return_only_outputs (bool) – boolean for 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[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – Callbacks to use for this chain run. If not provided, will
use the callbacks provided to the chain.
include_run_info (bool) – Whether to include run info in the response. Defaults
to False.
tags (Optional[List[str]]) –
Return type
Dict[str, Any]
apply(input_list, callbacks=None)
Call the chain on all inputs in the list.
Parameters
input_list (List[Dict[str, Any]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
Return type
List[Dict[str, str]]
async arun(*args, callbacks=None, tags=None, **kwargs)
Run the chain as text in, text out or multiple variables, text out.
Parameters
args (Any) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
str
dict(**kwargs)
Return dictionary representation of chain.
Parameters
kwargs (Any) –
Return type
Dict


classmethod from_llm(llm, *, qa_prompt=PromptTemplate(input_variables=['context', 'question'], output_parser=None, partial_variables={}, template="You are an assistant that helps to form nice and human understandable answers.\nThe information part contains the provided information that you must use to construct an answer.\nThe provided information is authorative, you must never doubt it or try to use your internal knowledge to correct it.\nMake the answer sound as a response to the question. Do not mention that you based the result on the given information.\nIf the provided information is empty, say that you don't know the answer.\nInformation:\n{context}\n\nQuestion: {question}\nHelpful Answer:", template_format='f-string', validate_template=True), cypher_prompt=PromptTemplate(input_variables=['schema', 'question'], output_parser=None, partial_variables={}, template='Task:Generate Kùzu Cypher statement to query a graph database.\n\nInstructions:\n\nGenerate statement with Kùzu Cypher dialect (rather than standard):\n1. do not use `WHERE EXISTS` clause to check the existence of a property because Kùzu database has a fixed schema.\n2. do not omit relationship pattern. Always use `()-[]->()` instead of `()->()`.\n3. do not include any notes or comments even if the statement does not produce the expected result.\n```\n\nUse only the provided relationship types and properties in the schema.\nDo not use any other relationship types or properties that are not provided.\nSchema:\n{schema}\nNote: Do not include any explanations or apologies in your responses.\nDo not respond to any questions that might ask anything else than for you to construct a Cypher statement.\nDo not include any text except the generated Cypher statement.\n\nThe question is:\n{question}', template_format='f-string', validate_template=True), **kwargs)[source]


Initialize from LLM.
Parameters
llm (langchain.base_language.BaseLanguageModel) –
qa_prompt (langchain.prompts.base.BasePromptTemplate) –
cypher_prompt (langchain.prompts.base.BasePromptTemplate) –
kwargs (Any) –
Return type
langchain.chains.graph_qa.kuzu.KuzuQAChain
prep_inputs(inputs)
Validate and prep inputs.
Parameters
inputs (Union[Dict[str, Any], Any]) –
Return type
Dict[str, str]
prep_outputs(inputs, outputs, return_only_outputs=False)
Validate and prep outputs.
Parameters
inputs (Dict[str, str]) –
outputs (Dict[str, str]) –
return_only_outputs (bool) –
Return type
Dict[str, str]
run(*args, callbacks=None, tags=None, **kwargs)
Run the chain as text in, text out or multiple variables, text out.
Parameters
args (Any) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
str
save(file_path)
Save the chain.
Parameters
file_path (Union[pathlib.Path, str]) – Path to file to save the chain to.
Return type
None
Example:
.. code-block:: python
chain.save(file_path=”path/chain.yaml”)
to_json()
Return type
Union[langchain.load.serializable.SerializedConstructor, langchain.load.serializable.SerializedNotImplemented]
to_json_not_implemented()
Return type
langchain.load.serializable.SerializedNotImplemented
property lc_attributes: Dict
Return a list of attribute names that should be included in the


Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool
Return whether or not the class is serializable.
class langchain.chains.LLMBashChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, llm_chain, llm=None, input_key='question', output_key='answer', prompt=PromptTemplate(input_variables=['question'], output_parser=BashOutputParser(), partial_variables={}, template='If someone asks you to perform a task, your job is to come up with a series of bash commands that will perform the task. There is no need to put "#!/bin/bash" in your answer. Make sure to reason step by step, using this format:\n\nQuestion: "copy the files in the directory named \'target\' into a new directory at the same level as target called \'myNewDirectory\'"\n\nI need to take the following actions:\n- List all files in the directory\n- Create a new directory\n- Copy the files from the first directory into the second directory\n```bash\nls\nmkdir myNewDirectory\ncp -r target/* myNewDirectory\n```\n\nThat is the format. Begin!\n\nQuestion: {question}', template_format='f-string', validate_template=True), bash_process=None)[source]
Bases: langchain.chains.base.Chain
Chain that interprets a prompt and executes bash code to perform bash operations.


Chain that interprets a prompt and executes bash code to perform bash operations.
Example
from langchain import LLMBashChain, OpenAI
llm_bash = LLMBashChain.from_llm(OpenAI())
Parameters
memory (Optional[langchain.schema.BaseMemory]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) –
verbose (bool) –
tags (Optional[List[str]]) –
llm_chain (langchain.chains.llm.LLMChain) –
llm (Optional[langchain.base_language.BaseLanguageModel]) –
input_key (str) –
output_key (str) –
prompt (langchain.prompts.base.BasePromptTemplate) –
bash_process (langchain.utilities.bash.BashProcess) –
Return type
None
attribute callback_manager: Optional[BaseCallbackManager] = None
Deprecated, use callbacks instead.
attribute 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.
attribute llm: Optional[BaseLanguageModel] = None
[Deprecated] LLM wrapper to use.
attribute llm_chain: LLMChain [Required]
attribute 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


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.
attribute prompt: BasePromptTemplate = PromptTemplate(input_variables=['question'], output_parser=BashOutputParser(), partial_variables={}, template='If someone asks you to perform a task, your job is to come up with a series of bash commands that will perform the task. There is no need to put "#!/bin/bash" in your answer. Make sure to reason step by step, using this format:\n\nQuestion: "copy the files in the directory named \'target\' into a new directory at the same level as target called \'myNewDirectory\'"\n\nI need to take the following actions:\n- List all files in the directory\n- Create a new directory\n- Copy the files from the first directory into the second directory\n```bash\nls\nmkdir myNewDirectory\ncp -r target/* myNewDirectory\n```\n\nThat is the format. Begin!\n\nQuestion: {question}', template_format='f-string', validate_template=True)
[Deprecated]
attribute 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.
attribute verbose: bool [Optional]
Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be printed to the console. Defaults to langchain.verbose value.
async acall(inputs, return_only_outputs=False, callbacks=None, *, tags=None, include_run_info=False)


Run the logic of this chain and add to output if desired.
Parameters
inputs (Union[Dict[str, Any], Any]) – Dictionary of inputs, or single input if chain expects
only one param.
return_only_outputs (bool) – boolean for 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[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – Callbacks to use for this chain run. If not provided, will
use the callbacks provided to the chain.
include_run_info (bool) – Whether to include run info in the response. Defaults
to False.
tags (Optional[List[str]]) –
Return type
Dict[str, Any]
apply(input_list, callbacks=None)
Call the chain on all inputs in the list.
Parameters
input_list (List[Dict[str, Any]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
Return type
List[Dict[str, str]]
async arun(*args, callbacks=None, tags=None, **kwargs)
Run the chain as text in, text out or multiple variables, text out.
Parameters
args (Any) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
str
dict(**kwargs)
Return dictionary representation of chain.
Parameters
kwargs (Any) –
Return type
Dict


Parameters
kwargs (Any) –
Return type
Dict
classmethod from_llm(llm, prompt=PromptTemplate(input_variables=['question'], output_parser=BashOutputParser(), partial_variables={}, template='If someone asks you to perform a task, your job is to come up with a series of bash commands that will perform the task. There is no need to put "#!/bin/bash" in your answer. Make sure to reason step by step, using this format:\n\nQuestion: "copy the files in the directory named \'target\' into a new directory at the same level as target called \'myNewDirectory\'"\n\nI need to take the following actions:\n- List all files in the directory\n- Create a new directory\n- Copy the files from the first directory into the second directory\n```bash\nls\nmkdir myNewDirectory\ncp -r target/* myNewDirectory\n```\n\nThat is the format. Begin!\n\nQuestion: {question}', template_format='f-string', validate_template=True), **kwargs)[source]
Parameters
llm (langchain.base_language.BaseLanguageModel) –
prompt (langchain.prompts.base.BasePromptTemplate) –
kwargs (Any) –
Return type
langchain.chains.llm_bash.base.LLMBashChain
prep_inputs(inputs)
Validate and prep inputs.
Parameters
inputs (Union[Dict[str, Any], Any]) –
Return type
Dict[str, str]
prep_outputs(inputs, outputs, return_only_outputs=False)
Validate and prep outputs.
Parameters
inputs (Dict[str, str]) –
outputs (Dict[str, str]) –
return_only_outputs (bool) –
Return type
Dict[str, str]
run(*args, callbacks=None, tags=None, **kwargs)


run(*args, callbacks=None, tags=None, **kwargs)
Run the chain as text in, text out or multiple variables, text out.
Parameters
args (Any) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
str
save(file_path)
Save the chain.
Parameters
file_path (Union[pathlib.Path, str]) – Path to file to save the chain to.
Return type
None
Example:
.. code-block:: python
chain.save(file_path=”path/chain.yaml”)
to_json()
Return type
Union[langchain.load.serializable.SerializedConstructor, langchain.load.serializable.SerializedNotImplemented]
to_json_not_implemented()
Return type
langchain.load.serializable.SerializedNotImplemented
property lc_attributes: Dict
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool
Return whether or not the class is serializable.
class langchain.chains.LLMChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, prompt, llm, output_key='text', output_parser=None, return_final_only=True, llm_kwargs=None)[source]


Bases: langchain.chains.base.Chain
Chain to run queries against LLMs.
Example
from langchain import LLMChain, OpenAI, PromptTemplate
prompt_template = "Tell me a {adjective} joke"
prompt = PromptTemplate(
    input_variables=["adjective"], template=prompt_template
)
llm = LLMChain(llm=OpenAI(), prompt=prompt)
Parameters
memory (Optional[langchain.schema.BaseMemory]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) –
verbose (bool) –
tags (Optional[List[str]]) –
prompt (langchain.prompts.base.BasePromptTemplate) –
llm (langchain.base_language.BaseLanguageModel) –
output_key (str) –
output_parser (langchain.schema.BaseLLMOutputParser) –
return_final_only (bool) –
llm_kwargs (dict) –
Return type
None
attribute callback_manager: Optional[BaseCallbackManager] = None
Deprecated, use callbacks instead.
attribute 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.
attribute llm: BaseLanguageModel [Required]
Language model to call.
attribute llm_kwargs: dict [Optional]
attribute memory: Optional[BaseMemory] = None
Optional memory object. Defaults to None.
Memory is a class that gets called at the start


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.
attribute output_parser: BaseLLMOutputParser [Optional]
Output parser to use.
Defaults to one that takes the most likely string but does not change it
otherwise.
attribute prompt: BasePromptTemplate [Required]
Prompt object to use.
attribute return_final_only: bool = True
Whether to return only the final parsed result. Defaults to True.
If false, will return a bunch of extra information about the generation.
attribute 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.
attribute verbose: bool [Optional]
Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be printed to the console. Defaults to langchain.verbose value.
async aapply(input_list, callbacks=None)[source]
Utilize the LLM generate method for speed gains.
Parameters
input_list (List[Dict[str, Any]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
Return type
List[Dict[str, str]]
async aapply_and_parse(input_list, callbacks=None)[source]
Call apply and then parse the results.
Parameters
input_list (List[Dict[str, Any]]) –


Parameters
input_list (List[Dict[str, Any]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
Return type
Sequence[Union[str, List[str], Dict[str, str]]]
async acall(inputs, return_only_outputs=False, callbacks=None, *, tags=None, include_run_info=False)
Run the logic of this chain and add to output if desired.
Parameters
inputs (Union[Dict[str, Any], Any]) – Dictionary of inputs, or single input if chain expects
only one param.
return_only_outputs (bool) – boolean for 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[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – Callbacks to use for this chain run. If not provided, will
use the callbacks provided to the chain.
include_run_info (bool) – Whether to include run info in the response. Defaults
to False.
tags (Optional[List[str]]) –
Return type
Dict[str, Any]
async agenerate(input_list, run_manager=None)[source]
Generate LLM result from inputs.
Parameters
input_list (List[Dict[str, Any]]) –
run_manager (Optional[langchain.callbacks.manager.AsyncCallbackManagerForChainRun]) –
Return type
langchain.schema.LLMResult
apply(input_list, callbacks=None)[source]
Utilize the LLM generate method for speed gains.
Parameters
input_list (List[Dict[str, Any]]) –


Parameters
input_list (List[Dict[str, Any]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
Return type
List[Dict[str, str]]
apply_and_parse(input_list, callbacks=None)[source]
Call apply and then parse the results.
Parameters
input_list (List[Dict[str, Any]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
Return type
Sequence[Union[str, List[str], Dict[str, str]]]
async apredict(callbacks=None, **kwargs)[source]
Format prompt with kwargs and pass to LLM.
Parameters
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – Callbacks to pass to LLMChain
**kwargs – Keys to pass to prompt template.
kwargs (Any) –
Returns
Completion from LLM.
Return type
str
Example
completion = llm.predict(adjective="funny")
async apredict_and_parse(callbacks=None, **kwargs)[source]
Call apredict and then parse the results.
Parameters
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
Union[str, List[str], Dict[str, str]]
async aprep_prompts(input_list, run_manager=None)[source]
Prepare prompts from inputs.
Parameters
input_list (List[Dict[str, Any]]) –
run_manager (Optional[langchain.callbacks.manager.AsyncCallbackManagerForChainRun]) –
Return type
Tuple[List[langchain.schema.PromptValue], Optional[List[str]]]


Return type
Tuple[List[langchain.schema.PromptValue], Optional[List[str]]]
async arun(*args, callbacks=None, tags=None, **kwargs)
Run the chain as text in, text out or multiple variables, text out.
Parameters
args (Any) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
str
create_outputs(llm_result)[source]
Create outputs from response.
Parameters
llm_result (langchain.schema.LLMResult) –
Return type
List[Dict[str, Any]]
dict(**kwargs)
Return dictionary representation of chain.
Parameters
kwargs (Any) –
Return type
Dict
classmethod from_string(llm, template)[source]
Create LLMChain from LLM and template.
Parameters
llm (langchain.base_language.BaseLanguageModel) –
template (str) –
Return type
langchain.chains.llm.LLMChain
generate(input_list, run_manager=None)[source]
Generate LLM result from inputs.
Parameters
input_list (List[Dict[str, Any]]) –
run_manager (Optional[langchain.callbacks.manager.CallbackManagerForChainRun]) –
Return type
langchain.schema.LLMResult
predict(callbacks=None, **kwargs)[source]
Format prompt with kwargs and pass to LLM.
Parameters
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – Callbacks to pass to LLMChain
**kwargs – Keys to pass to prompt template.
kwargs (Any) –
Returns
Completion from LLM.
Return type
str
Example


Returns
Completion from LLM.
Return type
str
Example
completion = llm.predict(adjective="funny")
predict_and_parse(callbacks=None, **kwargs)[source]
Call predict and then parse the results.
Parameters
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
Union[str, List[str], Dict[str, Any]]
prep_inputs(inputs)
Validate and prep inputs.
Parameters
inputs (Union[Dict[str, Any], Any]) –
Return type
Dict[str, str]
prep_outputs(inputs, outputs, return_only_outputs=False)
Validate and prep outputs.
Parameters
inputs (Dict[str, str]) –
outputs (Dict[str, str]) –
return_only_outputs (bool) –
Return type
Dict[str, str]
prep_prompts(input_list, run_manager=None)[source]
Prepare prompts from inputs.
Parameters
input_list (List[Dict[str, Any]]) –
run_manager (Optional[langchain.callbacks.manager.CallbackManagerForChainRun]) –
Return type
Tuple[List[langchain.schema.PromptValue], Optional[List[str]]]
run(*args, callbacks=None, tags=None, **kwargs)
Run the chain as text in, text out or multiple variables, text out.
Parameters
args (Any) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
str
save(file_path)
Save the chain.
Parameters
file_path (Union[pathlib.Path, str]) – Path to file to save the chain to.
Return type


Return type
None
Example:
.. code-block:: python
chain.save(file_path=”path/chain.yaml”)
to_json()
Return type
Union[langchain.load.serializable.SerializedConstructor, langchain.load.serializable.SerializedNotImplemented]
to_json_not_implemented()
Return type
langchain.load.serializable.SerializedNotImplemented
property lc_attributes: Dict
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool
Return whether or not the class is serializable.


property lc_serializable: bool
Return whether or not the class is serializable.
class langchain.chains.LLMCheckerChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, question_to_checked_assertions_chain, llm=None, create_draft_answer_prompt=PromptTemplate(input_variables=['question'], output_parser=None, partial_variables={}, template='{question}\n\n', template_format='f-string', validate_template=True), list_assertions_prompt=PromptTemplate(input_variables=['statement'], output_parser=None, partial_variables={}, template='Here is a statement:\n{statement}\nMake a bullet point list of the assumptions you made when producing the above statement.\n\n', template_format='f-string', validate_template=True), check_assertions_prompt=PromptTemplate(input_variables=['assertions'], output_parser=None, partial_variables={}, template='Here is a bullet point list of assertions:\n{assertions}\nFor each assertion, determine whether it is true or false. If it is false, explain why.\n\n', template_format='f-string', validate_template=True), revised_answer_prompt=PromptTemplate(input_variables=['checked_assertions', 'question'], output_parser=None, partial_variables={}, template="{checked_assertions}\n\nQuestion: In light of the above assertions and checks, how would you answer the question '{question}'?\n\nAnswer:", template_format='f-string', validate_template=True), input_key='query', output_key='result')[source]
Bases: langchain.chains.base.Chain
Chain for question-answering with self-verification.
Example
from langchain import OpenAI, LLMCheckerChain
llm = OpenAI(temperature=0.7)
checker_chain = LLMCheckerChain.from_llm(llm)
Parameters
memory (Optional[langchain.schema.BaseMemory]) –


Parameters
memory (Optional[langchain.schema.BaseMemory]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) –
verbose (bool) –
tags (Optional[List[str]]) –
question_to_checked_assertions_chain (langchain.chains.sequential.SequentialChain) –
llm (Optional[langchain.base_language.BaseLanguageModel]) –
create_draft_answer_prompt (langchain.prompts.prompt.PromptTemplate) –
list_assertions_prompt (langchain.prompts.prompt.PromptTemplate) –
check_assertions_prompt (langchain.prompts.prompt.PromptTemplate) –
revised_answer_prompt (langchain.prompts.prompt.PromptTemplate) –
input_key (str) –
output_key (str) –
Return type
None
attribute callback_manager: Optional[BaseCallbackManager] = None
Deprecated, use callbacks instead.
attribute 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.
attribute check_assertions_prompt: PromptTemplate = PromptTemplate(input_variables=['assertions'], output_parser=None, partial_variables={}, template='Here is a bullet point list of assertions:\n{assertions}\nFor each assertion, determine whether it is true or false. If it is false, explain why.\n\n', template_format='f-string', validate_template=True)
[Deprecated]


[Deprecated]
attribute create_draft_answer_prompt: PromptTemplate = PromptTemplate(input_variables=['question'], output_parser=None, partial_variables={}, template='{question}\n\n', template_format='f-string', validate_template=True)
[Deprecated]
attribute list_assertions_prompt: PromptTemplate = PromptTemplate(input_variables=['statement'], output_parser=None, partial_variables={}, template='Here is a statement:\n{statement}\nMake a bullet point list of the assumptions you made when producing the above statement.\n\n', template_format='f-string', validate_template=True)
[Deprecated]
attribute llm: Optional[BaseLanguageModel] = None
[Deprecated] LLM wrapper to use.
attribute 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.
attribute question_to_checked_assertions_chain: SequentialChain [Required]
attribute revised_answer_prompt: PromptTemplate = PromptTemplate(input_variables=['checked_assertions', 'question'], output_parser=None, partial_variables={}, template="{checked_assertions}\n\nQuestion: In light of the above assertions and checks, how would you answer the question '{question}'?\n\nAnswer:", template_format='f-string', validate_template=True)
[Deprecated] Prompt to use when questioning the documents.
attribute 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.


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.
attribute verbose: bool [Optional]
Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be printed to the console. Defaults to langchain.verbose value.
async acall(inputs, return_only_outputs=False, callbacks=None, *, tags=None, include_run_info=False)
Run the logic of this chain and add to output if desired.
Parameters
inputs (Union[Dict[str, Any], Any]) – Dictionary of inputs, or single input if chain expects
only one param.
return_only_outputs (bool) – boolean for 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[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – Callbacks to use for this chain run. If not provided, will
use the callbacks provided to the chain.
include_run_info (bool) – Whether to include run info in the response. Defaults
to False.
tags (Optional[List[str]]) –
Return type
Dict[str, Any]
apply(input_list, callbacks=None)
Call the chain on all inputs in the list.
Parameters
input_list (List[Dict[str, Any]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
Return type
List[Dict[str, str]]
async arun(*args, callbacks=None, tags=None, **kwargs)
Run the chain as text in, text out or multiple variables, text out.
Parameters


Run the chain as text in, text out or multiple variables, text out.
Parameters
args (Any) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
str
dict(**kwargs)
Return dictionary representation of chain.
Parameters
kwargs (Any) –
Return type
Dict
classmethod from_llm(llm, create_draft_answer_prompt=PromptTemplate(input_variables=['question'], output_parser=None, partial_variables={}, template='{question}\n\n', template_format='f-string', validate_template=True), list_assertions_prompt=PromptTemplate(input_variables=['statement'], output_parser=None, partial_variables={}, template='Here is a statement:\n{statement}\nMake a bullet point list of the assumptions you made when producing the above statement.\n\n', template_format='f-string', validate_template=True), check_assertions_prompt=PromptTemplate(input_variables=['assertions'], output_parser=None, partial_variables={}, template='Here is a bullet point list of assertions:\n{assertions}\nFor each assertion, determine whether it is true or false. If it is false, explain why.\n\n', template_format='f-string', validate_template=True), revised_answer_prompt=PromptTemplate(input_variables=['checked_assertions', 'question'], output_parser=None, partial_variables={}, template="{checked_assertions}\n\nQuestion: In light of the above assertions and checks, how would you answer the question '{question}'?\n\nAnswer:", template_format='f-string', validate_template=True), **kwargs)[source]
Parameters
llm (langchain.base_language.BaseLanguageModel) –
create_draft_answer_prompt (langchain.prompts.prompt.PromptTemplate) –
list_assertions_prompt (langchain.prompts.prompt.PromptTemplate) –


list_assertions_prompt (langchain.prompts.prompt.PromptTemplate) –
check_assertions_prompt (langchain.prompts.prompt.PromptTemplate) –
revised_answer_prompt (langchain.prompts.prompt.PromptTemplate) –
kwargs (Any) –
Return type
langchain.chains.llm_checker.base.LLMCheckerChain
prep_inputs(inputs)
Validate and prep inputs.
Parameters
inputs (Union[Dict[str, Any], Any]) –
Return type
Dict[str, str]
prep_outputs(inputs, outputs, return_only_outputs=False)
Validate and prep outputs.
Parameters
inputs (Dict[str, str]) –
outputs (Dict[str, str]) –
return_only_outputs (bool) –
Return type
Dict[str, str]
run(*args, callbacks=None, tags=None, **kwargs)
Run the chain as text in, text out or multiple variables, text out.
Parameters
args (Any) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
str
save(file_path)
Save the chain.
Parameters
file_path (Union[pathlib.Path, str]) – Path to file to save the chain to.
Return type
None
Example:
.. code-block:: python
chain.save(file_path=”path/chain.yaml”)
to_json()
Return type
Union[langchain.load.serializable.SerializedConstructor, langchain.load.serializable.SerializedNotImplemented]
to_json_not_implemented()
Return type
langchain.load.serializable.SerializedNotImplemented
property lc_attributes: Dict
Return a list of attribute names that should be included in the


Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool
Return whether or not the class is serializable.


property lc_serializable: bool
Return whether or not the class is serializable.
class langchain.chains.LLMMathChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, llm_chain, llm=None, prompt=PromptTemplate(input_variables=['question'], output_parser=None, partial_variables={}, 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 question.\n\nQuestion: ${{Question with math problem.}}\n```text\n${{single line mathematical expression that solves the problem}}\n```\n...numexpr.evaluate(text)...\n```output\n${{Output of running the code}}\n```\nAnswer: ${{Answer}}\n\nBegin.\n\nQuestion: What is 37593 * 67?\n```text\n37593 * 67\n```\n...numexpr.evaluate("37593 * 67")...\n```output\n2518731\n```\nAnswer: 2518731\n\nQuestion: 37593^(1/5)\n```text\n37593**(1/5)\n```\n...numexpr.evaluate("37593**(1/5)")...\n```output\n8.222831614237718\n```\nAnswer: 8.222831614237718\n\nQuestion: {question}\n', template_format='f-string', validate_template=True), input_key='question', output_key='answer')[source]
Bases: langchain.chains.base.Chain
Chain that interprets a prompt and executes python code to do math.
Example
from langchain import LLMMathChain, OpenAI
llm_math = LLMMathChain.from_llm(OpenAI())
Parameters
memory (Optional[langchain.schema.BaseMemory]) –


Parameters
memory (Optional[langchain.schema.BaseMemory]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) –
verbose (bool) –
tags (Optional[List[str]]) –
llm_chain (langchain.chains.llm.LLMChain) –
llm (Optional[langchain.base_language.BaseLanguageModel]) –
prompt (langchain.prompts.base.BasePromptTemplate) –
input_key (str) –
output_key (str) –
Return type
None
attribute callback_manager: Optional[BaseCallbackManager] = None
Deprecated, use callbacks instead.
attribute 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.
attribute llm: Optional[BaseLanguageModel] = None
[Deprecated] LLM wrapper to use.
attribute llm_chain: LLMChain [Required]
attribute 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.


There are many different types of memory - please see memory docs
for the full catalog.
attribute prompt: BasePromptTemplate = PromptTemplate(input_variables=['question'], output_parser=None, partial_variables={}, 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 question.\n\nQuestion: ${{Question with math problem.}}\n```text\n${{single line mathematical expression that solves the problem}}\n```\n...numexpr.evaluate(text)...\n```output\n${{Output of running the code}}\n```\nAnswer: ${{Answer}}\n\nBegin.\n\nQuestion: What is 37593 * 67?\n```text\n37593 * 67\n```\n...numexpr.evaluate("37593 * 67")...\n```output\n2518731\n```\nAnswer: 2518731\n\nQuestion: 37593^(1/5)\n```text\n37593**(1/5)\n```\n...numexpr.evaluate("37593**(1/5)")...\n```output\n8.222831614237718\n```\nAnswer: 8.222831614237718\n\nQuestion: {question}\n', template_format='f-string', validate_template=True)
[Deprecated] Prompt to use to translate to python if necessary.
attribute 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.
attribute verbose: bool [Optional]
Whether or not run in verbose mode. In verbose mode, some intermediate logs


Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be printed to the console. Defaults to langchain.verbose value.
async acall(inputs, return_only_outputs=False, callbacks=None, *, tags=None, include_run_info=False)
Run the logic of this chain and add to output if desired.
Parameters
inputs (Union[Dict[str, Any], Any]) – Dictionary of inputs, or single input if chain expects
only one param.
return_only_outputs (bool) – boolean for 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[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – Callbacks to use for this chain run. If not provided, will
use the callbacks provided to the chain.
include_run_info (bool) – Whether to include run info in the response. Defaults
to False.
tags (Optional[List[str]]) –
Return type
Dict[str, Any]
apply(input_list, callbacks=None)
Call the chain on all inputs in the list.
Parameters
input_list (List[Dict[str, Any]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
Return type
List[Dict[str, str]]
async arun(*args, callbacks=None, tags=None, **kwargs)
Run the chain as text in, text out or multiple variables, text out.
Parameters
args (Any) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –


tags (Optional[List[str]]) –
kwargs (Any) –
Return type
str
dict(**kwargs)
Return dictionary representation of chain.
Parameters
kwargs (Any) –
Return type
Dict
classmethod from_llm(llm, prompt=PromptTemplate(input_variables=['question'], output_parser=None, partial_variables={}, 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 question.\n\nQuestion: ${{Question with math problem.}}\n```text\n${{single line mathematical expression that solves the problem}}\n```\n...numexpr.evaluate(text)...\n```output\n${{Output of running the code}}\n```\nAnswer: ${{Answer}}\n\nBegin.\n\nQuestion: What is 37593 * 67?\n```text\n37593 * 67\n```\n...numexpr.evaluate("37593 * 67")...\n```output\n2518731\n```\nAnswer: 2518731\n\nQuestion: 37593^(1/5)\n```text\n37593**(1/5)\n```\n...numexpr.evaluate("37593**(1/5)")...\n```output\n8.222831614237718\n```\nAnswer: 8.222831614237718\n\nQuestion: {question}\n', template_format='f-string', validate_template=True), **kwargs)[source]
Parameters
llm (langchain.base_language.BaseLanguageModel) –
prompt (langchain.prompts.base.BasePromptTemplate) –
kwargs (Any) –
Return type
langchain.chains.llm_math.base.LLMMathChain
prep_inputs(inputs)
Validate and prep inputs.
Parameters


prep_inputs(inputs)
Validate and prep inputs.
Parameters
inputs (Union[Dict[str, Any], Any]) –
Return type
Dict[str, str]
prep_outputs(inputs, outputs, return_only_outputs=False)
Validate and prep outputs.
Parameters
inputs (Dict[str, str]) –
outputs (Dict[str, str]) –
return_only_outputs (bool) –
Return type
Dict[str, str]
run(*args, callbacks=None, tags=None, **kwargs)
Run the chain as text in, text out or multiple variables, text out.
Parameters
args (Any) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
str
save(file_path)
Save the chain.
Parameters
file_path (Union[pathlib.Path, str]) – Path to file to save the chain to.
Return type
None
Example:
.. code-block:: python
chain.save(file_path=”path/chain.yaml”)
to_json()
Return type
Union[langchain.load.serializable.SerializedConstructor, langchain.load.serializable.SerializedNotImplemented]
to_json_not_implemented()
Return type
langchain.load.serializable.SerializedNotImplemented
property lc_attributes: Dict
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]
Return a map of constructor argument names to secret ids.


Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool
Return whether or not the class is serializable.
class langchain.chains.LLMRequestsChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, llm_chain, requests_wrapper=None, text_length=8000, requests_key='requests_result', input_key='url', output_key='output')[source]
Bases: langchain.chains.base.Chain
Chain that hits a URL and then uses an LLM to parse results.
Parameters
memory (Optional[langchain.schema.BaseMemory]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) –
verbose (bool) –
tags (Optional[List[str]]) –
llm_chain (langchain.chains.llm.LLMChain) –
requests_wrapper (langchain.requests.TextRequestsWrapper) –
text_length (int) –
requests_key (str) –
input_key (str) –
output_key (str) –
Return type
None
attribute callback_manager: Optional[BaseCallbackManager] = None
Deprecated, use callbacks instead.
attribute 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.
attribute llm_chain: LLMChain [Required]


for full details.
attribute llm_chain: LLMChain [Required]
attribute 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.
attribute requests_wrapper: TextRequestsWrapper [Optional]
attribute 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.
attribute text_length: int = 8000
attribute verbose: bool [Optional]
Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be printed to the console. Defaults to langchain.verbose value.
async acall(inputs, return_only_outputs=False, callbacks=None, *, tags=None, include_run_info=False)
Run the logic of this chain and add to output if desired.
Parameters
inputs (Union[Dict[str, Any], Any]) – Dictionary of inputs, or single input if chain expects
only one param.
return_only_outputs (bool) – boolean for 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.


chain will be returned. Defaults to False.
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – Callbacks to use for this chain run. If not provided, will
use the callbacks provided to the chain.
include_run_info (bool) – Whether to include run info in the response. Defaults
to False.
tags (Optional[List[str]]) –
Return type
Dict[str, Any]
apply(input_list, callbacks=None)
Call the chain on all inputs in the list.
Parameters
input_list (List[Dict[str, Any]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
Return type
List[Dict[str, str]]
async arun(*args, callbacks=None, tags=None, **kwargs)
Run the chain as text in, text out or multiple variables, text out.
Parameters
args (Any) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
str
dict(**kwargs)
Return dictionary representation of chain.
Parameters
kwargs (Any) –
Return type
Dict
prep_inputs(inputs)
Validate and prep inputs.
Parameters
inputs (Union[Dict[str, Any], Any]) –
Return type
Dict[str, str]
prep_outputs(inputs, outputs, return_only_outputs=False)
Validate and prep outputs.
Parameters
inputs (Dict[str, str]) –
outputs (Dict[str, str]) –
return_only_outputs (bool) –
Return type
Dict[str, str]


return_only_outputs (bool) –
Return type
Dict[str, str]
run(*args, callbacks=None, tags=None, **kwargs)
Run the chain as text in, text out or multiple variables, text out.
Parameters
args (Any) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
str
save(file_path)
Save the chain.
Parameters
file_path (Union[pathlib.Path, str]) – Path to file to save the chain to.
Return type
None
Example:
.. code-block:: python
chain.save(file_path=”path/chain.yaml”)
to_json()
Return type
Union[langchain.load.serializable.SerializedConstructor, langchain.load.serializable.SerializedNotImplemented]
to_json_not_implemented()
Return type
langchain.load.serializable.SerializedNotImplemented
property lc_attributes: Dict
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool
Return whether or not the class is serializable.
class langchain.chains.LLMRouterChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, llm_chain)[source]


Bases: langchain.chains.router.base.RouterChain
A router chain that uses an LLM chain to perform routing.
Parameters
memory (Optional[langchain.schema.BaseMemory]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) –
verbose (bool) –
tags (Optional[List[str]]) –
llm_chain (langchain.chains.llm.LLMChain) –
Return type
None
attribute callback_manager: Optional[BaseCallbackManager] = None
Deprecated, use callbacks instead.
attribute 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.
attribute llm_chain: LLMChain [Required]
LLM chain used to perform routing
attribute 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.
attribute 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.


You can use these to eg identify a specific instance of a chain with its use case.
attribute verbose: bool [Optional]
Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be printed to the console. Defaults to langchain.verbose value.
async acall(inputs, return_only_outputs=False, callbacks=None, *, tags=None, include_run_info=False)
Run the logic of this chain and add to output if desired.
Parameters
inputs (Union[Dict[str, Any], Any]) – Dictionary of inputs, or single input if chain expects
only one param.
return_only_outputs (bool) – boolean for 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[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – Callbacks to use for this chain run. If not provided, will
use the callbacks provided to the chain.
include_run_info (bool) – Whether to include run info in the response. Defaults
to False.
tags (Optional[List[str]]) –
Return type
Dict[str, Any]
apply(input_list, callbacks=None)
Call the chain on all inputs in the list.
Parameters
input_list (List[Dict[str, Any]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
Return type
List[Dict[str, str]]
async aroute(inputs, callbacks=None)
Parameters
inputs (Dict[str, Any]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
Return type


Return type
langchain.chains.router.base.Route
async arun(*args, callbacks=None, tags=None, **kwargs)
Run the chain as text in, text out or multiple variables, text out.
Parameters
args (Any) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
str
dict(**kwargs)
Return dictionary representation of chain.
Parameters
kwargs (Any) –
Return type
Dict
classmethod from_llm(llm, prompt, **kwargs)[source]
Convenience constructor.
Parameters
llm (langchain.base_language.BaseLanguageModel) –
prompt (langchain.prompts.base.BasePromptTemplate) –
kwargs (Any) –
Return type
langchain.chains.router.llm_router.LLMRouterChain
prep_inputs(inputs)
Validate and prep inputs.
Parameters
inputs (Union[Dict[str, Any], Any]) –
Return type
Dict[str, str]
prep_outputs(inputs, outputs, return_only_outputs=False)
Validate and prep outputs.
Parameters
inputs (Dict[str, str]) –
outputs (Dict[str, str]) –
return_only_outputs (bool) –
Return type
Dict[str, str]
route(inputs, callbacks=None)
Parameters
inputs (Dict[str, Any]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
Return type
langchain.chains.router.base.Route
run(*args, callbacks=None, tags=None, **kwargs)
Run the chain as text in, text out or multiple variables, text out.
Parameters


Run the chain as text in, text out or multiple variables, text out.
Parameters
args (Any) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
str
save(file_path)
Save the chain.
Parameters
file_path (Union[pathlib.Path, str]) – Path to file to save the chain to.
Return type
None
Example:
.. code-block:: python
chain.save(file_path=”path/chain.yaml”)
to_json()
Return type
Union[langchain.load.serializable.SerializedConstructor, langchain.load.serializable.SerializedNotImplemented]
to_json_not_implemented()
Return type
langchain.load.serializable.SerializedNotImplemented
property lc_attributes: Dict
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool
Return whether or not the class is serializable.
property output_keys: List[str]
Output keys this chain expects.


class langchain.chains.LLMSummarizationCheckerChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, sequential_chain, llm=None, create_assertions_prompt=PromptTemplate(input_variables=['summary'], output_parser=None, partial_variables={}, template='Given some text, extract a list of facts from the text.\n\nFormat your output as a bulleted list.\n\nText:\n"""\n{summary}\n"""\n\nFacts:', template_format='f-string', validate_template=True), check_assertions_prompt=PromptTemplate(input_variables=['assertions'], output_parser=None, partial_variables={}, template='You are an expert fact checker. You have been hired by a major news organization to fact check a very important story.\n\nHere is a bullet point list of facts:\n"""\n{assertions}\n"""\n\nFor each fact, determine whether it is true or false about the subject. If you are unable to determine whether the fact is true or false, output "Undetermined".\nIf the fact is false, explain why.\n\n', template_format='f-string', validate_template=True), revised_summary_prompt=PromptTemplate(input_variables=['checked_assertions', 'summary'], output_parser=None, partial_variables={}, template='Below are some assertions that have been fact checked and are labeled as true or false. If the answer is false, a suggestion is given for a correction.\n\nChecked Assertions:\n"""\n{checked_assertions}\n"""\n\nOriginal Summary:\n"""\n{summary}\n"""\n\nUsing these checked assertions, rewrite the original summary to be completely true.\n\nThe output should have the same structure and formatting as the original summary.\n\nSummary:', template_format='f-string', validate_template=True), are_all_true_prompt=PromptTemplate(input_variables=['checked_assertions'], output_parser=None, partial_variables={}, template='Below are


output_parser=None, partial_variables={}, template='Below are some assertions that have been fact checked and are labeled as true or false.\n\nIf all of the assertions are true, return "True". If any of the assertions are false, return "False".\n\nHere are some examples:\n===\n\nChecked Assertions: """\n- The sky is red: False\n- Water is made of lava: False\n- The sun is a star: True\n"""\nResult: False\n\n===\n\nChecked Assertions: """\n- The sky is blue: True\n- Water is wet: True\n- The sun is a star: True\n"""\nResult: True\n\n===\n\nChecked Assertions: """\n- The sky is blue - True\n- Water is made of lava- False\n- The sun is a star - True\n"""\nResult: False\n\n===\n\nChecked Assertions:"""\n{checked_assertions}\n"""\nResult:', template_format='f-string', validate_template=True), input_key='query', output_key='result', max_checks=2)[source]


Bases: langchain.chains.base.Chain
Chain for question-answering with self-verification.
Example
from langchain import OpenAI, LLMSummarizationCheckerChain
llm = OpenAI(temperature=0.0)
checker_chain = LLMSummarizationCheckerChain.from_llm(llm)
Parameters
memory (Optional[langchain.schema.BaseMemory]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) –
verbose (bool) –
tags (Optional[List[str]]) –
sequential_chain (langchain.chains.sequential.SequentialChain) –
llm (Optional[langchain.base_language.BaseLanguageModel]) –
create_assertions_prompt (langchain.prompts.prompt.PromptTemplate) –
check_assertions_prompt (langchain.prompts.prompt.PromptTemplate) –
revised_summary_prompt (langchain.prompts.prompt.PromptTemplate) –
are_all_true_prompt (langchain.prompts.prompt.PromptTemplate) –
input_key (str) –
output_key (str) –
max_checks (int) –
Return type
None


max_checks (int) –
Return type
None
attribute are_all_true_prompt: PromptTemplate = PromptTemplate(input_variables=['checked_assertions'], output_parser=None, partial_variables={}, template='Below are some assertions that have been fact checked and are labeled as true or false.\n\nIf all of the assertions are true, return "True". If any of the assertions are false, return "False".\n\nHere are some examples:\n===\n\nChecked Assertions: """\n- The sky is red: False\n- Water is made of lava: False\n- The sun is a star: True\n"""\nResult: False\n\n===\n\nChecked Assertions: """\n- The sky is blue: True\n- Water is wet: True\n- The sun is a star: True\n"""\nResult: True\n\n===\n\nChecked Assertions: """\n- The sky is blue - True\n- Water is made of lava- False\n- The sun is a star - True\n"""\nResult: False\n\n===\n\nChecked Assertions:"""\n{checked_assertions}\n"""\nResult:', template_format='f-string', validate_template=True)
[Deprecated]
attribute callback_manager: Optional[BaseCallbackManager] = None
Deprecated, use callbacks instead.
attribute 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.


Each custom chain can optionally call additional callback methods, see Callback docs
for full details.
attribute check_assertions_prompt: PromptTemplate = PromptTemplate(input_variables=['assertions'], output_parser=None, partial_variables={}, template='You are an expert fact checker. You have been hired by a major news organization to fact check a very important story.\n\nHere is a bullet point list of facts:\n"""\n{assertions}\n"""\n\nFor each fact, determine whether it is true or false about the subject. If you are unable to determine whether the fact is true or false, output "Undetermined".\nIf the fact is false, explain why.\n\n', template_format='f-string', validate_template=True)
[Deprecated]
attribute create_assertions_prompt: PromptTemplate = PromptTemplate(input_variables=['summary'], output_parser=None, partial_variables={}, template='Given some text, extract a list of facts from the text.\n\nFormat your output as a bulleted list.\n\nText:\n"""\n{summary}\n"""\n\nFacts:', template_format='f-string', validate_template=True)
[Deprecated]
attribute llm: Optional[BaseLanguageModel] = None
[Deprecated] LLM wrapper to use.
attribute max_checks: int = 2
Maximum number of times to check the assertions. Default to double-checking.
attribute 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.


There are many different types of memory - please see memory docs
for the full catalog.
attribute revised_summary_prompt: PromptTemplate = PromptTemplate(input_variables=['checked_assertions', 'summary'], output_parser=None, partial_variables={}, template='Below are some assertions that have been fact checked and are labeled as true or false. If the answer is false, a suggestion is given for a correction.\n\nChecked Assertions:\n"""\n{checked_assertions}\n"""\n\nOriginal Summary:\n"""\n{summary}\n"""\n\nUsing these checked assertions, rewrite the original summary to be completely true.\n\nThe output should have the same structure and formatting as the original summary.\n\nSummary:', template_format='f-string', validate_template=True)
[Deprecated]
attribute sequential_chain: SequentialChain [Required]
attribute 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.
attribute verbose: bool [Optional]
Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be printed to the console. Defaults to langchain.verbose value.
async acall(inputs, return_only_outputs=False, callbacks=None, *, tags=None, include_run_info=False)
Run the logic of this chain and add to output if desired.
Parameters
inputs (Union[Dict[str, Any], Any]) – Dictionary of inputs, or single input if chain expects
only one param.
return_only_outputs (bool) – boolean for whether to return only outputs in the
response. If True, only new keys generated by this chain will be


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[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – Callbacks to use for this chain run. If not provided, will
use the callbacks provided to the chain.
include_run_info (bool) – Whether to include run info in the response. Defaults
to False.
tags (Optional[List[str]]) –
Return type
Dict[str, Any]
apply(input_list, callbacks=None)
Call the chain on all inputs in the list.
Parameters
input_list (List[Dict[str, Any]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
Return type
List[Dict[str, str]]
async arun(*args, callbacks=None, tags=None, **kwargs)
Run the chain as text in, text out or multiple variables, text out.
Parameters
args (Any) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
str
dict(**kwargs)
Return dictionary representation of chain.
Parameters
kwargs (Any) –
Return type
Dict


classmethod from_llm(llm, create_assertions_prompt=PromptTemplate(input_variables=['summary'], output_parser=None, partial_variables={}, template='Given some text, extract a list of facts from the text.\n\nFormat your output as a bulleted list.\n\nText:\n"""\n{summary}\n"""\n\nFacts:', template_format='f-string', validate_template=True), check_assertions_prompt=PromptTemplate(input_variables=['assertions'], output_parser=None, partial_variables={}, template='You are an expert fact checker. You have been hired by a major news organization to fact check a very important story.\n\nHere is a bullet point list of facts:\n"""\n{assertions}\n"""\n\nFor each fact, determine whether it is true or false about the subject. If you are unable to determine whether the fact is true or false, output "Undetermined".\nIf the fact is false, explain why.\n\n', template_format='f-string', validate_template=True), revised_summary_prompt=PromptTemplate(input_variables=['checked_assertions', 'summary'], output_parser=None, partial_variables={}, template='Below are some assertions that have been fact checked and are labeled as true or false. If the answer is false, a suggestion is given for a correction.\n\nChecked Assertions:\n"""\n{checked_assertions}\n"""\n\nOriginal Summary:\n"""\n{summary}\n"""\n\nUsing these checked assertions, rewrite the original summary to be completely true.\n\nThe output should have the same structure and formatting as the original summary.\n\nSummary:', template_format='f-string', validate_template=True), are_all_true_prompt=PromptTemplate(input_variables=['checked_assertions'], output_parser=None, partial_variables={}, template='Below are some assertions that have been fact checked and are labeled as true or false.\n\nIf all of the assertions are true, return "True". If any


true or false.\n\nIf all of the assertions are true, return "True". If any of the assertions are false, return "False".\n\nHere are some examples:\n===\n\nChecked Assertions: """\n- The sky is red: False\n- Water is made of lava: False\n- The sun is a star: True\n"""\nResult: False\n\n===\n\nChecked Assertions: """\n- The sky is blue: True\n- Water is wet: True\n- The sun is a star: True\n"""\nResult: True\n\n===\n\nChecked Assertions: """\n- The sky is blue - True\n- Water is made of lava- False\n- The sun is a star - True\n"""\nResult: False\n\n===\n\nChecked Assertions:"""\n{checked_assertions}\n"""\nResult:', template_format='f-string', validate_template=True), verbose=False, **kwargs)[source]


Parameters
llm (langchain.base_language.BaseLanguageModel) –
create_assertions_prompt (langchain.prompts.prompt.PromptTemplate) –
check_assertions_prompt (langchain.prompts.prompt.PromptTemplate) –
revised_summary_prompt (langchain.prompts.prompt.PromptTemplate) –
are_all_true_prompt (langchain.prompts.prompt.PromptTemplate) –
verbose (bool) –
kwargs (Any) –
Return type
langchain.chains.llm_summarization_checker.base.LLMSummarizationCheckerChain
prep_inputs(inputs)
Validate and prep inputs.
Parameters
inputs (Union[Dict[str, Any], Any]) –
Return type
Dict[str, str]
prep_outputs(inputs, outputs, return_only_outputs=False)
Validate and prep outputs.
Parameters
inputs (Dict[str, str]) –
outputs (Dict[str, str]) –
return_only_outputs (bool) –
Return type
Dict[str, str]
run(*args, callbacks=None, tags=None, **kwargs)
Run the chain as text in, text out or multiple variables, text out.
Parameters
args (Any) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
str
save(file_path)
Save the chain.
Parameters
file_path (Union[pathlib.Path, str]) – Path to file to save the chain to.
Return type
None
Example:
.. code-block:: python
chain.save(file_path=”path/chain.yaml”)
to_json()
Return type
Union[langchain.load.serializable.SerializedConstructor, langchain.load.serializable.SerializedNotImplemented]


to_json_not_implemented()
Return type
langchain.load.serializable.SerializedNotImplemented
property lc_attributes: Dict
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool
Return whether or not the class is serializable.
class langchain.chains.MapReduceChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, combine_documents_chain, text_splitter, input_key='input_text', output_key='output_text')[source]
Bases: langchain.chains.base.Chain
Map-reduce chain.
Parameters
memory (Optional[langchain.schema.BaseMemory]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) –
verbose (bool) –
tags (Optional[List[str]]) –
combine_documents_chain (langchain.chains.combine_documents.base.BaseCombineDocumentsChain) –
text_splitter (langchain.text_splitter.TextSplitter) –
input_key (str) –
output_key (str) –
Return type
None
attribute callback_manager: Optional[BaseCallbackManager] = None
Deprecated, use callbacks instead.
attribute callbacks: Callbacks = None
Optional list of callback handlers (or callback manager). Defaults to 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.
attribute combine_documents_chain: BaseCombineDocumentsChain [Required]
Chain to use to combine documents.
attribute 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.
attribute 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.
attribute text_splitter: TextSplitter [Required]
Text splitter to use.
attribute verbose: bool [Optional]
Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be printed to the console. Defaults to langchain.verbose value.
async acall(inputs, return_only_outputs=False, callbacks=None, *, tags=None, include_run_info=False)
Run the logic of this chain and add to output if desired.
Parameters
inputs (Union[Dict[str, Any], Any]) – Dictionary of inputs, or single input if chain expects
only one param.
return_only_outputs (bool) – boolean for whether to return only outputs in the


return_only_outputs (bool) – boolean for 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[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – Callbacks to use for this chain run. If not provided, will
use the callbacks provided to the chain.
include_run_info (bool) – Whether to include run info in the response. Defaults
to False.
tags (Optional[List[str]]) –
Return type
Dict[str, Any]
apply(input_list, callbacks=None)
Call the chain on all inputs in the list.
Parameters
input_list (List[Dict[str, Any]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
Return type
List[Dict[str, str]]
async arun(*args, callbacks=None, tags=None, **kwargs)
Run the chain as text in, text out or multiple variables, text out.
Parameters
args (Any) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
str
dict(**kwargs)
Return dictionary representation of chain.
Parameters
kwargs (Any) –
Return type
Dict
classmethod from_params(llm, prompt, text_splitter, callbacks=None, combine_chain_kwargs=None, reduce_chain_kwargs=None, **kwargs)[source]
Construct a map-reduce chain that uses the chain for map and reduce.
Parameters


Construct a map-reduce chain that uses the chain for map and reduce.
Parameters
llm (langchain.base_language.BaseLanguageModel) –
prompt (langchain.prompts.base.BasePromptTemplate) –
text_splitter (langchain.text_splitter.TextSplitter) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
combine_chain_kwargs (Optional[Mapping[str, Any]]) –
reduce_chain_kwargs (Optional[Mapping[str, Any]]) –
kwargs (Any) –
Return type
langchain.chains.mapreduce.MapReduceChain
prep_inputs(inputs)
Validate and prep inputs.
Parameters
inputs (Union[Dict[str, Any], Any]) –
Return type
Dict[str, str]
prep_outputs(inputs, outputs, return_only_outputs=False)
Validate and prep outputs.
Parameters
inputs (Dict[str, str]) –
outputs (Dict[str, str]) –
return_only_outputs (bool) –
Return type
Dict[str, str]
run(*args, callbacks=None, tags=None, **kwargs)
Run the chain as text in, text out or multiple variables, text out.
Parameters
args (Any) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
str
save(file_path)
Save the chain.
Parameters
file_path (Union[pathlib.Path, str]) – Path to file to save the chain to.
Return type
None
Example:
.. code-block:: python
chain.save(file_path=”path/chain.yaml”)
to_json()
Return type


chain.save(file_path=”path/chain.yaml”)
to_json()
Return type
Union[langchain.load.serializable.SerializedConstructor, langchain.load.serializable.SerializedNotImplemented]
to_json_not_implemented()
Return type
langchain.load.serializable.SerializedNotImplemented
property lc_attributes: Dict
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool
Return whether or not the class is serializable.
class langchain.chains.MultiPromptChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, router_chain, destination_chains, default_chain, silent_errors=False)[source]
Bases: langchain.chains.router.base.MultiRouteChain
A multi-route chain that uses an LLM router chain to choose amongst prompts.
Parameters
memory (Optional[langchain.schema.BaseMemory]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) –
verbose (bool) –
tags (Optional[List[str]]) –
router_chain (langchain.chains.router.base.RouterChain) –
destination_chains (Mapping[str, langchain.chains.llm.LLMChain]) –
default_chain (langchain.chains.llm.LLMChain) –


default_chain (langchain.chains.llm.LLMChain) –
silent_errors (bool) –
Return type
None
attribute callback_manager: Optional[BaseCallbackManager] = None
Deprecated, use callbacks instead.
attribute 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.
attribute default_chain: LLMChain [Required]
Default chain to use when router doesn’t map input to one of the destinations.
attribute destination_chains: Mapping[str, LLMChain] [Required]
Map of name to candidate chains that inputs can be routed to.
attribute 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.
attribute router_chain: RouterChain [Required]
Chain for deciding a destination chain and the input to it.
attribute silent_errors: bool = False
If True, use default_chain when an invalid destination name is provided.
Defaults to False.
attribute 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.


You can use these to eg identify a specific instance of a chain with its use case.
attribute verbose: bool [Optional]
Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be printed to the console. Defaults to langchain.verbose value.
async acall(inputs, return_only_outputs=False, callbacks=None, *, tags=None, include_run_info=False)
Run the logic of this chain and add to output if desired.
Parameters
inputs (Union[Dict[str, Any], Any]) – Dictionary of inputs, or single input if chain expects
only one param.
return_only_outputs (bool) – boolean for 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[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – Callbacks to use for this chain run. If not provided, will
use the callbacks provided to the chain.
include_run_info (bool) – Whether to include run info in the response. Defaults
to False.
tags (Optional[List[str]]) –
Return type
Dict[str, Any]
apply(input_list, callbacks=None)
Call the chain on all inputs in the list.
Parameters
input_list (List[Dict[str, Any]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
Return type
List[Dict[str, str]]
async arun(*args, callbacks=None, tags=None, **kwargs)
Run the chain as text in, text out or multiple variables, text out.
Parameters
args (Any) –


Parameters
args (Any) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
str
dict(**kwargs)
Return dictionary representation of chain.
Parameters
kwargs (Any) –
Return type
Dict
classmethod from_prompts(llm, prompt_infos, default_chain=None, **kwargs)[source]
Convenience constructor for instantiating from destination prompts.
Parameters
llm (langchain.base_language.BaseLanguageModel) –
prompt_infos (List[Dict[str, str]]) –
default_chain (Optional[langchain.chains.llm.LLMChain]) –
kwargs (Any) –
Return type
langchain.chains.router.multi_prompt.MultiPromptChain
prep_inputs(inputs)
Validate and prep inputs.
Parameters
inputs (Union[Dict[str, Any], Any]) –
Return type
Dict[str, str]
prep_outputs(inputs, outputs, return_only_outputs=False)
Validate and prep outputs.
Parameters
inputs (Dict[str, str]) –
outputs (Dict[str, str]) –
return_only_outputs (bool) –
Return type
Dict[str, str]
run(*args, callbacks=None, tags=None, **kwargs)
Run the chain as text in, text out or multiple variables, text out.
Parameters
args (Any) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
str
save(file_path)
Save the chain.
Parameters


Return type
str
save(file_path)
Save the chain.
Parameters
file_path (Union[pathlib.Path, str]) – Path to file to save the chain to.
Return type
None
Example:
.. code-block:: python
chain.save(file_path=”path/chain.yaml”)
to_json()
Return type
Union[langchain.load.serializable.SerializedConstructor, langchain.load.serializable.SerializedNotImplemented]
to_json_not_implemented()
Return type
langchain.load.serializable.SerializedNotImplemented
property lc_attributes: Dict
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool
Return whether or not the class is serializable.
class langchain.chains.MultiRetrievalQAChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, router_chain, destination_chains, default_chain, silent_errors=False)[source]
Bases: langchain.chains.router.base.MultiRouteChain
A multi-route chain that uses an LLM router chain to choose amongst retrieval
qa chains.
Parameters
memory (Optional[langchain.schema.BaseMemory]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) –
verbose (bool) –


verbose (bool) –
tags (Optional[List[str]]) –
router_chain (langchain.chains.router.llm_router.LLMRouterChain) –
destination_chains (Mapping[str, langchain.chains.retrieval_qa.base.BaseRetrievalQA]) –
default_chain (langchain.chains.base.Chain) –
silent_errors (bool) –
Return type
None
attribute callback_manager: Optional[BaseCallbackManager] = None
Deprecated, use callbacks instead.
attribute 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.
attribute default_chain: Chain [Required]
Default chain to use when router doesn’t map input to one of the destinations.
attribute destination_chains: Mapping[str, BaseRetrievalQA] [Required]
Map of name to candidate chains that inputs can be routed to.
attribute 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.
attribute router_chain: LLMRouterChain [Required]
Chain for deciding a destination chain and the input to it.
attribute silent_errors: bool = False
If True, use default_chain when an invalid destination name is provided.
Defaults to False.


If True, use default_chain when an invalid destination name is provided.
Defaults to False.
attribute 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.
attribute verbose: bool [Optional]
Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be printed to the console. Defaults to langchain.verbose value.
async acall(inputs, return_only_outputs=False, callbacks=None, *, tags=None, include_run_info=False)
Run the logic of this chain and add to output if desired.
Parameters
inputs (Union[Dict[str, Any], Any]) – Dictionary of inputs, or single input if chain expects
only one param.
return_only_outputs (bool) – boolean for 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[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – Callbacks to use for this chain run. If not provided, will
use the callbacks provided to the chain.
include_run_info (bool) – Whether to include run info in the response. Defaults
to False.
tags (Optional[List[str]]) –
Return type
Dict[str, Any]
apply(input_list, callbacks=None)
Call the chain on all inputs in the list.
Parameters
input_list (List[Dict[str, Any]]) –


Parameters
input_list (List[Dict[str, Any]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
Return type
List[Dict[str, str]]
async arun(*args, callbacks=None, tags=None, **kwargs)
Run the chain as text in, text out or multiple variables, text out.
Parameters
args (Any) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
str
dict(**kwargs)
Return dictionary representation of chain.
Parameters
kwargs (Any) –
Return type
Dict
classmethod from_retrievers(llm, retriever_infos, default_retriever=None, default_prompt=None, default_chain=None, **kwargs)[source]
Parameters
llm (langchain.base_language.BaseLanguageModel) –
retriever_infos (List[Dict[str, Any]]) –
default_retriever (Optional[langchain.schema.BaseRetriever]) –
default_prompt (Optional[langchain.prompts.prompt.PromptTemplate]) –
default_chain (Optional[langchain.chains.base.Chain]) –
kwargs (Any) –
Return type
langchain.chains.router.multi_retrieval_qa.MultiRetrievalQAChain
prep_inputs(inputs)
Validate and prep inputs.
Parameters
inputs (Union[Dict[str, Any], Any]) –
Return type
Dict[str, str]
prep_outputs(inputs, outputs, return_only_outputs=False)
Validate and prep outputs.
Parameters
inputs (Dict[str, str]) –
outputs (Dict[str, str]) –


inputs (Dict[str, str]) –
outputs (Dict[str, str]) –
return_only_outputs (bool) –
Return type
Dict[str, str]
run(*args, callbacks=None, tags=None, **kwargs)
Run the chain as text in, text out or multiple variables, text out.
Parameters
args (Any) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
str
save(file_path)
Save the chain.
Parameters
file_path (Union[pathlib.Path, str]) – Path to file to save the chain to.
Return type
None
Example:
.. code-block:: python
chain.save(file_path=”path/chain.yaml”)
to_json()
Return type
Union[langchain.load.serializable.SerializedConstructor, langchain.load.serializable.SerializedNotImplemented]
to_json_not_implemented()
Return type
langchain.load.serializable.SerializedNotImplemented
property lc_attributes: Dict
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool
Return whether or not the class is serializable.


property lc_serializable: bool
Return whether or not the class is serializable.
class langchain.chains.MultiRouteChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, router_chain, destination_chains, default_chain, silent_errors=False)[source]
Bases: langchain.chains.base.Chain
Use a single chain to route an input to one of multiple candidate chains.
Parameters
memory (Optional[langchain.schema.BaseMemory]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) –
verbose (bool) –
tags (Optional[List[str]]) –
router_chain (langchain.chains.router.base.RouterChain) –
destination_chains (Mapping[str, langchain.chains.base.Chain]) –
default_chain (langchain.chains.base.Chain) –
silent_errors (bool) –
Return type
None
attribute callback_manager: Optional[BaseCallbackManager] = None
Deprecated, use callbacks instead.
attribute 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.
attribute default_chain: Chain [Required]
Default chain to use when none of the destination chains are suitable.
attribute destination_chains: Mapping[str, Chain] [Required]
Chains that return final answer to inputs.
attribute memory: Optional[BaseMemory] = None
Optional memory object. Defaults to 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.
attribute router_chain: RouterChain [Required]
Chain that routes inputs to destination chains.
attribute silent_errors: bool = False
If True, use default_chain when an invalid destination name is provided.
Defaults to False.
attribute 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.
attribute verbose: bool [Optional]
Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be printed to the console. Defaults to langchain.verbose value.
async acall(inputs, return_only_outputs=False, callbacks=None, *, tags=None, include_run_info=False)
Run the logic of this chain and add to output if desired.
Parameters
inputs (Union[Dict[str, Any], Any]) – Dictionary of inputs, or single input if chain expects
only one param.
return_only_outputs (bool) – boolean for 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[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – Callbacks to use for this chain run. If not provided, will


use the callbacks provided to the chain.
include_run_info (bool) – Whether to include run info in the response. Defaults
to False.
tags (Optional[List[str]]) –
Return type
Dict[str, Any]
apply(input_list, callbacks=None)
Call the chain on all inputs in the list.
Parameters
input_list (List[Dict[str, Any]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
Return type
List[Dict[str, str]]
async arun(*args, callbacks=None, tags=None, **kwargs)
Run the chain as text in, text out or multiple variables, text out.
Parameters
args (Any) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
str
dict(**kwargs)
Return dictionary representation of chain.
Parameters
kwargs (Any) –
Return type
Dict
prep_inputs(inputs)
Validate and prep inputs.
Parameters
inputs (Union[Dict[str, Any], Any]) –
Return type
Dict[str, str]
prep_outputs(inputs, outputs, return_only_outputs=False)
Validate and prep outputs.
Parameters
inputs (Dict[str, str]) –
outputs (Dict[str, str]) –
return_only_outputs (bool) –
Return type
Dict[str, str]
run(*args, callbacks=None, tags=None, **kwargs)
Run the chain as text in, text out or multiple variables, text out.
Parameters
args (Any) –


Parameters
args (Any) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
str
save(file_path)
Save the chain.
Parameters
file_path (Union[pathlib.Path, str]) – Path to file to save the chain to.
Return type
None
Example:
.. code-block:: python
chain.save(file_path=”path/chain.yaml”)
to_json()
Return type
Union[langchain.load.serializable.SerializedConstructor, langchain.load.serializable.SerializedNotImplemented]
to_json_not_implemented()
Return type
langchain.load.serializable.SerializedNotImplemented
property lc_attributes: Dict
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool
Return whether or not the class is serializable.
class langchain.chains.NatBotChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, llm_chain, objective, llm=None, input_url_key='url', input_browser_content_key='browser_content', previous_command='', output_key='command')[source]
Bases: langchain.chains.base.Chain
Implement an LLM driven browser.
Example
from langchain import NatBotChain


Implement an LLM driven browser.
Example
from langchain import NatBotChain
natbot = NatBotChain.from_default("Buy me a new hat.")
Parameters
memory (Optional[langchain.schema.BaseMemory]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) –
verbose (bool) –
tags (Optional[List[str]]) –
llm_chain (langchain.chains.llm.LLMChain) –
objective (str) –
llm (Optional[langchain.base_language.BaseLanguageModel]) –
input_url_key (str) –
input_browser_content_key (str) –
previous_command (str) –
output_key (str) –
Return type
None
attribute callback_manager: Optional[BaseCallbackManager] = None
Deprecated, use callbacks instead.
attribute 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.
attribute llm: Optional[BaseLanguageModel] = None
[Deprecated] LLM wrapper to use.
attribute llm_chain: LLMChain [Required]
attribute 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.


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.
attribute objective: str [Required]
Objective that NatBot is tasked with completing.
attribute 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.
attribute verbose: bool [Optional]
Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be printed to the console. Defaults to langchain.verbose value.
async acall(inputs, return_only_outputs=False, callbacks=None, *, tags=None, include_run_info=False)
Run the logic of this chain and add to output if desired.
Parameters
inputs (Union[Dict[str, Any], Any]) – Dictionary of inputs, or single input if chain expects
only one param.
return_only_outputs (bool) – boolean for 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[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – Callbacks to use for this chain run. If not provided, will
use the callbacks provided to the chain.
include_run_info (bool) – Whether to include run info in the response. Defaults
to False.
tags (Optional[List[str]]) –
Return type
Dict[str, Any]
apply(input_list, callbacks=None)