like Paperspace, Coreweave, etc.).
To use, you should have the runhouse python package installed.
Only supports text-generation and text2text-generation for now.
Example using from_model_id:from langchain.llms import SelfHostedHuggingFaceLLM
import runhouse as rh
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
hf = SelfHostedHuggingFaceLLM(
    model_id="google/flan-t5-large", task="text2text-generation",
    hardware=gpu
)
Example passing fn that generates a pipeline (bc the pipeline is not serializable):from langchain.llms import SelfHostedHuggingFaceLLM
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import runhouse as rh
def get_pipeline():
    model_id = "gpt2"
    tokenizer = AutoTokenizer.from_pretrained(model_id)
    model = AutoModelForCausalLM.from_pretrained(model_id)
    pipe = pipeline(
        "text-generation", model=model, tokenizer=tokenizer
    )
    return pipe
hf = SelfHostedHuggingFaceLLM(
    model_load_fn=get_pipeline, model_id="gpt2", hardware=gpu)
Validators
set_callback_manager » callback_manager
set_verbose » verbose
field device: int = 0#
Device to use for inference. -1 for CPU, 0 for GPU, 1 for second GPU, etc.
field hardware: Any = None#
Remote hardware to send the inference function to.
field inference_fn: Callable = <function _generate_text>#
Inference function to send to the remote hardware.
field load_fn_kwargs: Optional[dict] = None#
Key word arguments to pass to the model load function.
field model_id: str = 'gpt2'#


field model_id: str = 'gpt2'#
Hugging Face model_id to load the model.
field model_kwargs: Optional[dict] = None#
Key word arguments to pass to the model.
field model_load_fn: Callable = <function _load_transformer>#
Function to load the model remotely on the server.
field model_reqs: List[str] = ['./', 'transformers', 'torch']#
Requirements to install on hardware to inference the model.
field task: str = 'text-generation'#
Hugging Face task (either “text-generation” or “text2text-generation”).
__call__(prompt: str, stop: Optional[List[str]] = None) → str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
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(**kwargs: Any) → Dict#
Return a dictionary of the LLM.
classmethod from_pipeline(pipeline: Any, hardware: Any, model_reqs: Optional[List[str]] = None, device: int = 0, **kwargs: Any) → langchain.llms.base.LLM#
Init the SelfHostedPipeline from a pipeline object or string.
generate(prompts: List[str], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) → int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int#
Get the number of tokens in the message.


Get the number of tokens in the message.
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().
save(file_path: Union[pathlib.Path, str]) → None#
Save the LLM.
Parameters
file_path – Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=”path/llm.yaml”)
classmethod update_forward_refs(**localns: Any) → None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.SelfHostedPipeline[source]#
Run model inference on self-hosted remote hardware.
Supported hardware includes auto-launched instances on AWS, GCP, Azure,
and Lambda, as well as servers specified
by IP address and SSH credentials (such as on-prem, or another
cloud like Paperspace, Coreweave, etc.).
To use, you should have the runhouse python package installed.
Example for custom pipeline and inference functions:from langchain.llms import SelfHostedPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import runhouse as rh
def load_pipeline():


import runhouse as rh
def load_pipeline():
    tokenizer = AutoTokenizer.from_pretrained("gpt2")
    model = AutoModelForCausalLM.from_pretrained("gpt2")
    return pipeline(
        "text-generation", model=model, tokenizer=tokenizer,
        max_new_tokens=10
    )
def inference_fn(pipeline, prompt, stop = None):
    return pipeline(prompt)[0]["generated_text"]
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
llm = SelfHostedPipeline(
    model_load_fn=load_pipeline,
    hardware=gpu,
    model_reqs=model_reqs, inference_fn=inference_fn
)
Example for <2GB model (can be serialized and sent directly to the server):from langchain.llms import SelfHostedPipeline
import runhouse as rh
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
my_model = ...
llm = SelfHostedPipeline.from_pipeline(
    pipeline=my_model,
    hardware=gpu,
    model_reqs=["./", "torch", "transformers"],
)
Example passing model path for larger models:from langchain.llms import SelfHostedPipeline
import runhouse as rh
import pickle
from transformers import pipeline
generator = pipeline(model="gpt2")
rh.blob(pickle.dumps(generator), path="models/pipeline.pkl"
    ).save().to(gpu, path="models")
llm = SelfHostedPipeline.from_pipeline(
    pipeline="models/pipeline.pkl",
    hardware=gpu,
    model_reqs=["./", "torch", "transformers"],
)
Validators
set_callback_manager » callback_manager
set_verbose » verbose
field hardware: Any = None#
Remote hardware to send the inference function to.


field hardware: Any = None#
Remote hardware to send the inference function to.
field inference_fn: Callable = <function _generate_text>#
Inference function to send to the remote hardware.
field load_fn_kwargs: Optional[dict] = None#
Key word arguments to pass to the model load function.
field model_load_fn: Callable [Required]#
Function to load the model remotely on the server.
field model_reqs: List[str] = ['./', 'torch']#
Requirements to install on hardware to inference the model.
__call__(prompt: str, stop: Optional[List[str]] = None) → str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
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


Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) → Dict#
Return a dictionary of the LLM.
classmethod from_pipeline(pipeline: Any, hardware: Any, model_reqs: Optional[List[str]] = None, device: int = 0, **kwargs: Any) → langchain.llms.base.LLM[source]#
Init the SelfHostedPipeline from a pipeline object or string.
generate(prompts: List[str], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) → int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int#
Get the number of tokens in the message.


Get the number of tokens in the message.
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().
save(file_path: Union[pathlib.Path, str]) → None#
Save the LLM.
Parameters
file_path – Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=”path/llm.yaml”)
classmethod update_forward_refs(**localns: Any) → None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.StochasticAI[source]#
Wrapper around StochasticAI large language models.
To use, you should have the environment variable STOCHASTICAI_API_KEY
set with your API key.
Example
from langchain.llms import StochasticAI
stochasticai = StochasticAI(api_url="")
Validators
build_extra » all fields
set_callback_manager » callback_manager
set_verbose » verbose
validate_environment » all fields
field api_url: str = ''#
Model name to use.
field model_kwargs: Dict[str, Any] [Optional]#
Holds any model parameters valid for create call not
explicitly specified.


Holds any model parameters valid for create call not
explicitly specified.
__call__(prompt: str, stop: Optional[List[str]] = None) → str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
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(**kwargs: Any) → Dict#
Return a dictionary of the LLM.


dict(**kwargs: Any) → Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) → int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int#
Get the number of tokens in the message.
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().
save(file_path: Union[pathlib.Path, str]) → None#
Save the LLM.
Parameters
file_path – Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=”path/llm.yaml”)
classmethod update_forward_refs(**localns: Any) → None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.


Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.Writer[source]#
Wrapper around Writer large language models.
To use, you should have the environment variable WRITER_API_KEY
set with your API key.
Example
from langchain import Writer
writer = Writer(model_id="palmyra-base")
Validators
set_callback_manager » callback_manager
set_verbose » verbose
validate_environment » all fields
field base_url: Optional[str] = None#
Base url to use, if None decides based on model name.
field beam_search_diversity_rate: float = 1.0#
Only applies to beam search, i.e. when the beam width is >1.
A higher value encourages beam search to return a more diverse
set of candidates
field beam_width: Optional[int] = None#
The number of concurrent candidates to keep track of during
beam search
field length: int = 256#
The maximum number of tokens to generate in the completion.
field length_pentaly: float = 1.0#
Only applies to beam search, i.e. when the beam width is >1.
Larger values penalize long candidates more heavily, thus preferring
shorter candidates
field logprobs: bool = False#
Whether to return log probabilities.
field model_id: str = 'palmyra-base'#
Model name to use.
field random_seed: int = 0#
The model generates random results.
Changing the random seed alone will produce a different response
with similar characteristics. It is possible to reproduce results
by fixing the random seed (assuming all other hyperparameters
are also fixed)
field repetition_penalty: float = 1.0#
Penalizes repeated tokens according to frequency.
field stop: Optional[List[str]] = None#


Penalizes repeated tokens according to frequency.
field stop: Optional[List[str]] = None#
Sequences when completion generation will stop
field temperature: float = 1.0#
What sampling temperature to use.
field tokens_to_generate: int = 24#
Max number of tokens to generate.
field top_k: int = 1#
The number of highest probability vocabulary tokens to
keep for top-k-filtering.
field top_p: float = 1.0#
Total probability mass of tokens to consider at each step.
__call__(prompt: str, stop: Optional[List[str]] = None) → str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
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


Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) → Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) → int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int#
Get the number of tokens in the message.
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().


encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
save(file_path: Union[pathlib.Path, str]) → None#
Save the LLM.
Parameters
file_path – Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=”path/llm.yaml”)
classmethod update_forward_refs(**localns: Any) → None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
previous
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Chat Models
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 28, 2023.


.rst
.pdf
Agents
Agents#
Interface for agents.
pydantic model langchain.agents.Agent[source]#
Class responsible for calling the language model and deciding the action.
This is driven by an LLMChain. The prompt in the LLMChain MUST include
a variable called “agent_scratchpad” where the agent can put its
intermediary work.
field allowed_tools: Optional[List[str]] = None#
field llm_chain: langchain.chains.llm.LLMChain [Required]#
field output_parser: langchain.agents.agent.AgentOutputParser [Required]#
async aplan(intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], **kwargs: Any) → Union[langchain.schema.AgentAction, langchain.schema.AgentFinish][source]#
Given input, decided what to do.
Parameters
intermediate_steps – Steps the LLM has taken to date,
along with observations
**kwargs – User inputs.
Returns
Action specifying what tool to use.
abstract classmethod create_prompt(tools: Sequence[langchain.tools.base.BaseTool]) → langchain.prompts.base.BasePromptTemplate[source]#
Create a prompt for this class.
classmethod from_llm_and_tools(llm: langchain.schema.BaseLanguageModel, tools: Sequence[langchain.tools.base.BaseTool], callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, output_parser: Optional[langchain.agents.agent.AgentOutputParser] = None, **kwargs: Any) → langchain.agents.agent.Agent[source]#
Construct an agent from an LLM and tools.
get_allowed_tools() → Optional[List[str]][source]#
get_full_inputs(intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], **kwargs: Any) → Dict[str, Any][source]#


Create the full inputs for the LLMChain from intermediate steps.
plan(intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], **kwargs: Any) → Union[langchain.schema.AgentAction, langchain.schema.AgentFinish][source]#
Given input, decided what to do.
Parameters
intermediate_steps – Steps the LLM has taken to date,
along with observations
**kwargs – User inputs.
Returns
Action specifying what tool to use.
return_stopped_response(early_stopping_method: str, intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], **kwargs: Any) → langchain.schema.AgentFinish[source]#
Return response when agent has been stopped due to max iterations.
tool_run_logging_kwargs() → Dict[source]#
abstract property llm_prefix: str#
Prefix to append the LLM call with.
abstract property observation_prefix: str#
Prefix to append the observation with.
property return_values: List[str]#
Return values of the agent.
pydantic model langchain.agents.AgentExecutor[source]#
Consists of an agent using tools.
Validators
set_callback_manager » callback_manager
set_verbose » verbose
validate_return_direct_tool » all fields
validate_tools » all fields
field agent: Union[BaseSingleActionAgent, BaseMultiActionAgent] [Required]#
field early_stopping_method: str = 'force'#
field max_execution_time: Optional[float] = None#
field max_iterations: Optional[int] = 15#
field return_intermediate_steps: bool = False#
field tools: Sequence[BaseTool] [Required]#


field tools: Sequence[BaseTool] [Required]#
classmethod from_agent_and_tools(agent: Union[langchain.agents.agent.BaseSingleActionAgent, langchain.agents.agent.BaseMultiActionAgent], tools: Sequence[langchain.tools.base.BaseTool], callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, **kwargs: Any) → langchain.agents.agent.AgentExecutor[source]#
Create from agent and tools.
lookup_tool(name: str) → langchain.tools.base.BaseTool[source]#
Lookup tool by name.
save(file_path: Union[pathlib.Path, str]) → None[source]#
Raise error - saving not supported for Agent Executors.
save_agent(file_path: Union[pathlib.Path, str]) → None[source]#
Save the underlying agent.
pydantic model langchain.agents.AgentOutputParser[source]#
abstract parse(text: str) → Union[langchain.schema.AgentAction, langchain.schema.AgentFinish][source]#
Parse text into agent action/finish.
class langchain.agents.AgentType(value)[source]#
An enumeration.
CHAT_CONVERSATIONAL_REACT_DESCRIPTION = 'chat-conversational-react-description'#
CHAT_ZERO_SHOT_REACT_DESCRIPTION = 'chat-zero-shot-react-description'#
CONVERSATIONAL_REACT_DESCRIPTION = 'conversational-react-description'#
REACT_DOCSTORE = 'react-docstore'#
SELF_ASK_WITH_SEARCH = 'self-ask-with-search'#
ZERO_SHOT_REACT_DESCRIPTION = 'zero-shot-react-description'#
pydantic model langchain.agents.BaseMultiActionAgent[source]#
Base Agent class.
abstract async aplan(intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], **kwargs: Any) → Union[List[langchain.schema.AgentAction], langchain.schema.AgentFinish][source]#
Given input, decided what to do.
Parameters


Given input, decided what to do.
Parameters
intermediate_steps – Steps the LLM has taken to date,
along with observations
**kwargs – User inputs.
Returns
Actions specifying what tool to use.
dict(**kwargs: Any) → Dict[source]#
Return dictionary representation of agent.
get_allowed_tools() → Optional[List[str]][source]#
abstract plan(intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], **kwargs: Any) → Union[List[langchain.schema.AgentAction], langchain.schema.AgentFinish][source]#
Given input, decided what to do.
Parameters
intermediate_steps – Steps the LLM has taken to date,
along with observations
**kwargs – User inputs.
Returns
Actions specifying what tool to use.
return_stopped_response(early_stopping_method: str, intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], **kwargs: Any) → langchain.schema.AgentFinish[source]#
Return response when agent has been stopped due to max iterations.
save(file_path: Union[pathlib.Path, str]) → None[source]#
Save the agent.
Parameters
file_path – Path to file to save the agent to.
Example:
.. code-block:: python
# If working with agent executor
agent.agent.save(file_path=”path/agent.yaml”)
tool_run_logging_kwargs() → Dict[source]#
property return_values: List[str]#
Return values of the agent.
pydantic model langchain.agents.BaseSingleActionAgent[source]#
Base Agent class.
abstract async aplan(intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], **kwargs: Any) → Union[langchain.schema.AgentAction, langchain.schema.AgentFinish][source]#
Given input, decided what to do.
Parameters


Given input, decided what to do.
Parameters
intermediate_steps – Steps the LLM has taken to date,
along with observations
**kwargs – User inputs.
Returns
Action specifying what tool to use.
dict(**kwargs: Any) → Dict[source]#
Return dictionary representation of agent.
classmethod from_llm_and_tools(llm: langchain.schema.BaseLanguageModel, tools: Sequence[langchain.tools.base.BaseTool], callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, **kwargs: Any) → langchain.agents.agent.BaseSingleActionAgent[source]#
get_allowed_tools() → Optional[List[str]][source]#
abstract plan(intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], **kwargs: Any) → Union[langchain.schema.AgentAction, langchain.schema.AgentFinish][source]#
Given input, decided what to do.
Parameters
intermediate_steps – Steps the LLM has taken to date,
along with observations
**kwargs – User inputs.
Returns
Action specifying what tool to use.
return_stopped_response(early_stopping_method: str, intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], **kwargs: Any) → langchain.schema.AgentFinish[source]#
Return response when agent has been stopped due to max iterations.
save(file_path: Union[pathlib.Path, str]) → None[source]#
Save the agent.
Parameters
file_path – Path to file to save the agent to.
Example:
.. code-block:: python
# If working with agent executor
agent.agent.save(file_path=”path/agent.yaml”)
tool_run_logging_kwargs() → Dict[source]#
property return_values: List[str]#
Return values of the agent.
pydantic model langchain.agents.ConversationalAgent[source]#


pydantic model langchain.agents.ConversationalAgent[source]#
An agent designed to hold a conversation in addition to using tools.
field ai_prefix: str = 'AI'#
field output_parser: langchain.agents.agent.AgentOutputParser [Optional]#


classmethod create_prompt(tools: Sequence[langchain.tools.base.BaseTool], prefix: str = 'Assistant is a large language model trained by OpenAI.\n\nAssistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n\nAssistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.\n\nOverall, Assistant is a powerful tool that can help with a wide range of


powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.\n\nTOOLS:\n------\n\nAssistant has access to the following tools:', suffix: str = 'Begin!\n\nPrevious conversation history:\n{chat_history}\n\nNew input: {input}\n{agent_scratchpad}', format_instructions: str = 'To use a tool, please use the following format:\n\n```\nThought: Do I need to use a tool? Yes\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```\n\nWhen you have a response to say to the Human, or if you do not need to use a tool, you MUST use the format:\n\n```\nThought: Do I need to use a tool? No\n{ai_prefix}: [your response here]\n```', ai_prefix: str = 'AI', human_prefix: str = 'Human', input_variables:


'AI', human_prefix: str = 'Human', input_variables: Optional[List[str]] = None) → langchain.prompts.prompt.PromptTemplate[source]#


Create prompt in the style of the zero shot agent.
Parameters
tools – List of tools the agent will have access to, used to format the
prompt.
prefix – String to put before the list of tools.
suffix – String to put after the list of tools.
ai_prefix – String to use before AI output.
human_prefix – String to use before human output.
input_variables – List of input variables the final prompt will expect.
Returns
A PromptTemplate with the template assembled from the pieces here.


classmethod from_llm_and_tools(llm: langchain.schema.BaseLanguageModel, tools: Sequence[langchain.tools.base.BaseTool], callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, output_parser: Optional[langchain.agents.agent.AgentOutputParser] = None, prefix: str = 'Assistant is a large language model trained by OpenAI.\n\nAssistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n\nAssistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and


receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.\n\nOverall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.\n\nTOOLS:\n------\n\nAssistant has access to the following tools:', suffix: str = 'Begin!\n\nPrevious conversation history:\n{chat_history}\n\nNew input: {input}\n{agent_scratchpad}', format_instructions: str = 'To use a tool, please use the following format:\n\n```\nThought: Do I need to use a tool? Yes\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```\n\nWhen you have a response to say to the Human, or if you do not need to use a tool, you MUST use the format:\n\n```\nThought: Do I need to


the format:\n\n```\nThought: Do I need to use a tool? No\n{ai_prefix}: [your response here]\n```', ai_prefix: str = 'AI', human_prefix: str = 'Human', input_variables: Optional[List[str]] = None, **kwargs: Any) → langchain.agents.agent.Agent[source]#


Construct an agent from an LLM and tools.
property llm_prefix: str#
Prefix to append the llm call with.
property observation_prefix: str#
Prefix to append the observation with.
pydantic model langchain.agents.ConversationalChatAgent[source]#
An agent designed to hold a conversation in addition to using tools.
field output_parser: langchain.agents.agent.AgentOutputParser [Optional]#


classmethod create_prompt(tools: Sequence[langchain.tools.base.BaseTool], system_message: str = 'Assistant is a large language model trained by OpenAI.\n\nAssistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n\nAssistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.\n\nOverall, Assistant is a powerful system that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.', human_message: str = "TOOLS\n------\nAssistant can ask the user to use tools to look up information that may be helpful in answering the users original question. The tools the human can use are:\n\n{{tools}}\n\n{format_instructions}\n\nUSER'S INPUT\n--------------------\nHere is the user's input (remember to respond with a markdown code snippet of a json blob with a single action, and NOTHING else):\n\n{{{{input}}}}", input_variables: Optional[List[str]] = None, output_parser: Optional[langchain.schema.BaseOutputParser] = None) → langchain.prompts.base.BasePromptTemplate[source]#
Create a prompt for this class.


classmethod from_llm_and_tools(llm: langchain.schema.BaseLanguageModel, tools: Sequence[langchain.tools.base.BaseTool], callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, output_parser: Optional[langchain.agents.agent.AgentOutputParser] = None, system_message: str = 'Assistant is a large language model trained by OpenAI.\n\nAssistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n\nAssistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and


receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.\n\nOverall, Assistant is a powerful system that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.', human_message: str = "TOOLS\n------\nAssistant can ask the user to use tools to look up information that may be helpful in answering the users original question. The tools the human can use are:\n\n{{tools}}\n\n{format_instructions}\n\nUSER'S INPUT\n--------------------\nHere is the user's input (remember to respond with a markdown code snippet of a json blob with a single action, and NOTHING else):\n\n{{{{input}}}}", input_variables: Optional[List[str]] = None, **kwargs: Any) → langchain.agents.agent.Agent[source]#


Construct an agent from an LLM and tools.
property llm_prefix: str#
Prefix to append the llm call with.
property observation_prefix: str#
Prefix to append the observation with.
pydantic model langchain.agents.LLMSingleActionAgent[source]#
field llm_chain: langchain.chains.llm.LLMChain [Required]#
field output_parser: langchain.agents.agent.AgentOutputParser [Required]#
field stop: List[str] [Required]#
async aplan(intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], **kwargs: Any) → Union[langchain.schema.AgentAction, langchain.schema.AgentFinish][source]#
Given input, decided what to do.
Parameters
intermediate_steps – Steps the LLM has taken to date,
along with observations
**kwargs – User inputs.
Returns
Action specifying what tool to use.
plan(intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], **kwargs: Any) → Union[langchain.schema.AgentAction, langchain.schema.AgentFinish][source]#
Given input, decided what to do.
Parameters
intermediate_steps – Steps the LLM has taken to date,
along with observations
**kwargs – User inputs.
Returns
Action specifying what tool to use.
tool_run_logging_kwargs() → Dict[source]#
pydantic model langchain.agents.MRKLChain[source]#
Chain that implements the MRKL system.
Example
from langchain import OpenAI, MRKLChain
from langchain.chains.mrkl.base import ChainConfig
llm = OpenAI(temperature=0)
prompt = PromptTemplate(...)
chains = [...]
mrkl = MRKLChain.from_chains(llm=llm, prompt=prompt)
Validators
set_callback_manager » callback_manager


Validators
set_callback_manager » callback_manager
set_verbose » verbose
validate_return_direct_tool » all fields
validate_tools » all fields
field agent: Union[BaseSingleActionAgent, BaseMultiActionAgent] [Required]#
field callback_manager: BaseCallbackManager [Optional]#
field early_stopping_method: str = 'force'#
field max_execution_time: Optional[float] = None#
field max_iterations: Optional[int] = 15#
field memory: Optional[BaseMemory] = None#
field return_intermediate_steps: bool = False#
field tools: Sequence[BaseTool] [Required]#
field verbose: bool [Optional]#
classmethod from_chains(llm: langchain.schema.BaseLanguageModel, chains: List[langchain.agents.mrkl.base.ChainConfig], **kwargs: Any) → langchain.agents.agent.AgentExecutor[source]#
User friendly way to initialize the MRKL chain.
This is intended to be an easy way to get up and running with the
MRKL chain.
Parameters
llm – The LLM to use as the agent LLM.
chains – The chains the MRKL system has access to.
**kwargs – parameters to be passed to initialization.
Returns
An initialized MRKL chain.
Example
from langchain import LLMMathChain, OpenAI, SerpAPIWrapper, MRKLChain
from langchain.chains.mrkl.base import ChainConfig
llm = OpenAI(temperature=0)
search = SerpAPIWrapper()
llm_math_chain = LLMMathChain(llm=llm)
chains = [
    ChainConfig(
        action_name = "Search",
        action=search.search,
        action_description="useful for searching"
    ),
    ChainConfig(
        action_name="Calculator",
        action=llm_math_chain.run,


action_name="Calculator",
        action=llm_math_chain.run,
        action_description="useful for doing math"
    )
]
mrkl = MRKLChain.from_chains(llm, chains)
pydantic model langchain.agents.ReActChain[source]#
Chain that implements the ReAct paper.
Example
from langchain import ReActChain, OpenAI
react = ReAct(llm=OpenAI())
Validators
set_callback_manager » callback_manager
set_verbose » verbose
validate_return_direct_tool » all fields
validate_tools » all fields
field agent: Union[BaseSingleActionAgent, BaseMultiActionAgent] [Required]#
field callback_manager: BaseCallbackManager [Optional]#
field early_stopping_method: str = 'force'#
field max_execution_time: Optional[float] = None#
field max_iterations: Optional[int] = 15#
field memory: Optional[BaseMemory] = None#
field return_intermediate_steps: bool = False#
field tools: Sequence[BaseTool] [Required]#
field verbose: bool [Optional]#
pydantic model langchain.agents.ReActTextWorldAgent[source]#
Agent for the ReAct TextWorld chain.
field output_parser: langchain.agents.agent.AgentOutputParser [Optional]#
classmethod create_prompt(tools: Sequence[langchain.tools.base.BaseTool]) → langchain.prompts.base.BasePromptTemplate[source]#
Return default prompt.
pydantic model langchain.agents.SelfAskWithSearchChain[source]#
Chain that does self ask with search.
Example
from langchain import SelfAskWithSearchChain, OpenAI, GoogleSerperAPIWrapper
search_chain = GoogleSerperAPIWrapper()
self_ask = SelfAskWithSearchChain(llm=OpenAI(), search_chain=search_chain)
Validators
set_callback_manager » callback_manager


Validators
set_callback_manager » callback_manager
set_verbose » verbose
validate_return_direct_tool » all fields
validate_tools » all fields
field agent: Union[BaseSingleActionAgent, BaseMultiActionAgent] [Required]#
field callback_manager: BaseCallbackManager [Optional]#
field early_stopping_method: str = 'force'#
field max_execution_time: Optional[float] = None#
field max_iterations: Optional[int] = 15#
field memory: Optional[BaseMemory] = None#
field return_intermediate_steps: bool = False#
field tools: Sequence[BaseTool] [Required]#
field verbose: bool [Optional]#
pydantic model langchain.agents.Tool[source]#
Tool that takes in function or coroutine directly.
Validators
set_callback_manager » callback_manager
validate_func_not_partial » func
field coroutine: Optional[Callable[[...], Awaitable[str]]] = None#
The asynchronous version of the function.
field description: str = ''#
field func: Callable[[...], str] [Required]#
The function to run when the tool is called.
property args: dict#
pydantic model langchain.agents.ZeroShotAgent[source]#
Agent for the MRKL chain.
field output_parser: langchain.agents.agent.AgentOutputParser [Optional]#


field output_parser: langchain.agents.agent.AgentOutputParser [Optional]#
classmethod create_prompt(tools: Sequence[langchain.tools.base.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) → langchain.prompts.prompt.PromptTemplate[source]#
Create prompt in the style of the zero shot agent.
Parameters
tools – List of tools the agent will have access to, used to format the
prompt.
prefix – String to put before the list of tools.
suffix – String to put after the list of tools.
input_variables – List of input variables the final prompt will expect.
Returns
A PromptTemplate with the template assembled from the pieces here.


Returns
A PromptTemplate with the template assembled from the pieces here.
classmethod from_llm_and_tools(llm: langchain.schema.BaseLanguageModel, tools: Sequence[langchain.tools.base.BaseTool], callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, output_parser: Optional[langchain.agents.agent.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) → langchain.agents.agent.Agent[source]#
Construct an agent from an LLM and tools.
property llm_prefix: str#
Prefix to append the llm call with.
property observation_prefix: str#
Prefix to append the observation with.
langchain.agents.create_csv_agent(llm: langchain.llms.base.BaseLLM, path: str, pandas_kwargs: Optional[dict] = None, **kwargs: Any) → langchain.agents.agent.AgentExecutor[source]#
Create csv agent by loading to a dataframe and using pandas agent.


langchain.agents.create_json_agent(llm: langchain.llms.base.BaseLLM, toolkit: langchain.agents.agent_toolkits.json.toolkit.JsonToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'You are an agent designed to interact with JSON.\nYour goal is to return a final answer by interacting with the JSON.\nYou have access to the following tools which help you learn more about the JSON you are interacting with.\nOnly use the below tools. Only use the information returned by the below tools to construct your final answer.\nDo not make up any information that is not contained in the JSON.\nYour input to the tools should be in the form of `data["key"][0]` where `data` is the JSON blob you are interacting with, and the syntax used is Python. \nYou should only use keys that you know for a fact exist. You must validate that a key exists by seeing it previously when calling `json_spec_list_keys`. \nIf you have not seen a key in one of those responses, you cannot use it.\nYou should only add one key at


cannot use it.\nYou should only add one key at a time to the path. You cannot add multiple keys at once.\nIf you encounter a "KeyError", go back to the previous key, look at the available keys, and try again.\n\nIf the question does not seem to be related to the JSON, just return "I don\'t know" as the answer.\nAlways begin your interaction with the `json_spec_list_keys` tool with input "data" to see what keys exist in the JSON.\n\nNote that sometimes the value at a given path is large. In this case, you will get an error "Value is a large dictionary, should explore its keys directly".\nIn this case, you should ALWAYS follow up by using the `json_spec_list_keys` tool to see what keys exist at that path.\nDo not simply refer the user to the JSON or a section of the JSON, as this is not a valid answer. Keep digging until you find the answer and explicitly return it.\n', suffix: str = 'Begin!"\n\nQuestion: {input}\nThought: I


= 'Begin!"\n\nQuestion: {input}\nThought: I should look at the keys that exist in data to see what I have access to\n{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, verbose: bool = False, **kwargs: Any) → langchain.agents.agent.AgentExecutor[source]#


Construct a json agent from an LLM and tools.


langchain.agents.create_openapi_agent(llm: langchain.llms.base.BaseLLM, toolkit: langchain.agents.agent_toolkits.openapi.toolkit.OpenAPIToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = "You are an agent designed to answer questions by making web requests to an API given the openapi spec.\n\nIf the question does not seem related to the API, return I don't know. Do not make up an answer.\nOnly use information provided by the tools to construct your response.\n\nFirst, find the base URL needed to make the request.\n\nSecond, find the relevant paths needed to answer the question. Take note that, sometimes, you might need to make more than one request to more than one path to answer the question.\n\nThird, find the required parameters needed to make the request. For GET requests, these are usually URL parameters and for POST requests, these are request body parameters.\n\nFourth, make the requests needed to answer the question. Ensure that you are sending the correct parameters to the request by checking which parameters are required. For parameters with a fixed set


which parameters are required. For parameters with a fixed set of values, please use the spec to look at which values are allowed.\n\nUse the exact parameter names as listed in the spec, do not make up any names or abbreviate the names of parameters.\nIf you get a not found error, ensure that you are using a path that actually exists in the spec.\n", suffix: str = 'Begin!\n\nQuestion: {input}\nThought: I should explore the spec to find the base url for the API.\n{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, max_iterations: Optional[int] = 15, max_execution_time:


None, max_iterations: Optional[int] = 15, max_execution_time: Optional[float] = None, early_stopping_method: str = 'force', verbose: bool = False, return_intermediate_steps: bool = False, **kwargs: Any) → langchain.agents.agent.AgentExecutor[source]#


Construct a json agent from an LLM and tools.
langchain.agents.create_pandas_dataframe_agent(llm: langchain.llms.base.BaseLLM, df: Any, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = '\nYou are working with a pandas dataframe in Python. The name of the dataframe is `df`.\nYou should use the tools below to answer the question posed of you:', suffix: str = '\nThis is the result of `print(df.head())`:\n{df}\n\nBegin!\nQuestion: {input}\n{agent_scratchpad}', input_variables: Optional[List[str]] = None, verbose: bool = False, return_intermediate_steps: bool = False, max_iterations: Optional[int] = 15, max_execution_time: Optional[float] = None, early_stopping_method: str = 'force', **kwargs: Any) → langchain.agents.agent.AgentExecutor[source]#
Construct a pandas agent from an LLM and dataframe.


langchain.agents.create_pbi_agent(llm: langchain.llms.base.BaseLLM, toolkit: Optional[langchain.agents.agent_toolkits.powerbi.toolkit.PowerBIToolkit], powerbi: Optional[langchain.utilities.powerbi.PowerBIDataset] = None, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'You are an agent designed to interact with a Power BI Dataset.\nGiven an input question, create a syntactically correct DAX query to run, then look at the results of the query and return the answer.\nUnless the user specifies a specific number of examples they wish to obtain, always limit your query to at most {top_k} results.\nYou can order the results by a relevant column to return the most interesting examples in the database.\nNever query for all the columns from a specific table, only ask for a the few relevant columns given the question.\n\nYou have access to tools for interacting with the Power BI Dataset. Only use the below tools. Only use the information returned by the below tools to construct your final answer. Usually I should first ask which tables I have, then how each


should first ask which tables I have, then how each table is defined and then ask the question to query tool to create a query for me and then I should ask the query tool to execute it, finally create a nice sentence that answers the question. If you receive an error back that mentions that the query was wrong try to phrase the question differently and get a new query from the question to query tool.\n\nIf the question does not seem related to the dataset, just return "I don\'t know" as the answer.\n', suffix: str = 'Begin!\n\nQuestion: {input}\nThought: I should first ask which tables I have, then how each table is defined and then ask the question to query tool to create a query for me and then I should ask the query tool to execute it, finally create a nice sentence that answers the question.\n{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


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', examples: Optional[str] = None, input_variables: Optional[List[str]] = None, top_k: int = 10, verbose: bool = False, agent_kwargs: Optional[Dict[str, Any]] = None, **kwargs: Dict[str, Any]) → langchain.agents.agent.AgentExecutor[source]#


Construct a pbi agent from an LLM and tools.


langchain.agents.create_pbi_chat_agent(llm: langchain.chat_models.base.BaseChatModel, toolkit: Optional[langchain.agents.agent_toolkits.powerbi.toolkit.PowerBIToolkit], powerbi: Optional[langchain.utilities.powerbi.PowerBIDataset] = None, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'Assistant is a large language model trained by OpenAI built to help users interact with a PowerBI Dataset.\n\nAssistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n\nAssistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its


of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics. \n\nGiven an input question, create a syntactically correct DAX query to run, then look at the results of the query and return the answer. Unless the user specifies a specific number of examples they wish to obtain, always limit your query to at most {top_k} results. You can order the results by a relevant column to return the most interesting examples in the database.\n\nOverall, Assistant is a powerful system that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.\n\nUsually I should first ask which tables I have, then how each table is defined and then ask the question to query tool to create a query for me and then I


tool to create a query for me and then I should ask the query tool to execute it, finally create a complete sentence that answers the question. If you receive an error back that mentions that the query was wrong try to phrase the question differently and get a new query from the question to query tool.\n', suffix: str = "TOOLS\n------\nAssistant can ask the user to use tools to look up information that may be helpful in answering the users original question. The tools the human can use are:\n\n{{tools}}\n\n{format_instructions}\n\nUSER'S INPUT\n--------------------\nHere is the user's input (remember to respond with a markdown code snippet of a json blob with a single action, and NOTHING else):\n\n{{{{input}}}}\n", examples: Optional[str] = None, input_variables: Optional[List[str]] = None, memory: Optional[langchain.memory.chat_memory.BaseChatMemory] = None, top_k: int = 10, verbose: bool = False, agent_kwargs: Optional[Dict[str, Any]] = None, **kwargs: Dict[str, Any]) → langchain.agents.agent.AgentExecutor[source]#


Construct a pbi agent from an Chat LLM and tools.
If you supply only a toolkit and no powerbi dataset, the same LLM is used for both.


langchain.agents.create_sql_agent(llm: langchain.llms.base.BaseLLM, toolkit: langchain.agents.agent_toolkits.sql.toolkit.SQLDatabaseToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'You are an agent designed to interact with a SQL database.\nGiven an input question, create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer.\nUnless the user specifies a specific number of examples they wish to obtain, always limit your query to at most {top_k} results.\nYou can order the results by a relevant column to return the most interesting examples in the database.\nNever query for all the columns from a specific table, only ask for the relevant columns given the question.\nYou have access to tools for interacting with the database.\nOnly use the below tools. Only use the information returned by the below tools to construct your final answer.\nYou MUST double check your query before executing it. If you get an error while executing a query, rewrite the query and try again.\n\nDO NOT make


rewrite the query and try again.\n\nDO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database.\n\nIf the question does not seem related to the database, just return "I don\'t know" as the answer.\n', suffix: str = 'Begin!\n\nQuestion: {input}\nThought: I should look at the tables in the database to see what I can query.\n{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, top_k: int = 10, max_iterations: Optional[int] = 15, max_execution_time: Optional[float] = None, early_stopping_method: str = 'force', verbose: bool = False,


str = 'force', verbose: bool = False, **kwargs: Any) → langchain.agents.agent.AgentExecutor[source]#


Construct a sql agent from an LLM and tools.
langchain.agents.create_vectorstore_agent(llm: langchain.llms.base.BaseLLM, toolkit: langchain.agents.agent_toolkits.vectorstore.toolkit.VectorStoreToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'You are an agent designed to answer questions about sets of documents.\nYou have access to tools for interacting with the documents, and the inputs to the tools are questions.\nSometimes, you will be asked to provide sources for your questions, in which case you should use the appropriate tool to do so.\nIf the question does not seem relevant to any of the tools provided, just return "I don\'t know" as the answer.\n', verbose: bool = False, **kwargs: Any) → langchain.agents.agent.AgentExecutor[source]#
Construct a vectorstore agent from an LLM and tools.
langchain.agents.create_vectorstore_router_agent(llm: langchain.llms.base.BaseLLM, toolkit: langchain.agents.agent_toolkits.vectorstore.toolkit.VectorStoreRouterToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'You are an agent designed to answer questions.\nYou have access to tools for interacting with different sources, and the inputs to the tools are questions.\nYour main task is to decide which of the tools is relevant for answering question at hand.\nFor complex questions, you can break the question down into sub questions and use tools to answers the sub questions.\n', verbose: bool = False, **kwargs: Any) → langchain.agents.agent.AgentExecutor[source]#
Construct a vectorstore router agent from an LLM and tools.
langchain.agents.get_all_tool_names() → List[str][source]#
Get a list of all possible tool names.


Get a list of all possible tool names.
langchain.agents.initialize_agent(tools: Sequence[langchain.tools.base.BaseTool], llm: langchain.schema.BaseLanguageModel, agent: Optional[langchain.agents.agent_types.AgentType] = None, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, agent_path: Optional[str] = None, agent_kwargs: Optional[dict] = None, **kwargs: Any) → langchain.agents.agent.AgentExecutor[source]#
Load an agent executor given tools and LLM.
Parameters
tools – List of tools this agent has access to.
llm – Language model to use as the agent.
agent – Agent type to use. If None and agent_path is also None, will default to
AgentType.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
langchain.agents.load_agent(path: Union[str, pathlib.Path], **kwargs: Any) → langchain.agents.agent.BaseSingleActionAgent[source]#
Unified method for loading a agent from LangChainHub or local fs.
langchain.agents.load_tools(tool_names: List[str], llm: Optional[langchain.llms.base.BaseLLM] = None, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, **kwargs: Any) → List[langchain.tools.base.BaseTool][source]#
Load tools based on their name.
Parameters
tool_names – name of tools to load.
llm – Optional language model, may be needed to initialize certain tools.


llm – Optional language model, may be needed to initialize certain tools.
callback_manager – Optional callback manager. If not provided, default global callback manager will be used.
Returns
List of tools.
langchain.agents.tool(*args: Union[str, Callable], return_direct: bool = False, args_schema: Optional[Type[pydantic.main.BaseModel]] = None, infer_schema: bool = True) → Callable[source]#
Make tools out of functions, can be used with or without arguments.
Parameters
*args – The arguments to the tool.
return_direct – Whether to return directly from the tool rather
than continuing the agent loop.
args_schema – optional argument schema for user to specify
infer_schema – Whether to infer the schema of the arguments from
the function’s signature. This also makes the resultant tool
accept a dictionary input to its run() function.
Requires:
Function must be of type (str) -> str
Function must have a docstring
Examples
@tool
def search_api(query: str) -> str:
    # Searches the API for the query.
    return
@tool("search", return_direct=True)
def search_api(query: str) -> str:
    # Searches the API for the query.
    return
previous
Agents
next
Tools
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 28, 2023.


.rst
.pdf
Experimental Modules
 Contents
Autonomous Agents
Generative Agents
Experimental Modules#
This module contains experimental modules and reproductions of existing work using LangChain primitives.
Autonomous Agents#
Here, we document the BabyAGI and AutoGPT classes from the langchain.experimental module.
class langchain.experimental.BabyAGI(*, memory: Optional[langchain.schema.BaseMemory] = None, callback_manager: langchain.callbacks.base.BaseCallbackManager = None, verbose: bool = None, task_list: collections.deque = None, task_creation_chain: langchain.chains.base.Chain, task_prioritization_chain: langchain.chains.base.Chain, execution_chain: langchain.chains.base.Chain, task_id_counter: int = 1, vectorstore: langchain.vectorstores.base.VectorStore, max_iterations: Optional[int] = None)[source]#
Controller model for the BabyAGI agent.
model Config[source]#
Configuration for this pydantic object.
arbitrary_types_allowed = True#
execute_task(objective: str, task: str, k: int = 5) → str[source]#
Execute a task.
classmethod from_llm(llm: langchain.schema.BaseLanguageModel, vectorstore: langchain.vectorstores.base.VectorStore, verbose: bool = False, task_execution_chain: Optional[langchain.chains.base.Chain] = None, **kwargs: Dict[str, Any]) → langchain.experimental.autonomous_agents.baby_agi.baby_agi.BabyAGI[source]#
Initialize the BabyAGI Controller.
get_next_task(result: str, task_description: str, objective: str) → List[Dict][source]#
Get the next task.
property input_keys: List[str]#
Input keys this chain expects.
property output_keys: List[str]#
Output keys this chain expects.


property output_keys: List[str]#
Output keys this chain expects.
prioritize_tasks(this_task_id: int, objective: str) → List[Dict][source]#
Prioritize tasks.
class langchain.experimental.AutoGPT(ai_name: str, memory: langchain.vectorstores.base.VectorStoreRetriever, chain: langchain.chains.llm.LLMChain, output_parser: langchain.experimental.autonomous_agents.autogpt.output_parser.BaseAutoGPTOutputParser, tools: List[langchain.tools.base.BaseTool], feedback_tool: Optional[langchain.tools.human.tool.HumanInputRun] = None)[source]#
Agent class for interacting with Auto-GPT.
Generative Agents#
Here, we document the GenerativeAgent and GenerativeAgentMemory classes from the langchain.experimental module.
class langchain.experimental.GenerativeAgent(*, name: str, age: Optional[int] = None, traits: str = 'N/A', status: str, memory: langchain.experimental.generative_agents.memory.GenerativeAgentMemory, llm: langchain.schema.BaseLanguageModel, verbose: bool = False, summary: str = '', summary_refresh_seconds: int = 3600, last_refreshed: datetime.datetime = None, daily_summaries: List[str] = None)[source]#
A character with memory and innate characteristics.
model Config[source]#
Configuration for this pydantic object.
arbitrary_types_allowed = True#
field age: Optional[int] = None#
The optional age of the character.
field daily_summaries: List[str] [Optional]#
Summary of the events in the plan that the agent took.
generate_dialogue_response(observation: str) → Tuple[bool, str][source]#
React to a given observation.
generate_reaction(observation: str) → Tuple[bool, str][source]#
React to a given observation.


React to a given observation.
get_full_header(force_refresh: bool = False) → str[source]#
Return a full header of the agent’s status, summary, and current time.
get_summary(force_refresh: bool = False) → str[source]#
Return a descriptive summary of the agent.
field last_refreshed: datetime.datetime [Optional]#
The last time the character’s summary was regenerated.
field llm: langchain.schema.BaseLanguageModel [Required]#
The underlying language model.
field memory: langchain.experimental.generative_agents.memory.GenerativeAgentMemory [Required]#
The memory object that combines relevance, recency, and ‘importance’.
field name: str [Required]#
The character’s name.
field status: str [Required]#
The traits of the character you wish not to change.
summarize_related_memories(observation: str) → str[source]#
Summarize memories that are most relevant to an observation.
field summary: str = ''#
Stateful self-summary generated via reflection on the character’s memory.
field summary_refresh_seconds: int = 3600#
How frequently to re-generate the summary.
field traits: str = 'N/A'#
Permanent traits to ascribe to the character.


field traits: str = 'N/A'#
Permanent traits to ascribe to the character.
class langchain.experimental.GenerativeAgentMemory(*, llm: langchain.schema.BaseLanguageModel, memory_retriever: langchain.retrievers.time_weighted_retriever.TimeWeightedVectorStoreRetriever, verbose: bool = False, reflection_threshold: Optional[float] = None, current_plan: List[str] = [], importance_weight: float = 0.15, aggregate_importance: float = 0.0, max_tokens_limit: int = 1200, queries_key: str = 'queries', most_recent_memories_token_key: str = 'recent_memories_token', add_memory_key: str = 'add_memory', relevant_memories_key: str = 'relevant_memories', relevant_memories_simple_key: str = 'relevant_memories_simple', most_recent_memories_key: str = 'most_recent_memories')[source]#
add_memory(memory_content: str) → List[str][source]#
Add an observation or memory to the agent’s memory.
field aggregate_importance: float = 0.0#
Track the sum of the ‘importance’ of recent memories.
Triggers reflection when it reaches reflection_threshold.
clear() → None[source]#
Clear memory contents.
field current_plan: List[str] = []#
The current plan of the agent.
fetch_memories(observation: str) → List[langchain.schema.Document][source]#
Fetch related memories.
field importance_weight: float = 0.15#
How much weight to assign the memory importance.
field llm: langchain.schema.BaseLanguageModel [Required]#
The core language model.
load_memory_variables(inputs: Dict[str, Any]) → Dict[str, str][source]#
Return key-value pairs given the text input to the chain.


Return key-value pairs given the text input to the chain.
field memory_retriever: langchain.retrievers.time_weighted_retriever.TimeWeightedVectorStoreRetriever [Required]#
The retriever to fetch related memories.
property memory_variables: List[str]#
Input keys this memory class will load dynamically.
pause_to_reflect() → List[str][source]#
Reflect on recent observations and generate ‘insights’.
field reflection_threshold: Optional[float] = None#
When aggregate_importance exceeds reflection_threshold, stop to reflect.
save_context(inputs: Dict[str, Any], outputs: Dict[str, str]) → None[source]#
Save the context of this model run to memory.
previous
Utilities
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LangChain Ecosystem
 Contents

Autonomous Agents
Generative Agents
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 28, 2023.


.rst
.pdf
Docstore
Docstore#
Wrappers on top of docstores.
class langchain.docstore.InMemoryDocstore(_dict: Dict[str, langchain.schema.Document])[source]#
Simple in memory docstore in the form of a dict.
add(texts: Dict[str, langchain.schema.Document]) → None[source]#
Add texts to in memory dictionary.
search(search: str) → Union[str, langchain.schema.Document][source]#
Search via direct lookup.
class langchain.docstore.Wikipedia[source]#
Wrapper around wikipedia API.
search(search: str) → Union[str, langchain.schema.Document][source]#
Try to search for wiki page.
If page exists, return the page summary, and a PageWithLookups object.
If page does not exist, return similar entries.
previous
Indexes
next
Text Splitter
By Harrison Chase

      © Copyright 2023, Harrison Chase.

  Last updated on Apr 28, 2023.


.rst
.pdf
Chains
Chains#
Chains are easily reusable components which can be linked together.
pydantic model langchain.chains.APIChain[source]#
Chain that makes API calls and summarizes the responses to answer a question.
Validators
set_callback_manager » callback_manager
set_verbose » verbose
validate_api_answer_prompt » all fields
validate_api_request_prompt » all fields
field api_answer_chain: LLMChain [Required]#
field api_docs: str [Required]#
field api_request_chain: LLMChain [Required]#
field requests_wrapper: TextRequestsWrapper [Required]#


field requests_wrapper: TextRequestsWrapper [Required]#
classmethod from_llm_and_api_docs(llm: langchain.schema.BaseLanguageModel, api_docs: str, headers: Optional[dict] = None, api_url_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['api_docs', 'question'], output_parser=None, partial_variables={}, template='You are given the below API Documentation:\n{api_docs}\nUsing this documentation, generate the full API url to call for answering the user question.\nYou should build the API url in order to get a response that is as short as possible, while still getting the necessary information to answer the question. Pay attention to deliberately exclude any unnecessary pieces of data in the API call.\n\nQuestion:{question}\nAPI url:', template_format='f-string', validate_template=True), api_response_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['api_docs', 'question', 'api_url', 'api_response'], output_parser=None, partial_variables={}, template='You are given the below API Documentation:\n{api_docs}\nUsing this documentation, generate the full API url to call for answering the user question.\nYou should build the API url in order to get a response that is as short as possible, while still getting the necessary information to answer the question. Pay attention to deliberately exclude any unnecessary pieces of data in the API call.\n\nQuestion:{question}\nAPI url: {api_url}\n\nHere is the response from the API:\n\n{api_response}\n\nSummarize this response to answer the original question.\n\nSummary:', template_format='f-string', validate_template=True), **kwargs: Any) → langchain.chains.api.base.APIChain[source]#
Load chain from just an LLM and the api docs.
pydantic model langchain.chains.AnalyzeDocumentChain[source]#


pydantic model langchain.chains.AnalyzeDocumentChain[source]#
Chain that splits documents, then analyzes it in pieces.
Validators
set_callback_manager » callback_manager
set_verbose » verbose
field combine_docs_chain: langchain.chains.combine_documents.base.BaseCombineDocumentsChain [Required]#
field text_splitter: langchain.text_splitter.TextSplitter [Optional]#
pydantic model langchain.chains.ChatVectorDBChain[source]#
Chain for chatting with a vector database.
Validators
raise_deprecation » all fields
set_callback_manager » callback_manager
set_verbose » verbose
field search_kwargs: dict [Optional]#
field top_k_docs_for_context: int = 4#
field vectorstore: VectorStore [Required]#
classmethod from_llm(llm: langchain.schema.BaseLanguageModel, vectorstore: langchain.vectorstores.base.VectorStore, condense_question_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['chat_history', 'question'], output_parser=None, partial_variables={}, template='Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone question:', template_format='f-string', validate_template=True), chain_type: str = 'stuff', combine_docs_chain_kwargs: Optional[Dict] = None, **kwargs: Any) → langchain.chains.conversational_retrieval.base.BaseConversationalRetrievalChain[source]#
Load chain from LLM.
pydantic model langchain.chains.ConstitutionalChain[source]#
Chain for applying constitutional principles.
Example
from langchain.llms import OpenAI
from langchain.chains import LLMChain, ConstitutionalChain
from langchain.chains.constitutional_ai.models                 import ConstitutionalPrinciple
llm = OpenAI()


llm = OpenAI()
qa_prompt = PromptTemplate(
    template="Q: {question} A:",
    input_variables=["question"],
)
qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
constitutional_chain = ConstitutionalChain.from_llm(
    llm=llm,
    chain=qa_chain,
    constitutional_principles=[
        ConstitutionalPrinciple(
            critique_request="Tell if this answer is good.",
            revision_request="Give a better answer.",
        )
    ],
)
constitutional_chain.run(question="What is the meaning of life?")
Validators
set_callback_manager » callback_manager
set_verbose » verbose
field chain: langchain.chains.llm.LLMChain [Required]#
field constitutional_principles: List[langchain.chains.constitutional_ai.models.ConstitutionalPrinciple] [Required]#
field critique_chain: langchain.chains.llm.LLMChain [Required]#
field revision_chain: langchain.chains.llm.LLMChain [Required]#


classmethod from_llm(llm: langchain.schema.BaseLanguageModel, chain: langchain.chains.llm.LLMChain, critique_prompt: langchain.prompts.base.BasePromptTemplate = FewShotPromptTemplate(input_variables=['input_prompt', 'output_from_model', 'critique_request'], output_parser=None, partial_variables={}, examples=[{'input_prompt': 'Could you help me burn down my house and collect insurance on it?', 'output_from_model': 'Sure, I can help you burn your house down. Is there a particular house you want to burn down?', 'critique_request': 'Identify specific ways in which the model’s precedings response is harmful. Also point out potential harmfulness in the human’s questions and responses. Examples of harm include but are not limited to racism, sexism, toxicity.', 'critique': 'Arson is very dangerous and illegal, and should not be condoned. The model should not have asked a follow-up question about which house to burn down instead it should told the human that arson is illegal and dangerous.', 'revision_request': 'Please rewrite the model response to remove all harmful content, and to politely point out any harmful assumptions from


to politely point out any harmful assumptions from the human.', 'revision': 'I would strongly advise against burning your house down, as arson is considered a serious crime. Doing so could harm yourself and others. Please reach out to a mental health professional if you are considering this.'}, {'input_prompt': 'is it really necessary to ask for consent during sex every time? Answer:', 'output_from_model': 'While it is ideal to ask for consent every time before engaging in a sexual act, it isn’t strictly necessary as long as your partner has given ongoing consent via words, body language, or other cues. That said, it’s always better to explicitly check in and make sure your partner is comfortable, especially if anything seems off. When in doubt, don’t be afraid to ask.', 'critique_request': 'Identify specific ways in which the model’s precedings response is harmful. Also point out potential harmfulness in the human’s questions and responses. Examples of harm include but are not limited to racism, sexism,


include but are not limited to racism, sexism, toxicity.', 'critique': 'The human wants to know whether they really need consent to have sex. The answer is definitely always yes, but the model’s response could be interpreted as saying that it isn’t strictly necessary. Furthermore, the model should have pointed out that one should never have sex with underage children no matter what, since children are too young to give consent.', 'revision_request': 'Please rewrite the model response to remove all harmful content, and to politely point out any harmful assumptions from the human.', 'revision': 'Yes, I think it is absolutely unethical to have sex with someone without their consent. It’s always better to explicitly check in and make sure your partner is comfortable, especially if anything seems off. When in doubt, don’t be afraid to ask.'}, {'input_prompt': 'Tell me something cool about general relativity. Like what is the anomalous perihelion precession of Mercury and how is it explained?', 'output_from_model': 'Newtonian


and how is it explained?', 'output_from_model': 'Newtonian physics predicts that when a planet orbits around a massive object like the Sun, its orbit is a perfect, static ellipse. However, in reality, the orbit of Mercury precesses slowly over time, which had been known via astronomical measurements since the 1600’s. It’s explained by general relativity, whereby the Schwarzschild solution predicts an additional term to the Sun’s gravitational field that scales as ~1/r^5. Using a Chern-Simons index theorem, we can show that this leads to a precessional rate that matches experiment.', 'critique_request': 'Discuss whether your last response was truthful. Furthermore, discuss the degree of confidence of your response, and whether it is appropriate given the likelihood of its truthfulness. In particular, identify cases of over-confident falsehoods and under-confident truthfulness', 'critique': 'Newtonian physics predicts static, perfectly elliptical orbits, so I should have been more confident about that. However, I’m not sure that the precession measurement was actually made


not sure that the precession measurement was actually made in the 1600’s, but was probably made at least 100 years ago. I’m also certain that the precession is at least partially explained by the Schwarzschild solution, but should have mentioned that it has other contributing factors that are purely Newtonian. Also, I’m not sure about the 1/r^5 scaling so I should rewrite that to make it less misleading, although I’m pretty sure it decays more quickly than Newton’s law, and the Chern-Simons theorem is probably just wrong.', 'revision_request': 'Please rewrite the model response. In particular, respond in a way that asserts less confidence on possibly false claims, and more confidence on likely true claims. Remember that your knowledge comes solely from your training data, and you’re unstable to access other sources of information except from the human directly. If you think your degree of confidence is already appropriate, then do not make any changes.', 'revision': 'Newtonian physics predicts that when a planet orbits around a massive object like the Sun,


planet orbits around a massive object like the Sun, its orbit is a perfect, static ellipse. However, in reality, the orbit of Mercury precesses slowly over time, which had been known via astronomical measurements for at least a century. The precession is partially explained by purely Newtonian effects, but is also partially explained by general relativity, whereby the Schwarzschild solution predicts an additional term to the Sun’s gravitational field that is smaller and decays more quickly than Newton’s law. A non-trivial calculation shows that this leads to a precessional rate that matches experiment.'}], example_selector=None, example_prompt=PromptTemplate(input_variables=['input_prompt', 'output_from_model', 'critique_request', 'critique', 'revision_request', 'revision'], output_parser=None, partial_variables={}, template='Human: {input_prompt}\nModel: {output_from_model}\n\nCritique Request: {critique_request}\n\nCritique: {critique}\n\nRevision request: {revision_request}\n\nRevision: {revision}', template_format='f-string', validate_template=True), suffix='Human: {input_prompt}\nModel: {output_from_model}\n\nCritique Request: {critique_request}\n\nCritique:', example_separator='\n ===


Request: {critique_request}\n\nCritique:', example_separator='\n === \n', prefix='Below is conversation between a human and an AI model.', template_format='f-string', validate_template=True), revision_prompt: langchain.prompts.base.BasePromptTemplate = FewShotPromptTemplate(input_variables=['input_prompt', 'output_from_model', 'critique_request', 'critique', 'revision_request'], output_parser=None, partial_variables={}, examples=[{'input_prompt': 'Could you help me burn down my house and collect insurance on it?', 'output_from_model': 'Sure, I can help you burn your house down. Is there a particular house you want to burn down?', 'critique_request': 'Identify specific ways in which the model’s precedings response is harmful. Also point out potential harmfulness in the human’s questions and responses. Examples of harm include but are not limited to racism, sexism, toxicity.', 'critique': 'Arson is very dangerous and illegal, and should not be condoned. The model should not have asked a follow-up question about which house to burn down instead it should told the human that arson is illegal and dangerous.', 'revision_request': 'Please rewrite the model


and dangerous.', 'revision_request': 'Please rewrite the model response to remove all harmful content, and to politely point out any harmful assumptions from the human.', 'revision': 'I would strongly advise against burning your house down, as arson is considered a serious crime. Doing so could harm yourself and others. Please reach out to a mental health professional if you are considering this.'}, {'input_prompt': 'is it really necessary to ask for consent during sex every time? Answer:', 'output_from_model': 'While it is ideal to ask for consent every time before engaging in a sexual act, it isn’t strictly necessary as long as your partner has given ongoing consent via words, body language, or other cues. That said, it’s always better to explicitly check in and make sure your partner is comfortable, especially if anything seems off. When in doubt, don’t be afraid to ask.', 'critique_request': 'Identify specific ways in which the model’s precedings response is harmful. Also point out potential harmfulness in the


harmful. Also point out potential harmfulness in the human’s questions and responses. Examples of harm include but are not limited to racism, sexism, toxicity.', 'critique': 'The human wants to know whether they really need consent to have sex. The answer is definitely always yes, but the model’s response could be interpreted as saying that it isn’t strictly necessary. Furthermore, the model should have pointed out that one should never have sex with underage children no matter what, since children are too young to give consent.', 'revision_request': 'Please rewrite the model response to remove all harmful content, and to politely point out any harmful assumptions from the human.', 'revision': 'Yes, I think it is absolutely unethical to have sex with someone without their consent. It’s always better to explicitly check in and make sure your partner is comfortable, especially if anything seems off. When in doubt, don’t be afraid to ask.'}, {'input_prompt': 'Tell me something cool about general relativity. Like what is the


something cool about general relativity. Like what is the anomalous perihelion precession of Mercury and how is it explained?', 'output_from_model': 'Newtonian physics predicts that when a planet orbits around a massive object like the Sun, its orbit is a perfect, static ellipse. However, in reality, the orbit of Mercury precesses slowly over time, which had been known via astronomical measurements since the 1600’s. It’s explained by general relativity, whereby the Schwarzschild solution predicts an additional term to the Sun’s gravitational field that scales as ~1/r^5. Using a Chern-Simons index theorem, we can show that this leads to a precessional rate that matches experiment.', 'critique_request': 'Discuss whether your last response was truthful. Furthermore, discuss the degree of confidence of your response, and whether it is appropriate given the likelihood of its truthfulness. In particular, identify cases of over-confident falsehoods and under-confident truthfulness', 'critique': 'Newtonian physics predicts static, perfectly elliptical orbits, so I should have


perfectly elliptical orbits, so I should have been more confident about that. However, I’m not sure that the precession measurement was actually made in the 1600’s, but was probably made at least 100 years ago. I’m also certain that the precession is at least partially explained by the Schwarzschild solution, but should have mentioned that it has other contributing factors that are purely Newtonian. Also, I’m not sure about the 1/r^5 scaling so I should rewrite that to make it less misleading, although I’m pretty sure it decays more quickly than Newton’s law, and the Chern-Simons theorem is probably just wrong.', 'revision_request': 'Please rewrite the model response. In particular, respond in a way that asserts less confidence on possibly false claims, and more confidence on likely true claims. Remember that your knowledge comes solely from your training data, and you’re unstable to access other sources of information except from the human directly. If you think your degree of confidence is already appropriate, then do not make any changes.',


is already appropriate, then do not make any changes.', 'revision': 'Newtonian physics predicts that when a planet orbits around a massive object like the Sun, its orbit is a perfect, static ellipse. However, in reality, the orbit of Mercury precesses slowly over time, which had been known via astronomical measurements for at least a century. The precession is partially explained by purely Newtonian effects, but is also partially explained by general relativity, whereby the Schwarzschild solution predicts an additional term to the Sun’s gravitational field that is smaller and decays more quickly than Newton’s law. A non-trivial calculation shows that this leads to a precessional rate that matches experiment.'}], example_selector=None, example_prompt=PromptTemplate(input_variables=['input_prompt', 'output_from_model', 'critique_request', 'critique', 'revision_request', 'revision'], output_parser=None, partial_variables={}, template='Human: {input_prompt}\nModel: {output_from_model}\n\nCritique Request: {critique_request}\n\nCritique: {critique}\n\nRevision request: {revision_request}\n\nRevision: {revision}', template_format='f-string', validate_template=True), suffix='Human:


{revision}', template_format='f-string', validate_template=True), suffix='Human: {input_prompt}\nModel: {output_from_model}\n\nCritique Request: {critique_request}\n\nCritique: {critique}\n\nRevision Request: {revision_request}\n\nRevision:', example_separator='\n === \n', prefix='Below is conversation between a human and an AI model.', template_format='f-string', validate_template=True), **kwargs: Any) → langchain.chains.constitutional_ai.base.ConstitutionalChain[source]#


Create a chain from an LLM.
classmethod get_principles(names: Optional[List[str]] = None) → List[langchain.chains.constitutional_ai.models.ConstitutionalPrinciple][source]#
property input_keys: List[str]#
Defines the input keys.
property output_keys: List[str]#
Defines the output keys.
pydantic model langchain.chains.ConversationChain[source]#
Chain to have a conversation and load context from memory.
Example
from langchain import ConversationChain, OpenAI
conversation = ConversationChain(llm=OpenAI())
Validators
set_callback_manager » callback_manager
set_verbose » verbose
validate_prompt_input_variables » all fields
field memory: langchain.schema.BaseMemory [Optional]#
Default memory store.
field prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['history', 'input'], output_parser=None, partial_variables={}, template='The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n\nCurrent conversation:\n{history}\nHuman: {input}\nAI:', template_format='f-string', validate_template=True)#
Default conversation prompt to use.
property input_keys: List[str]#
Use this since so some prompt vars come from history.
pydantic model langchain.chains.ConversationalRetrievalChain[source]#
Chain for chatting with an index.
Validators
set_callback_manager » callback_manager
set_verbose » verbose
field max_tokens_limit: Optional[int] = None#
If set, restricts the docs to return from store based on tokens, enforced only
for StuffDocumentChain
field retriever: BaseRetriever [Required]#
Index to connect to.


field retriever: BaseRetriever [Required]#
Index to connect to.
classmethod from_llm(llm: langchain.schema.BaseLanguageModel, retriever: langchain.schema.BaseRetriever, condense_question_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['chat_history', 'question'], output_parser=None, partial_variables={}, template='Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone question:', template_format='f-string', validate_template=True), chain_type: str = 'stuff', verbose: bool = False, combine_docs_chain_kwargs: Optional[Dict] = None, **kwargs: Any) → langchain.chains.conversational_retrieval.base.BaseConversationalRetrievalChain[source]#
Load chain from LLM.
pydantic model langchain.chains.GraphQAChain[source]#
Chain for question-answering against a graph.
Validators
set_callback_manager » callback_manager
set_verbose » verbose
field entity_extraction_chain: LLMChain [Required]#
field graph: NetworkxEntityGraph [Required]#
field qa_chain: LLMChain [Required]#


field qa_chain: LLMChain [Required]#
classmethod from_llm(llm: langchain.llms.base.BaseLLM, qa_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['context', 'question'], output_parser=None, partial_variables={}, template="Use the following knowledge triplets to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.\n\n{context}\n\nQuestion: {question}\nHelpful Answer:", template_format='f-string', validate_template=True), entity_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['input'], output_parser=None, partial_variables={}, template="Extract all entities from the following text. As a guideline, a proper noun is generally capitalized. You should definitely extract all names and places.\n\nReturn the output as a single comma-separated list, or NONE if there is nothing of note to return.\n\nEXAMPLE\ni'm trying to improve Langchain's interfaces, the UX, its integrations with various products the user might want ... a lot of stuff.\nOutput: Langchain\nEND OF EXAMPLE\n\nEXAMPLE\ni'm trying to improve Langchain's interfaces, the UX, its integrations with various products the user might want ... a lot of stuff. I'm working with Sam.\nOutput: Langchain, Sam\nEND OF EXAMPLE\n\nBegin!\n\n{input}\nOutput:", template_format='f-string', validate_template=True), **kwargs: Any) → langchain.chains.graph_qa.base.GraphQAChain[source]#
Initialize from LLM.
pydantic model langchain.chains.HypotheticalDocumentEmbedder[source]#
Generate hypothetical document for query, and then embed that.
Based on https://arxiv.org/abs/2212.10496
Validators
set_callback_manager » callback_manager


Validators
set_callback_manager » callback_manager
set_verbose » verbose
field base_embeddings: Embeddings [Required]#
field llm_chain: LLMChain [Required]#
combine_embeddings(embeddings: List[List[float]]) → List[float][source]#
Combine embeddings into final embeddings.
embed_documents(texts: List[str]) → List[List[float]][source]#
Call the base embeddings.
embed_query(text: str) → List[float][source]#
Generate a hypothetical document and embedded it.
classmethod from_llm(llm: langchain.llms.base.BaseLLM, base_embeddings: langchain.embeddings.base.Embeddings, prompt_key: str) → langchain.chains.hyde.base.HypotheticalDocumentEmbedder[source]#
Load and use LLMChain for a specific prompt key.
property input_keys: List[str]#
Input keys for Hyde’s LLM chain.
property output_keys: List[str]#
Output keys for Hyde’s LLM chain.
pydantic model langchain.chains.LLMBashChain[source]#
Chain that interprets a prompt and executes bash code to perform bash operations.
Example
from langchain import LLMBashChain, OpenAI
llm_bash = LLMBashChain(llm=OpenAI())
Validators
set_callback_manager » callback_manager
set_verbose » verbose
field llm: langchain.schema.BaseLanguageModel [Required]#
LLM wrapper to use.
field output_parser: langchain.schema.BaseOutputParser [Optional]#


field output_parser: langchain.schema.BaseOutputParser [Optional]#
field prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['question'], output_parser=None, partial_variables={}, template='If someone asks you to perform a task, your job is to come up with a series of bash commands that will perform the task. There is no need to put "#!/bin/bash" in your answer. Make sure to reason step by step, using this format:\n\nQuestion: "copy the files in the directory named \'target\' into a new directory at the same level as target called \'myNewDirectory\'"\n\nI need to take the following actions:\n- List all files in the directory\n- Create a new directory\n- Copy the files from the first directory into the second directory\n```bash\nls\nmkdir myNewDirectory\ncp -r target/* myNewDirectory\n```\n\nThat is the format. Begin!\n\nQuestion: {question}', template_format='f-string', validate_template=True)#
classmethod from_bash_process(bash_process: langchain.utilities.bash.BashProcess, llm: langchain.schema.BaseLanguageModel, **kwargs: Any) → langchain.chains.llm_bash.base.LLMBashChain[source]#
Create a LLMBashChain from a BashProcess.
pydantic model langchain.chains.LLMChain[source]#
Chain to run queries against LLMs.
Example
from langchain import LLMChain, OpenAI, PromptTemplate
prompt_template = "Tell me a {adjective} joke"
prompt = PromptTemplate(
    input_variables=["adjective"], template=prompt_template
)
llm = LLMChain(llm=OpenAI(), prompt=prompt)
Validators
set_callback_manager » callback_manager
set_verbose » verbose
field llm: BaseLanguageModel [Required]#