Tony20100/langchain_full huggingface dataset:
langchain.agents.self_ask_with_search.base.SelfAskWithSearchAgent¶
class langchain.agents.self_ask_with_search.base.SelfAskWithSearchAgent[source]¶
Bases: Agent
[Deprecated] Agent for the self-ask-with-search paper.
Notes
Deprecated since version 0.1.0: Use create_self_ask_with_search instead.
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 allowed_tools: Optional[List[str]] = None¶
param llm_chain: LLMChain [Required]¶
param output_parser: AgentOutputParser [Optional]¶
async aplan(intermediate_steps: List[Tuple[AgentAction, str]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Union[AgentAction, AgentFinish]¶
Given input, decided what to do.
Parameters
intermediate_steps (List[Tuple[AgentAction, str]]) – Steps the LLM has taken to date,
along with observations
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – Callbacks to run.
**kwargs (Any) – User inputs.
Returns
Action specifying what tool to use.
Return type
Union[AgentAction, AgentFinish]
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
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
classmethod create_prompt(tools: Sequence[BaseTool]) → BasePromptTemplate[source]¶
Prompt does not depend on tools.
Parameters
tools (Sequence[BaseTool]) – 
Return type
BasePromptTemplate
dict(**kwargs: Any) → Dict¶
Return dictionary representation of agent.
Parameters
kwargs (Any) – 
Return type
Dict
classmethod from_llm_and_tools(llm: BaseLanguageModel, tools: Sequence[BaseTool], callback_manager: Optional[BaseCallbackManager] = None, output_parser: Optional[AgentOutputParser] = None, **kwargs: Any) → Agent¶
Construct an agent from an LLM and tools.
Parameters
llm (BaseLanguageModel) – 
tools (Sequence[BaseTool]) – 
callback_manager (Optional[BaseCallbackManager]) –
from langchain.agents import AgentExecutor
from langchain_core.agents import AgentFinish
from langchain.tools import E2BDataAnalysisTool
tools = [E2BDataAnalysisTool(api_key="...")]
agent = OpenAIAssistantRunnable.create_assistant(
    name="langchain assistant e2b tool",
    instructions="You are a personal math tutor. Write and run code to answer math questions.",
    tools=tools,
    model="gpt-4-1106-preview",
    as_agent=True
)
def execute_agent(agent, tools, input):
    tool_map = {tool.name: tool for tool in tools}
    response = agent.invoke(input)
    while not isinstance(response, AgentFinish):
        tool_outputs = []
        for action in response:
            tool_output = tool_map[action.tool].invoke(action.tool_input)
            tool_outputs.append({"output": tool_output, "tool_call_id": action.tool_call_id})
        response = agent.invoke(
            {
                "tool_outputs": tool_outputs,
                "run_id": action.run_id,
                "thread_id": action.thread_id
            }
        )
    return response
response = execute_agent(agent, tools, {"content": "What's 10 - 4 raised to the 2.7"})
next_response = execute_agent(agent, tools, {"content": "now add 17.241", "thread_id": response.thread_id})
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 as_agent: bool = False¶
Use as a LangChain agent, compatible with the AgentExecutor.
param assistant_id: str [Required]¶
OpenAI assistant id.
param async_client: Any = None¶
Return type
AsyncIterator[Tuple[int, Union[Output, Exception]]]
async classmethod acreate_assistant(name: str, instructions: str, tools: Sequence[Union[BaseTool, dict]], model: str, *, async_client: Optional[Union[openai.AsyncOpenAI, openai.AsyncAzureOpenAI]] = None, **kwargs: Any) → OpenAIAssistantRunnable[source]¶
Create an AsyncOpenAI Assistant and instantiate the Runnable.
Parameters
name (str) – Assistant name.
instructions (str) – Assistant instructions.
tools (Sequence[Union[BaseTool, dict]]) – Assistant tools. Can be passed in OpenAI format or as BaseTools.
model (str) – Assistant model to use.
async_client (Optional[Union[openai.AsyncOpenAI, openai.AsyncAzureOpenAI]]) – AsyncOpenAI client.
specified. (Will create default async_client if not) – 
kwargs (Any) – 
Returns
AsyncOpenAIAssistantRunnable configured to run using the created assistant.
Return type
OpenAIAssistantRunnable
async ainvoke(input: dict, config: Optional[RunnableConfig] = None, **kwargs: Any) → OutputType[source]¶
Async invoke assistant.
Parameters
input (dict) – Runnable input dict that can have:
content: User message when starting a new run.
thread_id: Existing thread to use.
run_id: Existing run to use. Should only be supplied when providing
the tool output for a required action after an initial invocation.
file_ids: File ids to include in new run. Used for retrieval.
message_metadata: Metadata to associate with new message.
thread_metadata: Metadata to associate with new thread. Only relevant
when new thread being created.
instructions: Additional run instructions.
model: Override Assistant model for this run.
tools: Override Assistant tools for this run.
model: Override Assistant model for this run.
tools: Override Assistant tools for this run.
run_metadata: Metadata to associate with new run.
config (Optional[RunnableConfig]) – Runnable config:
kwargs (Any) – 
Returns
If self.as_agent, will returnUnion[List[OpenAIAssistantAction], OpenAIAssistantFinish]. Otherwise,
will return OpenAI types
Union[List[ThreadMessage], List[RequiredActionFunctionToolCall]].
Return type
OutputType
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.
from langchain_community.llms.fake import FakeStreamingListLLM
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import SystemMessagePromptTemplate
from langchain_core.runnables import Runnable
from operator import itemgetter
prompt = (
    SystemMessagePromptTemplate.from_template("You are a nice assistant.")
    + "{question}"
)
llm = FakeStreamingListLLM(responses=["foo-lish"])
chain: Runnable = prompt | llm | {"str": StrOutputParser()}
chain_with_assign = chain.assign(hello=itemgetter("str") | llm)
print(chain_with_assign.input_schema.schema())
# {'title': 'PromptInput', 'type': 'object', 'properties':
{'question': {'title': 'Question', 'type': 'string'}}}
print(chain_with_assign.output_schema.schema()) #
{'title': 'RunnableSequenceOutput', 'type': 'object', 'properties':
{'str': {'title': 'Str',
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
classmethod create_assistant(name: str, instructions: str, tools: Sequence[Union[BaseTool, dict]], model: str, *, client: Optional[Union[openai.OpenAI, openai.AzureOpenAI]] = None, **kwargs: Any) → OpenAIAssistantRunnable[source]¶
Create an OpenAI Assistant and instantiate the Runnable.
Parameters
name (str) – Assistant name.
instructions (str) – Assistant instructions.
tools (Sequence[Union[BaseTool, dict]]) – Assistant tools. Can be passed in OpenAI format or as BaseTools.
model (str) – Assistant model to use.
client (Optional[Union[openai.OpenAI, openai.AzureOpenAI]]) – OpenAI or AzureOpenAI client.
Will create default OpenAI client if not specified.
kwargs (Any) –
Will create default OpenAI client if not specified.
kwargs (Any) – 
Returns
OpenAIAssistantRunnable configured to run using the created assistant.
Return type
OpenAIAssistantRunnable
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.
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) – 
Return type
DictStrAny
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
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, config: Optional[RunnableConfig] = None) → OutputType[source]¶
Invoke assistant.
Parameters
input (dict) – Runnable input dict that can have:
Invoke assistant.
Parameters
input (dict) – Runnable input dict that can have:
content: User message when starting a new run.
thread_id: Existing thread to use.
run_id: Existing run to use. Should only be supplied when providing
the tool output for a required action after an initial invocation.
file_ids: File ids to include in new run. Used for retrieval.
message_metadata: Metadata to associate with new message.
thread_metadata: Metadata to associate with new thread. Only relevant
when new thread being created.
instructions: Additional run instructions.
model: Override Assistant model for this run.
tools: Override Assistant tools for this run.
run_metadata: Metadata to associate with new run.
config (Optional[RunnableConfig]) – Runnable config:
Returns
If self.as_agent, will returnUnion[List[OpenAIAssistantAction], OpenAIAssistantFinish]. Otherwise,
will return OpenAI types
Union[List[ThreadMessage], List[RequiredActionFunctionToolCall]].
Return type
OutputType
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
langchain.agents.react.base.DocstoreExplorer¶
class langchain.agents.react.base.DocstoreExplorer(docstore: Docstore)[source]¶
[Deprecated] Class to assist with exploration of a document store.
Notes
Deprecated since version 0.1.0.
Initialize with a docstore, and set initial document to None.
Methods
__init__(docstore)
Initialize with a docstore, and set initial document to None.
lookup(term)
Lookup a term in document (if saved).
search(term)
Search for a term in the docstore, and if found save.
Parameters
docstore (Docstore) – 
__init__(docstore: Docstore)[source]¶
Initialize with a docstore, and set initial document to None.
Parameters
docstore (Docstore) – 
lookup(term: str) → str[source]¶
Lookup a term in document (if saved).
Parameters
term (str) – 
Return type
str
search(term: str) → str[source]¶
Search for a term in the docstore, and if found save.
Parameters
term (str) – 
Return type
str
langchain.agents.loading.load_agent_from_config¶
langchain.agents.loading.load_agent_from_config(config: dict, llm: Optional[BaseLanguageModel] = None, tools: Optional[List[Tool]] = None, **kwargs: Any) → Union[BaseSingleActionAgent, BaseMultiActionAgent][source]¶
[Deprecated] Load agent from Config Dict.
Parameters
config (dict) – Config dict to load agent from.
llm (Optional[BaseLanguageModel]) – Language model to use as the agent.
tools (Optional[List[Tool]]) – List of tools this agent has access to.
**kwargs (Any) – Additional keyword arguments passed to the agent executor.
Returns
An agent executor.
Return type
Union[BaseSingleActionAgent, BaseMultiActionAgent]
Notes
Deprecated since version 0.1.0.
langchain.agents.mrkl.base.ZeroShotAgent¶
class langchain.agents.mrkl.base.ZeroShotAgent[source]¶
Bases: Agent
[Deprecated] Agent for the MRKL chain.
Notes
Deprecated since version 0.1.0: Use create_react_agent instead.
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 allowed_tools: Optional[List[str]] = None¶
param llm_chain: LLMChain [Required]¶
param output_parser: AgentOutputParser [Optional]¶
async aplan(intermediate_steps: List[Tuple[AgentAction, str]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Union[AgentAction, AgentFinish]¶
Given input, decided what to do.
Parameters
intermediate_steps (List[Tuple[AgentAction, str]]) – Steps the LLM has taken to date,
along with observations
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – Callbacks to run.
**kwargs (Any) – User inputs.
Returns
Action specifying what tool to use.
Return type
Union[AgentAction, AgentFinish]
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
self (Model) – 
Returns
new model instance
Return type
Model
classmethod create_prompt(tools: Sequence[BaseTool], prefix: str = 'Answer the following questions as best you can. You have access to the following tools:', suffix: str = 'Begin!\n\nQuestion: {input}\nThought:{agent_scratchpad}', format_instructions: str = 'Use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question', input_variables: Optional[List[str]] = None) → PromptTemplate[source]¶
Create prompt in the style of the zero shot agent.
Parameters
tools (Sequence[BaseTool]) – List of tools the agent will have access to, used to format the
prompt.
prefix (str) – String to put before the list of tools.
suffix (str) – String to put after the list of tools.
input_variables (Optional[List[str]]) – List of input variables the final prompt will expect.
format_instructions (str) – 
Returns
A PromptTemplate with the template assembled from the pieces here.
Return type
PromptTemplate
dict(**kwargs: Any) → Dict¶
Return dictionary representation of agent.
Parameters
kwargs (Any) – 
Return type
Dict
Parameters
kwargs (Any) – 
Return type
Dict
classmethod from_llm_and_tools(llm: BaseLanguageModel, tools: Sequence[BaseTool], callback_manager: Optional[BaseCallbackManager] = None, output_parser: Optional[AgentOutputParser] = None, prefix: str = 'Answer the following questions as best you can. You have access to the following tools:', suffix: str = 'Begin!\n\nQuestion: {input}\nThought:{agent_scratchpad}', format_instructions: str = 'Use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question', input_variables: Optional[List[str]] = None, **kwargs: Any) → Agent[source]¶
Construct an agent from an LLM and tools.
Parameters
llm (BaseLanguageModel) – 
tools (Sequence[BaseTool]) – 
callback_manager (Optional[BaseCallbackManager]) – 
output_parser (Optional[AgentOutputParser]) – 
prefix (str) – 
suffix (str) – 
format_instructions (str) – 
input_variables (Optional[List[str]]) – 
kwargs (Any) – 
Return type
Agent
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
get_allowed_tools() → Optional[List[str]]¶
Return type
Optional[List[str]]
get_allowed_tools() → Optional[List[str]]¶
Return type
Optional[List[str]]
get_full_inputs(intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any) → Dict[str, Any]¶
Create the full inputs for the LLMChain from intermediate steps.
Parameters
intermediate_steps (List[Tuple[AgentAction, str]]) – 
kwargs (Any) – 
Return type
Dict[str, Any]
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
dumps_kwargs (Any) – 
Return type
unicode
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
plan(intermediate_steps: List[Tuple[AgentAction, str]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Union[AgentAction, AgentFinish]¶
Given input, decided what to do.
Parameters
intermediate_steps (List[Tuple[AgentAction, str]]) – Steps the LLM has taken to date,
along with observations
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – Callbacks to run.
**kwargs (Any) – User inputs.
Returns
Action specifying what tool to use.
Return type
Union[AgentAction, AgentFinish]
return_stopped_response(early_stopping_method: str, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any) → AgentFinish¶
Return response when agent has been stopped due to max iterations.
Parameters
early_stopping_method (str) – 
intermediate_steps (List[Tuple[AgentAction, str]]) – 
kwargs (Any) – 
Return type
AgentFinish
save(file_path: Union[Path, str]) → None¶
Save the agent.
Parameters
file_path (Union[Path, str]) – Path to file to save the agent to.
Return type
None
Example:
.. code-block:: python
# If working with agent executor
agent.agent.save(file_path=”path/agent.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
tool_run_logging_kwargs() → Dict¶
Return type
Dict
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
property llm_prefix: str¶
Prefix to append the llm call with.
property observation_prefix: str¶
Prefix to append the observation with.
property return_values: List[str]¶
Return values of the agent.