id stringlengths 14 16 | text stringlengths 13 2.7k | source stringlengths 57 178 |
|---|---|---|
a1ca0a7be866-4 | Returns
A dict of named outputs. Should contain all outputs specified inChain.output_keys.
async agenerate(input_list: List[Dict[str, Any]], run_manager: Optional[AsyncCallbackManagerForChainRun] = None) → LLMResult¶
Generate LLM result from inputs.
async ainvoke(input: Dict[str, Any], config: Optional[RunnableConfig] ... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.flare.base.QuestionGeneratorChain.html |
a1ca0a7be866-5 | Call apredict and then parse the results.
async aprep_prompts(input_list: List[Dict[str, Any]], run_manager: Optional[AsyncCallbackManagerForChainRun] = None) → Tuple[List[PromptValue], Optional[List[str]]]¶
Prepare prompts from inputs.
async arun(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCal... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.flare.base.QuestionGeneratorChain.html |
a1ca0a7be866-6 | question = "What's the temperature in Boise, Idaho?"
context = "Weather report for Boise, Idaho on 07/03/23..."
await chain.arun(question=question, context=context)
# -> "The temperature in Boise is..."
async astream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.flare.base.QuestionGeneratorChain.html |
a1ca0a7be866-7 | input is still being generated.
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation runs invoke in parallel using a thread pool executor.
The default implementation of batch w... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.flare.base.QuestionGeneratorChain.html |
a1ca0a7be866-8 | 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... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.flare.base.QuestionGeneratorChain.html |
a1ca0a7be866-9 | 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 confi... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.flare.base.QuestionGeneratorChain.html |
a1ca0a7be866-10 | for more details.
Returns
The output of the runnable.
classmethod is_lc_serializable() → bool¶
Is this class serializable?
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[b... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.flare.base.QuestionGeneratorChain.html |
a1ca0a7be866-11 | Format prompt with kwargs and pass to LLM.
Parameters
callbacks – Callbacks to pass to LLMChain
**kwargs – Keys to pass to prompt template.
Returns
Completion from LLM.
Example
completion = llm.predict(adjective="funny")
predict_and_parse(callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.flare.base.QuestionGeneratorChain.html |
a1ca0a7be866-12 | Prepare prompts from inputs.
run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Convenience method for executing chain.
The main difference between this method and Chain.__c... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.flare.base.QuestionGeneratorChain.html |
a1ca0a7be866-13 | save(file_path: Union[Path, str]) → None¶
Save the chain.
Expects Chain._chain_type property to be implemented and for memory to benull.
Parameters
file_path – Path to file to save the chain to.
Example
chain.save(file_path="path/chain.yaml")
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definiti... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.flare.base.QuestionGeneratorChain.html |
a1ca0a7be866-14 | Add fallbacks to a runnable, returning a new Runnable.
Parameters
fallbacks – A sequence of runnables to try if the original runnable fails.
exceptions_to_handle – A tuple of exception types to handle.
Returns
A new Runnable that will try the original runnable, and then each
fallback in order, upon failures.
with_liste... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.flare.base.QuestionGeneratorChain.html |
a1ca0a7be866-15 | Bind input and output types to a Runnable, returning a new Runnable.
property InputType: Type[langchain.schema.runnable.utils.Input]¶
The type of input this runnable accepts specified as a type annotation.
property OutputType: Type[langchain.schema.runnable.utils.Output]¶
The type of output this runnable produces speci... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.flare.base.QuestionGeneratorChain.html |
85661d716d7b-0 | langchain.chains.query_constructor.schema.AttributeInfo¶
class langchain.chains.query_constructor.schema.AttributeInfo[source]¶
Bases: BaseModel
Information about a data source attribute.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be p... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.query_constructor.schema.AttributeInfo.html |
85661d716d7b-1 | 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, ex... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.query_constructor.schema.AttributeInfo.html |
85661d716d7b-2 | 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¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.query_constructor.schema.AttributeInfo.html |
604ab3c8e754-0 | langchain.chains.llm_math.base.LLMMathChain¶
class langchain.chains.llm_math.base.LLMMathChain[source]¶
Bases: Chain
Chain that interprets a prompt and executes python code to do math.
Example
from langchain.chains import LLMMathChain
from langchain.llms import OpenAI
llm_math = LLMMathChain.from_llm(OpenAI())
Create a... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.llm_math.base.LLMMathChain.html |
604ab3c8e754-1 | and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param prompt: BasePromptTemplate = PromptTemplate(input_variables=['question'], template='Translate a math problem into a expression that can be executed using Python\'s numex... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.llm_math.base.LLMMathChain.html |
604ab3c8e754-2 | will be printed to the console. Defaults to the global verbose value,
accessible via langchain.globals.get_verbose().
__call__(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, me... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.llm_math.base.LLMMathChain.html |
604ab3c8e754-3 | 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 acall(inputs: Union[Dict... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.llm_math.base.LLMMathChain.html |
604ab3c8e754-4 | Returns
A dict of named outputs. Should contain all outputs specified inChain.output_keys.
async ainvoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any) → Dict[str, Any]¶
Default implementation of ainvoke, calls invoke from a thread.
The default implementation allows usage of async code e... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.llm_math.base.LLMMathChain.html |
604ab3c8e754-5 | directly as keyword arguments.
Returns
The chain output.
Example
# Suppose we have a single-input chain that takes a 'question' string:
await chain.arun("What's the temperature in Boise, Idaho?")
# -> "The temperature in Boise is..."
# Suppose we have a multi-input chain that takes a 'question' string
# and 'context' s... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.llm_math.base.LLMMathChain.html |
604ab3c8e754-6 | The jsonpatch ops can be applied in order to construct state.
async atransform(input: AsyncIterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of atransform, which buffers input and calls astream.
Subclasses should override this method if th... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.llm_math.base.LLMMathChain.html |
604ab3c8e754-7 | 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... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.llm_math.base.LLMMathChain.html |
604ab3c8e754-8 | # -> {"_type": "foo", "verbose": False, ...}
classmethod from_llm(llm: BaseLanguageModel, prompt: BasePromptTemplate = PromptTemplate(input_variables=['question'], template='Translate a math problem into a expression that can be executed using Python\'s numexpr library. Use the output of running this code to answer the... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.llm_math.base.LLMMathChain.html |
604ab3c8e754-9 | 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]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the
namespace is [“langchain”, “llms”, “open... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.llm_math.base.LLMMathChain.html |
604ab3c8e754-10 | classmethod is_lc_serializable() → bool¶
Is this class 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_defa... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.llm_math.base.LLMMathChain.html |
604ab3c8e754-11 | Parameters
inputs – Dictionary of raw inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
Returns
A dictionary of all inputs, including those added by the chain’s memory.
prep_outputs(inputs: Dict[str,... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.llm_math.base.LLMMathChain.html |
604ab3c8e754-12 | these runtime tags will propagate to calls to other objects.
**kwargs – If the chain expects multiple inputs, they can be passed in
directly as keyword arguments.
Returns
The chain output.
Example
# Suppose we have a single-input chain that takes a 'question' string:
chain.run("What's the temperature in Boise, Idaho?")... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.llm_math.base.LLMMathChain.html |
604ab3c8e754-13 | Default implementation of transform, which buffers input and then calls stream.
Subclasses should override this method if they can start producing output while
input is still being generated.
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and lo... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.llm_math.base.LLMMathChain.html |
604ab3c8e754-14 | added to the run.
with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, stop_after_attempt: int = 3) → Runnable[Input, Output]¶
Create a new Runnable that retries the original runnable on exceptions.
Parameters
retry_if_exc... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.llm_math.base.LLMMathChain.html |
604ab3c8e754-15 | property output_schema: Type[pydantic.main.BaseModel]¶
The type of output this runnable produces specified as a pydantic model. | lang/api.python.langchain.com/en/latest/chains/langchain.chains.llm_math.base.LLMMathChain.html |
da2896eeec33-0 | langchain.chains.graph_qa.falkordb.FalkorDBQAChain¶
class langchain.chains.graph_qa.falkordb.FalkorDBQAChain[source]¶
Bases: Chain
Chain for question-answering against a graph by generating Cypher statements.
Security note: Make sure that the database connection uses credentialsthat are narrowly-scoped to only include ... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.falkordb.FalkorDBQAChain.html |
da2896eeec33-1 | them along in the chain. At the end, it saves any returned variables.
There are many different types of memory - please see memory docs
for the full catalog.
param metadata: Optional[Dict[str, Any]] = None¶
Optional metadata associated with the chain. Defaults to None.
This metadata will be associated with each call to... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.falkordb.FalkorDBQAChain.html |
da2896eeec33-2 | Execute the chain.
Parameters
inputs – Dictionary of inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
return_only_outputs – Whether to return only outputs in the
response. If True, only new keys gen... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.falkordb.FalkorDBQAChain.html |
da2896eeec33-3 | e.g., if the underlying runnable uses an API which supports a batch mode.
async acall(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, ... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.falkordb.FalkorDBQAChain.html |
da2896eeec33-4 | 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.
apply(input_list: List[Dict[str, Any]], callbacks: Optional... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.falkordb.FalkorDBQAChain.html |
da2896eeec33-5 | # -> "The temperature in Boise is..."
# Suppose we have a multi-input chain that takes a 'question' string
# and 'context' string:
question = "What's the temperature in Boise, Idaho?"
context = "Weather report for Boise, Idaho on 07/03/23..."
await chain.arun(question=question, context=context)
# -> "The temperature in... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.falkordb.FalkorDBQAChain.html |
da2896eeec33-6 | Default implementation of atransform, which buffers input and calls astream.
Subclasses should override this method if they can start producing output while
input is still being generated.
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.falkordb.FalkorDBQAChain.html |
da2896eeec33-7 | 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... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.falkordb.FalkorDBQAChain.html |
da2896eeec33-8 | # -> {"_type": "foo", "verbose": False, ...}
classmethod from_llm(llm: BaseLanguageModel, *, qa_prompt: BasePromptTemplate = PromptTemplate(input_variables=['context', 'question'], template="You are an assistant that helps to form nice and human understandable answers.\nThe information part contains the provided inform... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.falkordb.FalkorDBQAChain.html |
da2896eeec33-9 | 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_... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.falkordb.FalkorDBQAChain.html |
da2896eeec33-10 | classmethod is_lc_serializable() → bool¶
Is this class 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_defa... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.falkordb.FalkorDBQAChain.html |
da2896eeec33-11 | Parameters
inputs – Dictionary of raw inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
Returns
A dictionary of all inputs, including those added by the chain’s memory.
prep_outputs(inputs: Dict[str,... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.falkordb.FalkorDBQAChain.html |
da2896eeec33-12 | these runtime tags will propagate to calls to other objects.
**kwargs – If the chain expects multiple inputs, they can be passed in
directly as keyword arguments.
Returns
The chain output.
Example
# Suppose we have a single-input chain that takes a 'question' string:
chain.run("What's the temperature in Boise, Idaho?")... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.falkordb.FalkorDBQAChain.html |
da2896eeec33-13 | Default implementation of transform, which buffers input and then calls stream.
Subclasses should override this method if they can start producing output while
input is still being generated.
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and lo... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.falkordb.FalkorDBQAChain.html |
da2896eeec33-14 | added to the run.
with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, stop_after_attempt: int = 3) → Runnable[Input, Output]¶
Create a new Runnable that retries the original runnable on exceptions.
Parameters
retry_if_exc... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.falkordb.FalkorDBQAChain.html |
da2896eeec33-15 | property output_schema: Type[pydantic.main.BaseModel]¶
The type of output this runnable produces specified as a pydantic model.
Examples using FalkorDBQAChain¶
FalkorDBQAChain | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.falkordb.FalkorDBQAChain.html |
424a1dc0acc3-0 | langchain.chains.transform.TransformChain¶
class langchain.chains.transform.TransformChain[source]¶
Bases: Chain
Chain that transforms the chain output.
Example
from langchain.chains import TransformChain
transform_chain = TransformChain(input_variables=["text"],
output_variables["entities"], transform=func())
Create ... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.transform.TransformChain.html |
424a1dc0acc3-1 | and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param output_variables: List[str] [Required]¶
The keys returned by the transform’s output dictionary.
param tags: Optional[List[str]] = None¶
Optional list of tags associated ... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.transform.TransformChain.html |
424a1dc0acc3-2 | chain will be returned. Defaults to False.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.transform.TransformChain.html |
424a1dc0acc3-3 | Chain.input_keys except for inputs that will be set by the chain’s
memory.
return_only_outputs – Whether to return only outputs in the
response. If True, only new keys generated by this chain will be
returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False.
callbacks ... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.transform.TransformChain.html |
424a1dc0acc3-4 | Convenience method for executing chain.
The main difference between this method and Chain.__call__ is that this
method expects inputs to be passed directly in as positional arguments or
keyword arguments, whereas Chain.__call__ expects a single input dictionary
with all the inputs
Parameters
*args – If the chain expect... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.transform.TransformChain.html |
424a1dc0acc3-5 | Subclasses should override this method if they support streaming output.
async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, diff: bool = True, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names:... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.transform.TransformChain.html |
424a1dc0acc3-6 | e.g., if the underlying runnable uses an API which supports a batch mode.
bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable.
config_schema(*, include: Optional[Sequence[str]] = None) → Type[BaseModel]¶
The type of config this runnable accepts specified as a pydantic m... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.transform.TransformChain.html |
424a1dc0acc3-7 | 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(**kw... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.transform.TransformChain.html |
424a1dc0acc3-8 | 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 schem... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.transform.TransformChain.html |
424a1dc0acc3-9 | 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... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.transform.TransformChain.html |
424a1dc0acc3-10 | Returns
A dict of the final chain outputs.
run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Convenience method for executing chain.
The main difference between this method... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.transform.TransformChain.html |
424a1dc0acc3-11 | save(file_path: Union[Path, str]) → None¶
Save the chain.
Expects Chain._chain_type property to be implemented and for memory to benull.
Parameters
file_path – Path to file to save the chain to.
Example
chain.save(file_path="path/chain.yaml")
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definiti... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.transform.TransformChain.html |
424a1dc0acc3-12 | Add fallbacks to a runnable, returning a new Runnable.
Parameters
fallbacks – A sequence of runnables to try if the original runnable fails.
exceptions_to_handle – A tuple of exception types to handle.
Returns
A new Runnable that will try the original runnable, and then each
fallback in order, upon failures.
with_liste... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.transform.TransformChain.html |
424a1dc0acc3-13 | Bind input and output types to a Runnable, returning a new Runnable.
property InputType: Type[langchain.schema.runnable.utils.Input]¶
The type of input this runnable accepts specified as a type annotation.
property OutputType: Type[langchain.schema.runnable.utils.Output]¶
The type of output this runnable produces speci... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.transform.TransformChain.html |
73e36be6c858-0 | langchain.chains.sequential.SequentialChain¶
class langchain.chains.sequential.SequentialChain[source]¶
Bases: Chain
Chain where the outputs of one chain feed directly into next.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to ... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.sequential.SequentialChain.html |
73e36be6c858-1 | These tags will be associated with each call to this chain,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param verbose: bool [Optional]¶
Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.sequential.SequentialChain.html |
73e36be6c858-2 | include_run_info – Whether to include run info in the response. Defaults
to False.
Returns
A dict of named outputs. Should contain all outputs specified inChain.output_keys.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwar... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.sequential.SequentialChain.html |
73e36be6c858-3 | tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the chain during construction, but only
these runtime tags will propagate to calls to other objects.
metadata – Optional metadata associated with the chain. Defaults to None
include_run_info – Whether to include run ... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.sequential.SequentialChain.html |
73e36be6c858-4 | addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the chain during construction, but only
these runtime tags will propagate to c... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.sequential.SequentialChain.html |
73e36be6c858-5 | Stream all output from a runnable, as reported to the callback system.
This includes all inner runs of LLMs, Retrievers, Tools, etc.
Output is streamed as Log objects, which include a list of
jsonpatch ops that describe how the state of the run has changed in each
step, and the final state of the run.
The jsonpatch ops... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.sequential.SequentialChain.html |
73e36be6c858-6 | Returns
A pydantic model that can be used to validate config.
configurable_alternatives(which: ConfigurableField, default_key: str = 'default', **kwargs: Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) → RunnableSerializable[Input, Output]¶
configurable_fields(**kwargs: Union[ConfigurableField, C... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.sequential.SequentialChain.html |
73e36be6c858-7 | method.
Returns
A dictionary representation of the chain.
Example
chain.dict(exclude_unset=True)
# -> {"_type": "foo", "verbose": False, ...}
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 th... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.sequential.SequentialChain.html |
73e36be6c858-8 | 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.
Retur... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.sequential.SequentialChain.html |
73e36be6c858-9 | 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¶
prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]¶
Validate and prepare chain inputs, including ad... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.sequential.SequentialChain.html |
73e36be6c858-10 | sole positional argument.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be passed in
additi... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.sequential.SequentialChain.html |
73e36be6c858-11 | Default implementation of stream, which calls invoke.
Subclasses should override this method if they support streaming output.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
transform(input: Iterator[Input], config: Optional[RunnableConfig] = No... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.sequential.SequentialChain.html |
73e36be6c858-12 | on_start: Called before the runnable starts running, with the Run object.
on_end: Called after the runnable finishes running, with the Run object.
on_error: Called if the runnable throws an error, with the Run object.
The Run object contains information about the run, including its id,
type, input, output, error, start... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.sequential.SequentialChain.html |
73e36be6c858-13 | 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: T... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.sequential.SequentialChain.html |
be5b19f3ff3a-0 | langchain.chains.combine_documents.reduce.collapse_docs¶
langchain.chains.combine_documents.reduce.collapse_docs(docs: List[Document], combine_document_func: CombineDocsProtocol, **kwargs: Any) → Document[source]¶
Execute a collapse function on a set of documents and merge their metadatas.
Parameters
docs – A list of D... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.reduce.collapse_docs.html |
2830d551cd12-0 | langchain.chains.hyde.base.HypotheticalDocumentEmbedder¶
class langchain.chains.hyde.base.HypotheticalDocumentEmbedder[source]¶
Bases: Chain, Embeddings
Generate hypothetical document for query, and then embed that.
Based on https://arxiv.org/abs/2212.10496
Create a new model by parsing and validating input data from k... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html |
2830d551cd12-1 | param tags: Optional[List[str]] = None¶
Optional list of tags associated with the chain. Defaults to None.
These tags will be associated with each call to this chain,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param ve... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html |
2830d551cd12-2 | these runtime tags will propagate to calls to other objects.
metadata – Optional metadata associated with the chain. Defaults to None
include_run_info – Whether to include run info in the response. Defaults
to False.
Returns
A dict of named outputs. Should contain all outputs specified inChain.output_keys.
async abatch... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html |
2830d551cd12-3 | addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the chain during construction, but only
these runtime tags will propagate to c... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html |
2830d551cd12-4 | method expects inputs to be passed directly in as positional arguments or
keyword arguments, whereas Chain.__call__ expects a single input dictionary
with all the inputs
Parameters
*args – If the chain expects a single input, it can be passed in as the
sole positional argument.
callbacks – Callbacks to use for this cha... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html |
2830d551cd12-5 | Subclasses should override this method if they support streaming output.
async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, diff: bool = True, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names:... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html |
2830d551cd12-6 | e.g., if the underlying runnable uses an API which supports a batch mode.
bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable.
combine_embeddings(embeddings: List[List[float]]) → List[float][source]¶
Combine embeddings into final embeddings.
config_schema(*, include: Op... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html |
2830d551cd12-7 | 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 creat... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html |
2830d551cd12-8 | 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]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the
namespace is [“langchain”, “llms”, “open... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html |
2830d551cd12-9 | classmethod is_lc_serializable() → bool¶
Is this class 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_defa... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html |
2830d551cd12-10 | Parameters
inputs – Dictionary of raw inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
Returns
A dictionary of all inputs, including those added by the chain’s memory.
prep_outputs(inputs: Dict[str,... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html |
2830d551cd12-11 | these runtime tags will propagate to calls to other objects.
**kwargs – If the chain expects multiple inputs, they can be passed in
directly as keyword arguments.
Returns
The chain output.
Example
# Suppose we have a single-input chain that takes a 'question' string:
chain.run("What's the temperature in Boise, Idaho?")... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html |
2830d551cd12-12 | Default implementation of transform, which buffers input and then calls stream.
Subclasses should override this method if they can start producing output while
input is still being generated.
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and lo... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html |
2830d551cd12-13 | added to the run.
with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, stop_after_attempt: int = 3) → Runnable[Input, Output]¶
Create a new Runnable that retries the original runnable on exceptions.
Parameters
retry_if_exc... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html |
2830d551cd12-14 | For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_keys: List[str]¶
Output keys for Hyde’s LLM chain.
property output_schema: Type[pydantic.main.BaseModel]¶
The type of output this runnable produces specified as a pydantic model.
Examples using HypotheticalDocumentEmbedder¶
Improve document indexing with ... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html |
00818d234c72-0 | langchain.chains.llm_checker.base.LLMCheckerChain¶
class langchain.chains.llm_checker.base.LLMCheckerChain[source]¶
Bases: Chain
Chain for question-answering with self-verification.
Example
from langchain.llms import OpenAI
from langchain.chains import LLMCheckerChain
llm = OpenAI(temperature=0.7)
checker_chain = LLMCh... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.llm_checker.base.LLMCheckerChain.html |
00818d234c72-1 | [Deprecated] LLM wrapper to use.
param memory: Optional[BaseMemory] = None¶
Optional memory object. Defaults to None.
Memory is a class that gets called at the start
and at the end of every chain. At the start, memory loads variables and passes
them along in the chain. At the end, it saves any returned variables.
There... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.llm_checker.base.LLMCheckerChain.html |
00818d234c72-2 | accessible via langchain.globals.get_verbose().
__call__(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[str] = Non... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.llm_checker.base.LLMCheckerChain.html |
00818d234c72-3 | 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 acall(inputs: Union[Dict... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.llm_checker.base.LLMCheckerChain.html |
00818d234c72-4 | Returns
A dict of named outputs. Should contain all outputs specified inChain.output_keys.
async ainvoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any) → Dict[str, Any]¶
Default implementation of ainvoke, calls invoke from a thread.
The default implementation allows usage of async code e... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.llm_checker.base.LLMCheckerChain.html |
00818d234c72-5 | directly as keyword arguments.
Returns
The chain output.
Example
# Suppose we have a single-input chain that takes a 'question' string:
await chain.arun("What's the temperature in Boise, Idaho?")
# -> "The temperature in Boise is..."
# Suppose we have a multi-input chain that takes a 'question' string
# and 'context' s... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.llm_checker.base.LLMCheckerChain.html |
00818d234c72-6 | The jsonpatch ops can be applied in order to construct state.
async atransform(input: AsyncIterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of atransform, which buffers input and calls astream.
Subclasses should override this method if th... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.llm_checker.base.LLMCheckerChain.html |
00818d234c72-7 | 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... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.llm_checker.base.LLMCheckerChain.html |
00818d234c72-8 | # -> {"_type": "foo", "verbose": False, ...}
classmethod from_llm(llm: BaseLanguageModel, create_draft_answer_prompt: PromptTemplate = PromptTemplate(input_variables=['question'], template='{question}\n\n'), list_assertions_prompt: PromptTemplate = PromptTemplate(input_variables=['statement'], template='Here is a state... | lang/api.python.langchain.com/en/latest/chains/langchain.chains.llm_checker.base.LLMCheckerChain.html |
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