langchain_community.chat_models.edenai.ChatEdenAI

Note

ChatEdenAI implements the standard Runnable Interface. 🏃

class langchain_community.chat_models.edenai.ChatEdenAI[source]

Bases: BaseChatModel

EdenAI chat large language models.

EdenAI is a versatile platform that allows you to access various language models from different providers such as Google, OpenAI, Cohere, Mistral and more.

To get started, make sure you have the environment variable EDENAI_API_KEY set with your API key, or pass it as a named parameter to the constructor.

Additionally, EdenAI provides the flexibility to choose from a variety of models, including the ones like “gpt-4”.

Example

from langchain_community.chat_models import ChatEdenAI
from langchain_core.messages import HumanMessage

# Initialize `ChatEdenAI` with the desired configuration
chat = ChatEdenAI(
    provider="openai",
    model="gpt-4",
    max_tokens=256,
    temperature=0.75)

# Create a list of messages to interact with the model
messages = [HumanMessage(content="hello")]

# Invoke the model with the provided messages
chat.invoke(messages)

EdenAI goes beyond mere model invocation. It empowers you with advanced features :

  • Multiple Providers: access to a diverse range of llms offered by various

providers giving you the freedom to choose the best-suited model for your use case.

  • Fallback Mechanism: Set a fallback mechanism to ensure seamless operations

    even if the primary provider is unavailable, you can easily switches to an alternative provider.

  • Usage Statistics: Track usage statistics on a per-project

and per-API key basis. This feature allows you to monitor and manage resource consumption effectively.

  • Monitoring and Observability: EdenAI provides comprehensive monitoring

and observability tools on the platform.

Example of setting up a fallback mechanism:
# Initialize `ChatEdenAI` with a fallback provider
chat_with_fallback = ChatEdenAI(
    provider="openai",
    model="gpt-4",
    max_tokens=256,
    temperature=0.75,
    fallback_provider="google")

you can find more details here : https://docs.edenai.co/reference/text_chat_create

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

param cache: Union[BaseCache, bool, None] = None

Whether to cache the response.

  • If true, will use the global cache.

  • If false, will not use a cache

  • If None, will use the global cache if it’s set, otherwise no cache.

  • If instance of BaseCache, will use the provided cache.

Caching is not currently supported for streaming methods of models.

param callback_manager: Optional[BaseCallbackManager] = None

[DEPRECATED] Callback manager to add to the run trace.

param callbacks: Callbacks = None

Callbacks to add to the run trace.

param custom_get_token_ids: Optional[Callable[[str], List[int]]] = None

Optional encoder to use for counting tokens.

param edenai_api_key: Optional[SecretStr] = None

EdenAI API Token

Constraints
  • type = string

  • writeOnly = True

  • format = password

param edenai_api_url: str = 'https://api.edenai.run/v2'
param fallback_providers: Optional[str] = None

Providers in this will be used as fallback if the call to provider fails.

param max_tokens: int = 256

Denotes the number of tokens to predict per generation.

param metadata: Optional[Dict[str, Any]] = None

Metadata to add to the run trace.

param model: Optional[str] = None

model name for above provider (eg: ‘gpt-4’ for openai) available models are shown on https://docs.edenai.co/ under ‘available providers’

param provider: str = 'openai'

chat provider to use (eg: openai,google etc.)

param streaming: bool = False

Whether to stream the results.

param tags: Optional[List[str]] = None

Tags to add to the run trace.

param temperature: Optional[float] = 0

A non-negative float that tunes the degree of randomness in generation.

param verbose: bool [Optional]

Whether to print out response text.

__call__(messages: List[BaseMessage], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) BaseMessage

[Deprecated]

Notes

Deprecated since version langchain-core==0.1.7: Use invoke instead.

Parameters
Return type

BaseMessage

async agenerate(messages: List[List[BaseMessage]], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[str] = None, run_id: Optional[UUID] = None, **kwargs: Any) LLMResult

Asynchronously pass a sequence of prompts to a model and return generations.

This method should make use of batched calls for models that expose a batched API.

Use this method when you want to:
  1. take advantage of batched calls,

  2. need more output from the model than just the top generated value,

  3. are building chains that are agnostic to the underlying language model

    type (e.g., pure text completion models vs chat models).

Parameters
  • messages (List[List[BaseMessage]]) – List of list of messages.

  • stop (Optional[List[str]]) – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.

  • callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation.

  • **kwargs (Any) – Arbitrary additional keyword arguments. These are usually passed to the model provider API call.

  • tags (Optional[List[str]]) –

  • metadata (Optional[Dict[str, Any]]) –

  • run_name (Optional[str]) –

  • run_id (Optional[UUID]) –

  • **kwargs

Returns

An LLMResult, which contains a list of candidate Generations for each input

prompt and additional model provider-specific output.

Return type

LLMResult

async agenerate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) LLMResult

Asynchronously pass a sequence of prompts and return model generations.

This method should make use of batched calls for models that expose a batched API.

Use this method when you want to:
  1. take advantage of batched calls,

  2. need more output from the model than just the top generated value,

  3. are building chains that are agnostic to the underlying language model

    type (e.g., pure text completion models vs chat models).

Parameters
  • prompts (List[PromptValue]) – List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models).

  • stop (Optional[List[str]]) – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.

  • callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation.

  • **kwargs (Any) – Arbitrary additional keyword arguments. These are usually passed to the model provider API call.

Returns

An LLMResult, which contains a list of candidate Generations for each input

prompt and additional model provider-specific output.

Return type

LLMResult

async apredict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) str

[Deprecated]

Notes

Deprecated since version langchain-core==0.1.7: Use ainvoke instead.

Parameters
  • text (str) –

  • stop (Optional[Sequence[str]]) –

  • kwargs (Any) –

Return type

str

async apredict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) BaseMessage

[Deprecated]

Notes

Deprecated since version langchain-core==0.1.7: Use ainvoke instead.

Parameters
  • messages (List[BaseMessage]) –

  • stop (Optional[Sequence[str]]) –

  • kwargs (Any) –

Return type

BaseMessage

bind_tools(tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]], *, tool_choice: Optional[Union[dict, str, Literal['auto', 'none', 'required', 'any'], bool]] = None, **kwargs: Any) Runnable[Union[PromptValue, str, Sequence[Union[BaseMessage, List[str], Tuple[str, str], str, Dict[str, Any]]]], BaseMessage][source]
Parameters
  • tools (Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]]) –

  • tool_choice (Optional[Union[dict, str, Literal['auto', 'none', 'required', 'any'], bool]]) –

  • kwargs (Any) –

Return type

Runnable[Union[PromptValue, str, Sequence[Union[BaseMessage, List[str], Tuple[str, str], str, Dict[str, Any]]]], BaseMessage]

call_as_llm(message: str, stop: Optional[List[str]] = None, **kwargs: Any) str

[Deprecated]

Notes

Deprecated since version langchain-core==0.1.7: Use invoke instead.

Parameters
  • message (str) –

  • stop (Optional[List[str]]) –

  • kwargs (Any) –

Return type

str

generate(messages: List[List[BaseMessage]], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[str] = None, run_id: Optional[UUID] = None, **kwargs: Any) LLMResult

Pass a sequence of prompts to the model and return model generations.

This method should make use of batched calls for models that expose a batched API.

Use this method when you want to:
  1. take advantage of batched calls,

  2. need more output from the model than just the top generated value,

  3. are building chains that are agnostic to the underlying language model

    type (e.g., pure text completion models vs chat models).

Parameters
  • messages (List[List[BaseMessage]]) – List of list of messages.

  • stop (Optional[List[str]]) – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.

  • callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation.

  • **kwargs (Any) – Arbitrary additional keyword arguments. These are usually passed to the model provider API call.

  • tags (Optional[List[str]]) –

  • metadata (Optional[Dict[str, Any]]) –

  • run_name (Optional[str]) –

  • run_id (Optional[UUID]) –

  • **kwargs

Returns

An LLMResult, which contains a list of candidate Generations for each input

prompt and additional model provider-specific output.

Return type

LLMResult

generate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) LLMResult

Pass a sequence of prompts to the model and return model generations.

This method should make use of batched calls for models that expose a batched API.

Use this method when you want to:
  1. take advantage of batched calls,

  2. need more output from the model than just the top generated value,

  3. are building chains that are agnostic to the underlying language model

    type (e.g., pure text completion models vs chat models).

Parameters
  • prompts (List[PromptValue]) – List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models).

  • stop (Optional[List[str]]) – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.

  • callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation.

  • **kwargs (Any) – Arbitrary additional keyword arguments. These are usually passed to the model provider API call.

Returns

An LLMResult, which contains a list of candidate Generations for each input

prompt and additional model provider-specific output.

Return type

LLMResult

get_num_tokens(text: str) int

Get the number of tokens present in the text.

Useful for checking if an input will fit in a model’s context window.

Parameters

text (str) – The string input to tokenize.

Returns

The integer number of tokens in the text.

Return type

int

get_num_tokens_from_messages(messages: List[BaseMessage]) int

Get the number of tokens in the messages.

Useful for checking if an input will fit in a model’s context window.

Parameters

messages (List[BaseMessage]) – The message inputs to tokenize.

Returns

The sum of the number of tokens across the messages.

Return type

int

get_token_ids(text: str) List[int]

Return the ordered ids of the tokens in a text.

Parameters

text (str) – The string input to tokenize.

Returns

A list of ids corresponding to the tokens in the text, in order they occur

in the text.

Return type

List[int]

static get_user_agent() str[source]
Return type

str

predict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) str

[Deprecated]

Notes

Deprecated since version langchain-core==0.1.7: Use invoke instead.

Parameters
  • text (str) –

  • stop (Optional[Sequence[str]]) –

  • kwargs (Any) –

Return type

str

predict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) BaseMessage

[Deprecated]

Notes

Deprecated since version langchain-core==0.1.7: Use invoke instead.

Parameters
  • messages (List[BaseMessage]) –

  • stop (Optional[Sequence[str]]) –

  • kwargs (Any) –

Return type

BaseMessage

with_structured_output(schema: Union[Dict, Type[BaseModel]], *, include_raw: bool = False, **kwargs: Any) Runnable[Union[PromptValue, str, Sequence[Union[BaseMessage, List[str], Tuple[str, str], str, Dict[str, Any]]]], Union[Dict, BaseModel]][source]

Model wrapper that returns outputs formatted to match the given schema.

Parameters
  • schema (Union[Dict, Type[BaseModel]]) – The output schema as a dict or a Pydantic class. If a Pydantic class then the model output will be an object of that class. If a dict then the model output will be a dict. With a Pydantic class the returned attributes will be validated, whereas with a dict they will not be. If method is “function_calling” and schema is a dict, then the dict must match the OpenAI function-calling spec.

  • include_raw (bool) – If False then only the parsed structured output is returned. If an error occurs during model output parsing it will be raised. If True then both the raw model response (a BaseMessage) and the parsed model response will be returned. If an error occurs during output parsing it will be caught and returned as well. The final output is always a dict with keys “raw”, “parsed”, and “parsing_error”.

  • kwargs (Any) –

Returns

If include_raw is True then a dict with keys:

raw: BaseMessage parsed: Optional[_DictOrPydantic] parsing_error: Optional[BaseException]

If include_raw is False then just _DictOrPydantic is returned, where _DictOrPydantic depends on the schema:

If schema is a Pydantic class then _DictOrPydantic is the Pydantic

class.

If schema is a dict then _DictOrPydantic is a dict.

Return type

A Runnable that takes any ChatModel input and returns as output

Example: Function-calling, Pydantic schema (method=”function_calling”, include_raw=False):
from langchain_core.pydantic_v1 import BaseModel

class AnswerWithJustification(BaseModel):
    '''An answer to the user question along with justification for the answer.'''
    answer: str
    justification: str

llm = ChatModel(model="model-name", temperature=0)
structured_llm = llm.with_structured_output(AnswerWithJustification)

structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")

# -> AnswerWithJustification(
#     answer='They weigh the same',
#     justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
# )
Example: Function-calling, Pydantic schema (method=”function_calling”, include_raw=True):
from langchain_core.pydantic_v1 import BaseModel

class AnswerWithJustification(BaseModel):
    '''An answer to the user question along with justification for the answer.'''
    answer: str
    justification: str

llm = ChatModel(model="model-name", temperature=0)
structured_llm = llm.with_structured_output(AnswerWithJustification, include_raw=True)

structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
# -> {
#     'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}),
#     'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'),
#     'parsing_error': None
# }
Example: Function-calling, dict schema (method=”function_calling”, include_raw=False):
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.utils.function_calling import convert_to_openai_tool

class AnswerWithJustification(BaseModel):
    '''An answer to the user question along with justification for the answer.'''
    answer: str
    justification: str

dict_schema = convert_to_openai_tool(AnswerWithJustification)
llm = ChatModel(model="model-name", temperature=0)
structured_llm = llm.with_structured_output(dict_schema)

structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
# -> {
#     'answer': 'They weigh the same',
#     'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
# }

Examples using ChatEdenAI