camel.types package

Submodules

camel.types.enums module

class camel.types.enums.AudioModelType(value)

Bases: Enum

An enumeration.

TTS_1 = 'tts-1'

TTS_1_HD = 'tts-1-hd'

property is_openai: bool

Returns whether this type of audio models is an OpenAI-released model.

class camel.types.enums.EmbeddingModelType(value)

Bases: Enum

An enumeration.

MISTRAL_EMBED = 'mistral-embed'

TEXT_EMBEDDING_3_LARGE = 'text-embedding-3-large'

TEXT_EMBEDDING_3_SMALL = 'text-embedding-3-small'

TEXT_EMBEDDING_ADA_2 = 'text-embedding-ada-002'

property is_mistral: bool

Returns whether this type of models is an Mistral-released model.

property is_openai: bool

Returns whether this type of models is an OpenAI-released model.

property output_dim: int

class camel.types.enums.JinaReturnFormat(value)

Bases: Enum

An enumeration.

DEFAULT = None

HTML = 'html'

MARKDOWN = 'markdown'

TEXT = 'text'

class camel.types.enums.ModelPlatformType(value)

Bases: Enum

An enumeration.

ANTHROPIC = 'anthropic'

AZURE = 'azure'

COHERE = 'cohere'

DEFAULT = 'openai'

GEMINI = 'gemini'

GROQ = 'groq'

LITELLM = 'litellm'

MISTRAL = 'mistral'

OLLAMA = 'ollama'

OPENAI = 'openai'

OPENAI_COMPATIBLE_MODEL = 'openai-compatible-model'

QWEN = 'tongyi-qianwen'

REKA = 'reka'

SAMBA = 'samba-nova'

TOGETHER = 'together'

VLLM = 'vllm'

YI = 'lingyiwanwu'

ZHIPU = 'zhipuai'

property is_anthropic: bool

Returns whether this platform is anthropic.

property is_azure: bool

Returns whether this platform is azure.

property is_cohere: bool

Returns whether this platform is Cohere.

property is_gemini: bool

Returns whether this platform is Gemini.

property is_groq: bool

Returns whether this platform is groq.

property is_litellm: bool

Returns whether this platform is litellm.

property is_mistral: bool

Returns whether this platform is mistral.

property is_ollama: bool

Returns whether this platform is ollama.

property is_openai: bool

Returns whether this platform is openai.

property is_openai_compatible_model: bool

Returns whether this is a platform supporting openai compatibility

property is_qwen: bool

Returns whether this platform is Qwen.

property is_reka: bool

Returns whether this platform is Reka.

property is_samba: bool

Returns whether this platform is Samba Nova.

property is_together: bool

Returns whether this platform is together.

property is_vllm: bool

Returns whether this platform is vllm.

property is_yi: bool

Returns whether this platform is Yi.

property is_zhipuai: bool

Returns whether this platform is zhipu.

class camel.types.enums.OpenAIBackendRole(value)

Bases: Enum

An enumeration.

ASSISTANT = 'assistant'

FUNCTION = 'function'

SYSTEM = 'system'

TOOL = 'tool'

USER = 'user'

class camel.types.enums.OpenAIImageType(value)

Bases: Enum

Image types supported by OpenAI vision model.

GIF = 'gif'

JPEG = 'jpeg'

JPG = 'jpg'

PNG = 'png'

WEBP = 'webp'

class camel.types.enums.OpenAIImageTypeMeta(cls, bases, classdict, **kwds)

Bases: EnumMeta

class camel.types.enums.OpenAIVisionDetailType(value)

Bases: Enum

An enumeration.

AUTO = 'auto'

HIGH = 'high'

LOW = 'low'

class camel.types.enums.OpenAPIName(value)

Bases: Enum

An enumeration.

BIZTOC = 'biztoc'

COURSERA = 'coursera'

CREATE_QR_CODE = 'create_qr_code'

KLARNA = 'klarna'

NASA_APOD = 'nasa_apod'

OUTSCHOOL = 'outschool'

SPEAK = 'speak'

WEB_SCRAPER = 'web_scraper'

class camel.types.enums.RoleType(value)

Bases: Enum

An enumeration.

ASSISTANT = 'assistant'

CRITIC = 'critic'

DEFAULT = 'default'

EMBODIMENT = 'embodiment'

USER = 'user'

class camel.types.enums.StorageType(value)

Bases: Enum

An enumeration.

MILVUS = 'milvus'

QDRANT = 'qdrant'

class camel.types.enums.TaskType(value)

Bases: Enum

An enumeration.

AI_SOCIETY = 'ai_society'

CODE = 'code'

DEFAULT = 'default'

EVALUATION = 'evaluation'

GENERATE_TEXT_EMBEDDING_DATA = 'generate_text_embedding_data'

IMAGE_CRAFT = 'image_craft'

MISALIGNMENT = 'misalignment'

MULTI_CONDITION_IMAGE_CRAFT = 'multi_condition_image_craft'

OBJECT_RECOGNITION = 'object_recognition'

ROLE_DESCRIPTION = 'role_description'

SOLUTION_EXTRACTION = 'solution_extraction'

TRANSLATION = 'translation'

VIDEO_DESCRIPTION = 'video_description'

class camel.types.enums.TerminationMode(value)

Bases: Enum

An enumeration.

ALL = 'all'

ANY = 'any'

class camel.types.enums.VectorDistance(value)

Bases: Enum

Distance metrics used in a vector database.

COSINE = 'cosine'

//en.wikipedia.org/wiki/Cosine_similarity

Type:

Cosine similarity. https

DOT = 'dot'

//en.wikipedia.org/wiki/Dot_product

Type:

Dot product. https

EUCLIDEAN = 'euclidean'

//en.wikipedia.org/wiki/Euclidean_distance

Type:

Euclidean distance. https

class camel.types.enums.VoiceType(value)

Bases: Enum

An enumeration.

ALLOY = 'alloy'

ECHO = 'echo'

FABLE = 'fable'

NOVA = 'nova'

ONYX = 'onyx'

SHIMMER = 'shimmer'

property is_openai: bool

Returns whether this type of voice is an OpenAI-released voice.

camel.types.openai_types module

Module contents

class camel.types.AudioModelType(value)

Bases: Enum

An enumeration.

TTS_1 = 'tts-1'

TTS_1_HD = 'tts-1-hd'

property is_openai: bool

Returns whether this type of audio models is an OpenAI-released model.

class camel.types.ChatCompletion(**data: Any)

Bases: BaseModel

choices: List[Choice]

A list of chat completion choices.

Can be more than one if n is greater than 1.

created: int

The Unix timestamp (in seconds) of when the chat completion was created.

id: str

A unique identifier for the chat completion.

model: str

The model used for the chat completion.

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'defer_build': True, 'extra': 'allow'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'choices': FieldInfo(annotation=List[Choice], required=True), 'created': FieldInfo(annotation=int, required=True), 'id': FieldInfo(annotation=str, required=True), 'model': FieldInfo(annotation=str, required=True), 'object': FieldInfo(annotation=Literal['chat.completion'], required=True), 'service_tier': FieldInfo(annotation=Union[Literal['scale', 'default'], NoneType], required=False, default=None), 'system_fingerprint': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'usage': FieldInfo(annotation=Union[CompletionUsage, NoneType], required=False, default=None)}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

object: Literal['chat.completion']

The object type, which is always chat.completion.

service_tier: Literal['scale', 'default'] | None

The service tier used for processing the request.

This field is only included if the service_tier parameter is specified in the request.

system_fingerprint: str | None

This fingerprint represents the backend configuration that the model runs with.

Can be used in conjunction with the seed request parameter to understand when backend changes have been made that might impact determinism.

usage: CompletionUsage | None

Usage statistics for the completion request.

class camel.types.ChatCompletionAssistantMessageParam

Bases: TypedDict

audio: Audio | None

Data about a previous audio response from the model.

[Learn more](https://platform.openai.com/docs/guides/audio).

content: str | Iterable[ChatCompletionContentPartTextParam | ChatCompletionContentPartRefusalParam] | None

The contents of the assistant message.

Required unless tool_calls or function_call is specified.

function_call: FunctionCall | None

Deprecated and replaced by tool_calls.

The name and arguments of a function that should be called, as generated by the model.

name: str

An optional name for the participant.

Provides the model information to differentiate between participants of the same role.

refusal: str | None

The refusal message by the assistant.

role: Required[Literal['assistant']]

The role of the messages author, in this case assistant.

tool_calls: Iterable[ChatCompletionMessageToolCallParam]

The tool calls generated by the model, such as function calls.

class camel.types.ChatCompletionChunk(**data: Any)

Bases: BaseModel

choices: List[Choice]

A list of chat completion choices.

Can contain more than one elements if n is greater than 1. Can also be empty for the last chunk if you set stream_options: {“include_usage”: true}.

created: int

The Unix timestamp (in seconds) of when the chat completion was created.

Each chunk has the same timestamp.

id: str

A unique identifier for the chat completion. Each chunk has the same ID.

model: str

The model to generate the completion.

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'defer_build': True, 'extra': 'allow'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'choices': FieldInfo(annotation=List[Choice], required=True), 'created': FieldInfo(annotation=int, required=True), 'id': FieldInfo(annotation=str, required=True), 'model': FieldInfo(annotation=str, required=True), 'object': FieldInfo(annotation=Literal['chat.completion.chunk'], required=True), 'service_tier': FieldInfo(annotation=Union[Literal['scale', 'default'], NoneType], required=False, default=None), 'system_fingerprint': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'usage': FieldInfo(annotation=Union[CompletionUsage, NoneType], required=False, default=None)}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

object: Literal['chat.completion.chunk']

The object type, which is always chat.completion.chunk.

service_tier: Literal['scale', 'default'] | None

The service tier used for processing the request.

This field is only included if the service_tier parameter is specified in the request.

system_fingerprint: str | None

This fingerprint represents the backend configuration that the model runs with. Can be used in conjunction with the seed request parameter to understand when backend changes have been made that might impact determinism.

usage: CompletionUsage | None

An optional field that will only be present when you set stream_options: {“include_usage”: true} in your request. When present, it contains a null value except for the last chunk which contains the token usage statistics for the entire request.

class camel.types.ChatCompletionFunctionMessageParam

Bases: TypedDict

content: Required[str | None]

The contents of the function message.

name: Required[str]

The name of the function to call.

role: Required[Literal['function']]

The role of the messages author, in this case function.

class camel.types.ChatCompletionMessage(**data: Any)

Bases: BaseModel

audio: ChatCompletionAudio | None

If the audio output modality is requested, this object contains data about the audio response from the model. [Learn more](https://platform.openai.com/docs/guides/audio).

content: str | None

The contents of the message.

function_call: FunctionCall | None

Deprecated and replaced by tool_calls.

The name and arguments of a function that should be called, as generated by the model.

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'defer_build': True, 'extra': 'allow'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'audio': FieldInfo(annotation=Union[ChatCompletionAudio, NoneType], required=False, default=None), 'content': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'function_call': FieldInfo(annotation=Union[FunctionCall, NoneType], required=False, default=None), 'refusal': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'role': FieldInfo(annotation=Literal['assistant'], required=True), 'tool_calls': FieldInfo(annotation=Union[List[ChatCompletionMessageToolCall], NoneType], required=False, default=None)}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

refusal: str | None

The refusal message generated by the model.

role: Literal['assistant']

The role of the author of this message.

tool_calls: List[ChatCompletionMessageToolCall] | None

The tool calls generated by the model, such as function calls.

class camel.types.ChatCompletionSystemMessageParam

Bases: TypedDict

content: Required[str | Iterable[ChatCompletionContentPartTextParam]]

The contents of the system message.

name: str

An optional name for the participant.

Provides the model information to differentiate between participants of the same role.

role: Required[Literal['system']]

The role of the messages author, in this case system.

class camel.types.ChatCompletionUserMessageParam

Bases: TypedDict

content: Required[str | Iterable[ChatCompletionContentPartTextParam | ChatCompletionContentPartImageParam | ChatCompletionContentPartInputAudioParam]]

The contents of the user message.

name: str

An optional name for the participant.

Provides the model information to differentiate between participants of the same role.

role: Required[Literal['user']]

The role of the messages author, in this case user.

class camel.types.Choice(**data: Any)

Bases: BaseModel

finish_reason: Literal['stop', 'length', 'tool_calls', 'content_filter', 'function_call']

The reason the model stopped generating tokens.

This will be stop if the model hit a natural stop point or a provided stop sequence, length if the maximum number of tokens specified in the request was reached, content_filter if content was omitted due to a flag from our content filters, tool_calls if the model called a tool, or function_call (deprecated) if the model called a function.

index: int

The index of the choice in the list of choices.

logprobs: ChoiceLogprobs | None

Log probability information for the choice.

message: ChatCompletionMessage

A chat completion message generated by the model.

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'defer_build': True, 'extra': 'allow'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'finish_reason': FieldInfo(annotation=Literal['stop', 'length', 'tool_calls', 'content_filter', 'function_call'], required=True), 'index': FieldInfo(annotation=int, required=True), 'logprobs': FieldInfo(annotation=Union[ChoiceLogprobs, NoneType], required=False, default=None), 'message': FieldInfo(annotation=ChatCompletionMessage, required=True)}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

class camel.types.CompletionUsage(**data: Any)

Bases: BaseModel

completion_tokens: int

Number of tokens in the generated completion.

completion_tokens_details: CompletionTokensDetails | None

Breakdown of tokens used in a completion.

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'defer_build': True, 'extra': 'allow'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'completion_tokens': FieldInfo(annotation=int, required=True), 'completion_tokens_details': FieldInfo(annotation=Union[CompletionTokensDetails, NoneType], required=False, default=None), 'prompt_tokens': FieldInfo(annotation=int, required=True), 'prompt_tokens_details': FieldInfo(annotation=Union[PromptTokensDetails, NoneType], required=False, default=None), 'total_tokens': FieldInfo(annotation=int, required=True)}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

prompt_tokens: int

Number of tokens in the prompt.

prompt_tokens_details: PromptTokensDetails | None

Breakdown of tokens used in the prompt.

total_tokens: int

Total number of tokens used in the request (prompt + completion).

class camel.types.EmbeddingModelType(value)

Bases: Enum

An enumeration.

MISTRAL_EMBED = 'mistral-embed'

TEXT_EMBEDDING_3_LARGE = 'text-embedding-3-large'

TEXT_EMBEDDING_3_SMALL = 'text-embedding-3-small'

TEXT_EMBEDDING_ADA_2 = 'text-embedding-ada-002'

property is_mistral: bool

Returns whether this type of models is an Mistral-released model.

property is_openai: bool

Returns whether this type of models is an OpenAI-released model.

property output_dim: int

class camel.types.ModelPlatformType(value)

Bases: Enum

An enumeration.

ANTHROPIC = 'anthropic'

AZURE = 'azure'

COHERE = 'cohere'

DEFAULT = 'openai'

GEMINI = 'gemini'

GROQ = 'groq'

LITELLM = 'litellm'

MISTRAL = 'mistral'

OLLAMA = 'ollama'

OPENAI = 'openai'

OPENAI_COMPATIBLE_MODEL = 'openai-compatible-model'

QWEN = 'tongyi-qianwen'

REKA = 'reka'

SAMBA = 'samba-nova'

TOGETHER = 'together'

VLLM = 'vllm'

YI = 'lingyiwanwu'

ZHIPU = 'zhipuai'

property is_anthropic: bool

Returns whether this platform is anthropic.

property is_azure: bool

Returns whether this platform is azure.

property is_cohere: bool

Returns whether this platform is Cohere.

property is_gemini: bool

Returns whether this platform is Gemini.

property is_groq: bool

Returns whether this platform is groq.

property is_litellm: bool

Returns whether this platform is litellm.

property is_mistral: bool

Returns whether this platform is mistral.

property is_ollama: bool

Returns whether this platform is ollama.

property is_openai: bool

Returns whether this platform is openai.

property is_openai_compatible_model: bool

Returns whether this is a platform supporting openai compatibility

property is_qwen: bool

Returns whether this platform is Qwen.

property is_reka: bool

Returns whether this platform is Reka.

property is_samba: bool

Returns whether this platform is Samba Nova.

property is_together: bool

Returns whether this platform is together.

property is_vllm: bool

Returns whether this platform is vllm.

property is_yi: bool

Returns whether this platform is Yi.

property is_zhipuai: bool

Returns whether this platform is zhipu.

class camel.types.ModelType(value)

Bases: UnifiedModelType, Enum

An enumeration.

CLAUDE_2_0 = ModelType.CLAUDE_2_0

CLAUDE_2_1 = ModelType.CLAUDE_2_1

CLAUDE_3_5_SONNET = ModelType.CLAUDE_3_5_SONNET

CLAUDE_3_HAIKU = ModelType.CLAUDE_3_HAIKU

CLAUDE_3_OPUS = ModelType.CLAUDE_3_OPUS

CLAUDE_3_SONNET = ModelType.CLAUDE_3_SONNET

CLAUDE_INSTANT_1_2 = ModelType.CLAUDE_INSTANT_1_2

COHERE_COMMAND = ModelType.COHERE_COMMAND

COHERE_COMMAND_LIGHT = ModelType.COHERE_COMMAND_LIGHT

COHERE_COMMAND_NIGHTLY = ModelType.COHERE_COMMAND_NIGHTLY

COHERE_COMMAND_R = ModelType.COHERE_COMMAND_R

COHERE_COMMAND_R_PLUS = ModelType.COHERE_COMMAND_R_PLUS

DEFAULT = ModelType.GPT_4O_MINI

GEMINI_1_5_FLASH = ModelType.GEMINI_1_5_FLASH

GEMINI_1_5_PRO = ModelType.GEMINI_1_5_PRO

GEMINI_EXP_1114 = ModelType.GEMINI_EXP_1114

GLM_3_TURBO = ModelType.GLM_3_TURBO

GLM_4 = ModelType.GLM_4

GLM_4V = ModelType.GLM_4V

GPT_3_5_TURBO = ModelType.GPT_3_5_TURBO

GPT_4 = ModelType.GPT_4

GPT_4O = ModelType.GPT_4O

GPT_4O_MINI = ModelType.GPT_4O_MINI

GPT_4_TURBO = ModelType.GPT_4_TURBO

GROQ_GEMMA_2_9B_IT = ModelType.GROQ_GEMMA_2_9B_IT

GROQ_GEMMA_7B_IT = ModelType.GROQ_GEMMA_7B_IT

GROQ_LLAMA_3_1_405B = ModelType.GROQ_LLAMA_3_1_405B

GROQ_LLAMA_3_1_70B = ModelType.GROQ_LLAMA_3_1_70B

GROQ_LLAMA_3_1_8B = ModelType.GROQ_LLAMA_3_1_8B

GROQ_LLAMA_3_70B = ModelType.GROQ_LLAMA_3_70B

GROQ_LLAMA_3_8B = ModelType.GROQ_LLAMA_3_8B

GROQ_MIXTRAL_8_7B = ModelType.GROQ_MIXTRAL_8_7B

MISTRAL_3B = ModelType.MISTRAL_3B

MISTRAL_7B = ModelType.MISTRAL_7B

MISTRAL_8B = ModelType.MISTRAL_8B

MISTRAL_CODESTRAL = ModelType.MISTRAL_CODESTRAL

MISTRAL_CODESTRAL_MAMBA = ModelType.MISTRAL_CODESTRAL_MAMBA

MISTRAL_LARGE = ModelType.MISTRAL_LARGE

MISTRAL_MIXTRAL_8x22B = ModelType.MISTRAL_MIXTRAL_8x22B

MISTRAL_MIXTRAL_8x7B = ModelType.MISTRAL_MIXTRAL_8x7B

MISTRAL_NEMO = ModelType.MISTRAL_NEMO

MISTRAL_PIXTRAL_12B = ModelType.MISTRAL_PIXTRAL_12B

NEMOTRON_4_REWARD = ModelType.NEMOTRON_4_REWARD

O1_MINI = ModelType.O1_MINI

O1_PREVIEW = ModelType.O1_PREVIEW

QWEN_2_5_14B = ModelType.QWEN_2_5_14B

QWEN_2_5_32B = ModelType.QWEN_2_5_32B

QWEN_2_5_72B = ModelType.QWEN_2_5_72B

QWEN_2_5_CODER_32B = ModelType.QWEN_2_5_CODER_32B

QWEN_CODER_TURBO = ModelType.QWEN_CODER_TURBO

QWEN_LONG = ModelType.QWEN_LONG

QWEN_MATH_PLUS = ModelType.QWEN_MATH_PLUS

QWEN_MATH_TURBO = ModelType.QWEN_MATH_TURBO

QWEN_MAX = ModelType.QWEN_MAX

QWEN_PLUS = ModelType.QWEN_PLUS

QWEN_TURBO = ModelType.QWEN_TURBO

QWEN_VL_MAX = ModelType.QWEN_VL_MAX

QWEN_VL_PLUS = ModelType.QWEN_VL_PLUS

REKA_CORE = ModelType.REKA_CORE

REKA_EDGE = ModelType.REKA_EDGE

REKA_FLASH = ModelType.REKA_FLASH

STUB = ModelType.STUB

YI_LARGE = ModelType.YI_LARGE

YI_LARGE_FC = ModelType.YI_LARGE_FC

YI_LARGE_RAG = ModelType.YI_LARGE_RAG

YI_LARGE_TURBO = ModelType.YI_LARGE_TURBO

YI_LIGHTNING = ModelType.YI_LIGHTNING

YI_MEDIUM = ModelType.YI_MEDIUM

YI_MEDIUM_200K = ModelType.YI_MEDIUM_200K

YI_SPARK = ModelType.YI_SPARK

YI_VISION = ModelType.YI_VISION

property is_anthropic: bool

Returns whether this type of models is Anthropic-released model.

Returns:

Whether this type of models is anthropic.

Return type:

bool

property is_azure_openai: bool

Returns whether this type of models is an OpenAI-released model from Azure.

property is_cohere: bool

Returns whether this type of models is a Cohere model.

Returns:

Whether this type of models is Cohere.

Return type:

bool

property is_gemini: bool

Returns whether this type of models is Gemini model.

Returns:

Whether this type of models is gemini.

Return type:

bool

property is_groq: bool

Returns whether this type of models is served by Groq.

property is_mistral: bool

Returns whether this type of models is served by Mistral.

property is_nvidia: bool

Returns whether this type of models is Nvidia-released model.

Returns:

Whether this type of models is nvidia.

Return type:

bool

property is_openai: bool

Returns whether this type of models is an OpenAI-released model.

property is_qwen: bool

Returns whether the model is a Qwen model.

property is_reka: bool

Returns whether this type of models is Reka model.

Returns:

Whether this type of models is Reka.

Return type:

bool

property is_yi: bool

Returns whether this type of models is Yi model.

Returns:

Whether this type of models is Yi.

Return type:

bool

property is_zhipuai: bool

Returns whether this type of models is an ZhipuAI model.

property support_native_tool_calling: bool

Returns whether the model supports native tool calling.

property token_limit: int

Returns the maximum token limit for a given model.

Returns:

The maximum token limit for the given model.

Return type:

int

property value_for_tiktoken: str

Returns the model name for TikToken.

class camel.types.NotGiven

Bases: object

A sentinel singleton class used to distinguish omitted keyword arguments from those passed in with the value None (which may have different behavior).

For example:

```py def get(timeout: Union[int, NotGiven, None] = NotGiven()) -> Response: …

get(timeout=1) # 1s timeout get(timeout=None) # No timeout get() # Default timeout behavior, which may not be statically known at the method definition. ```

class camel.types.OpenAIBackendRole(value)

Bases: Enum

An enumeration.

ASSISTANT = 'assistant'

FUNCTION = 'function'

SYSTEM = 'system'

TOOL = 'tool'

USER = 'user'

class camel.types.OpenAIImageType(value)

Bases: Enum

Image types supported by OpenAI vision model.

GIF = 'gif'

JPEG = 'jpeg'

JPG = 'jpg'

PNG = 'png'

WEBP = 'webp'

class camel.types.OpenAIVisionDetailType(value)

Bases: Enum

An enumeration.

AUTO = 'auto'

HIGH = 'high'

LOW = 'low'

class camel.types.OpenAPIName(value)

Bases: Enum

An enumeration.

BIZTOC = 'biztoc'

COURSERA = 'coursera'

CREATE_QR_CODE = 'create_qr_code'

KLARNA = 'klarna'

NASA_APOD = 'nasa_apod'

OUTSCHOOL = 'outschool'

SPEAK = 'speak'

WEB_SCRAPER = 'web_scraper'

class camel.types.ParsedChatCompletion(*, id: str, choices: List[ParsedChoice], created: int, model: str, object: Literal['chat.completion'], service_tier: Literal['scale', 'default'] | None = None, system_fingerprint: str | None = None, usage: CompletionUsage | None = None, **extra_data: Any)

Bases: ChatCompletion, GenericModel, Generic[ContentType]

choices: List[ParsedChoice]

A list of chat completion choices.

Can be more than one if n is greater than 1.

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'defer_build': True, 'extra': 'allow'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'choices': FieldInfo(annotation=List[ParsedChoice], required=True), 'created': FieldInfo(annotation=int, required=True), 'id': FieldInfo(annotation=str, required=True), 'model': FieldInfo(annotation=str, required=True), 'object': FieldInfo(annotation=Literal['chat.completion'], required=True), 'service_tier': FieldInfo(annotation=Union[Literal['scale', 'default'], NoneType], required=False, default=None), 'system_fingerprint': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'usage': FieldInfo(annotation=Union[CompletionUsage, NoneType], required=False, default=None)}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

class camel.types.RoleType(value)

Bases: Enum

An enumeration.

ASSISTANT = 'assistant'

CRITIC = 'critic'

DEFAULT = 'default'

EMBODIMENT = 'embodiment'

USER = 'user'

class camel.types.StorageType(value)

Bases: Enum

An enumeration.

MILVUS = 'milvus'

QDRANT = 'qdrant'

class camel.types.TaskType(value)

Bases: Enum

An enumeration.

AI_SOCIETY = 'ai_society'

CODE = 'code'

DEFAULT = 'default'

EVALUATION = 'evaluation'

GENERATE_TEXT_EMBEDDING_DATA = 'generate_text_embedding_data'

IMAGE_CRAFT = 'image_craft'

MISALIGNMENT = 'misalignment'

MULTI_CONDITION_IMAGE_CRAFT = 'multi_condition_image_craft'

OBJECT_RECOGNITION = 'object_recognition'

ROLE_DESCRIPTION = 'role_description'

SOLUTION_EXTRACTION = 'solution_extraction'

TRANSLATION = 'translation'

VIDEO_DESCRIPTION = 'video_description'

class camel.types.TerminationMode(value)

Bases: Enum

An enumeration.

ALL = 'all'

ANY = 'any'

class camel.types.UnifiedModelType(value: ModelType | str)

Bases: str

Class used for support both ModelType and str to be used to represent a model type in a unified way. This class is a subclass of str so that it can be used as string seamlessly.

Parameters:

value (Union[ModelType, str]) – The value of the model type.

property is_anthropic: bool

Returns whether the model is an Anthropic model.

property is_azure_openai: bool

Returns whether the model is an Azure OpenAI model.

property is_cohere: bool

Returns whether the model is a Cohere model.

property is_gemini: bool

Returns whether the model is a Gemini model.

property is_groq: bool

Returns whether the model is a Groq served model.

property is_mistral: bool

Returns whether the model is a Mistral model.

property is_openai: bool

Returns whether the model is an OpenAI model.

property is_qwen: bool

Returns whether the model is a Qwen model.

property is_reka: bool

Returns whether the model is a Reka model.

property is_yi: bool

Returns whether the model is a Yi model.

property is_zhipuai: bool

Returns whether the model is a Zhipuai model.

property support_native_tool_calling: bool

Returns whether the model supports native tool calling.

property token_limit: int

Returns the token limit for the model. Here we set the default value as 999_999_999 if it’s not provided from model_config_dict

property value_for_tiktoken: str

Returns the model name for TikToken.

class camel.types.VectorDistance(value)

Bases: Enum

Distance metrics used in a vector database.

COSINE = 'cosine'

//en.wikipedia.org/wiki/Cosine_similarity

Type:

Cosine similarity. https

DOT = 'dot'

//en.wikipedia.org/wiki/Dot_product

Type:

Dot product. https

EUCLIDEAN = 'euclidean'

//en.wikipedia.org/wiki/Euclidean_distance

Type:

Euclidean distance. https

class camel.types.VoiceType(value)

Bases: Enum

An enumeration.

ALLOY = 'alloy'

ECHO = 'echo'

FABLE = 'fable'

NOVA = 'nova'

ONYX = 'onyx'

SHIMMER = 'shimmer'

property is_openai: bool

Returns whether this type of voice is an OpenAI-released voice.