id stringlengths 14 15 | text stringlengths 49 2.47k | source stringlengths 61 166 |
|---|---|---|
e81b777a91d3-1 | batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, max_concurrency: Optional[int] = None) → List[Output]¶
bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable.
classmethod construct(_fields_set: Optional[SetStr] = None, **... | https://api.python.langchain.com/en/latest/chains/langchain.chains.router.llm_router.RouterOutputParser.html |
e81b777a91d3-2 | 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.router.llm_router.RouterOutputParser.html |
e81b777a91d3-3 | Structured output.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
Parse the output of an LLM call with the input prompt for context.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
compl... | https://api.python.langchain.com/en/latest/chains/langchain.chains.router.llm_router.RouterOutputParser.html |
e81b777a91d3-4 | 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.
Examples using RouterOutputParser¶
Router | https://api.python.langchain.com/en/latest/chains/langchain.chains.router.llm_router.RouterOutputParser.html |
29782b7549a7-0 | langchain.chains.transform.TransformChain¶
class langchain.chains.transform.TransformChain[source]¶
Bases: Chain
Chain that transforms the chain output.
Example
from langchain import TransformChain
transform_chain = TransformChain(input_variables=["text"],
output_variables["entities"], transform=func())
Create a new m... | https://api.python.langchain.com/en/latest/chains/langchain.chains.transform.TransformChain.html |
29782b7549a7-1 | 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 output_variables: List[str] [Required]¶
The keys returned by the transform’s output dictionary.
param tags: Optional[List[str]] = None¶
Optional list of tags associated ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.transform.TransformChain.html |
29782b7549a7-2 | 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.transform.TransformChain.html |
29782b7549a7-3 | 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.transform.TransformChain.html |
29782b7549a7-4 | 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.transform.TransformChain.html |
29782b7549a7-5 | 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.transform.TransformChain.html |
29782b7549a7-6 | 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.transform.TransformChain.html |
29782b7549a7-7 | 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.transform.TransformChain.html |
29782b7549a7-8 | # 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.transform.TransformChain.html |
29782b7549a7-9 | 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.transform.TransformChain.html |
0f66434117ac-0 | langchain.chains.query_constructor.base.StructuredQueryOutputParser¶
class langchain.chains.query_constructor.base.StructuredQueryOutputParser[source]¶
Bases: BaseOutputParser[StructuredQuery]
Output parser that parses a structured query.
Create a new model by parsing and validating input data from keyword arguments.
R... | https://api.python.langchain.com/en/latest/chains/langchain.chains.query_constructor.base.StructuredQueryOutputParser.html |
0f66434117ac-1 | bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no othe... | https://api.python.langchain.com/en/latest/chains/langchain.chains.query_constructor.base.StructuredQueryOutputParser.html |
0f66434117ac-2 | get_format_instructions() → str¶
Instructions on how the LLM output should be formatted.
invoke(input: str | langchain.schema.messages.BaseMessage, config: langchain.schema.runnable.RunnableConfig | None = None) → T¶
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[A... | https://api.python.langchain.com/en/latest/chains/langchain.chains.query_constructor.base.StructuredQueryOutputParser.html |
0f66434117ac-3 | Parameters
result – A list of Generations to be parsed. The Generations are assumed
to be different candidate outputs for a single model input.
Returns
Structured output.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
Parse the output of an LLM call with the input prompt for context.
The prompt is large... | https://api.python.langchain.com/en/latest/chains/langchain.chains.query_constructor.base.StructuredQueryOutputParser.html |
0f66434117ac-4 | 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.query_constructor.base.StructuredQueryOutputParser.html |
d3e516f73951-0 | langchain.chains.llm_requests.LLMRequestsChain¶
class langchain.chains.llm_requests.LLMRequestsChain[source]¶
Bases: Chain
Chain that requests a URL and then uses an LLM to parse results.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be p... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_requests.LLMRequestsChain.html |
d3e516f73951-1 | 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 text_length: int = 8000¶
param verbose: bool [Optional]¶
Whether or not run in verbose mode. In verbose mode,... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_requests.LLMRequestsChain.html |
d3e516f73951-2 | 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, List[RunnableConfig]]] = None, *, max_concurrency: Optional[int] = None) → List[Output]¶
async acall(inputs: Union[Dict[str, Any], Any], return_on... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_requests.LLMRequestsChain.html |
d3e516f73951-3 | 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) → List[Dic... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_requests.LLMRequestsChain.html |
d3e516f73951-4 | # -> "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.llm_requests.LLMRequestsChain.html |
d3e516f73951-5 | 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.llm_requests.LLMRequestsChain.html |
d3e516f73951-6 | 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¶
prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]¶
Validate and prepare chain inputs, including ad... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_requests.LLMRequestsChain.html |
d3e516f73951-7 | 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.llm_requests.LLMRequestsChain.html |
d3e516f73951-8 | to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
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(fallba... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_requests.LLMRequestsChain.html |
228d6d65ddd7-0 | langchain.chains.llm_bash.base.LLMBashChain¶
class langchain.chains.llm_bash.base.LLMBashChain[source]¶
Bases: Chain
Chain that interprets a prompt and executes bash operations.
Example
from langchain import LLMBashChain, OpenAI
llm_bash = LLMBashChain.from_llm(OpenAI())
Create a new model by parsing and validating inp... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_bash.base.LLMBashChain.html |
228d6d65ddd7-1 | 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 prompt: BasePromptTemplate = PromptTemplate(input_variables=['question'], output_parser=BashOutputParser(), partial_variables={}, template='If someone asks you to perfor... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_bash.base.LLMBashChain.html |
228d6d65ddd7-2 | will be printed to the console. Defaults to langchain.verbose value.
__call__(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, include_... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_bash.base.LLMBashChain.html |
228d6d65ddd7-3 | async acall(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, include_run_info: bool = False) → Dict[str, Any]¶
Asynchronously execute t... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_bash.base.LLMBashChain.html |
228d6d65ddd7-4 | Call the chain on all inputs in the list.
async arun(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Convenience method for executing chain.
The main difference between this ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_bash.base.LLMBashChain.html |
228d6d65ddd7-5 | # -> "The temperature in Boise is..."
async astream(input: Input, config: Optional[RunnableConfig] = None) → AsyncIterator[Output]¶
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, max_concurrency: Optional[int] = None) → List[Output]¶
bind(**kwargs: Any) → Runnable[In... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_bash.base.LLMBashChain.html |
228d6d65ddd7-6 | **kwargs – Keyword arguments passed to default pydantic.BaseModel.dict
method.
Returns
A dictionary representation of the chain.
Example
..code-block:: python
chain.dict(exclude_unset=True)
# -> {“_type”: “foo”, “verbose”: False, …}
classmethod from_llm(llm: BaseLanguageModel, prompt: BasePromptTemplate = PromptTemplat... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_bash.base.LLMBashChain.html |
228d6d65ddd7-7 | 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.llm_bash.base.LLMBashChain.html |
228d6d65ddd7-8 | 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.llm_bash.base.LLMBashChain.html |
228d6d65ddd7-9 | # 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.llm_bash.base.LLMBashChain.html |
228d6d65ddd7-10 | 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.llm_bash.base.LLMBashChain.html |
8a28e145fe5e-0 | langchain.chains.flare.prompts.FinishedOutputParser¶
class langchain.chains.flare.prompts.FinishedOutputParser[source]¶
Bases: BaseOutputParser[Tuple[str, bool]]
Output parser that checks if the output is finished.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if... | https://api.python.langchain.com/en/latest/chains/langchain.chains.flare.prompts.FinishedOutputParser.html |
8a28e145fe5e-1 | bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no othe... | https://api.python.langchain.com/en/latest/chains/langchain.chains.flare.prompts.FinishedOutputParser.html |
8a28e145fe5e-2 | 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.flare.prompts.FinishedOutputParser.html |
8a28e145fe5e-3 | Structured output.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
Parse the output of an LLM call with the input prompt for context.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
compl... | https://api.python.langchain.com/en/latest/chains/langchain.chains.flare.prompts.FinishedOutputParser.html |
8a28e145fe5e-4 | 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. | https://api.python.langchain.com/en/latest/chains/langchain.chains.flare.prompts.FinishedOutputParser.html |
70a49469fb04-0 | langchain.chains.conversational_retrieval.base.ConversationalRetrievalChain¶
class langchain.chains.conversational_retrieval.base.ConversationalRetrievalChain[source]¶
Bases: BaseConversationalRetrievalChain
Chain for having a conversation based on retrieved documents.
This chain takes in chat history (a list of messag... | https://api.python.langchain.com/en/latest/chains/langchain.chains.conversational_retrieval.base.ConversationalRetrievalChain.html |
70a49469fb04-1 | )
prompt = PromptTemplate.from_template(template)
llm = OpenAI()
question_generator_chain = LLMChain(llm=llm, prompt=prompt)
chain = ConversationalRetrievalChain(
combine_docs_chain=combine_docs_chain,
retriever=retriever,
question_generator=question_generator_chain,
)
Create a new model by parsing and vali... | https://api.python.langchain.com/en/latest/chains/langchain.chains.conversational_retrieval.base.ConversationalRetrievalChain.html |
70a49469fb04-2 | 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 the chain. Defaults to None.
This metadata will be associated with each call to this chain,
and passed as arguments to the handlers defined in callba... | https://api.python.langchain.com/en/latest/chains/langchain.chains.conversational_retrieval.base.ConversationalRetrievalChain.html |
70a49469fb04-3 | param 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.
__call__(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallba... | https://api.python.langchain.com/en/latest/chains/langchain.chains.conversational_retrieval.base.ConversationalRetrievalChain.html |
70a49469fb04-4 | Returns
A dict of named outputs. Should contain all outputs specified inChain.output_keys.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, max_concurrency: Optional[int] = None) → List[Output]¶
async acall(inputs: Union[Dict[str, Any], Any], return_only_outputs... | https://api.python.langchain.com/en/latest/chains/langchain.chains.conversational_retrieval.base.ConversationalRetrievalChain.html |
70a49469fb04-5 | 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) → List[Dic... | https://api.python.langchain.com/en/latest/chains/langchain.chains.conversational_retrieval.base.ConversationalRetrievalChain.html |
70a49469fb04-6 | # -> "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.conversational_retrieval.base.ConversationalRetrievalChain.html |
70a49469fb04-7 | 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.conversational_retrieval.base.ConversationalRetrievalChain.html |
70a49469fb04-8 | retriever – The retriever to use to fetch relevant documents from.
condense_question_prompt – The prompt to use to condense the chat history
and new question into a standalone question.
chain_type – The chain type to use to create the combine_docs_chain, will
be sent to load_qa_chain.
verbose – Verbosity flag for loggi... | https://api.python.langchain.com/en/latest/chains/langchain.chains.conversational_retrieval.base.ConversationalRetrievalChain.html |
70a49469fb04-9 | classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto... | https://api.python.langchain.com/en/latest/chains/langchain.chains.conversational_retrieval.base.ConversationalRetrievalChain.html |
70a49469fb04-10 | method expects inputs to be passed directly in as positional arguments or
keyword arguments, whereas Chain.__call__ expects a single input dictionary
with all the inputs
Parameters
*args – If the chain expects a single input, it can be passed in as the
sole positional argument.
callbacks – Callbacks to use for this cha... | https://api.python.langchain.com/en/latest/chains/langchain.chains.conversational_retrieval.base.ConversationalRetrievalChain.html |
70a49469fb04-11 | classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
stream(input: Input, config: Optional[RunnableConfig] = None) → Iterator[Output]¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedN... | https://api.python.langchain.com/en/latest/chains/langchain.chains.conversational_retrieval.base.ConversationalRetrievalChain.html |
70a49469fb04-12 | Structure answers with OpenAI functions
QA using Activeloop’s DeepLake
Analysis of Twitter the-algorithm source code with LangChain, GPT4 and Activeloop’s Deep Lake
Use LangChain, GPT and Activeloop’s Deep Lake to work with code base
Retrieval QA using OpenAI functions | https://api.python.langchain.com/en/latest/chains/langchain.chains.conversational_retrieval.base.ConversationalRetrievalChain.html |
820c3d4ef315-0 | langchain.chains.qa_with_sources.loading.load_qa_with_sources_chain¶
langchain.chains.qa_with_sources.loading.load_qa_with_sources_chain(llm: BaseLanguageModel, chain_type: str = 'stuff', verbose: Optional[bool] = None, **kwargs: Any) → BaseCombineDocumentsChain[source]¶
Load a question answering with sources chain.
Pa... | https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_with_sources.loading.load_qa_with_sources_chain.html |
cdb68b850007-0 | langchain.chains.openai_functions.citation_fuzzy_match.create_citation_fuzzy_match_chain¶
langchain.chains.openai_functions.citation_fuzzy_match.create_citation_fuzzy_match_chain(llm: BaseLanguageModel) → LLMChain[source]¶
Create a citation fuzzy match chain.
Parameters
llm – Language model to use for the chain.
Return... | https://api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.citation_fuzzy_match.create_citation_fuzzy_match_chain.html |
7f16d187cdc3-0 | langchain.chains.router.base.MultiRouteChain¶
class langchain.chains.router.base.MultiRouteChain[source]¶
Bases: Chain
Use a single chain to route an input to one of multiple candidate chains.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot... | https://api.python.langchain.com/en/latest/chains/langchain.chains.router.base.MultiRouteChain.html |
7f16d187cdc3-1 | param router_chain: RouterChain [Required]¶
Chain that routes inputs to destination chains.
param silent_errors: bool = False¶
If True, use default_chain when an invalid destination name is provided.
Defaults to False.
param tags: Optional[List[str]] = None¶
Optional list of tags associated with the chain. Defaults to ... | https://api.python.langchain.com/en/latest/chains/langchain.chains.router.base.MultiRouteChain.html |
7f16d187cdc3-2 | 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.router.base.MultiRouteChain.html |
7f16d187cdc3-3 | 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.router.base.MultiRouteChain.html |
7f16d187cdc3-4 | **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.router.base.MultiRouteChain.html |
7f16d187cdc3-5 | 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.router.base.MultiRouteChain.html |
7f16d187cdc3-6 | classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto... | https://api.python.langchain.com/en/latest/chains/langchain.chains.router.base.MultiRouteChain.html |
7f16d187cdc3-7 | method expects inputs to be passed directly in as positional arguments or
keyword arguments, whereas Chain.__call__ expects a single input dictionary
with all the inputs
Parameters
*args – If the chain expects a single input, it can be passed in as the
sole positional argument.
callbacks – Callbacks to use for this cha... | https://api.python.langchain.com/en/latest/chains/langchain.chains.router.base.MultiRouteChain.html |
7f16d187cdc3-8 | classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
stream(input: Input, config: Optional[RunnableConfig] = None) → Iterator[Output]¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedN... | https://api.python.langchain.com/en/latest/chains/langchain.chains.router.base.MultiRouteChain.html |
0e352b8ea5bb-0 | langchain.chains.loading.load_chain¶
langchain.chains.loading.load_chain(path: Union[str, Path], **kwargs: Any) → Chain[source]¶
Unified method for loading a chain from LangChainHub or local fs.
Examples using load_chain¶
Serialization
Loading from LangChainHub | https://api.python.langchain.com/en/latest/chains/langchain.chains.loading.load_chain.html |
7b217a648370-0 | langchain.chains.llm_checker.base.LLMCheckerChain¶
class langchain.chains.llm_checker.base.LLMCheckerChain[source]¶
Bases: Chain
Chain for question-answering with self-verification.
Example
from langchain import OpenAI, LLMCheckerChain
llm = OpenAI(temperature=0.7)
checker_chain = LLMCheckerChain.from_llm(llm)
Create a... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_checker.base.LLMCheckerChain.html |
7b217a648370-1 | [Deprecated]
param list_assertions_prompt: PromptTemplate = PromptTemplate(input_variables=['statement'], output_parser=None, partial_variables={}, template='Here is a statement:\n{statement}\nMake a bullet point list of the assumptions you made when producing the above statement.\n\n', template_format='f-string', vali... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_checker.base.LLMCheckerChain.html |
7b217a648370-2 | 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 verbose: bool [Optional]¶
Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_checker.base.LLMCheckerChain.html |
7b217a648370-3 | 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, List[RunnableConfig]]] = None, *, max_concurrency: Optional[int] = None) → List[Output]¶
async acall(inputs: Union[Dict[str, Any], Any], return_on... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_checker.base.LLMCheckerChain.html |
7b217a648370-4 | 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) → List[Dic... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_checker.base.LLMCheckerChain.html |
7b217a648370-5 | # -> "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.llm_checker.base.LLMCheckerChain.html |
7b217a648370-6 | 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.llm_checker.base.LLMCheckerChain.html |
7b217a648370-7 | 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, skip_defaults: Opti... | https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_checker.base.LLMCheckerChain.html |
7b217a648370-8 | 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.llm_checker.base.LLMCheckerChain.html |
7b217a648370-9 | 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.llm_checker.base.LLMCheckerChain.html |
7b217a648370-10 | 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.llm_checker.base.LLMCheckerChain.html |
9683a23993ed-0 | langchain.chains.api.base.APIChain¶
class langchain.chains.api.base.APIChain[source]¶
Bases: Chain
Chain that makes API calls and summarizes the responses to answer a question.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to fo... | https://api.python.langchain.com/en/latest/chains/langchain.chains.api.base.APIChain.html |
9683a23993ed-1 | 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 verbose: bool [Optional]¶
Whether or not r... | https://api.python.langchain.com/en/latest/chains/langchain.chains.api.base.APIChain.html |
9683a23993ed-2 | 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, List[RunnableConfig]]] = None, *, max_concurrency: Optional[int] = None) → List[Output]¶
async acall(inputs: Union[Dict[str, Any], Any], return_on... | https://api.python.langchain.com/en/latest/chains/langchain.chains.api.base.APIChain.html |
9683a23993ed-3 | 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) → List[Dic... | https://api.python.langchain.com/en/latest/chains/langchain.chains.api.base.APIChain.html |
9683a23993ed-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.api.base.APIChain.html |
9683a23993ed-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.api.base.APIChain.html |
9683a23993ed-6 | # -> {“_type”: “foo”, “verbose”: False, …}
classmethod from_llm_and_api_docs(llm: BaseLanguageModel, api_docs: str, headers: Optional[dict] = None, api_url_prompt: BasePromptTemplate = PromptTemplate(input_variables=['api_docs', 'question'], output_parser=None, partial_variables={}, template='You are given the below AP... | https://api.python.langchain.com/en/latest/chains/langchain.chains.api.base.APIChain.html |
9683a23993ed-7 | 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, skip_defaults: Opti... | https://api.python.langchain.com/en/latest/chains/langchain.chains.api.base.APIChain.html |
9683a23993ed-8 | 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.api.base.APIChain.html |
9683a23993ed-9 | 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.api.base.APIChain.html |
9683a23993ed-10 | 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.api.base.APIChain.html |
0baa4de7ad18-0 | langchain.chains.router.embedding_router.EmbeddingRouterChain¶
class langchain.chains.router.embedding_router.EmbeddingRouterChain[source]¶
Bases: RouterChain
Chain that uses embeddings to route between options.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if th... | https://api.python.langchain.com/en/latest/chains/langchain.chains.router.embedding_router.EmbeddingRouterChain.html |
0baa4de7ad18-1 | 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 vectorstore: VectorStore [Required]¶
param verbose: bool [Optional]¶
Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be printed to the c... | https://api.python.langchain.com/en/latest/chains/langchain.chains.router.embedding_router.EmbeddingRouterChain.html |
0baa4de7ad18-2 | Returns
A dict of named outputs. Should contain all outputs specified inChain.output_keys.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, max_concurrency: Optional[int] = None) → List[Output]¶
async acall(inputs: Union[Dict[str, Any], Any], return_only_outputs... | https://api.python.langchain.com/en/latest/chains/langchain.chains.router.embedding_router.EmbeddingRouterChain.html |
0baa4de7ad18-3 | 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) → List[Dic... | https://api.python.langchain.com/en/latest/chains/langchain.chains.router.embedding_router.EmbeddingRouterChain.html |
0baa4de7ad18-4 | 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.router.embedding_router.EmbeddingRouterChain.html |
0baa4de7ad18-5 | 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 creating
the new model: you should trust this data
deep – set to True to make a deep co... | https://api.python.langchain.com/en/latest/chains/langchain.chains.router.embedding_router.EmbeddingRouterChain.html |
0baa4de7ad18-6 | Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol... | https://api.python.langchain.com/en/latest/chains/langchain.chains.router.embedding_router.EmbeddingRouterChain.html |
0baa4de7ad18-7 | Route inputs to a destination chain.
Parameters
inputs – inputs to the chain
callbacks – callbacks to use for the chain
Returns
a Route object
run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, *... | https://api.python.langchain.com/en/latest/chains/langchain.chains.router.embedding_router.EmbeddingRouterChain.html |
0baa4de7ad18-8 | 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 implemented and for memory to benull.
Parameters
file_path – Path to file to save the chain to.
Example
chain.save(file_path="path/chain.... | https://api.python.langchain.com/en/latest/chains/langchain.chains.router.embedding_router.EmbeddingRouterChain.html |
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