`langchain.chains.graph_qa.ontotext_graphdb`.OntotextGraphDBQAChain¶

class langchain.chains.graph_qa.ontotext_graphdb.OntotextGraphDBQAChain[source]¶

Bases: Chain

Question-answering against Ontotext GraphDB

https://graphdb.ontotext.com/ by generating SPARQL queries.

Security note: Make sure that the database connection uses credentials

that 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: OntotextGraphDBGraph [Required]¶

param max_fix_retries: int [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.

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 sparql_fix_chain: LLMChain [Required]¶

param sparql_generation_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]¶

[Deprecated] Execute the chain.

Parameters

inputs (Union[Dict[str, Any], Any]) – 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 (bool) – Whether to return only outputs in the response. If True, only new keys generated by this chain will be returned. If False, both input keys and new keys generated by this chain will be returned. Defaults to False.
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – 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 (Optional[List[str]]) – 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[Dict[str, Any]]) – Optional metadata associated with the chain. Defaults to None
include_run_info (bool) – Whether to include run info in the response. Defaults to False.
run_name (Optional[str]) –

Returns

A dict of named outputs. Should contain all outputs specified in: Chain.output_keys.

Return type

Dict[str, Any]

Notes

Deprecated since version 0.1.0: Use invoke instead.

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.

Parameters

inputs (List[Input]) –
config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) –
return_exceptions (bool) –
kwargs (Optional[Any]) –

Return type

List[Output]

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]¶

[Deprecated] Asynchronously execute the chain.

Parameters

inputs (Union[Dict[str, Any], Any]) – 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 (bool) – Whether to return only outputs in the response. If True, only new keys generated by this chain will be returned. If False, both input keys and new keys generated by this chain will be returned. Defaults to False.
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – 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 (Optional[List[str]]) – 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[Dict[str, Any]]) – Optional metadata associated with the chain. Defaults to None
include_run_info (bool) – Whether to include run info in the response. Defaults to False.
run_name (Optional[str]) –

Returns

A dict of named outputs. Should contain all outputs specified in: Chain.output_keys.

Return type

Dict[str, Any]

Notes

Deprecated since version 0.1.0: Use ainvoke instead.

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.

Parameters

input (Dict[str, Any]) –
config (Optional[RunnableConfig]) –
kwargs (Any) –

Return type

Dict[str, Any]

apply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[Dict[str, str]]¶

[Deprecated] Call the chain on all inputs in the list.

Notes

Deprecated since version 0.1.0: Use batch instead.

Parameters

input_list (List[Dict[str, Any]]) –
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) –

Return type

List[Dict[str, str]]

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¶

[Deprecated] 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 (Any) – If the chain expects a single input, it can be passed in as the sole positional argument.
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – 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 (Optional[List[str]]) – 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 (Any) – If the chain expects multiple inputs, they can be passed in directly as keyword arguments.
metadata (Optional[Dict[str, Any]]) –
**kwargs –

Returns

The chain output.

Return type

Any

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..."

Notes

Deprecated since version 0.1.0: Use ainvoke instead.

assign(**kwargs: Union[Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any], Mapping[str, Union[Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any]]]]) → RunnableSerializable[Any, Any]¶

Assigns new fields to the dict output of this runnable. Returns a new runnable.

Parameters: kwargs (Union[Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any], Mapping[str, Union[Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any]]]]) –
Return type: RunnableSerializable[Any, Any]

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.

Parameters

input (Input) –
config (Optional[RunnableConfig]) –
kwargs (Optional[Any]) –

Return type

AsyncIterator[Output]

astream_events(input: Any, config: Optional[RunnableConfig] = None, *, version: Literal['v1'], 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: Any) → AsyncIterator[StreamEvent]¶

[Beta] Generate a stream of events.

Use to create an iterator over StreamEvents that provide real-time information about the progress of the runnable, including StreamEvents from intermediate results.

A StreamEvent is a dictionary with the following schema:

event: str - Event names are of the
format: on_[runnable_type]_(start|stream|end).
name: str - The name of the runnable that generated the event.
run_id: str - randomly generated ID associated with the given execution of
the runnable that emitted the event. A child runnable that gets invoked as part of the execution of a parent runnable is assigned its own unique ID.
tags: Optional[List[str]] - The tags of the runnable that generated
the event.
metadata: Optional[Dict[str, Any]] - The metadata of the runnable
that generated the event.
data: Dict[str, Any]

Below is a table that illustrates some evens that might be emitted by various chains. Metadata fields have been omitted from the table for brevity. Chain definitions have been included after the table.

event	name	chunk	input	output
on_chat_model_start	[model name]		{“messages”: [[SystemMessage, HumanMessage]]}
on_chat_model_stream	[model name]	AIMessageChunk(content=”hello”)
on_chat_model_end	[model name]		{“messages”: [[SystemMessage, HumanMessage]]}	{“generations”: […], “llm_output”: None, …}
on_llm_start	[model name]		{‘input’: ‘hello’}
on_llm_stream	[model name]	‘Hello’
on_llm_end	[model name]		‘Hello human!’
on_chain_start	format_docs
on_chain_stream	format_docs	“hello world!, goodbye world!”
on_chain_end	format_docs		[Document(…)]	“hello world!, goodbye world!”
on_tool_start	some_tool		{“x”: 1, “y”: “2”}
on_tool_stream	some_tool	{“x”: 1, “y”: “2”}
on_tool_end	some_tool			{“x”: 1, “y”: “2”}
on_retriever_start	[retriever name]		{“query”: “hello”}
on_retriever_chunk	[retriever name]	{documents: […]}
on_retriever_end	[retriever name]		{“query”: “hello”}	{documents: […]}
on_prompt_start	[template_name]		{“question”: “hello”}
on_prompt_end	[template_name]		{“question”: “hello”}	ChatPromptValue(messages: [SystemMessage, …])

Here are declarations associated with the events shown above:

format_docs:

def format_docs(docs: List[Document]) -> str:
    '''Format the docs.'''
    return ", ".join([doc.page_content for doc in docs])

format_docs = RunnableLambda(format_docs)

some_tool:

@tool
def some_tool(x: int, y: str) -> dict:
    '''Some_tool.'''
    return {"x": x, "y": y}

prompt:

template = ChatPromptTemplate.from_messages(
    [("system", "You are Cat Agent 007"), ("human", "{question}")]
).with_config({"run_name": "my_template", "tags": ["my_template"]})

Example:

from langchain_core.runnables import RunnableLambda

async def reverse(s: str) -> str:
    return s[::-1]

chain = RunnableLambda(func=reverse)

events = [
    event async for event in chain.astream_events("hello", version="v1")
]

# will produce the following events (run_id has been omitted for brevity):
[
    {
        "data": {"input": "hello"},
        "event": "on_chain_start",
        "metadata": {},
        "name": "reverse",
        "tags": [],
    },
    {
        "data": {"chunk": "olleh"},
        "event": "on_chain_stream",
        "metadata": {},
        "name": "reverse",
        "tags": [],
    },
    {
        "data": {"output": "olleh"},
        "event": "on_chain_end",
        "metadata": {},
        "name": "reverse",
        "tags": [],
    },
]

Parameters

input (Any) – The input to the runnable.
config (Optional[RunnableConfig]) – The config to use for the runnable.
version (Literal['v1']) – The version of the schema to use. Currently only version 1 is available. No default will be assigned until the API is stabilized.
include_names (Optional[Sequence[str]]) – Only include events from runnables with matching names.
include_types (Optional[Sequence[str]]) – Only include events from runnables with matching types.
include_tags (Optional[Sequence[str]]) – Only include events from runnables with matching tags.
exclude_names (Optional[Sequence[str]]) – Exclude events from runnables with matching names.
exclude_types (Optional[Sequence[str]]) – Exclude events from runnables with matching types.
exclude_tags (Optional[Sequence[str]]) – Exclude events from runnables with matching tags.
kwargs (Any) – Additional keyword arguments to pass to the runnable. These will be passed to astream_log as this implementation of astream_events is built on top of astream_log.

Returns

An async stream of StreamEvents.

Return type

AsyncIterator[StreamEvent]

Notes

async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, diff: bool = True, with_streamed_output_list: 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: 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.

Parameters

input (Any) – The input to the runnable.
config (Optional[RunnableConfig]) – The config to use for the runnable.
diff (bool) – Whether to yield diffs between each step, or the current state.
with_streamed_output_list (bool) – Whether to yield the streamed_output list.
include_names (Optional[Sequence[str]]) – Only include logs with these names.
include_types (Optional[Sequence[str]]) – Only include logs with these types.
include_tags (Optional[Sequence[str]]) – Only include logs with these tags.
exclude_names (Optional[Sequence[str]]) – Exclude logs with these names.
exclude_types (Optional[Sequence[str]]) – Exclude logs with these types.
exclude_tags (Optional[Sequence[str]]) – Exclude logs with these tags.
kwargs (Any) –

Return type

Union[AsyncIterator[RunLogPatch], AsyncIterator[RunLog]]

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.

Parameters

input (AsyncIterator[Input]) –
config (Optional[RunnableConfig]) –
kwargs (Optional[Any]) –

Return type

AsyncIterator[Output]

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.

Parameters

inputs (List[Input]) –
config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) –
return_exceptions (bool) –
kwargs (Optional[Any]) –

Return type

List[Output]

bind(**kwargs: Any) → Runnable[Input, Output]¶

Bind arguments to a Runnable, returning a new Runnable.

Parameters: kwargs (Any) –
Return type: Runnable[Input, Output]

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 (Optional[Sequence[str]]) – A list of fields to include in the config schema.
Returns: A pydantic model that can be used to validate config.
Return type: Type[BaseModel]

configurable_alternatives(which: ConfigurableField, *, default_key: str = 'default', prefix_keys: bool = False, **kwargs: Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) → RunnableSerializable[Input, Output]¶

Parameters

which (ConfigurableField) –
default_key (str) –
prefix_keys (bool) –
kwargs (Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) –

Return type

RunnableSerializable[Input, Output]

configurable_fields(**kwargs: Union[ConfigurableField, ConfigurableFieldSingleOption, ConfigurableFieldMultiOption]) → RunnableSerializable[Input, Output]¶

Parameters: kwargs (Union[ConfigurableField, ConfigurableFieldSingleOption, ConfigurableFieldMultiOption]) –
Return type: 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

Parameters

_fields_set (Optional[SetStr]) –
values (Any) –

Return type

Model

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 (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – 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 (bool) – set to True to make a deep copy of the model
self (Model) –

Returns

new model instance

Return type

Model

dict(**kwargs: Any) → Dict¶

Dictionary representation of chain.

Expects Chain._chain_type property to be implemented and for memory to be: null.

Parameters: **kwargs (Any) – Keyword arguments passed to default pydantic.BaseModel.dict method.
Returns: A dictionary representation of the chain.
Return type: Dict

Example

chain.dict(exclude_unset=True)
# -> {"_type": "foo", "verbose": False, ...}

classmethod from_llm(llm: BaseLanguageModel, *, sparql_generation_prompt: BasePromptTemplate = PromptTemplate(input_variables=['prompt', 'schema'], template='\nWrite a SPARQL SELECT query for querying a graph database.\nThe ontology schema delimited by triple backticks in Turtle format is:\n```\n{schema}\n```\nUse only the classes and properties provided in the schema to construct the SPARQL query.\nDo not use any classes or properties that are not explicitly provided in the SPARQL query.\nInclude all necessary prefixes.\nDo not include any explanations or apologies in your responses.\nDo not wrap the query in backticks.\nDo not include any text except the SPARQL query generated.\nThe question delimited by triple backticks is:\n```\n{prompt}\n```\n'), sparql_fix_prompt: BasePromptTemplate = PromptTemplate(input_variables=['error_message', 'generated_sparql', 'schema'], template='\nThis following SPARQL query delimited by triple backticks\n```\n{generated_sparql}\n```\nis not valid.\nThe error delimited by triple backticks is\n```\n{error_message}\n```\nGive me a correct version of the SPARQL query.\nDo not change the logic of the query.\nDo not include any explanations or apologies in your responses.\nDo not wrap the query in backticks.\nDo not include any text except the SPARQL query generated.\nThe ontology schema delimited by triple backticks in Turtle format is:\n```\n{schema}\n```\n'), max_fix_retries: int = 5, qa_prompt: BasePromptTemplate = PromptTemplate(input_variables=['context', 'prompt'], template="Task: Generate a natural language response from the results of a SPARQL query.\nYou are an assistant that creates well-written and human understandable answers.\nThe information part contains the information provided, which you can use to construct an answer.\nThe information provided is authoritative, you must never doubt it or try to use your internal knowledge to correct it.\nMake your response sound like the information is coming from an AI assistant, but don't add any information.\nDon't use internal knowledge to answer the question, just say you don't know if no information is available.\nInformation:\n{context}\n\nQuestion: {prompt}\nHelpful Answer:"), **kwargs: Any) → OntotextGraphDBQAChain[source]¶

Initialize from LLM.

Parameters

llm (BaseLanguageModel) –
sparql_generation_prompt (BasePromptTemplate) –
sparql_fix_prompt (BasePromptTemplate) –
max_fix_retries (int) –
qa_prompt (BasePromptTemplate) –
kwargs (Any) –

Return type

OntotextGraphDBQAChain

classmethod from_orm(obj: Any) → Model¶

Parameters: obj (Any) –
Return type: Model

get_graph(config: Optional[RunnableConfig] = None) → Graph¶

Return a graph representation of this runnable.

Parameters: config (Optional[RunnableConfig]) –
Return type: Graph

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 (Optional[RunnableConfig]) – A config to use when generating the schema.
Returns: A pydantic model that can be used to validate input.
Return type: Type[BaseModel]

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”]

Return type: List[str]

get_name(suffix: Optional[str] = None, *, name: Optional[str] = None) → str¶

Get the name of the runnable.

Parameters

suffix (Optional[str]) –
name (Optional[str]) –

Return type

str

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 (Optional[RunnableConfig]) – A config to use when generating the schema.
Returns: A pydantic model that can be used to validate output.
Return type: Type[BaseModel]

get_prompts(config: Optional[RunnableConfig] = None) → List[BasePromptTemplate]¶

Parameters: config (Optional[RunnableConfig]) –
Return type: List[BasePromptTemplate]

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 (Dict[str, Any]) – The input to the runnable.
config (Optional[RunnableConfig]) – 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.
kwargs (Any) –

Returns

The output of the runnable.

Return type

Dict[str, Any]

classmethod is_lc_serializable() → bool¶

Is this class serializable?

Return type: bool

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().

Parameters

include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
encoder (Optional[Callable[[Any], Any]]) –
models_as_dict (bool) –
dumps_kwargs (Any) –

Return type

unicode

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.

Return type: List[str]

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.

Return type: Runnable[List[Input], List[Output]]

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

Parameters

path (Union[str, Path]) –
content_type (unicode) –
encoding (unicode) –
proto (Protocol) –
allow_pickle (bool) –

Return type

Model

classmethod parse_obj(obj: Any) → Model¶

Parameters: obj (Any) –
Return type: Model

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶

Parameters

b (Union[str, bytes]) –
content_type (unicode) –
encoding (unicode) –
proto (Protocol) –
allow_pickle (bool) –

Return type

Model

pick(keys: Union[str, List[str]]) → RunnableSerializable[Any, Any]¶

Pick keys from the dict output of this runnable. Returns a new runnable.

Parameters: keys (Union[str, List[str]]) –
Return type: RunnableSerializable[Any, Any]

pipe(*others: Union[Runnable[Any, Other], Callable[[Any], Other]], name: Optional[str] = None) → RunnableSerializable[Input, Other]¶

Compose this runnable with another object to create a RunnableSequence.

Parameters

others (Union[Runnable[Any, Other], Callable[[Any], Other]]) –
name (Optional[str]) –

Return type

RunnableSerializable[Input, Other]

prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]¶

Prepare chain inputs, including adding inputs from memory.

Parameters: inputs (Union[Dict[str, Any], Any]) – 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.
Return type: Dict[str, str]

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 (Dict[str, str]) – Dictionary of chain inputs, including any inputs added by chain memory.
outputs (Dict[str, str]) – Dictionary of initial chain outputs.
return_only_outputs (bool) – 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.

Return type

Dict[str, str]

run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶

[Deprecated] 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 (Any) – If the chain expects a single input, it can be passed in as the sole positional argument.
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – 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 (Optional[List[str]]) – 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 (Any) – If the chain expects multiple inputs, they can be passed in directly as keyword arguments.
metadata (Optional[Dict[str, Any]]) –
**kwargs –

Returns

The chain output.

Return type

Any

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..."

Notes

Deprecated since version 0.1.0: Use invoke instead.

save(file_path: Union[Path, str]) → None¶

Save the chain.

Expects Chain._chain_type property to be implemented and for memory to be: null.

Parameters: file_path (Union[Path, str]) – Path to file to save the chain to.
Return type: None

Example

chain.save(file_path="path/chain.yaml")

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶

Parameters

by_alias (bool) –
ref_template (unicode) –

Return type

DictStrAny

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶

Parameters

by_alias (bool) –
ref_template (unicode) –
dumps_kwargs (Any) –

Return type

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.

Parameters

input (Input) –
config (Optional[RunnableConfig]) –
kwargs (Optional[Any]) –

Return type

Iterator[Output]

to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶

Serialize the runnable to JSON.

Return type: Union[SerializedConstructor, SerializedNotImplemented]

to_json_not_implemented() → SerializedNotImplemented¶

Return type: 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.

Parameters

input (Iterator[Input]) –
config (Optional[RunnableConfig]) –
kwargs (Optional[Any]) –

Return type

Iterator[Output]

classmethod update_forward_refs(**localns: Any) → None¶

Try to update ForwardRefs on fields based on this Model, globalns and localns.

Parameters: localns (Any) –
Return type: None

classmethod validate(value: Any) → Model¶

Parameters: value (Any) –
Return type: Model

with_config(config: Optional[RunnableConfig] = None, **kwargs: Any) → Runnable[Input, Output]¶

Bind config to a Runnable, returning a new Runnable.

Parameters

config (Optional[RunnableConfig]) –
kwargs (Any) –

Return type

Runnable[Input, Output]

with_fallbacks(fallbacks: Sequence[Runnable[Input, Output]], *, exceptions_to_handle: Tuple[Type[BaseException], ...] = (<class 'Exception'>,), exception_key: Optional[str] = None) → RunnableWithFallbacksT[Input, Output]¶

Add fallbacks to a runnable, returning a new Runnable.

Parameters

fallbacks (Sequence[Runnable[Input, Output]]) – A sequence of runnables to try if the original runnable fails.
exceptions_to_handle (Tuple[Type[BaseException], ...]) – A tuple of exception types to handle.
exception_key (Optional[str]) – If string is specified then handled exceptions will be passed to fallbacks as part of the input under the specified key. If None, exceptions will not be passed to fallbacks. If used, the base runnable and its fallbacks must accept a dictionary as input.

Returns

A new Runnable that will try the original runnable, and then each fallback in order, upon failures.

Return type

RunnableWithFallbacksT[Input, Output]

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.

Parameters

on_start (Optional[Listener]) –
on_end (Optional[Listener]) –
on_error (Optional[Listener]) –

Return type

Runnable[Input, Output]

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 (Tuple[Type[BaseException], ...]) – A tuple of exception types to retry on
wait_exponential_jitter (bool) – Whether to add jitter to the wait time between retries
stop_after_attempt (int) – The maximum number of attempts to make before giving up

Returns

A new Runnable that retries the original runnable on exceptions.

Return type

Runnable[Input, Output]

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.

Parameters

input_type (Optional[Type[Input]]) –
output_type (Optional[Type[Output]]) –

Return type

Runnable[Input, Output]

property InputType: Type[Input]¶: The type of input this runnable accepts specified as a type annotation.

property OutputType: Type[Output]¶: The type of output this runnable produces specified as a type annotation.

property config_specs: List[ConfigurableFieldSpec]¶: List configurable fields for this runnable.

property input_keys: List[str]¶: Keys expected to be in the chain input.

property input_schema: Type[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”}

name: Optional[str] = None¶: The name of the runnable. Used for debugging and tracing.

property output_keys: List[str]¶: Keys expected to be in the chain output.

property output_schema: Type[BaseModel]¶: The type of output this runnable produces specified as a pydantic model.

Examples using OntotextGraphDBQAChain¶

Ontotext GraphDB QA chain

langchain.chains.graph_qa.ontotext_graphdb.OntotextGraphDBQAChain¶

Examples using OntotextGraphDBQAChain¶

`langchain.chains.graph_qa.ontotext_graphdb`.OntotextGraphDBQAChain¶