Can we implement callbacks for from_chain_type() - langchain.chains.retrieval_qa.base.RetrievalQA
class langchain.chains.retrieval_qa.base.RetrievalQA[source]
Bases: BaseRetrievalQA

Chain for question-answering against an index.

Example

from langchain_community.llms import OpenAI
from langchain.chains import RetrievalQA
from langchain_community.vectorstores import FAISS
from langchain_core.vectorstores import VectorStoreRetriever
retriever = VectorStoreRetriever(vectorstore=FAISS(...))
retrievalQA = RetrievalQA.from_llm(llm=OpenAI(), retriever=retriever)
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 callback_manager: Optional[BaseCallbackManager] = None
[DEPRECATED] Use callbacks instead.

param callbacks: Callbacks = None
Optional list of callback handlers (or callback manager). Defaults to None. Callback handlers are called throughout the lifecycle of a call to a chain, starting with on_chain_start, ending with on_chain_end or on_chain_error. Each custom chain can optionally call additional callback methods, see Callback docs for full details.

param combine_documents_chain: BaseCombineDocumentsChain [Required]
Chain to use to combine the documents.

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 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 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 retriever: BaseRetriever [Required]
param return_source_documents: bool = False
Return the source documents or not.

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 verbose: bool [Optional]
Whether or not run in verbose mode. In verbose mode, some intermediate logs 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, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[str] = None, include_run_info: bool = False) → Dict[str, Any]
[Deprecated] Execute the chain.

Parameters
inputs (Union[Dict[str, Any], Any]) – 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 (bool) – 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 (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – 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 (Optional[List[str]]) – 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[Dict[str, Any]]) – Optional metadata associated with the chain. Defaults to None

include_run_info (bool) – Whether to include run info in the response. Defaults to False.

run_name (Optional[str]) –

Returns
A dict of named outputs. Should contain all outputs specified in
Chain.output_keys.

Return type
Dict[str, Any]

Notes

Deprecated since version 0.1.0: Use invoke instead.
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 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, run_name: Optional[str] = None, include_run_info: bool = False) → Dict[str, Any]
[Deprecated] Asynchronously execute the chain.

Parameters
inputs (Union[Dict[str, Any], Any]) – 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 (bool) – 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 (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – 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 (Optional[List[str]]) – 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[Dict[str, Any]]) – Optional metadata associated with the chain. Defaults to None

include_run_info (bool) – Whether to include run info in the response. Defaults to False.

run_name (Optional[str]) –

Returns
A dict of named outputs. Should contain all outputs specified in
Chain.output_keys.

Return type
Dict[str, Any]

Notes

Deprecated since version 0.1.0: Use ainvoke instead.
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 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[str, Any]) –

config (Optional[RunnableConfig]) –

kwargs (Any) –

Return type
Dict[str, Any]

apply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[Dict[str, str]]
[Deprecated] Call the chain on all inputs in the list.

Notes

Deprecated since version 0.1.0: Use batch instead.
Parameters
input_list (List[Dict[str, Any]]) –

callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) –

Return type
List[Dict[str, str]]

async arun(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any
[Deprecated] 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 (Any) – If the chain expects a single input, it can be passed in as the sole positional argument.

callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – 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 (Optional[List[str]]) – 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.

**kwargs (Any) – If the chain expects multiple inputs, they can be passed in directly as keyword arguments.

metadata (Optional[Dict[str, Any]]) –

**kwargs –

Returns
The chain output.

Return type
Any

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' 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 Boise is..."
Notes

Deprecated since version 0.1.0: Use ainvoke instead.
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.

Parameters
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]]]]) –

Return type
RunnableSerializable[Any, Any]

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.

Parameters
input (Input) –

config (Optional[RunnableConfig]) –

kwargs (Optional[Any]) –

Return type
AsyncIterator[Output]

astream_events(input: Any, config: Optional[RunnableConfig] = None, *, version: Literal['v1'], include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Any) → AsyncIterator[StreamEvent]
[Beta] Generate a stream of events.

Use to create an iterator over StreamEvents that provide real-time information about the progress of the runnable, including StreamEvents from intermediate results.

A StreamEvent is a dictionary with the following schema:

event: str - Event names are of the
format: on_[runnable_type]_(start|stream|end).

name: str - The name of the runnable that generated the event.

run_id: str - randomly generated ID associated with the given execution of
the runnable that emitted the event. A child runnable that gets invoked as part of the execution of a parent runnable is assigned its own unique ID.

tags: Optional[List[str]] - The tags of the runnable that generated
the event.

metadata: Optional[Dict[str, Any]] - The metadata of the runnable
that generated the event.

data: Dict[str, Any]

Below is a table that illustrates some evens that might be emitted by various chains. Metadata fields have been omitted from the table for brevity. Chain definitions have been included after the table.

event

name

chunk

input

output

on_chat_model_start

[model name]

{“messages”: [[SystemMessage, HumanMessage]]}

on_chat_model_stream

[model name]

AIMessageChunk(content=”hello”)

on_chat_model_end

[model name]

{“messages”: [[SystemMessage, HumanMessage]]}

{“generations”: […], “llm_output”: None, …}

on_llm_start

[model name]

{‘input’: ‘hello’}

on_llm_stream

[model name]

‘Hello’

on_llm_end

[model name]

‘Hello human!’

on_chain_start

format_docs

on_chain_stream

format_docs

“hello world!, goodbye world!”

on_chain_end

format_docs

[Document(…)]

“hello world!, goodbye world!”

on_tool_start

some_tool

{“x”: 1, “y”: “2”}

on_tool_stream

some_tool

{“x”: 1, “y”: “2”}

on_tool_end

some_tool

{“x”: 1, “y”: “2”}

on_retriever_start

[retriever name]

{“query”: “hello”}

on_retriever_chunk

[retriever name]

{documents: […]}

on_retriever_end

[retriever name]

{“query”: “hello”}

{documents: […]}

on_prompt_start

[template_name]

{“question”: “hello”}

on_prompt_end

[template_name]

{“question”: “hello”}

ChatPromptValue(messages: [SystemMessage, …])

Here are declarations associated with the events shown above:

format_docs:

def format_docs(docs: List[Document]) -> str:
    '''Format the docs.'''
    return ", ".join([doc.page_content for doc in docs])

format_docs = RunnableLambda(format_docs)
some_tool:

@tool
def some_tool(x: int, y: str) -> dict:
    '''Some_tool.'''
    return {"x": x, "y": y}
prompt:

template = ChatPromptTemplate.from_messages(
    [("system", "You are Cat Agent 007"), ("human", "{question}")]
).with_config({"run_name": "my_template", "tags": ["my_template"]})
Example:

from langchain_core.runnables import RunnableLambda

async def reverse(s: str) -> str:
    return s[::-1]

chain = RunnableLambda(func=reverse)

events = [
    event async for event in chain.astream_events("hello", version="v1")
]

# will produce the following events (run_id has been omitted for brevity):
[
    {
        "data": {"input": "hello"},
        "event": "on_chain_start",
        "metadata": {},
        "name": "reverse",
        "tags": [],
    },
    {
        "data": {"chunk": "olleh"},
        "event": "on_chain_stream",
        "metadata": {},
        "name": "reverse",
        "tags": [],
    },
    {
        "data": {"output": "olleh"},
        "event": "on_chain_end",
        "metadata": {},
        "name": "reverse",
        "tags": [],
    },
]
Parameters
input (Any) – The input to the runnable.

config (Optional[RunnableConfig]) – The config to use for the runnable.

version (Literal['v1']) – The version of the schema to use. Currently only version 1 is available. No default will be assigned until the API is stabilized.

include_names (Optional[Sequence[str]]) – Only include events from runnables with matching names.

include_types (Optional[Sequence[str]]) – Only include events from runnables with matching types.

include_tags (Optional[Sequence[str]]) – Only include events from runnables with matching tags.

exclude_names (Optional[Sequence[str]]) – Exclude events from runnables with matching names.

exclude_types (Optional[Sequence[str]]) – Exclude events from runnables with matching types.

exclude_tags (Optional[Sequence[str]]) – Exclude events from runnables with matching tags.

kwargs (Any) – Additional keyword arguments to pass to the runnable. These will be passed to astream_log as this implementation of astream_events is built on top of astream_log.

Returns
An async stream of StreamEvents.

Return type
AsyncIterator[StreamEvent]

Notes

async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, diff: bool = True, with_streamed_output_list: bool = True, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Any) → Union[AsyncIterator[RunLogPatch], AsyncIterator[RunLog]]
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 can be applied in order to construct state.

Parameters
input (Any) – The input to the runnable.

config (Optional[RunnableConfig]) – The config to use for the runnable.

diff (bool) – Whether to yield diffs between each step, or the current state.

with_streamed_output_list (bool) – Whether to yield the streamed_output list.

include_names (Optional[Sequence[str]]) – Only include logs with these names.

include_types (Optional[Sequence[str]]) – Only include logs with these types.

include_tags (Optional[Sequence[str]]) – Only include logs with these tags.

exclude_names (Optional[Sequence[str]]) – Exclude logs with these names.

exclude_types (Optional[Sequence[str]]) – Exclude logs with these types.

exclude_tags (Optional[Sequence[str]]) – Exclude logs with these tags.

kwargs (Any) –

Return type
Union[AsyncIterator[RunLogPatch], AsyncIterator[RunLog]]

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 they can start producing output while input is still being generated.

Parameters
input (AsyncIterator[Input]) –

config (Optional[RunnableConfig]) –

kwargs (Optional[Any]) –

Return type
AsyncIterator[Output]

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 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]

bind(**kwargs: Any) → Runnable[Input, Output]
Bind arguments to a Runnable, returning a new Runnable.

Parameters
kwargs (Any) –

Return type
Runnable[Input, Output]

config_schema(*, include: Optional[Sequence[str]] = None) → Type[BaseModel]
The type of config this runnable accepts specified as a pydantic model.

To mark a field as configurable, see the configurable_fields and configurable_alternatives methods.

Parameters
include (Optional[Sequence[str]]) – A list of fields to include in the config schema.

Returns
A pydantic model that can be used to validate config.

Return type
Type[BaseModel]

configurable_alternatives(which: ConfigurableField, *, default_key: str = 'default', prefix_keys: bool = False, **kwargs: Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) → RunnableSerializable[Input, Output]
Parameters
which (ConfigurableField) –

default_key (str) –

prefix_keys (bool) –

kwargs (Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) –

Return type
RunnableSerializable[Input, Output]

configurable_fields(**kwargs: Union[ConfigurableField, ConfigurableFieldSingleOption, ConfigurableFieldMultiOption]) → RunnableSerializable[Input, Output]
Parameters
kwargs (Union[ConfigurableField, ConfigurableFieldSingleOption, ConfigurableFieldMultiOption]) –

Return type
RunnableSerializable[Input, Output]

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

Parameters
_fields_set (Optional[SetStr]) –

values (Any) –

Return type
Model

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model
Duplicate a model, optionally choose which fields to include, exclude and change.

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

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

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

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

self (Model) –

Returns
new model instance

Return type
Model

dict(**kwargs: Any) → Dict
Dictionary representation of chain.

Expects Chain._chain_type property to be implemented and for memory to be
null.

Parameters
**kwargs (Any) – Keyword arguments passed to default pydantic.BaseModel.dict method.

Returns
A dictionary representation of the chain.

Return type
Dict

Example

chain.dict(exclude_unset=True)
# -> {"_type": "foo", "verbose": False, ...}
classmethod from_chain_type(llm: BaseLanguageModel, chain_type: str = 'stuff', chain_type_kwargs: Optional[dict] = None, **kwargs: Any) → BaseRetrievalQA
Load chain from chain type.

Parameters
llm (BaseLanguageModel) –

chain_type (str) –

chain_type_kwargs (Optional[dict]) –

kwargs (Any) –

Return type
BaseRetrievalQA

classmethod from_llm(llm: BaseLanguageModel, prompt: Optional[PromptTemplate] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, llm_chain_kwargs: Optional[dict] = None, **kwargs: Any) → BaseRetrievalQA
Initialize from LLM.

Parameters
llm (BaseLanguageModel) –

prompt (Optional[PromptTemplate]) –

callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) –

llm_chain_kwargs (Optional[dict]) –

kwargs (Any) –

Return type
BaseRetrievalQA

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]

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”, “openai”]

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[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any) → Dict[str, Any]
Transform a single input into an output. Override to implement.

Parameters
input (Dict[str, Any]) – 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.

kwargs (Any) –

Returns
The output of the runnable.

Return type
Dict[str, Any]

classmethod is_lc_serializable() → bool
Is this class 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]

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.

Return type
Runnable[List[Input], List[Output]]

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

pick(keys: Union[str, List[str]]) → RunnableSerializable[Any, Any]
Pick keys from the dict output of this runnable. Returns a new runnable.

Parameters
keys (Union[str, List[str]]) –

Return type
RunnableSerializable[Any, Any]

pipe(*others: Union[Runnable[Any, Other], Callable[[Any], Other]], name: Optional[str] = None) → RunnableSerializable[Input, Other]
Compose this runnable with another object to create a RunnableSequence.

Parameters
others (Union[Runnable[Any, Other], Callable[[Any], Other]]) –

name (Optional[str]) –

Return type
RunnableSerializable[Input, Other]

prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]
Prepare chain inputs, including adding inputs from memory.

Parameters
inputs (Union[Dict[str, Any], Any]) – 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.

Return type
Dict[str, str]

prep_outputs(inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False) → Dict[str, str]
Validate and prepare chain outputs, and save info about this run to memory.

Parameters
inputs (Dict[str, str]) – Dictionary of chain inputs, including any inputs added by chain memory.

outputs (Dict[str, str]) – Dictionary of initial chain outputs.

return_only_outputs (bool) – Whether to only return the chain outputs. If False, inputs are also added to the final outputs.

Returns
A dict of the final chain outputs.

Return type
Dict[str, str]

run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any
[Deprecated] 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 (Any) – If the chain expects a single input, it can be passed in as the sole positional argument.

callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – 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 (Optional[List[str]]) – 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.

**kwargs (Any) – If the chain expects multiple inputs, they can be passed in directly as keyword arguments.

metadata (Optional[Dict[str, Any]]) –

**kwargs –

Returns
The chain output.

Return type
Any

Example

# Suppose we have a single-input chain that takes a 'question' string:
chain.run("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' string:
question = "What's the temperature in Boise, Idaho?"
context = "Weather report for Boise, Idaho on 07/03/23..."
chain.run(question=question, context=context)
# -> "The temperature in Boise is..."
Notes

Deprecated since version 0.1.0: Use invoke instead.
save(file_path: Union[Path, str]) → None
Save the chain.

Expects Chain._chain_type property to be implemented and for memory to be
null.

Parameters
file_path (Union[Path, str]) – Path to file to save the chain to.

Return type
None

Example

chain.save(file_path="path/chain.yaml")
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

stream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]
Default implementation of stream, which calls invoke. Subclasses should override this method if they support streaming output.

Parameters
input (Input) –

config (Optional[RunnableConfig]) –

kwargs (Optional[Any]) –

Return type
Iterator[Output]

to_json() → Union[SerializedConstructor, SerializedNotImplemented]
Serialize the runnable to JSON.

Return type
Union[SerializedConstructor, SerializedNotImplemented]

to_json_not_implemented() → SerializedNotImplemented
Return type
SerializedNotImplemented

transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]
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.

Parameters
input (Iterator[Input]) –

config (Optional[RunnableConfig]) –

kwargs (Optional[Any]) –

Return type
Iterator[Output]

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

with_config(config: Optional[RunnableConfig] = None, **kwargs: Any) → Runnable[Input, Output]
Bind config to a Runnable, returning a new Runnable.

Parameters
config (Optional[RunnableConfig]) –

kwargs (Any) –

Return type
Runnable[Input, Output]

with_fallbacks(fallbacks: Sequence[Runnable[Input, Output]], *, exceptions_to_handle: Tuple[Type[BaseException], ...] = (<class 'Exception'>,), exception_key: Optional[str] = None) → RunnableWithFallbacksT[Input, Output]
Add fallbacks to a runnable, returning a new Runnable.

Parameters
fallbacks (Sequence[Runnable[Input, Output]]) – A sequence of runnables to try if the original runnable fails.

exceptions_to_handle (Tuple[Type[BaseException], ...]) – A tuple of exception types to handle.

exception_key (Optional[str]) – If string is specified then handled exceptions will be passed to fallbacks as part of the input under the specified key. If None, exceptions will not be passed to fallbacks. If used, the base runnable and its fallbacks must accept a dictionary as input.

Returns
A new Runnable that will try the original runnable, and then each fallback in order, upon failures.

Return type
RunnableWithFallbacksT[Input, Output]

with_listeners(*, on_start: Optional[Listener] = None, on_end: Optional[Listener] = None, on_error: Optional[Listener] = None) → Runnable[Input, Output]
Bind lifecycle listeners to a Runnable, returning a new Runnable.

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_time, end_time, and any tags or metadata added to the run.

Parameters
on_start (Optional[Listener]) –

on_end (Optional[Listener]) –

on_error (Optional[Listener]) –

Return type
Runnable[Input, Output]

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_exception_type (Tuple[Type[BaseException], ...]) – A tuple of exception types to retry on

wait_exponential_jitter (bool) – Whether to add jitter to the wait time between retries

stop_after_attempt (int) – The maximum number of attempts to make before giving up

Returns
A new Runnable that retries the original runnable on exceptions.

Return type
Runnable[Input, Output]

with_types(*, input_type: Optional[Type[Input]] = None, output_type: Optional[Type[Output]] = None) → Runnable[Input, Output]
Bind input and output types to a Runnable, returning a new Runnable.

Parameters
input_type (Optional[Type[Input]]) –

output_type (Optional[Type[Output]]) –

Return type
Runnable[Input, Output]

property InputType: Type[Input]
The type of input this runnable accepts specified as a type annotation.

property OutputType: Type[Output]
The type of output this runnable produces specified as a type annotation.

property config_specs: List[ConfigurableFieldSpec]
List configurable fields for this runnable.

property input_schema: Type[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”}

name: Optional[str] = None
The name of the runnable. Used for debugging and tracing.

property output_schema: Type[BaseModel]
The type of output this runnable produces specified as a pydantic model.