters=model_parameters, + tools=tools, + stop=stop, + stream=stream, + user=user, + ) + except Exception as e: + if callback.raise_error: + raise e + else: + logger.warning(f"Callback {callback.__class__.__name__} on_invoke_error failed with error {e}") + + def _validate_and_filter_model_parameters(self, model: str, model_parameters: dict, credentials: dict) -> dict: + """ + Validate model parameters + + :param model: model name + :param model_parameters: model parameters + :param credentials: model credentials + :return: + """ + parameter_rules = self.get_parameter_rules(model, credentials) + + # validate model parameters + filtered_model_parameters = {} + for parameter_rule in parameter_rules: + parameter_name = parameter_rule.name + parameter_value = model_parameters.get(parameter_name) + if parameter_value is None: + if parameter_rule.use_template and parameter_rule.use_template in model_parameters: + # if parameter value is None, use template value variable name instead + parameter_value = model_parameters[parameter_rule.use_template] + else: + if parameter_rule.required: + if parameter_rule.default is not None: + filtered_model_parameters[parameter_name] = parameter_rule.default + continue + else: + raise ValueError(f"Model Parameter {parameter_name} is required.") + else: + continue + + # validate parameter value type + if parameter_rule.type == ParameterType.INT: + if not isinstance(parameter_value, int): + raise ValueError(f"Model Parameter {parameter_name} should be int.") + + # validate parameter value range + if parameter_rule.min is not None and parameter_value < parameter_rule.min: + raise ValueError( + f"Model Parameter {parameter_name} should be greater than or equal to {parameter_rule.min}." + ) + + if parameter_rule.max is not None and parameter_value > parameter_rule.max: + raise ValueError( + f"Model Parameter {parameter_name} should be less than or equal to {parameter_rule.max}." + ) + elif parameter_rule.type == ParameterType.FLOAT: + if not isinstance(parameter_value, float | int): + raise ValueError(f"Model Parameter {parameter_name} should be float.") + + # validate parameter value precision + if parameter_rule.precision is not None: + if parameter_rule.precision == 0: + if parameter_value != int(parameter_value): + raise ValueError(f"Model Parameter {parameter_name} should be int.") + else: + if parameter_value != round(parameter_value, parameter_rule.precision): + raise ValueError( + f"Model Parameter {parameter_name} should be round to {parameter_rule.precision}" + f" decimal places." + ) + + # validate parameter value range + if parameter_rule.min is not None and parameter_value < parameter_rule.min: + raise ValueError( + f"Model Parameter {parameter_name} should be greater than or equal to {parameter_rule.min}." + ) + + if parameter_rule.max is not None and parameter_value > parameter_rule.max: + raise ValueError( + f"Model Parameter {parameter_name} should be less than or equal to {parameter_rule.max}." + ) + elif parameter_rule.type == ParameterType.BOOLEAN: + if not isinstance(parameter_value, bool): + raise ValueError(f"Model Parameter {parameter_name} should be bool.") + elif parameter_rule.type == ParameterType.STRING: + if not isinstance(parameter_value, str): + raise ValueError(f"Model Parameter {parameter_name} should be string.") + + # validate options + if parameter_rule.options and parameter_value not in parameter_rule.options: + raise ValueError(f"Model Parameter {parameter_name} should be one of {parameter_rule.options}.") + elif parameter_rule.type == ParameterType.TEXT: + if not isinstance(parameter_value, str): + raise ValueError(f"Model Parameter {parameter_name} should be text.") + + # validate options + if parameter_rule.options and parameter_value not in parameter_rule.options: + raise ValueError(f"Model Parameter {parameter_name} should be one of {parameter_rule.options}.") + else: + raise ValueError(f"Model Parameter {parameter_name} type {parameter_rule.type} is not supported.") + + filtered_model_parameters[parameter_name] = parameter_value + + return filtered_model_parameters diff --git a/api/core/model_runtime/model_providers/__base/model_provider.py b/api/core/model_runtime/model_providers/__base/model_provider.py new file mode 100644 index 0000000000000000000000000000000000000000..36e3e7bd5571633ca8366261a858de65ea395f8c --- /dev/null +++ b/api/core/model_runtime/model_providers/__base/model_provider.py @@ -0,0 +1,121 @@ +import os +from abc import ABC, abstractmethod +from typing import Optional + +from core.helper.module_import_helper import get_subclasses_from_module, import_module_from_source +from core.model_runtime.entities.model_entities import AIModelEntity, ModelType +from core.model_runtime.entities.provider_entities import ProviderEntity +from core.model_runtime.model_providers.__base.ai_model import AIModel +from core.tools.utils.yaml_utils import load_yaml_file + + +class ModelProvider(ABC): + provider_schema: Optional[ProviderEntity] = None + model_instance_map: dict[str, AIModel] = {} + + @abstractmethod + def validate_provider_credentials(self, credentials: dict) -> None: + """ + Validate provider credentials + You can choose any validate_credentials method of model type or implement validate method by yourself, + such as: get model list api + + if validate failed, raise exception + + :param credentials: provider credentials, credentials form defined in `provider_credential_schema`. + """ + raise NotImplementedError + + def get_provider_schema(self) -> ProviderEntity: + """ + Get provider schema + + :return: provider schema + """ + if self.provider_schema: + return self.provider_schema + + # get dirname of the current path + provider_name = self.__class__.__module__.split(".")[-1] + + # get the path of the model_provider classes + base_path = os.path.abspath(__file__) + current_path = os.path.join(os.path.dirname(os.path.dirname(base_path)), provider_name) + + # read provider schema from yaml file + yaml_path = os.path.join(current_path, f"{provider_name}.yaml") + yaml_data = load_yaml_file(yaml_path) + + try: + # yaml_data to entity + provider_schema = ProviderEntity(**yaml_data) + except Exception as e: + raise Exception(f"Invalid provider schema for {provider_name}: {str(e)}") + + # cache schema + self.provider_schema = provider_schema + + return provider_schema + + def models(self, model_type: ModelType) -> list[AIModelEntity]: + """ + Get all models for given model type + + :param model_type: model type defined in `ModelType` + :return: list of models + """ + provider_schema = self.get_provider_schema() + if model_type not in provider_schema.supported_model_types: + return [] + + # get model instance of the model type + model_instance = self.get_model_instance(model_type) + + # get predefined models (predefined_models) + models = model_instance.predefined_models() + + # return models + return models + + def get_model_instance(self, model_type: ModelType) -> AIModel: + """ + Get model instance + + :param model_type: model type defined in `ModelType` + :return: + """ + # get dirname of the current path + provider_name = self.__class__.__module__.split(".")[-1] + + if f"{provider_name}.{model_type.value}" in self.model_instance_map: + return self.model_instance_map[f"{provider_name}.{model_type.value}"] + + # get the path of the model type classes + base_path = os.path.abspath(__file__) + model_type_name = model_type.value.replace("-", "_") + model_type_path = os.path.join(os.path.dirname(os.path.dirname(base_path)), provider_name, model_type_name) + model_type_py_path = os.path.join(model_type_path, f"{model_type_name}.py") + + if not os.path.isdir(model_type_path) or not os.path.exists(model_type_py_path): + raise Exception(f"Invalid model type {model_type} for provider {provider_name}") + + # Dynamic loading {model_type_name}.py file and find the subclass of AIModel + parent_module = ".".join(self.__class__.__module__.split(".")[:-1]) + mod = import_module_from_source( + module_name=f"{parent_module}.{model_type_name}.{model_type_name}", py_file_path=model_type_py_path + ) + # FIXME "type" has no attribute "__abstractmethods__" ignore it for now fix it later + model_class = next( + filter( + lambda x: x.__module__ == mod.__name__ and not x.__abstractmethods__, # type: ignore + get_subclasses_from_module(mod, AIModel), + ), + None, + ) + if not model_class: + raise Exception(f"Missing AIModel Class for model type {model_type} in {model_type_py_path}") + + model_instance_map = model_class() + self.model_instance_map[f"{provider_name}.{model_type.value}"] = model_instance_map + + return model_instance_map diff --git a/api/core/model_runtime/model_providers/__base/moderation_model.py b/api/core/model_runtime/model_providers/__base/moderation_model.py new file mode 100644 index 0000000000000000000000000000000000000000..d04414ccb87a63f3ad4745811a7660b0013bc36a --- /dev/null +++ b/api/core/model_runtime/model_providers/__base/moderation_model.py @@ -0,0 +1,49 @@ +import time +from abc import abstractmethod +from typing import Optional + +from pydantic import ConfigDict + +from core.model_runtime.entities.model_entities import ModelType +from core.model_runtime.model_providers.__base.ai_model import AIModel + + +class ModerationModel(AIModel): + """ + Model class for moderation model. + """ + + model_type: ModelType = ModelType.MODERATION + + # pydantic configs + model_config = ConfigDict(protected_namespaces=()) + + def invoke(self, model: str, credentials: dict, text: str, user: Optional[str] = None) -> bool: + """ + Invoke moderation model + + :param model: model name + :param credentials: model credentials + :param text: text to moderate + :param user: unique user id + :return: false if text is safe, true otherwise + """ + self.started_at = time.perf_counter() + + try: + return self._invoke(model, credentials, text, user) + except Exception as e: + raise self._transform_invoke_error(e) + + @abstractmethod + def _invoke(self, model: str, credentials: dict, text: str, user: Optional[str] = None) -> bool: + """ + Invoke large language model + + :param model: model name + :param credentials: model credentials + :param text: text to moderate + :param user: unique user id + :return: false if text is safe, true otherwise + """ + raise NotImplementedError diff --git a/api/core/model_runtime/model_providers/__base/rerank_model.py b/api/core/model_runtime/model_providers/__base/rerank_model.py new file mode 100644 index 0000000000000000000000000000000000000000..5fb960474255928ec74aaa83a1d6eb1cb51e9bfa --- /dev/null +++ b/api/core/model_runtime/model_providers/__base/rerank_model.py @@ -0,0 +1,69 @@ +import time +from abc import abstractmethod +from typing import Optional + +from core.model_runtime.entities.model_entities import ModelType +from core.model_runtime.entities.rerank_entities import RerankResult +from core.model_runtime.model_providers.__base.ai_model import AIModel + + +class RerankModel(AIModel): + """ + Base Model class for rerank model. + """ + + model_type: ModelType = ModelType.RERANK + + 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 + """ + self.started_at = time.perf_counter() + + try: + return self._invoke(model, credentials, query, docs, score_threshold, top_n, user) + except Exception as e: + raise self._transform_invoke_error(e) + + @abstractmethod + 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 + """ + raise NotImplementedError diff --git a/api/core/model_runtime/model_providers/__base/speech2text_model.py b/api/core/model_runtime/model_providers/__base/speech2text_model.py new file mode 100644 index 0000000000000000000000000000000000000000..b6b0b737436d9c25212dac5746dbf5d17b9f88ed --- /dev/null +++ b/api/core/model_runtime/model_providers/__base/speech2text_model.py @@ -0,0 +1,59 @@ +import os +from abc import abstractmethod +from typing import IO, Optional + +from pydantic import ConfigDict + +from core.model_runtime.entities.model_entities import ModelType +from core.model_runtime.model_providers.__base.ai_model import AIModel + + +class Speech2TextModel(AIModel): + """ + Model class for speech2text model. + """ + + model_type: ModelType = ModelType.SPEECH2TEXT + + # pydantic configs + model_config = ConfigDict(protected_namespaces=()) + + def invoke(self, model: str, credentials: dict, file: IO[bytes], user: Optional[str] = None) -> str: + """ + Invoke large language model + + :param model: model name + :param credentials: model credentials + :param file: audio file + :param user: unique user id + :return: text for given audio file + """ + try: + return self._invoke(model, credentials, file, user) + except Exception as e: + raise self._transform_invoke_error(e) + + @abstractmethod + def _invoke(self, model: str, credentials: dict, file: IO[bytes], user: Optional[str] = None) -> str: + """ + Invoke large language model + + :param model: model name + :param credentials: model credentials + :param file: audio file + :param user: unique user id + :return: text for given audio file + """ + raise NotImplementedError + + def _get_demo_file_path(self) -> str: + """ + Get demo file for given model + + :return: demo file + """ + # Get the directory of the current file + current_dir = os.path.dirname(os.path.abspath(__file__)) + + # Construct the path to the audio file + return os.path.join(current_dir, "audio.mp3") diff --git a/api/core/model_runtime/model_providers/__base/text2img_model.py b/api/core/model_runtime/model_providers/__base/text2img_model.py new file mode 100644 index 0000000000000000000000000000000000000000..a5810e2f0e4b0925da496a1ec3f5f3527de2d583 --- /dev/null +++ b/api/core/model_runtime/model_providers/__base/text2img_model.py @@ -0,0 +1,54 @@ +from abc import abstractmethod +from typing import IO, Optional + +from pydantic import ConfigDict + +from core.model_runtime.entities.model_entities import ModelType +from core.model_runtime.model_providers.__base.ai_model import AIModel + + +class Text2ImageModel(AIModel): + """ + Model class for text2img model. + """ + + model_type: ModelType = ModelType.TEXT2IMG + + # pydantic configs + model_config = ConfigDict(protected_namespaces=()) + + def invoke( + self, model: str, credentials: dict, prompt: str, model_parameters: dict, user: Optional[str] = None + ) -> list[IO[bytes]]: + """ + Invoke Text2Image model + + :param model: model name + :param credentials: model credentials + :param prompt: prompt for image generation + :param model_parameters: model parameters + :param user: unique user id + + :return: image bytes + """ + try: + return self._invoke(model, credentials, prompt, model_parameters, user) + except Exception as e: + raise self._transform_invoke_error(e) + + @abstractmethod + def _invoke( + self, model: str, credentials: dict, prompt: str, model_parameters: dict, user: Optional[str] = None + ) -> list[IO[bytes]]: + """ + Invoke Text2Image model + + :param model: model name + :param credentials: model credentials + :param prompt: prompt for image generation + :param model_parameters: model parameters + :param user: unique user id + + :return: image bytes + """ + raise NotImplementedError diff --git a/api/core/model_runtime/model_providers/__base/text_embedding_model.py b/api/core/model_runtime/model_providers/__base/text_embedding_model.py new file mode 100644 index 0000000000000000000000000000000000000000..33135129082b1dd2b0fb4c0b2f4c71632a1bc6af --- /dev/null +++ b/api/core/model_runtime/model_providers/__base/text_embedding_model.py @@ -0,0 +1,111 @@ +import time +from abc import abstractmethod +from typing import Optional + +from pydantic import ConfigDict + +from core.entities.embedding_type import EmbeddingInputType +from core.model_runtime.entities.model_entities import ModelPropertyKey, ModelType +from core.model_runtime.entities.text_embedding_entities import TextEmbeddingResult +from core.model_runtime.model_providers.__base.ai_model import AIModel + + +class TextEmbeddingModel(AIModel): + """ + Model class for text embedding model. + """ + + model_type: ModelType = ModelType.TEXT_EMBEDDING + + # pydantic configs + model_config = ConfigDict(protected_namespaces=()) + + def invoke( + self, + model: str, + credentials: dict, + texts: list[str], + user: Optional[str] = None, + input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT, + ) -> TextEmbeddingResult: + """ + Invoke text embedding model + + :param model: model name + :param credentials: model credentials + :param texts: texts to embed + :param user: unique user id + :param input_type: input type + :return: embeddings result + """ + self.started_at = time.perf_counter() + + try: + return self._invoke(model, credentials, texts, user, input_type) + except Exception as e: + raise self._transform_invoke_error(e) + + @abstractmethod + def _invoke( + self, + model: str, + credentials: dict, + texts: list[str], + user: Optional[str] = None, + input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT, + ) -> TextEmbeddingResult: + """ + Invoke text embedding model + + :param model: model name + :param credentials: model credentials + :param texts: texts to embed + :param user: unique user id + :param input_type: input type + :return: embeddings result + """ + raise NotImplementedError + + @abstractmethod + def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int: + """ + Get number of tokens for given prompt messages + + :param model: model name + :param credentials: model credentials + :param texts: texts to embed + :return: + """ + raise NotImplementedError + + def _get_context_size(self, model: str, credentials: dict) -> int: + """ + Get context size for given embedding model + + :param model: model name + :param credentials: model credentials + :return: context size + """ + model_schema = self.get_model_schema(model, credentials) + + if model_schema and ModelPropertyKey.CONTEXT_SIZE in model_schema.model_properties: + content_size: int = model_schema.model_properties[ModelPropertyKey.CONTEXT_SIZE] + return content_size + + return 1000 + + def _get_max_chunks(self, model: str, credentials: dict) -> int: + """ + Get max chunks for given embedding model + + :param model: model name + :param credentials: model credentials + :return: max chunks + """ + model_schema = self.get_model_schema(model, credentials) + + if model_schema and ModelPropertyKey.MAX_CHUNKS in model_schema.model_properties: + max_chunks: int = model_schema.model_properties[ModelPropertyKey.MAX_CHUNKS] + return max_chunks + + return 1 diff --git a/api/core/model_runtime/model_providers/__base/tokenizers/gpt2/merges.txt b/api/core/model_runtime/model_providers/__base/tokenizers/gpt2/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..226b0752cac7789c48f0cb3ec53eda48b7be36cc --- /dev/null +++ b/api/core/model_runtime/model_providers/__base/tokenizers/gpt2/merges.txt @@ -0,0 +1,50001 @@ +#version: 0.2 +Ġ t +Ġ a +h e +i n +r e +o n +Ġt he +e r +Ġ s +a t +Ġ w +Ġ o +e n +Ġ c +i t +i s +a n +o r +e s +Ġ b +e d +Ġ f +in g +Ġ p +o u +Ġa n +a l +a r +Ġt o +Ġ m +Ġo f +Ġ in +Ġ d +Ġ h +Ġan d +i c +a s +l e +Ġt h +i on +o m +l l +en t +Ġ n +Ġ l +s t +Ġ re +v e +Ġ e +r o +l y +Ġb e +Ġ g +Ġ T +c t +Ġ S +i d +o t +Ġ I +u t +e t +Ġ A +Ġ is +Ġ on +i m +a m +o w +a y +a d 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