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5a4ee1ff70f3-2
include_run_info – Whether to include run info in the response. Defaults to False. async acall(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, include_run_info: bool = False) → Dict[str, Any]¶ Run the logic of this chain and add to output if desired. Parameters inputs – Dictionary of inputs, or single input if chain expects only one param. return_only_outputs – 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 – Callbacks to use for this chain run. If not provided, will use the callbacks provided to the chain. include_run_info – Whether to include run info in the response. Defaults to False. apply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[Dict[str, str]]¶ Call the chain on all inputs in the list. async arun(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, **kwargs: Any) → str¶ Run the chain as text in, text out or multiple variables, text out. dict(**kwargs: Any) → Dict¶ Return dictionary representation of chain. classmethod from_chain_type(llm: BaseLanguageModel, chain_type: str = 'stuff', chain_type_kwargs: Optional[dict] = None, **kwargs: Any) → BaseQAWithSourcesChain[source]¶ Load chain from chain type.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.qa_with_sources.base.BaseQAWithSourcesChain.html
5a4ee1ff70f3-3
classmethod from_llm(llm: BaseLanguageModel, document_prompt: BasePromptTemplate = 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: BasePromptTemplate = 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: BasePromptTemplate = 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
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.qa_with_sources.base.BaseQAWithSourcesChain.html
5a4ee1ff70f3-4
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
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.qa_with_sources.base.BaseQAWithSourcesChain.html
5a4ee1ff70f3-5
\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
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.qa_with_sources.base.BaseQAWithSourcesChain.html
5a4ee1ff70f3-6
\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.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.qa_with_sources.base.BaseQAWithSourcesChain.html
5a4ee1ff70f3-7
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: Any) → BaseQAWithSourcesChain[source]¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.qa_with_sources.base.BaseQAWithSourcesChain.html
5a4ee1ff70f3-8
Construct the chain from an LLM. prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]¶ Validate and prep inputs. prep_outputs(inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False) → Dict[str, str]¶ Validate and prep outputs. validator raise_deprecation  »  all fields¶ Raise deprecation warning if callback_manager is used. run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, **kwargs: Any) → str¶ Run the chain as text in, text out or multiple variables, text out. save(file_path: Union[Path, str]) → None¶ Save the chain. Parameters file_path – Path to file to save the chain to. Example: .. code-block:: python chain.save(file_path=”path/chain.yaml”) validator set_verbose  »  verbose¶ If verbose is None, set it. This allows users to pass in None as verbose to access the global setting. to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶ to_json_not_implemented() → SerializedNotImplemented¶ validator validate_naming  »  all fields[source]¶ Fix backwards compatability in naming. 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.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.qa_with_sources.base.BaseQAWithSourcesChain.html
5a4ee1ff70f3-9
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. model Config[source]¶ Bases: object Configuration for this pydantic object. arbitrary_types_allowed = True¶ extra = 'forbid'¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.qa_with_sources.base.BaseQAWithSourcesChain.html
2e89b7eb0dd0-0
langchain.chains.flare.base.QuestionGeneratorChain¶ class langchain.chains.flare.base.QuestionGeneratorChain(*, memory: Optional[BaseMemory] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, verbose: bool = None, tags: Optional[List[str]] = None, prompt: BasePromptTemplate = PromptTemplate(input_variables=['user_input', 'current_response', 'uncertain_span'], output_parser=None, partial_variables={}, template='Given a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:\n\n>>> USER INPUT: {user_input}\n>>> EXISTING PARTIAL RESPONSE: {current_response}\n\nThe question to which the answer is the term/entity/phrase "{uncertain_span}" is:', template_format='f-string', validate_template=True), llm: BaseLanguageModel, output_key: str = 'text', output_parser: BaseLLMOutputParser = None, return_final_only: bool = True, llm_kwargs: dict = None)[source]¶ Bases: LLMChain Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot be parsed to form a valid model. param callback_manager: Optional[BaseCallbackManager] = None¶ Deprecated, use callbacks instead. param callbacks: Callbacks = None¶ Optional list of callback handlers (or callback manager). Defaults to None. Callback handlers are called throughout the lifecycle of a call to a chain, starting with on_chain_start, ending with on_chain_end or on_chain_error. Each custom chain can optionally call additional callback methods, see Callback docs for full details. param llm: BaseLanguageModel [Required]¶ Language model to call.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.flare.base.QuestionGeneratorChain.html
2e89b7eb0dd0-1
param llm: BaseLanguageModel [Required]¶ Language model to call. param llm_kwargs: dict [Optional]¶ param memory: Optional[BaseMemory] = None¶ Optional memory object. Defaults to None. Memory is a class that gets called at the start and at the end of every chain. At the start, memory loads variables and passes them along in the chain. At the end, it saves any returned variables. There are many different types of memory - please see memory docs for the full catalog. param output_parser: BaseLLMOutputParser [Optional]¶ Output parser to use. Defaults to one that takes the most likely string but does not change it otherwise. param prompt: BasePromptTemplate = PromptTemplate(input_variables=['user_input', 'current_response', 'uncertain_span'], output_parser=None, partial_variables={}, template='Given a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase:\n\n>>> USER INPUT: {user_input}\n>>> EXISTING PARTIAL RESPONSE: {current_response}\n\nThe question to which the answer is the term/entity/phrase "{uncertain_span}" is:', template_format='f-string', validate_template=True)¶ Prompt object to use. param 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. param tags: Optional[List[str]] = None¶ Optional list of tags associated with the chain. Defaults to None These tags will be associated with each call to this chain, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a chain with its use case. param verbose: bool [Optional]¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.flare.base.QuestionGeneratorChain.html
2e89b7eb0dd0-2
param verbose: bool [Optional]¶ Whether or not run in verbose mode. In verbose mode, some intermediate logs will be printed to the console. Defaults to langchain.verbose value. __call__(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, include_run_info: bool = False) → Dict[str, Any]¶ Run the logic of this chain and add to output if desired. Parameters inputs – Dictionary of inputs, or single input if chain expects only one param. return_only_outputs – 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 – Callbacks to use for this chain run. If not provided, will use the callbacks provided to the chain. include_run_info – Whether to include run info in the response. Defaults to False. async aapply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[Dict[str, str]]¶ Utilize the LLM generate method for speed gains. async aapply_and_parse(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → Sequence[Union[str, List[str], Dict[str, str]]]¶ Call apply and then parse the results.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.flare.base.QuestionGeneratorChain.html
2e89b7eb0dd0-3
Call apply and then parse the results. async acall(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, include_run_info: bool = False) → Dict[str, Any]¶ Run the logic of this chain and add to output if desired. Parameters inputs – Dictionary of inputs, or single input if chain expects only one param. return_only_outputs – 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 – Callbacks to use for this chain run. If not provided, will use the callbacks provided to the chain. include_run_info – Whether to include run info in the response. Defaults to False. async agenerate(input_list: List[Dict[str, Any]], run_manager: Optional[AsyncCallbackManagerForChainRun] = None) → LLMResult¶ Generate LLM result from inputs. apply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[Dict[str, str]]¶ Utilize the LLM generate method for speed gains. apply_and_parse(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → Sequence[Union[str, List[str], Dict[str, str]]]¶ Call apply and then parse the results. async apredict(callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → str¶ Format prompt with kwargs and pass to LLM.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.flare.base.QuestionGeneratorChain.html
2e89b7eb0dd0-4
Format prompt with kwargs and pass to LLM. Parameters callbacks – Callbacks to pass to LLMChain **kwargs – Keys to pass to prompt template. Returns Completion from LLM. Example completion = llm.predict(adjective="funny") async apredict_and_parse(callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Union[str, List[str], Dict[str, str]]¶ Call apredict and then parse the results. async aprep_prompts(input_list: List[Dict[str, Any]], run_manager: Optional[AsyncCallbackManagerForChainRun] = None) → Tuple[List[PromptValue], Optional[List[str]]]¶ Prepare prompts from inputs. async arun(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, **kwargs: Any) → str¶ Run the chain as text in, text out or multiple variables, text out. create_outputs(llm_result: LLMResult) → List[Dict[str, Any]]¶ Create outputs from response. dict(**kwargs: Any) → Dict¶ Return dictionary representation of chain. classmethod from_string(llm: BaseLanguageModel, template: str) → LLMChain¶ Create LLMChain from LLM and template. generate(input_list: List[Dict[str, Any]], run_manager: Optional[CallbackManagerForChainRun] = None) → LLMResult¶ Generate LLM result from inputs. predict(callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → str¶ Format prompt with kwargs and pass to LLM. Parameters callbacks – Callbacks to pass to LLMChain **kwargs – Keys to pass to prompt template. Returns
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.flare.base.QuestionGeneratorChain.html
2e89b7eb0dd0-5
**kwargs – Keys to pass to prompt template. Returns Completion from LLM. Example completion = llm.predict(adjective="funny") predict_and_parse(callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Union[str, List[str], Dict[str, Any]]¶ Call predict and then parse the results. prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]¶ Validate and prep inputs. prep_outputs(inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False) → Dict[str, str]¶ Validate and prep outputs. prep_prompts(input_list: List[Dict[str, Any]], run_manager: Optional[CallbackManagerForChainRun] = None) → Tuple[List[PromptValue], Optional[List[str]]]¶ Prepare prompts from inputs. validator raise_deprecation  »  all fields¶ Raise deprecation warning if callback_manager is used. run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, **kwargs: Any) → str¶ Run the chain as text in, text out or multiple variables, text out. save(file_path: Union[Path, str]) → None¶ Save the chain. Parameters file_path – Path to file to save the chain to. Example: .. code-block:: python chain.save(file_path=”path/chain.yaml”) validator set_verbose  »  verbose¶ If verbose is None, set it. This allows users to pass in None as verbose to access the global setting. to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶ to_json_not_implemented() → SerializedNotImplemented¶ property lc_attributes: Dict¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.flare.base.QuestionGeneratorChain.html
2e89b7eb0dd0-6
to_json_not_implemented() → 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. model Config¶ Bases: object Configuration for this pydantic object. arbitrary_types_allowed = True¶ extra = 'forbid'¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.flare.base.QuestionGeneratorChain.html
9af991117cf4-0
langchain.chains.qa_with_sources.loading.LoadingCallable¶ class langchain.chains.qa_with_sources.loading.LoadingCallable(*args, **kwargs)[source]¶ Bases: Protocol Interface for loading the combine documents chain. Methods __init__(*args, **kwargs) __call__(llm: BaseLanguageModel, **kwargs: Any) → BaseCombineDocumentsChain[source]¶ Callable to load the combine documents chain.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.qa_with_sources.loading.LoadingCallable.html
cb0801196c5f-0
langchain.chains.router.llm_router.RouterOutputParser¶ class langchain.chains.router.llm_router.RouterOutputParser(*, default_destination: str = 'DEFAULT', next_inputs_type: ~typing.Type = <class 'str'>, next_inputs_inner_key: str = 'input')[source]¶ Bases: BaseOutputParser[Dict[str, str]] Parser for output of router chain int he multi-prompt chain. Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot be parsed to form a valid model. param default_destination: str = 'DEFAULT'¶ param next_inputs_inner_key: str = 'input'¶ param next_inputs_type: Type = <class 'str'>¶ dict(**kwargs: Any) → Dict¶ Return dictionary representation of output parser. get_format_instructions() → str¶ Instructions on how the LLM output should be formatted. parse(text: str) → Dict[str, Any][source]¶ Parse the output of an LLM call. A method which takes in a string (assumed output of a language model ) and parses it into some structure. Parameters text – output of language model Returns structured output parse_result(result: List[Generation]) → T¶ Parse LLM Result. parse_with_prompt(completion: str, prompt: PromptValue) → Any¶ Optional method to parse the output of an LLM call with a prompt. The prompt is largely provided in the event the OutputParser wants to retry or fix the output in some way, and needs information from the prompt to do so. Parameters completion – output of language model prompt – prompt value Returns structured output to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶ to_json_not_implemented() → SerializedNotImplemented¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.router.llm_router.RouterOutputParser.html
cb0801196c5f-1
to_json_not_implemented() → 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. model Config¶ Bases: object extra = 'ignore'¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.router.llm_router.RouterOutputParser.html
184f1e3ddc49-0
langchain.chains.qa_with_sources.base.QAWithSourcesChain¶ class langchain.chains.qa_with_sources.base.QAWithSourcesChain(*, memory: Optional[BaseMemory] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, verbose: bool = None, tags: Optional[List[str]] = None, combine_documents_chain: BaseCombineDocumentsChain, question_key: str = 'question', input_docs_key: str = 'docs', answer_key: str = 'answer', sources_answer_key: str = 'sources', return_source_documents: bool = False)[source]¶ Bases: BaseQAWithSourcesChain Question answering with sources over documents. Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot be parsed to form a valid model. param callback_manager: Optional[BaseCallbackManager] = None¶ Deprecated, use callbacks instead. param callbacks: Callbacks = None¶ Optional list of callback handlers (or callback manager). Defaults to None. Callback handlers are called throughout the lifecycle of a call to a chain, starting with on_chain_start, ending with on_chain_end or on_chain_error. Each custom chain can optionally call additional callback methods, see Callback docs for full details. param combine_documents_chain: BaseCombineDocumentsChain [Required]¶ Chain to use to combine documents. param memory: Optional[BaseMemory] = None¶ Optional memory object. Defaults to None. Memory is a class that gets called at the start and at the end of every chain. At the start, memory loads variables and passes them along in the chain. At the end, it saves any returned variables. There are many different types of memory - please see memory docs for the full catalog.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.qa_with_sources.base.QAWithSourcesChain.html
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There are many different types of memory - please see memory docs for the full catalog. param return_source_documents: bool = False¶ Return the source documents. param tags: Optional[List[str]] = None¶ Optional list of tags associated with the chain. Defaults to None These tags will be associated with each call to this chain, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a chain with its use case. param verbose: bool [Optional]¶ Whether or not run in verbose mode. In verbose mode, some intermediate logs will be printed to the console. Defaults to langchain.verbose value. __call__(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, include_run_info: bool = False) → Dict[str, Any]¶ Run the logic of this chain and add to output if desired. Parameters inputs – Dictionary of inputs, or single input if chain expects only one param. return_only_outputs – 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 – Callbacks to use for this chain run. If not provided, will use the callbacks provided to the chain. include_run_info – Whether to include run info in the response. Defaults to False.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.qa_with_sources.base.QAWithSourcesChain.html
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include_run_info – Whether to include run info in the response. Defaults to False. async acall(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, include_run_info: bool = False) → Dict[str, Any]¶ Run the logic of this chain and add to output if desired. Parameters inputs – Dictionary of inputs, or single input if chain expects only one param. return_only_outputs – 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 – Callbacks to use for this chain run. If not provided, will use the callbacks provided to the chain. include_run_info – Whether to include run info in the response. Defaults to False. apply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[Dict[str, str]]¶ Call the chain on all inputs in the list. async arun(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, **kwargs: Any) → str¶ Run the chain as text in, text out or multiple variables, text out. dict(**kwargs: Any) → Dict¶ Return dictionary representation of chain. classmethod from_chain_type(llm: BaseLanguageModel, chain_type: str = 'stuff', chain_type_kwargs: Optional[dict] = None, **kwargs: Any) → BaseQAWithSourcesChain¶ Load chain from chain type.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.qa_with_sources.base.QAWithSourcesChain.html
184f1e3ddc49-3
classmethod from_llm(llm: BaseLanguageModel, document_prompt: BasePromptTemplate = 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: BasePromptTemplate = 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: BasePromptTemplate = 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
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.qa_with_sources.base.QAWithSourcesChain.html
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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
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.qa_with_sources.base.QAWithSourcesChain.html
184f1e3ddc49-5
\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
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.qa_with_sources.base.QAWithSourcesChain.html
184f1e3ddc49-6
\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.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.qa_with_sources.base.QAWithSourcesChain.html
184f1e3ddc49-7
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: Any) → BaseQAWithSourcesChain¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.qa_with_sources.base.QAWithSourcesChain.html
184f1e3ddc49-8
Construct the chain from an LLM. prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]¶ Validate and prep inputs. prep_outputs(inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False) → Dict[str, str]¶ Validate and prep outputs. validator raise_deprecation  »  all fields¶ Raise deprecation warning if callback_manager is used. run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, **kwargs: Any) → str¶ Run the chain as text in, text out or multiple variables, text out. save(file_path: Union[Path, str]) → None¶ Save the chain. Parameters file_path – Path to file to save the chain to. Example: .. code-block:: python chain.save(file_path=”path/chain.yaml”) validator set_verbose  »  verbose¶ If verbose is None, set it. This allows users to pass in None as verbose to access the global setting. to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶ to_json_not_implemented() → SerializedNotImplemented¶ validator validate_naming  »  all fields¶ Fix backwards compatability in naming. 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.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.qa_with_sources.base.QAWithSourcesChain.html
184f1e3ddc49-9
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. model Config¶ Bases: object Configuration for this pydantic object. arbitrary_types_allowed = True¶ extra = 'forbid'¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.qa_with_sources.base.QAWithSourcesChain.html
89d7430796a8-0
langchain.chains.openai_functions.qa_with_structure.create_qa_with_structure_chain¶ langchain.chains.openai_functions.qa_with_structure.create_qa_with_structure_chain(llm: BaseLanguageModel, schema: Union[dict, Type[BaseModel]], output_parser: str = 'base', prompt: Optional[Union[PromptTemplate, ChatPromptTemplate]] = None) → LLMChain[source]¶ Create a question answering chain that returns an answer with sources. Parameters llm – Language model to use for the chain. schema – Pydantic schema to use for the output. output_parser – Output parser to use. Should be one of pydantic or base. Default to base. prompt – Optional prompt to use for the chain. Returns:
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.openai_functions.qa_with_structure.create_qa_with_structure_chain.html
ddb24bff2be0-0
langchain.chains.query_constructor.ir.Expr¶ class langchain.chains.query_constructor.ir.Expr[source]¶ Bases: BaseModel Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot be parsed to form a valid model. accept(visitor: Visitor) → Any[source]¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.query_constructor.ir.Expr.html
1d2471e71a5a-0
langchain.chains.prompt_selector.ConditionalPromptSelector¶ class langchain.chains.prompt_selector.ConditionalPromptSelector(*, default_prompt: BasePromptTemplate, conditionals: List[Tuple[Callable[[BaseLanguageModel], bool], BasePromptTemplate]] = None)[source]¶ Bases: BasePromptSelector Prompt collection that goes through conditionals. Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot be parsed to form a valid model. param conditionals: List[Tuple[Callable[[langchain.base_language.BaseLanguageModel], bool], langchain.prompts.base.BasePromptTemplate]] [Optional]¶ param default_prompt: langchain.prompts.base.BasePromptTemplate [Required]¶ get_prompt(llm: BaseLanguageModel) → BasePromptTemplate[source]¶ Get default prompt for a language model.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.prompt_selector.ConditionalPromptSelector.html
690fe996f798-0
langchain.chains.loading.load_chain_from_config¶ langchain.chains.loading.load_chain_from_config(config: dict, **kwargs: Any) → Chain[source]¶ Load chain from Config Dict.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.loading.load_chain_from_config.html
a5ba814752ef-0
langchain.chains.question_answering.__init__.load_qa_chain¶ langchain.chains.question_answering.__init__.load_qa_chain(llm: BaseLanguageModel, chain_type: str = 'stuff', verbose: Optional[bool] = None, callback_manager: Optional[BaseCallbackManager] = None, **kwargs: Any) → BaseCombineDocumentsChain[source]¶ Load question answering chain. Parameters llm – Language Model to use in the chain. chain_type – Type of document combining chain to use. Should be one of “stuff”, “map_reduce”, “map_rerank”, and “refine”. verbose – Whether chains should be run in verbose mode or not. Note that this applies to all chains that make up the final chain. callback_manager – Callback manager to use for the chain. Returns A chain to use for question answering.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.question_answering.__init__.load_qa_chain.html
338457bc5331-0
langchain.chains.query_constructor.ir.StructuredQuery¶ class langchain.chains.query_constructor.ir.StructuredQuery(*, query: str, filter: Optional[FilterDirective] = None, limit: Optional[int] = None)[source]¶ Bases: Expr Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot be parsed to form a valid model. param filter: Optional[langchain.chains.query_constructor.ir.FilterDirective] = None¶ param limit: Optional[int] = None¶ param query: str [Required]¶ accept(visitor: Visitor) → Any¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.query_constructor.ir.StructuredQuery.html
6364f872f66b-0
langchain.chains.query_constructor.ir.Comparator¶ class langchain.chains.query_constructor.ir.Comparator(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶ Bases: str, Enum Enumerator of the comparison operators. Methods __init__(*args, **kwds) capitalize() Return a capitalized version of the string. casefold() Return a version of the string suitable for caseless comparisons. center(width[, fillchar]) Return a centered string of length width. count(sub[, start[, end]]) Return the number of non-overlapping occurrences of substring sub in string S[start:end]. encode([encoding, errors]) Encode the string using the codec registered for encoding. endswith(suffix[, start[, end]]) Return True if S ends with the specified suffix, False otherwise. expandtabs([tabsize]) Return a copy where all tab characters are expanded using spaces. find(sub[, start[, end]]) Return the lowest index in S where substring sub is found, such that sub is contained within S[start:end]. format(*args, **kwargs) Return a formatted version of S, using substitutions from args and kwargs. format_map(mapping) Return a formatted version of S, using substitutions from mapping. index(sub[, start[, end]]) Return the lowest index in S where substring sub is found, such that sub is contained within S[start:end]. isalnum() Return True if the string is an alpha-numeric string, False otherwise. isalpha() Return True if the string is an alphabetic string, False otherwise. isascii() Return True if all characters in the string are ASCII, False otherwise. isdecimal() Return True if the string is a decimal string, False otherwise.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.query_constructor.ir.Comparator.html
6364f872f66b-1
isdecimal() Return True if the string is a decimal string, False otherwise. isdigit() Return True if the string is a digit string, False otherwise. isidentifier() Return True if the string is a valid Python identifier, False otherwise. islower() Return True if the string is a lowercase string, False otherwise. isnumeric() Return True if the string is a numeric string, False otherwise. isprintable() Return True if the string is printable, False otherwise. isspace() Return True if the string is a whitespace string, False otherwise. istitle() Return True if the string is a title-cased string, False otherwise. isupper() Return True if the string is an uppercase string, False otherwise. join(iterable, /) Concatenate any number of strings. ljust(width[, fillchar]) Return a left-justified string of length width. lower() Return a copy of the string converted to lowercase. lstrip([chars]) Return a copy of the string with leading whitespace removed. maketrans Return a translation table usable for str.translate(). partition(sep, /) Partition the string into three parts using the given separator. removeprefix(prefix, /) Return a str with the given prefix string removed if present. removesuffix(suffix, /) Return a str with the given suffix string removed if present. replace(old, new[, count]) Return a copy with all occurrences of substring old replaced by new. rfind(sub[, start[, end]]) Return the highest index in S where substring sub is found, such that sub is contained within S[start:end]. rindex(sub[, start[, end]]) Return the highest index in S where substring sub is found, such that sub is contained within S[start:end].
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.query_constructor.ir.Comparator.html
6364f872f66b-2
rjust(width[, fillchar]) Return a right-justified string of length width. rpartition(sep, /) Partition the string into three parts using the given separator. rsplit([sep, maxsplit]) Return a list of the substrings in the string, using sep as the separator string. rstrip([chars]) Return a copy of the string with trailing whitespace removed. split([sep, maxsplit]) Return a list of the substrings in the string, using sep as the separator string. splitlines([keepends]) Return a list of the lines in the string, breaking at line boundaries. startswith(prefix[, start[, end]]) Return True if S starts with the specified prefix, False otherwise. strip([chars]) Return a copy of the string with leading and trailing whitespace removed. swapcase() Convert uppercase characters to lowercase and lowercase characters to uppercase. title() Return a version of the string where each word is titlecased. translate(table, /) Replace each character in the string using the given translation table. upper() Return a copy of the string converted to uppercase. zfill(width, /) Pad a numeric string with zeros on the left, to fill a field of the given width. Attributes EQ GT GTE LT LTE CONTAIN LIKE capitalize()¶ Return a capitalized version of the string. More specifically, make the first character have upper case and the rest lower case. casefold()¶ Return a version of the string suitable for caseless comparisons. center(width, fillchar=' ', /)¶ Return a centered string of length width. Padding is done using the specified fill character (default is a space). count(sub[, start[, end]]) → int¶ Return the number of non-overlapping occurrences of substring sub in
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.query_constructor.ir.Comparator.html
6364f872f66b-3
Return the number of non-overlapping occurrences of substring sub in string S[start:end]. Optional arguments start and end are interpreted as in slice notation. encode(encoding='utf-8', errors='strict')¶ Encode the string using the codec registered for encoding. encodingThe encoding in which to encode the string. errorsThe error handling scheme to use for encoding errors. The default is ‘strict’ meaning that encoding errors raise a UnicodeEncodeError. Other possible values are ‘ignore’, ‘replace’ and ‘xmlcharrefreplace’ as well as any other name registered with codecs.register_error that can handle UnicodeEncodeErrors. endswith(suffix[, start[, end]]) → bool¶ Return True if S ends with the specified suffix, False otherwise. With optional start, test S beginning at that position. With optional end, stop comparing S at that position. suffix can also be a tuple of strings to try. expandtabs(tabsize=8)¶ Return a copy where all tab characters are expanded using spaces. If tabsize is not given, a tab size of 8 characters is assumed. find(sub[, start[, end]]) → int¶ Return the lowest index in S where substring sub is found, such that sub is contained within S[start:end]. Optional arguments start and end are interpreted as in slice notation. Return -1 on failure. format(*args, **kwargs) → str¶ Return a formatted version of S, using substitutions from args and kwargs. The substitutions are identified by braces (‘{’ and ‘}’). format_map(mapping) → str¶ Return a formatted version of S, using substitutions from mapping. The substitutions are identified by braces (‘{’ and ‘}’). index(sub[, start[, end]]) → int¶ Return the lowest index in S where substring sub is found,
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.query_constructor.ir.Comparator.html
6364f872f66b-4
Return the lowest index in S where substring sub is found, such that sub is contained within S[start:end]. Optional arguments start and end are interpreted as in slice notation. Raises ValueError when the substring is not found. isalnum()¶ Return True if the string is an alpha-numeric string, False otherwise. A string is alpha-numeric if all characters in the string are alpha-numeric and there is at least one character in the string. isalpha()¶ Return True if the string is an alphabetic string, False otherwise. A string is alphabetic if all characters in the string are alphabetic and there is at least one character in the string. isascii()¶ Return True if all characters in the string are ASCII, False otherwise. ASCII characters have code points in the range U+0000-U+007F. Empty string is ASCII too. isdecimal()¶ Return True if the string is a decimal string, False otherwise. A string is a decimal string if all characters in the string are decimal and there is at least one character in the string. isdigit()¶ Return True if the string is a digit string, False otherwise. A string is a digit string if all characters in the string are digits and there is at least one character in the string. isidentifier()¶ Return True if the string is a valid Python identifier, False otherwise. Call keyword.iskeyword(s) to test whether string s is a reserved identifier, such as “def” or “class”. islower()¶ Return True if the string is a lowercase string, False otherwise. A string is lowercase if all cased characters in the string are lowercase and there is at least one cased character in the string. isnumeric()¶ Return True if the string is a numeric string, False otherwise.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.query_constructor.ir.Comparator.html
6364f872f66b-5
isnumeric()¶ Return True if the string is a numeric string, False otherwise. A string is numeric if all characters in the string are numeric and there is at least one character in the string. isprintable()¶ Return True if the string is printable, False otherwise. A string is printable if all of its characters are considered printable in repr() or if it is empty. isspace()¶ Return True if the string is a whitespace string, False otherwise. A string is whitespace if all characters in the string are whitespace and there is at least one character in the string. istitle()¶ Return True if the string is a title-cased string, False otherwise. In a title-cased string, upper- and title-case characters may only follow uncased characters and lowercase characters only cased ones. isupper()¶ Return True if the string is an uppercase string, False otherwise. A string is uppercase if all cased characters in the string are uppercase and there is at least one cased character in the string. join(iterable, /)¶ Concatenate any number of strings. The string whose method is called is inserted in between each given string. The result is returned as a new string. Example: ‘.’.join([‘ab’, ‘pq’, ‘rs’]) -> ‘ab.pq.rs’ ljust(width, fillchar=' ', /)¶ Return a left-justified string of length width. Padding is done using the specified fill character (default is a space). lower()¶ Return a copy of the string converted to lowercase. lstrip(chars=None, /)¶ Return a copy of the string with leading whitespace removed. If chars is given and not None, remove characters in chars instead. static maketrans()¶ Return a translation table usable for str.translate().
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.query_constructor.ir.Comparator.html
6364f872f66b-6
static maketrans()¶ Return a translation table usable for str.translate(). If there is only one argument, it must be a dictionary mapping Unicode ordinals (integers) or characters to Unicode ordinals, strings or None. Character keys will be then converted to ordinals. If there are two arguments, they must be strings of equal length, and in the resulting dictionary, each character in x will be mapped to the character at the same position in y. If there is a third argument, it must be a string, whose characters will be mapped to None in the result. partition(sep, /)¶ Partition the string into three parts using the given separator. This will search for the separator in the string. If the separator is found, returns a 3-tuple containing the part before the separator, the separator itself, and the part after it. If the separator is not found, returns a 3-tuple containing the original string and two empty strings. removeprefix(prefix, /)¶ Return a str with the given prefix string removed if present. If the string starts with the prefix string, return string[len(prefix):]. Otherwise, return a copy of the original string. removesuffix(suffix, /)¶ Return a str with the given suffix string removed if present. If the string ends with the suffix string and that suffix is not empty, return string[:-len(suffix)]. Otherwise, return a copy of the original string. replace(old, new, count=- 1, /)¶ Return a copy with all occurrences of substring old replaced by new. countMaximum number of occurrences to replace. -1 (the default value) means replace all occurrences. If the optional argument count is given, only the first count occurrences are replaced. rfind(sub[, start[, end]]) → int¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.query_constructor.ir.Comparator.html
6364f872f66b-7
replaced. rfind(sub[, start[, end]]) → int¶ Return the highest index in S where substring sub is found, such that sub is contained within S[start:end]. Optional arguments start and end are interpreted as in slice notation. Return -1 on failure. rindex(sub[, start[, end]]) → int¶ Return the highest index in S where substring sub is found, such that sub is contained within S[start:end]. Optional arguments start and end are interpreted as in slice notation. Raises ValueError when the substring is not found. rjust(width, fillchar=' ', /)¶ Return a right-justified string of length width. Padding is done using the specified fill character (default is a space). rpartition(sep, /)¶ Partition the string into three parts using the given separator. This will search for the separator in the string, starting at the end. If the separator is found, returns a 3-tuple containing the part before the separator, the separator itself, and the part after it. If the separator is not found, returns a 3-tuple containing two empty strings and the original string. rsplit(sep=None, maxsplit=- 1)¶ Return a list of the substrings in the string, using sep as the separator string. sepThe separator used to split the string. When set to None (the default value), will split on any whitespace character (including \n \r \t \f and spaces) and will discard empty strings from the result. maxsplitMaximum number of splits (starting from the left). -1 (the default value) means no limit. Splitting starts at the end of the string and works to the front. rstrip(chars=None, /)¶ Return a copy of the string with trailing whitespace removed.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.query_constructor.ir.Comparator.html
6364f872f66b-8
rstrip(chars=None, /)¶ Return a copy of the string with trailing whitespace removed. If chars is given and not None, remove characters in chars instead. split(sep=None, maxsplit=- 1)¶ Return a list of the substrings in the string, using sep as the separator string. sepThe separator used to split the string. When set to None (the default value), will split on any whitespace character (including \n \r \t \f and spaces) and will discard empty strings from the result. maxsplitMaximum number of splits (starting from the left). -1 (the default value) means no limit. Note, str.split() is mainly useful for data that has been intentionally delimited. With natural text that includes punctuation, consider using the regular expression module. splitlines(keepends=False)¶ Return a list of the lines in the string, breaking at line boundaries. Line breaks are not included in the resulting list unless keepends is given and true. startswith(prefix[, start[, end]]) → bool¶ Return True if S starts with the specified prefix, False otherwise. With optional start, test S beginning at that position. With optional end, stop comparing S at that position. prefix can also be a tuple of strings to try. strip(chars=None, /)¶ Return a copy of the string with leading and trailing whitespace removed. If chars is given and not None, remove characters in chars instead. swapcase()¶ Convert uppercase characters to lowercase and lowercase characters to uppercase. title()¶ Return a version of the string where each word is titlecased. More specifically, words start with uppercased characters and all remaining cased characters have lower case. translate(table, /)¶ Replace each character in the string using the given translation table.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.query_constructor.ir.Comparator.html
6364f872f66b-9
translate(table, /)¶ Replace each character in the string using the given translation table. tableTranslation table, which must be a mapping of Unicode ordinals to Unicode ordinals, strings, or None. The table must implement lookup/indexing via __getitem__, for instance a dictionary or list. If this operation raises LookupError, the character is left untouched. Characters mapped to None are deleted. upper()¶ Return a copy of the string converted to uppercase. zfill(width, /)¶ Pad a numeric string with zeros on the left, to fill a field of the given width. The string is never truncated. CONTAIN = 'contain'¶ EQ = 'eq'¶ GT = 'gt'¶ GTE = 'gte'¶ LIKE = 'like'¶ LT = 'lt'¶ LTE = 'lte'¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.query_constructor.ir.Comparator.html
58af5c8350d7-0
langchain.chains.query_constructor.schema.AttributeInfo¶ class langchain.chains.query_constructor.schema.AttributeInfo(*, name: str, description: str, type: str)[source]¶ Bases: BaseModel Information about a data source attribute. Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot be parsed to form a valid model. param description: str [Required]¶ param name: str [Required]¶ param type: str [Required]¶ model Config[source]¶ Bases: object Configuration for this pydantic object. arbitrary_types_allowed = True¶ frozen = True¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.query_constructor.schema.AttributeInfo.html
e17d12f16e46-0
langchain.chains.combine_documents.stuff.StuffDocumentsChain¶ class langchain.chains.combine_documents.stuff.StuffDocumentsChain(*, memory: Optional[BaseMemory] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, verbose: bool = None, tags: Optional[List[str]] = None, input_key: str = 'input_documents', output_key: str = 'output_text', llm_chain: LLMChain, document_prompt: BasePromptTemplate = None, document_variable_name: str, document_separator: str = '\n\n')[source]¶ Bases: BaseCombineDocumentsChain Chain that combines documents by stuffing into context. Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot be parsed to form a valid model. param callback_manager: Optional[BaseCallbackManager] = None¶ Deprecated, use callbacks instead. param callbacks: Callbacks = None¶ Optional list of callback handlers (or callback manager). Defaults to None. Callback handlers are called throughout the lifecycle of a call to a chain, starting with on_chain_start, ending with on_chain_end or on_chain_error. Each custom chain can optionally call additional callback methods, see Callback docs for full details. param document_prompt: langchain.prompts.base.BasePromptTemplate [Optional]¶ Prompt to use to format each document. param document_separator: str = '\n\n'¶ The string with which to join the formatted documents param 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. param llm_chain: langchain.chains.llm.LLMChain [Required]¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.combine_documents.stuff.StuffDocumentsChain.html
e17d12f16e46-1
param llm_chain: langchain.chains.llm.LLMChain [Required]¶ LLM wrapper to use after formatting documents. param memory: Optional[BaseMemory] = None¶ Optional memory object. Defaults to None. Memory is a class that gets called at the start and at the end of every chain. At the start, memory loads variables and passes them along in the chain. At the end, it saves any returned variables. There are many different types of memory - please see memory docs for the full catalog. param tags: Optional[List[str]] = None¶ Optional list of tags associated with the chain. Defaults to None These tags will be associated with each call to this chain, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a chain with its use case. param verbose: bool [Optional]¶ Whether or not run in verbose mode. In verbose mode, some intermediate logs will be printed to the console. Defaults to langchain.verbose value. __call__(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, include_run_info: bool = False) → Dict[str, Any]¶ Run the logic of this chain and add to output if desired. Parameters inputs – Dictionary of inputs, or single input if chain expects only one param. return_only_outputs – 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 – Callbacks to use for this chain run. If not provided, will
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.combine_documents.stuff.StuffDocumentsChain.html
e17d12f16e46-2
callbacks – Callbacks to use for this chain run. If not provided, will use the callbacks provided to the chain. include_run_info – Whether to include run info in the response. Defaults to False. async acall(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, include_run_info: bool = False) → Dict[str, Any]¶ Run the logic of this chain and add to output if desired. Parameters inputs – Dictionary of inputs, or single input if chain expects only one param. return_only_outputs – 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 – Callbacks to use for this chain run. If not provided, will use the callbacks provided to the chain. include_run_info – Whether to include run info in the response. Defaults to False. async acombine_docs(docs: List[Document], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Tuple[str, dict][source]¶ Stuff all documents into one prompt and pass to LLM. apply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[Dict[str, str]]¶ Call the chain on all inputs in the list. async arun(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, **kwargs: Any) → str¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.combine_documents.stuff.StuffDocumentsChain.html
e17d12f16e46-3
Run the chain as text in, text out or multiple variables, text out. combine_docs(docs: List[Document], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Tuple[str, dict][source]¶ Stuff all documents into one prompt and pass to LLM. dict(**kwargs: Any) → Dict¶ Return dictionary representation of chain. validator get_default_document_variable_name  »  all fields[source]¶ Get default document variable name, if not provided. prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]¶ Validate and prep inputs. prep_outputs(inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False) → Dict[str, str]¶ Validate and prep outputs. prompt_length(docs: List[Document], **kwargs: Any) → Optional[int][source]¶ Get the prompt length by formatting the prompt. validator raise_deprecation  »  all fields¶ Raise deprecation warning if callback_manager is used. run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, **kwargs: Any) → str¶ Run the chain as text in, text out or multiple variables, text out. save(file_path: Union[Path, str]) → None¶ Save the chain. Parameters file_path – Path to file to save the chain to. Example: .. code-block:: python chain.save(file_path=”path/chain.yaml”) validator set_verbose  »  verbose¶ If verbose is None, set it. This allows users to pass in None as verbose to access the global setting. to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.combine_documents.stuff.StuffDocumentsChain.html
e17d12f16e46-4
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶ to_json_not_implemented() → 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. model Config[source]¶ Bases: object Configuration for this pydantic object. arbitrary_types_allowed = True¶ extra = 'forbid'¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.combine_documents.stuff.StuffDocumentsChain.html
494de32ea590-0
langchain.chains.combine_documents.map_reduce.CombineDocsProtocol¶ class langchain.chains.combine_documents.map_reduce.CombineDocsProtocol(*args, **kwargs)[source]¶ Bases: Protocol Interface for the combine_docs method. Methods __init__(*args, **kwargs) __call__(docs: List[Document], **kwargs: Any) → str[source]¶ Interface for the combine_docs method.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.combine_documents.map_reduce.CombineDocsProtocol.html
c6e571ae23ba-0
langchain.chains.graph_qa.base.GraphQAChain¶ class langchain.chains.graph_qa.base.GraphQAChain(*, memory: Optional[BaseMemory] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, verbose: bool = None, tags: Optional[List[str]] = None, graph: NetworkxEntityGraph, entity_extraction_chain: LLMChain, qa_chain: LLMChain, input_key: str = 'query', output_key: str = 'result')[source]¶ Bases: Chain Chain for question-answering against a graph. Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot be parsed to form a valid model. param callback_manager: Optional[BaseCallbackManager] = None¶ Deprecated, use callbacks instead. param callbacks: Callbacks = None¶ Optional list of callback handlers (or callback manager). Defaults to None. Callback handlers are called throughout the lifecycle of a call to a chain, starting with on_chain_start, ending with on_chain_end or on_chain_error. Each custom chain can optionally call additional callback methods, see Callback docs for full details. param entity_extraction_chain: LLMChain [Required]¶ param graph: NetworkxEntityGraph [Required]¶ param memory: Optional[BaseMemory] = None¶ Optional memory object. Defaults to None. Memory is a class that gets called at the start and at the end of every chain. At the start, memory loads variables and passes them along in the chain. At the end, it saves any returned variables. There are many different types of memory - please see memory docs for the full catalog. param qa_chain: LLMChain [Required]¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.graph_qa.base.GraphQAChain.html
c6e571ae23ba-1
for the full catalog. param qa_chain: LLMChain [Required]¶ param tags: Optional[List[str]] = None¶ Optional list of tags associated with the chain. Defaults to None These tags will be associated with each call to this chain, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a chain with its use case. param verbose: bool [Optional]¶ Whether or not run in verbose mode. In verbose mode, some intermediate logs will be printed to the console. Defaults to langchain.verbose value. __call__(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, include_run_info: bool = False) → Dict[str, Any]¶ Run the logic of this chain and add to output if desired. Parameters inputs – Dictionary of inputs, or single input if chain expects only one param. return_only_outputs – 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 – Callbacks to use for this chain run. If not provided, will use the callbacks provided to the chain. include_run_info – Whether to include run info in the response. Defaults to False. async acall(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, include_run_info: bool = False) → Dict[str, Any]¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.graph_qa.base.GraphQAChain.html
c6e571ae23ba-2
Run the logic of this chain and add to output if desired. Parameters inputs – Dictionary of inputs, or single input if chain expects only one param. return_only_outputs – 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 – Callbacks to use for this chain run. If not provided, will use the callbacks provided to the chain. include_run_info – Whether to include run info in the response. Defaults to False. apply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[Dict[str, str]]¶ Call the chain on all inputs in the list. async arun(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, **kwargs: Any) → str¶ Run the chain as text in, text out or multiple variables, text out. dict(**kwargs: Any) → Dict¶ Return dictionary representation of chain.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.graph_qa.base.GraphQAChain.html
c6e571ae23ba-3
dict(**kwargs: Any) → Dict¶ Return dictionary representation of chain. classmethod from_llm(llm: BaseLanguageModel, qa_prompt: BasePromptTemplate = 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: BasePromptTemplate = 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: Any) → GraphQAChain[source]¶ Initialize from LLM. prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]¶ Validate and prep inputs. prep_outputs(inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False) → Dict[str, str]¶ Validate and prep outputs.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.graph_qa.base.GraphQAChain.html
c6e571ae23ba-4
Validate and prep outputs. validator raise_deprecation  »  all fields¶ Raise deprecation warning if callback_manager is used. run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, **kwargs: Any) → str¶ Run the chain as text in, text out or multiple variables, text out. save(file_path: Union[Path, str]) → None¶ Save the chain. Parameters file_path – Path to file to save the chain to. Example: .. code-block:: python chain.save(file_path=”path/chain.yaml”) validator set_verbose  »  verbose¶ If verbose is None, set it. This allows users to pass in None as verbose to access the global setting. to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶ to_json_not_implemented() → 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. model Config¶ Bases: object Configuration for this pydantic object. arbitrary_types_allowed = True¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.graph_qa.base.GraphQAChain.html
a5290f139821-0
langchain.chains.retrieval_qa.base.BaseRetrievalQA¶ class langchain.chains.retrieval_qa.base.BaseRetrievalQA(*, memory: Optional[BaseMemory] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, verbose: bool = None, tags: Optional[List[str]] = None, combine_documents_chain: BaseCombineDocumentsChain, input_key: str = 'query', output_key: str = 'result', return_source_documents: bool = False)[source]¶ Bases: Chain Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot be parsed to form a valid model. param callback_manager: Optional[BaseCallbackManager] = None¶ Deprecated, use callbacks instead. param callbacks: Callbacks = None¶ Optional list of callback handlers (or callback manager). Defaults to None. Callback handlers are called throughout the lifecycle of a call to a chain, starting with on_chain_start, ending with on_chain_end or on_chain_error. Each custom chain can optionally call additional callback methods, see Callback docs for full details. param combine_documents_chain: BaseCombineDocumentsChain [Required]¶ Chain to use to combine the documents. param memory: Optional[BaseMemory] = None¶ Optional memory object. Defaults to None. Memory is a class that gets called at the start and at the end of every chain. At the start, memory loads variables and passes them along in the chain. At the end, it saves any returned variables. There are many different types of memory - please see memory docs for the full catalog. param return_source_documents: bool = False¶ Return the source documents. param tags: Optional[List[str]] = None¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.retrieval_qa.base.BaseRetrievalQA.html
a5290f139821-1
Return the source documents. param tags: Optional[List[str]] = None¶ Optional list of tags associated with the chain. Defaults to None These tags will be associated with each call to this chain, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a chain with its use case. param verbose: bool [Optional]¶ Whether or not run in verbose mode. In verbose mode, some intermediate logs will be printed to the console. Defaults to langchain.verbose value. __call__(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, include_run_info: bool = False) → Dict[str, Any]¶ Run the logic of this chain and add to output if desired. Parameters inputs – Dictionary of inputs, or single input if chain expects only one param. return_only_outputs – 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 – Callbacks to use for this chain run. If not provided, will use the callbacks provided to the chain. include_run_info – Whether to include run info in the response. Defaults to False. async acall(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, include_run_info: bool = False) → Dict[str, Any]¶ Run the logic of this chain and add to output if desired. Parameters
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.retrieval_qa.base.BaseRetrievalQA.html
a5290f139821-2
Run the logic of this chain and add to output if desired. Parameters inputs – Dictionary of inputs, or single input if chain expects only one param. return_only_outputs – 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 – Callbacks to use for this chain run. If not provided, will use the callbacks provided to the chain. include_run_info – Whether to include run info in the response. Defaults to False. apply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[Dict[str, str]]¶ Call the chain on all inputs in the list. async arun(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, **kwargs: Any) → str¶ Run the chain as text in, text out or multiple variables, text out. dict(**kwargs: Any) → Dict¶ Return dictionary representation of chain. classmethod from_chain_type(llm: BaseLanguageModel, chain_type: str = 'stuff', chain_type_kwargs: Optional[dict] = None, **kwargs: Any) → BaseRetrievalQA[source]¶ Load chain from chain type. classmethod from_llm(llm: BaseLanguageModel, prompt: Optional[PromptTemplate] = None, **kwargs: Any) → BaseRetrievalQA[source]¶ Initialize from LLM. prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]¶ Validate and prep inputs.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.retrieval_qa.base.BaseRetrievalQA.html
a5290f139821-3
Validate and prep inputs. prep_outputs(inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False) → Dict[str, str]¶ Validate and prep outputs. validator raise_deprecation  »  all fields¶ Raise deprecation warning if callback_manager is used. run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, **kwargs: Any) → str¶ Run the chain as text in, text out or multiple variables, text out. save(file_path: Union[Path, str]) → None¶ Save the chain. Parameters file_path – Path to file to save the chain to. Example: .. code-block:: python chain.save(file_path=”path/chain.yaml”) validator set_verbose  »  verbose¶ If verbose is None, set it. This allows users to pass in None as verbose to access the global setting. to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶ to_json_not_implemented() → 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. model Config[source]¶ Bases: object Configuration for this pydantic object. allow_population_by_field_name = True¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.retrieval_qa.base.BaseRetrievalQA.html
a5290f139821-4
Configuration for this pydantic object. allow_population_by_field_name = True¶ arbitrary_types_allowed = True¶ extra = 'forbid'¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.retrieval_qa.base.BaseRetrievalQA.html
c9ad42f93f51-0
langchain.chains.natbot.crawler.ElementInViewPort¶ class langchain.chains.natbot.crawler.ElementInViewPort[source]¶ Bases: TypedDict A typed dictionary containing information about elements in the viewport. Methods __init__(*args, **kwargs) clear() copy() fromkeys([value]) Create a new dictionary with keys from iterable and values set to value. get(key[, default]) Return the value for key if key is in the dictionary, else default. items() keys() pop(k[,d]) If the key is not found, return the default if given; otherwise, raise a KeyError. popitem() Remove and return a (key, value) pair as a 2-tuple. setdefault(key[, default]) Insert key with a value of default if key is not in the dictionary. update([E, ]**F) If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k] values() Attributes node_index backend_node_id node_name node_value node_meta is_clickable origin_x origin_y center_x center_y clear() → None.  Remove all items from D.¶ copy() → a shallow copy of D¶ fromkeys(value=None, /)¶ Create a new dictionary with keys from iterable and values set to value. get(key, default=None, /)¶ Return the value for key if key is in the dictionary, else default.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.natbot.crawler.ElementInViewPort.html
c9ad42f93f51-1
Return the value for key if key is in the dictionary, else default. items() → a set-like object providing a view on D's items¶ keys() → a set-like object providing a view on D's keys¶ pop(k[, d]) → v, remove specified key and return the corresponding value.¶ If the key is not found, return the default if given; otherwise, raise a KeyError. popitem()¶ Remove and return a (key, value) pair as a 2-tuple. Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty. setdefault(key, default=None, /)¶ Insert key with a value of default if key is not in the dictionary. Return the value for key if key is in the dictionary, else default. update([E, ]**F) → None.  Update D from dict/iterable E and F.¶ If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k] values() → an object providing a view on D's values¶ backend_node_id: int¶ center_x: int¶ center_y: int¶ is_clickable: bool¶ node_index: str¶ node_meta: List[str]¶ node_name: Optional[str]¶ node_value: Optional[str]¶ origin_x: int¶ origin_y: int¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.natbot.crawler.ElementInViewPort.html
9eea31dfd92e-0
langchain.chains.llm_checker.base.LLMCheckerChain¶ class langchain.chains.llm_checker.base.LLMCheckerChain(*, memory: Optional[BaseMemory] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, verbose: bool = None, tags: Optional[List[str]] = None, question_to_checked_assertions_chain: SequentialChain, llm: Optional[BaseLanguageModel] = None, 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), 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), 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), 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), input_key: str = 'query', output_key: str = 'result')[source]¶ Bases: Chain Chain for question-answering with self-verification. Example
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.llm_checker.base.LLMCheckerChain.html
9eea31dfd92e-1
Bases: 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) Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot be parsed to form a valid model. param callback_manager: Optional[BaseCallbackManager] = None¶ Deprecated, use callbacks instead. param callbacks: Callbacks = None¶ Optional list of callback handlers (or callback manager). Defaults to None. Callback handlers are called throughout the lifecycle of a call to a chain, starting with on_chain_start, ending with on_chain_end or on_chain_error. Each custom chain can optionally call additional callback methods, see Callback docs for full details. param 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] param 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] param 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] param llm: Optional[BaseLanguageModel] = None¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.llm_checker.base.LLMCheckerChain.html
9eea31dfd92e-2
[Deprecated] param llm: Optional[BaseLanguageModel] = None¶ [Deprecated] LLM wrapper to use. param memory: Optional[BaseMemory] = None¶ Optional memory object. Defaults to None. Memory is a class that gets called at the start and at the end of every chain. At the start, memory loads variables and passes them along in the chain. At the end, it saves any returned variables. There are many different types of memory - please see memory docs for the full catalog. param question_to_checked_assertions_chain: SequentialChain [Required]¶ param 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. param tags: Optional[List[str]] = None¶ Optional list of tags associated with the chain. Defaults to None These tags will be associated with each call to this chain, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a chain with its use case. param verbose: bool [Optional]¶ Whether or not run in verbose mode. In verbose mode, some intermediate logs will be printed to the console. Defaults to langchain.verbose value. __call__(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, include_run_info: bool = False) → Dict[str, Any]¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.llm_checker.base.LLMCheckerChain.html
9eea31dfd92e-3
Run the logic of this chain and add to output if desired. Parameters inputs – Dictionary of inputs, or single input if chain expects only one param. return_only_outputs – 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 – Callbacks to use for this chain run. If not provided, will use the callbacks provided to the chain. include_run_info – Whether to include run info in the response. Defaults to False. async acall(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, include_run_info: bool = False) → Dict[str, Any]¶ Run the logic of this chain and add to output if desired. Parameters inputs – Dictionary of inputs, or single input if chain expects only one param. return_only_outputs – 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 – Callbacks to use for this chain run. If not provided, will use the callbacks provided to the chain. include_run_info – Whether to include run info in the response. Defaults to False. apply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[Dict[str, str]]¶ Call the chain on all inputs in the list.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.llm_checker.base.LLMCheckerChain.html
9eea31dfd92e-4
Call the chain on all inputs in the list. async arun(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, **kwargs: Any) → str¶ Run the chain as text in, text out or multiple variables, text out. dict(**kwargs: Any) → Dict¶ Return dictionary representation of chain. classmethod from_llm(llm: BaseLanguageModel, 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), 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), 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), 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), **kwargs: Any) → LLMCheckerChain[source]¶ prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]¶ Validate and prep inputs.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.llm_checker.base.LLMCheckerChain.html
9eea31dfd92e-5
Validate and prep inputs. prep_outputs(inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False) → Dict[str, str]¶ Validate and prep outputs. validator raise_deprecation  »  all fields, all fields[source]¶ Raise deprecation warning if callback_manager is used. run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, **kwargs: Any) → str¶ Run the chain as text in, text out or multiple variables, text out. save(file_path: Union[Path, str]) → None¶ Save the chain. Parameters file_path – Path to file to save the chain to. Example: .. code-block:: python chain.save(file_path=”path/chain.yaml”) validator set_verbose  »  verbose¶ If verbose is None, set it. This allows users to pass in None as verbose to access the global setting. to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶ to_json_not_implemented() → 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. model Config[source]¶ Bases: object Configuration for this pydantic object.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.llm_checker.base.LLMCheckerChain.html
9eea31dfd92e-6
model Config[source]¶ Bases: object Configuration for this pydantic object. arbitrary_types_allowed = True¶ extra = 'forbid'¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.llm_checker.base.LLMCheckerChain.html
76310ec6b5f0-0
langchain.chains.base.Chain¶ class langchain.chains.base.Chain(*, memory: Optional[BaseMemory] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, verbose: bool = None, tags: Optional[List[str]] = None)[source]¶ Bases: Serializable, ABC Base interface that all chains should implement. Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot be parsed to form a valid model. param callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None¶ Deprecated, use callbacks instead. param callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None¶ Optional list of callback handlers (or callback manager). Defaults to None. Callback handlers are called throughout the lifecycle of a call to a chain, starting with on_chain_start, ending with on_chain_end or on_chain_error. Each custom chain can optionally call additional callback methods, see Callback docs for full details. param memory: Optional[langchain.schema.BaseMemory] = None¶ Optional memory object. Defaults to None. Memory is a class that gets called at the start and at the end of every chain. At the start, memory loads variables and passes them along in the chain. At the end, it saves any returned variables. There are many different types of memory - please see memory docs for the full catalog. param 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.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.base.Chain.html
76310ec6b5f0-1
and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a chain with its use case. param 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. __call__(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, include_run_info: bool = False) → Dict[str, Any][source]¶ Run the logic of this chain and add to output if desired. Parameters inputs – Dictionary of inputs, or single input if chain expects only one param. return_only_outputs – 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 – Callbacks to use for this chain run. If not provided, will use the callbacks provided to the chain. include_run_info – Whether to include run info in the response. Defaults to False. async acall(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, include_run_info: bool = False) → Dict[str, Any][source]¶ Run the logic of this chain and add to output if desired. Parameters inputs – Dictionary of inputs, or single input if chain expects only one param. return_only_outputs – boolean for whether to return only outputs in the
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.base.Chain.html
76310ec6b5f0-2
only one param. return_only_outputs – 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 – Callbacks to use for this chain run. If not provided, will use the callbacks provided to the chain. include_run_info – Whether to include run info in the response. Defaults to False. apply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[Dict[str, str]][source]¶ Call the chain on all inputs in the list. async arun(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, **kwargs: Any) → str[source]¶ Run the chain as text in, text out or multiple variables, text out. dict(**kwargs: Any) → Dict[source]¶ Return dictionary representation of chain. prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str][source]¶ Validate and prep inputs. prep_outputs(inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False) → Dict[str, str][source]¶ Validate and prep outputs. validator raise_deprecation  »  all fields[source]¶ Raise deprecation warning if callback_manager is used. run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, **kwargs: Any) → str[source]¶ Run the chain as text in, text out or multiple variables, text out.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.base.Chain.html
76310ec6b5f0-3
Run the chain as text in, text out or multiple variables, text out. save(file_path: Union[Path, str]) → None[source]¶ Save the chain. Parameters file_path – Path to file to save the chain to. Example: .. code-block:: python chain.save(file_path=”path/chain.yaml”) validator set_verbose  »  verbose[source]¶ If verbose is None, set it. This allows users to pass in None as verbose to access the global setting. to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶ to_json_not_implemented() → 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. abstract property output_keys: List[str]¶ Output keys this chain expects. model Config[source]¶ Bases: object Configuration for this pydantic object. arbitrary_types_allowed = True¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.base.Chain.html
e7e98b673432-0
langchain.chains.combine_documents.base.BaseCombineDocumentsChain¶ class langchain.chains.combine_documents.base.BaseCombineDocumentsChain(*, memory: Optional[BaseMemory] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, verbose: bool = None, tags: Optional[List[str]] = None, input_key: str = 'input_documents', output_key: str = 'output_text')[source]¶ Bases: Chain, ABC Base interface for chains combining documents. Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot be parsed to form a valid model. param callback_manager: Optional[BaseCallbackManager] = None¶ Deprecated, use callbacks instead. param callbacks: Callbacks = None¶ Optional list of callback handlers (or callback manager). Defaults to None. Callback handlers are called throughout the lifecycle of a call to a chain, starting with on_chain_start, ending with on_chain_end or on_chain_error. Each custom chain can optionally call additional callback methods, see Callback docs for full details. param memory: Optional[BaseMemory] = None¶ Optional memory object. Defaults to None. Memory is a class that gets called at the start and at the end of every chain. At the start, memory loads variables and passes them along in the chain. At the end, it saves any returned variables. There are many different types of memory - please see memory docs for the full catalog. param 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.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.combine_documents.base.BaseCombineDocumentsChain.html
e7e98b673432-1
and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a chain with its use case. param 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. __call__(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, include_run_info: bool = False) → Dict[str, Any]¶ Run the logic of this chain and add to output if desired. Parameters inputs – Dictionary of inputs, or single input if chain expects only one param. return_only_outputs – 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 – Callbacks to use for this chain run. If not provided, will use the callbacks provided to the chain. include_run_info – Whether to include run info in the response. Defaults to False. async acall(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, include_run_info: bool = False) → Dict[str, Any]¶ Run the logic of this chain and add to output if desired. Parameters inputs – Dictionary of inputs, or single input if chain expects only one param. return_only_outputs – boolean for whether to return only outputs in the response. If True, only new keys generated by this chain will be
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.combine_documents.base.BaseCombineDocumentsChain.html
e7e98b673432-2
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 – Callbacks to use for this chain run. If not provided, will use the callbacks provided to the chain. include_run_info – Whether to include run info in the response. Defaults to False. abstract async acombine_docs(docs: List[Document], **kwargs: Any) → Tuple[str, dict][source]¶ Combine documents into a single string asynchronously. apply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[Dict[str, str]]¶ Call the chain on all inputs in the list. async arun(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, **kwargs: Any) → str¶ Run the chain as text in, text out or multiple variables, text out. abstract combine_docs(docs: List[Document], **kwargs: Any) → Tuple[str, dict][source]¶ Combine documents into a single string. dict(**kwargs: Any) → Dict¶ Return dictionary representation of chain. prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]¶ Validate and prep inputs. prep_outputs(inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False) → Dict[str, str]¶ Validate and prep outputs. prompt_length(docs: List[Document], **kwargs: Any) → Optional[int][source]¶ Return the prompt length given the documents passed in. Returns None if the method does not depend on the prompt length.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.combine_documents.base.BaseCombineDocumentsChain.html
e7e98b673432-3
Returns None if the method does not depend on the prompt length. validator raise_deprecation  »  all fields¶ Raise deprecation warning if callback_manager is used. run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, **kwargs: Any) → str¶ Run the chain as text in, text out or multiple variables, text out. save(file_path: Union[Path, str]) → None¶ Save the chain. Parameters file_path – Path to file to save the chain to. Example: .. code-block:: python chain.save(file_path=”path/chain.yaml”) validator set_verbose  »  verbose¶ If verbose is None, set it. This allows users to pass in None as verbose to access the global setting. to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶ to_json_not_implemented() → 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. model Config¶ Bases: object Configuration for this pydantic object. arbitrary_types_allowed = True¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.combine_documents.base.BaseCombineDocumentsChain.html
aff9233cb2df-0
langchain.chains.combine_documents.map_rerank.MapRerankDocumentsChain¶ class langchain.chains.combine_documents.map_rerank.MapRerankDocumentsChain(*, memory: Optional[BaseMemory] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, verbose: bool = None, tags: Optional[List[str]] = None, input_key: str = 'input_documents', output_key: str = 'output_text', llm_chain: LLMChain, document_variable_name: str, rank_key: str, answer_key: str, metadata_keys: Optional[List[str]] = None, return_intermediate_steps: bool = False)[source]¶ Bases: BaseCombineDocumentsChain Combining documents by mapping a chain over them, then reranking results. Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot be parsed to form a valid model. param answer_key: str [Required]¶ Key in output of llm_chain to return as answer. param callback_manager: Optional[BaseCallbackManager] = None¶ Deprecated, use callbacks instead. param callbacks: Callbacks = None¶ Optional list of callback handlers (or callback manager). Defaults to None. Callback handlers are called throughout the lifecycle of a call to a chain, starting with on_chain_start, ending with on_chain_end or on_chain_error. Each custom chain can optionally call additional callback methods, see Callback docs for full details. param 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. param llm_chain: LLMChain [Required]¶ Chain to apply to each document individually.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.combine_documents.map_rerank.MapRerankDocumentsChain.html
aff9233cb2df-1
Chain to apply to each document individually. param memory: Optional[BaseMemory] = None¶ Optional memory object. Defaults to None. Memory is a class that gets called at the start and at the end of every chain. At the start, memory loads variables and passes them along in the chain. At the end, it saves any returned variables. There are many different types of memory - please see memory docs for the full catalog. param metadata_keys: Optional[List[str]] = None¶ param rank_key: str [Required]¶ Key in output of llm_chain to rank on. param return_intermediate_steps: bool = False¶ param tags: Optional[List[str]] = None¶ Optional list of tags associated with the chain. Defaults to None These tags will be associated with each call to this chain, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a chain with its use case. param verbose: bool [Optional]¶ Whether or not run in verbose mode. In verbose mode, some intermediate logs will be printed to the console. Defaults to langchain.verbose value. __call__(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, include_run_info: bool = False) → Dict[str, Any]¶ Run the logic of this chain and add to output if desired. Parameters inputs – Dictionary of inputs, or single input if chain expects only one param. return_only_outputs – 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
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.combine_documents.map_rerank.MapRerankDocumentsChain.html
aff9233cb2df-2
returned. If False, both input keys and new keys generated by this chain will be returned. Defaults to False. callbacks – Callbacks to use for this chain run. If not provided, will use the callbacks provided to the chain. include_run_info – Whether to include run info in the response. Defaults to False. async acall(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, include_run_info: bool = False) → Dict[str, Any]¶ Run the logic of this chain and add to output if desired. Parameters inputs – Dictionary of inputs, or single input if chain expects only one param. return_only_outputs – 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 – Callbacks to use for this chain run. If not provided, will use the callbacks provided to the chain. include_run_info – Whether to include run info in the response. Defaults to False. async acombine_docs(docs: List[Document], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Tuple[str, dict][source]¶ Combine documents in a map rerank manner. Combine by mapping first chain over all documents, then reranking the results. apply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[Dict[str, str]]¶ Call the chain on all inputs in the list.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.combine_documents.map_rerank.MapRerankDocumentsChain.html
aff9233cb2df-3
Call the chain on all inputs in the list. async arun(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, **kwargs: Any) → str¶ Run the chain as text in, text out or multiple variables, text out. combine_docs(docs: List[Document], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Tuple[str, dict][source]¶ Combine documents in a map rerank manner. Combine by mapping first chain over all documents, then reranking the results. dict(**kwargs: Any) → Dict¶ Return dictionary representation of chain. validator get_default_document_variable_name  »  all fields[source]¶ Get default document variable name, if not provided. prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]¶ Validate and prep inputs. prep_outputs(inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False) → Dict[str, str]¶ Validate and prep outputs. prompt_length(docs: List[Document], **kwargs: Any) → Optional[int]¶ Return the prompt length given the documents passed in. Returns None if the method does not depend on the prompt length. validator raise_deprecation  »  all fields¶ Raise deprecation warning if callback_manager is used. run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, **kwargs: Any) → str¶ Run the chain as text in, text out or multiple variables, text out. save(file_path: Union[Path, str]) → None¶ Save the chain. Parameters
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.combine_documents.map_rerank.MapRerankDocumentsChain.html
aff9233cb2df-4
Save the chain. Parameters file_path – Path to file to save the chain to. Example: .. code-block:: python chain.save(file_path=”path/chain.yaml”) validator set_verbose  »  verbose¶ If verbose is None, set it. This allows users to pass in None as verbose to access the global setting. to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶ to_json_not_implemented() → SerializedNotImplemented¶ validator validate_llm_output  »  all fields[source]¶ Validate that the combine chain outputs a dictionary. 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. model Config[source]¶ Bases: object Configuration for this pydantic object. arbitrary_types_allowed = True¶ extra = 'forbid'¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.combine_documents.map_rerank.MapRerankDocumentsChain.html
f120f7811c97-0
langchain.chains.prompt_selector.BasePromptSelector¶ class langchain.chains.prompt_selector.BasePromptSelector[source]¶ Bases: BaseModel, ABC Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot be parsed to form a valid model. abstract get_prompt(llm: BaseLanguageModel) → BasePromptTemplate[source]¶ Get default prompt for a language model.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.prompt_selector.BasePromptSelector.html
f70a6700d66a-0
langchain.chains.constitutional_ai.models.ConstitutionalPrinciple¶ class langchain.chains.constitutional_ai.models.ConstitutionalPrinciple(*, critique_request: str, revision_request: str, name: str = 'Constitutional Principle')[source]¶ Bases: BaseModel Class for a constitutional principle. Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot be parsed to form a valid model. param critique_request: str [Required]¶ param name: str = 'Constitutional Principle'¶ param revision_request: str [Required]¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.constitutional_ai.models.ConstitutionalPrinciple.html
a9a8ccb09a89-0
langchain.chains.query_constructor.ir.FilterDirective¶ class langchain.chains.query_constructor.ir.FilterDirective[source]¶ Bases: Expr, ABC A filtering expression. Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot be parsed to form a valid model. accept(visitor: Visitor) → Any¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.query_constructor.ir.FilterDirective.html
7ce230a69aae-0
langchain.chains.llm_requests.LLMRequestsChain¶ class langchain.chains.llm_requests.LLMRequestsChain(*, memory: Optional[BaseMemory] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, verbose: bool = None, tags: Optional[List[str]] = None, llm_chain: LLMChain, requests_wrapper: TextRequestsWrapper = None, text_length: int = 8000, requests_key: str = 'requests_result', input_key: str = 'url', output_key: str = 'output')[source]¶ Bases: Chain Chain that hits a URL and then uses an LLM to parse results. Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot be parsed to form a valid model. param callback_manager: Optional[BaseCallbackManager] = None¶ Deprecated, use callbacks instead. param callbacks: Callbacks = None¶ Optional list of callback handlers (or callback manager). Defaults to None. Callback handlers are called throughout the lifecycle of a call to a chain, starting with on_chain_start, ending with on_chain_end or on_chain_error. Each custom chain can optionally call additional callback methods, see Callback docs for full details. param llm_chain: LLMChain [Required]¶ param memory: Optional[BaseMemory] = None¶ Optional memory object. Defaults to None. Memory is a class that gets called at the start and at the end of every chain. At the start, memory loads variables and passes them along in the chain. At the end, it saves any returned variables. There are many different types of memory - please see memory docs for the full catalog. param requests_wrapper: TextRequestsWrapper [Optional]¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.llm_requests.LLMRequestsChain.html
7ce230a69aae-1
for the full catalog. param requests_wrapper: TextRequestsWrapper [Optional]¶ param tags: Optional[List[str]] = None¶ Optional list of tags associated with the chain. Defaults to None These tags will be associated with each call to this chain, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a chain with its use case. param text_length: int = 8000¶ param verbose: bool [Optional]¶ Whether or not run in verbose mode. In verbose mode, some intermediate logs will be printed to the console. Defaults to langchain.verbose value. __call__(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, include_run_info: bool = False) → Dict[str, Any]¶ Run the logic of this chain and add to output if desired. Parameters inputs – Dictionary of inputs, or single input if chain expects only one param. return_only_outputs – 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 – Callbacks to use for this chain run. If not provided, will use the callbacks provided to the chain. include_run_info – Whether to include run info in the response. Defaults to False.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.llm_requests.LLMRequestsChain.html
7ce230a69aae-2
include_run_info – Whether to include run info in the response. Defaults to False. async acall(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, include_run_info: bool = False) → Dict[str, Any]¶ Run the logic of this chain and add to output if desired. Parameters inputs – Dictionary of inputs, or single input if chain expects only one param. return_only_outputs – 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 – Callbacks to use for this chain run. If not provided, will use the callbacks provided to the chain. include_run_info – Whether to include run info in the response. Defaults to False. apply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[Dict[str, str]]¶ Call the chain on all inputs in the list. async arun(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, **kwargs: Any) → str¶ Run the chain as text in, text out or multiple variables, text out. dict(**kwargs: Any) → Dict¶ Return dictionary representation of chain. prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]¶ Validate and prep inputs.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.llm_requests.LLMRequestsChain.html
7ce230a69aae-3
Validate and prep inputs. prep_outputs(inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False) → Dict[str, str]¶ Validate and prep outputs. validator raise_deprecation  »  all fields¶ Raise deprecation warning if callback_manager is used. run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, **kwargs: Any) → str¶ Run the chain as text in, text out or multiple variables, text out. save(file_path: Union[Path, str]) → None¶ Save the chain. Parameters file_path – Path to file to save the chain to. Example: .. code-block:: python chain.save(file_path=”path/chain.yaml”) validator set_verbose  »  verbose¶ If verbose is None, set it. This allows users to pass in None as verbose to access the global setting. to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶ to_json_not_implemented() → SerializedNotImplemented¶ validator validate_environment  »  all fields[source]¶ Validate that api key and python package exists in environment. 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. model Config[source]¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.llm_requests.LLMRequestsChain.html
7ce230a69aae-4
Return whether or not the class is serializable. model Config[source]¶ Bases: object Configuration for this pydantic object. arbitrary_types_allowed = True¶ extra = 'forbid'¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.llm_requests.LLMRequestsChain.html
da51672846db-0
langchain.chains.router.embedding_router.EmbeddingRouterChain¶ class langchain.chains.router.embedding_router.EmbeddingRouterChain(*, memory: Optional[BaseMemory] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, verbose: bool = None, tags: Optional[List[str]] = None, vectorstore: VectorStore, routing_keys: List[str] = ['query'])[source]¶ Bases: RouterChain Class that uses embeddings to route between options. Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot be parsed to form a valid model. param callback_manager: Optional[BaseCallbackManager] = None¶ Deprecated, use callbacks instead. param callbacks: Callbacks = None¶ Optional list of callback handlers (or callback manager). Defaults to None. Callback handlers are called throughout the lifecycle of a call to a chain, starting with on_chain_start, ending with on_chain_end or on_chain_error. Each custom chain can optionally call additional callback methods, see Callback docs for full details. param memory: Optional[BaseMemory] = None¶ Optional memory object. Defaults to None. Memory is a class that gets called at the start and at the end of every chain. At the start, memory loads variables and passes them along in the chain. At the end, it saves any returned variables. There are many different types of memory - please see memory docs for the full catalog. param routing_keys: List[str] = ['query']¶ param tags: Optional[List[str]] = None¶ Optional list of tags associated with the chain. Defaults to None These tags will be associated with each call to this chain, and passed as arguments to the handlers defined in callbacks.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.router.embedding_router.EmbeddingRouterChain.html
da51672846db-1
and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a chain with its use case. param vectorstore: VectorStore [Required]¶ param verbose: bool [Optional]¶ Whether or not run in verbose mode. In verbose mode, some intermediate logs will be printed to the console. Defaults to langchain.verbose value. __call__(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, include_run_info: bool = False) → Dict[str, Any]¶ Run the logic of this chain and add to output if desired. Parameters inputs – Dictionary of inputs, or single input if chain expects only one param. return_only_outputs – 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 – Callbacks to use for this chain run. If not provided, will use the callbacks provided to the chain. include_run_info – Whether to include run info in the response. Defaults to False. async acall(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, include_run_info: bool = False) → Dict[str, Any]¶ Run the logic of this chain and add to output if desired. Parameters inputs – Dictionary of inputs, or single input if chain expects only one param. return_only_outputs – boolean for whether to return only outputs in the
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.router.embedding_router.EmbeddingRouterChain.html
da51672846db-2
only one param. return_only_outputs – 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 – Callbacks to use for this chain run. If not provided, will use the callbacks provided to the chain. include_run_info – Whether to include run info in the response. Defaults to False. apply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[Dict[str, str]]¶ Call the chain on all inputs in the list. async aroute(inputs: Dict[str, Any], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → Route¶ async arun(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, **kwargs: Any) → str¶ Run the chain as text in, text out or multiple variables, text out. dict(**kwargs: Any) → Dict¶ Return dictionary representation of chain. classmethod from_names_and_descriptions(names_and_descriptions: Sequence[Tuple[str, Sequence[str]]], vectorstore_cls: Type[VectorStore], embeddings: Embeddings, **kwargs: Any) → EmbeddingRouterChain[source]¶ Convenience constructor. prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]¶ Validate and prep inputs. prep_outputs(inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False) → Dict[str, str]¶ Validate and prep outputs. validator raise_deprecation  »  all fields¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.router.embedding_router.EmbeddingRouterChain.html
da51672846db-3
Validate and prep outputs. validator raise_deprecation  »  all fields¶ Raise deprecation warning if callback_manager is used. route(inputs: Dict[str, Any], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → Route¶ run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, **kwargs: Any) → str¶ Run the chain as text in, text out or multiple variables, text out. save(file_path: Union[Path, str]) → None¶ Save the chain. Parameters file_path – Path to file to save the chain to. Example: .. code-block:: python chain.save(file_path=”path/chain.yaml”) validator set_verbose  »  verbose¶ If verbose is None, set it. This allows users to pass in None as verbose to access the global setting. to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶ to_json_not_implemented() → 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. model Config[source]¶ Bases: object Configuration for this pydantic object.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.router.embedding_router.EmbeddingRouterChain.html
da51672846db-4
model Config[source]¶ Bases: object Configuration for this pydantic object. arbitrary_types_allowed = True¶ extra = 'forbid'¶
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.router.embedding_router.EmbeddingRouterChain.html
589559ef7bbf-0
langchain.chains.graph_qa.kuzu.KuzuQAChain¶ class langchain.chains.graph_qa.kuzu.KuzuQAChain(*, memory: Optional[BaseMemory] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, verbose: bool = None, tags: Optional[List[str]] = None, graph: KuzuGraph, cypher_generation_chain: LLMChain, qa_chain: LLMChain, input_key: str = 'query', output_key: str = 'result')[source]¶ Bases: Chain Chain for question-answering against a graph by generating Cypher statements for Kùzu. Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot be parsed to form a valid model. param callback_manager: Optional[BaseCallbackManager] = None¶ Deprecated, use callbacks instead. param callbacks: Callbacks = None¶ Optional list of callback handlers (or callback manager). Defaults to None. Callback handlers are called throughout the lifecycle of a call to a chain, starting with on_chain_start, ending with on_chain_end or on_chain_error. Each custom chain can optionally call additional callback methods, see Callback docs for full details. param cypher_generation_chain: LLMChain [Required]¶ param graph: KuzuGraph [Required]¶ param memory: Optional[BaseMemory] = None¶ Optional memory object. Defaults to None. Memory is a class that gets called at the start and at the end of every chain. At the start, memory loads variables and passes them along in the chain. At the end, it saves any returned variables. There are many different types of memory - please see memory docs for the full catalog.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.graph_qa.kuzu.KuzuQAChain.html
589559ef7bbf-1
There are many different types of memory - please see memory docs for the full catalog. param qa_chain: LLMChain [Required]¶ param tags: Optional[List[str]] = None¶ Optional list of tags associated with the chain. Defaults to None These tags will be associated with each call to this chain, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a chain with its use case. param verbose: bool [Optional]¶ Whether or not run in verbose mode. In verbose mode, some intermediate logs will be printed to the console. Defaults to langchain.verbose value. __call__(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, include_run_info: bool = False) → Dict[str, Any]¶ Run the logic of this chain and add to output if desired. Parameters inputs – Dictionary of inputs, or single input if chain expects only one param. return_only_outputs – 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 – Callbacks to use for this chain run. If not provided, will use the callbacks provided to the chain. include_run_info – Whether to include run info in the response. Defaults to False.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.graph_qa.kuzu.KuzuQAChain.html
589559ef7bbf-2
include_run_info – Whether to include run info in the response. Defaults to False. async acall(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, include_run_info: bool = False) → Dict[str, Any]¶ Run the logic of this chain and add to output if desired. Parameters inputs – Dictionary of inputs, or single input if chain expects only one param. return_only_outputs – 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 – Callbacks to use for this chain run. If not provided, will use the callbacks provided to the chain. include_run_info – Whether to include run info in the response. Defaults to False. apply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[Dict[str, str]]¶ Call the chain on all inputs in the list. async arun(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, **kwargs: Any) → str¶ Run the chain as text in, text out or multiple variables, text out. dict(**kwargs: Any) → Dict¶ Return dictionary representation of chain.
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.graph_qa.kuzu.KuzuQAChain.html
589559ef7bbf-3
classmethod from_llm(llm: BaseLanguageModel, *, qa_prompt: BasePromptTemplate = 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: BasePromptTemplate = 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',
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https://langchain.readthedocs.io/en/latest/chains/langchain.chains.graph_qa.kuzu.KuzuQAChain.html