Tony20100/langchain_full huggingface dataset:
langchain.agents.agent.LLMSingleActionAgent¶
class langchain.agents.agent.LLMSingleActionAgent[source]¶
Bases: BaseSingleActionAgent
[Deprecated] Base class for single action agents.
Notes
Deprecated since version 0.1.0: Use Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc. 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 llm_chain: LLMChain [Required]¶
LLMChain to use for agent.
param output_parser: AgentOutputParser [Required]¶
Output parser to use for agent.
param stop: List[str] [Required]¶
List of strings to stop on.
async aplan(intermediate_steps: List[Tuple[AgentAction, str]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Union[AgentAction, AgentFinish][source]¶
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
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[source]¶
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, **kwargs: Any) → BaseSingleActionAgent¶
Parameters
llm (BaseLanguageModel) – 
tools (Sequence[BaseTool]) – 
callback_manager (Optional[BaseCallbackManager]) – 
kwargs (Any) – 
Return type
BaseSingleActionAgent
classmethod from_orm(obj: Any) → Model¶
Parameters
BaseSingleActionAgent
classmethod from_orm(obj: Any) → Model¶
Parameters
obj (Any) – 
Return type
Model
get_allowed_tools() → Optional[List[str]]¶
Return type
Optional[List[str]]
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 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) –
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][source]¶
Given input, decided what to do.
Parameters
intermediate_steps (List[Tuple[AgentAction, str]]) – Steps the LLM has taken to date,
along with the 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
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[source]¶
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 input_keys: List[str]¶
Return the input keys.
Returns
List of input keys.
property return_values: List[str]¶
Return values of the agent.
langchain.agents.format_scratchpad.xml.format_xml¶
langchain.agents.format_scratchpad.xml.format_xml(intermediate_steps: List[Tuple[AgentAction, str]]) → str[source]¶
Format the intermediate steps as XML.
Parameters
intermediate_steps (List[Tuple[AgentAction, str]]) – The intermediate steps.
Returns
The intermediate steps as XML.
Return type
str
langchain.agents.agent_toolkits.conversational_retrieval.openai_functions.create_conversational_retrieval_agent¶
langchain.agents.agent_toolkits.conversational_retrieval.openai_functions.create_conversational_retrieval_agent(llm: BaseLanguageModel, tools: List[BaseTool], remember_intermediate_steps: bool = True, memory_key: str = 'chat_history', system_message: Optional[SystemMessage] = None, verbose: bool = False, max_token_limit: int = 2000, **kwargs: Any) → AgentExecutor[source]¶
A convenience method for creating a conversational retrieval agent.
Parameters
llm (BaseLanguageModel) – The language model to use, should be ChatOpenAI
tools (List[BaseTool]) – A list of tools the agent has access to
remember_intermediate_steps (bool) – Whether the agent should remember intermediate
steps or not. Intermediate steps refer to prior action/observation
pairs from previous questions. The benefit of remembering these is if
there is relevant information in there, the agent can use it to answer
follow up questions. The downside is it will take up more tokens.
memory_key (str) – The name of the memory key in the prompt.
system_message (Optional[SystemMessage]) – The system message to use. By default, a basic one will
be used.
verbose (bool) – Whether or not the final AgentExecutor should be verbose or not,
defaults to False.
max_token_limit (int) – The max number of tokens to keep around in memory.
Defaults to 2000.
kwargs (Any) – 
Returns
An agent executor initialized appropriately
Return type
AgentExecutor
Examples using create_conversational_retrieval_agent¶
# Cogniswitch Tools
langchain.agents.chat.base.ChatAgent¶
class langchain.agents.chat.base.ChatAgent[source]¶
Bases: Agent
[Deprecated] Chat Agent.
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]¶
Output parser for the agent.
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], system_message_prefix: str = 'Answer the following questions as best you can. You have access to the following tools:', system_message_suffix: str = 'Begin! Reminder to always use the exact characters `Final Answer` when responding.', human_message: str = '{input}\n\n{agent_scratchpad}', format_instructions: str = 'The way you use the tools is by specifying a json blob.\nSpecifically, this json should have a `action` key (with the name of the tool to use) and a `action_input` key (with the input to the tool going here).\n\nThe only values that should be in the "action" field are: {tool_names}\n\nThe $JSON_BLOB should only contain a SINGLE action, do NOT return a list of multiple actions. Here is an example of a valid $JSON_BLOB:\n\n```\n{{{{\n  "action": $TOOL_NAME,\n  "action_input": $INPUT\n}}}}\n```\n\nALWAYS use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction:\n```\n$JSON_BLOB\n```\nObservation: the result of the action\n... (this Thought/Action/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) → BasePromptTemplate[source]¶
Create a prompt for this class.
Parameters
tools (Sequence[BaseTool]) – 
system_message_prefix (str) – 
system_message_suffix (str) – 
human_message (str) –
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, system_message_prefix: str = 'Answer the following questions as best you can. You have access to the following tools:', system_message_suffix: str = 'Begin! Reminder to always use the exact characters `Final Answer` when responding.', human_message: str = '{input}\n\n{agent_scratchpad}', format_instructions: str = 'The way you use the tools is by specifying a json blob.\nSpecifically, this json should have a `action` key (with the name of the tool to use) and a `action_input` key (with the input to the tool going here).\n\nThe only values that should be in the "action" field are: {tool_names}\n\nThe $JSON_BLOB should only contain a SINGLE action, do NOT return a list of multiple actions. Here is an example of a valid $JSON_BLOB:\n\n```\n{{{{\n  "action": $TOOL_NAME,\n  "action_input": $INPUT\n}}}}\n```\n\nALWAYS use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction:\n```\n$JSON_BLOB\n```\nObservation: the result of the action\n... (this Thought/Action/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
Construct an agent from an LLM and tools.
Parameters
llm (BaseLanguageModel) – 
tools (Sequence[BaseTool]) – 
callback_manager (Optional[BaseCallbackManager]) – 
output_parser (Optional[AgentOutputParser]) – 
system_message_prefix (str) – 
system_message_suffix (str) – 
human_message (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_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
langchain.agents.output_parsers.react_single_input.ReActSingleInputOutputParser¶
class langchain.agents.output_parsers.react_single_input.ReActSingleInputOutputParser[source]¶
Bases: AgentOutputParser
Parses ReAct-style LLM calls that have a single tool input.
Expects output to be in one of two formats.
If the output signals that an action should be taken,
should be in the below format. This will result in an AgentAction
being returned.
`
Thought: agent thought here
Action: search
Action Input: what is the temperature in SF?
`
If the output signals that a final answer should be given,
should be in the below format. This will result in an AgentFinish
being returned.
`
Thought: agent thought here
Final Answer: The temperature is 100 degrees
`
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.
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]
langchain_core.agents.AgentAction¶
class langchain_core.agents.AgentAction[source]¶
Bases: Serializable
A full description of an action for an ActionAgent to execute.
Override init to support instantiation by position for backward compat.
param log: str [Required]¶
Additional information to log about the action.
This log can be used in a few ways. First, it can be used to audit
what exactly the LLM predicted to lead to this (tool, tool_input).
Second, it can be used in future iterations to show the LLMs prior
thoughts. This is useful when (tool, tool_input) does not contain
full information about the LLM prediction (for example, any thought
before the tool/tool_input).
param tool: str [Required]¶
The name of the Tool to execute.
param tool_input: Union[str, dict] [Required]¶
The input to pass in to the Tool.
param type: Literal['AgentAction'] = 'AgentAction'¶
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
SerializedNotImplemented
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 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 messages: Sequence[BaseMessage]¶
Return the messages that correspond to this action.
langchain_experimental.agents.agent_toolkits.csv.base.create_csv_agent¶
langchain_experimental.agents.agent_toolkits.csv.base.create_csv_agent(llm: LanguageModelLike, path: Union[str, IOBase, List[Union[str, IOBase]]], pandas_kwargs: Optional[dict] = None, **kwargs: Any) → AgentExecutor[source]¶
Create pandas dataframe agent by loading csv to a dataframe.
Parameters
llm (LanguageModelLike) – Language model to use for the agent.
path (Union[str, IOBase, List[Union[str, IOBase]]]) – A string path, file-like object or a list of string paths/file-like
objects that can be read in as pandas DataFrames with pd.read_csv().
pandas_kwargs (Optional[dict]) – Named arguments to pass to pd.read_csv().
**kwargs (Any) – Additional kwargs to pass to langchain_experimental.agents.agent_toolkits.pandas.base.create_pandas_dataframe_agent().
Returns
An AgentExecutor with the specified agent_type agent and access to
a PythonAstREPLTool with the loaded DataFrame(s) and any user-provided extra_tools.
Return type
AgentExecutor
Example
from langchain_openai import ChatOpenAI
from langchain_experimental.agents import create_csv_agent
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
agent_executor = create_pandas_dataframe_agent(
    llm,
    "titanic.csv",
    agent_type="openai-tools",
    verbose=True
)
Examples using create_csv_agent¶
CSV
langchain.agents.output_parsers.tools.ToolAgentAction¶
class langchain.agents.output_parsers.tools.ToolAgentAction[source]¶
Bases: AgentActionMessageLog
Override init to support instantiation by position for backward compat.
param log: str [Required]¶
Additional information to log about the action.
This log can be used in a few ways. First, it can be used to audit
what exactly the LLM predicted to lead to this (tool, tool_input).
Second, it can be used in future iterations to show the LLMs prior
thoughts. This is useful when (tool, tool_input) does not contain
full information about the LLM prediction (for example, any thought
before the tool/tool_input).
param message_log: Sequence[BaseMessage] [Required]¶
Similar to log, this can be used to pass along extra
information about what exact messages were predicted by the LLM
before parsing out the (tool, tool_input). This is again useful
if (tool, tool_input) cannot be used to fully recreate the LLM
prediction, and you need that LLM prediction (for future agent iteration).
Compared to log, this is useful when the underlying LLM is a
ChatModel (and therefore returns messages rather than a string).
param tool: str [Required]¶
The name of the Tool to execute.
param tool_call_id: str [Required]¶
Tool call that this message is responding to.
param tool_input: Union[str, dict] [Required]¶
The input to pass in to the Tool.
param type: Literal['AgentActionMessageLog'] = 'AgentActionMessageLog'¶
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.
langchain.agents.output_parsers.tools.ToolsAgentOutputParser¶
class langchain.agents.output_parsers.tools.ToolsAgentOutputParser[source]¶
Bases: MultiActionAgentOutputParser
Parses a message into agent actions/finish.
If a tool_calls parameter is passed, then that is used to get
the tool names and tool inputs.
If one is not passed, then the AIMessage is assumed to be the final output.
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.
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 abatch_as_completed(inputs: Sequence[Input], config: Optional[Union[RunnableConfig, Sequence[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → AsyncIterator[Tuple[int, Union[Output, Exception]]]¶
Run ainvoke in parallel on a list of inputs,
yielding results as they complete.
Parameters
inputs (Sequence[Input]) – 
config (Optional[Union[RunnableConfig, Sequence[RunnableConfig]]]) – 
return_exceptions (bool) –