id stringlengths 14 16 | text stringlengths 31 2.41k | source stringlengths 54 121 |
|---|---|---|
09aa860bdfb8-9 | 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 |
09aa860bdfb8-10 | Callback handlers are called throughout the lifecycle of a call to a chain,
starting with on_chain_start, ending with on_chain_end or on_chain_error.
Each custom chain can optionally call additional callback methods, see Callback docs
for full details.
attribute combine_docs_chain: BaseCombineDocumentsChain [Required]... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-11 | 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 |
09aa860bdfb8-12 | Parameters
kwargs (Any) –
Return type
Dict
classmethod from_llm(llm, vectorstore, condense_question_prompt=PromptTemplate(input_variables=['chat_history', 'question'], output_parser=None, partial_variables={}, template='Given the following conversation and a follow up question, rephrase the follow up question to be a ... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-13 | 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 |
09aa860bdfb8-14 | 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 |
09aa860bdfb8-15 | 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/modules/chains.html |
09aa860bdfb8-16 | 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 |
09aa860bdfb8-17 | 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 |
09aa860bdfb8-18 | 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 |
09aa860bdfb8-19 | 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 |
09aa860bdfb8-20 | Master Yoda's unique speaking style quite well, including the key characteristics of his speech. There are no apparent discrepancies between this sentence and Yoda's typical speaking style. No critique needed.", 'revision': 'No revisions needed.'}], example_selector=None, example_prompt=PromptTemplate(input_variables=[... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-21 | toxicity.', 'critique': 'Arson is very dangerous and illegal, and should not be condoned. The model should not have asked a follow-up question about which house to burn down instead it should told the human that arson is illegal and dangerous. Critique Needed.', 'revision_request': 'Please rewrite the model response to... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-22 | harmful assumptions from the human.', 'revision': 'Yes, I think it is absolutely unethical to have sex with someone without their consent. It’s always better to explicitly check in and make sure your partner is comfortable, especially if anything seems off. When in doubt, don’t be afraid to ask.'}, {'input_prompt': 'Te... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-23 | I should rewrite that to make it less misleading, although I’m pretty sure it decays more quickly than Newton’s law, and the Chern-Simons theorem is probably just wrong. Critique Needed.', 'revision_request': 'Please rewrite the model response. In particular, respond in a way that asserts less confidence on possibly fa... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-24 | 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 |
09aa860bdfb8-25 | 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 |
09aa860bdfb8-26 | chain.save(file_path=”path/chain.yaml”)
to_json()
Return type
Union[langchain.load.serializable.SerializedConstructor, langchain.load.serializable.SerializedNotImplemented]
to_json_not_implemented()
Return type
langchain.load.serializable.SerializedNotImplemented
property input_keys: List[str]
Defines the input keys... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-27 | 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 |
09aa860bdfb8-28 | Defaults to one that takes the most likely string but does not change it
otherwise.
attribute prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['history', 'input'], output_parser=None, partial_variables={}, template='The following is a friendly conversation between a human and an AI. T... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-29 | Parameters
input_list (List[Dict[str, Any]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
Return type
Sequence[Union[str, List[str], Dict[str, str]]]
async acall(inputs, return_only_outputs=False, callbacks=None, *, tags=None, includ... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-30 | Parameters
input_list (List[Dict[str, Any]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
Return type
List[Dict[str, str]]
apply_and_parse(input_list, callbacks=None)
Call apply and then parse the results.
Parameters
input_list (Lis... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-31 | Return type
Tuple[List[langchain.schema.PromptValue], Optional[List[str]]]
async arun(*args, callbacks=None, tags=None, **kwargs)
Run the chain as text in, text out or multiple variables, text out.
Parameters
args (Any) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callback... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-32 | Returns
Completion from LLM.
Return type
str
Example
completion = llm.predict(adjective="funny")
predict_and_parse(callbacks=None, **kwargs)
Call predict and then parse the results.
Parameters
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) ... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-33 | 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 |
09aa860bdfb8-34 | verbose (bool) –
tags (Optional[List[str]]) –
combine_docs_chain (langchain.chains.combine_documents.base.BaseCombineDocumentsChain) –
question_generator (langchain.chains.llm.LLMChain) –
output_key (str) –
return_source_documents (bool) –
return_generated_question (bool) –
get_chat_history (Optional[Callable[[U... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-35 | There are many different types of memory - please see memory docs
for the full catalog.
attribute output_key: str = 'answer'
attribute question_generator: LLMChain [Required]
attribute retriever: BaseRetriever [Required]
Index to connect to.
attribute return_generated_question: bool = False
attribute return_source_... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-36 | 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 |
09aa860bdfb8-37 | 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 |
09aa860bdfb8-38 | to_json_not_implemented()
Return type
langchain.load.serializable.SerializedNotImplemented
property input_keys: List[str]
Input keys.
property lc_attributes: Dict
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_names... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-39 | retriever (langchain.schema.BaseRetriever) –
min_prob (float) –
min_token_gap (int) –
num_pad_tokens (int) –
max_iter (int) –
start_with_retrieval (bool) –
Return type
None
attribute callback_manager: Optional[BaseCallbackManager] = None
Deprecated, use callbacks instead.
attribute callbacks: Callbacks = None
O... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-40 | Optional list of tags associated with the chain. Defaults to None
These tags will be associated with each call to this chain,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
attribute verbose: bool [Optional]
Whether or no... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-41 | 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 |
09aa860bdfb8-42 | Return type
str
save(file_path)
Save the chain.
Parameters
file_path (Union[pathlib.Path, str]) – Path to file to save the chain to.
Return type
None
Example:
.. code-block:: python
chain.save(file_path=”path/chain.yaml”)
to_json()
Return type
Union[langchain.load.serializable.SerializedConstructor, langchain.load.se... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-43 | Parameters
memory (Optional[langchain.schema.BaseMemory]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) –
verbose (bool) –
tags (Optional[List[str]]) –
graph... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-44 | for the full catalog.
attribute qa_chain: LLMChain [Required]
attribute return_direct: bool = False
Whether or not to return the result of querying the graph directly.
attribute return_intermediate_steps: bool = False
Whether or not to return the intermediate steps along with the final answer.
attribute tags: Option... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-45 | 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 |
09aa860bdfb8-46 | Parameters
kwargs (Any) –
Return type
Dict
classmethod from_llm(llm, *, qa_prompt=PromptTemplate(input_variables=['context', 'question'], output_parser=None, partial_variables={}, template="You are an assistant that helps to form nice and human understandable answers.\nThe information part contains the provided inform... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-47 | Return type
langchain.chains.graph_qa.cypher.GraphCypherQAChain
prep_inputs(inputs)
Validate and prep inputs.
Parameters
inputs (Union[Dict[str, Any], Any]) –
Return type
Dict[str, str]
prep_outputs(inputs, outputs, return_only_outputs=False)
Validate and prep outputs.
Parameters
inputs (Dict[str, str]) –
outputs (... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-48 | eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool
Return whether or not the class is serializable.
class langchain.chains.GraphQAChain(*, memory=None, callbacks=None,... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-49 | Each custom chain can optionally call additional callback methods, see Callback docs
for full details.
attribute entity_extraction_chain: LLMChain [Required]
attribute graph: NetworkxEntityGraph [Required]
attribute memory: Optional[BaseMemory] = None
Optional memory object. Defaults to None.
Memory is a class that ... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-50 | 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 |
09aa860bdfb8-51 | Parameters
kwargs (Any) –
Return type
Dict
classmethod from_llm(llm, qa_prompt=PromptTemplate(input_variables=['context', 'question'], output_parser=None, partial_variables={}, template="Use the following knowledge triplets to answer the question at the end. If you don't know the answer, just say that you don't know, ... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-52 | prep_inputs(inputs)
Validate and prep inputs.
Parameters
inputs (Union[Dict[str, Any], Any]) –
Return type
Dict[str, str]
prep_outputs(inputs, outputs, return_only_outputs=False)
Validate and prep outputs.
Parameters
inputs (Dict[str, str]) –
outputs (Dict[str, str]) –
return_only_outputs (bool) –
Return type
Dic... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-53 | Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool
Return whether or not the class is serializable.
class langchain.chains.HypotheticalDocumentEmbedder(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, base_emb... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-54 | 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 |
09aa860bdfb8-55 | tags (Optional[List[str]]) –
Return type
Dict[str, Any]
apply(input_list, callbacks=None)
Call the chain on all inputs in the list.
Parameters
input_list (List[Dict[str, Any]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
Return ty... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-56 | prompt_key (str) –
kwargs (Any) –
Return type
langchain.chains.hyde.base.HypotheticalDocumentEmbedder
prep_inputs(inputs)
Validate and prep inputs.
Parameters
inputs (Union[Dict[str, Any], Any]) –
Return type
Dict[str, str]
prep_outputs(inputs, outputs, return_only_outputs=False)
Validate and prep outputs.
Paramet... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-57 | constructor.
property lc_namespace: List[str]
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool
Return whether or not t... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-58 | 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/modules/chains.html |
09aa860bdfb8-59 | return_only_outputs (bool) – boolean for whether to return only outputs in the
response. If True, only new keys generated by this chain will be
returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False.
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallba... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-60 | 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 |
09aa860bdfb8-61 | Initialize from LLM.
Parameters
llm (langchain.base_language.BaseLanguageModel) –
qa_prompt (langchain.prompts.base.BasePromptTemplate) –
cypher_prompt (langchain.prompts.base.BasePromptTemplate) –
kwargs (Any) –
Return type
langchain.chains.graph_qa.kuzu.KuzuQAChain
prep_inputs(inputs)
Validate and prep inputs.
P... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-62 | Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]
Return a map of constructor ar... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-63 | Chain that interprets a prompt and executes bash code to perform bash operations.
Example
from langchain import LLMBashChain, OpenAI
llm_bash = LLMBashChain.from_llm(OpenAI())
Parameters
memory (Optional[langchain.schema.BaseMemory]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langc... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-64 | and at the end of every chain. At the start, memory loads variables and passes
them along in the chain. At the end, it saves any returned variables.
There are many different types of memory - please see memory docs
for the full catalog.
attribute prompt: BasePromptTemplate = PromptTemplate(input_variables=['question'],... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-65 | 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 |
09aa860bdfb8-66 | Parameters
kwargs (Any) –
Return type
Dict
classmethod from_llm(llm, prompt=PromptTemplate(input_variables=['question'], output_parser=BashOutputParser(), partial_variables={}, template='If someone asks you to perform a task, your job is to come up with a series of bash commands that will perform the task. There is no... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-67 | run(*args, callbacks=None, tags=None, **kwargs)
Run the chain as text in, text out or multiple variables, text out.
Parameters
args (Any) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Ret... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-68 | Bases: langchain.chains.base.Chain
Chain to run queries against LLMs.
Example
from langchain import LLMChain, OpenAI, PromptTemplate
prompt_template = "Tell me a {adjective} joke"
prompt = PromptTemplate(
input_variables=["adjective"], template=prompt_template
)
llm = LLMChain(llm=OpenAI(), prompt=prompt)
Parameter... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-69 | 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 |
09aa860bdfb8-70 | Parameters
input_list (List[Dict[str, Any]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
Return type
Sequence[Union[str, List[str], Dict[str, str]]]
async acall(inputs, return_only_outputs=False, callbacks=None, *, tags=None, includ... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-71 | Parameters
input_list (List[Dict[str, Any]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
Return type
List[Dict[str, str]]
apply_and_parse(input_list, callbacks=None)[source]
Call apply and then parse the results.
Parameters
input_l... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-72 | Return type
Tuple[List[langchain.schema.PromptValue], Optional[List[str]]]
async arun(*args, callbacks=None, tags=None, **kwargs)
Run the chain as text in, text out or multiple variables, text out.
Parameters
args (Any) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callback... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-73 | Returns
Completion from LLM.
Return type
str
Example
completion = llm.predict(adjective="funny")
predict_and_parse(callbacks=None, **kwargs)[source]
Call predict and then parse the results.
Parameters
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackMan... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-74 | 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 |
09aa860bdfb8-75 | property lc_serializable: bool
Return whether or not the class is serializable.
class langchain.chains.LLMCheckerChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, question_to_checked_assertions_chain, llm=None, create_draft_answer_prompt=PromptTemplate(input_variables=['question'], ... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-76 | 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]]) –
quest... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-77 | [Deprecated]
attribute create_draft_answer_prompt: PromptTemplate = PromptTemplate(input_variables=['question'], output_parser=None, partial_variables={}, template='{question}\n\n', template_format='f-string', validate_template=True)
[Deprecated]
attribute list_assertions_prompt: PromptTemplate = PromptTemplate(input_... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-78 | 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 |
09aa860bdfb8-79 | 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 |
09aa860bdfb8-80 | list_assertions_prompt (langchain.prompts.prompt.PromptTemplate) –
check_assertions_prompt (langchain.prompts.prompt.PromptTemplate) –
revised_answer_prompt (langchain.prompts.prompt.PromptTemplate) –
kwargs (Any) –
Return type
langchain.chains.llm_checker.base.LLMCheckerChain
prep_inputs(inputs)
Validate and prep... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-81 | Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]
Return a map of constructor ar... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-82 | property lc_serializable: bool
Return whether or not the class is serializable.
class langchain.chains.LLMMathChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, llm_chain, llm=None, prompt=PromptTemplate(input_variables=['question'], output_parser=None, partial_variables={}, template... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-83 | Parameters
memory (Optional[langchain.schema.BaseMemory]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) –
verbose (bool) –
tags (Optional[List[str]]) –
llm_c... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-84 | There are many different types of memory - please see memory docs
for the full catalog.
attribute prompt: BasePromptTemplate = PromptTemplate(input_variables=['question'], output_parser=None, partial_variables={}, template='Translate a math problem into a expression that can be executed using Python\'s numexpr library.... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-85 | Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be printed to the console. Defaults to langchain.verbose value.
async acall(inputs, return_only_outputs=False, callbacks=None, *, tags=None, include_run_info=False)
Run the logic of this chain and add to output if desired.
Parameters
inpu... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-86 | tags (Optional[List[str]]) –
kwargs (Any) –
Return type
str
dict(**kwargs)
Return dictionary representation of chain.
Parameters
kwargs (Any) –
Return type
Dict
classmethod from_llm(llm, prompt=PromptTemplate(input_variables=['question'], output_parser=None, partial_variables={}, template='Translate a math problem ... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-87 | prep_inputs(inputs)
Validate and prep inputs.
Parameters
inputs (Union[Dict[str, Any], Any]) –
Return type
Dict[str, str]
prep_outputs(inputs, outputs, return_only_outputs=False)
Validate and prep outputs.
Parameters
inputs (Dict[str, str]) –
outputs (Dict[str, str]) –
return_only_outputs (bool) –
Return type
Dic... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-88 | Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool
Return whether or not the class is serializable.
class langchain.chains.LLMRequestsChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, llm_chain, requests_... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-89 | for full details.
attribute llm_chain: LLMChain [Required]
attribute memory: Optional[BaseMemory] = None
Optional memory object. Defaults to None.
Memory is a class that gets called at the start
and at the end of every chain. At the start, memory loads variables and passes
them along in the chain. At the end, it save... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-90 | 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 |
09aa860bdfb8-91 | return_only_outputs (bool) –
Return type
Dict[str, str]
run(*args, callbacks=None, tags=None, **kwargs)
Run the chain as text in, text out or multiple variables, text out.
Parameters
args (Any) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManage... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-92 | Bases: langchain.chains.router.base.RouterChain
A router chain that uses an LLM chain to perform routing.
Parameters
memory (Optional[langchain.schema.BaseMemory]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
callback_manager (Option... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-93 | You can use these to eg identify a specific instance of a chain with its use case.
attribute verbose: bool [Optional]
Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be printed to the console. Defaults to langchain.verbose value.
async acall(inputs, return_only_outputs=False, callbacks... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-94 | Return type
langchain.chains.router.base.Route
async arun(*args, callbacks=None, tags=None, **kwargs)
Run the chain as text in, text out or multiple variables, text out.
Parameters
args (Any) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-95 | 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 |
09aa860bdfb8-96 | class langchain.chains.LLMSummarizationCheckerChain(*, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, sequential_chain, llm=None, create_assertions_prompt=PromptTemplate(input_variables=['summary'], output_parser=None, partial_variables={}, template='Given some text, extract a list of fact... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-97 | output_parser=None, partial_variables={}, template='Below are some assertions that have been fact checked and are labeled as true or false.\n\nIf all of the assertions are true, return "True". If any of the assertions are false, return "False".\n\nHere are some examples:\n===\n\nChecked Assertions: """\n- The sky is re... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-98 | Bases: langchain.chains.base.Chain
Chain for question-answering with self-verification.
Example
from langchain import OpenAI, LLMSummarizationCheckerChain
llm = OpenAI(temperature=0.0)
checker_chain = LLMSummarizationCheckerChain.from_llm(llm)
Parameters
memory (Optional[langchain.schema.BaseMemory]) –
callbacks (Opti... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-99 | max_checks (int) –
Return type
None
attribute are_all_true_prompt: PromptTemplate = PromptTemplate(input_variables=['checked_assertions'], output_parser=None, partial_variables={}, template='Below are some assertions that have been fact checked and are labeled as true or false.\n\nIf all of the assertions are true, re... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-100 | Each custom chain can optionally call additional callback methods, see Callback docs
for full details.
attribute check_assertions_prompt: PromptTemplate = PromptTemplate(input_variables=['assertions'], output_parser=None, partial_variables={}, template='You are an expert fact checker. You have been hired by a major new... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-101 | There are many different types of memory - please see memory docs
for the full catalog.
attribute revised_summary_prompt: PromptTemplate = PromptTemplate(input_variables=['checked_assertions', 'summary'], output_parser=None, partial_variables={}, template='Below are some assertions that have been fact checked and are l... | https://api.python.langchain.com/en/latest/modules/chains.html |
09aa860bdfb8-102 | 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 |
09aa860bdfb8-103 | 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 |
09aa860bdfb8-104 | 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 |
09aa860bdfb8-105 | 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 |
09aa860bdfb8-106 | 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 |
09aa860bdfb8-107 | 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/modules/chains.html |
09aa860bdfb8-108 | return_only_outputs (bool) – boolean for whether to return only outputs in the
response. If True, only new keys generated by this chain will be
returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False.
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallba... | https://api.python.langchain.com/en/latest/modules/chains.html |
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