langchain_core.messages.human.HumanMessage

class langchain_core.messages.human.HumanMessage

Bases: BaseMessage

Message from a human.

Pass in content as positional arg.

param additional_kwargs: dict [Optional]

Reserved for additional payload data associated with the message.

For example, for a message from an AI, this could include tool calls.

param content: Union[str, List[Union[str, Dict]]] [Required]

The string contents of the message.

param example: bool = False

Whether this Message is being passed in to the model as part of an example conversation.

param id: Optional[str] = None

An optional unique identifier for the message. This should ideally be provided by the provider/model which created the message.

param name: Optional[str] = None

param response_metadata: dict [Optional]

Response metadata. For example: response headers, logprobs, token counts.

param type: Literal['human'] = 'human'

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model

Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

Parameters

• _fields_set (Optional[SetStr]) –

• values (Any) –

Return type

Model

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

• include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model

• exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include

• update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data

• deep (bool) – set to True to make a deep copy of the model

• self (Model) –

Returns

new model instance

Return type

Model

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters

• include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

• exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

• by_alias (bool) –

• skip_defaults (Optional[bool]) –

• exclude_unset (bool) –

• exclude_defaults (bool) –

• exclude_none (bool) –

Return type

DictStrAny

classmethod from_orm(obj: Any) → Model

Parameters

obj (Any) –

Return type

Model

classmethod get_lc_namespace() → List[str]

Get the namespace of the langchain object.

Return type

List[str]

classmethod is_lc_serializable() → bool

Return whether this class is serializable.

Return type

bool

json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

Parameters

• include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

• exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

• by_alias (bool) –

• skip_defaults (Optional[bool]) –

• exclude_unset (bool) –

• exclude_defaults (bool) –

• exclude_none (bool) –

• encoder (Optional[Callable[[Any], Any]]) –

• models_as_dict (bool) –

• dumps_kwargs (Any) –

Return type

unicode

classmethod lc_id() → List[str]

A unique identifier for this class for serialization purposes.

The unique identifier is a list of strings that describes the path to the object.

Return type

List[str]

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model

Parameters

• path (Union[str, Path]) –

• content_type (unicode) –

• encoding (unicode) –

• proto (Protocol) –

• allow_pickle (bool) –

Return type

Model

classmethod parse_obj(obj: Any) → Model

Parameters

obj (Any) –

Return type

Model

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model

Parameters

• b (Union[str, bytes]) –

• content_type (unicode) –

• encoding (unicode) –

• proto (Protocol) –

• allow_pickle (bool) –

Return type

Model

pretty_print() → None

Return type

None

pretty_repr(html: bool = False) → str

Parameters

html (bool) –

Return type

str

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny

Parameters

• by_alias (bool) –

• ref_template (unicode) –

Return type

DictStrAny

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode

Parameters

• by_alias (bool) –

• ref_template (unicode) –

• dumps_kwargs (Any) –

Return type

unicode

to_json() → Union[SerializedConstructor, SerializedNotImplemented]

Return type

Union[SerializedConstructor, SerializedNotImplemented]

to_json_not_implemented() → SerializedNotImplemented

Return type

SerializedNotImplemented

classmethod update_forward_refs(**localns: Any) → None

Try to update ForwardRefs on fields based on this Model, globalns and localns.

Parameters

localns (Any) –

Return type

None

classmethod validate(value: Any) → Model

Parameters

value (Any) –

Return type

Model

property lc_attributes: Dict

List of attribute names that should be included in the serialized kwargs.

These attributes must be accepted by the constructor.

property lc_secrets: Dict[str, str]

A map of constructor argument names to secret ids.

For example,

{“openai_api_key”: “OPENAI_API_KEY”}

Examples using HumanMessage

• # activeloop token is needed if you are not signed in using CLI: `activeloop login -u <USERNAME> -p <PASSWORD>`

• %pip install -qU langchain langchain-openai

• %pip install -qU langchain-core langchain-openai

• -> content=’Hello! How can I assist you today?’

• Chat Bot Feedback Template

• Cohere

• Construct the Tools agent

• Define a custom prompt to provide instructions and any additional context.

• First step is to set up the env variable.

• Flyte

• Generate your api key from: https://platform.moonshot.cn/console/api-keys

• Get the prompt to use - you can modify this!

• Google

• If api_key is not passed, default behavior is to use the `MISTRAL_API_KEY` environment variable.

• If running a Databricks notebook attached to an interactive cluster in “single user”

• Install the package

• Javelin AI Gateway

• Konko {#konko}

• LLMonitor

• LangChain supports many other chat models. Here, we’re using Ollama

• Let’s try out LLAMA model offered on EverlyAI Hosted Endpoints {#lets-try-out-llama-model-offered-on-everlyai-hosted-endpoints}

• Let’s try out each model offered on Anyscale Endpoints {#lets-try-out-each-model-offered-on-anyscale-endpoints}

• Log10

• MLflow AI Gateway

• MLflow Deployments for LLMs

• Merge consecutive messages from the same sender into a single message

• Model I/O

• Note that each chunk may contain more than one “token”

• Or via the async API

• PremAI

• Quick reference {#quick-reference}

• Quickstart

• Quickstart {#quickstart}

• Remembers

• Schema

• Set env var OPENAI_API_KEY or load from a .env file:

• Set this to your Zep server URL

• Setup {#setup}

• Step 4: Embeddings Example {#step-4-embeddings-example}

• To enable streaming, we pass in `streaming=True` to the ChatModel constructor

• Tool calling {#tool-calling}

• Uncomment the below to use LangSmith. Not required.

• alibaba_cloud_pai_eas.md

• arthur_tracking.md

• azure_chat_openai.md

• azure_ml.md

• azureml_chat_endpoint.md

• baichuan.md

• baidu_qianfan_endpoint.md

• bedrock.md

• composition.md

• context.md

• custom_agent.md

• custom_chat_model.md

• dappier.md

• edenai.md

• ernie.md

• for running these examples in the notebook:

• friendli.md

• get a chat completion from the formatted messages

• get a new token: https://deepinfra.com/login?from=%2Fdash

• gigachat.md

• google_vertex_ai_palm.md

• gpt_router.md

• import dotenv

• labelstudio.md

• litellm.md

• litellm_router.md

• minimax.md

• octoai.md

• open ../../../static/img/brand/wordmark.png as base64 str

• openai.md

• os.environ[“LANGCHAIN_TRACING_V2”] = “true”

• promptlayer.md

• promptlayer_chatopenai.md

• service url

• setup tools

• sparkllm.md

• tencent_hunyuan.md

• tools_as_openai_functions.md

• trubrics.md

• yandex.md

• yuan2.md

• zhipuai.md

• 🦜️🏓 LangServe

• 🦜🕸️LangGraph