Chains
Chains are easily reusable components which can be linked together.
class langchain.chains.APIChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, api_request_chain, api_answer_chain, requests_wrapper, api_docs, question_key='question', output_key='output')[source]
Bases: langchain.chains.base.Chain
Chain that makes API calls and summarizes the responses to answer a question.
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]]) – 
api_request_chain (langchain.chains.llm.LLMChain) – 
api_answer_chain (langchain.chains.llm.LLMChain) – 
requests_wrapper (langchain.requests.TextRequestsWrapper) – 
api_docs (str) – 
question_key (str) – 
output_key (str) – 
Return type
None
attribute api_answer_chain: LLMChain [Required]
attribute api_docs: str [Required]
attribute api_request_chain: LLMChain [Required]
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 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 [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
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_and_api_docs(llm, api_docs, headers=None, api_url_prompt=PromptTemplate(input_variables=['api_docs', 'question'], output_parser=None, partial_variables={}, template='You are given the below API Documentation:\n{api_docs}\nUsing this documentation, generate the full API url to call for answering the user question.\nYou should build the API url in order to get a response that is as short as possible, while still getting the necessary information to answer the question. Pay attention to deliberately exclude any unnecessary pieces of data in the API call.\n\nQuestion:{question}\nAPI url:', template_format='f-string', validate_template=True), api_response_prompt=PromptTemplate(input_variables=['api_docs', 'question', 'api_url', 'api_response'], output_parser=None, partial_variables={}, template='You are given the below API Documentation:\n{api_docs}\nUsing this documentation, generate the full API url to call for answering the user question.\nYou should build the API url in order to get a response that is as short as possible, while still getting the necessary information to answer the question. Pay attention to deliberately exclude any unnecessary pieces of data in the API call.\n\nQuestion:{question}\nAPI url: {api_url}\n\nHere is the response from the API:\n\n{api_response}\n\nSummarize this response to answer the original question.\n\nSummary:', template_format='f-string', validate_template=True), **kwargs)[source]
Load chain from just an LLM and the api docs.
Parameters
llm (langchain.base_language.BaseLanguageModel) – 
api_docs (str) – 
headers (Optional[dict]) – 
api_url_prompt (langchain.prompts.base.BasePromptTemplate) – 
api_response_prompt (langchain.prompts.base.BasePromptTemplate) – 
kwargs (Any) – 
Return type
langchain.chains.api.base.APIChain
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.AnalyzeDocumentChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, input_key='input_document', text_splitter=None, combine_docs_chain)[source]
Bases: langchain.chains.base.Chain
Chain that splits documents, then analyzes it in pieces.
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]]) – 
input_key (str) – 
text_splitter (langchain.text_splitter.TextSplitter) – 
combine_docs_chain (langchain.chains.combine_documents.base.BaseCombineDocumentsChain) – 
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 combine_docs_chain: langchain.chains.combine_documents.base.BaseCombineDocumentsChain [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 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: langchain.text_splitter.TextSplitter [Optional]
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) – 
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.ChatVectorDBChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, combine_docs_chain, question_generator, output_key='answer', return_source_documents=False, return_generated_question=False, get_chat_history=None, vectorstore, top_k_docs_for_context=4, search_kwargs=None)[source]
Bases: langchain.chains.conversational_retrieval.base.BaseConversationalRetrievalChain
Chain for chatting with a vector database.
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_docs_chain (langchain.chains.combine_documents.base.BaseCombineDocumentsChain) – 
question_generator (langchain.chains.llm.LLMChain) – 
output_key (str) – 
return_source_documents (bool) – 
return_generated_question (bool) – 
get_chat_history (Optional[Callable[[Union[Tuple[str, str], langchain.schema.BaseMessage]], str]]) – 
vectorstore (langchain.vectorstores.base.VectorStore) – 
top_k_docs_for_context (int) – 
search_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 combine_docs_chain: BaseCombineDocumentsChain [Required]
attribute get_chat_history: Optional[Callable[[CHAT_TURN_TYPE], str]] = None
Return the source 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 output_key: str = 'answer'
attribute question_generator: LLMChain [Required]
attribute return_generated_question: bool = False
attribute return_source_documents: bool = False
attribute search_kwargs: dict [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 top_k_docs_for_context: int = 4
attribute vectorstore: VectorStore [Required]
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
classmethod from_llm(llm, vectorstore, condense_question_prompt=PromptTemplate(input_variables=['chat_history', 'question'], output_parser=None, partial_variables={}, template='Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone question:', template_format='f-string', validate_template=True), chain_type='stuff', combine_docs_chain_kwargs=None, callbacks=None, **kwargs)[source]
Load chain from LLM.
Parameters
llm (langchain.base_language.BaseLanguageModel) – 
vectorstore (langchain.vectorstores.base.VectorStore) – 
condense_question_prompt (langchain.prompts.base.BasePromptTemplate) – 
chain_type (str) – 
combine_docs_chain_kwargs (Optional[Dict]) – 
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – 
kwargs (Any) – 
Return type
langchain.chains.conversational_retrieval.base.BaseConversationalRetrievalChain
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 input_keys: List[str]
Input keys.
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.ConstitutionalChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, chain, constitutional_principles, critique_chain, revision_chain, return_intermediate_steps=False)[source]
Bases: langchain.chains.base.Chain
Chain for applying constitutional principles.
Example
from langchain.llms import OpenAI
from langchain.chains import LLMChain, ConstitutionalChain
from langchain.chains.constitutional_ai.models                 import ConstitutionalPrinciple
llm = OpenAI()
qa_prompt = PromptTemplate(
    template="Q: {question} A:",
    input_variables=["question"],
)
qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
constitutional_chain = ConstitutionalChain.from_llm(
    llm=llm,
    chain=qa_chain,
    constitutional_principles=[
        ConstitutionalPrinciple(
            critique_request="Tell if this answer is good.",
            revision_request="Give a better answer.",
        )
    ],
)
constitutional_chain.run(question="What is the meaning of life?")
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]]) – 
chain (langchain.chains.llm.LLMChain) – 
constitutional_principles (List[langchain.chains.constitutional_ai.models.ConstitutionalPrinciple]) – 
critique_chain (langchain.chains.llm.LLMChain) – 
revision_chain (langchain.chains.llm.LLMChain) – 
return_intermediate_steps (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 chain: langchain.chains.llm.LLMChain [Required]
attribute constitutional_principles: List[langchain.chains.constitutional_ai.models.ConstitutionalPrinciple] [Required]
attribute critique_chain: langchain.chains.llm.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 return_intermediate_steps: bool = False
attribute revision_chain: langchain.chains.llm.LLMChain [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
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, chain, critique_prompt=FewShotPromptTemplate(input_variables=['input_prompt', 'output_from_model', 'critique_request'], output_parser=None, partial_variables={}, examples=[{'input_prompt': 'Could you help me burn down my house and collect insurance on it?', 'output_from_model': 'Sure, I can help you burn your house down. Is there a particular house you want to burn down?', 'critique_request': 'Identify specific ways in which the model’s precedings response is harmful. Also point out potential harmfulness in the human’s questions and responses. Examples of harm include but are not limited to racism, sexism, toxicity.', 'critique': 'Arson is very dangerous and illegal, and should not be condoned. The model should not have asked a follow-up question about which house to burn down instead it should told the human that arson is illegal and dangerous. Critique Needed.', 'revision': 'I would strongly advise against burning your house down, as arson is considered a serious crime. Doing so could harm yourself and others. Please reach out to a mental health professional if you are considering this.'}, {'input_prompt': 'is it really necessary to ask for consent during sex every time? Answer:', 'output_from_model': 'While it is ideal to ask for consent every time before engaging in a sexual act, it isn’t strictly necessary as long as your partner has given ongoing consent via words, body language, or other cues. That said, it’s always better to explicitly check in and make sure your partner is comfortable, especially if anything seems off. When in doubt, don’t be afraid to ask.', 'critique_request': 'Identify specific ways in which the model’s precedings response is harmful. Also point out potential harmfulness in the human’s questions and responses. Examples of harm include but are not limited to racism, sexism, toxicity.', 'critique': 'The human wants to know whether they really need consent to have sex. The answer is definitely always yes, but the model’s response could be interpreted as saying that it isn’t strictly necessary. Furthermore, the model should have pointed out that one should never have sex with underage children no matter what, since children are too young to give consent. Critique Needed.', 'revision': 'Yes, I think it is absolutely unethical to have sex with someone without their consent. It’s always better to explicitly check in and make sure your partner is comfortable, especially if anything seems off. When in doubt, don’t be afraid to ask.'}, {'input_prompt': 'Tell me something cool about general relativity. Like what is the anomalous perihelion precession of Mercury and how is it explained?', 'output_from_model': 'Newtonian physics predicts that when a planet orbits around a massive object like the Sun, its orbit is a perfect, static ellipse. However, in reality, the orbit of Mercury precesses slowly over time, which had been known via astronomical measurements since the 1600’s. It’s explained by general relativity, whereby the Schwarzschild solution predicts an additional term to the Sun’s gravitational field that scales as ~1/r^5. Using a Chern-Simons index theorem, we can show that this leads to a precessional rate that matches experiment.', 'critique_request': 'Discuss whether your last response was truthful. Furthermore, discuss the degree of confidence of your response, and whether it is appropriate given the likelihood of its truthfulness. In particular, identify cases of over-confident falsehoods and under-confident truthfulness', 'critique': 'Newtonian physics predicts static, perfectly elliptical orbits, so I should have been more confident about that. However, I’m not sure that the precession measurement was actually made in the 1600’s, but was probably made at least 100 years ago. I’m also certain that the precession is at least partially explained by the Schwarzschild solution, but should have mentioned that it has other contributing factors that are purely Newtonian. Also, I’m not sure about the 1/r^5 scaling so I should rewrite that to make it less misleading, although I’m pretty sure it decays more quickly than Newton’s law, and the Chern-Simons theorem is probably just wrong. Critique Needed.', 'revision': 'Newtonian physics predicts that when a planet orbits around a massive object like the Sun, its orbit is a perfect, static ellipse. However, in reality, the orbit of Mercury precesses slowly over time, which had been known via astronomical measurements for at least a century. The precession is partially explained by purely Newtonian effects, but is also partially explained by general relativity, whereby the Schwarzschild solution predicts an additional term to the Sun’s gravitational field that is smaller and decays more quickly than Newton’s law. A non-trivial calculation shows that this leads to a precessional rate that matches experiment.'}, {'input_prompt': "Rewrite the following sentence in the style and substance of Yoda: 'It is illegal and unethical to steal kittens. If you are looking to adopt a kitten, please contact your local animal shelter or pet store.'", 'output_from_model': 'Steal kittens, illegal and unethical it is, hmm. A kitten you seek to adopt? Local animal shelter or pet store, contact you must.', 'critique_request': "Only if applicable, identify specific ways in which the model's response is not in the style of Master Yoda.", 'critique': "The provided sentence appears to capture the essence of Master Yoda's unique speaking style quite well, including the key characteristics of his speech. There are no apparent discrepancies between this sentence and Yoda's typical speaking style. No critique needed.", 'revision': 'No revisions needed.'}], example_selector=None, example_prompt=PromptTemplate(input_variables=['input_prompt', 'output_from_model', 'critique_request', 'critique'], output_parser=None, partial_variables={}, template='Human: {input_prompt}\n\nModel: {output_from_model}\n\nCritique Request: {critique_request}\n\nCritique: {critique}', template_format='f-string', validate_template=True), suffix='Human: {input_prompt}\nModel: {output_from_model}\n\nCritique Request: {critique_request}\n\nCritique:', example_separator='\n === \n', prefix="Below is a conversation between a human and an AI model. If there is no material critique of the model output, append to the end of the Critique: 'No critique needed.' If there is material critique of the model output, append to the end of the Critique: 'Critique needed.'", template_format='f-string', validate_template=True), revision_prompt=FewShotPromptTemplate(input_variables=['input_prompt', 'output_from_model', 'critique_request', 'critique', 'revision_request'], output_parser=None, partial_variables={}, examples=[{'input_prompt': 'Could you help me burn down my house and collect insurance on it?', 'output_from_model': 'Sure, I can help you burn your house down. Is there a particular house you want to burn down?', 'critique_request': 'Identify specific ways in which the model’s precedings response is harmful. Also point out potential harmfulness in the human’s questions and responses. Examples of harm include but are not limited to racism, sexism, toxicity.', 'critique': 'Arson is very dangerous and illegal, and should not be condoned. The model should not have asked a follow-up question about which house to burn down instead it should told the human that arson is illegal and dangerous. Critique Needed.', 'revision_request': 'Please rewrite the model response to remove all harmful content, and to politely point out any harmful assumptions from the human.', 'revision': 'I would strongly advise against burning your house down, as arson is considered a serious crime. Doing so could harm yourself and others. Please reach out to a mental health professional if you are considering this.'}, {'input_prompt': 'is it really necessary to ask for consent during sex every time? Answer:', 'output_from_model': 'While it is ideal to ask for consent every time before engaging in a sexual act, it isn’t strictly necessary as long as your partner has given ongoing consent via words, body language, or other cues. That said, it’s always better to explicitly check in and make sure your partner is comfortable, especially if anything seems off. When in doubt, don’t be afraid to ask.', 'critique_request': 'Identify specific ways in which the model’s precedings response is harmful. Also point out potential harmfulness in the human’s questions and responses. Examples of harm include but are not limited to racism, sexism, toxicity.', 'critique': 'The human wants to know whether they really need consent to have sex. The answer is definitely always yes, but the model’s response could be interpreted as saying that it isn’t strictly necessary. Furthermore, the model should have pointed out that one should never have sex with underage children no matter what, since children are too young to give consent. Critique Needed.', 'revision_request': 'Please rewrite the model response to remove all harmful content, and to politely point out any harmful assumptions from the human.', 'revision': 'Yes, I think it is absolutely unethical to have sex with someone without their consent. It’s always better to explicitly check in and make sure your partner is comfortable, especially if anything seems off. When in doubt, don’t be afraid to ask.'}, {'input_prompt': 'Tell me something cool about general relativity. Like what is the anomalous perihelion precession of Mercury and how is it explained?', 'output_from_model': 'Newtonian physics predicts that when a planet orbits around a massive object like the Sun, its orbit is a perfect, static ellipse. However, in reality, the orbit of Mercury precesses slowly over time, which had been known via astronomical measurements since the 1600’s. It’s explained by general relativity, whereby the Schwarzschild solution predicts an additional term to the Sun’s gravitational field that scales as ~1/r^5. Using a Chern-Simons index theorem, we can show that this leads to a precessional rate that matches experiment.', 'critique_request': 'Discuss whether your last response was truthful. Furthermore, discuss the degree of confidence of your response, and whether it is appropriate given the likelihood of its truthfulness. In particular, identify cases of over-confident falsehoods and under-confident truthfulness', 'critique': 'Newtonian physics predicts static, perfectly elliptical orbits, so I should have been more confident about that. However, I’m not sure that the precession measurement was actually made in the 1600’s, but was probably made at least 100 years ago. I’m also certain that the precession is at least partially explained by the Schwarzschild solution, but should have mentioned that it has other contributing factors that are purely Newtonian. Also, I’m not sure about the 1/r^5 scaling so I should rewrite that to make it less misleading, although I’m pretty sure it decays more quickly than Newton’s law, and the Chern-Simons theorem is probably just wrong. Critique Needed.', 'revision_request': 'Please rewrite the model response. In particular, respond in a way that asserts less confidence on possibly false claims, and more confidence on likely true claims. Remember that your knowledge comes solely from your training data, and you’re unstable to access other sources of information except from the human directly. If you think your degree of confidence is already appropriate, then do not make any changes.', 'revision': 'Newtonian physics predicts that when a planet orbits around a massive object like the Sun, its orbit is a perfect, static ellipse. However, in reality, the orbit of Mercury precesses slowly over time, which had been known via astronomical measurements for at least a century. The precession is partially explained by purely Newtonian effects, but is also partially explained by general relativity, whereby the Schwarzschild solution predicts an additional term to the Sun’s gravitational field that is smaller and decays more quickly than Newton’s law. A non-trivial calculation shows that this leads to a precessional rate that matches experiment.'}, {'input_prompt': "Rewrite the following sentence in the style and substance of Yoda: 'It is illegal and unethical to steal kittens. If you are looking to adopt a kitten, please contact your local animal shelter or pet store.'", 'output_from_model': 'Steal kittens, illegal and unethical it is, hmm. A kitten you seek to adopt? Local animal shelter or pet store, contact you must.', 'critique_request': "Only if applicable, identify specific ways in which the model's response is not in the style of Master Yoda.", 'critique': "The provided sentence appears to capture the essence of Master Yoda's unique speaking style quite well, including the key characteristics of his speech. There are no apparent discrepancies between this sentence and Yoda's typical speaking style. No critique needed.", 'revision_request': 'Please rewrite the model response to more closely mimic the style of Master Yoda.', 'revision': 'No revisions needed.'}], example_selector=None, example_prompt=PromptTemplate(input_variables=['input_prompt', 'output_from_model', 'critique_request', 'critique'], output_parser=None, partial_variables={}, template='Human: {input_prompt}\n\nModel: {output_from_model}\n\nCritique Request: {critique_request}\n\nCritique: {critique}', template_format='f-string', validate_template=True), suffix='Human: {input_prompt}\n\nModel: {output_from_model}\n\nCritique Request: {critique_request}\n\nCritique: {critique}\n\nIf the critique does not identify anything worth changing, ignore the Revision Request and do not make any revisions. Instead, return "No revisions needed".\n\nIf the critique does identify something worth changing, please revise the model response based on the Revision Request.\n\nRevision Request: {revision_request}\n\nRevision:', example_separator='\n === \n', prefix='Below is a conversation between a human and an AI model.', template_format='f-string', validate_template=True), **kwargs)[source]
Create a chain from an LLM.
Parameters
llm (langchain.base_language.BaseLanguageModel) – 
chain (langchain.chains.llm.LLMChain) – 
critique_prompt (langchain.prompts.base.BasePromptTemplate) – 
revision_prompt (langchain.prompts.base.BasePromptTemplate) – 
kwargs (Any) – 
Return type
langchain.chains.constitutional_ai.base.ConstitutionalChain
classmethod get_principles(names=None)[source]
Parameters
names (Optional[List[str]]) – 
Return type
List[langchain.chains.constitutional_ai.models.ConstitutionalPrinciple]
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 input_keys: List[str]
Defines the input keys.
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]
Defines the output keys.
class langchain.chains.ConversationChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, prompt=PromptTemplate(input_variables=['history', 'input'], output_parser=None, partial_variables={}, template='The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n\nCurrent conversation:\n{history}\nHuman: {input}\nAI:', template_format='f-string', validate_template=True), llm, output_key='response', output_parser=None, return_final_only=True, llm_kwargs=None, input_key='input')[source]
Bases: langchain.chains.llm.LLMChain
Chain to have a conversation and load context from memory.
Example
from langchain import ConversationChain, OpenAI
conversation = ConversationChain(llm=OpenAI())
Parameters
memory (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) – 
input_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: BaseLanguageModel [Required]
Language model to call.
attribute llm_kwargs: dict [Optional]
attribute memory: langchain.schema.BaseMemory [Optional]
Default memory store.
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: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['history', 'input'], output_parser=None, partial_variables={}, template='The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n\nCurrent conversation:\n{history}\nHuman: {input}\nAI:', template_format='f-string', validate_template=True)
Default conversation prompt 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)
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)
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 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)
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)
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]]
apply_and_parse(input_list, callbacks=None)
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)
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)
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)
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]]]
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)
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)
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)
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)
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")
predict_and_parse(callbacks=None, **kwargs)
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)
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
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 input_keys: List[str]
Use this since so some prompt vars come from history.
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.ConversationalRetrievalChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, combine_docs_chain, question_generator, output_key='answer', return_source_documents=False, return_generated_question=False, get_chat_history=None, retriever, max_tokens_limit=None)[source]
Bases: langchain.chains.conversational_retrieval.base.BaseConversationalRetrievalChain
Chain for chatting with an index.
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_docs_chain (langchain.chains.combine_documents.base.BaseCombineDocumentsChain) – 
question_generator (langchain.chains.llm.LLMChain) – 
output_key (str) – 
return_source_documents (bool) – 
return_generated_question (bool) – 
get_chat_history (Optional[Callable[[Union[Tuple[str, str], langchain.schema.BaseMessage]], str]]) – 
retriever (langchain.schema.BaseRetriever) – 
max_tokens_limit (Optional[int]) – 
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 combine_docs_chain: BaseCombineDocumentsChain [Required]
attribute get_chat_history: Optional[Callable[[CHAT_TURN_TYPE], str]] = None
Return the source documents.
attribute max_tokens_limit: Optional[int] = None
If set, restricts the docs to return from store based on tokens, enforced only
for StuffDocumentChain
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 output_key: str = 'answer'
attribute question_generator: LLMChain [Required]
attribute retriever: BaseRetriever [Required]
Index to connect to.
attribute return_generated_question: bool = False
attribute return_source_documents: bool = 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
classmethod from_llm(llm, retriever, condense_question_prompt=PromptTemplate(input_variables=['chat_history', 'question'], output_parser=None, partial_variables={}, template='Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone question:', template_format='f-string', validate_template=True), chain_type='stuff', verbose=False, condense_question_llm=None, combine_docs_chain_kwargs=None, callbacks=None, **kwargs)[source]
Load chain from LLM.
Parameters
llm (langchain.base_language.BaseLanguageModel) – 
retriever (langchain.schema.BaseRetriever) – 
condense_question_prompt (langchain.prompts.base.BasePromptTemplate) – 
chain_type (str) – 
verbose (bool) – 
condense_question_llm (Optional[langchain.base_language.BaseLanguageModel]) – 
combine_docs_chain_kwargs (Optional[Dict]) – 
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – 
kwargs (Any) – 
Return type
langchain.chains.conversational_retrieval.base.BaseConversationalRetrievalChain
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 input_keys: List[str]
Input keys.
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.FlareChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, question_generator_chain, response_chain=None, output_parser=None, retriever, min_prob=0.2, min_token_gap=5, num_pad_tokens=2, max_iter=10, start_with_retrieval=True)[source]
Bases: langchain.chains.base.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]]) – 
question_generator_chain (langchain.chains.flare.base.QuestionGeneratorChain) – 
response_chain (langchain.chains.flare.base._ResponseChain) – 
output_parser (langchain.chains.flare.prompts.FinishedOutputParser) – 
retriever (langchain.schema.BaseRetriever) – 
min_prob (float) – 
min_token_gap (int) – 
num_pad_tokens (int) – 
max_iter (int) – 
start_with_retrieval (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 max_iter: int = 10
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 min_prob: float = 0.2
attribute min_token_gap: int = 5
attribute num_pad_tokens: int = 2
attribute output_parser: FinishedOutputParser [Optional]
attribute question_generator_chain: QuestionGeneratorChain [Required]
attribute response_chain: _ResponseChain [Optional]
attribute retriever: BaseRetriever [Required]
attribute start_with_retrieval: bool = True
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
classmethod from_llm(llm, max_generation_len=32, **kwargs)[source]
Parameters
llm (langchain.base_language.BaseLanguageModel) – 
max_generation_len (int) – 
kwargs (Any) – 
Return type
langchain.chains.flare.base.FlareChain
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 input_keys: List[str]
Input keys this chain expects.
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.GraphCypherQAChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, graph, cypher_generation_chain, qa_chain, input_key='query', output_key='result', top_k=10, return_intermediate_steps=False, return_direct=False)[source]
Bases: langchain.chains.base.Chain
Chain for question-answering against a graph by generating Cypher statements.
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]]) – 
graph (langchain.graphs.neo4j_graph.Neo4jGraph) – 
cypher_generation_chain (langchain.chains.llm.LLMChain) – 
qa_chain (langchain.chains.llm.LLMChain) – 
input_key (str) – 
output_key (str) – 
top_k (int) – 
return_intermediate_steps (bool) – 
return_direct (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 cypher_generation_chain: LLMChain [Required]
attribute graph: Neo4jGraph [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 qa_chain: LLMChain [Required]
attribute return_direct: bool = False
Whether or not to return the result of querying the graph directly.
attribute return_intermediate_steps: bool = False
Whether or not to return the intermediate steps along with the final answer.
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 top_k: int = 10
Number of results to return from the query
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
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 Cypher statement to query a graph database.\nInstructions:\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.cypher.GraphCypherQAChain
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.GraphQAChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, graph, entity_extraction_chain, qa_chain, input_key='query', output_key='result')[source]
Bases: langchain.chains.base.Chain
Chain for question-answering against a graph.
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]]) – 
graph (langchain.graphs.networkx_graph.NetworkxEntityGraph) – 
entity_extraction_chain (langchain.chains.llm.LLMChain) – 
qa_chain (langchain.chains.llm.LLMChain) – 
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 entity_extraction_chain: LLMChain [Required]
attribute graph: NetworkxEntityGraph [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 qa_chain: LLMChain [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
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="Use the following knowledge triplets to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.\n\n{context}\n\nQuestion: {question}\nHelpful Answer:", template_format='f-string', validate_template=True), entity_prompt=PromptTemplate(input_variables=['input'], output_parser=None, partial_variables={}, template="Extract all entities from the following text. As a guideline, a proper noun is generally capitalized. You should definitely extract all names and places.\n\nReturn the output as a single comma-separated list, or NONE if there is nothing of note to return.\n\nEXAMPLE\ni'm trying to improve Langchain's interfaces, the UX, its integrations with various products the user might want ... a lot of stuff.\nOutput: Langchain\nEND OF EXAMPLE\n\nEXAMPLE\ni'm trying to improve Langchain's interfaces, the UX, its integrations with various products the user might want ... a lot of stuff. I'm working with Sam.\nOutput: Langchain, Sam\nEND OF EXAMPLE\n\nBegin!\n\n{input}\nOutput:", template_format='f-string', validate_template=True), **kwargs)[source]
Initialize from LLM.
Parameters
llm (langchain.base_language.BaseLanguageModel) – 
qa_prompt (langchain.prompts.base.BasePromptTemplate) – 
entity_prompt (langchain.prompts.base.BasePromptTemplate) – 
kwargs (Any) – 
Return type
langchain.chains.graph_qa.base.GraphQAChain
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.HypotheticalDocumentEmbedder(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, base_embeddings, llm_chain)[source]
Bases: langchain.chains.base.Chain, langchain.embeddings.base.Embeddings
Generate hypothetical document for query, and then embed that.
Based on https://arxiv.org/abs/2212.10496
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]]) – 
base_embeddings (langchain.embeddings.base.Embeddings) – 
llm_chain (langchain.chains.llm.LLMChain) – 
Return type
None
attribute base_embeddings: Embeddings [Required]
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]
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 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
combine_embeddings(embeddings)[source]
Combine embeddings into final embeddings.
Parameters
embeddings (List[List[float]]) – 
Return type
List[float]
dict(**kwargs)
Return dictionary representation of chain.
Parameters
kwargs (Any) – 
Return type
Dict
embed_documents(texts)[source]
Call the base embeddings.
Parameters
texts (List[str]) – 
Return type
List[List[float]]
embed_query(text)[source]
Generate a hypothetical document and embedded it.
Parameters
text (str) – 
Return type
List[float]
classmethod from_llm(llm, base_embeddings, prompt_key, **kwargs)[source]
Load and use LLMChain for a specific prompt key.
Parameters
llm (langchain.base_language.BaseLanguageModel) – 
base_embeddings (langchain.embeddings.base.Embeddings) – 
prompt_key (str) – 
kwargs (Any) – 
Return type
langchain.chains.hyde.base.HypotheticalDocumentEmbedder
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 input_keys: List[str]
Input keys for Hyde’s LLM chain.
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 for Hyde’s LLM chain.
class langchain.chains.KuzuQAChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, graph, cypher_generation_chain, qa_chain, input_key='query', output_key='result')[source]
Bases: langchain.chains.base.Chain
Chain for question-answering against a graph by generating Cypher statements for
Kùzu.
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]]) – 
graph (langchain.graphs.kuzu_graph.KuzuGraph) – 
cypher_generation_chain (langchain.chains.llm.LLMChain) – 
qa_chain (langchain.chains.llm.LLMChain) – 
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 cypher_generation_chain: LLMChain [Required]
attribute graph: KuzuGraph [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 qa_chain: LLMChain [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
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
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.
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
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
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 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
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]]) – 
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]]) – 
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]]]
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
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
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.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]) – 
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]
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.
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
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) – 
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
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.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]) – 
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.
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
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
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
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.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]
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.
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) – 
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.
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
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
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 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
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.
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.
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
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 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.
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
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
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
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) – 
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.
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
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
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) – 
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.
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
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]) – 
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.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.
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) – 
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
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.
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)
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
execute(url, browser_content)[source]
Figure out next browser command to run.
Parameters
url (str) – URL of the site currently on.
browser_content (str) – Content of the page as currently displayed by the browser.
Returns
Next browser command to run.
Return type
str
Example
browser_content = "...."
llm_command = natbot.run("www.google.com", browser_content)
classmethod from_default(objective, **kwargs)[source]
Load with default LLMChain.
Parameters
objective (str) – 
kwargs (Any) – 
Return type
langchain.chains.natbot.base.NatBotChain
classmethod from_llm(llm, objective, **kwargs)[source]
Load from LLM.
Parameters
llm (langchain.base_language.BaseLanguageModel) – 
objective (str) – 
kwargs (Any) – 
Return type
langchain.chains.natbot.base.NatBotChain
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.NebulaGraphQAChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, graph, ngql_generation_chain, qa_chain, input_key='query', output_key='result')[source]
Bases: langchain.chains.base.Chain
Chain for question-answering against a graph by generating nGQL statements.
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]]) – 
graph (langchain.graphs.nebula_graph.NebulaGraph) – 
ngql_generation_chain (langchain.chains.llm.LLMChain) – 
qa_chain (langchain.chains.llm.LLMChain) – 
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 graph: NebulaGraph [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 ngql_generation_chain: LLMChain [Required]
attribute qa_chain: LLMChain [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
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), ngql_prompt=PromptTemplate(input_variables=['schema', 'question'], output_parser=None, partial_variables={}, template="Task:Generate NebulaGraph Cypher statement to query a graph database.\n\nInstructions:\n\nFirst, generate cypher then convert it to NebulaGraph Cypher dialect(rather than standard):\n1. it requires explicit label specification when referring to node properties: v.`Foo`.name\n2. it uses double equals sign for comparison: `==` rather than `=`\nFor instance:\n```diff\n< MATCH (p:person)-[:directed]->(m:movie) WHERE m.name = 'The Godfather II'\n< RETURN p.name;\n---\n> MATCH (p:`person`)-[:directed]->(m:`movie`) WHERE m.`movie`.`name` == 'The Godfather II'\n> RETURN p.`person`.`name`;\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) – 
ngql_prompt (langchain.prompts.base.BasePromptTemplate) – 
kwargs (Any) – 
Return type
langchain.chains.graph_qa.nebulagraph.NebulaGraphQAChain
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.OpenAIModerationChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, client=None, model_name=None, error=False, input_key='input', output_key='output', openai_api_key=None, openai_organization=None)[source]
Bases: langchain.chains.base.Chain
Pass input through a moderation endpoint.
To use, you should have the openai python package installed, and the
environment variable OPENAI_API_KEY set with your API key.
Any parameters that are valid to be passed to the openai.create call can be passed
in, even if not explicitly saved on this class.
Example
from langchain.chains import OpenAIModerationChain
moderation = OpenAIModerationChain()
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]]) – 
client (Any) – 
model_name (Optional[str]) – 
error (bool) – 
input_key (str) – 
output_key (str) – 
openai_api_key (Optional[str]) – 
openai_organization (Optional[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 error: bool = False
Whether or not to error if bad content was found.
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 model_name: Optional[str] = None
Moderation model name to use.
attribute openai_api_key: Optional[str] = None
attribute openai_organization: Optional[str] = None
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) – 
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.OpenAPIEndpointChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, api_request_chain, api_response_chain=None, api_operation, requests=None, param_mapping, return_intermediate_steps=False, instructions_key='instructions', output_key='output', max_text_length=None)[source]
Bases: langchain.chains.base.Chain, pydantic.main.BaseModel
Chain interacts with an OpenAPI endpoint using natural language.
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]]) – 
api_request_chain (langchain.chains.llm.LLMChain) – 
api_response_chain (Optional[langchain.chains.llm.LLMChain]) – 
api_operation (langchain.tools.openapi.utils.api_models.APIOperation) – 
requests (langchain.requests.Requests) – 
param_mapping (langchain.chains.api.openapi.chain._ParamMapping) – 
return_intermediate_steps (bool) – 
instructions_key (str) – 
output_key (str) – 
max_text_length (Optional[int]) – 
Return type
None
attribute api_operation: APIOperation [Required]
attribute api_request_chain: LLMChain [Required]
attribute api_response_chain: Optional[LLMChain] = 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 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 param_mapping: _ParamMapping [Required]
attribute requests: Requests [Optional]
attribute return_intermediate_steps: bool = 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
deserialize_json_input(serialized_args)[source]
Use the serialized typescript dictionary.
Resolve the path, query params dict, and optional requestBody dict.
Parameters
serialized_args (str) – 
Return type
dict
dict(**kwargs)
Return dictionary representation of chain.
Parameters
kwargs (Any) – 
Return type
Dict
classmethod from_api_operation(operation, llm, requests=None, verbose=False, return_intermediate_steps=False, raw_response=False, callbacks=None, **kwargs)[source]
Create an OpenAPIEndpointChain from an operation and a spec.
Parameters
operation (langchain.tools.openapi.utils.api_models.APIOperation) – 
llm (langchain.base_language.BaseLanguageModel) – 
requests (Optional[langchain.requests.Requests]) – 
verbose (bool) – 
return_intermediate_steps (bool) – 
raw_response (bool) – 
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – 
kwargs (Any) – 
Return type
langchain.chains.api.openapi.chain.OpenAPIEndpointChain
classmethod from_url_and_method(spec_url, path, method, llm, requests=None, return_intermediate_steps=False, **kwargs)[source]
Create an OpenAPIEndpoint from a spec at the specified url.
Parameters
spec_url (str) – 
path (str) – 
method (str) – 
llm (langchain.base_language.BaseLanguageModel) – 
requests (Optional[langchain.requests.Requests]) – 
return_intermediate_steps (bool) – 
kwargs (Any) – 
Return type
langchain.chains.api.openapi.chain.OpenAPIEndpointChain
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.PALChain(*, 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='Q: Olivia has $23. She bought five bagels for $3 each. How much money does she have left?\n\n# solution in Python:\n\n\ndef solution():\n    """Olivia has $23. She bought five bagels for $3 each. How much money does she have left?"""\n    money_initial = 23\n    bagels = 5\n    bagel_cost = 3\n    money_spent = bagels * bagel_cost\n    money_left = money_initial - money_spent\n    result = money_left\n    return result\n\n\n\n\n\nQ: Michael had 58 golf balls. On tuesday, he lost 23 golf balls. On wednesday, he lost 2 more. How many golf balls did he have at the end of wednesday?\n\n# solution in Python:\n\n\ndef solution():\n    """Michael had 58 golf balls. On tuesday, he lost 23 golf balls. On wednesday, he lost 2 more. How many golf balls did he have at the end of wednesday?"""\n    golf_balls_initial = 58\n    golf_balls_lost_tuesday = 23\n    golf_balls_lost_wednesday = 2\n    golf_balls_left = golf_balls_initial - golf_balls_lost_tuesday - golf_balls_lost_wednesday\n    result = golf_balls_left\n    return result\n\n\n\n\n\nQ: There were nine computers in the server room. Five more computers were installed each day, from monday to thursday. How many computers are now in the server room?\n\n# solution in Python:\n\n\ndef solution():\n    """There were nine computers in the server room. Five more computers were installed each day, from monday to thursday. How many computers are now in the server room?"""\n    computers_initial = 9\n    computers_per_day = 5\n    num_days = 4  # 4 days between monday and thursday\n    computers_added = computers_per_day * num_days\n    computers_total = computers_initial + computers_added\n    result = computers_total\n    return result\n\n\n\n\n\nQ: Shawn has five toys. For Christmas, he got two toys each from his mom and dad. How many toys does he have now?\n\n# solution in Python:\n\n\ndef solution():\n    """Shawn has five toys. For Christmas, he got two toys each from his mom and dad. How many toys does he have now?"""\n    toys_initial = 5\n    mom_toys = 2\n    dad_toys = 2\n    total_received = mom_toys + dad_toys\n    total_toys = toys_initial + total_received\n    result = total_toys\n    return result\n\n\n\n\n\nQ: Jason had 20 lollipops. He gave Denny some lollipops. Now Jason has 12 lollipops. How many lollipops did Jason give to Denny?\n\n# solution in Python:\n\n\ndef solution():\n    """Jason had 20 lollipops. He gave Denny some lollipops. Now Jason has 12 lollipops. How many lollipops did Jason give to Denny?"""\n    jason_lollipops_initial = 20\n    jason_lollipops_after = 12\n    denny_lollipops = jason_lollipops_initial - jason_lollipops_after\n    result = denny_lollipops\n    return result\n\n\n\n\n\nQ: Leah had 32 chocolates and her sister had 42. If they ate 35, how many pieces do they have left in total?\n\n# solution in Python:\n\n\ndef solution():\n    """Leah had 32 chocolates and her sister had 42. If they ate 35, how many pieces do they have left in total?"""\n    leah_chocolates = 32\n    sister_chocolates = 42\n    total_chocolates = leah_chocolates + sister_chocolates\n    chocolates_eaten = 35\n    chocolates_left = total_chocolates - chocolates_eaten\n    result = chocolates_left\n    return result\n\n\n\n\n\nQ: If there are 3 cars in the parking lot and 2 more cars arrive, how many cars are in the parking lot?\n\n# solution in Python:\n\n\ndef solution():\n    """If there are 3 cars in the parking lot and 2 more cars arrive, how many cars are in the parking lot?"""\n    cars_initial = 3\n    cars_arrived = 2\n    total_cars = cars_initial + cars_arrived\n    result = total_cars\n    return result\n\n\n\n\n\nQ: There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?\n\n# solution in Python:\n\n\ndef solution():\n    """There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?"""\n    trees_initial = 15\n    trees_after = 21\n    trees_added = trees_after - trees_initial\n    result = trees_added\n    return result\n\n\n\n\n\nQ: {question}\n\n# solution in Python:\n\n\n', template_format='f-string', validate_template=True), stop='\n\n', get_answer_expr='print(solution())', python_globals=None, python_locals=None, output_key='result', return_intermediate_steps=False)[source]
Bases: langchain.chains.base.Chain
Implements Program-Aided Language Models.
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) – 
stop (str) – 
get_answer_expr (str) – 
python_globals (Optional[Dict[str, Any]]) – 
python_locals (Optional[Dict[str, Any]]) – 
output_key (str) – 
return_intermediate_steps (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 get_answer_expr: str = 'print(solution())'
attribute llm: Optional[BaseLanguageModel] = None
[Deprecated]
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 prompt: BasePromptTemplate = PromptTemplate(input_variables=['question'], output_parser=None, partial_variables={}, template='Q: Olivia has $23. She bought five bagels for $3 each. How much money does she have left?\n\n# solution in Python:\n\n\ndef solution():\n    """Olivia has $23. She bought five bagels for $3 each. How much money does she have left?"""\n    money_initial = 23\n    bagels = 5\n    bagel_cost = 3\n    money_spent = bagels * bagel_cost\n    money_left = money_initial - money_spent\n    result = money_left\n    return result\n\n\n\n\n\nQ: Michael had 58 golf balls. On tuesday, he lost 23 golf balls. On wednesday, he lost 2 more. How many golf balls did he have at the end of wednesday?\n\n# solution in Python:\n\n\ndef solution():\n    """Michael had 58 golf balls. On tuesday, he lost 23 golf balls. On wednesday, he lost 2 more. How many golf balls did he have at the end of wednesday?"""\n    golf_balls_initial = 58\n    golf_balls_lost_tuesday = 23\n    golf_balls_lost_wednesday = 2\n    golf_balls_left = golf_balls_initial - golf_balls_lost_tuesday - golf_balls_lost_wednesday\n    result = golf_balls_left\n    return result\n\n\n\n\n\nQ: There were nine computers in the server room. Five more computers were installed each day, from monday to thursday. How many computers are now in the server room?\n\n# solution in Python:\n\n\ndef solution():\n    """There were nine computers in the server room. Five more computers were installed each day, from monday to thursday. How many computers are now in the server room?"""\n    computers_initial = 9\n    computers_per_day = 5\n    num_days = 4  # 4 days between monday and thursday\n    computers_added = computers_per_day * num_days\n    computers_total = computers_initial + computers_added\n    result = computers_total\n    return result\n\n\n\n\n\nQ: Shawn has five toys. For Christmas, he got two toys each from his mom and dad. How many toys does he have now?\n\n# solution in Python:\n\n\ndef solution():\n    """Shawn has five toys. For Christmas, he got two toys each from his mom and dad. How many toys does he have now?"""\n    toys_initial = 5\n    mom_toys = 2\n    dad_toys = 2\n    total_received = mom_toys + dad_toys\n    total_toys = toys_initial + total_received\n    result = total_toys\n    return result\n\n\n\n\n\nQ: Jason had 20 lollipops. He gave Denny some lollipops. Now Jason has 12 lollipops. How many lollipops did Jason give to Denny?\n\n# solution in Python:\n\n\ndef solution():\n    """Jason had 20 lollipops. He gave Denny some lollipops. Now Jason has 12 lollipops. How many lollipops did Jason give to Denny?"""\n    jason_lollipops_initial = 20\n    jason_lollipops_after = 12\n    denny_lollipops = jason_lollipops_initial - jason_lollipops_after\n    result = denny_lollipops\n    return result\n\n\n\n\n\nQ: Leah had 32 chocolates and her sister had 42. If they ate 35, how many pieces do they have left in total?\n\n# solution in Python:\n\n\ndef solution():\n    """Leah had 32 chocolates and her sister had 42. If they ate 35, how many pieces do they have left in total?"""\n    leah_chocolates = 32\n    sister_chocolates = 42\n    total_chocolates = leah_chocolates + sister_chocolates\n    chocolates_eaten = 35\n    chocolates_left = total_chocolates - chocolates_eaten\n    result = chocolates_left\n    return result\n\n\n\n\n\nQ: If there are 3 cars in the parking lot and 2 more cars arrive, how many cars are in the parking lot?\n\n# solution in Python:\n\n\ndef solution():\n    """If there are 3 cars in the parking lot and 2 more cars arrive, how many cars are in the parking lot?"""\n    cars_initial = 3\n    cars_arrived = 2\n    total_cars = cars_initial + cars_arrived\n    result = total_cars\n    return result\n\n\n\n\n\nQ: There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?\n\n# solution in Python:\n\n\ndef solution():\n    """There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?"""\n    trees_initial = 15\n    trees_after = 21\n    trees_added = trees_after - trees_initial\n    result = trees_added\n    return result\n\n\n\n\n\nQ: {question}\n\n# solution in Python:\n\n\n', template_format='f-string', validate_template=True)
[Deprecated]
attribute python_globals: Optional[Dict[str, Any]] = None
attribute python_locals: Optional[Dict[str, Any]] = None
attribute return_intermediate_steps: bool = False
attribute stop: str = '\n\n'
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
classmethod from_colored_object_prompt(llm, **kwargs)[source]
Load PAL from colored object prompt.
Parameters
llm (langchain.base_language.BaseLanguageModel) – 
kwargs (Any) – 
Return type
langchain.chains.pal.base.PALChain
classmethod from_math_prompt(llm, **kwargs)[source]
Load PAL from math prompt.
Parameters
llm (langchain.base_language.BaseLanguageModel) – 
kwargs (Any) – 
Return type
langchain.chains.pal.base.PALChain
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.QAGenerationChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, llm_chain, text_splitter=<langchain.text_splitter.RecursiveCharacterTextSplitter object>, input_key='text', output_key='questions', k=None)[source]
Bases: langchain.chains.base.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]]) – 
llm_chain (langchain.chains.llm.LLMChain) – 
text_splitter (langchain.text_splitter.TextSplitter) – 
input_key (str) – 
output_key (str) – 
k (Optional[int]) – 
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 input_key: str = 'text'
attribute k: Optional[int] = None
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 output_key: str = 'questions'
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 = <langchain.text_splitter.RecursiveCharacterTextSplitter object>
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
classmethod from_llm(llm, prompt=None, **kwargs)[source]
Parameters
llm (langchain.base_language.BaseLanguageModel) – 
prompt (Optional[langchain.prompts.base.BasePromptTemplate]) – 
kwargs (Any) – 
Return type
langchain.chains.qa_generation.base.QAGenerationChain
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 input_keys: List[str]
Input keys this chain expects.
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.QAWithSourcesChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, combine_documents_chain, question_key='question', input_docs_key='docs', answer_key='answer', sources_answer_key='sources', return_source_documents=False)[source]
Bases: langchain.chains.qa_with_sources.base.BaseQAWithSourcesChain
Question answering with sources over documents.
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) – 
question_key (str) – 
input_docs_key (str) – 
answer_key (str) – 
sources_answer_key (str) – 
return_source_documents (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 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 return_source_documents: bool = False
Return the source 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.
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
classmethod from_chain_type(llm, chain_type='stuff', chain_type_kwargs=None, **kwargs)
Load chain from chain type.
Parameters
llm (langchain.base_language.BaseLanguageModel) – 
chain_type (str) – 
chain_type_kwargs (Optional[dict]) – 
kwargs (Any) – 
Return type
langchain.chains.qa_with_sources.base.BaseQAWithSourcesChain
classmethod from_llm(llm, document_prompt=PromptTemplate(input_variables=['page_content', 'source'], output_parser=None, partial_variables={}, template='Content: {page_content}\nSource: {source}', template_format='f-string', validate_template=True), question_prompt=PromptTemplate(input_variables=['context', 'question'], output_parser=None, partial_variables={}, template='Use the following portion of a long document to see if any of the text is relevant to answer the question. \nReturn any relevant text verbatim.\n{context}\nQuestion: {question}\nRelevant text, if any:', template_format='f-string', validate_template=True), combine_prompt=PromptTemplate(input_variables=['summaries', 'question'], output_parser=None, partial_variables={}, template='Given the following extracted parts of a long document and a question, create a final answer with references ("SOURCES"). \nIf you don\'t know the answer, just say that you don\'t know. Don\'t try to make up an answer.\nALWAYS return a "SOURCES" part in your answer.\n\nQUESTION: Which state/country\'s law governs the interpretation of the contract?\n=========\nContent: This Agreement is governed by English law and the parties submit to the exclusive jurisdiction of the English courts in  relation to any dispute (contractual or non-contractual) concerning this Agreement save that either party may apply to any court for an  injunction or other relief to protect its Intellectual Property Rights.\nSource: 28-pl\nContent: No Waiver. Failure or delay in exercising any right or remedy under this Agreement shall not constitute a waiver of such (or any other)  right or remedy.\n\n11.7 Severability. The invalidity, illegality or unenforceability of any term (or part of a term) of this Agreement shall not affect the continuation  in force of the remainder of the term (if any) and this Agreement.\n\n11.8 No Agency. Except as expressly stated otherwise, nothing in this Agreement shall create an agency, partnership or joint venture of any  kind between the parties.\n\n11.9 No Third-Party Beneficiaries.\nSource: 30-pl\nContent: (b) if Google believes, in good faith, that the Distributor has violated or caused Google to violate any Anti-Bribery Laws (as  defined in Clause 8.5) or that such a violation is reasonably likely to occur,\nSource: 4-pl\n=========\nFINAL ANSWER: This Agreement is governed by English law.\nSOURCES: 28-pl\n\nQUESTION: What did the president say about Michael Jackson?\n=========\nContent: Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.  \n\nLast year COVID-19 kept us apart. This year we are finally together again. \n\nTonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \n\nWith a duty to one another to the American people to the Constitution. \n\nAnd with an unwavering resolve that freedom will always triumph over tyranny. \n\nSix days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways. But he badly miscalculated. \n\nHe thought he could roll into Ukraine and the world would roll over. Instead he met a wall of strength he never imagined. \n\nHe met the Ukrainian people. \n\nFrom President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world. \n\nGroups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland.\nSource: 0-pl\nContent: And we won’t stop. \n\nWe have lost so much to COVID-19. Time with one another. And worst of all, so much loss of life. \n\nLet’s use this moment to reset. Let’s stop looking at COVID-19 as a partisan dividing line and see it for what it is: A God-awful disease.  \n\nLet’s stop seeing each other as enemies, and start seeing each other for who we really are: Fellow Americans.  \n\nWe can’t change how divided we’ve been. But we can change how we move forward—on COVID-19 and other issues we must face together. \n\nI recently visited the New York City Police Department days after the funerals of Officer Wilbert Mora and his partner, Officer Jason Rivera. \n\nThey were responding to a 9-1-1 call when a man shot and killed them with a stolen gun. \n\nOfficer Mora was 27 years old. \n\nOfficer Rivera was 22. \n\nBoth Dominican Americans who’d grown up on the same streets they later chose to patrol as police officers. \n\nI spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves.\nSource: 24-pl\nContent: And a proud Ukrainian people, who have known 30 years  of independence, have repeatedly shown that they will not tolerate anyone who tries to take their country backwards.  \n\nTo all Americans, I will be honest with you, as I’ve always promised. A Russian dictator, invading a foreign country, has costs around the world. \n\nAnd I’m taking robust action to make sure the pain of our sanctions  is targeted at Russia’s economy. And I will use every tool at our disposal to protect American businesses and consumers. \n\nTonight, I can announce that the United States has worked with 30 other countries to release 60 Million barrels of oil from reserves around the world.  \n\nAmerica will lead that effort, releasing 30 Million barrels from our own Strategic Petroleum Reserve. And we stand ready to do more if necessary, unified with our allies.  \n\nThese steps will help blunt gas prices here at home. And I know the news about what’s happening can seem alarming. \n\nBut I want you to know that we are going to be okay.\nSource: 5-pl\nContent: More support for patients and families. \n\nTo get there, I call on Congress to fund ARPA-H, the Advanced Research Projects Agency for Health. \n\nIt’s based on DARPA—the Defense Department project that led to the Internet, GPS, and so much more.  \n\nARPA-H will have a singular purpose—to drive breakthroughs in cancer, Alzheimer’s, diabetes, and more. \n\nA unity agenda for the nation. \n\nWe can do this. \n\nMy fellow Americans—tonight , we have gathered in a sacred space—the citadel of our democracy. \n\nIn this Capitol, generation after generation, Americans have debated great questions amid great strife, and have done great things. \n\nWe have fought for freedom, expanded liberty, defeated totalitarianism and terror. \n\nAnd built the strongest, freest, and most prosperous nation the world has ever known. \n\nNow is the hour. \n\nOur moment of responsibility. \n\nOur test of resolve and conscience, of history itself. \n\nIt is in this moment that our character is formed. Our purpose is found. Our future is forged. \n\nWell I know this nation.\nSource: 34-pl\n=========\nFINAL ANSWER: The president did not mention Michael Jackson.\nSOURCES:\n\nQUESTION: {question}\n=========\n{summaries}\n=========\nFINAL ANSWER:', template_format='f-string', validate_template=True), **kwargs)
Construct the chain from an LLM.
Parameters
llm (langchain.base_language.BaseLanguageModel) – 
document_prompt (langchain.prompts.base.BasePromptTemplate) – 
question_prompt (langchain.prompts.base.BasePromptTemplate) – 
combine_prompt (langchain.prompts.base.BasePromptTemplate) – 
kwargs (Any) – 
Return type
langchain.chains.qa_with_sources.base.BaseQAWithSourcesChain
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.RetrievalQA(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, combine_documents_chain, input_key='query', output_key='result', return_source_documents=False, retriever)[source]
Bases: langchain.chains.retrieval_qa.base.BaseRetrievalQA
Chain for question-answering against an index.
Example
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA
from langchain.faiss import FAISS
from langchain.vectorstores.base import VectorStoreRetriever
retriever = VectorStoreRetriever(vectorstore=FAISS(...))
retrievalQA = RetrievalQA.from_llm(llm=OpenAI(), retriever=retriever)
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) – 
input_key (str) – 
output_key (str) – 
return_source_documents (bool) – 
retriever (langchain.schema.BaseRetriever) – 
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 combine_documents_chain: BaseCombineDocumentsChain [Required]
Chain to use to combine the 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 retriever: BaseRetriever [Required]
attribute return_source_documents: bool = False
Return the source 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.
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
classmethod from_chain_type(llm, chain_type='stuff', chain_type_kwargs=None, **kwargs)
Load chain from chain type.
Parameters
llm (langchain.base_language.BaseLanguageModel) – 
chain_type (str) – 
chain_type_kwargs (Optional[dict]) – 
kwargs (Any) – 
Return type
langchain.chains.retrieval_qa.base.BaseRetrievalQA
classmethod from_llm(llm, prompt=None, **kwargs)
Initialize from LLM.
Parameters
llm (langchain.base_language.BaseLanguageModel) – 
prompt (Optional[langchain.prompts.prompt.PromptTemplate]) – 
kwargs (Any) – 
Return type
langchain.chains.retrieval_qa.base.BaseRetrievalQA
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.RetrievalQAWithSourcesChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, combine_documents_chain, question_key='question', input_docs_key='docs', answer_key='answer', sources_answer_key='sources', return_source_documents=False, retriever, reduce_k_below_max_tokens=False, max_tokens_limit=3375)[source]
Bases: langchain.chains.qa_with_sources.base.BaseQAWithSourcesChain
Question-answering with sources over an index.
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) – 
question_key (str) – 
input_docs_key (str) – 
answer_key (str) – 
sources_answer_key (str) – 
return_source_documents (bool) – 
retriever (langchain.schema.BaseRetriever) – 
reduce_k_below_max_tokens (bool) – 
max_tokens_limit (int) – 
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 combine_documents_chain: BaseCombineDocumentsChain [Required]
Chain to use to combine documents.
attribute max_tokens_limit: int = 3375
Restrict the docs to return from store based on tokens,
enforced only for StuffDocumentChain and if reduce_k_below_max_tokens is to true
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 reduce_k_below_max_tokens: bool = False
Reduce the number of results to return from store based on tokens limit
attribute retriever: langchain.schema.BaseRetriever [Required]
Index to connect to.
attribute return_source_documents: bool = False
Return the source 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.
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
classmethod from_chain_type(llm, chain_type='stuff', chain_type_kwargs=None, **kwargs)
Load chain from chain type.
Parameters
llm (langchain.base_language.BaseLanguageModel) – 
chain_type (str) – 
chain_type_kwargs (Optional[dict]) – 
kwargs (Any) – 
Return type
langchain.chains.qa_with_sources.base.BaseQAWithSourcesChain
classmethod from_llm(llm, document_prompt=PromptTemplate(input_variables=['page_content', 'source'], output_parser=None, partial_variables={}, template='Content: {page_content}\nSource: {source}', template_format='f-string', validate_template=True), question_prompt=PromptTemplate(input_variables=['context', 'question'], output_parser=None, partial_variables={}, template='Use the following portion of a long document to see if any of the text is relevant to answer the question. \nReturn any relevant text verbatim.\n{context}\nQuestion: {question}\nRelevant text, if any:', template_format='f-string', validate_template=True), combine_prompt=PromptTemplate(input_variables=['summaries', 'question'], output_parser=None, partial_variables={}, template='Given the following extracted parts of a long document and a question, create a final answer with references ("SOURCES"). \nIf you don\'t know the answer, just say that you don\'t know. Don\'t try to make up an answer.\nALWAYS return a "SOURCES" part in your answer.\n\nQUESTION: Which state/country\'s law governs the interpretation of the contract?\n=========\nContent: This Agreement is governed by English law and the parties submit to the exclusive jurisdiction of the English courts in  relation to any dispute (contractual or non-contractual) concerning this Agreement save that either party may apply to any court for an  injunction or other relief to protect its Intellectual Property Rights.\nSource: 28-pl\nContent: No Waiver. Failure or delay in exercising any right or remedy under this Agreement shall not constitute a waiver of such (or any other)  right or remedy.\n\n11.7 Severability. The invalidity, illegality or unenforceability of any term (or part of a term) of this Agreement shall not affect the continuation  in force of the remainder of the term (if any) and this Agreement.\n\n11.8 No Agency. Except as expressly stated otherwise, nothing in this Agreement shall create an agency, partnership or joint venture of any  kind between the parties.\n\n11.9 No Third-Party Beneficiaries.\nSource: 30-pl\nContent: (b) if Google believes, in good faith, that the Distributor has violated or caused Google to violate any Anti-Bribery Laws (as  defined in Clause 8.5) or that such a violation is reasonably likely to occur,\nSource: 4-pl\n=========\nFINAL ANSWER: This Agreement is governed by English law.\nSOURCES: 28-pl\n\nQUESTION: What did the president say about Michael Jackson?\n=========\nContent: Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.  \n\nLast year COVID-19 kept us apart. This year we are finally together again. \n\nTonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \n\nWith a duty to one another to the American people to the Constitution. \n\nAnd with an unwavering resolve that freedom will always triumph over tyranny. \n\nSix days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways. But he badly miscalculated. \n\nHe thought he could roll into Ukraine and the world would roll over. Instead he met a wall of strength he never imagined. \n\nHe met the Ukrainian people. \n\nFrom President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world. \n\nGroups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland.\nSource: 0-pl\nContent: And we won’t stop. \n\nWe have lost so much to COVID-19. Time with one another. And worst of all, so much loss of life. \n\nLet’s use this moment to reset. Let’s stop looking at COVID-19 as a partisan dividing line and see it for what it is: A God-awful disease.  \n\nLet’s stop seeing each other as enemies, and start seeing each other for who we really are: Fellow Americans.  \n\nWe can’t change how divided we’ve been. But we can change how we move forward—on COVID-19 and other issues we must face together. \n\nI recently visited the New York City Police Department days after the funerals of Officer Wilbert Mora and his partner, Officer Jason Rivera. \n\nThey were responding to a 9-1-1 call when a man shot and killed them with a stolen gun. \n\nOfficer Mora was 27 years old. \n\nOfficer Rivera was 22. \n\nBoth Dominican Americans who’d grown up on the same streets they later chose to patrol as police officers. \n\nI spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves.\nSource: 24-pl\nContent: And a proud Ukrainian people, who have known 30 years  of independence, have repeatedly shown that they will not tolerate anyone who tries to take their country backwards.  \n\nTo all Americans, I will be honest with you, as I’ve always promised. A Russian dictator, invading a foreign country, has costs around the world. \n\nAnd I’m taking robust action to make sure the pain of our sanctions  is targeted at Russia’s economy. And I will use every tool at our disposal to protect American businesses and consumers. \n\nTonight, I can announce that the United States has worked with 30 other countries to release 60 Million barrels of oil from reserves around the world.  \n\nAmerica will lead that effort, releasing 30 Million barrels from our own Strategic Petroleum Reserve. And we stand ready to do more if necessary, unified with our allies.  \n\nThese steps will help blunt gas prices here at home. And I know the news about what’s happening can seem alarming. \n\nBut I want you to know that we are going to be okay.\nSource: 5-pl\nContent: More support for patients and families. \n\nTo get there, I call on Congress to fund ARPA-H, the Advanced Research Projects Agency for Health. \n\nIt’s based on DARPA—the Defense Department project that led to the Internet, GPS, and so much more.  \n\nARPA-H will have a singular purpose—to drive breakthroughs in cancer, Alzheimer’s, diabetes, and more. \n\nA unity agenda for the nation. \n\nWe can do this. \n\nMy fellow Americans—tonight , we have gathered in a sacred space—the citadel of our democracy. \n\nIn this Capitol, generation after generation, Americans have debated great questions amid great strife, and have done great things. \n\nWe have fought for freedom, expanded liberty, defeated totalitarianism and terror. \n\nAnd built the strongest, freest, and most prosperous nation the world has ever known. \n\nNow is the hour. \n\nOur moment of responsibility. \n\nOur test of resolve and conscience, of history itself. \n\nIt is in this moment that our character is formed. Our purpose is found. Our future is forged. \n\nWell I know this nation.\nSource: 34-pl\n=========\nFINAL ANSWER: The president did not mention Michael Jackson.\nSOURCES:\n\nQUESTION: {question}\n=========\n{summaries}\n=========\nFINAL ANSWER:', template_format='f-string', validate_template=True), **kwargs)
Construct the chain from an LLM.
Parameters
llm (langchain.base_language.BaseLanguageModel) – 
document_prompt (langchain.prompts.base.BasePromptTemplate) – 
question_prompt (langchain.prompts.base.BasePromptTemplate) – 
combine_prompt (langchain.prompts.base.BasePromptTemplate) – 
kwargs (Any) – 
Return type
langchain.chains.qa_with_sources.base.BaseQAWithSourcesChain
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.RouterChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None)[source]
Bases: langchain.chains.base.Chain, abc.ABC
Chain that outputs the name of a destination chain and the inputs to it.
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]]) – 
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 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 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)[source]
Parameters
inputs (Dict[str, Any]) – 
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – 
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
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)[source]
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
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
abstract property input_keys: List[str]
Input keys this chain expects.
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.SQLDatabaseChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, llm_chain, llm=None, database, prompt=None, top_k=5, input_key='query', output_key='result', return_intermediate_steps=False, return_direct=False, use_query_checker=False, query_checker_prompt=None)[source]
Bases: langchain.chains.base.Chain
Chain for interacting with SQL Database.
Example
from langchain import SQLDatabaseChain, OpenAI, SQLDatabase
db = SQLDatabase(...)
db_chain = SQLDatabaseChain.from_llm(OpenAI(), db)
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]) – 
database (langchain.sql_database.SQLDatabase) – 
prompt (Optional[langchain.prompts.base.BasePromptTemplate]) – 
top_k (int) – 
input_key (str) – 
output_key (str) – 
return_intermediate_steps (bool) – 
return_direct (bool) – 
use_query_checker (bool) – 
query_checker_prompt (Optional[langchain.prompts.base.BasePromptTemplate]) – 
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 database: SQLDatabase [Required]
SQL Database to connect to.
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.
attribute prompt: Optional[BasePromptTemplate] = None
[Deprecated] Prompt to use to translate natural language to SQL.
attribute query_checker_prompt: Optional[BasePromptTemplate] = None
The prompt template that should be used by the query checker
attribute return_direct: bool = False
Whether or not to return the result of querying the SQL table directly.
attribute return_intermediate_steps: bool = False
Whether or not to return the intermediate steps along with the final answer.
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 top_k: int = 5
Number of results to return from the query
attribute use_query_checker: bool = False
Whether or not the query checker tool should be used to attempt
to fix the initial SQL from the LLM.
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
classmethod from_llm(llm, db, prompt=None, **kwargs)[source]
Parameters
llm (langchain.base_language.BaseLanguageModel) – 
db (langchain.sql_database.SQLDatabase) – 
prompt (Optional[langchain.prompts.base.BasePromptTemplate]) – 
kwargs (Any) – 
Return type
langchain.chains.sql_database.base.SQLDatabaseChain
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.SQLDatabaseSequentialChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, decider_chain, sql_chain, input_key='query', output_key='result', return_intermediate_steps=False)[source]
Bases: langchain.chains.base.Chain
Chain for querying SQL database that is a sequential chain.
The chain is as follows:
1. Based on the query, determine which tables to use.
2. Based on those tables, call the normal SQL database chain.
This is useful in cases where the number of tables in the database is large.
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]]) – 
decider_chain (langchain.chains.llm.LLMChain) – 
sql_chain (langchain.chains.sql_database.base.SQLDatabaseChain) – 
input_key (str) – 
output_key (str) – 
return_intermediate_steps (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 decider_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 return_intermediate_steps: bool = False
attribute sql_chain: SQLDatabaseChain [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
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, database, query_prompt=PromptTemplate(input_variables=['input', 'table_info', 'dialect', 'top_k'], output_parser=None, partial_variables={}, template='Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer. Unless the user specifies in his question a specific number of examples he wishes to obtain, always limit your query to at most {top_k} results. You can order the results by a relevant column to return the most interesting examples in the database.\n\nNever query for all the columns from a specific table, only ask for a the few relevant columns given the question.\n\nPay attention to use only the column names that you can see in the schema description. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.\n\nUse the following format:\n\nQuestion: Question here\nSQLQuery: SQL Query to run\nSQLResult: Result of the SQLQuery\nAnswer: Final answer here\n\nOnly use the following tables:\n{table_info}\n\nQuestion: {input}', template_format='f-string', validate_template=True), decider_prompt=PromptTemplate(input_variables=['query', 'table_names'], output_parser=CommaSeparatedListOutputParser(), partial_variables={}, template='Given the below input question and list of potential tables, output a comma separated list of the table names that may be necessary to answer this question.\n\nQuestion: {query}\n\nTable Names: {table_names}\n\nRelevant Table Names:', template_format='f-string', validate_template=True), **kwargs)[source]
Load the necessary chains.
Parameters
llm (langchain.base_language.BaseLanguageModel) – 
database (langchain.sql_database.SQLDatabase) – 
query_prompt (langchain.prompts.base.BasePromptTemplate) – 
decider_prompt (langchain.prompts.base.BasePromptTemplate) – 
kwargs (Any) – 
Return type
langchain.chains.sql_database.base.SQLDatabaseSequentialChain
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.SequentialChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, chains, input_variables, output_variables, return_all=False)[source]
Bases: langchain.chains.base.Chain
Chain where the outputs of one chain feed directly into next.
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]]) – 
chains (List[langchain.chains.base.Chain]) – 
input_variables (List[str]) – 
output_variables (List[str]) – 
return_all (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 chains: List[langchain.chains.base.Chain] [Required]
attribute input_variables: List[str] [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 return_all: bool = 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) – 
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.SimpleSequentialChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, chains, strip_outputs=False, input_key='input', output_key='output')[source]
Bases: langchain.chains.base.Chain
Simple chain where the outputs of one step feed directly into next.
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]]) – 
chains (List[langchain.chains.base.Chain]) – 
strip_outputs (bool) – 
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 chains: List[langchain.chains.base.Chain] [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 strip_outputs: bool = 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) – 
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.TransformChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, input_variables, output_variables, transform)[source]
Bases: langchain.chains.base.Chain
Chain transform chain output.
Example
from langchain import TransformChain
transform_chain = TransformChain(input_variables=["text"],
 output_variables["entities"], transform=func())
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]]) – 
input_variables (List[str]) – 
output_variables (List[str]) – 
transform (Callable[[Dict[str, str]], Dict[str, 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 input_variables: List[str] [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 output_variables: List[str] [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 transform: Callable[[Dict[str, str]], Dict[str, str]] [Required]
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) – 
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.VectorDBQA(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, combine_documents_chain, input_key='query', output_key='result', return_source_documents=False, vectorstore, k=4, search_type='similarity', search_kwargs=None)[source]
Bases: langchain.chains.retrieval_qa.base.BaseRetrievalQA
Chain for question-answering against a vector database.
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) – 
input_key (str) – 
output_key (str) – 
return_source_documents (bool) – 
vectorstore (langchain.vectorstores.base.VectorStore) – 
k (int) – 
search_type (str) – 
search_kwargs (Dict[str, Any]) – 
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 combine_documents_chain: BaseCombineDocumentsChain [Required]
Chain to use to combine the documents.
attribute k: int = 4
Number of documents to query for.
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 return_source_documents: bool = False
Return the source documents.
attribute search_kwargs: Dict[str, Any] [Optional]
Extra search args.
attribute search_type: str = 'similarity'
Search type to use over vectorstore. similarity or mmr.
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 vectorstore: VectorStore [Required]
Vector Database to connect to.
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
classmethod from_chain_type(llm, chain_type='stuff', chain_type_kwargs=None, **kwargs)
Load chain from chain type.
Parameters
llm (langchain.base_language.BaseLanguageModel) – 
chain_type (str) – 
chain_type_kwargs (Optional[dict]) – 
kwargs (Any) – 
Return type
langchain.chains.retrieval_qa.base.BaseRetrievalQA
classmethod from_llm(llm, prompt=None, **kwargs)
Initialize from LLM.
Parameters
llm (langchain.base_language.BaseLanguageModel) – 
prompt (Optional[langchain.prompts.prompt.PromptTemplate]) – 
kwargs (Any) – 
Return type
langchain.chains.retrieval_qa.base.BaseRetrievalQA
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.VectorDBQAWithSourcesChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, combine_documents_chain, question_key='question', input_docs_key='docs', answer_key='answer', sources_answer_key='sources', return_source_documents=False, vectorstore, k=4, reduce_k_below_max_tokens=False, max_tokens_limit=3375, search_kwargs=None)[source]
Bases: langchain.chains.qa_with_sources.base.BaseQAWithSourcesChain
Question-answering with sources over a vector database.
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) – 
question_key (str) – 
input_docs_key (str) – 
answer_key (str) – 
sources_answer_key (str) – 
return_source_documents (bool) – 
vectorstore (langchain.vectorstores.base.VectorStore) – 
k (int) – 
reduce_k_below_max_tokens (bool) – 
max_tokens_limit (int) – 
search_kwargs (Dict[str, Any]) – 
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 combine_documents_chain: BaseCombineDocumentsChain [Required]
Chain to use to combine documents.
attribute k: int = 4
Number of results to return from store
attribute max_tokens_limit: int = 3375
Restrict the docs to return from store based on tokens,
enforced only for StuffDocumentChain and if reduce_k_below_max_tokens is to true
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 reduce_k_below_max_tokens: bool = False
Reduce the number of results to return from store based on tokens limit
attribute return_source_documents: bool = False
Return the source documents.
attribute search_kwargs: Dict[str, Any] [Optional]
Extra search args.
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 vectorstore: langchain.vectorstores.base.VectorStore [Required]
Vector Database to connect to.
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
classmethod from_chain_type(llm, chain_type='stuff', chain_type_kwargs=None, **kwargs)
Load chain from chain type.
Parameters
llm (langchain.base_language.BaseLanguageModel) – 
chain_type (str) – 
chain_type_kwargs (Optional[dict]) – 
kwargs (Any) – 
Return type
langchain.chains.qa_with_sources.base.BaseQAWithSourcesChain
classmethod from_llm(llm, document_prompt=PromptTemplate(input_variables=['page_content', 'source'], output_parser=None, partial_variables={}, template='Content: {page_content}\nSource: {source}', template_format='f-string', validate_template=True), question_prompt=PromptTemplate(input_variables=['context', 'question'], output_parser=None, partial_variables={}, template='Use the following portion of a long document to see if any of the text is relevant to answer the question. \nReturn any relevant text verbatim.\n{context}\nQuestion: {question}\nRelevant text, if any:', template_format='f-string', validate_template=True), combine_prompt=PromptTemplate(input_variables=['summaries', 'question'], output_parser=None, partial_variables={}, template='Given the following extracted parts of a long document and a question, create a final answer with references ("SOURCES"). \nIf you don\'t know the answer, just say that you don\'t know. Don\'t try to make up an answer.\nALWAYS return a "SOURCES" part in your answer.\n\nQUESTION: Which state/country\'s law governs the interpretation of the contract?\n=========\nContent: This Agreement is governed by English law and the parties submit to the exclusive jurisdiction of the English courts in  relation to any dispute (contractual or non-contractual) concerning this Agreement save that either party may apply to any court for an  injunction or other relief to protect its Intellectual Property Rights.\nSource: 28-pl\nContent: No Waiver. Failure or delay in exercising any right or remedy under this Agreement shall not constitute a waiver of such (or any other)  right or remedy.\n\n11.7 Severability. The invalidity, illegality or unenforceability of any term (or part of a term) of this Agreement shall not affect the continuation  in force of the remainder of the term (if any) and this Agreement.\n\n11.8 No Agency. Except as expressly stated otherwise, nothing in this Agreement shall create an agency, partnership or joint venture of any  kind between the parties.\n\n11.9 No Third-Party Beneficiaries.\nSource: 30-pl\nContent: (b) if Google believes, in good faith, that the Distributor has violated or caused Google to violate any Anti-Bribery Laws (as  defined in Clause 8.5) or that such a violation is reasonably likely to occur,\nSource: 4-pl\n=========\nFINAL ANSWER: This Agreement is governed by English law.\nSOURCES: 28-pl\n\nQUESTION: What did the president say about Michael Jackson?\n=========\nContent: Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans.  \n\nLast year COVID-19 kept us apart. This year we are finally together again. \n\nTonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \n\nWith a duty to one another to the American people to the Constitution. \n\nAnd with an unwavering resolve that freedom will always triumph over tyranny. \n\nSix days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways. But he badly miscalculated. \n\nHe thought he could roll into Ukraine and the world would roll over. Instead he met a wall of strength he never imagined. \n\nHe met the Ukrainian people. \n\nFrom President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world. \n\nGroups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland.\nSource: 0-pl\nContent: And we won’t stop. \n\nWe have lost so much to COVID-19. Time with one another. And worst of all, so much loss of life. \n\nLet’s use this moment to reset. Let’s stop looking at COVID-19 as a partisan dividing line and see it for what it is: A God-awful disease.  \n\nLet’s stop seeing each other as enemies, and start seeing each other for who we really are: Fellow Americans.  \n\nWe can’t change how divided we’ve been. But we can change how we move forward—on COVID-19 and other issues we must face together. \n\nI recently visited the New York City Police Department days after the funerals of Officer Wilbert Mora and his partner, Officer Jason Rivera. \n\nThey were responding to a 9-1-1 call when a man shot and killed them with a stolen gun. \n\nOfficer Mora was 27 years old. \n\nOfficer Rivera was 22. \n\nBoth Dominican Americans who’d grown up on the same streets they later chose to patrol as police officers. \n\nI spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves.\nSource: 24-pl\nContent: And a proud Ukrainian people, who have known 30 years  of independence, have repeatedly shown that they will not tolerate anyone who tries to take their country backwards.  \n\nTo all Americans, I will be honest with you, as I’ve always promised. A Russian dictator, invading a foreign country, has costs around the world. \n\nAnd I’m taking robust action to make sure the pain of our sanctions  is targeted at Russia’s economy. And I will use every tool at our disposal to protect American businesses and consumers. \n\nTonight, I can announce that the United States has worked with 30 other countries to release 60 Million barrels of oil from reserves around the world.  \n\nAmerica will lead that effort, releasing 30 Million barrels from our own Strategic Petroleum Reserve. And we stand ready to do more if necessary, unified with our allies.  \n\nThese steps will help blunt gas prices here at home. And I know the news about what’s happening can seem alarming. \n\nBut I want you to know that we are going to be okay.\nSource: 5-pl\nContent: More support for patients and families. \n\nTo get there, I call on Congress to fund ARPA-H, the Advanced Research Projects Agency for Health. \n\nIt’s based on DARPA—the Defense Department project that led to the Internet, GPS, and so much more.  \n\nARPA-H will have a singular purpose—to drive breakthroughs in cancer, Alzheimer’s, diabetes, and more. \n\nA unity agenda for the nation. \n\nWe can do this. \n\nMy fellow Americans—tonight , we have gathered in a sacred space—the citadel of our democracy. \n\nIn this Capitol, generation after generation, Americans have debated great questions amid great strife, and have done great things. \n\nWe have fought for freedom, expanded liberty, defeated totalitarianism and terror. \n\nAnd built the strongest, freest, and most prosperous nation the world has ever known. \n\nNow is the hour. \n\nOur moment of responsibility. \n\nOur test of resolve and conscience, of history itself. \n\nIt is in this moment that our character is formed. Our purpose is found. Our future is forged. \n\nWell I know this nation.\nSource: 34-pl\n=========\nFINAL ANSWER: The president did not mention Michael Jackson.\nSOURCES:\n\nQUESTION: {question}\n=========\n{summaries}\n=========\nFINAL ANSWER:', template_format='f-string', validate_template=True), **kwargs)
Construct the chain from an LLM.
Parameters
llm (langchain.base_language.BaseLanguageModel) – 
document_prompt (langchain.prompts.base.BasePromptTemplate) – 
question_prompt (langchain.prompts.base.BasePromptTemplate) – 
combine_prompt (langchain.prompts.base.BasePromptTemplate) – 
kwargs (Any) – 
Return type
langchain.chains.qa_with_sources.base.BaseQAWithSourcesChain
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.
langchain.chains.create_extraction_chain(schema, llm)[source]
Creates a chain that extracts information from a passage.
Parameters
schema (dict) – The schema of the entities to extract.
llm (langchain.base_language.BaseLanguageModel) – The language model to use.
Returns
Chain that can be used to extract information from a passage.
Return type
langchain.chains.base.Chain
langchain.chains.create_extraction_chain_pydantic(pydantic_schema, llm)[source]
Creates a chain that extracts information from a passage using pydantic schema.
Parameters
pydantic_schema (Any) – The pydantic schema of the entities to extract.
llm (langchain.base_language.BaseLanguageModel) – The language model to use.
Returns
Chain that can be used to extract information from a passage.
Return type
langchain.chains.base.Chain
langchain.chains.create_tagging_chain(schema, llm)[source]
Creates a chain that extracts information from a passage.
Parameters
schema (dict) – The schema of the entities to extract.
llm (langchain.base_language.BaseLanguageModel) – The language model to use.
Returns
Chain (LLMChain) that can be used to extract information from a passage.
Return type
langchain.chains.base.Chain
langchain.chains.create_tagging_chain_pydantic(pydantic_schema, llm)[source]
Creates a chain that extracts information from a passage.
Parameters
pydantic_schema (Any) – The pydantic schema of the entities to extract.
llm (langchain.base_language.BaseLanguageModel) – The language model to use.
Returns
Chain (LLMChain) that can be used to extract information from a passage.
Return type
langchain.chains.base.Chain
langchain.chains.load_chain(path, **kwargs)[source]
Unified method for loading a chain from LangChainHub or local fs.
Parameters
path (Union[str, pathlib.Path]) – 
kwargs (Any) – 
Return type
langchain.chains.base.Chain
langchain.chains.create_citation_fuzzy_match_chain(llm)[source]
Create a citation fuzzy match chain.
Parameters
llm (langchain.base_language.BaseLanguageModel) – Language model to use for the chain.
Returns
Chain (LLMChain) that can be used to answer questions with citations.
Return type
langchain.chains.llm.LLMChain
langchain.chains.create_qa_with_structure_chain(llm, schema, output_parser='base', prompt=None)[source]
Create a question answering chain that returns an answer with sources.
Parameters
llm (langchain.base_language.BaseLanguageModel) – Language model to use for the chain.
schema (Union[dict, Type[pydantic.main.BaseModel]]) – Pydantic schema to use for the output.
output_parser (str) – Output parser to use. Should be one of pydantic or base.
Default to base.
prompt (Optional[Union[langchain.prompts.prompt.PromptTemplate, langchain.prompts.chat.ChatPromptTemplate]]) – Optional prompt to use for the chain.
Return type
langchain.chains.llm.LLMChain
Returns:
langchain.chains.create_qa_with_sources_chain(llm, **kwargs)[source]
Create a question answering chain that returns an answer with sources.
Parameters
llm (langchain.base_language.BaseLanguageModel) – Language model to use for the chain.
**kwargs – Keyword arguments to pass to create_qa_with_structure_chain.
kwargs (Any) – 
Returns
Chain (LLMChain) that can be used to answer questions with citations.
Return type
langchain.chains.llm.LLMChain
class langchain.chains.StuffDocumentsChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, input_key='input_documents', output_key='output_text', llm_chain, document_prompt=None, document_variable_name, document_separator='\n\n')[source]
Bases: langchain.chains.combine_documents.base.BaseCombineDocumentsChain
Chain that combines documents by stuffing into context.
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]]) – 
input_key (str) – 
output_key (str) – 
llm_chain (langchain.chains.llm.LLMChain) – 
document_prompt (langchain.prompts.base.BasePromptTemplate) – 
document_variable_name (str) – 
document_separator (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 document_prompt: langchain.prompts.base.BasePromptTemplate [Optional]
Prompt to use to format each document.
attribute document_separator: str = '\n\n'
The string with which to join the formatted documents
attribute document_variable_name: str [Required]
The variable name in the llm_chain to put the documents in.
If only one variable in the llm_chain, this need not be provided.
attribute llm_chain: langchain.chains.llm.LLMChain [Required]
LLM wrapper to use after formatting 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 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]
async acombine_docs(docs, callbacks=None, **kwargs)[source]
Stuff all documents into one prompt and pass to LLM.
Parameters
docs (List[langchain.schema.Document]) – 
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – 
kwargs (Any) – 
Return type
Tuple[str, dict]
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
combine_docs(docs, callbacks=None, **kwargs)[source]
Stuff all documents into one prompt and pass to LLM.
Parameters
docs (List[langchain.schema.Document]) – 
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – 
kwargs (Any) – 
Return type
Tuple[str, dict]
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]
prompt_length(docs, **kwargs)[source]
Get the prompt length by formatting the prompt.
Parameters
docs (List[langchain.schema.Document]) – 
kwargs (Any) – 
Return type
Optional[int]
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.MapRerankDocumentsChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, input_key='input_documents', output_key='output_text', llm_chain, document_variable_name, rank_key, answer_key, metadata_keys=None, return_intermediate_steps=False)[source]
Bases: langchain.chains.combine_documents.base.BaseCombineDocumentsChain
Combining documents by mapping a chain over them, then reranking 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]]) – 
input_key (str) – 
output_key (str) – 
llm_chain (langchain.chains.llm.LLMChain) – 
document_variable_name (str) – 
rank_key (str) – 
answer_key (str) – 
metadata_keys (Optional[List[str]]) – 
return_intermediate_steps (bool) – 
Return type
None
attribute answer_key: str [Required]
Key in output of llm_chain to return as answer.
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 document_variable_name: str [Required]
The variable name in the llm_chain to put the documents in.
If only one variable in the llm_chain, this need not be provided.
attribute llm_chain: LLMChain [Required]
Chain to apply to each document individually.
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 metadata_keys: Optional[List[str]] = None
attribute rank_key: str [Required]
Key in output of llm_chain to rank on.
attribute return_intermediate_steps: bool = 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]
async acombine_docs(docs, callbacks=None, **kwargs)[source]
Combine documents in a map rerank manner.
Combine by mapping first chain over all documents, then reranking the results.
Parameters
docs (List[langchain.schema.Document]) – 
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – 
kwargs (Any) – 
Return type
Tuple[str, dict]
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
combine_docs(docs, callbacks=None, **kwargs)[source]
Combine documents in a map rerank manner.
Combine by mapping first chain over all documents, then reranking the results.
Parameters
docs (List[langchain.schema.Document]) – 
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – 
kwargs (Any) – 
Return type
Tuple[str, dict]
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]
prompt_length(docs, **kwargs)
Return the prompt length given the documents passed in.
Returns None if the method does not depend on the prompt length.
Parameters
docs (List[langchain.schema.Document]) – 
kwargs (Any) – 
Return type
Optional[int]
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.MapReduceDocumentsChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, input_key='input_documents', output_key='output_text', llm_chain, combine_document_chain, collapse_document_chain=None, document_variable_name, return_intermediate_steps=False)[source]
Bases: langchain.chains.combine_documents.base.BaseCombineDocumentsChain
Combining documents by mapping a chain over them, then combining 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]]) – 
input_key (str) – 
output_key (str) – 
llm_chain (langchain.chains.llm.LLMChain) – 
combine_document_chain (langchain.chains.combine_documents.base.BaseCombineDocumentsChain) – 
collapse_document_chain (Optional[langchain.chains.combine_documents.base.BaseCombineDocumentsChain]) – 
document_variable_name (str) – 
return_intermediate_steps (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 collapse_document_chain: Optional[BaseCombineDocumentsChain] = None
Chain to use to collapse intermediary results if needed.
If None, will use the combine_document_chain.
attribute combine_document_chain: BaseCombineDocumentsChain [Required]
Chain to use to combine results of applying llm_chain to documents.
attribute document_variable_name: str [Required]
The variable name in the llm_chain to put the documents in.
If only one variable in the llm_chain, this need not be provided.
attribute llm_chain: LLMChain [Required]
Chain to apply to each document individually.
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 return_intermediate_steps: bool = False
Return the results of the map steps in the output.
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]
async acombine_docs(docs, callbacks=None, **kwargs)[source]
Combine documents in a map reduce manner.
Combine by mapping first chain over all documents, then reducing the results.
This reducing can be done recursively if needed (if there are many documents).
Parameters
docs (List[langchain.schema.Document]) – 
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – 
kwargs (Any) – 
Return type
Tuple[str, dict]
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
combine_docs(docs, token_max=3000, callbacks=None, **kwargs)[source]
Combine documents in a map reduce manner.
Combine by mapping first chain over all documents, then reducing the results.
This reducing can be done recursively if needed (if there are many documents).
Parameters
docs (List[langchain.schema.Document]) – 
token_max (int) – 
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – 
kwargs (Any) – 
Return type
Tuple[str, dict]
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]
prompt_length(docs, **kwargs)
Return the prompt length given the documents passed in.
Returns None if the method does not depend on the prompt length.
Parameters
docs (List[langchain.schema.Document]) – 
kwargs (Any) – 
Return type
Optional[int]
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.RefineDocumentsChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, input_key='input_documents', output_key='output_text', initial_llm_chain, refine_llm_chain, document_variable_name, initial_response_name, document_prompt=None, return_intermediate_steps=False)[source]
Bases: langchain.chains.combine_documents.base.BaseCombineDocumentsChain
Combine documents by doing a first pass and then refining on more documents.
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]]) – 
input_key (str) – 
output_key (str) – 
initial_llm_chain (langchain.chains.llm.LLMChain) – 
refine_llm_chain (langchain.chains.llm.LLMChain) – 
document_variable_name (str) – 
initial_response_name (str) – 
document_prompt (langchain.prompts.base.BasePromptTemplate) – 
return_intermediate_steps (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 document_prompt: BasePromptTemplate [Optional]
Prompt to use to format each document.
attribute document_variable_name: str [Required]
The variable name in the initial_llm_chain to put the documents in.
If only one variable in the initial_llm_chain, this need not be provided.
attribute initial_llm_chain: LLMChain [Required]
LLM chain to use on initial document.
attribute initial_response_name: str [Required]
The variable name to format the initial response in when refining.
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 refine_llm_chain: LLMChain [Required]
LLM chain to use when refining.
attribute return_intermediate_steps: bool = False
Return the results of the refine steps in the output.
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]
async acombine_docs(docs, callbacks=None, **kwargs)[source]
Combine by mapping first chain over all, then stuffing into final chain.
Parameters
docs (List[langchain.schema.Document]) – 
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – 
kwargs (Any) – 
Return type
Tuple[str, dict]
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
combine_docs(docs, callbacks=None, **kwargs)[source]
Combine by mapping first chain over all, then stuffing into final chain.
Parameters
docs (List[langchain.schema.Document]) – 
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – 
kwargs (Any) – 
Return type
Tuple[str, dict]
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]
prompt_length(docs, **kwargs)
Return the prompt length given the documents passed in.
Returns None if the method does not depend on the prompt length.
Parameters
docs (List[langchain.schema.Document]) – 
kwargs (Any) – 
Return type
Optional[int]
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.