langchain_core.prompts.chat.MessagesPlaceholder

class langchain_core.prompts.chat.MessagesPlaceholder

Bases: BaseMessagePromptTemplate

Prompt template that assumes variable is already list of messages.

A placeholder which can be used to pass in a list of messages.

Direct usage:

from langchain_core.prompts import MessagesPlaceholder

prompt = MessagesPlaceholder("history")
prompt.format_messages() # raises KeyError

prompt = MessagesPlaceholder("history", optional=True)
prompt.format_messages() # returns empty list []

prompt.format_messages(
    history=[
        ("system", "You are an AI assistant."),
        ("human", "Hello!"),
    ]
)
# -> [
#     SystemMessage(content="You are an AI assistant."),
#     HumanMessage(content="Hello!"),
# ]

Building a prompt with chat history:

from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder

prompt = ChatPromptTemplate.from_messages(
    [
        ("system", "You are a helpful assistant."),
        MessagesPlaceholder("history"),
        ("human", "{question}")
    ]
)
prompt.invoke(
   {
       "history": [("human", "what's 5 + 2"), ("ai", "5 + 2 is 7")],
       "question": "now multiply that by 4"
   }
)
# -> ChatPromptValue(messages=[
#     SystemMessage(content="You are a helpful assistant."),
#     HumanMessage(content="what's 5 + 2"),
#     AIMessage(content="5 + 2 is 7"),
#     HumanMessage(content="now multiply that by 4"),
# ])

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 optional: bool = False

If True format_messages can be called with no arguments and will return an empty list. If False then a named argument with name variable_name must be passed in, even if the value is an empty list.

param variable_name: str [Required]

Name of variable to use as messages.

async aformat_messages(**kwargs: Any) → List[BaseMessage]

Format messages from kwargs. Should return a list of BaseMessages.

Parameters

**kwargs (Any) – Keyword arguments to use for formatting.

Returns

List of BaseMessages.

Return type

List[BaseMessage]

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

format_messages(**kwargs: Any) → List[BaseMessage]

Format messages from kwargs.

Parameters

**kwargs (Any) – Keyword arguments to use for formatting.

Returns

List of BaseMessage.

Return type

List[BaseMessage]

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 or not the 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

Human-readable representation.

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 input_variables: List[str]

Input variables for this prompt template.

Returns

List of input variable names.

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 MessagesPlaceholder

• !pip3 install text-generation

• # Automatically restart kernel after installs so that your environment can access the new packages

• %pip install -qU langchain langchain-openai

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

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

• Download the content

• Install a model capable of tool calling

• Load in document to retrieve over

• Optionally, specify your own session_state key for storing messages

• Quick reference {#quick-reference}

• Quickstart

• Remembers

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

• Set your environment variables using os.environ

• Uncomment if you want to log to LangSmith

• Uncomment the below to use LangSmith. Not required.

• [Beta] Memory

• adding_memory.md

• copy from tidb cloud console

• custom_agent.md

• import dotenv

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

• prompt_size.md

• redis_chat_message_history.md

• sql_database.md

• 🦜🕸️LangGraph