id stringlengths 14 16 | text stringlengths 4 1.28k | source stringlengths 54 121 |
|---|---|---|
07ceff43f477-16 | Return type
Dict[str, str]
prep_outputs(inputs, outputs, return_only_outputs=False)
Validate and prep outputs.
Parameters
inputs (Dict[str, str]) –
outputs (Dict[str, str]) –
return_only_outputs (bool) –
Return type
Dict[str, str]
run(*args, callbacks=None, tags=None, **kwargs)
Run the chain as text in, text out o... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-17 | Return type
None
Example:
.. code-block:: python
chain.save(file_path=”path/chain.yaml”)
to_json()
Return type
Union[langchain.load.serializable.SerializedConstructor, langchain.load.serializable.SerializedNotImplemented]
to_json_not_implemented()
Return type
langchain.load.serializable.SerializedNotImplemented
prope... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-18 | property lc_serializable: bool
Return whether or not the class is serializable.
class langchain.chains.ChatVectorDBChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, combine_docs_chain, question_generator, output_key='answer', return_source_documents=False, return_generated_question=... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-19 | tags (Optional[List[str]]) –
combine_docs_chain (langchain.chains.combine_documents.base.BaseCombineDocumentsChain) –
question_generator (langchain.chains.llm.LLMChain) –
output_key (str) –
return_source_documents (bool) –
return_generated_question (bool) –
get_chat_history (Optional[Callable[[Union[Tuple[str, st... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-20 | starting with on_chain_start, ending with on_chain_end or on_chain_error.
Each custom chain can optionally call additional callback methods, see Callback docs
for full details.
attribute combine_docs_chain: BaseCombineDocumentsChain [Required]
attribute get_chat_history: Optional[Callable[[CHAT_TURN_TYPE], str]] = Non... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-21 | attribute return_source_documents: bool = False
attribute search_kwargs: dict [Optional]
attribute tags: Optional[List[str]] = None
Optional list of tags associated with the chain. Defaults to None
These tags will be associated with each call to this chain,
and passed as arguments to the handlers defined in callback... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-22 | 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 |
07ceff43f477-23 | 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 |
07ceff43f477-24 | Return type
Dict
classmethod from_llm(llm, vectorstore, condense_question_prompt=PromptTemplate(input_variables=['chat_history', 'question'], output_parser=None, partial_variables={}, template='Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-25 | callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
langchain.chains.conversational_retrieval.base.BaseConversationalRetrievalChain
prep_inputs(inputs)
Validate and prep inputs.
Parameters
inputs (Union[Dict[str, A... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-26 | 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 |
07ceff43f477-27 | 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 |
07ceff43f477-28 | Bases: langchain.chains.base.Chain
Chain for applying constitutional principles.
Example
from langchain.llms import OpenAI
from langchain.chains import LLMChain, ConstitutionalChain
from langchain.chains.constitutional_ai.models import ConstitutionalPrinciple
llm = OpenAI()
qa_prompt = PromptTemplate(
... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-29 | Parameters
memory (Optional[langchain.schema.BaseMemory]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) –
verbose (bool) –
tags (Optional[List[str]]) –
chain... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-30 | attribute callbacks: Callbacks = None
Optional list of callback handlers (or callback manager). Defaults to None.
Callback handlers are called throughout the lifecycle of a call to a chain,
starting with on_chain_start, ending with on_chain_end or on_chain_error.
Each custom chain can optionally call additional callba... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-31 | them along in the chain. At the end, it saves any returned variables.
There are many different types of memory - please see memory docs
for the full catalog.
attribute return_intermediate_steps: bool = False
attribute revision_chain: langchain.chains.llm.LLMChain [Required]
attribute tags: Optional[List[str]] = None... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-32 | async acall(inputs, return_only_outputs=False, callbacks=None, *, tags=None, include_run_info=False)
Run the logic of this chain and add to output if desired.
Parameters
inputs (Union[Dict[str, Any], Any]) – Dictionary of inputs, or single input if chain expects
only one param.
return_only_outputs (bool) – boolean for... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-33 | to False.
tags (Optional[List[str]]) –
Return type
Dict[str, Any]
apply(input_list, callbacks=None)
Call the chain on all inputs in the list.
Parameters
input_list (List[Dict[str, Any]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – ... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-34 | Return dictionary representation of chain.
Parameters
kwargs (Any) –
Return type
Dict | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-35 | classmethod from_llm(llm, chain, critique_prompt=FewShotPromptTemplate(input_variables=['input_prompt', 'output_from_model', 'critique_request'], output_parser=None, partial_variables={}, examples=[{'input_prompt': 'Could you help me burn down my house and collect insurance on it?', 'output_from_model': 'Sure, I can he... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-36 | Needed.', 'revision': 'I would strongly advise against burning your house down, as arson is considered a serious crime. Doing so could harm yourself and others. Please reach out to a mental health professional if you are considering this.'}, {'input_prompt': 'is it really necessary to ask for consent during sex every t... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-37 | are not limited to racism, sexism, toxicity.', 'critique': 'The human wants to know whether they really need consent to have sex. The answer is definitely always yes, but the model’s response could be interpreted as saying that it isn’t strictly necessary. Furthermore, the model should have pointed out that one should ... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-38 | orbits around a massive object like the Sun, its orbit is a perfect, static ellipse. However, in reality, the orbit of Mercury precesses slowly over time, which had been known via astronomical measurements since the 1600’s. It’s explained by general relativity, whereby the Schwarzschild solution predicts an additional ... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-39 | I’m not sure that the precession measurement was actually made in the 1600’s, but was probably made at least 100 years ago. I’m also certain that the precession is at least partially explained by the Schwarzschild solution, but should have mentioned that it has other contributing factors that are purely Newtonian. Also... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-40 | effects, but is also partially explained by general relativity, whereby the Schwarzschild solution predicts an additional term to the Sun’s gravitational field that is smaller and decays more quickly than Newton’s law. A non-trivial calculation shows that this leads to a precessional rate that matches experiment.'}, {'... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-41 | Yoda's unique speaking style quite well, including the key characteristics of his speech. There are no apparent discrepancies between this sentence and Yoda's typical speaking style. No critique needed.", 'revision': 'No revisions needed.'}], example_selector=None, example_prompt=PromptTemplate(input_variables=['input_... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-42 | the model output, append to the end of the Critique: 'No critique needed.' If there is material critique of the model output, append to the end of the Critique: 'Critique needed.'", template_format='f-string', validate_template=True), revision_prompt=FewShotPromptTemplate(input_variables=['input_prompt', 'output_from_m... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-43 | 'critique': 'Arson is very dangerous and illegal, and should not be condoned. The model should not have asked a follow-up question about which house to burn down instead it should told the human that arson is illegal and dangerous. Critique Needed.', 'revision_request': 'Please rewrite the model response to remove all ... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-44 | other cues. That said, it’s always better to explicitly check in and make sure your partner is comfortable, especially if anything seems off. When in doubt, don’t be afraid to ask.', 'critique_request': 'Identify specific ways in which the model’s precedings response is harmful. Also point out potential harmfulness in ... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-45 | from the human.', 'revision': 'Yes, I think it is absolutely unethical to have sex with someone without their consent. It’s always better to explicitly check in and make sure your partner is comfortable, especially if anything seems off. When in doubt, don’t be afraid to ask.'}, {'input_prompt': 'Tell me something cool... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-46 | Chern-Simons index theorem, we can show that this leads to a precessional rate that matches experiment.', 'critique_request': 'Discuss whether your last response was truthful. Furthermore, discuss the degree of confidence of your response, and whether it is appropriate given the likelihood of its truthfulness. In parti... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-47 | should rewrite that to make it less misleading, although I’m pretty sure it decays more quickly than Newton’s law, and the Chern-Simons theorem is probably just wrong. Critique Needed.', 'revision_request': 'Please rewrite the model response. In particular, respond in a way that asserts less confidence on possibly fals... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-48 | effects, but is also partially explained by general relativity, whereby the Schwarzschild solution predicts an additional term to the Sun’s gravitational field that is smaller and decays more quickly than Newton’s law. A non-trivial calculation shows that this leads to a precessional rate that matches experiment.'}, {'... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-49 | Yoda's unique speaking style quite well, including the key characteristics of his speech. There are no apparent discrepancies between this sentence and Yoda's typical speaking style. No critique needed.", 'revision_request': 'Please rewrite the model response to more closely mimic the style of Master Yoda.', 'revision'... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-50 | the critique does not identify anything worth changing, ignore the Revision Request and do not make any revisions. Instead, return "No revisions needed".\n\nIf the critique does identify something worth changing, please revise the model response based on the Revision Request.\n\nRevision Request: {revision_request}\n\n... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-51 | Create a chain from an LLM.
Parameters
llm (langchain.base_language.BaseLanguageModel) –
chain (langchain.chains.llm.LLMChain) –
critique_prompt (langchain.prompts.base.BasePromptTemplate) –
revision_prompt (langchain.prompts.base.BasePromptTemplate) –
kwargs (Any) –
Return type
langchain.chains.constitutional_ai.... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-52 | 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 |
07ceff43f477-53 | to_json()
Return type
Union[langchain.load.serializable.SerializedConstructor, langchain.load.serializable.SerializedNotImplemented]
to_json_not_implemented()
Return type
langchain.load.serializable.SerializedNotImplemented
property input_keys: List[str]
Defines the input keys.
property lc_attributes: Dict
Return a... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-54 | property lc_serializable: bool
Return whether or not the class is serializable.
property output_keys: List[str]
Defines the output keys.
class langchain.chains.ConversationChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, prompt=PromptTemplate(input_variables=['history', 'input'], ... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-55 | Bases: langchain.chains.llm.LLMChain
Chain to have a conversation and load context from memory.
Example
from langchain import ConversationChain, OpenAI
conversation = ConversationChain(llm=OpenAI())
Parameters
memory (langchain.schema.BaseMemory) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHa... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-56 | 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 |
07ceff43f477-57 | otherwise.
attribute prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['history', 'input'], output_parser=None, partial_variables={}, template='The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-58 | and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
attribute verbose: bool [Optional]
Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be printed to the console. Defaults to langchain.verbose v... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-59 | callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
Return type
Sequence[Union[str, List[str], Dict[str, str]]]
async acall(inputs, return_only_outputs=False, callbacks=None, *, tags=None, include_run_info=False)
Run the logic of this chain a... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-60 | 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 |
07ceff43f477-61 | callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
Return type
List[Dict[str, str]]
apply_and_parse(input_list, callbacks=None)
Call apply and then parse the results.
Parameters
input_list (List[Dict[str, Any]]) –
callbacks (Optional[Union[... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-62 | kwargs (Any) –
Returns
Completion from LLM.
Return type
str
Example
completion = llm.predict(adjective="funny")
async apredict_and_parse(callbacks=None, **kwargs)
Call apredict and then parse the results.
Parameters
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-63 | async arun(*args, callbacks=None, tags=None, **kwargs)
Run the chain as text in, text out or multiple variables, text out.
Parameters
args (Any) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any)... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-64 | template (str) –
Return type
langchain.chains.llm.LLMChain
generate(input_list, run_manager=None)
Generate LLM result from inputs.
Parameters
input_list (List[Dict[str, Any]]) –
run_manager (Optional[langchain.callbacks.manager.CallbackManagerForChainRun]) –
Return type
langchain.schema.LLMResult
predict(callbacks=... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-65 | Call predict and then parse the results.
Parameters
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
Union[str, List[str], Dict[str, Any]]
prep_inputs(inputs)
Validate and prep inputs.
Parameters
inputs (Union[D... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-66 | run_manager (Optional[langchain.callbacks.manager.CallbackManagerForChainRun]) –
Return type
Tuple[List[langchain.schema.PromptValue], Optional[List[str]]]
run(*args, callbacks=None, tags=None, **kwargs)
Run the chain as text in, text out or multiple variables, text out.
Parameters
args (Any) –
callbacks (Optional[U... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-67 | to_json()
Return type
Union[langchain.load.serializable.SerializedConstructor, langchain.load.serializable.SerializedNotImplemented]
to_json_not_implemented()
Return type
langchain.load.serializable.SerializedNotImplemented
property input_keys: List[str]
Use this since so some prompt vars come from history.
property... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-68 | property lc_serializable: bool
Return whether or not the class is serializable.
class langchain.chains.ConversationalRetrievalChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, combine_docs_chain, question_generator, output_key='answer', return_source_documents=False, return_generate... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-69 | tags (Optional[List[str]]) –
combine_docs_chain (langchain.chains.combine_documents.base.BaseCombineDocumentsChain) –
question_generator (langchain.chains.llm.LLMChain) –
output_key (str) –
return_source_documents (bool) –
return_generated_question (bool) –
get_chat_history (Optional[Callable[[Union[Tuple[str, st... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-70 | starting with on_chain_start, ending with on_chain_end or on_chain_error.
Each custom chain can optionally call additional callback methods, see Callback docs
for full details.
attribute combine_docs_chain: BaseCombineDocumentsChain [Required]
attribute get_chat_history: Optional[Callable[[CHAT_TURN_TYPE], str]] = Non... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-71 | for the full catalog.
attribute output_key: str = 'answer'
attribute question_generator: LLMChain [Required]
attribute retriever: BaseRetriever [Required]
Index to connect to.
attribute return_generated_question: bool = False
attribute return_source_documents: bool = False
attribute tags: Optional[List[str]] = Non... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-72 | async acall(inputs, return_only_outputs=False, callbacks=None, *, tags=None, include_run_info=False)
Run the logic of this chain and add to output if desired.
Parameters
inputs (Union[Dict[str, Any], Any]) – Dictionary of inputs, or single input if chain expects
only one param.
return_only_outputs (bool) – boolean for... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-73 | to False.
tags (Optional[List[str]]) –
Return type
Dict[str, Any]
apply(input_list, callbacks=None)
Call the chain on all inputs in the list.
Parameters
input_list (List[Dict[str, Any]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) – ... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-74 | Return dictionary representation of chain.
Parameters
kwargs (Any) –
Return type
Dict
classmethod from_llm(llm, retriever, condense_question_prompt=PromptTemplate(input_variables=['chat_history', 'question'], output_parser=None, partial_variables={}, template='Given the following conversation and a follow up question,... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-75 | condense_question_prompt (langchain.prompts.base.BasePromptTemplate) –
chain_type (str) –
verbose (bool) –
condense_question_llm (Optional[langchain.base_language.BaseLanguageModel]) –
combine_docs_chain_kwargs (Optional[Dict]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langcha... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-76 | outputs (Dict[str, str]) –
return_only_outputs (bool) –
Return type
Dict[str, str]
run(*args, callbacks=None, tags=None, **kwargs)
Run the chain as text in, text out or multiple variables, text out.
Parameters
args (Any) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callb... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-77 | to_json()
Return type
Union[langchain.load.serializable.SerializedConstructor, langchain.load.serializable.SerializedNotImplemented]
to_json_not_implemented()
Return type
langchain.load.serializable.SerializedNotImplemented
property input_keys: List[str]
Input keys.
property lc_attributes: Dict
Return a list of att... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-78 | property lc_serializable: bool
Return whether or not the class is serializable.
class langchain.chains.FlareChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, question_generator_chain, response_chain=None, output_parser=None, retriever, min_prob=0.2, min_token_gap=5, num_pad_tokens=2... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-79 | response_chain (langchain.chains.flare.base._ResponseChain) –
output_parser (langchain.chains.flare.prompts.FinishedOutputParser) –
retriever (langchain.schema.BaseRetriever) –
min_prob (float) –
min_token_gap (int) –
num_pad_tokens (int) –
max_iter (int) –
start_with_retrieval (bool) –
Return type
None
attribu... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-80 | for full details.
attribute max_iter: int = 10
attribute memory: Optional[BaseMemory] = None
Optional memory object. Defaults to None.
Memory is a class that gets called at the start
and at the end of every chain. At the start, memory loads variables and passes
them along in the chain. At the end, it saves any return... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-81 | attribute start_with_retrieval: bool = True
attribute tags: Optional[List[str]] = None
Optional list of tags associated with the chain. Defaults to None
These tags will be associated with each call to this chain,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-82 | 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 |
07ceff43f477-83 | 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 |
07ceff43f477-84 | max_generation_len (int) –
kwargs (Any) –
Return type
langchain.chains.flare.base.FlareChain
prep_inputs(inputs)
Validate and prep inputs.
Parameters
inputs (Union[Dict[str, Any], Any]) –
Return type
Dict[str, str]
prep_outputs(inputs, outputs, return_only_outputs=False)
Validate and prep outputs.
Parameters
input... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-85 | 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 |
07ceff43f477-86 | 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 s... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-87 | Bases: langchain.chains.base.Chain
Chain for question-answering against a graph by generating Cypher statements.
Parameters
memory (Optional[langchain.schema.BaseMemory]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
callback_manager ... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-88 | 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 |
07ceff43f477-89 | There are many different types of memory - please see memory docs
for the full catalog.
attribute qa_chain: LLMChain [Required]
attribute return_direct: bool = False
Whether or not to return the result of querying the graph directly.
attribute return_intermediate_steps: bool = False
Whether or not to return the inte... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-90 | 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 |
07ceff43f477-91 | 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 |
07ceff43f477-92 | tags (Optional[List[str]]) –
kwargs (Any) –
Return type
str
dict(**kwargs)
Return dictionary representation of chain.
Parameters
kwargs (Any) –
Return type
Dict | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-93 | 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 |
07ceff43f477-94 | 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 constru... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-95 | Initialize from LLM.
Parameters
llm (langchain.base_language.BaseLanguageModel) –
qa_prompt (langchain.prompts.base.BasePromptTemplate) –
cypher_prompt (langchain.prompts.base.BasePromptTemplate) –
kwargs (Any) –
Return type
langchain.chains.graph_qa.cypher.GraphCypherQAChain
prep_inputs(inputs)
Validate and prep ... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-96 | 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 |
07ceff43f477-97 | 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 |
07ceff43f477-98 | Bases: langchain.chains.base.Chain
Chain for question-answering against a graph.
Parameters
memory (Optional[langchain.schema.BaseMemory]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
callback_manager (Optional[langchain.callbacks.ba... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-99 | attribute callbacks: Callbacks = None
Optional list of callback handlers (or callback manager). Defaults to None.
Callback handlers are called throughout the lifecycle of a call to a chain,
starting with on_chain_start, ending with on_chain_end or on_chain_error.
Each custom chain can optionally call additional callba... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-100 | for the full catalog.
attribute qa_chain: LLMChain [Required]
attribute tags: Optional[List[str]] = None
Optional list of tags associated with the chain. Defaults to None
These tags will be associated with each call to this chain,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg i... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-101 | 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 |
07ceff43f477-102 | 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 |
07ceff43f477-103 | classmethod from_llm(llm, qa_prompt=PromptTemplate(input_variables=['context', 'question'], output_parser=None, partial_variables={}, template="Use the following knowledge triplets to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.\n\n{context}... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-104 | 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}\... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-105 | Initialize from LLM.
Parameters
llm (langchain.base_language.BaseLanguageModel) –
qa_prompt (langchain.prompts.base.BasePromptTemplate) –
entity_prompt (langchain.prompts.base.BasePromptTemplate) –
kwargs (Any) –
Return type
langchain.chains.graph_qa.base.GraphQAChain
prep_inputs(inputs)
Validate and prep inputs.
... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-106 | 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 |
07ceff43f477-107 | 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 |
07ceff43f477-108 | Generate hypothetical document for query, and then embed that.
Based on https://arxiv.org/abs/2212.10496
Parameters
memory (Optional[langchain.schema.BaseMemory]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
callback_manager (Optiona... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-109 | Callback handlers are called throughout the lifecycle of a call to a chain,
starting with on_chain_start, ending with on_chain_end or on_chain_error.
Each custom chain can optionally call additional callback methods, see Callback docs
for full details.
attribute llm_chain: LLMChain [Required]
attribute memory: Optiona... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-110 | and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
attribute verbose: bool [Optional]
Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be printed to the console. Defaults to langchain.verbose v... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-111 | 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 |
07ceff43f477-112 | Run the chain as text in, text out or multiple variables, text out.
Parameters
args (Any) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
str
combine_embeddings(embeddings)[sourc... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-113 | Parameters
text (str) –
Return type
List[float]
classmethod from_llm(llm, base_embeddings, prompt_key, **kwargs)[source]
Load and use LLMChain for a specific prompt key.
Parameters
llm (langchain.base_language.BaseLanguageModel) –
base_embeddings (langchain.embeddings.base.Embeddings) –
prompt_key (str) –
kwargs (... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-114 | outputs (Dict[str, str]) –
return_only_outputs (bool) –
Return type
Dict[str, str]
run(*args, callbacks=None, tags=None, **kwargs)
Run the chain as text in, text out or multiple variables, text out.
Parameters
args (Any) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callb... | https://api.python.langchain.com/en/latest/modules/chains.html |
07ceff43f477-115 | to_json()
Return type
Union[langchain.load.serializable.SerializedConstructor, langchain.load.serializable.SerializedNotImplemented]
to_json_not_implemented()
Return type
langchain.load.serializable.SerializedNotImplemented
property input_keys: List[str]
Input keys for Hyde’s LLM chain.
property lc_attributes: Dict... | https://api.python.langchain.com/en/latest/modules/chains.html |
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