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537e185b4210-0 | langchain.chains.query_constructor.ir.Operation¶
class langchain.chains.query_constructor.ir.Operation[source]¶
Bases: FilterDirective
A logical operation over other directives.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param arguments: List[langchain.chains.query_constructor.ir.FilterDirective] [Required]¶
param operator: langchain.chains.query_constructor.ir.Operator [Required]¶
accept(visitor: Visitor) → Any¶
Accept a visitor.
Parameters
visitor – visitor to accept
Returns
result of visiting
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 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] = None, deep: bool = False) → Model¶
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 creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance | lang/api.python.langchain.com/en/latest/chains/langchain.chains.query_constructor.ir.Operation.html |
537e185b4210-1 | 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, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶
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[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
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 = 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: Protocol = None, allow_pickle: bool = False) → Model¶ | lang/api.python.langchain.com/en/latest/chains/langchain.chains.query_constructor.ir.Operation.html |
537e185b4210-2 | 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 fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶ | lang/api.python.langchain.com/en/latest/chains/langchain.chains.query_constructor.ir.Operation.html |
6db02a8073e6-0 | langchain.chains.sql_database.query.create_sql_query_chain¶
langchain.chains.sql_database.query.create_sql_query_chain(llm: BaseLanguageModel, db: SQLDatabase, prompt: Optional[BasePromptTemplate] = None, k: int = 5) → Runnable[Union[SQLInput, SQLInputWithTables], str][source]¶
Create a chain that generates SQL queries.
Security Note: This chain generates SQL queries for the given database.
The SQLDatabase class provides a get_table_info method that can be used
to get column information as well as sample data from the table.
To mitigate risk of leaking sensitive data, limit permissions
to read and scope to the tables that are needed.
Optionally, use the SQLInputWithTables input type to specify which tables
are allowed to be accessed.
Control access to who can submit requests to this chain.
See https://python.langchain.com/docs/security for more information.
Parameters
llm – The language model to use
db – The SQLDatabase to generate the query for
prompt – The prompt to use. If none is provided, will choose one
based on dialect. Defaults to None.
k – The number of results per select statement to return. Defaults to 5.
Returns
A chain that takes in a question and generates a SQL query that answers
that question.
Examples using create_sql_query_chain¶
Multiple Retrieval Sources
Set env var OPENAI_API_KEY or load from a .env file
SQL | lang/api.python.langchain.com/en/latest/chains/langchain.chains.sql_database.query.create_sql_query_chain.html |
1ddb28b50002-0 | langchain.chains.query_constructor.ir.Comparison¶
class langchain.chains.query_constructor.ir.Comparison[source]¶
Bases: FilterDirective
A comparison to a value.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param attribute: str [Required]¶
param comparator: langchain.chains.query_constructor.ir.Comparator [Required]¶
param value: Any = None¶
accept(visitor: Visitor) → Any¶
Accept a visitor.
Parameters
visitor – visitor to accept
Returns
result of visiting
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 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] = None, deep: bool = False) → Model¶
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 creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance | lang/api.python.langchain.com/en/latest/chains/langchain.chains.query_constructor.ir.Comparison.html |
1ddb28b50002-1 | 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, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶
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[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
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 = 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: Protocol = None, allow_pickle: bool = False) → Model¶ | lang/api.python.langchain.com/en/latest/chains/langchain.chains.query_constructor.ir.Comparison.html |
1ddb28b50002-2 | 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 fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶ | lang/api.python.langchain.com/en/latest/chains/langchain.chains.query_constructor.ir.Comparison.html |
8d56a00e0365-0 | 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 the input data cannot be parsed to form a valid model.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated, use callbacks instead.
param callbacks: Callbacks = None¶
Optional list of callback 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 details.
param memory: Optional[BaseMemory] = None¶
Optional memory object. Defaults to None.
Memory is a class that gets called at the start
and at the end of every chain. At the start, memory loads variables and passes
them along in the chain. At 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 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 callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param routing_keys: List[str] = ['query']¶
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. | lang/api.python.langchain.com/en/latest/chains/langchain.chains.router.embedding_router.EmbeddingRouterChain.html |
8d56a00e0365-1 | 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 console. Defaults to the global verbose value,
accessible via langchain.globals.get_verbose().
__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, run_name: Optional[str] = None, include_run_info: bool = False) → Dict[str, Any]¶
Execute the chain.
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 chain will be
returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False.
callbacks – Callbacks to use for this chain run. 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 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. | lang/api.python.langchain.com/en/latest/chains/langchain.chains.router.embedding_router.EmbeddingRouterChain.html |
8d56a00e0365-2 | 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, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation runs ainvoke in parallel using asyncio.gather.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode.
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, run_name: Optional[str] = None, include_run_info: bool = False) → Dict[str, Any]¶
Asynchronously execute the chain.
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 chain will be
returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False.
callbacks – Callbacks to use for this chain run. 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 | lang/api.python.langchain.com/en/latest/chains/langchain.chains.router.embedding_router.EmbeddingRouterChain.html |
8d56a00e0365-3 | 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 info in the response. Defaults
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, **kwargs: Any) → Dict[str, Any]¶
Default implementation of ainvoke, calls invoke from a thread.
The default implementation allows usage of async code even if
the runnable did not implement a native async version of invoke.
Subclasses should override this method if they can run asynchronously.
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 aroute(inputs: Dict[str, Any], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → Route¶
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 method and Chain.__call__ is that this
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. | lang/api.python.langchain.com/en/latest/chains/langchain.chains.router.embedding_router.EmbeddingRouterChain.html |
8d56a00e0365-4 | 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
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 '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' 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 Boise is..."
async astream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of astream, which calls ainvoke.
Subclasses should override this method if they support streaming output. | lang/api.python.langchain.com/en/latest/chains/langchain.chains.router.embedding_router.EmbeddingRouterChain.html |
8d56a00e0365-5 | Subclasses should override this method if they support streaming output.
async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, diff: bool = True, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Optional[Any]) → Union[AsyncIterator[RunLogPatch], AsyncIterator[RunLog]]¶
Stream all output from a runnable, as reported to the callback system.
This includes all inner runs of LLMs, Retrievers, Tools, etc.
Output is streamed as Log objects, which include a list of
jsonpatch ops that describe how the state of the run has changed in each
step, and the final state of the run.
The jsonpatch ops can be applied in order to construct state.
async atransform(input: AsyncIterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of atransform, which buffers input and calls astream.
Subclasses should override this method if they can start producing output while
input is still being generated.
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation runs invoke in parallel using a thread pool executor.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode. | lang/api.python.langchain.com/en/latest/chains/langchain.chains.router.embedding_router.EmbeddingRouterChain.html |
8d56a00e0365-6 | e.g., if the underlying runnable uses an API which supports a batch mode.
bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable.
config_schema(*, include: Optional[Sequence[str]] = None) → Type[BaseModel]¶
The type of config this runnable accepts specified as a pydantic model.
To mark a field as configurable, see the configurable_fields
and configurable_alternatives methods.
Parameters
include – A list of fields to include in the config schema.
Returns
A pydantic model that can be used to validate config.
configurable_alternatives(which: ConfigurableField, default_key: str = 'default', **kwargs: Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) → RunnableSerializable[Input, Output]¶
configurable_fields(**kwargs: Union[ConfigurableField, ConfigurableFieldSingleOption, ConfigurableFieldMultiOption]) → RunnableSerializable[Input, Output]¶
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 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] = None, deep: bool = False) → Model¶
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 | lang/api.python.langchain.com/en/latest/chains/langchain.chains.router.embedding_router.EmbeddingRouterChain.html |
8d56a00e0365-7 | 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 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 default pydantic.BaseModel.dict
method.
Returns
A dictionary representation of the chain.
Example
chain.dict(exclude_unset=True)
# -> {"_type": "foo", "verbose": False, ...}
classmethod from_names_and_descriptions(names_and_descriptions: Sequence[Tuple[str, Sequence[str]]], vectorstore_cls: Type[VectorStore], embeddings: Embeddings, **kwargs: Any) → EmbeddingRouterChain[source]¶
Convenience constructor.
classmethod from_orm(obj: Any) → Model¶
get_input_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
Get a pydantic model that can be used to validate input to the runnable.
Runnables that leverage the configurable_fields and configurable_alternatives
methods will have a dynamic input schema that depends on which
configuration the runnable is invoked with.
This method allows to get an input schema for a specific configuration.
Parameters
config – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate input.
classmethod get_lc_namespace() → List[str]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the
namespace is [“langchain”, “llms”, “openai”] | lang/api.python.langchain.com/en/latest/chains/langchain.chains.router.embedding_router.EmbeddingRouterChain.html |
8d56a00e0365-8 | namespace is [“langchain”, “llms”, “openai”]
get_output_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
Get a pydantic model that can be used to validate output to the runnable.
Runnables that leverage the configurable_fields and configurable_alternatives
methods will have a dynamic output schema that depends on which
configuration the runnable is invoked with.
This method allows to get an output schema for a specific configuration.
Parameters
config – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate output.
invoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any) → Dict[str, Any]¶
Transform a single input into an output. Override to implement.
Parameters
input – The input to the runnable.
config – A config to use when invoking the runnable.
The config supports standard keys like ‘tags’, ‘metadata’ for tracing
purposes, ‘max_concurrency’ for controlling how much work to do
in parallel, and other keys. Please refer to the RunnableConfig
for more details.
Returns
The output of the runnable.
classmethod is_lc_serializable() → bool¶
Is this class serializable?
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[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶ | lang/api.python.langchain.com/en/latest/chains/langchain.chains.router.embedding_router.EmbeddingRouterChain.html |
8d56a00e0365-9 | 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 lc_id() → List[str]¶
A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
map() → Runnable[List[Input], List[Output]]¶
Return a new Runnable that maps a list of inputs to a list of outputs,
by calling invoke() with each input.
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: Protocol = None, allow_pickle: bool = False) → Model¶
prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]¶
Validate and prepare chain inputs, including adding inputs from memory.
Parameters
inputs – Dictionary of raw 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.
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, including any inputs added by chain
memory.
outputs – Dictionary of initial chain outputs. | lang/api.python.langchain.com/en/latest/chains/langchain.chains.router.embedding_router.EmbeddingRouterChain.html |
8d56a00e0365-10 | 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 final outputs.
Returns
A dict of the final chain outputs.
route(inputs: Dict[str, Any], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → Route¶
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, **kwargs: Any) → Any¶
Convenience method for executing chain.
The main difference between this method and Chain.__call__ is that this
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 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 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 'question' string:
chain.run("What's the temperature in Boise, Idaho?") | lang/api.python.langchain.com/en/latest/chains/langchain.chains.router.embedding_router.EmbeddingRouterChain.html |
8d56a00e0365-11 | 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:
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 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 = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
stream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of stream, which calls invoke.
Subclasses should override this method if they support streaming output.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of transform, which buffers input and then calls stream.
Subclasses should override this method if they can start producing output while
input is still being generated.
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns. | lang/api.python.langchain.com/en/latest/chains/langchain.chains.router.embedding_router.EmbeddingRouterChain.html |
8d56a00e0365-12 | Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
with_config(config: Optional[RunnableConfig] = None, **kwargs: Any) → Runnable[Input, Output]¶
Bind config to a Runnable, returning a new Runnable.
with_fallbacks(fallbacks: Sequence[Runnable[Input, Output]], *, exceptions_to_handle: Tuple[Type[BaseException], ...] = (<class 'Exception'>,)) → RunnableWithFallbacksT[Input, Output]¶
Add fallbacks to a runnable, returning a new Runnable.
Parameters
fallbacks – A sequence of runnables to try if the original runnable fails.
exceptions_to_handle – A tuple of exception types to handle.
Returns
A new Runnable that will try the original runnable, and then each
fallback in order, upon failures.
with_listeners(*, on_start: Optional[Listener] = None, on_end: Optional[Listener] = None, on_error: Optional[Listener] = None) → Runnable[Input, Output]¶
Bind lifecycle listeners to a Runnable, returning a new Runnable.
on_start: Called before the runnable starts running, with the Run object.
on_end: Called after the runnable finishes running, with the Run object.
on_error: Called if the runnable throws an error, with the Run object.
The Run object contains information about the run, including its id,
type, input, output, error, start_time, end_time, and any tags or metadata
added to the run.
with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, stop_after_attempt: int = 3) → Runnable[Input, Output]¶ | lang/api.python.langchain.com/en/latest/chains/langchain.chains.router.embedding_router.EmbeddingRouterChain.html |
8d56a00e0365-13 | Create a new Runnable that retries the original runnable on exceptions.
Parameters
retry_if_exception_type – A tuple of exception types to retry on
wait_exponential_jitter – Whether to add jitter to the wait time
between retries
stop_after_attempt – The maximum number of attempts to make before giving up
Returns
A new Runnable that retries the original runnable on exceptions.
with_types(*, input_type: Optional[Type[Input]] = None, output_type: Optional[Type[Output]] = None) → Runnable[Input, Output]¶
Bind input and output types to a Runnable, returning a new Runnable.
property InputType: Type[langchain.schema.runnable.utils.Input]¶
The type of input this runnable accepts specified as a type annotation.
property OutputType: Type[langchain.schema.runnable.utils.Output]¶
The type of output this runnable produces specified as a type annotation.
property config_specs: List[langchain.schema.runnable.utils.ConfigurableFieldSpec]¶
List configurable fields for this runnable.
property input_schema: Type[pydantic.main.BaseModel]¶
The type of input this runnable accepts specified as a pydantic model.
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_keys: List[str]¶
Keys expected to be in the chain output.
property output_schema: Type[pydantic.main.BaseModel]¶
The type of output this runnable produces specified as a pydantic model.
Examples using EmbeddingRouterChain¶
Router | lang/api.python.langchain.com/en/latest/chains/langchain.chains.router.embedding_router.EmbeddingRouterChain.html |
72f2103065a4-0 | 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 parsed to form a valid model.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated, use callbacks instead.
param callbacks: Callbacks = None¶
Optional list of callback handlers (or callback manager). Defaults to None.
Callback handlers are called throughout the lifecycle of a call to a chain,
starting with on_chain_start, ending with on_chain_end or on_chain_error.
Each custom chain can optionally call additional callback methods, see Callback docs
for full details.
param combine_documents_chain: BaseCombineDocumentsChain [Required]¶
Chain to use to combine the documents.
param k: int = 4¶
Number of documents to query for.
param memory: Optional[BaseMemory] = None¶
Optional memory object. Defaults to None.
Memory is a class that gets called at the start
and at the end of every chain. At the start, memory loads variables and passes
them along in the chain. At 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 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 callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param return_source_documents: bool = False¶
Return the source documents or not. | lang/api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.VectorDBQA.html |
72f2103065a4-1 | 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 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 vectorstore: VectorStore [Required]¶
Vector Database to connect to.
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 the global verbose value,
accessible via langchain.globals.get_verbose().
__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, run_name: Optional[str] = None, include_run_info: bool = False) → Dict[str, Any]¶
Execute the chain.
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 chain will be
returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False. | lang/api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.VectorDBQA.html |
72f2103065a4-2 | 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 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 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, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation runs ainvoke in parallel using asyncio.gather.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode.
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, run_name: Optional[str] = None, include_run_info: bool = False) → Dict[str, Any]¶
Asynchronously execute the chain.
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. | lang/api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.VectorDBQA.html |
72f2103065a4-3 | 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 – 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 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. Should contain all outputs specified inChain.output_keys.
async ainvoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any) → Dict[str, Any]¶
Default implementation of ainvoke, calls invoke from a thread.
The default implementation allows usage of async code even if
the runnable did not implement a native async version of invoke.
Subclasses should override this method if they can run asynchronously.
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]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶ | lang/api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.VectorDBQA.html |
72f2103065a4-4 | Convenience method for executing chain.
The main difference between this method and Chain.__call__ is that this
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 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 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 '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' 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 Boise is..."
async astream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of astream, which calls ainvoke.
Subclasses should override this method if they support streaming output. | lang/api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.VectorDBQA.html |
72f2103065a4-5 | Subclasses should override this method if they support streaming output.
async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, diff: bool = True, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Optional[Any]) → Union[AsyncIterator[RunLogPatch], AsyncIterator[RunLog]]¶
Stream all output from a runnable, as reported to the callback system.
This includes all inner runs of LLMs, Retrievers, Tools, etc.
Output is streamed as Log objects, which include a list of
jsonpatch ops that describe how the state of the run has changed in each
step, and the final state of the run.
The jsonpatch ops can be applied in order to construct state.
async atransform(input: AsyncIterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of atransform, which buffers input and calls astream.
Subclasses should override this method if they can start producing output while
input is still being generated.
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation runs invoke in parallel using a thread pool executor.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode. | lang/api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.VectorDBQA.html |
72f2103065a4-6 | e.g., if the underlying runnable uses an API which supports a batch mode.
bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable.
config_schema(*, include: Optional[Sequence[str]] = None) → Type[BaseModel]¶
The type of config this runnable accepts specified as a pydantic model.
To mark a field as configurable, see the configurable_fields
and configurable_alternatives methods.
Parameters
include – A list of fields to include in the config schema.
Returns
A pydantic model that can be used to validate config.
configurable_alternatives(which: ConfigurableField, default_key: str = 'default', **kwargs: Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) → RunnableSerializable[Input, Output]¶
configurable_fields(**kwargs: Union[ConfigurableField, ConfigurableFieldSingleOption, ConfigurableFieldMultiOption]) → RunnableSerializable[Input, Output]¶
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 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] = None, deep: bool = False) → Model¶
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 | lang/api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.VectorDBQA.html |
72f2103065a4-7 | 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 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 default pydantic.BaseModel.dict
method.
Returns
A dictionary representation of the chain.
Example
chain.dict(exclude_unset=True)
# -> {"_type": "foo", "verbose": False, ...}
classmethod from_chain_type(llm: BaseLanguageModel, chain_type: str = 'stuff', chain_type_kwargs: Optional[dict] = None, **kwargs: Any) → BaseRetrievalQA¶
Load chain from chain type.
classmethod from_llm(llm: BaseLanguageModel, prompt: Optional[PromptTemplate] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → BaseRetrievalQA¶
Initialize from LLM.
classmethod from_orm(obj: Any) → Model¶
get_input_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
Get a pydantic model that can be used to validate input to the runnable.
Runnables that leverage the configurable_fields and configurable_alternatives
methods will have a dynamic input schema that depends on which
configuration the runnable is invoked with.
This method allows to get an input schema for a specific configuration.
Parameters
config – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate input. | lang/api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.VectorDBQA.html |
72f2103065a4-8 | Returns
A pydantic model that can be used to validate input.
classmethod get_lc_namespace() → List[str]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the
namespace is [“langchain”, “llms”, “openai”]
get_output_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
Get a pydantic model that can be used to validate output to the runnable.
Runnables that leverage the configurable_fields and configurable_alternatives
methods will have a dynamic output schema that depends on which
configuration the runnable is invoked with.
This method allows to get an output schema for a specific configuration.
Parameters
config – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate output.
invoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any) → Dict[str, Any]¶
Transform a single input into an output. Override to implement.
Parameters
input – The input to the runnable.
config – A config to use when invoking the runnable.
The config supports standard keys like ‘tags’, ‘metadata’ for tracing
purposes, ‘max_concurrency’ for controlling how much work to do
in parallel, and other keys. Please refer to the RunnableConfig
for more details.
Returns
The output of the runnable.
classmethod is_lc_serializable() → bool¶
Is this class serializable? | lang/api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.VectorDBQA.html |
72f2103065a4-9 | classmethod is_lc_serializable() → bool¶
Is this class serializable?
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[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
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 lc_id() → List[str]¶
A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
map() → Runnable[List[Input], List[Output]]¶
Return a new Runnable that maps a list of inputs to a list of outputs,
by calling invoke() with each input.
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: Protocol = None, allow_pickle: bool = False) → Model¶
prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]¶
Validate and prepare chain inputs, including adding inputs from memory.
Parameters
inputs – Dictionary of raw inputs, or single input if chain expects | lang/api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.VectorDBQA.html |
72f2103065a4-10 | Parameters
inputs – Dictionary of raw 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.
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, 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 final outputs.
Returns
A dict of the final chain outputs.
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.__call__ is that this
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 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 chain during construction, but only
these runtime tags will propagate to calls to other objects. | lang/api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.VectorDBQA.html |
72f2103065a4-11 | 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:
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:
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 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 = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
stream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of stream, which calls invoke.
Subclasses should override this method if they support streaming output.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶ | lang/api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.VectorDBQA.html |
72f2103065a4-12 | Default implementation of transform, which buffers input and then calls stream.
Subclasses should override this method if they can start producing output while
input is still being generated.
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_config(config: Optional[RunnableConfig] = None, **kwargs: Any) → Runnable[Input, Output]¶
Bind config to a Runnable, returning a new Runnable.
with_fallbacks(fallbacks: Sequence[Runnable[Input, Output]], *, exceptions_to_handle: Tuple[Type[BaseException], ...] = (<class 'Exception'>,)) → RunnableWithFallbacksT[Input, Output]¶
Add fallbacks to a runnable, returning a new Runnable.
Parameters
fallbacks – A sequence of runnables to try if the original runnable fails.
exceptions_to_handle – A tuple of exception types to handle.
Returns
A new Runnable that will try the original runnable, and then each
fallback in order, upon failures.
with_listeners(*, on_start: Optional[Listener] = None, on_end: Optional[Listener] = None, on_error: Optional[Listener] = None) → Runnable[Input, Output]¶
Bind lifecycle listeners to a Runnable, returning a new Runnable.
on_start: Called before the runnable starts running, with the Run object.
on_end: Called after the runnable finishes running, with the Run object.
on_error: Called if the runnable throws an error, with the Run object.
The Run object contains information about the run, including its id,
type, input, output, error, start_time, end_time, and any tags or metadata
added to the run. | lang/api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.VectorDBQA.html |
72f2103065a4-13 | added to the run.
with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, stop_after_attempt: int = 3) → Runnable[Input, Output]¶
Create a new Runnable that retries the original runnable on exceptions.
Parameters
retry_if_exception_type – A tuple of exception types to retry on
wait_exponential_jitter – Whether to add jitter to the wait time
between retries
stop_after_attempt – The maximum number of attempts to make before giving up
Returns
A new Runnable that retries the original runnable on exceptions.
with_types(*, input_type: Optional[Type[Input]] = None, output_type: Optional[Type[Output]] = None) → Runnable[Input, Output]¶
Bind input and output types to a Runnable, returning a new Runnable.
property InputType: Type[langchain.schema.runnable.utils.Input]¶
The type of input this runnable accepts specified as a type annotation.
property OutputType: Type[langchain.schema.runnable.utils.Output]¶
The type of output this runnable produces specified as a type annotation.
property config_specs: List[langchain.schema.runnable.utils.ConfigurableFieldSpec]¶
List configurable fields for this runnable.
property input_schema: Type[pydantic.main.BaseModel]¶
The type of input this runnable accepts specified as a pydantic model.
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_schema: Type[pydantic.main.BaseModel]¶ | lang/api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.VectorDBQA.html |
72f2103065a4-14 | property output_schema: Type[pydantic.main.BaseModel]¶
The type of output this runnable produces specified as a pydantic model. | lang/api.python.langchain.com/en/latest/chains/langchain.chains.retrieval_qa.base.VectorDBQA.html |
8a860f0ab822-0 | langchain.chains.graph_qa.nebulagraph.NebulaGraphQAChain¶
class langchain.chains.graph_qa.nebulagraph.NebulaGraphQAChain[source]¶
Bases: Chain
Chain for question-answering against a graph by generating nGQL statements.
Security note: Make sure that the database connection uses credentialsthat are narrowly-scoped to only include necessary permissions.
Failure to do so may result in data corruption or loss, since the calling
code may attempt commands that would result in deletion, mutation
of data if appropriately prompted or reading sensitive data if such
data is present in the database.
The best way to guard against such negative outcomes is to (as appropriate)
limit the permissions granted to the credentials used with this tool.
See https://python.langchain.com/docs/security for more information.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated, use callbacks instead.
param callbacks: Callbacks = None¶
Optional list of callback 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 details.
param graph: NebulaGraph [Required]¶
param memory: Optional[BaseMemory] = None¶
Optional memory object. Defaults to None.
Memory is a class that gets called at the start
and at the end of every chain. At the start, memory loads variables and passes
them along in the chain. At the end, it saves any returned variables.
There are many different types of memory - please see memory docs
for the full catalog. | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.nebulagraph.NebulaGraphQAChain.html |
8a860f0ab822-1 | 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 callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param ngql_generation_chain: LLMChain [Required]¶
param qa_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 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 printed to the console. Defaults to the global verbose value,
accessible via langchain.globals.get_verbose().
__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, run_name: Optional[str] = None, include_run_info: bool = False) → Dict[str, Any]¶
Execute the chain.
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 chain will be | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.nebulagraph.NebulaGraphQAChain.html |
8a860f0ab822-2 | response. If True, only new keys generated by this chain will be
returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False.
callbacks – Callbacks to use for this chain run. 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 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. Should contain all outputs specified inChain.output_keys.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation runs ainvoke in parallel using asyncio.gather.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode.
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, run_name: Optional[str] = None, include_run_info: bool = False) → Dict[str, Any]¶
Asynchronously execute the chain.
Parameters
inputs – Dictionary of inputs, or single input if chain expects | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.nebulagraph.NebulaGraphQAChain.html |
8a860f0ab822-3 | 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 chain will be
returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False.
callbacks – Callbacks to use for this chain run. 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 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. Should contain all outputs specified inChain.output_keys.
async ainvoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any) → Dict[str, Any]¶
Default implementation of ainvoke, calls invoke from a thread.
The default implementation allows usage of async code even if
the runnable did not implement a native async version of invoke.
Subclasses should override this method if they can run asynchronously.
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. | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.nebulagraph.NebulaGraphQAChain.html |
8a860f0ab822-4 | 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 method and Chain.__call__ is that this
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 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 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 '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' 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 Boise is..." | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.nebulagraph.NebulaGraphQAChain.html |
8a860f0ab822-5 | # -> "The temperature in Boise is..."
async astream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of astream, which calls ainvoke.
Subclasses should override this method if they support streaming output.
async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, diff: bool = True, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Optional[Any]) → Union[AsyncIterator[RunLogPatch], AsyncIterator[RunLog]]¶
Stream all output from a runnable, as reported to the callback system.
This includes all inner runs of LLMs, Retrievers, Tools, etc.
Output is streamed as Log objects, which include a list of
jsonpatch ops that describe how the state of the run has changed in each
step, and the final state of the run.
The jsonpatch ops can be applied in order to construct state.
async atransform(input: AsyncIterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of atransform, which buffers input and calls astream.
Subclasses should override this method if they can start producing output while
input is still being generated.
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation runs invoke in parallel using a thread pool executor. | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.nebulagraph.NebulaGraphQAChain.html |
8a860f0ab822-6 | Default implementation runs invoke in parallel using a thread pool executor.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode.
bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable.
config_schema(*, include: Optional[Sequence[str]] = None) → Type[BaseModel]¶
The type of config this runnable accepts specified as a pydantic model.
To mark a field as configurable, see the configurable_fields
and configurable_alternatives methods.
Parameters
include – A list of fields to include in the config schema.
Returns
A pydantic model that can be used to validate config.
configurable_alternatives(which: ConfigurableField, default_key: str = 'default', **kwargs: Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) → RunnableSerializable[Input, Output]¶
configurable_fields(**kwargs: Union[ConfigurableField, ConfigurableFieldSingleOption, ConfigurableFieldMultiOption]) → RunnableSerializable[Input, Output]¶
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 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] = None, deep: bool = False) → Model¶ | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.nebulagraph.NebulaGraphQAChain.html |
8a860f0ab822-7 | 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 creating
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 default pydantic.BaseModel.dict
method.
Returns
A dictionary representation of the chain.
Example
chain.dict(exclude_unset=True)
# -> {"_type": "foo", "verbose": False, ...} | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.nebulagraph.NebulaGraphQAChain.html |
8a860f0ab822-8 | classmethod from_llm(llm: BaseLanguageModel, *, qa_prompt: BasePromptTemplate = PromptTemplate(input_variables=['context', 'question'], 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 answer.\nThe provided information is authoritative, you must never doubt it or try to use your internal knowledge to correct it.\nMake the answer sound as a response to the question. Do not mention that you based the result on the given information.\nIf the provided information is empty, say that you don't know the answer.\nInformation:\n{context}\n\nQuestion: {question}\nHelpful Answer:"), ngql_prompt: BasePromptTemplate = PromptTemplate(input_variables=['question', 'schema'], template="Task:Generate NebulaGraph Cypher statement to query a graph database.\n\nInstructions:\n\nFirst, generate cypher then convert it to NebulaGraph Cypher dialect(rather than standard):\n1. it requires explicit label specification only when referring to node properties: v.`Foo`.name\n2. note explicit label specification is not needed for edge properties, so it's e.name instead of e.`Bar`.name\n3. it uses double equals sign for comparison: `==` rather than `=`\nFor instance:\n```diff\n< MATCH (p:person)-[e:directed]->(m:movie) WHERE m.name = 'The Godfather II'\n< RETURN p.name, e.year, m.name;\n---\n> MATCH (p:`person`)-[e:directed]->(m:`movie`) WHERE m.`movie`.`name` == 'The Godfather II'\n> RETURN p.`person`.`name`, e.year, m.`movie`.`name`;\n```\n\nUse only the provided relationship types and properties in the schema.\nDo not use any other relationship | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.nebulagraph.NebulaGraphQAChain.html |
8a860f0ab822-9 | only the provided relationship types and properties in the schema.\nDo not use any other relationship types or properties that are not provided.\nSchema:\n{schema}\nNote: Do not include any explanations or apologies in your responses.\nDo not respond to any questions that might ask anything else than for you to construct a Cypher statement.\nDo not include any text except the generated Cypher statement.\n\nThe question is:\n{question}"), **kwargs: Any) → NebulaGraphQAChain[source]¶ | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.nebulagraph.NebulaGraphQAChain.html |
8a860f0ab822-10 | Initialize from LLM.
classmethod from_orm(obj: Any) → Model¶
get_input_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
Get a pydantic model that can be used to validate input to the runnable.
Runnables that leverage the configurable_fields and configurable_alternatives
methods will have a dynamic input schema that depends on which
configuration the runnable is invoked with.
This method allows to get an input schema for a specific configuration.
Parameters
config – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate input.
classmethod get_lc_namespace() → List[str]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the
namespace is [“langchain”, “llms”, “openai”]
get_output_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
Get a pydantic model that can be used to validate output to the runnable.
Runnables that leverage the configurable_fields and configurable_alternatives
methods will have a dynamic output schema that depends on which
configuration the runnable is invoked with.
This method allows to get an output schema for a specific configuration.
Parameters
config – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate output.
invoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any) → Dict[str, Any]¶
Transform a single input into an output. Override to implement.
Parameters
input – The input to the runnable.
config – A config to use when invoking the runnable.
The config supports standard keys like ‘tags’, ‘metadata’ for tracing | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.nebulagraph.NebulaGraphQAChain.html |
8a860f0ab822-11 | The config supports standard keys like ‘tags’, ‘metadata’ for tracing
purposes, ‘max_concurrency’ for controlling how much work to do
in parallel, and other keys. Please refer to the RunnableConfig
for more details.
Returns
The output of the runnable.
classmethod is_lc_serializable() → bool¶
Is this class serializable?
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[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
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 lc_id() → List[str]¶
A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
map() → Runnable[List[Input], List[Output]]¶
Return a new Runnable that maps a list of inputs to a list of outputs,
by calling invoke() with each input.
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¶ | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.nebulagraph.NebulaGraphQAChain.html |
8a860f0ab822-12 | 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 adding inputs from memory.
Parameters
inputs – Dictionary of raw 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.
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, 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 final outputs.
Returns
A dict of the final chain outputs.
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.__call__ is that this
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. | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.nebulagraph.NebulaGraphQAChain.html |
8a860f0ab822-13 | 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
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 '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:
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 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 = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
stream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶ | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.nebulagraph.NebulaGraphQAChain.html |
8a860f0ab822-14 | Default implementation of stream, which calls invoke.
Subclasses should override this method if they support streaming output.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of transform, which buffers input and then calls stream.
Subclasses should override this method if they can start producing output while
input is still being generated.
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_config(config: Optional[RunnableConfig] = None, **kwargs: Any) → Runnable[Input, Output]¶
Bind config to a Runnable, returning a new Runnable.
with_fallbacks(fallbacks: Sequence[Runnable[Input, Output]], *, exceptions_to_handle: Tuple[Type[BaseException], ...] = (<class 'Exception'>,)) → RunnableWithFallbacksT[Input, Output]¶
Add fallbacks to a runnable, returning a new Runnable.
Parameters
fallbacks – A sequence of runnables to try if the original runnable fails.
exceptions_to_handle – A tuple of exception types to handle.
Returns
A new Runnable that will try the original runnable, and then each
fallback in order, upon failures.
with_listeners(*, on_start: Optional[Listener] = None, on_end: Optional[Listener] = None, on_error: Optional[Listener] = None) → Runnable[Input, Output]¶
Bind lifecycle listeners to a Runnable, returning a new Runnable.
on_start: Called before the runnable starts running, with the Run object. | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.nebulagraph.NebulaGraphQAChain.html |
8a860f0ab822-15 | on_start: Called before the runnable starts running, with the Run object.
on_end: Called after the runnable finishes running, with the Run object.
on_error: Called if the runnable throws an error, with the Run object.
The Run object contains information about the run, including its id,
type, input, output, error, start_time, end_time, and any tags or metadata
added to the run.
with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, stop_after_attempt: int = 3) → Runnable[Input, Output]¶
Create a new Runnable that retries the original runnable on exceptions.
Parameters
retry_if_exception_type – A tuple of exception types to retry on
wait_exponential_jitter – Whether to add jitter to the wait time
between retries
stop_after_attempt – The maximum number of attempts to make before giving up
Returns
A new Runnable that retries the original runnable on exceptions.
with_types(*, input_type: Optional[Type[Input]] = None, output_type: Optional[Type[Output]] = None) → Runnable[Input, Output]¶
Bind input and output types to a Runnable, returning a new Runnable.
property InputType: Type[langchain.schema.runnable.utils.Input]¶
The type of input this runnable accepts specified as a type annotation.
property OutputType: Type[langchain.schema.runnable.utils.Output]¶
The type of output this runnable produces specified as a type annotation.
property config_specs: List[langchain.schema.runnable.utils.ConfigurableFieldSpec]¶
List configurable fields for this runnable.
property input_schema: Type[pydantic.main.BaseModel]¶
The type of input this runnable accepts specified as a pydantic model.
property lc_attributes: Dict¶ | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.nebulagraph.NebulaGraphQAChain.html |
8a860f0ab822-16 | property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_schema: Type[pydantic.main.BaseModel]¶
The type of output this runnable produces specified as a pydantic model.
Examples using NebulaGraphQAChain¶
NebulaGraphQAChain | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.nebulagraph.NebulaGraphQAChain.html |
fcfb3051fc85-0 | langchain.chains.query_constructor.base.load_query_constructor_chain¶
langchain.chains.query_constructor.base.load_query_constructor_chain(llm: ~langchain.schema.language_model.BaseLanguageModel, document_contents: str, attribute_info: ~typing.Sequence[~typing.Union[~langchain.chains.query_constructor.schema.AttributeInfo, dict]], examples: ~typing.Optional[~typing.List] = None, allowed_comparators: ~typing.Sequence[~langchain.chains.query_constructor.ir.Comparator] = (<Comparator.EQ: 'eq'>, <Comparator.NE: 'ne'>, <Comparator.GT: 'gt'>, <Comparator.GTE: 'gte'>, <Comparator.LT: 'lt'>, <Comparator.LTE: 'lte'>, <Comparator.CONTAIN: 'contain'>, <Comparator.LIKE: 'like'>, <Comparator.IN: 'in'>, <Comparator.NIN: 'nin'>), allowed_operators: ~typing.Sequence[~langchain.chains.query_constructor.ir.Operator] = (<Operator.AND: 'and'>, <Operator.OR: 'or'>, <Operator.NOT: 'not'>), enable_limit: bool = False, schema_prompt: ~typing.Optional[~langchain.schema.prompt_template.BasePromptTemplate] = None, **kwargs: ~typing.Any) → LLMChain[source]¶
Load a query constructor chain.
Parameters
llm – BaseLanguageModel to use for the chain.
document_contents – The contents of the document to be queried.
attribute_info – Sequence of attributes in the document.
examples – Optional list of examples to use for the chain.
allowed_comparators – Sequence of allowed comparators. Defaults to all
Comparators.
allowed_operators – Sequence of allowed operators. Defaults to all Operators.
enable_limit – Whether to enable the limit operator. Defaults to False.
schema_prompt – Prompt for describing query schema. Should have string input | lang/api.python.langchain.com/en/latest/chains/langchain.chains.query_constructor.base.load_query_constructor_chain.html |
fcfb3051fc85-1 | schema_prompt – Prompt for describing query schema. Should have string input
variables allowed_comparators and allowed_operators.
**kwargs – Arbitrary named params to pass to LLMChain.
Returns
A LLMChain that can be used to construct queries. | lang/api.python.langchain.com/en/latest/chains/langchain.chains.query_constructor.base.load_query_constructor_chain.html |
fc706b9a28a7-0 | langchain.chains.openai_tools.extraction.create_extraction_chain_pydantic¶
langchain.chains.openai_tools.extraction.create_extraction_chain_pydantic(pydantic_schemas: Union[List[Type[BaseModel]], Type[BaseModel]], llm: BaseLanguageModel, system_message: str = 'Extract and save the relevant entities mentioned in the following passage together with their properties.\n\nIf a property is not present and is not required in the function parameters, do not include it in the output.') → Runnable[source]¶
Examples using create_extraction_chain_pydantic¶
Set env var OPENAI_API_KEY or load from a .env file: | lang/api.python.langchain.com/en/latest/chains/langchain.chains.openai_tools.extraction.create_extraction_chain_pydantic.html |
0c8386b8c88d-0 | langchain.chains.openai_functions.tagging.create_tagging_chain_pydantic¶
langchain.chains.openai_functions.tagging.create_tagging_chain_pydantic(pydantic_schema: Any, llm: BaseLanguageModel, prompt: Optional[ChatPromptTemplate] = None, **kwargs: Any) → Chain[source]¶
Creates a chain that extracts information from a passagebased on a pydantic schema.
Parameters
pydantic_schema – The pydantic schema of the entities to extract.
llm – The language model to use.
Returns
Chain (LLMChain) that can be used to extract information from a passage.
Examples using create_tagging_chain_pydantic¶
Set env var OPENAI_API_KEY or load from a .env file: | lang/api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.tagging.create_tagging_chain_pydantic.html |
c2d85b6dc335-0 | 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, passing
that first document in with the variable name document_variable_name, and
produces a new variable with the variable name initial_response_name.
Then, it loops over every remaining document. This is called the “refine” step.
It calls refine_llm_chain,
passing in that document with the variable name document_variable_name
as well as the previous response with the variable name initial_response_name.
Example
from langchain.chains import RefineDocumentsChain, LLMChain
from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI
# This controls how each document will be formatted. Specifically,
# it will be passed to `format_document` - see that function for more
# details.
document_prompt = PromptTemplate(
input_variables=["page_content"],
template="{page_content}"
)
document_variable_name = "context"
llm = OpenAI()
# The prompt here should take as an input variable the
# `document_variable_name`
prompt = PromptTemplate.from_template(
"Summarize this content: {context}"
)
initial_llm_chain = LLMChain(llm=llm, prompt=prompt)
initial_response_name = "prev_response"
# The prompt here should take as an input variable the
# `document_variable_name` as well as `initial_response_name`
prompt_refine = PromptTemplate.from_template(
"Here's your first summary: {prev_response}. "
"Now add to it based on the following context: {context}"
) | lang/api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.refine.RefineDocumentsChain.html |
c2d85b6dc335-1 | "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,
initial_response_name=initial_response_name,
)
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated, use callbacks instead.
param callbacks: Callbacks = None¶
Optional list of callback 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 details.
param document_prompt: BasePromptTemplate [Optional]¶
Prompt to use to format each document, gets passed to format_document.
param document_variable_name: str [Required]¶
The variable name in the initial_llm_chain to put the documents in.
If only one variable in the initial_llm_chain, this need not be provided.
param initial_llm_chain: LLMChain [Required]¶
LLM chain to use on initial document.
param initial_response_name: str [Required]¶
The variable name to format the initial response in when refining.
param memory: Optional[BaseMemory] = None¶
Optional memory object. Defaults to None.
Memory is a class that gets called at the start
and at the end of every chain. At the start, memory loads variables and passes | lang/api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.refine.RefineDocumentsChain.html |
c2d85b6dc335-2 | 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 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 callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param refine_llm_chain: LLMChain [Required]¶
LLM chain to use when refining.
param return_intermediate_steps: bool = False¶
Return the results of the refine steps in the output.
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 verbose: bool [Optional]¶
Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be printed to the console. Defaults to the global verbose value,
accessible via langchain.globals.get_verbose().
__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, run_name: Optional[str] = None, include_run_info: bool = False) → Dict[str, Any]¶
Execute the chain.
Parameters
inputs – Dictionary of inputs, or single input if chain expects | lang/api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.refine.RefineDocumentsChain.html |
c2d85b6dc335-3 | Execute the chain.
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 chain will be
returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False.
callbacks – Callbacks to use for this chain run. 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 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. Should contain all outputs specified inChain.output_keys.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation runs ainvoke in parallel using asyncio.gather.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode. | lang/api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.refine.RefineDocumentsChain.html |
c2d85b6dc335-4 | e.g., if the underlying runnable uses an API which supports a batch mode.
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, run_name: Optional[str] = None, include_run_info: bool = False) → Dict[str, Any]¶
Asynchronously execute the chain.
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 chain will be
returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False.
callbacks – Callbacks to use for this chain run. 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 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. Should contain all outputs specified inChain.output_keys.
async acombine_docs(docs: List[Document], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Tuple[str, dict][source]¶ | lang/api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.refine.RefineDocumentsChain.html |
c2d85b6dc335-5 | Async combine by mapping a first chain over all, then stuffinginto a final chain.
Parameters
docs – List of documents to combine
callbacks – Callbacks to be passed through
**kwargs – additional parameters to be passed to LLM calls (like other
input variables besides the documents)
Returns
The first element returned is the single string output. The second
element returned is a dictionary of other keys to return.
async ainvoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any) → Dict[str, Any]¶
Default implementation of ainvoke, calls invoke from a thread.
The default implementation allows usage of async code even if
the runnable did not implement a native async version of invoke.
Subclasses should override this method if they can run asynchronously.
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]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Convenience method for executing chain.
The main difference between this method and Chain.__call__ is that this
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 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. | lang/api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.refine.RefineDocumentsChain.html |
c2d85b6dc335-6 | 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, 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 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 Boise is..."
async astream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of astream, which calls ainvoke.
Subclasses should override this method if they support streaming output.
async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, diff: bool = True, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Optional[Any]) → Union[AsyncIterator[RunLogPatch], AsyncIterator[RunLog]]¶
Stream all output from a runnable, as reported to the callback system. | lang/api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.refine.RefineDocumentsChain.html |
c2d85b6dc335-7 | Stream all output from a runnable, as reported to the callback system.
This includes all inner runs of LLMs, Retrievers, Tools, etc.
Output is streamed as Log objects, which include a list of
jsonpatch ops that describe how the state of the run has changed in each
step, and the final state of the run.
The jsonpatch ops can be applied in order to construct state.
async atransform(input: AsyncIterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of atransform, which buffers input and calls astream.
Subclasses should override this method if they can start producing output while
input is still being generated.
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation runs invoke in parallel using a thread pool executor.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode.
bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable.
combine_docs(docs: List[Document], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Tuple[str, dict][source]¶
Combine by mapping first chain over all, then stuffing into final chain.
Parameters
docs – List of documents to combine
callbacks – Callbacks to be passed through
**kwargs – additional parameters to be passed to LLM calls (like other
input variables besides the documents)
Returns | lang/api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.refine.RefineDocumentsChain.html |
c2d85b6dc335-8 | input variables besides the documents)
Returns
The first element returned is the single string output. The second
element returned is a dictionary of other keys to return.
config_schema(*, include: Optional[Sequence[str]] = None) → Type[BaseModel]¶
The type of config this runnable accepts specified as a pydantic model.
To mark a field as configurable, see the configurable_fields
and configurable_alternatives methods.
Parameters
include – A list of fields to include in the config schema.
Returns
A pydantic model that can be used to validate config.
configurable_alternatives(which: ConfigurableField, default_key: str = 'default', **kwargs: Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) → RunnableSerializable[Input, Output]¶
configurable_fields(**kwargs: Union[ConfigurableField, ConfigurableFieldSingleOption, ConfigurableFieldMultiOption]) → RunnableSerializable[Input, Output]¶
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 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] = None, deep: bool = False) → Model¶
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 | lang/api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.refine.RefineDocumentsChain.html |
c2d85b6dc335-9 | 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 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 default pydantic.BaseModel.dict
method.
Returns
A dictionary representation of the chain.
Example
chain.dict(exclude_unset=True)
# -> {"_type": "foo", "verbose": False, ...}
classmethod from_orm(obj: Any) → Model¶
get_input_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
Get a pydantic model that can be used to validate input to the runnable.
Runnables that leverage the configurable_fields and configurable_alternatives
methods will have a dynamic input schema that depends on which
configuration the runnable is invoked with.
This method allows to get an input schema for a specific configuration.
Parameters
config – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate input.
classmethod get_lc_namespace() → List[str]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the
namespace is [“langchain”, “llms”, “openai”]
get_output_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
Get a pydantic model that can be used to validate output to the runnable.
Runnables that leverage the configurable_fields and configurable_alternatives | lang/api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.refine.RefineDocumentsChain.html |
c2d85b6dc335-10 | Runnables that leverage the configurable_fields and configurable_alternatives
methods will have a dynamic output schema that depends on which
configuration the runnable is invoked with.
This method allows to get an output schema for a specific configuration.
Parameters
config – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate output.
invoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any) → Dict[str, Any]¶
Transform a single input into an output. Override to implement.
Parameters
input – The input to the runnable.
config – A config to use when invoking the runnable.
The config supports standard keys like ‘tags’, ‘metadata’ for tracing
purposes, ‘max_concurrency’ for controlling how much work to do
in parallel, and other keys. Please refer to the RunnableConfig
for more details.
Returns
The output of the runnable.
classmethod is_lc_serializable() → bool¶
Is this class serializable?
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[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
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 lc_id() → List[str]¶
A unique identifier for this class for serialization purposes. | lang/api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.refine.RefineDocumentsChain.html |
c2d85b6dc335-11 | A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
map() → Runnable[List[Input], List[Output]]¶
Return a new Runnable that maps a list of inputs to a list of outputs,
by calling invoke() with each input.
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: Protocol = None, allow_pickle: bool = False) → Model¶
prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]¶
Validate and prepare chain inputs, including adding inputs from memory.
Parameters
inputs – Dictionary of raw 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.
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, 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 final outputs.
Returns
A dict of the final chain outputs.
prompt_length(docs: List[Document], **kwargs: Any) → Optional[int]¶ | lang/api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.refine.RefineDocumentsChain.html |
c2d85b6dc335-12 | prompt_length(docs: List[Document], **kwargs: Any) → Optional[int]¶
Return the prompt length given the documents passed in.
This can be used by a caller to determine whether passing in a list
of documents would exceed a certain prompt length. This useful when
trying to ensure that the size of a prompt remains below a certain
context limit.
Parameters
docs – List[Document], a list of documents to use to calculate the
total prompt length.
Returns
Returns None if the method does not depend on the prompt length,
otherwise the length of the prompt in tokens.
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.__call__ is that this
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 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 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 'question' string: | lang/api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.refine.RefineDocumentsChain.html |
c2d85b6dc335-13 | 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:
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 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 = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
stream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of stream, which calls invoke.
Subclasses should override this method if they support streaming output.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of transform, which buffers input and then calls stream.
Subclasses should override this method if they can start producing output while
input is still being generated.
classmethod update_forward_refs(**localns: Any) → None¶ | lang/api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.refine.RefineDocumentsChain.html |
c2d85b6dc335-14 | 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_config(config: Optional[RunnableConfig] = None, **kwargs: Any) → Runnable[Input, Output]¶
Bind config to a Runnable, returning a new Runnable.
with_fallbacks(fallbacks: Sequence[Runnable[Input, Output]], *, exceptions_to_handle: Tuple[Type[BaseException], ...] = (<class 'Exception'>,)) → RunnableWithFallbacksT[Input, Output]¶
Add fallbacks to a runnable, returning a new Runnable.
Parameters
fallbacks – A sequence of runnables to try if the original runnable fails.
exceptions_to_handle – A tuple of exception types to handle.
Returns
A new Runnable that will try the original runnable, and then each
fallback in order, upon failures.
with_listeners(*, on_start: Optional[Listener] = None, on_end: Optional[Listener] = None, on_error: Optional[Listener] = None) → Runnable[Input, Output]¶
Bind lifecycle listeners to a Runnable, returning a new Runnable.
on_start: Called before the runnable starts running, with the Run object.
on_end: Called after the runnable finishes running, with the Run object.
on_error: Called if the runnable throws an error, with the Run object.
The Run object contains information about the run, including its id,
type, input, output, error, start_time, end_time, and any tags or metadata
added to the run. | lang/api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.refine.RefineDocumentsChain.html |
c2d85b6dc335-15 | added to the run.
with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, stop_after_attempt: int = 3) → Runnable[Input, Output]¶
Create a new Runnable that retries the original runnable on exceptions.
Parameters
retry_if_exception_type – A tuple of exception types to retry on
wait_exponential_jitter – Whether to add jitter to the wait time
between retries
stop_after_attempt – The maximum number of attempts to make before giving up
Returns
A new Runnable that retries the original runnable on exceptions.
with_types(*, input_type: Optional[Type[Input]] = None, output_type: Optional[Type[Output]] = None) → Runnable[Input, Output]¶
Bind input and output types to a Runnable, returning a new Runnable.
property InputType: Type[langchain.schema.runnable.utils.Input]¶
The type of input this runnable accepts specified as a type annotation.
property OutputType: Type[langchain.schema.runnable.utils.Output]¶
The type of output this runnable produces specified as a type annotation.
property config_specs: List[langchain.schema.runnable.utils.ConfigurableFieldSpec]¶
List configurable fields for this runnable.
property input_schema: Type[pydantic.main.BaseModel]¶
The type of input this runnable accepts specified as a pydantic model.
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_schema: Type[pydantic.main.BaseModel]¶ | lang/api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.refine.RefineDocumentsChain.html |
c2d85b6dc335-16 | property output_schema: Type[pydantic.main.BaseModel]¶
The type of output this runnable produces specified as a pydantic model. | lang/api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.refine.RefineDocumentsChain.html |
d92a5c83fde0-0 | langchain.chains.graph_qa.cypher_utils.CypherQueryCorrector¶
class langchain.chains.graph_qa.cypher_utils.CypherQueryCorrector(schemas: List[Schema])[source]¶
Used to correct relationship direction in generated Cypher statements.
This code is copied from the winner’s submission to the Cypher competition:
https://github.com/sakusaku-rich/cypher-direction-competition
Parameters
schemas – list of schemas
Attributes
node_pattern
node_relation_node_pattern
path_pattern
property_pattern
relation_type_pattern
Methods
__init__(schemas)
param schemas
list of schemas
clean_node(node)
param node
node in string format
correct_query(query)
param query
cypher query
detect_labels(str_node, node_variable_dict)
param str_node
node in string format
detect_node_variables(query)
param query
cypher query
detect_relation_types(str_relation)
param str_relation
relation in string format
extract_node_variable(part)
param part
node in string format
extract_paths(query)
param query
cypher query
judge_direction(relation)
param relation
relation in string format
verify_schema(from_node_labels, ...)
param from_node_labels
labels of the from node
__init__(schemas: List[Schema])[source]¶
Parameters
schemas – list of schemas
clean_node(node: str) → str[source]¶
Parameters
node – node in string format
correct_query(query: str) → str[source]¶
Parameters
query – cypher query
detect_labels(str_node: str, node_variable_dict: Dict[str, Any]) → List[str][source]¶
Parameters
str_node – node in string format
node_variable_dict – dictionary of node variables
detect_node_variables(query: str) → Dict[str, List[str]][source]¶
Parameters
query – cypher query | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.cypher_utils.CypherQueryCorrector.html |
d92a5c83fde0-1 | Parameters
query – cypher query
detect_relation_types(str_relation: str) → Tuple[str, List[str]][source]¶
Parameters
str_relation – relation in string format
extract_node_variable(part: str) → Optional[str][source]¶
Parameters
part – node in string format
extract_paths(query: str) → List[str][source]¶
Parameters
query – cypher query
judge_direction(relation: str) → str[source]¶
Parameters
relation – relation in string format
verify_schema(from_node_labels: List[str], relation_types: List[str], to_node_labels: List[str]) → bool[source]¶
Parameters
from_node_labels – labels of the from node
relation_type – type of the relation
to_node_labels – labels of the to node | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.cypher_utils.CypherQueryCorrector.html |
e27bcbfeeb6e-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.
param filter: Optional[langchain.chains.query_constructor.ir.FilterDirective] = None¶
Filtering expression.
param limit: Optional[int] = None¶
Limit on the number of results.
param query: str [Required]¶
Query string.
accept(visitor: Visitor) → Any¶
Accept a visitor.
Parameters
visitor – visitor to accept
Returns
result of visiting
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 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] = None, deep: bool = False) → Model¶
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 creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance | lang/api.python.langchain.com/en/latest/chains/langchain.chains.query_constructor.ir.StructuredQuery.html |
e27bcbfeeb6e-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, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶
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[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
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 = 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: Protocol = None, allow_pickle: bool = False) → Model¶ | lang/api.python.langchain.com/en/latest/chains/langchain.chains.query_constructor.ir.StructuredQuery.html |
e27bcbfeeb6e-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 fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶ | lang/api.python.langchain.com/en/latest/chains/langchain.chains.query_constructor.ir.StructuredQuery.html |
a9dc984ff559-0 | langchain.chains.openai_functions.qa_with_structure.create_qa_with_sources_chain¶
langchain.chains.openai_functions.qa_with_structure.create_qa_with_sources_chain(llm: BaseLanguageModel, verbose: bool = False, **kwargs: Any) → LLMChain[source]¶
Create a question answering chain that returns an answer with sources.
Parameters
llm – Language model to use for the chain.
verbose – Whether to print the details of the chain
**kwargs – Keyword arguments to pass to create_qa_with_structure_chain.
Returns
Chain (LLMChain) that can be used to answer questions with citations.
Examples using create_qa_with_sources_chain¶
Structure answers with OpenAI functions | lang/api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.qa_with_structure.create_qa_with_sources_chain.html |
a77f06ab2f74-0 | langchain.chains.sql_database.query.SQLInput¶
class langchain.chains.sql_database.query.SQLInput[source]¶
Input for a SQL Chain.
question: str¶ | lang/api.python.langchain.com/en/latest/chains/langchain.chains.sql_database.query.SQLInput.html |
759ef41e852d-0 | langchain.chains.graph_qa.base.GraphQAChain¶
class langchain.chains.graph_qa.base.GraphQAChain[source]¶
Bases: Chain
Chain for question-answering against a graph.
Security note: Make sure that the database connection uses credentialsthat are narrowly-scoped to only include necessary permissions.
Failure to do so may result in data corruption or loss, since the calling
code may attempt commands that would result in deletion, mutation
of data if appropriately prompted or reading sensitive data if such
data is present in the database.
The best way to guard against such negative outcomes is to (as appropriate)
limit the permissions granted to the credentials used with this tool.
See https://python.langchain.com/docs/security for more information.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated, use callbacks instead.
param callbacks: Callbacks = None¶
Optional list of callback 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 details.
param entity_extraction_chain: LLMChain [Required]¶
param graph: NetworkxEntityGraph [Required]¶
param memory: Optional[BaseMemory] = None¶
Optional memory object. Defaults to None.
Memory is a class that gets called at the start
and at the end of every chain. At the start, memory loads variables and passes
them along in the chain. At the end, it saves any returned variables.
There are many different types of memory - please see memory docs
for the full catalog. | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.base.GraphQAChain.html |
759ef41e852d-1 | 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 callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param qa_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 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 printed to the console. Defaults to the global verbose value,
accessible via langchain.globals.get_verbose().
__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, run_name: Optional[str] = None, include_run_info: bool = False) → Dict[str, Any]¶
Execute the chain.
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 chain will be
returned. If False, both input keys and new keys generated by this | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.base.GraphQAChain.html |
759ef41e852d-2 | returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False.
callbacks – Callbacks to use for this chain run. 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 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. Should contain all outputs specified inChain.output_keys.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation runs ainvoke in parallel using asyncio.gather.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode.
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, run_name: Optional[str] = None, include_run_info: bool = False) → Dict[str, Any]¶
Asynchronously execute the chain.
Parameters
inputs – Dictionary of inputs, or single input if chain expects
only one param. Should contain all inputs specified in | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.base.GraphQAChain.html |
759ef41e852d-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 this
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 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 info in the response. Defaults
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, **kwargs: Any) → Dict[str, Any]¶
Default implementation of ainvoke, calls invoke from a thread.
The default implementation allows usage of async code even if
the runnable did not implement a native async version of invoke.
Subclasses should override this method if they can run asynchronously.
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. | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.base.GraphQAChain.html |
759ef41e852d-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 method and Chain.__call__ is that this
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 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 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 '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' 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 Boise is..." | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.base.GraphQAChain.html |
759ef41e852d-5 | # -> "The temperature in Boise is..."
async astream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of astream, which calls ainvoke.
Subclasses should override this method if they support streaming output.
async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, diff: bool = True, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Optional[Any]) → Union[AsyncIterator[RunLogPatch], AsyncIterator[RunLog]]¶
Stream all output from a runnable, as reported to the callback system.
This includes all inner runs of LLMs, Retrievers, Tools, etc.
Output is streamed as Log objects, which include a list of
jsonpatch ops that describe how the state of the run has changed in each
step, and the final state of the run.
The jsonpatch ops can be applied in order to construct state.
async atransform(input: AsyncIterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of atransform, which buffers input and calls astream.
Subclasses should override this method if they can start producing output while
input is still being generated.
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation runs invoke in parallel using a thread pool executor. | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.base.GraphQAChain.html |
759ef41e852d-6 | Default implementation runs invoke in parallel using a thread pool executor.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode.
bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable.
config_schema(*, include: Optional[Sequence[str]] = None) → Type[BaseModel]¶
The type of config this runnable accepts specified as a pydantic model.
To mark a field as configurable, see the configurable_fields
and configurable_alternatives methods.
Parameters
include – A list of fields to include in the config schema.
Returns
A pydantic model that can be used to validate config.
configurable_alternatives(which: ConfigurableField, default_key: str = 'default', **kwargs: Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) → RunnableSerializable[Input, Output]¶
configurable_fields(**kwargs: Union[ConfigurableField, ConfigurableFieldSingleOption, ConfigurableFieldMultiOption]) → RunnableSerializable[Input, Output]¶
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 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] = None, deep: bool = False) → Model¶ | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.base.GraphQAChain.html |
759ef41e852d-7 | 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 creating
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 default pydantic.BaseModel.dict
method.
Returns
A dictionary representation of the chain.
Example
chain.dict(exclude_unset=True)
# -> {"_type": "foo", "verbose": False, ...} | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.base.GraphQAChain.html |
759ef41e852d-8 | # -> {"_type": "foo", "verbose": False, ...}
classmethod from_llm(llm: BaseLanguageModel, qa_prompt: BasePromptTemplate = PromptTemplate(input_variables=['context', 'question'], template="Use the following knowledge triplets to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.\n\n{context}\n\nQuestion: {question}\nHelpful Answer:"), entity_prompt: BasePromptTemplate = PromptTemplate(input_variables=['input'], template="Extract all entities from the following text. As a guideline, a proper noun is generally capitalized. You should definitely extract all names and places.\n\nReturn the output as a single comma-separated list, or NONE if there is nothing of note to return.\n\nEXAMPLE\ni'm trying to improve Langchain's interfaces, the UX, its integrations with various products the user might want ... a lot of stuff.\nOutput: Langchain\nEND OF EXAMPLE\n\nEXAMPLE\ni'm trying to improve Langchain's interfaces, the UX, its integrations with various products the user might want ... a lot of stuff. I'm working with Sam.\nOutput: Langchain, Sam\nEND OF EXAMPLE\n\nBegin!\n\n{input}\nOutput:"), **kwargs: Any) → GraphQAChain[source]¶
Initialize from LLM.
classmethod from_orm(obj: Any) → Model¶
get_input_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
Get a pydantic model that can be used to validate input to the runnable.
Runnables that leverage the configurable_fields and configurable_alternatives
methods will have a dynamic input schema that depends on which
configuration the runnable is invoked with.
This method allows to get an input schema for a specific configuration.
Parameters | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.base.GraphQAChain.html |
759ef41e852d-9 | This method allows to get an input schema for a specific configuration.
Parameters
config – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate input.
classmethod get_lc_namespace() → List[str]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the
namespace is [“langchain”, “llms”, “openai”]
get_output_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
Get a pydantic model that can be used to validate output to the runnable.
Runnables that leverage the configurable_fields and configurable_alternatives
methods will have a dynamic output schema that depends on which
configuration the runnable is invoked with.
This method allows to get an output schema for a specific configuration.
Parameters
config – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate output.
invoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any) → Dict[str, Any]¶
Transform a single input into an output. Override to implement.
Parameters
input – The input to the runnable.
config – A config to use when invoking the runnable.
The config supports standard keys like ‘tags’, ‘metadata’ for tracing
purposes, ‘max_concurrency’ for controlling how much work to do
in parallel, and other keys. Please refer to the RunnableConfig
for more details.
Returns
The output of the runnable.
classmethod is_lc_serializable() → bool¶
Is this class serializable? | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.base.GraphQAChain.html |
759ef41e852d-10 | classmethod is_lc_serializable() → bool¶
Is this class serializable?
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[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
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 lc_id() → List[str]¶
A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
map() → Runnable[List[Input], List[Output]]¶
Return a new Runnable that maps a list of inputs to a list of outputs,
by calling invoke() with each input.
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: Protocol = None, allow_pickle: bool = False) → Model¶
prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]¶
Validate and prepare chain inputs, including adding inputs from memory.
Parameters
inputs – Dictionary of raw inputs, or single input if chain expects | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.base.GraphQAChain.html |
759ef41e852d-11 | Parameters
inputs – Dictionary of raw 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.
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, 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 final outputs.
Returns
A dict of the final chain outputs.
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.__call__ is that this
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 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 chain during construction, but only
these runtime tags will propagate to calls to other objects. | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.base.GraphQAChain.html |
759ef41e852d-12 | 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:
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:
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 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 = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
stream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of stream, which calls invoke.
Subclasses should override this method if they support streaming output.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶ | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.base.GraphQAChain.html |
759ef41e852d-13 | Default implementation of transform, which buffers input and then calls stream.
Subclasses should override this method if they can start producing output while
input is still being generated.
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_config(config: Optional[RunnableConfig] = None, **kwargs: Any) → Runnable[Input, Output]¶
Bind config to a Runnable, returning a new Runnable.
with_fallbacks(fallbacks: Sequence[Runnable[Input, Output]], *, exceptions_to_handle: Tuple[Type[BaseException], ...] = (<class 'Exception'>,)) → RunnableWithFallbacksT[Input, Output]¶
Add fallbacks to a runnable, returning a new Runnable.
Parameters
fallbacks – A sequence of runnables to try if the original runnable fails.
exceptions_to_handle – A tuple of exception types to handle.
Returns
A new Runnable that will try the original runnable, and then each
fallback in order, upon failures.
with_listeners(*, on_start: Optional[Listener] = None, on_end: Optional[Listener] = None, on_error: Optional[Listener] = None) → Runnable[Input, Output]¶
Bind lifecycle listeners to a Runnable, returning a new Runnable.
on_start: Called before the runnable starts running, with the Run object.
on_end: Called after the runnable finishes running, with the Run object.
on_error: Called if the runnable throws an error, with the Run object.
The Run object contains information about the run, including its id,
type, input, output, error, start_time, end_time, and any tags or metadata
added to the run. | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.base.GraphQAChain.html |
759ef41e852d-14 | added to the run.
with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, stop_after_attempt: int = 3) → Runnable[Input, Output]¶
Create a new Runnable that retries the original runnable on exceptions.
Parameters
retry_if_exception_type – A tuple of exception types to retry on
wait_exponential_jitter – Whether to add jitter to the wait time
between retries
stop_after_attempt – The maximum number of attempts to make before giving up
Returns
A new Runnable that retries the original runnable on exceptions.
with_types(*, input_type: Optional[Type[Input]] = None, output_type: Optional[Type[Output]] = None) → Runnable[Input, Output]¶
Bind input and output types to a Runnable, returning a new Runnable.
property InputType: Type[langchain.schema.runnable.utils.Input]¶
The type of input this runnable accepts specified as a type annotation.
property OutputType: Type[langchain.schema.runnable.utils.Output]¶
The type of output this runnable produces specified as a type annotation.
property config_specs: List[langchain.schema.runnable.utils.ConfigurableFieldSpec]¶
List configurable fields for this runnable.
property input_schema: Type[pydantic.main.BaseModel]¶
The type of input this runnable accepts specified as a pydantic model.
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_schema: Type[pydantic.main.BaseModel]¶ | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.base.GraphQAChain.html |
759ef41e852d-15 | property output_schema: Type[pydantic.main.BaseModel]¶
The type of output this runnable produces specified as a pydantic model.
Examples using GraphQAChain¶
Graph QA | lang/api.python.langchain.com/en/latest/chains/langchain.chains.graph_qa.base.GraphQAChain.html |
9ac93cce92fe-0 | langchain.chains.openai_functions.extraction.create_extraction_chain_pydantic¶
langchain.chains.openai_functions.extraction.create_extraction_chain_pydantic(pydantic_schema: Any, llm: BaseLanguageModel, prompt: Optional[BasePromptTemplate] = None, verbose: bool = False) → Chain[source]¶
Creates a chain that extracts information from a passage using pydantic schema.
Parameters
pydantic_schema – The pydantic schema of the entities to extract.
llm – The language model to use.
prompt – The prompt to use for extraction.
verbose – Whether to run in verbose mode. In verbose mode, some intermediate
logs will be printed to the console. Defaults to the global verbose value,
accessible via langchain.globals.get_verbose()
Returns
Chain that can be used to extract information from a passage.
Examples using create_extraction_chain_pydantic¶
Set env var OPENAI_API_KEY or load from a .env file: | lang/api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.extraction.create_extraction_chain_pydantic.html |
7b93328a4732-0 | langchain.chains.natbot.crawler.Crawler¶
class langchain.chains.natbot.crawler.Crawler[source]¶
A crawler for web pages.
Security Note: This is an implementation of a crawler that uses a browser viaPlaywright.
This crawler can be used to load arbitrary webpages INCLUDING content
from the local file system.
Control access to who can submit crawling requests and what network access
the crawler has.
Make sure to scope permissions to the minimal permissions necessary for
the application.
See https://python.langchain.com/docs/security for more information.
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_page(url: str) → None[source]¶
scroll(direction: str) → None[source]¶
type(id: Union[str, int], text: str) → None[source]¶ | lang/api.python.langchain.com/en/latest/chains/langchain.chains.natbot.crawler.Crawler.html |
e4cb4ebc25e2-0 | langchain.chains.combine_documents.base.BaseCombineDocumentsChain¶
class langchain.chains.combine_documents.base.BaseCombineDocumentsChain[source]¶
Bases: Chain, ABC
Base interface for chains combining documents.
Subclasses of this chain deal with combining documents in a variety of
ways. This base class exists to add some uniformity in the interface these types
of chains should expose. Namely, they expect an input key related to the documents
to use (default input_documents), and then also expose a method to calculate
the length of a prompt from documents (useful for outside callers to use to
determine whether it’s safe to pass a list of documents into this chain or whether
that will longer than the context length).
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated, use callbacks instead.
param callbacks: Callbacks = None¶
Optional list of callback 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 details.
param memory: Optional[BaseMemory] = None¶
Optional memory object. Defaults to None.
Memory is a class that gets called at the start
and at the end of every chain. At the start, memory loads variables and passes
them along in the chain. At 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 the chain. Defaults to None. | lang/api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.base.BaseCombineDocumentsChain.html |
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