Langchain Integrations

Agent Tools + Functions

Llama integration with Langchain agents.

class llama_index.langchain_helpers.agents.IndexToolConfig(*, query_engine: BaseQueryEngine, name: str, description: str, tool_kwargs: Dict = None)

Configuration for LlamaIndex index tool.

class Config

Configuration for this pydantic object.

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

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.

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 update_forward_refs(**localns: Any) → None

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

class llama_index.langchain_helpers.agents.LlamaIndexTool(*, name: str, description: str, args_schema: Optional[Type[BaseModel]] = None, return_direct: bool = False, verbose: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False, query_engine: BaseQueryEngine, return_sources: bool = False)

Tool for querying a LlamaIndex.

class Config

Configuration for this pydantic object.

args_schema: Optional[Type[BaseModel]]

Pydantic model class to validate and parse the tool’s input arguments.

async arun(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any

Run the tool asynchronously.

callback_manager: Optional[BaseCallbackManager]

Deprecated. Please use callbacks instead.

callbacks: Callbacks

Callbacks to be called during tool execution.

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

description: str

Used to tell the model how/when/why to use the tool.

You can provide few-shot examples as a part of the description.

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_tool_config(tool_config: IndexToolConfig) → LlamaIndexTool

Create a tool from a tool config.

handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]]

Handle the content of the ToolException thrown.

property is_single_input: bool

Whether the tool only accepts a single input.

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().

name: str

The unique name of the tool that clearly communicates its purpose.

classmethod raise_deprecation(values: Dict) → Dict

Raise deprecation warning if callback_manager is used.

return_direct: bool

Whether to return the tool’s output directly. Setting this to True means

that after the tool is called, the AgentExecutor will stop looping.

run(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → Any

Run the tool.

classmethod update_forward_refs(**localns: Any) → None

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

verbose: bool

Whether to log the tool’s progress.

class llama_index.langchain_helpers.agents.LlamaToolkit(*, index_configs: List[IndexToolConfig] = None)

Toolkit for interacting with Llama indices.

class Config

Configuration for this pydantic object.

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

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.

get_tools() → List[BaseTool]

Get the tools in the toolkit.

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 update_forward_refs(**localns: Any) → None

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

llama_index.langchain_helpers.agents.create_llama_agent(toolkit: LlamaToolkit, llm: BaseLLM, agent: Optional[AgentType] = None, callback_manager: Optional[BaseCallbackManager] = None, agent_path: Optional[str] = None, agent_kwargs: Optional[dict] = None, **kwargs: Any) → AgentExecutor

Load an agent executor given a Llama Toolkit and LLM.

NOTE: this is a light wrapper around initialize_agent in langchain.

Parameters

• toolkit – LlamaToolkit to use.

• llm – Language model to use as the agent.

• agent –

  A string that specified the agent type to use. Valid options are:

  zero-shot-react-description react-docstore self-ask-with-search conversational-react-description chat-zero-shot-react-description, chat-conversational-react-description,

  If None and agent_path is also None, will default to

  zero-shot-react-description.

• callback_manager – CallbackManager to use. Global callback manager is used if not provided. Defaults to None.

• agent_path – Path to serialized agent to use.

• agent_kwargs – Additional key word arguments to pass to the underlying agent

• **kwargs – Additional key word arguments passed to the agent executor

Returns

An agent executor

llama_index.langchain_helpers.agents.create_llama_chat_agent(toolkit: LlamaToolkit, llm: BaseLLM, callback_manager: Optional[BaseCallbackManager] = None, agent_kwargs: Optional[dict] = None, **kwargs: Any) → AgentExecutor

Load a chat llama agent given a Llama Toolkit and LLM.

Parameters

• toolkit – LlamaToolkit to use.

• llm – Language model to use as the agent.

• callback_manager – CallbackManager to use. Global callback manager is used if not provided. Defaults to None.

• agent_kwargs – Additional key word arguments to pass to the underlying agent

• **kwargs – Additional key word arguments passed to the agent executor

Returns

An agent executor

Memory Module

Langchain memory wrapper (for LlamaIndex).

class llama_index.langchain_helpers.memory_wrapper.GPTIndexChatMemory(*, chat_memory: BaseChatMessageHistory = None, output_key: Optional[str] = None, input_key: Optional[str] = None, return_messages: bool = False, human_prefix: str = 'Human', ai_prefix: str = 'AI', memory_key: str = 'history', index: BaseIndex, query_kwargs: Dict = None, return_source: bool = False, id_to_message: Dict[str, BaseMessage] = None)

Langchain chat memory wrapper (for LlamaIndex).

Parameters

• human_prefix (str) – Prefix for human input. Defaults to “Human”.

• ai_prefix (str) – Prefix for AI output. Defaults to “AI”.

• memory_key (str) – Key for memory. Defaults to “history”.

• index (BaseIndex) – LlamaIndex instance.

• query_kwargs (Dict[str, Any]) – Keyword arguments for LlamaIndex query.

• input_key (Optional[str]) – Input key. Defaults to None.

• output_key (Optional[str]) – Output key. Defaults to None.

class Config

Configuration for this pydantic object.

clear() → None

Clear memory contents.

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

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.

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().

load_memory_variables(inputs: Dict[str, Any]) → Dict[str, str]

Return key-value pairs given the text input to the chain.

property memory_variables: List[str]

Return memory variables.

save_context(inputs: Dict[str, Any], outputs: Dict[str, str]) → None

Save the context of this model run to memory.

classmethod update_forward_refs(**localns: Any) → None

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

class llama_index.langchain_helpers.memory_wrapper.GPTIndexMemory(*, human_prefix: str = 'Human', ai_prefix: str = 'AI', memory_key: str = 'history', index: BaseIndex, query_kwargs: Dict = None, output_key: Optional[str] = None, input_key: Optional[str] = None)

Langchain memory wrapper (for LlamaIndex).

Parameters

• human_prefix (str) – Prefix for human input. Defaults to “Human”.

• ai_prefix (str) – Prefix for AI output. Defaults to “AI”.

• memory_key (str) – Key for memory. Defaults to “history”.

• index (BaseIndex) – LlamaIndex instance.

• query_kwargs (Dict[str, Any]) – Keyword arguments for LlamaIndex query.

• input_key (Optional[str]) – Input key. Defaults to None.

• output_key (Optional[str]) – Output key. Defaults to None.

class Config

Configuration for this pydantic object.

clear() → None

Clear memory contents.

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

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.

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().

load_memory_variables(inputs: Dict[str, Any]) → Dict[str, str]

Return key-value pairs given the text input to the chain.

property memory_variables: List[str]

Return memory variables.

save_context(inputs: Dict[str, Any], outputs: Dict[str, str]) → None

Save the context of this model run to memory.

classmethod update_forward_refs(**localns: Any) → None

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

llama_index.langchain_helpers.memory_wrapper.get_prompt_input_key(inputs: Dict[str, Any], memory_variables: List[str]) → str

Get prompt input key.

Copied over from langchain.