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response. If True, only new keys generated by this chain will be returned. If False, both input keys and new keys generated by this chain will be returned. Defaults to False. callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – Callbacks to use ...
https://api.python.langchain.com/en/latest/modules/chains.html
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Return type List[Dict[str, str]] async arun(*args, callbacks=None, tags=None, **kwargs) Run the chain as text in, text out or multiple variables, text out. Parameters args (Any) – callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – tags (Opt...
https://api.python.langchain.com/en/latest/modules/chains.html
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classmethod from_llm(llm, create_assertions_prompt=PromptTemplate(input_variables=['summary'], output_parser=None, partial_variables={}, template='Given some text, extract a list of facts from the text.\n\nFormat your output as a bulleted list.\n\nText:\n"""\n{summary}\n"""\n\nFacts:', template_format='f-string', valid...
https://api.python.langchain.com/en/latest/modules/chains.html
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fact is false, explain why.\n\n', template_format='f-string', validate_template=True), revised_summary_prompt=PromptTemplate(input_variables=['checked_assertions', 'summary'], output_parser=None, partial_variables={}, template='Below are some assertions that have been fact checked and are labeled as true or false. If t...
https://api.python.langchain.com/en/latest/modules/chains.html
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true or false.\n\nIf all of the assertions are true, return "True". If any of the assertions are false, return "False".\n\nHere are some examples:\n===\n\nChecked Assertions: """\n- The sky is red: False\n- Water is made of lava: False\n- The sun is a star: True\n"""\nResult: False\n\n===\n\nChecked Assertions: """\n- ...
https://api.python.langchain.com/en/latest/modules/chains.html
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template_format='f-string', validate_template=True), verbose=False, **kwargs)[source]
https://api.python.langchain.com/en/latest/modules/chains.html
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Parameters llm (langchain.base_language.BaseLanguageModel) – create_assertions_prompt (langchain.prompts.prompt.PromptTemplate) – check_assertions_prompt (langchain.prompts.prompt.PromptTemplate) – revised_summary_prompt (langchain.prompts.prompt.PromptTemplate) – are_all_true_prompt (langchain.prompts.prompt.Promp...
https://api.python.langchain.com/en/latest/modules/chains.html
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inputs (Dict[str, str]) – outputs (Dict[str, str]) – return_only_outputs (bool) – Return type Dict[str, str] run(*args, callbacks=None, tags=None, **kwargs) Run the chain as text in, text out or multiple variables, text out. Parameters args (Any) – callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallba...
https://api.python.langchain.com/en/latest/modules/chains.html
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to_json() Return type Union[langchain.load.serializable.SerializedConstructor, langchain.load.serializable.SerializedNotImplemented] to_json_not_implemented() Return type langchain.load.serializable.SerializedNotImplemented property lc_attributes: Dict Return a list of attribute names that should be included in the ...
https://api.python.langchain.com/en/latest/modules/chains.html
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class langchain.chains.MapReduceChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, combine_documents_chain, text_splitter, input_key='input_text', output_key='output_text')[source] Bases: langchain.chains.base.Chain Map-reduce chain. Parameters memory (Optional[langchain.schema.BaseM...
https://api.python.langchain.com/en/latest/modules/chains.html
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Return type None attribute callback_manager: Optional[BaseCallbackManager] = None Deprecated, use callbacks instead. attribute callbacks: Callbacks = None Optional list of callback handlers (or callback manager). Defaults to None. Callback handlers are called throughout the lifecycle of a call to a chain, starting wi...
https://api.python.langchain.com/en/latest/modules/chains.html
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There are many different types of memory - please see memory docs for the full catalog. attribute tags: Optional[List[str]] = None Optional list of tags associated with the chain. Defaults to None These tags will be associated with each call to this chain, and passed as arguments to the handlers defined in callbacks. ...
https://api.python.langchain.com/en/latest/modules/chains.html
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Parameters inputs (Union[Dict[str, Any], Any]) – Dictionary of inputs, or single input if chain expects only one param. return_only_outputs (bool) – boolean for whether to return only outputs in the response. If True, only new keys generated by this chain will be returned. If False, both input keys and new keys generat...
https://api.python.langchain.com/en/latest/modules/chains.html
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Parameters input_list (List[Dict[str, Any]]) – callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – Return type List[Dict[str, str]] async arun(*args, callbacks=None, tags=None, **kwargs) Run the chain as text in, text out or multiple variabl...
https://api.python.langchain.com/en/latest/modules/chains.html
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Return type Dict classmethod from_params(llm, prompt, text_splitter, callbacks=None, combine_chain_kwargs=None, reduce_chain_kwargs=None, **kwargs)[source] Construct a map-reduce chain that uses the chain for map and reduce. Parameters llm (langchain.base_language.BaseLanguageModel) – prompt (langchain.prompts.base.B...
https://api.python.langchain.com/en/latest/modules/chains.html
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Validate and prep inputs. Parameters inputs (Union[Dict[str, Any], Any]) – Return type Dict[str, str] prep_outputs(inputs, outputs, return_only_outputs=False) Validate and prep outputs. Parameters inputs (Dict[str, str]) – outputs (Dict[str, str]) – return_only_outputs (bool) – Return type Dict[str, str] run(*args...
https://api.python.langchain.com/en/latest/modules/chains.html
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Save the chain. Parameters file_path (Union[pathlib.Path, str]) – Path to file to save the chain to. Return type None Example: .. code-block:: python chain.save(file_path=”path/chain.yaml”) to_json() Return type Union[langchain.load.serializable.SerializedConstructor, langchain.load.serializable.SerializedNotImplement...
https://api.python.langchain.com/en/latest/modules/chains.html
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Return a map of constructor argument names to secret ids. eg. {“openai_api_key”: “OPENAI_API_KEY”} property lc_serializable: bool Return whether or not the class is serializable. class langchain.chains.MultiPromptChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, router_chain, destin...
https://api.python.langchain.com/en/latest/modules/chains.html
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tags (Optional[List[str]]) – router_chain (langchain.chains.router.base.RouterChain) – destination_chains (Mapping[str, langchain.chains.llm.LLMChain]) – default_chain (langchain.chains.llm.LLMChain) – silent_errors (bool) – Return type None attribute callback_manager: Optional[BaseCallbackManager] = None Depreca...
https://api.python.langchain.com/en/latest/modules/chains.html
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Default chain to use when router doesn’t map input to one of the destinations. attribute destination_chains: Mapping[str, LLMChain] [Required] Map of name to candidate chains that inputs can be routed to. attribute memory: Optional[BaseMemory] = None Optional memory object. Defaults to None. Memory is a class that ge...
https://api.python.langchain.com/en/latest/modules/chains.html
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Defaults to False. attribute tags: Optional[List[str]] = None Optional list of tags associated with the chain. Defaults to None These tags will be associated with each call to this chain, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a chain with ...
https://api.python.langchain.com/en/latest/modules/chains.html
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only one param. return_only_outputs (bool) – boolean for whether to return only outputs in the response. If True, only new keys generated by this chain will be returned. If False, both input keys and new keys generated by this chain will be returned. Defaults to False. callbacks (Optional[Union[List[langchain.callbacks...
https://api.python.langchain.com/en/latest/modules/chains.html
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callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – Return type List[Dict[str, str]] async arun(*args, callbacks=None, tags=None, **kwargs) Run the chain as text in, text out or multiple variables, text out. Parameters args (Any) – callbacks...
https://api.python.langchain.com/en/latest/modules/chains.html
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Parameters llm (langchain.base_language.BaseLanguageModel) – prompt_infos (List[Dict[str, str]]) – default_chain (Optional[langchain.chains.llm.LLMChain]) – kwargs (Any) – Return type langchain.chains.router.multi_prompt.MultiPromptChain prep_inputs(inputs) Validate and prep inputs. Parameters inputs (Union[Dict[s...
https://api.python.langchain.com/en/latest/modules/chains.html
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Parameters args (Any) – callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – tags (Optional[List[str]]) – kwargs (Any) – Return type str save(file_path) Save the chain. Parameters file_path (Union[pathlib.Path, str]) – Path to file to save ...
https://api.python.langchain.com/en/latest/modules/chains.html
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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_K...
https://api.python.langchain.com/en/latest/modules/chains.html
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qa chains. Parameters memory (Optional[langchain.schema.BaseMemory]) – callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) – verbose (bool) – tags (Optional[List[str]...
https://api.python.langchain.com/en/latest/modules/chains.html
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Callback handlers are called throughout the lifecycle of a call to a chain, starting with on_chain_start, ending with on_chain_end or on_chain_error. Each custom chain can optionally call additional callback methods, see Callback docs for full details. attribute default_chain: Chain [Required] Default chain to use whe...
https://api.python.langchain.com/en/latest/modules/chains.html
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for the full catalog. attribute router_chain: LLMRouterChain [Required] Chain for deciding a destination chain and the input to it. attribute silent_errors: bool = False If True, use default_chain when an invalid destination name is provided. Defaults to False. attribute tags: Optional[List[str]] = None Optional lis...
https://api.python.langchain.com/en/latest/modules/chains.html
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Run the logic of this chain and add to output if desired. Parameters inputs (Union[Dict[str, Any], Any]) – Dictionary of inputs, or single input if chain expects only one param. return_only_outputs (bool) – boolean for whether to return only outputs in the response. If True, only new keys generated by this chain will b...
https://api.python.langchain.com/en/latest/modules/chains.html
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apply(input_list, callbacks=None) Call the chain on all inputs in the list. Parameters input_list (List[Dict[str, Any]]) – callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – Return type List[Dict[str, str]] async arun(*args, callbacks=None,...
https://api.python.langchain.com/en/latest/modules/chains.html
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Return type Dict classmethod from_retrievers(llm, retriever_infos, default_retriever=None, default_prompt=None, default_chain=None, **kwargs)[source] Parameters llm (langchain.base_language.BaseLanguageModel) – retriever_infos (List[Dict[str, Any]]) – default_retriever (Optional[langchain.schema.BaseRetriever]) – d...
https://api.python.langchain.com/en/latest/modules/chains.html
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Validate and prep outputs. Parameters inputs (Dict[str, str]) – outputs (Dict[str, str]) – return_only_outputs (bool) – Return type Dict[str, str] run(*args, callbacks=None, tags=None, **kwargs) Run the chain as text in, text out or multiple variables, text out. Parameters args (Any) – callbacks (Optional[Union[Li...
https://api.python.langchain.com/en/latest/modules/chains.html
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to_json() Return type Union[langchain.load.serializable.SerializedConstructor, langchain.load.serializable.SerializedNotImplemented] to_json_not_implemented() Return type langchain.load.serializable.SerializedNotImplemented property lc_attributes: Dict Return a list of attribute names that should be included in the ...
https://api.python.langchain.com/en/latest/modules/chains.html
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class langchain.chains.MultiRouteChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, router_chain, destination_chains, default_chain, silent_errors=False)[source] Bases: langchain.chains.base.Chain Use a single chain to route an input to one of multiple candidate chains. Parameters me...
https://api.python.langchain.com/en/latest/modules/chains.html
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Return type None attribute callback_manager: Optional[BaseCallbackManager] = None Deprecated, use callbacks instead. attribute callbacks: Callbacks = None Optional list of callback handlers (or callback manager). Defaults to None. Callback handlers are called throughout the lifecycle of a call to a chain, starting wi...
https://api.python.langchain.com/en/latest/modules/chains.html
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and at the end of every chain. At the start, memory loads variables and passes them along in the chain. At the end, it saves any returned variables. There are many different types of memory - please see memory docs for the full catalog. attribute router_chain: RouterChain [Required] Chain that routes inputs to destina...
https://api.python.langchain.com/en/latest/modules/chains.html
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will be printed to the console. Defaults to langchain.verbose value. async acall(inputs, return_only_outputs=False, callbacks=None, *, tags=None, include_run_info=False) Run the logic of this chain and add to output if desired. Parameters inputs (Union[Dict[str, Any], Any]) – Dictionary of inputs, or single input if c...
https://api.python.langchain.com/en/latest/modules/chains.html
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use the callbacks provided to the chain. include_run_info (bool) – Whether to include run info in the response. Defaults to False. tags (Optional[List[str]]) – Return type Dict[str, Any] apply(input_list, callbacks=None) Call the chain on all inputs in the list. Parameters input_list (List[Dict[str, Any]]) – callbac...
https://api.python.langchain.com/en/latest/modules/chains.html
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tags (Optional[List[str]]) – kwargs (Any) – Return type str dict(**kwargs) Return dictionary representation of chain. Parameters kwargs (Any) – Return type Dict prep_inputs(inputs) Validate and prep inputs. Parameters inputs (Union[Dict[str, Any], Any]) – Return type Dict[str, str] prep_outputs(inputs, outputs, r...
https://api.python.langchain.com/en/latest/modules/chains.html
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Parameters args (Any) – callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – tags (Optional[List[str]]) – kwargs (Any) – Return type str save(file_path) Save the chain. Parameters file_path (Union[pathlib.Path, str]) – Path to file to save ...
https://api.python.langchain.com/en/latest/modules/chains.html
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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_K...
https://api.python.langchain.com/en/latest/modules/chains.html
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Implement an LLM driven browser. Example from langchain import NatBotChain natbot = NatBotChain.from_default("Buy me a new hat.") Parameters memory (Optional[langchain.schema.BaseMemory]) – callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – ...
https://api.python.langchain.com/en/latest/modules/chains.html
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Return type None attribute callback_manager: Optional[BaseCallbackManager] = None Deprecated, use callbacks instead. attribute callbacks: Callbacks = None Optional list of callback handlers (or callback manager). Defaults to None. Callback handlers are called throughout the lifecycle of a call to a chain, starting wi...
https://api.python.langchain.com/en/latest/modules/chains.html
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them along in the chain. At the end, it saves any returned variables. There are many different types of memory - please see memory docs for the full catalog. attribute objective: str [Required] Objective that NatBot is tasked with completing. attribute tags: Optional[List[str]] = None Optional list of tags associated...
https://api.python.langchain.com/en/latest/modules/chains.html
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Run the logic of this chain and add to output if desired. Parameters inputs (Union[Dict[str, Any], Any]) – Dictionary of inputs, or single input if chain expects only one param. return_only_outputs (bool) – boolean for whether to return only outputs in the response. If True, only new keys generated by this chain will b...
https://api.python.langchain.com/en/latest/modules/chains.html
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apply(input_list, callbacks=None) Call the chain on all inputs in the list. Parameters input_list (List[Dict[str, Any]]) – callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – Return type List[Dict[str, str]] async arun(*args, callbacks=None,...
https://api.python.langchain.com/en/latest/modules/chains.html
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Return type Dict execute(url, browser_content)[source] Figure out next browser command to run. Parameters url (str) – URL of the site currently on. browser_content (str) – Content of the page as currently displayed by the browser. Returns Next browser command to run. Return type str Example browser_content = "...." ll...
https://api.python.langchain.com/en/latest/modules/chains.html
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objective (str) – kwargs (Any) – Return type langchain.chains.natbot.base.NatBotChain prep_inputs(inputs) Validate and prep inputs. Parameters inputs (Union[Dict[str, Any], Any]) – Return type Dict[str, str] prep_outputs(inputs, outputs, return_only_outputs=False) Validate and prep outputs. Parameters inputs (Dict...
https://api.python.langchain.com/en/latest/modules/chains.html
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tags (Optional[List[str]]) – kwargs (Any) – Return type str save(file_path) Save the chain. Parameters file_path (Union[pathlib.Path, str]) – Path to file to save the chain to. Return type None Example: .. code-block:: python chain.save(file_path=”path/chain.yaml”) to_json() Return type Union[langchain.load.seriali...
https://api.python.langchain.com/en/latest/modules/chains.html
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Return the namespace of the langchain object. eg. [“langchain”, “llms”, “openai”] property lc_secrets: Dict[str, str] Return a map of constructor argument names to secret ids. eg. {“openai_api_key”: “OPENAI_API_KEY”} property lc_serializable: bool Return whether or not the class is serializable. class langchain.chain...
https://api.python.langchain.com/en/latest/modules/chains.html
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callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) – verbose (bool) – tags (Optional[List[str]]) – graph (langchain.graphs.nebula_graph.NebulaGraph) – ngql_generati...
https://api.python.langchain.com/en/latest/modules/chains.html
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starting with on_chain_start, ending with on_chain_end or on_chain_error. Each custom chain can optionally call additional callback methods, see Callback docs for full details. attribute graph: NebulaGraph [Required] attribute memory: Optional[BaseMemory] = None Optional memory object. Defaults to None. Memory is a c...
https://api.python.langchain.com/en/latest/modules/chains.html
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These tags will be associated with each call to this chain, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a chain with its use case. attribute verbose: bool [Optional] Whether or not run in verbose mode. In verbose mode, some intermediate logs wil...
https://api.python.langchain.com/en/latest/modules/chains.html
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returned. If False, both input keys and new keys generated by this chain will be returned. Defaults to False. callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – Callbacks to use for this chain run. If not provided, will use the callbacks provi...
https://api.python.langchain.com/en/latest/modules/chains.html
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List[Dict[str, str]] async arun(*args, callbacks=None, tags=None, **kwargs) Run the chain as text in, text out or multiple variables, text out. Parameters args (Any) – callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – tags (Optional[List[s...
https://api.python.langchain.com/en/latest/modules/chains.html
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classmethod from_llm(llm, *, qa_prompt=PromptTemplate(input_variables=['context', 'question'], output_parser=None, partial_variables={}, template="You are an assistant that helps to form nice and human understandable answers.\nThe information part contains the provided information that you must use to construct an answ...
https://api.python.langchain.com/en/latest/modules/chains.html
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NebulaGraph Cypher statement to query a graph database.\n\nInstructions:\n\nFirst, generate cypher then convert it to NebulaGraph Cypher dialect(rather than standard):\n1. it requires explicit label specification when referring to node properties: v.`Foo`.name\n2. it uses double equals sign for comparison: `==` rather ...
https://api.python.langchain.com/en/latest/modules/chains.html
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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...
https://api.python.langchain.com/en/latest/modules/chains.html
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Initialize from LLM. Parameters llm (langchain.base_language.BaseLanguageModel) – qa_prompt (langchain.prompts.base.BasePromptTemplate) – ngql_prompt (langchain.prompts.base.BasePromptTemplate) – kwargs (Any) – Return type langchain.chains.graph_qa.nebulagraph.NebulaGraphQAChain prep_inputs(inputs) Validate and pr...
https://api.python.langchain.com/en/latest/modules/chains.html
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Run the chain as text in, text out or multiple variables, text out. Parameters args (Any) – callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – tags (Optional[List[str]]) – kwargs (Any) – Return type str save(file_path) Save the chain. Par...
https://api.python.langchain.com/en/latest/modules/chains.html
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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] ...
https://api.python.langchain.com/en/latest/modules/chains.html
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Bases: langchain.chains.base.Chain Pass input through a moderation endpoint. To use, you should have the openai python package installed, and the environment variable OPENAI_API_KEY set with your API key. Any parameters that are valid to be passed to the openai.create call can be passed in, even if not explicitly saved...
https://api.python.langchain.com/en/latest/modules/chains.html
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error (bool) – input_key (str) – output_key (str) – openai_api_key (Optional[str]) – openai_organization (Optional[str]) – Return type None attribute callback_manager: Optional[BaseCallbackManager] = None Deprecated, use callbacks instead. attribute callbacks: Callbacks = None Optional list of callback handlers ...
https://api.python.langchain.com/en/latest/modules/chains.html
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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 ca...
https://api.python.langchain.com/en/latest/modules/chains.html
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attribute verbose: bool [Optional] Whether or not run in verbose mode. In verbose mode, some intermediate logs will be printed to the console. Defaults to langchain.verbose value. async acall(inputs, return_only_outputs=False, callbacks=None, *, tags=None, include_run_info=False) Run the logic of this chain and add t...
https://api.python.langchain.com/en/latest/modules/chains.html
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chain will be returned. Defaults to False. callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – Callbacks to use for this chain run. If not provided, will use the callbacks provided to the chain. include_run_info (bool) – Whether to include run ...
https://api.python.langchain.com/en/latest/modules/chains.html
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Run the chain as text in, text out or multiple variables, text out. Parameters args (Any) – callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – tags (Optional[List[str]]) – kwargs (Any) – Return type str dict(**kwargs) Return dictionary re...
https://api.python.langchain.com/en/latest/modules/chains.html
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Return type Dict[str, str] run(*args, callbacks=None, tags=None, **kwargs) Run the chain as text in, text out or multiple variables, text out. Parameters args (Any) – callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – tags (Optional[List[st...
https://api.python.langchain.com/en/latest/modules/chains.html
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to_json_not_implemented() Return type langchain.load.serializable.SerializedNotImplemented property lc_attributes: Dict Return a list of attribute names that should be included in the serialized kwargs. These attributes must be accepted by the constructor. property lc_namespace: List[str] Return the namespace of the...
https://api.python.langchain.com/en/latest/modules/chains.html
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class langchain.chains.OpenAPIEndpointChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, api_request_chain, api_response_chain=None, api_operation, requests=None, param_mapping, return_intermediate_steps=False, instructions_key='instructions', output_key='output', max_text_length=None...
https://api.python.langchain.com/en/latest/modules/chains.html
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api_response_chain (Optional[langchain.chains.llm.LLMChain]) – api_operation (langchain.tools.openapi.utils.api_models.APIOperation) – requests (langchain.requests.Requests) – param_mapping (langchain.chains.api.openapi.chain._ParamMapping) – return_intermediate_steps (bool) – instructions_key (str) – output_key ...
https://api.python.langchain.com/en/latest/modules/chains.html
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Callback handlers are called throughout the lifecycle of a call to a chain, starting with on_chain_start, ending with on_chain_end or on_chain_error. Each custom chain can optionally call additional callback methods, see Callback docs for full details. attribute memory: Optional[BaseMemory] = None Optional memory obje...
https://api.python.langchain.com/en/latest/modules/chains.html
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These tags will be associated with each call to this chain, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a chain with its use case. attribute verbose: bool [Optional] Whether or not run in verbose mode. In verbose mode, some intermediate logs wil...
https://api.python.langchain.com/en/latest/modules/chains.html
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returned. If False, both input keys and new keys generated by this chain will be returned. Defaults to False. callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – Callbacks to use for this chain run. If not provided, will use the callbacks provi...
https://api.python.langchain.com/en/latest/modules/chains.html
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List[Dict[str, str]] async arun(*args, callbacks=None, tags=None, **kwargs) Run the chain as text in, text out or multiple variables, text out. Parameters args (Any) – callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – tags (Optional[List[s...
https://api.python.langchain.com/en/latest/modules/chains.html
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Return type Dict classmethod from_api_operation(operation, llm, requests=None, verbose=False, return_intermediate_steps=False, raw_response=False, callbacks=None, **kwargs)[source] Create an OpenAPIEndpointChain from an operation and a spec. Parameters operation (langchain.tools.openapi.utils.api_models.APIOperation) ...
https://api.python.langchain.com/en/latest/modules/chains.html
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classmethod from_url_and_method(spec_url, path, method, llm, requests=None, return_intermediate_steps=False, **kwargs)[source] Create an OpenAPIEndpoint from a spec at the specified url. Parameters spec_url (str) – path (str) – method (str) – llm (langchain.base_language.BaseLanguageModel) – requests (Optional[lan...
https://api.python.langchain.com/en/latest/modules/chains.html
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inputs (Dict[str, str]) – outputs (Dict[str, str]) – return_only_outputs (bool) – Return type Dict[str, str] run(*args, callbacks=None, tags=None, **kwargs) Run the chain as text in, text out or multiple variables, text out. Parameters args (Any) – callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallba...
https://api.python.langchain.com/en/latest/modules/chains.html
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to_json() Return type Union[langchain.load.serializable.SerializedConstructor, langchain.load.serializable.SerializedNotImplemented] to_json_not_implemented() Return type langchain.load.serializable.SerializedNotImplemented property lc_attributes: Dict Return a list of attribute names that should be included in the ...
https://api.python.langchain.com/en/latest/modules/chains.html
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class langchain.chains.PALChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, llm_chain, llm=None, prompt=PromptTemplate(input_variables=['question'], output_parser=None, partial_variables={}, template='Q: Olivia has $23. She bought five bagels for $3 each. How much money does she have...
https://api.python.langchain.com/en/latest/modules/chains.html
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On tuesday, he lost 23 golf balls. On wednesday, he lost 2 more. How many golf balls did he have at the end of wednesday?\n\n# solution in Python:\n\n\ndef solution():\n    """Michael had 58 golf balls. On tuesday, he lost 23 golf balls. On wednesday, he lost 2 more. How many golf balls did he have at the end of wednes...
https://api.python.langchain.com/en/latest/modules/chains.html
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from monday to thursday. How many computers are now in the server room?\n\n# solution in Python:\n\n\ndef solution():\n    """There were nine computers in the server room. Five more computers were installed each day, from monday to thursday. How many computers are now in the server room?"""\n    computers_initial = 9\n...
https://api.python.langchain.com/en/latest/modules/chains.html
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Christmas, he got two toys each from his mom and dad. How many toys does he have now?"""\n    toys_initial = 5\n    mom_toys = 2\n    dad_toys = 2\n    total_received = mom_toys + dad_toys\n    total_toys = toys_initial + total_received\n    result = total_toys\n    return result\n\n\n\n\n\nQ: Jason had 20 lollipops. H...
https://api.python.langchain.com/en/latest/modules/chains.html
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jason_lollipops_initial = 20\n    jason_lollipops_after = 12\n    denny_lollipops = jason_lollipops_initial - jason_lollipops_after\n    result = denny_lollipops\n    return result\n\n\n\n\n\nQ: Leah had 32 chocolates and her sister had 42. If they ate 35, how many pieces do they have left in total?\n\n# solution in Py...
https://api.python.langchain.com/en/latest/modules/chains.html
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chocolates_left = total_chocolates - chocolates_eaten\n    result = chocolates_left\n    return result\n\n\n\n\n\nQ: If there are 3 cars in the parking lot and 2 more cars arrive, how many cars are in the parking lot?\n\n# solution in Python:\n\n\ndef solution():\n    """If there are 3 cars in the parking lot and 2 mor...
https://api.python.langchain.com/en/latest/modules/chains.html
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workers plant today?\n\n# solution in Python:\n\n\ndef solution():\n    """There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?"""\n    trees_initial = 15\n    trees_after = 21\n    trees_added ...
https://api.python.langchain.com/en/latest/modules/chains.html
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Bases: langchain.chains.base.Chain Implements Program-Aided Language Models. Parameters memory (Optional[langchain.schema.BaseMemory]) – callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – callback_manager (Optional[langchain.callbacks.base.B...
https://api.python.langchain.com/en/latest/modules/chains.html
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return_intermediate_steps (bool) – Return type None attribute callback_manager: Optional[BaseCallbackManager] = None Deprecated, use callbacks instead. attribute callbacks: Callbacks = None Optional list of callback handlers (or callback manager). Defaults to None. Callback handlers are called throughout the lifecyc...
https://api.python.langchain.com/en/latest/modules/chains.html
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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.
https://api.python.langchain.com/en/latest/modules/chains.html
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attribute prompt: BasePromptTemplate = PromptTemplate(input_variables=['question'], output_parser=None, partial_variables={}, template='Q: Olivia has $23. She bought five bagels for $3 each. How much money does she have left?\n\n# solution in Python:\n\n\ndef solution():\n    """Olivia has $23. She bought five bagels f...
https://api.python.langchain.com/en/latest/modules/chains.html
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at the end of wednesday?\n\n# solution in Python:\n\n\ndef solution():\n    """Michael had 58 golf balls. On tuesday, he lost 23 golf balls. On wednesday, he lost 2 more. How many golf balls did he have at the end of wednesday?"""\n    golf_balls_initial = 58\n    golf_balls_lost_tuesday = 23\n    golf_balls_lost_wedne...
https://api.python.langchain.com/en/latest/modules/chains.html
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solution():\n    """There were nine computers in the server room. Five more computers were installed each day, from monday to thursday. How many computers are now in the server room?"""\n    computers_initial = 9\n    computers_per_day = 5\n    num_days = 4  # 4 days between monday and thursday\n    computers_added = c...
https://api.python.langchain.com/en/latest/modules/chains.html
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= 5\n    mom_toys = 2\n    dad_toys = 2\n    total_received = mom_toys + dad_toys\n    total_toys = toys_initial + total_received\n    result = total_toys\n    return result\n\n\n\n\n\nQ: Jason had 20 lollipops. He gave Denny some lollipops. Now Jason has 12 lollipops. How many lollipops did Jason give to Denny?\n\n# s...
https://api.python.langchain.com/en/latest/modules/chains.html
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denny_lollipops = jason_lollipops_initial - jason_lollipops_after\n    result = denny_lollipops\n    return result\n\n\n\n\n\nQ: Leah had 32 chocolates and her sister had 42. If they ate 35, how many pieces do they have left in total?\n\n# solution in Python:\n\n\ndef solution():\n    """Leah had 32 chocolates and her ...
https://api.python.langchain.com/en/latest/modules/chains.html
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If there are 3 cars in the parking lot and 2 more cars arrive, how many cars are in the parking lot?\n\n# solution in Python:\n\n\ndef solution():\n    """If there are 3 cars in the parking lot and 2 more cars arrive, how many cars are in the parking lot?"""\n    cars_initial = 3\n    cars_arrived = 2\n    total_cars =...
https://api.python.langchain.com/en/latest/modules/chains.html
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will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?"""\n    trees_initial = 15\n    trees_after = 21\n    trees_added = trees_after - trees_initial\n    result = trees_added\n    return result\n\n\n\n\n\nQ: {question}\n\n# solution in Pytho...
https://api.python.langchain.com/en/latest/modules/chains.html
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[Deprecated] attribute python_globals: Optional[Dict[str, Any]] = None attribute python_locals: Optional[Dict[str, Any]] = None attribute return_intermediate_steps: bool = False attribute stop: str = '\n\n' attribute tags: Optional[List[str]] = None Optional list of tags associated with the chain. Defaults to None...
https://api.python.langchain.com/en/latest/modules/chains.html
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Run the logic of this chain and add to output if desired. Parameters inputs (Union[Dict[str, Any], Any]) – Dictionary of inputs, or single input if chain expects only one param. return_only_outputs (bool) – boolean for whether to return only outputs in the response. If True, only new keys generated by this chain will b...
https://api.python.langchain.com/en/latest/modules/chains.html
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apply(input_list, callbacks=None) Call the chain on all inputs in the list. Parameters input_list (List[Dict[str, Any]]) – callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – Return type List[Dict[str, str]] async arun(*args, callbacks=None,...
https://api.python.langchain.com/en/latest/modules/chains.html