viruPVS/langchain-docs huggingface dataset:
langchain_core.prompts.chat.SystemMessagePromptTemplate¶
class langchain_core.prompts.chat.SystemMessagePromptTemplate[source]¶
Bases: BaseStringMessagePromptTemplate
System message prompt template.
This is a message that is not sent to 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: langchain_core.prompts.string.StringPromptTemplate [Required]¶
String prompt template.
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
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 – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – 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 – set to True to make a deep copy of the model
Returns
new model instance
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → 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 SystemMessagePromptTemplate¶
Anthropic
🚅 LiteLLM
Konko
OpenAI
Google Cloud Platform Vertex AI PaLM 
JinaChat
Figma
Set env var OPENAI_API_KEY or load from a .env file:
CAMEL Role-Playing Autonomous Cooperative Agents
Code writing
langchain_core.prompts.few_shot_with_templates.FewShotPromptWithTemplates¶
class langchain_core.prompts.few_shot_with_templates.FewShotPromptWithTemplates[source]¶
Bases: 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: langchain_core.prompts.prompt.PromptTemplate [Required]¶
PromptTemplate used to format an individual example.
param example_selector: Any = 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 output_parser: Optional[BaseOutputParser] = None¶
How to parse the output of calling an LLM on this formatted prompt.
param partial_variables: Mapping[str, Union[str, Callable[[], str]]] [Optional]¶
param prefix: Optional[langchain_core.prompts.string.StringPromptTemplate] = None¶
A PromptTemplate to put before the examples.
param suffix: langchain_core.prompts.string.StringPromptTemplate [Required]¶
A PromptTemplate to put after the examples.
param template_format: str = 'f-string'¶
param template_format: str = '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.
async ainvoke(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Any) → Output¶
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.
async astream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of astream, which calls ainvoke.
Subclasses should override this method if they support streaming output.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
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 – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – 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 – set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) → Dict¶
Return dictionary representation of prompt.
format(**kwargs: Any) → str[source]¶
Format the prompt with the inputs.
Parameters
kwargs – Any arguments to be passed to the prompt template.
Returns
A formatted string.
Example:
prompt.format(variable1="foo")
format_prompt(**kwargs: Any) → PromptValue¶
Create Chat Messages.
classmethod from_orm(obj: Any) → Model¶
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 – A config to use when generating the schema.
Returns
Parameters
config – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate input.
classmethod get_lc_namespace() → List[str][source]¶
Get the namespace of the langchain object.
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 – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate output.
invoke(input: Dict, config: Optional[RunnableConfig] = None) → PromptValue¶
Transform a single input into an output. Override to implement.
Parameters
input – The input to the runnable.
config – 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.
classmethod is_lc_serializable() → bool¶
Return whether this class is serializable.
classmethod is_lc_serializable() → bool¶
Return whether this class is serializable.
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().
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.
map() → Runnable[List[Input], List[Output]]¶
Return a new Runnable that maps a list of inputs to a list of outputs,
by calling invoke() with each input.
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
partial(**kwargs: Union[str, Callable[[], str]]) → BasePromptTemplate¶
Return a partial of the prompt template.
save(file_path: Union[Path, str]) → None[source]¶
Save the prompt.
Parameters
Bind input and output types to a Runnable, returning a new Runnable.
property InputType: Type[langchain_core.runnables.utils.Input]¶
The type of input this runnable accepts specified as a type annotation.
property OutputType: Any¶
The type of output this runnable produces specified as a type annotation.
property config_specs: List[langchain_core.runnables.utils.ConfigurableFieldSpec]¶
List configurable fields for this runnable.
property input_schema: Type[pydantic.main.BaseModel]¶
The type of input this runnable accepts specified as a pydantic 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”}
property output_schema: Type[pydantic.main.BaseModel]¶
The type of output this runnable produces specified as a pydantic model.
langchain_core.prompts.chat.BaseStringMessagePromptTemplate¶
class langchain_core.prompts.chat.BaseStringMessagePromptTemplate[source]¶
Bases: BaseMessagePromptTemplate, ABC
Base class for message prompt templates that use a string 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: langchain_core.prompts.string.StringPromptTemplate [Required]¶
String prompt template.
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
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 – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – 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 – set to True to make a deep copy of the model
Returns
new model instance
deep – set to True to make a deep copy of the model
Returns
new model instance
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.
abstract format(**kwargs: Any) → BaseMessage[source]¶
Format the prompt template.
Parameters
**kwargs – Keyword arguments to use for formatting.
Returns
Formatted message.
format_messages(**kwargs: Any) → List[BaseMessage][source]¶
Format messages from kwargs.
Parameters
**kwargs – Keyword arguments to use for formatting.
Returns
List of BaseMessages.
classmethod from_orm(obj: Any) → Model¶
classmethod from_template(template: str, template_format: str = 'f-string', partial_variables: Optional[Dict[str, Any]] = None, **kwargs: Any) → MessagePromptTemplateT[source]¶
Create a class from a string template.
Parameters
template – a template.
template_format – format of the template.
partial_variables – 
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 – keyword arguments to pass to the constructor.
Returns
A new instance of this class.
Returns
A new instance of this class.
classmethod from_template_file(template_file: Union[str, Path], input_variables: List[str], **kwargs: Any) → MessagePromptTemplateT[source]¶
Create a class from a template file.
Parameters
template_file – path to a template file. String or Path.
input_variables – list of input variables.
**kwargs – keyword arguments to pass to the constructor.
Returns
A new instance of this class.
classmethod get_lc_namespace() → List[str][source]¶
Get the namespace of the langchain object.
classmethod is_lc_serializable() → bool¶
Return whether or not the class is serializable.
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().
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.
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → 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.base.format_document¶
langchain_core.prompts.base.format_document(doc: Document, prompt: BasePromptTemplate) → str[source]¶
Format a document into a string based on a prompt template.
First, this pulls information from the document from two sources:
page_content:This takes the information from the document.page_content
and assigns it to a variable named page_content.
metadata:This takes information from document.metadata and assigns
it to variables of the same name.
Those variables are then passed into the prompt to produce a formatted string.
Parameters
doc – Document, the page_content and metadata will be used to create
the final string.
prompt – BasePromptTemplate, will be used to format the page_content
and metadata into the final string.
Returns
string of the document formatted.
Example
from langchain_core import Document
from langchain_core.prompts import PromptTemplate
doc = Document(page_content="This is a joke", metadata={"page": "1"})
prompt = PromptTemplate.from_template("Page {page}: {page_content}")
format_document(doc, prompt)
>>> "Page 1: This is a joke"
Examples using format_document¶
First we add a step to load memory
langchain_core.prompts.pipeline.PipelinePromptTemplate¶
class langchain_core.prompts.pipeline.PipelinePromptTemplate[source]¶
Bases: BasePromptTemplate
A prompt template for composing multiple prompt templates together.
This can be useful when you want to reuse parts of prompts.
A PipelinePrompt consists of two main parts:
final_prompt: This is the final prompt that is returned
pipeline_prompts: This is a list of tuples, consistingof a string (name) and a Prompt Template.
Each PromptTemplate will be formatted and then passed
to future prompt templates as a variable with
the same name as name
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 final_prompt: langchain_core.prompts.base.BasePromptTemplate [Required]¶
The final prompt that is returned.
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 output_parser: Optional[BaseOutputParser] = None¶
How to parse the output of calling an LLM on this formatted prompt.
param partial_variables: Mapping[str, Union[str, Callable[[], str]]] [Optional]¶
param pipeline_prompts: List[Tuple[str, langchain_core.prompts.base.BasePromptTemplate]] [Required]¶
A list of tuples, consisting of a string (name) and a Prompt Template.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
dict(**kwargs: Any) → Dict¶
Return dictionary representation of prompt.
format(**kwargs: Any) → str[source]¶
Format the prompt with the inputs.
Parameters
kwargs – Any arguments to be passed to the prompt template.
Returns
A formatted string.
Example:
prompt.format(variable1="foo")
format_prompt(**kwargs: Any) → PromptValue[source]¶
Create Chat Messages.
classmethod from_orm(obj: Any) → Model¶
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 – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate input.
classmethod get_lc_namespace() → List[str][source]¶
Get the namespace of the langchain object.
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 – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate output.
invoke(input: Dict, config: Optional[RunnableConfig] = None) → PromptValue¶
Transform a single input into an output. Override to implement.
Parameters
input – The input to the runnable.
Parameters
input – The input to the runnable.
config – 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.
classmethod is_lc_serializable() → bool¶
Return whether this class is serializable.
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().
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.
map() → Runnable[List[Input], List[Output]]¶
Return a new Runnable that maps a list of inputs to a list of outputs,
by calling invoke() with each input.
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶