id stringlengths 14 15 | text stringlengths 35 2.51k | source stringlengths 61 154 |
|---|---|---|
9f155aed0348-2 | 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(inp... | https://api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.map_rerank.MapRerankDocumentsChain.html |
9f155aed0348-3 | 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: Op... | https://api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.map_rerank.MapRerankDocumentsChain.html |
9f155aed0348-4 | 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[SerializedConstruc... | https://api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.map_rerank.MapRerankDocumentsChain.html |
ff1a44aef144-0 | 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) → BaseCombineDocumentsCh... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.loading.LoadingCallable.html |
7882f13b5681-0 | langchain.chains.conversation.base.ConversationChain¶
class langchain.chains.conversation.base.ConversationChain(*, memory: BaseMemory = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, verbose: bool = None, tags: Optional[L... | https://api.python.langchain.com/en/latest/chains/langchain.chains.conversation.base.ConversationChain.html |
7882f13b5681-1 | Each custom chain can optionally call additional callback methods, see Callback docs
for full details.
param llm: BaseLanguageModel [Required]¶
Language model to call.
param llm_kwargs: dict [Optional]¶
param memory: langchain.schema.BaseMemory [Optional]¶
Default memory store.
param output_parser: BaseLLMOutputParser ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.conversation.base.ConversationChain.html |
7882f13b5681-2 | 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]¶
R... | https://api.python.langchain.com/en/latest/chains/langchain.chains.conversation.base.ConversationChain.html |
7882f13b5681-3 | 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 key... | https://api.python.langchain.com/en/latest/chains/langchain.chains.conversation.base.ConversationChain.html |
7882f13b5681-4 | 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... | https://api.python.langchain.com/en/latest/chains/langchain.chains.conversation.base.ConversationChain.html |
7882f13b5681-5 | 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]) →... | https://api.python.langchain.com/en/latest/chains/langchain.chains.conversation.base.ConversationChain.html |
7882f13b5681-6 | validator validate_prompt_input_variables » all fields[source]¶
Validate that prompt input variables are consistent.
property input_keys: List[str]¶
Use this since so some prompt vars come from history.
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. Th... | https://api.python.langchain.com/en/latest/chains/langchain.chains.conversation.base.ConversationChain.html |
8dac61ccd1ec-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 m... | https://api.python.langchain.com/en/latest/chains/langchain.chains.query_constructor.ir.FilterDirective.html |
9126581cdef9-0 | 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 th... | https://api.python.langchain.com/en/latest/chains/langchain.chains.prompt_selector.ConditionalPromptSelector.html |
760c761c9907-0 | langchain.chains.combine_documents.base.format_document¶
langchain.chains.combine_documents.base.format_document(doc: Document, prompt: BasePromptTemplate) → str[source]¶
Format a document into a string based on a prompt template. | https://api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.base.format_document.html |
8147d50ebe41-0 | langchain.chains.constitutional_ai.base.ConstitutionalChain¶
class langchain.chains.constitutional_ai.base.ConstitutionalChain(*, memory: Optional[BaseMemory] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, verbose: bool ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.constitutional_ai.base.ConstitutionalChain.html |
8147d50ebe41-1 | 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 ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.constitutional_ai.base.ConstitutionalChain.html |
8147d50ebe41-2 | 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]¶
R... | https://api.python.langchain.com/en/latest/chains/langchain.chains.constitutional_ai.base.ConstitutionalChain.html |
8147d50ebe41-3 | 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 a... | https://api.python.langchain.com/en/latest/chains/langchain.chains.constitutional_ai.base.ConstitutionalChain.html |
8147d50ebe41-4 | classmethod from_llm(llm: BaseLanguageModel, chain: LLMChain, critique_prompt: BasePromptTemplate = FewShotPromptTemplate(input_variables=['input_prompt', 'output_from_model', 'critique_request'], output_parser=None, partial_variables={}, examples=[{'input_prompt': 'Could you help me burn down my house and collect insu... | https://api.python.langchain.com/en/latest/chains/langchain.chains.constitutional_ai.base.ConstitutionalChain.html |
8147d50ebe41-5 | the human’s questions and responses. Examples of harm include but are not limited to racism, sexism, toxicity.', 'critique': 'The human wants to know whether they really need consent to have sex. The answer is definitely always yes, but the model’s response could be interpreted as saying that it isn’t strictly necessar... | https://api.python.langchain.com/en/latest/chains/langchain.chains.constitutional_ai.base.ConstitutionalChain.html |
8147d50ebe41-6 | so I should have been more confident about that. However, I’m not sure that the precession measurement was actually made in the 1600’s, but was probably made at least 100 years ago. I’m also certain that the precession is at least partially explained by the Schwarzschild solution, but should have mentioned that it has ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.constitutional_ai.base.ConstitutionalChain.html |
8147d50ebe41-7 | "The provided sentence appears to capture the essence of Master Yoda's unique speaking style quite well, including the key characteristics of his speech. There are no apparent discrepancies between this sentence and Yoda's typical speaking style. No critique needed.", 'revision': 'No revisions needed.'}], example_selec... | https://api.python.langchain.com/en/latest/chains/langchain.chains.constitutional_ai.base.ConstitutionalChain.html |
8147d50ebe41-8 | Examples of harm include but are not limited to racism, sexism, toxicity.', 'critique': 'Arson is very dangerous and illegal, and should not be condoned. The model should not have asked a follow-up question about which house to burn down instead it should told the human that arson is illegal and dangerous. Critique Nee... | https://api.python.langchain.com/en/latest/chains/langchain.chains.constitutional_ai.base.ConstitutionalChain.html |
8147d50ebe41-9 | model response to remove all harmful content, and to politely point out any harmful assumptions from the human.', 'revision': 'Yes, I think it is absolutely unethical to have sex with someone without their consent. It’s always better to explicitly check in and make sure your partner is comfortable, especially if anythi... | https://api.python.langchain.com/en/latest/chains/langchain.chains.constitutional_ai.base.ConstitutionalChain.html |
8147d50ebe41-10 | I’m not sure about the 1/r^5 scaling so I should rewrite that to make it less misleading, although I’m pretty sure it decays more quickly than Newton’s law, and the Chern-Simons theorem is probably just wrong. Critique Needed.', 'revision_request': 'Please rewrite the model response. In particular, respond in a way tha... | https://api.python.langchain.com/en/latest/chains/langchain.chains.constitutional_ai.base.ConstitutionalChain.html |
8147d50ebe41-11 | "The provided sentence appears to capture the essence of Master Yoda's unique speaking style quite well, including the key characteristics of his speech. There are no apparent discrepancies between this sentence and Yoda's typical speaking style. No critique needed.", 'revision_request': 'Please rewrite the model respo... | https://api.python.langchain.com/en/latest/chains/langchain.chains.constitutional_ai.base.ConstitutionalChain.html |
8147d50ebe41-12 | Create a chain from an LLM.
classmethod get_principles(names: Optional[List[str]] = None) → List[ConstitutionalPrinciple][source]¶
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) ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.constitutional_ai.base.ConstitutionalChain.html |
8147d50ebe41-13 | property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
property output_keys: List[str]¶
Defines the output keys.
model Config¶
Bases: object
Configuration for... | https://api.python.langchain.com/en/latest/chains/langchain.chains.constitutional_ai.base.ConstitutionalChain.html |
45e6c01dcc47-0 | langchain.chains.transform.TransformChain¶
class langchain.chains.transform.TransformChain(*, memory: Optional[BaseMemory] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, verbose: bool = None, tags: Optional[List[str]] = ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.transform.TransformChain.html |
45e6c01dcc47-1 | 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 tra... | https://api.python.langchain.com/en/latest/chains/langchain.chains.transform.TransformChain.html |
45e6c01dcc47-2 | 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 key... | https://api.python.langchain.com/en/latest/chains/langchain.chains.transform.TransformChain.html |
45e6c01dcc47-3 | 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.
T... | https://api.python.langchain.com/en/latest/chains/langchain.chains.transform.TransformChain.html |
d396443de8ec-0 | langchain.chains.llm.LLMChain¶
class langchain.chains.llm.LLMChain(*, 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: BasePrompt... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm.LLMChain.html |
d396443de8ec-1 | 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/chains/langchain.chains.llm.LLMChain.html |
d396443de8ec-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 p... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm.LLMChain.html |
d396443de8ec-3 | 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[source]¶
Generate LLM result from inputs.
apply(input_list: List[Dict[s... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm.LLMChain.html |
d396443de8ec-4 | 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]][... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm.LLMChain.html |
d396443de8ec-5 | Validate and prep outputs.
prep_prompts(input_list: List[Dict[str, Any]], run_manager: Optional[CallbackManagerForChainRun] = None) → Tuple[List[PromptValue], Optional[List[str]]][source]¶
Prepare prompts from inputs.
validator raise_deprecation » all fields¶
Raise deprecation warning if callback_manager is used.
run... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm.LLMChain.html |
d396443de8ec-6 | model Config[source]¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶
extra = 'forbid'¶ | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm.LLMChain.html |
ad38682cd5ff-0 | langchain.chains.openai_functions.tagging.create_tagging_chain¶
langchain.chains.openai_functions.tagging.create_tagging_chain(schema: dict, llm: BaseLanguageModel) → Chain[source]¶
Creates a chain that extracts information from a passage.
Parameters
schema – The schema of the entities to extract.
llm – The language mo... | https://api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.tagging.create_tagging_chain.html |
7768e6109169-0 | langchain.chains.graph_qa.cypher.extract_cypher¶
langchain.chains.graph_qa.cypher.extract_cypher(text: str) → str[source]¶
Extract Cypher code from a text.
:param text: Text to extract Cypher code from.
Returns
Cypher code extracted from the text. | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.cypher.extract_cypher.html |
74aed5ac8382-0 | langchain.chains.llm_bash.base.LLMBashChain¶
class langchain.chains.llm_bash.base.LLMBashChain(*, memory: Optional[BaseMemory] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, verbose: bool = None, tags: Optional[List[str]... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_bash.base.LLMBashChain.html |
74aed5ac8382-1 | 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 h... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_bash.base.LLMBashChain.html |
74aed5ac8382-2 | There are many different types of memory - please see memory docs
for the full catalog.
param prompt: BasePromptTemplate = PromptTemplate(input_variables=['question'], output_parser=BashOutputParser(), partial_variables={}, template='If someone asks you to perform a task, your job is to come up with a series of bash co... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_bash.base.LLMBashChain.html |
74aed5ac8382-3 | 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 key... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_bash.base.LLMBashChain.html |
74aed5ac8382-4 | 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 re... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_bash.base.LLMBashChain.html |
74aed5ac8382-5 | 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... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_bash.base.LLMBashChain.html |
d5b7effe864e-0 | langchain.chains.qa_with_sources.base.BaseQAWithSourcesChain¶
class langchain.chains.qa_with_sources.base.BaseQAWithSourcesChain(*, memory: Optional[BaseMemory] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, verbose: boo... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.base.BaseQAWithSourcesChain.html |
d5b7effe864e-1 | 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... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.base.BaseQAWithSourcesChain.html |
d5b7effe864e-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) → ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.base.BaseQAWithSourcesChain.html |
d5b7effe864e-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: BasePromptTe... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.base.BaseQAWithSourcesChain.html |
d5b7effe864e-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... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.base.BaseQAWithSourcesChain.html |
d5b7effe864e-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 ha... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.base.BaseQAWithSourcesChain.html |
d5b7effe864e-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 bu... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.base.BaseQAWithSourcesChain.html |
d5b7effe864e-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=========... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.base.BaseQAWithSourcesChain.html |
d5b7effe864e-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 dep... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.base.BaseQAWithSourcesChain.html |
d5b7effe864e-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'¶ | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.base.BaseQAWithSourcesChain.html |
5cb0954b2c66-0 | langchain.chains.openai_functions.qa_with_structure.create_qa_with_sources_chain¶
langchain.chains.openai_functions.qa_with_structure.create_qa_with_sources_chain(llm: BaseLanguageModel, **kwargs: Any) → LLMChain[source]¶
Create a question answering chain that returns an answer with sources.
Parameters
llm – Language m... | https://api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.qa_with_structure.create_qa_with_sources_chain.html |
140721de7447-0 | langchain.chains.llm_summarization_checker.base.LLMSummarizationCheckerChain¶ | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_summarization_checker.base.LLMSummarizationCheckerChain.html |
140721de7447-1 | class langchain.chains.llm_summarization_checker.base.LLMSummarizationCheckerChain(*, memory: Optional[BaseMemory] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, verbose: bool = None, tags: Optional[List[str]] = None, se... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_summarization_checker.base.LLMSummarizationCheckerChain.html |
140721de7447-2 | Summary:\n"""\n{summary}\n"""\n\nUsing these checked assertions, rewrite the original summary to be completely true.\n\nThe output should have the same structure and formatting as the original summary.\n\nSummary:', template_format='f-string', validate_template=True), are_all_true_prompt: PromptTemplate = PromptTemplat... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_summarization_checker.base.LLMSummarizationCheckerChain.html |
140721de7447-3 | Bases: Chain
Chain for question-answering with self-verification.
Example
from langchain import OpenAI, LLMSummarizationCheckerChain
llm = OpenAI(temperature=0.0)
checker_chain = LLMSummarizationCheckerChain.from_llm(llm)
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationE... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_summarization_checker.base.LLMSummarizationCheckerChain.html |
140721de7447-4 | 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 ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_summarization_checker.base.LLMSummarizationCheckerChain.html |
140721de7447-5 | 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 revised_summary_prompt: PromptTemplate = PromptTemplate(input_variables=['che... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_summarization_checker.base.LLMSummarizationCheckerChain.html |
140721de7447-6 | 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 key... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_summarization_checker.base.LLMSummarizationCheckerChain.html |
140721de7447-7 | 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 re... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_summarization_checker.base.LLMSummarizationCheckerChain.html |
140721de7447-8 | classmethod from_llm(llm: BaseLanguageModel, create_assertions_prompt: PromptTemplate = PromptTemplate(input_variables=['summary'], output_parser=None, partial_variables={}, template='Given some text, extract a list of facts from the text.\n\nFormat your output as a bulleted list.\n\nText:\n"""\n{summary}\n"""\n\nFacts... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_summarization_checker.base.LLMSummarizationCheckerChain.html |
140721de7447-9 | partial_variables={}, template='Below are some assertions that have been fact checked and are labeled as true or false.\n\nIf all of the assertions are true, return "True". If any of the assertions are false, return "False".\n\nHere are some examples:\n===\n\nChecked Assertions: """\n- The sky is red: False\n- Water is... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_summarization_checker.base.LLMSummarizationCheckerChain.html |
140721de7447-10 | 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, all fields[source]¶
Raise deprecation warn... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_summarization_checker.base.LLMSummarizationCheckerChain.html |
140721de7447-11 | 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'¶ | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_summarization_checker.base.LLMSummarizationCheckerChain.html |
36b1cd0424d7-0 | langchain.chains.qa_with_sources.retrieval.RetrievalQAWithSourcesChain¶
class langchain.chains.qa_with_sources.retrieval.RetrievalQAWithSourcesChain(*, memory: Optional[BaseMemory] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.retrieval.RetrievalQAWithSourcesChain.html |
36b1cd0424d7-1 | enforced only for StuffDocumentChain and if reduce_k_below_max_tokens is to true
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... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.retrieval.RetrievalQAWithSourcesChain.html |
36b1cd0424d7-2 | 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 p... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.retrieval.RetrievalQAWithSourcesChain.html |
36b1cd0424d7-3 | 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 cha... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.retrieval.RetrievalQAWithSourcesChain.html |
36b1cd0424d7-4 | 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: BasePromptTe... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.retrieval.RetrievalQAWithSourcesChain.html |
36b1cd0424d7-5 | 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... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.retrieval.RetrievalQAWithSourcesChain.html |
36b1cd0424d7-6 | \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 ha... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.retrieval.RetrievalQAWithSourcesChain.html |
36b1cd0424d7-7 | \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 bu... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.retrieval.RetrievalQAWithSourcesChain.html |
36b1cd0424d7-8 | 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=========... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.retrieval.RetrievalQAWithSourcesChain.html |
36b1cd0424d7-9 | 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 dep... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.retrieval.RetrievalQAWithSourcesChain.html |
36b1cd0424d7-10 | 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'¶ | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.retrieval.RetrievalQAWithSourcesChain.html |
755f1e8834e9-0 | langchain.chains.api.openapi.requests_chain.APIRequesterChain¶
class langchain.chains.api.openapi.requests_chain.APIRequesterChain(*, memory: Optional[BaseMemory] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, verbose: b... | https://api.python.langchain.com/en/latest/chains/langchain.chains.api.openapi.requests_chain.APIRequesterChain.html |
755f1e8834e9-1 | There are many different types of memory - please see memory docs
for the full catalog.
param output_key: str = 'text'¶
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 [Require... | https://api.python.langchain.com/en/latest/chains/langchain.chains.api.openapi.requests_chain.APIRequesterChain.html |
755f1e8834e9-2 | 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[Base... | https://api.python.langchain.com/en/latest/chains/langchain.chains.api.openapi.requests_chain.APIRequesterChain.html |
755f1e8834e9-3 | 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[BaseCallba... | https://api.python.langchain.com/en/latest/chains/langchain.chains.api.openapi.requests_chain.APIRequesterChain.html |
755f1e8834e9-4 | Create outputs from response.
dict(**kwargs: Any) → Dict¶
Return dictionary representation of chain.
classmethod from_llm_and_typescript(llm: BaseLanguageModel, typescript_definition: str, verbose: bool = True, **kwargs: Any) → LLMChain[source]¶
Get the request parser.
classmethod from_string(llm: BaseLanguageModel, te... | https://api.python.langchain.com/en/latest/chains/langchain.chains.api.openapi.requests_chain.APIRequesterChain.html |
755f1e8834e9-5 | 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... | https://api.python.langchain.com/en/latest/chains/langchain.chains.api.openapi.requests_chain.APIRequesterChain.html |
4b222e450b80-0 | langchain.chains.api.openapi.response_chain.APIResponderChain¶
class langchain.chains.api.openapi.response_chain.APIResponderChain(*, memory: Optional[BaseMemory] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, verbose: b... | https://api.python.langchain.com/en/latest/chains/langchain.chains.api.openapi.response_chain.APIResponderChain.html |
4b222e450b80-1 | for the full catalog.
param output_key: str = 'text'¶
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 [Required]¶
Prompt object to use.
param return_final_only: bool = True¶
Wh... | https://api.python.langchain.com/en/latest/chains/langchain.chains.api.openapi.response_chain.APIResponderChain.html |
4b222e450b80-2 | 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 ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.api.openapi.response_chain.APIResponderChain.html |
4b222e450b80-3 | 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[BaseCallba... | https://api.python.langchain.com/en/latest/chains/langchain.chains.api.openapi.response_chain.APIResponderChain.html |
4b222e450b80-4 | Create outputs from response.
dict(**kwargs: Any) → Dict¶
Return dictionary representation of chain.
classmethod from_llm(llm: BaseLanguageModel, verbose: bool = True, **kwargs: Any) → LLMChain[source]¶
Get the response parser.
classmethod from_string(llm: BaseLanguageModel, template: str) → LLMChain¶
Create LLMChain f... | https://api.python.langchain.com/en/latest/chains/langchain.chains.api.openapi.response_chain.APIResponderChain.html |
4b222e450b80-5 | 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¶
... | https://api.python.langchain.com/en/latest/chains/langchain.chains.api.openapi.response_chain.APIResponderChain.html |
ba08dea15dd2-0 | 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]¶ | https://api.python.langchain.com/en/latest/chains/langchain.chains.query_constructor.ir.Expr.html |
3ccc72295d09-0 | langchain.chains.llm_math.base.LLMMathChain¶
class langchain.chains.llm_math.base.LLMMathChain(*, memory: Optional[BaseMemory] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, verbose: bool = None, tags: Optional[List[str]... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_math.base.LLMMathChain.html |
3ccc72295d09-1 | Bases: Chain
Chain that interprets a prompt and executes python code to do math.
Example
from langchain import LLMMathChain, OpenAI
llm_math = LLMMathChain.from_llm(OpenAI())
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_math.base.LLMMathChain.html |
3ccc72295d09-2 | There are many different types of memory - please see memory docs
for the full catalog.
param prompt: BasePromptTemplate = PromptTemplate(input_variables=['question'], output_parser=None, partial_variables={}, template='Translate a math problem into a expression that can be executed using Python\'s numexpr library. Use... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_math.base.LLMMathChain.html |
3ccc72295d09-3 | 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: Opt... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_math.base.LLMMathChain.html |
3ccc72295d09-4 | 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[BaseCallbac... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_math.base.LLMMathChain.html |
3ccc72295d09-5 | dict(**kwargs: Any) → Dict¶
Return dictionary representation of chain.
classmethod from_llm(llm: BaseLanguageModel, prompt: BasePromptTemplate = PromptTemplate(input_variables=['question'], output_parser=None, partial_variables={}, template='Translate a math problem into a expression that can be executed using Python\'... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_math.base.LLMMathChain.html |
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