id stringlengths 14 15 | text stringlengths 49 2.47k | source stringlengths 61 166 |
|---|---|---|
0baa4de7ad18-9 | 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]¶
Keys expected to be in the chain output.
Examples using EmbeddingRoute... | https://api.python.langchain.com/en/latest/chains/langchain.chains.router.embedding_router.EmbeddingRouterChain.html |
1e1cf6fb9d4d-0 | langchain.chains.combine_documents.refine.RefineDocumentsChain¶
class langchain.chains.combine_documents.refine.RefineDocumentsChain[source]¶
Bases: BaseCombineDocumentsChain
Combine documents by doing a first pass and then refining on more documents.
This algorithm first calls initial_llm_chain on the first document, ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.refine.RefineDocumentsChain.html |
1e1cf6fb9d4d-1 | "Now add to it based on the following context: {context}"
)
refine_llm_chain = LLMChain(llm=llm, prompt=prompt_refine)
chain = RefineDocumentsChain(
initial_llm_chain=initial_llm_chain,
refine_llm_chain=refine_llm_chain,
document_prompt=document_prompt,
document_variable_name=document_variable_name,
... | https://api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.refine.RefineDocumentsChain.html |
1e1cf6fb9d4d-2 | and at the end of every chain. At the start, memory loads variables and passes
them along in the chain. At the end, it saves any returned variables.
There are many different types of memory - please see memory docs
for the full catalog.
param metadata: Optional[Dict[str, Any]] = None¶
Optional metadata associated with ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.refine.RefineDocumentsChain.html |
1e1cf6fb9d4d-3 | only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
return_only_outputs – 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 thi... | https://api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.refine.RefineDocumentsChain.html |
1e1cf6fb9d4d-4 | Chain.input_keys except for inputs that will be set by the chain’s
memory.
return_only_outputs – 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 ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.refine.RefineDocumentsChain.html |
1e1cf6fb9d4d-5 | apply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[Dict[str, str]]¶
Call the chain on all inputs in the list.
async arun(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]]... | https://api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.refine.RefineDocumentsChain.html |
1e1cf6fb9d4d-6 | question = "What's the temperature in Boise, Idaho?"
context = "Weather report for Boise, Idaho on 07/03/23..."
await chain.arun(question=question, context=context)
# -> "The temperature in Boise is..."
async astream(input: Input, config: Optional[RunnableConfig] = None) → AsyncIterator[Output]¶
batch(inputs: List[Inpu... | https://api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.refine.RefineDocumentsChain.html |
1e1cf6fb9d4d-7 | Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally... | https://api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.refine.RefineDocumentsChain.html |
1e1cf6fb9d4d-8 | json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Cal... | https://api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.refine.RefineDocumentsChain.html |
1e1cf6fb9d4d-9 | Validate and prepare chain outputs, and save info about this run to memory.
Parameters
inputs – Dictionary of chain inputs, including any inputs added by chain
memory.
outputs – Dictionary of initial chain outputs.
return_only_outputs – Whether to only return the chain outputs. If False,
inputs are also added to the fi... | https://api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.refine.RefineDocumentsChain.html |
1e1cf6fb9d4d-10 | these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the chain during construction, but only
these runtime tags will propagate to calls to other objects.
**kwargs – If the chain expects multiple inputs, ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.refine.RefineDocumentsChain.html |
1e1cf6fb9d4d-11 | classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.Runnable[~langchain.schema.runnable.Input, ~langchain.schema.runnable.Out... | https://api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.refine.RefineDocumentsChain.html |
b0ca07a025a6-0 | langchain.chains.natbot.crawler.Crawler¶
class langchain.chains.natbot.crawler.Crawler[source]¶
Methods
__init__()
click(id)
crawl()
enter()
go_to_page(url)
scroll(direction)
type(id, text)
__init__() → None[source]¶
click(id: Union[str, int]) → None[source]¶
crawl() → List[str][source]¶
enter() → None[source]¶
go_to_p... | https://api.python.langchain.com/en/latest/chains/langchain.chains.natbot.crawler.Crawler.html |
e29068e19d6f-0 | langchain.chains.api.openapi.requests_chain.APIRequesterChain¶
class langchain.chains.api.openapi.requests_chain.APIRequesterChain[source]¶
Bases: LLMChain
Get the request parser.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to... | https://api.python.langchain.com/en/latest/chains/langchain.chains.api.openapi.requests_chain.APIRequesterChain.html |
e29068e19d6f-1 | 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¶
Whether to return only the final parsed result. Defaults... | https://api.python.langchain.com/en/latest/chains/langchain.chains.api.openapi.requests_chain.APIRequesterChain.html |
e29068e19d6f-2 | callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the c... | https://api.python.langchain.com/en/latest/chains/langchain.chains.api.openapi.requests_chain.APIRequesterChain.html |
e29068e19d6f-3 | Parameters
inputs – Dictionary of inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
return_only_outputs – Whether to return only outputs in the
response. If True, only new keys generated by this chai... | https://api.python.langchain.com/en/latest/chains/langchain.chains.api.openapi.requests_chain.APIRequesterChain.html |
e29068e19d6f-4 | Utilize the LLM generate method for speed gains.
apply_and_parse(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → Sequence[Union[str, List[str], Dict[str, str]]]¶
Call apply and then parse the results.
async apredict(callbacks: Optional[Union[List[Ba... | https://api.python.langchain.com/en/latest/chains/langchain.chains.api.openapi.requests_chain.APIRequesterChain.html |
e29068e19d6f-5 | sole positional argument.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be passed in
additi... | https://api.python.langchain.com/en/latest/chains/langchain.chains.api.openapi.requests_chain.APIRequesterChain.html |
e29068e19d6f-6 | Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny... | https://api.python.langchain.com/en/latest/chains/langchain.chains.api.openapi.requests_chain.APIRequesterChain.html |
e29068e19d6f-7 | classmethod from_string(llm: BaseLanguageModel, template: str) → LLMChain¶
Create LLMChain from LLM and template.
generate(input_list: List[Dict[str, Any]], run_manager: Optional[CallbackManagerForChainRun] = None) → LLMResult¶
Generate LLM result from inputs.
invoke(input: Dict[str, Any], config: Optional[RunnableConf... | https://api.python.langchain.com/en/latest/chains/langchain.chains.api.openapi.requests_chain.APIRequesterChain.html |
e29068e19d6f-8 | Parameters
callbacks – Callbacks to pass to LLMChain
**kwargs – Keys to pass to prompt template.
Returns
Completion from LLM.
Example
completion = llm.predict(adjective="funny")
predict_and_parse(callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Union[str, List[str], Di... | https://api.python.langchain.com/en/latest/chains/langchain.chains.api.openapi.requests_chain.APIRequesterChain.html |
e29068e19d6f-9 | Prepare prompts from inputs.
run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Convenience method for executing chain.
The main difference between this method and Chain.__c... | https://api.python.langchain.com/en/latest/chains/langchain.chains.api.openapi.requests_chain.APIRequesterChain.html |
e29068e19d6f-10 | save(file_path: Union[Path, str]) → None¶
Save the chain.
Expects Chain._chain_type property to be implemented and for memory to benull.
Parameters
file_path – Path to file to save the chain to.
Example
chain.save(file_path="path/chain.yaml")
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definiti... | https://api.python.langchain.com/en/latest/chains/langchain.chains.api.openapi.requests_chain.APIRequesterChain.html |
e29068e19d6f-11 | 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. | https://api.python.langchain.com/en/latest/chains/langchain.chains.api.openapi.requests_chain.APIRequesterChain.html |
c72551ef4487-0 | langchain.chains.query_constructor.ir.StructuredQuery¶
class langchain.chains.query_constructor.ir.StructuredQuery[source]¶
Bases: Expr
A structured query.
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.
par... | https://api.python.langchain.com/en/latest/chains/langchain.chains.query_constructor.ir.StructuredQuery.html |
c72551ef4487-1 | deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, ex... | https://api.python.langchain.com/en/latest/chains/langchain.chains.query_constructor.ir.StructuredQuery.html |
c72551ef4487-2 | classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on... | https://api.python.langchain.com/en/latest/chains/langchain.chains.query_constructor.ir.StructuredQuery.html |
5f368a6ebd2a-0 | langchain.chains.openai_functions.qa_with_structure.create_qa_with_structure_chain¶
langchain.chains.openai_functions.qa_with_structure.create_qa_with_structure_chain(llm: BaseLanguageModel, schema: Union[dict, Type[BaseModel]], output_parser: str = 'base', prompt: Optional[Union[PromptTemplate, ChatPromptTemplate]] = ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.qa_with_structure.create_qa_with_structure_chain.html |
4ce64f8b0571-0 | 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 |
4ce64f8b0571-1 | deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, ex... | https://api.python.langchain.com/en/latest/chains/langchain.chains.query_constructor.ir.FilterDirective.html |
4ce64f8b0571-2 | classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on... | https://api.python.langchain.com/en/latest/chains/langchain.chains.query_constructor.ir.FilterDirective.html |
96684fb18998-0 | langchain.chains.hyde.base.HypotheticalDocumentEmbedder¶
class langchain.chains.hyde.base.HypotheticalDocumentEmbedder[source]¶
Bases: Chain, Embeddings
Generate hypothetical document for query, and then embed that.
Based on https://arxiv.org/abs/2212.10496
Create a new model by parsing and validating input data from k... | https://api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html |
96684fb18998-1 | 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 ve... | https://api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html |
96684fb18998-2 | metadata – Optional metadata associated with the chain. Defaults to None
include_run_info – Whether to include run info in the response. Defaults
to False.
Returns
A dict of named outputs. Should contain all outputs specified inChain.output_keys.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html |
96684fb18998-3 | to False.
Returns
A dict of named outputs. Should contain all outputs specified inChain.output_keys.
async aembed_documents(texts: List[str]) → List[List[float]]¶
Asynchronous Embed search docs.
async aembed_query(text: str) → List[float]¶
Asynchronous Embed query text.
async ainvoke(input: Dict[str, Any], config: Opti... | https://api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html |
96684fb18998-4 | directly as keyword arguments.
Returns
The chain output.
Example
# Suppose we have a single-input chain that takes a 'question' string:
await chain.arun("What's the temperature in Boise, Idaho?")
# -> "The temperature in Boise is..."
# Suppose we have a multi-input chain that takes a 'question' string
# and 'context' s... | https://api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html |
96684fb18998-5 | Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creat... | https://api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html |
96684fb18998-6 | json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Cal... | https://api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html |
96684fb18998-7 | Validate and prepare chain outputs, and save info about this run to memory.
Parameters
inputs – Dictionary of chain inputs, including any inputs added by chain
memory.
outputs – Dictionary of initial chain outputs.
return_only_outputs – Whether to only return the chain outputs. If False,
inputs are also added to the fi... | https://api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html |
96684fb18998-8 | # and 'context' string:
question = "What's the temperature in Boise, Idaho?"
context = "Weather report for Boise, Idaho on 07/03/23..."
chain.run(question=question, context=context)
# -> "The temperature in Boise is..."
save(file_path: Union[Path, str]) → None¶
Save the chain.
Expects Chain._chain_type property to be i... | https://api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html |
96684fb18998-9 | serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_K... | https://api.python.langchain.com/en/latest/chains/langchain.chains.hyde.base.HypotheticalDocumentEmbedder.html |
9c16c4c7e51e-0 | langchain.chains.combine_documents.reduce.AsyncCombineDocsProtocol¶
class langchain.chains.combine_documents.reduce.AsyncCombineDocsProtocol(*args, **kwargs)[source]¶
Interface for the combine_docs method.
Methods
__init__(*args, **kwargs)
__init__(*args, **kwargs)¶ | https://api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.reduce.AsyncCombineDocsProtocol.html |
de12c2fc8f8a-0 | langchain.chains.openai_functions.citation_fuzzy_match.FactWithEvidence¶
class langchain.chains.openai_functions.citation_fuzzy_match.FactWithEvidence[source]¶
Bases: BaseModel
Class representing a single statement.
Each fact has a body and a list of sources.
If there are multiple facts make sure to break them apart
su... | https://api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.citation_fuzzy_match.FactWithEvidence.html |
de12c2fc8f8a-1 | the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[boo... | https://api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.citation_fuzzy_match.FactWithEvidence.html |
de12c2fc8f8a-2 | classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmet... | https://api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.citation_fuzzy_match.FactWithEvidence.html |
c2e55184e9be-0 | langchain.chains.qa_with_sources.retrieval.RetrievalQAWithSourcesChain¶
class langchain.chains.qa_with_sources.retrieval.RetrievalQAWithSourcesChain[source]¶
Bases: BaseQAWithSourcesChain
Question-answering with sources over an index.
Create a new model by parsing and validating input data from keyword arguments.
Raise... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.retrieval.RetrievalQAWithSourcesChain.html |
c2e55184e9be-1 | and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param reduce_k_below_max_tokens: bool = False¶
Reduce the number of results to return from store based on tokens limit
param retriever: langchain.schema.retriever.BaseRetrieve... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.retrieval.RetrievalQAWithSourcesChain.html |
c2e55184e9be-2 | chain will be returned. Defaults to False.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.retrieval.RetrievalQAWithSourcesChain.html |
c2e55184e9be-3 | callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the c... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.retrieval.RetrievalQAWithSourcesChain.html |
c2e55184e9be-4 | these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the chain during construction, but only
these runtime tags will propagate to calls to other objects.
**kwargs – If the chain expects multiple inputs, ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.retrieval.RetrievalQAWithSourcesChain.html |
c2e55184e9be-5 | Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.retrieval.RetrievalQAWithSourcesChain.html |
c2e55184e9be-6 | 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 |
c2e55184e9be-7 | 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 |
c2e55184e9be-8 | \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 |
c2e55184e9be-9 | \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 |
c2e55184e9be-10 | 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 |
c2e55184e9be-11 | Construct the chain from an LLM.
classmethod from_orm(obj: Any) → Model¶
invoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None) → Dict[str, Any]¶
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.retrieval.RetrievalQAWithSourcesChain.html |
c2e55184e9be-12 | Returns
A dictionary of all inputs, including those added by the chain’s memory.
prep_outputs(inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False) → Dict[str, str]¶
Validate and prepare chain outputs, and save info about this run to memory.
Parameters
inputs – Dictionary of chain inputs, ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.retrieval.RetrievalQAWithSourcesChain.html |
c2e55184e9be-13 | directly as keyword arguments.
Returns
The chain output.
Example
# Suppose we have a single-input chain that takes a 'question' string:
chain.run("What's the temperature in Boise, Idaho?")
# -> "The temperature in Boise is..."
# Suppose we have a multi-input chain that takes a 'question' string
# and 'context' string:
... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.retrieval.RetrievalQAWithSourcesChain.html |
c2e55184e9be-14 | classmethod validate(value: Any) → Model¶
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.Runnable[~langchain.schema.runnable.Input, ~langchain.schema.runnable.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Type[BaseException]] = (<class 'Exception'>,)) → RunnableWithFallbacks[Input, Out... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.retrieval.RetrievalQAWithSourcesChain.html |
e11c7e809dd5-0 | langchain.chains.graph_qa.cypher.GraphCypherQAChain¶
class langchain.chains.graph_qa.cypher.GraphCypherQAChain[source]¶
Bases: Chain
Chain for question-answering against a graph by generating Cypher statements.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.cypher.GraphCypherQAChain.html |
e11c7e809dd5-1 | Whether or not to return the result of querying the graph directly.
param return_intermediate_steps: bool = False¶
Whether or not to return the intermediate steps along with the final answer.
param tags: Optional[List[str]] = None¶
Optional list of tags associated with the chain. Defaults to None.
These tags will be as... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.cypher.GraphCypherQAChain.html |
e11c7e809dd5-2 | these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the chain during construction, but only
these runtime tags will propagate to calls to other objects.
metadata – Optional metadata associated with the ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.cypher.GraphCypherQAChain.html |
e11c7e809dd5-3 | tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the chain during construction, but only
these runtime tags will propagate to calls to other objects.
metadata – Optional metadata associated with the chain. Defaults to None
include_run_info – Whether to include run ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.cypher.GraphCypherQAChain.html |
e11c7e809dd5-4 | these runtime tags will propagate to calls to other objects.
**kwargs – If the chain expects multiple inputs, they can be passed in
directly as keyword arguments.
Returns
The chain output.
Example
# Suppose we have a single-input chain that takes a 'question' string:
await chain.arun("What's the temperature in Boise, I... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.cypher.GraphCypherQAChain.html |
e11c7e809dd5-5 | Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creat... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.cypher.GraphCypherQAChain.html |
e11c7e809dd5-6 | # -> {“_type”: “foo”, “verbose”: False, …}
classmethod from_llm(llm: BaseLanguageModel, *, qa_prompt: BasePromptTemplate = PromptTemplate(input_variables=['context', 'question'], output_parser=None, partial_variables={}, template="You are an assistant that helps to form nice and human understandable answers.\nThe infor... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.cypher.GraphCypherQAChain.html |
e11c7e809dd5-7 | json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Cal... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.cypher.GraphCypherQAChain.html |
e11c7e809dd5-8 | Validate and prepare chain outputs, and save info about this run to memory.
Parameters
inputs – Dictionary of chain inputs, including any inputs added by chain
memory.
outputs – Dictionary of initial chain outputs.
return_only_outputs – Whether to only return the chain outputs. If False,
inputs are also added to the fi... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.cypher.GraphCypherQAChain.html |
e11c7e809dd5-9 | # and 'context' string:
question = "What's the temperature in Boise, Idaho?"
context = "Weather report for Boise, Idaho on 07/03/23..."
chain.run(question=question, context=context)
# -> "The temperature in Boise is..."
save(file_path: Union[Path, str]) → None¶
Save the chain.
Expects Chain._chain_type property to be i... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.cypher.GraphCypherQAChain.html |
e11c7e809dd5-10 | 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/chains/langchain.chains.graph_qa.cypher.GraphCypherQAChain.html |
fea1ee1c721c-0 | langchain.chains.retrieval_qa.base.VectorDBQA¶
class langchain.chains.retrieval_qa.base.VectorDBQA[source]¶
Bases: BaseRetrievalQA
Chain for question-answering against a vector database.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be pa... | https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.VectorDBQA.html |
fea1ee1c721c-1 | param return_source_documents: bool = False¶
Return the source documents or not.
param search_kwargs: Dict[str, Any] [Optional]¶
Extra search args.
param search_type: str = 'similarity'¶
Search type to use over vectorstore. similarity or mmr.
param tags: Optional[List[str]] = None¶
Optional list of tags associated with... | https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.VectorDBQA.html |
fea1ee1c721c-2 | addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the chain during construction, but only
these runtime tags will propagate to c... | https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.VectorDBQA.html |
fea1ee1c721c-3 | these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the chain during construction, but only
these runtime tags will propagate to calls to other objects.
metadata – Optional metadata associated with the ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.VectorDBQA.html |
fea1ee1c721c-4 | addition to tags passed to the chain during construction, but only
these runtime tags will propagate to calls to other objects.
**kwargs – If the chain expects multiple inputs, they can be passed in
directly as keyword arguments.
Returns
The chain output.
Example
# Suppose we have a single-input chain that takes a 'que... | https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.VectorDBQA.html |
fea1ee1c721c-5 | Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally... | https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.VectorDBQA.html |
fea1ee1c721c-6 | Initialize from LLM.
classmethod from_orm(obj: Any) → Model¶
invoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None) → Dict[str, Any]¶
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False... | https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.VectorDBQA.html |
fea1ee1c721c-7 | Returns
A dictionary of all inputs, including those added by the chain’s memory.
prep_outputs(inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False) → Dict[str, str]¶
Validate and prepare chain outputs, and save info about this run to memory.
Parameters
inputs – Dictionary of chain inputs, ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.VectorDBQA.html |
fea1ee1c721c-8 | directly as keyword arguments.
Returns
The chain output.
Example
# Suppose we have a single-input chain that takes a 'question' string:
chain.run("What's the temperature in Boise, Idaho?")
# -> "The temperature in Boise is..."
# Suppose we have a multi-input chain that takes a 'question' string
# and 'context' string:
... | https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.VectorDBQA.html |
fea1ee1c721c-9 | classmethod validate(value: Any) → Model¶
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.Runnable[~langchain.schema.runnable.Input, ~langchain.schema.runnable.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Type[BaseException]] = (<class 'Exception'>,)) → RunnableWithFallbacks[Input, Out... | https://api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.VectorDBQA.html |
f9f6a95db43f-0 | langchain.chains.graph_qa.kuzu.KuzuQAChain¶
class langchain.chains.graph_qa.kuzu.KuzuQAChain[source]¶
Bases: Chain
Chain for question-answering against a graph by generating Cypher statements for
Kùzu.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input da... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.kuzu.KuzuQAChain.html |
f9f6a95db43f-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 ve... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.kuzu.KuzuQAChain.html |
f9f6a95db43f-2 | metadata – Optional metadata associated with the chain. Defaults to None
include_run_info – Whether to include run info in the response. Defaults
to False.
Returns
A dict of named outputs. Should contain all outputs specified inChain.output_keys.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.kuzu.KuzuQAChain.html |
f9f6a95db43f-3 | to False.
Returns
A dict of named outputs. Should contain all outputs specified inChain.output_keys.
async ainvoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None) → Dict[str, Any]¶
apply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.kuzu.KuzuQAChain.html |
f9f6a95db43f-4 | # -> "The temperature in Boise is..."
# Suppose we have a multi-input chain that takes a 'question' string
# and 'context' string:
question = "What's the temperature in Boise, Idaho?"
context = "Weather report for Boise, Idaho on 07/03/23..."
await chain.arun(question=question, context=context)
# -> "The temperature in... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.kuzu.KuzuQAChain.html |
f9f6a95db43f-5 | the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) → Dict¶
Dictionary representation of chain.
Expects Chain._chain_type property to be implemented and for memory to benull.
Parameters
**kwargs – Keyword arguments passed to defaul... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.kuzu.KuzuQAChain.html |
f9f6a95db43f-6 | classmethod from_llm(llm: BaseLanguageModel, *, qa_prompt: BasePromptTemplate = PromptTemplate(input_variables=['context', 'question'], output_parser=None, partial_variables={}, template="You are an assistant that helps to form nice and human understandable answers.\nThe information part contains the provided informati... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.kuzu.KuzuQAChain.html |
f9f6a95db43f-7 | Cypher statement.\n\nThe question is:\n{question}', template_format='f-string', validate_template=True), **kwargs: Any) → KuzuQAChain[source]¶ | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.kuzu.KuzuQAChain.html |
f9f6a95db43f-8 | Initialize from LLM.
classmethod from_orm(obj: Any) → Model¶
invoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None) → Dict[str, Any]¶
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.kuzu.KuzuQAChain.html |
f9f6a95db43f-9 | Returns
A dictionary of all inputs, including those added by the chain’s memory.
prep_outputs(inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False) → Dict[str, str]¶
Validate and prepare chain outputs, and save info about this run to memory.
Parameters
inputs – Dictionary of chain inputs, ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.kuzu.KuzuQAChain.html |
f9f6a95db43f-10 | directly as keyword arguments.
Returns
The chain output.
Example
# Suppose we have a single-input chain that takes a 'question' string:
chain.run("What's the temperature in Boise, Idaho?")
# -> "The temperature in Boise is..."
# Suppose we have a multi-input chain that takes a 'question' string
# and 'context' string:
... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.kuzu.KuzuQAChain.html |
f9f6a95db43f-11 | classmethod validate(value: Any) → Model¶
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.Runnable[~langchain.schema.runnable.Input, ~langchain.schema.runnable.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Type[BaseException]] = (<class 'Exception'>,)) → RunnableWithFallbacks[Input, Out... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.kuzu.KuzuQAChain.html |
5a0bda88820e-0 | langchain.chains.graph_qa.sparql.GraphSparqlQAChain¶
class langchain.chains.graph_qa.sparql.GraphSparqlQAChain[source]¶
Bases: Chain
Chain for question-answering against an RDF or OWL graph by generating
SPARQL statements.
Create a new model by parsing and validating input data from keyword arguments.
Raises Validation... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.sparql.GraphSparqlQAChain.html |
5a0bda88820e-1 | param sparql_generation_select_chain: LLMChain [Required]¶
param sparql_generation_update_chain: LLMChain [Required]¶
param sparql_intent_chain: LLMChain [Required]¶
param tags: Optional[List[str]] = None¶
Optional list of tags associated with the chain. Defaults to None.
These tags will be associated with each call to... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.sparql.GraphSparqlQAChain.html |
5a0bda88820e-2 | tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the chain during construction, but only
these runtime tags will propagate to calls to other objects.
metadata – Optional metadata associated with the chain. Defaults to None
include_run_info – Whether to include run ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.sparql.GraphSparqlQAChain.html |
5a0bda88820e-3 | addition to tags passed to the chain during construction, but only
these runtime tags will propagate to calls to other objects.
metadata – Optional metadata associated with the chain. Defaults to None
include_run_info – Whether to include run info in the response. Defaults
to False.
Returns
A dict of named outputs. Sho... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.sparql.GraphSparqlQAChain.html |
5a0bda88820e-4 | **kwargs – If the chain expects multiple inputs, they can be passed in
directly as keyword arguments.
Returns
The chain output.
Example
# Suppose we have a single-input chain that takes a 'question' string:
await chain.arun("What's the temperature in Boise, Idaho?")
# -> "The temperature in Boise is..."
# Suppose we ha... | https://api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.sparql.GraphSparqlQAChain.html |
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