Tony20100/langchain_full huggingface dataset:
MessagePromptTemplateT
classmethod get_lc_namespace() → List[str][source]¶
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]
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[source]¶
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
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”}
langchain_core.prompts.chat.HumanMessagePromptTemplate¶
class langchain_core.prompts.chat.HumanMessagePromptTemplate[source]¶
Bases: _StringImageMessagePromptTemplate
Human message prompt template. This is a message sent from the user.
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 additional_kwargs: dict [Optional]¶
Additional keyword arguments to pass to the prompt template.
param prompt: Union[StringPromptTemplate, List[Union[StringPromptTemplate, ImagePromptTemplate]]] [Required]¶
Prompt template.
async aformat(**kwargs: Any) → BaseMessage¶
Format the prompt template.
Parameters
**kwargs (Any) – Keyword arguments to use for formatting.
Returns
Formatted message.
Return type
BaseMessage
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
exclude_unset (bool) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
Return type
DictStrAny
format(**kwargs: Any) → BaseMessage¶
Format the prompt template.
Parameters
**kwargs (Any) – Keyword arguments to use for formatting.
Returns
Formatted message.
Return type
BaseMessage
format_messages(**kwargs: Any) → List[BaseMessage]¶
Format messages from kwargs.
Parameters
**kwargs (Any) – Keyword arguments to use for formatting.
Returns
List of BaseMessages.
Return type
List[BaseMessage]
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
classmethod from_template(template: Union[str, List[Union[str, _TextTemplateParam, _ImageTemplateParam]]], template_format: str = 'f-string', *, partial_variables: Optional[Dict[str, Any]] = None, **kwargs: Any) → _StringImageMessagePromptTemplateT¶
Create a class from a string template.
Parameters
template (Union[str, List[Union[str, _TextTemplateParam, _ImageTemplateParam]]]) – a template.
template_format (str) – format of the template.
partial_variables (Optional[Dict[str, Any]]) – A dictionary of variables that can be used too partially.
**kwargs (Any) – keyword arguments to pass to the constructor.
Returns
A new instance of this class.
Return type
_StringImageMessagePromptTemplateT
classmethod from_template_file(template_file: Union[str, Path], input_variables: List[str], **kwargs: Any) → _StringImageMessagePromptTemplateT¶
Create a class from a template file.
Parameters
template_file (Union[str, Path]) – path to a template file. String or Path.
template_file (Union[str, Path]) – path to a template file. String or Path.
input_variables (List[str]) – list of input variables.
**kwargs (Any) – keyword arguments to pass to the constructor.
Returns
A new instance of this class.
Return type
_StringImageMessagePromptTemplateT
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]¶
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) –
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 HumanMessagePromptTemplate¶
Activeloop Deep Memory
Context
Figma
JinaChat
Llama2Chat
SQL Database
Yellowbrick
vLLM Chat
langchain_core.prompts.chat.ChatMessagePromptTemplate¶
class langchain_core.prompts.chat.ChatMessagePromptTemplate[source]¶
Bases: BaseStringMessagePromptTemplate
Chat message prompt template.
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 additional_kwargs: dict [Optional]¶
Additional keyword arguments to pass to the prompt template.
param prompt: StringPromptTemplate [Required]¶
String prompt template.
param role: str [Required]¶
Role of the message.
async aformat(**kwargs: Any) → BaseMessage[source]¶
Format the prompt template.
Parameters
**kwargs (Any) – Keyword arguments to use for formatting.
Returns
Formatted message.
Return type
BaseMessage
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
exclude_unset (bool) – 
exclude_defaults (bool) – 
exclude_none (bool) – 
Return type
DictStrAny
format(**kwargs: Any) → BaseMessage[source]¶
Format the prompt template.
Parameters
**kwargs (Any) – Keyword arguments to use for formatting.
Returns
Formatted message.
Return type
BaseMessage
format_messages(**kwargs: Any) → List[BaseMessage]¶
Format messages from kwargs.
Parameters
**kwargs (Any) – Keyword arguments to use for formatting.
Returns
List of BaseMessages.
Return type
List[BaseMessage]
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
classmethod from_template(template: str, template_format: str = 'f-string', partial_variables: Optional[Dict[str, Any]] = None, **kwargs: Any) → MessagePromptTemplateT¶
Create a class from a string template.
Parameters
template (str) – a template.
template_format (str) – format of the template.
partial_variables (Optional[Dict[str, Any]]) – 
A dictionary of variables that can be used to partiallyfill in the template. For example, if the template is
”{variable1} {variable2}”, and partial_variables is
{“variable1”: “foo”}, then the final prompt will be
“foo {variable2}”.
**kwargs (Any) – keyword arguments to pass to the constructor.
Returns
A new instance of this class.
Return type
MessagePromptTemplateT
classmethod from_template_file(template_file: Union[str, Path], input_variables: List[str], **kwargs: Any) → MessagePromptTemplateT¶
Create a class from a template file.
Parameters
template_file (Union[str, Path]) – path to a template file. String or Path.
template_file (Union[str, Path]) – path to a template file. String or Path.
input_variables (List[str]) – list of input variables.
**kwargs (Any) – keyword arguments to pass to the constructor.
Returns
A new instance of this class.
Return type
MessagePromptTemplateT
classmethod get_lc_namespace() → List[str][source]¶
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]¶
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”}
langchain_core.prompts.few_shot.FewShotPromptTemplate¶
class langchain_core.prompts.few_shot.FewShotPromptTemplate[source]¶
Bases: _FewShotPromptTemplateMixin, StringPromptTemplate
Prompt template that contains few shot examples.
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 example_prompt: PromptTemplate [Required]¶
PromptTemplate used to format an individual example.
param example_selector: Optional[BaseExampleSelector] = None¶
ExampleSelector to choose the examples to format into the prompt.
Either this or examples should be provided.
param example_separator: str = '\n\n'¶
String separator used to join the prefix, the examples, and suffix.
param examples: Optional[List[dict]] = None¶
Examples to format into the prompt.
Either this or example_selector should be provided.
param input_types: Dict[str, Any] [Optional]¶
A dictionary of the types of the variables the prompt template expects.
If not provided, all variables are assumed to be strings.
param input_variables: List[str] [Required]¶
A list of the names of the variables the prompt template expects.
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to be used for tracing.
param output_parser: Optional[BaseOutputParser] = None¶
How to parse the output of calling an LLM on this formatted prompt.
param partial_variables: Mapping[str, Any] [Optional]¶
A dictionary of the partial variables the prompt template carries.
Partial variables populate the template so that you don’t need to
pass them in every time you call the prompt.
param prefix: str = ''¶
A prompt template string to put before the examples.
param suffix: str [Required]¶
A prompt template string to put before the examples.
param suffix: str [Required]¶
A prompt template string to put after the examples.
param tags: Optional[List[str]] = None¶
Tags to be used for tracing.
param template_format: Literal['f-string', 'jinja2'] = 'f-string'¶
The format of the prompt template. Options are: ‘f-string’, ‘jinja2’.
param validate_template: bool = False¶
Whether or not to try validating the template.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation runs ainvoke in parallel using asyncio.gather.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode.
Parameters
inputs (List[Input]) – 
config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) – 
return_exceptions (bool) – 
kwargs (Optional[Any]) – 
Return type
List[Output]
async abatch_as_completed(inputs: Sequence[Input], config: Optional[Union[RunnableConfig, Sequence[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → AsyncIterator[Tuple[int, Union[Output, Exception]]]¶
Run ainvoke in parallel on a list of inputs,
yielding results as they complete.
Parameters
inputs (Sequence[Input]) – 
config (Optional[Union[RunnableConfig, Sequence[RunnableConfig]]]) – 
return_exceptions (bool) – 
kwargs (Optional[Any]) – 
Return type
return_exceptions (bool) – 
kwargs (Optional[Any]) – 
Return type
AsyncIterator[Tuple[int, Union[Output, Exception]]]
async aformat(**kwargs: Any) → str[source]¶
Format the prompt with the inputs.
Parameters
kwargs (Any) – Any arguments to be passed to the prompt template.
Returns
A formatted string.
Return type
str
Example:
await prompt.aformat(variable1="foo")
async aformat_prompt(**kwargs: Any) → PromptValue¶
Create Prompt Value.
Parameters
kwargs (Any) – 
Return type
PromptValue
async ainvoke(input: Dict, config: Optional[RunnableConfig] = None, **kwargs: Any) → PromptValue¶
Default implementation of ainvoke, calls invoke from a thread.
The default implementation allows usage of async code even if
the runnable did not implement a native async version of invoke.
Subclasses should override this method if they can run asynchronously.
Parameters
input (Dict) – 
config (Optional[RunnableConfig]) – 
kwargs (Any) – 
Return type
PromptValue
assign(**kwargs: Union[Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any], Mapping[str, Union[Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any]]]]) → RunnableSerializable[Any, Any]¶
Assigns new fields to the dict output of this runnable.
Returns a new runnable.
from langchain_community.llms.fake import FakeStreamingListLLM
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import SystemMessagePromptTemplate
from langchain_core.runnables import Runnable
from operator import itemgetter
prompt = (
    SystemMessagePromptTemplate.from_template("You are a nice assistant.")
    + "{question}"
)
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(**kwargs: Any) → Dict¶
Return dictionary representation of prompt.
Parameters
kwargs (Any) – 
Return type
Dict
format(**kwargs: Any) → str[source]¶
Format the prompt with the inputs.
Parameters
kwargs (Any) – Any arguments to be passed to the prompt template.
Returns
A formatted string.
Return type
str
Example:
prompt.format(variable1="foo")
format_prompt(**kwargs: Any) → PromptValue¶
Create Prompt Value.
Parameters
kwargs (Any) – 
Return type
PromptValue
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
get_graph(config: Optional[RunnableConfig] = None) → Graph¶
Return a graph representation of this runnable.
Parameters
config (Optional[RunnableConfig]) – 
Return type
Graph
get_input_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
Get a pydantic model that can be used to validate input to the runnable.
Runnables that leverage the configurable_fields and configurable_alternatives
methods will have a dynamic input schema that depends on which
configuration the runnable is invoked with.
This method allows to get an input schema for a specific configuration.
Parameters
config (Optional[RunnableConfig]) – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate input.
Return type
Type[BaseModel]
Return type
Type[BaseModel]
classmethod get_lc_namespace() → List[str]¶
Get the namespace of the langchain object.
Return type
List[str]
get_name(suffix: Optional[str] = None, *, name: Optional[str] = None) → str¶
Get the name of the runnable.
Parameters
suffix (Optional[str]) – 
name (Optional[str]) – 
Return type
str
get_output_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
Get a pydantic model that can be used to validate output to the runnable.
Runnables that leverage the configurable_fields and configurable_alternatives
methods will have a dynamic output schema that depends on which
configuration the runnable is invoked with.
This method allows to get an output schema for a specific configuration.
Parameters
config (Optional[RunnableConfig]) – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate output.
Return type
Type[BaseModel]
get_prompts(config: Optional[RunnableConfig] = None) → List[BasePromptTemplate]¶
Parameters
config (Optional[RunnableConfig]) – 
Return type
List[BasePromptTemplate]
invoke(input: Dict, config: Optional[RunnableConfig] = None) → PromptValue¶
Transform a single input into an output. Override to implement.
Parameters
input (Dict) – The input to the runnable.
config (Optional[RunnableConfig]) – A config to use when invoking the runnable.
The config supports standard keys like ‘tags’, ‘metadata’ for tracing
purposes, ‘max_concurrency’ for controlling how much work to do
in parallel, and other keys. Please refer to the RunnableConfig
for more details.
Returns
The output of the runnable.
Return type
PromptValue