id stringlengths 14 16 | text stringlengths 44 2.73k | source stringlengths 49 115 |
|---|---|---|
ad57987e20ef-50 | Remove input text from API response
field repetition_penalty: float = 1.0#
Penalizes repeated tokens. 1.0 means no penalty.
field temperature: float = 0.7#
What sampling temperature to use.
field top_k: int = 50#
The number of highest probability tokens to keep for top-k filtering.
field top_p: int = 1#
Total probabili... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-51 | Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β 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 β set to True to make a deep co... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-52 | save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localn... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-53 | 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 = βallowβ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclu... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-54 | get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
get_sub_prompts(params: Dict[str, Any], prompts: List[str], stop: Optional[List[str]] = None) β List[List[str]]#
Get the sub prompts for llm call.
json(*, include: Optional[Union[AbstractSetIntStr,... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-55 | Prepare the params for streaming.
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
stream(prompt: str, stop: Optional[List[str]] = None) β Generator#
Call OpenAI with streaming flag... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-56 | Set of special tokens that are allowedγ
field disallowed_special: Union[Literal['all'], Collection[str]] = 'all'#
Set of special tokens that are not allowedγ
field max_retries: int = 6#
Maximum number of retries to make when generating.
field model_kwargs: Dict[str, Any] [Optional]#
Holds any model parameters valid for... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-57 | Behaves as if Config.extra = βallowβ 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) β Model#
Duplicate a model, optionally... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-58 | 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_... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-59 | Whether or not to use sampling; use greedy decoding otherwise.
field max_length: Optional[int] = None#
The maximum length of the sequence to be generated.
field max_new_tokens: int = 256#
The maximum number of new tokens to generate in the completion.
field model_kwargs: Dict[str, Any] [Optional]#
Holds any model param... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-60 | Behaves as if Config.extra = βallowβ 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) β Model#
Duplicate a model, optionally... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-61 | 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_... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-62 | field pipeline_kwargs: Dict[str, Any] [Optional]#
Holds any pipeline parameters valid for create call not
explicitly specified.
__call__(prompt: str, stop: Optional[List[str]] = None) β str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) ... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-63 | Returns
new model instance
dict(**kwargs: Any) β Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β ... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-64 | Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.PredictionGuard[source]#
Wrapper around Prediction Guard large language models.
To use, you should have the predictionguard python package installed, and the
environment variable PREDICTIONGUARD_TOKEN set with y... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-65 | Behaves as if Config.extra = βallowβ 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) β Model#
Duplicate a model, optionally... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-66 | 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_... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-67 | returned in the generation_info field of the
Generation object.
Example
from langchain.llms import PromptLayerOpenAI
openai = PromptLayerOpenAI(model_name="text-davinci-003")
Validators
build_extra Β» all fields
set_callback_manager Β» callback_manager
set_verbose Β» verbose
validate_environment Β» all fields
__call__(prom... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-68 | update β 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 β set to True to make a deep copy of the model
Returns
new model instance
create_llm_result(choices: Any, prompts: List[str], token_usage: Dict[str, int]) β langchain.schema.LLM... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-69 | 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().
max_tokens_for_prompt(prompt: str) β int#
Calculate the maximum number of tokens possible to generate for a prompt.
Paramet... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-70 | for token in generator:
yield token
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.PromptLayerOpenAIChat[source]#
Wrapper around OpenAI large language models.
To use, you should have the openai and ... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-71 | Model name to use.
field prefix_messages: List [Optional]#
Series of messages for Chat input.
field streaming: bool = False#
Whether to stream the results or not.
__call__(prompt: str, stop: Optional[List[str]] = None) β str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str],... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-72 | deep β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schem... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-73 | classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.RWKV[source]#
Wrapper around RWKV language models.
To use, you should have the rwkv python package installed, the
pre-trained model file, and the modelβs c... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-74 | The top-p value to use for sampling.
__call__(prompt: str, stop: Optional[List[str]] = None) β str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
async agenerate_pr... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-75 | dict(**kwargs: Any) β Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-76 | Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.Replicate[source]#
Wrapper around Replicate models.
To use, you should have the replicate python package installed,
and the environment variable REPLICATE_API_TOKEN set with your API token.
You can find your tok... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-77 | Behaves as if Config.extra = βallowβ 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) β Model#
Duplicate a model, optionally... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-78 | 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_... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-79 | Make sure the credentials / roles used have the required policies to
access the Sagemaker endpoint.
See: https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies.html
Validators
set_callback_manager Β» callback_manager
set_verbose Β» verbose
validate_environment Β» all fields
field content_handler: langchain.llms.... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-80 | Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β Model#
Crea... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-81 | Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
json(*, include: Optional[Union[AbstractSetIntStr, MappingInt... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-82 | 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")... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-83 | 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',... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-84 | Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β values to change/add in the new model. Note: the data is not validated before creat... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-85 | 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_... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-86 | 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):
... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-87 | 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 [R... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-88 | Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β values to change/add in the new model. Note: the data is not validated before creat... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-89 | 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_... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-90 | Holds any model parameters valid for create call not
explicitly specified.
__call__(prompt: str, stop: Optional[List[str]] = None) β str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the give... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-91 | dict(**kwargs: Any) β Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-92 | 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="... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-93 | 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 pro... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-94 | Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β values to change/add in the new model. Note: the data is not validated before creat... | https://python.langchain.com/en/latest/reference/modules/llms.html |
ad57987e20ef-95 | 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]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forwar... | https://python.langchain.com/en/latest/reference/modules/llms.html |
c8659f94f92c-0 | .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 βagent_scratchpadβ where the agent can put its
intermediary wor... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-1 | Create the full inputs for the LLMChain from intermediate steps.
plan(intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], **kwargs: Any) β Union[langchain.schema.AgentAction, langchain.schema.AgentFinish][source]#
Given input, decided what to do.
Parameters
intermediate_steps β Steps the LLM has taken t... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-2 | 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:... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-3 | Given input, decided what to do.
Parameters
intermediate_steps β Steps the LLM has taken to date,
along with observations
**kwargs β User inputs.
Returns
Actions specifying what tool to use.
dict(**kwargs: Any) β Dict[source]#
Return dictionary representation of agent.
get_allowed_tools() β Optional[List[str]][source]#... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-4 | Given input, decided what to do.
Parameters
intermediate_steps β Steps the LLM has taken to date,
along with observations
**kwargs β User inputs.
Returns
Action specifying what tool to use.
dict(**kwargs: Any) β Dict[source]#
Return dictionary representation of agent.
classmethod from_llm_and_tools(llm: langchain.schem... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-5 | 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]# | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-6 | 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 ... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-7 | 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 follo... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-8 | 'AI', human_prefix: str = 'Human', input_variables: Optional[List[str]] = None) β langchain.prompts.prompt.PromptTemplate[source]# | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-9 | Create prompt in the style of the zero shot agent.
Parameters
tools β List of tools the agent will have access to, used to format the
prompt.
prefix β String to put before the list of tools.
suffix β String to put after the list of tools.
ai_prefix β String to use before AI output.
human_prefix β String to use before h... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-10 | 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 ... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-11 | 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... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-12 | 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) β langchain.agents.agent.Agent[source]# | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-13 | 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 out... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-14 | 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 r... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-15 | 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 l... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-16 | 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 ... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-17 | 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: l... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-18 | Validators
set_callback_manager Β» callback_manager
set_verbose Β» verbose
validate_return_direct_tool Β» all fields
validate_tools Β» all fields
field agent: Union[BaseSingleActionAgent, BaseMultiActionAgent] [Required]#
field callback_manager: BaseCallbackManager [Optional]#
field early_stopping_method: str = 'force'#
fi... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-19 | 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=... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-20 | Validators
set_callback_manager Β» callback_manager
set_verbose Β» verbose
validate_return_direct_tool Β» all fields
validate_tools Β» all fields
field agent: Union[BaseSingleActionAgent, BaseMultiActionAgent] [Required]#
field callback_manager: BaseCallbackManager [Optional]#
field early_stopping_method: str = 'force'#
fi... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-21 | 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_scratchp... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-22 | 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.age... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-23 | 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 ... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-24 | 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 ... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-25 | = '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, ... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-26 | Construct a json agent from an LLM and tools. | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-27 | 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 ... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-28 | 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 ... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-29 | 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) β langchain.agents.agent.AgentExecutor[source]# | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-30 | 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`.\... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-31 | 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... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-32 | 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 p... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-33 | 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 questio... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-34 | Construct a pbi agent from an LLM and tools. | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-35 | 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, ... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-36 | 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 q... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-37 | 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 ... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-38 | 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. | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-39 | 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... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-40 | 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 databas... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-41 | str = 'force', verbose: bool = False, **kwargs: Any) β langchain.agents.agent.AgentExecutor[source]# | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-42 | 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 desig... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-43 | 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: O... | https://python.langchain.com/en/latest/reference/modules/agents.html |
c8659f94f92c-44 | llm β Optional language model, may be needed to initialize certain tools.
callback_manager β 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[pydanti... | https://python.langchain.com/en/latest/reference/modules/agents.html |
0e37f39b8177-0 | .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 ... | https://python.langchain.com/en/latest/reference/modules/experimental.html |
0e37f39b8177-1 | property output_keys: List[str]#
Output keys this chain expects.
prioritize_tasks(this_task_id: int, objective: str) β List[Dict][source]#
Prioritize tasks.
class langchain.experimental.AutoGPT(ai_name: str, memory: langchain.vectorstores.base.VectorStoreRetriever, chain: langchain.chains.llm.LLMChain, output_parser: l... | https://python.langchain.com/en/latest/reference/modules/experimental.html |
0e37f39b8177-2 | React to a given observation.
get_full_header(force_refresh: bool = False) β str[source]#
Return a full header of the agentβs status, summary, and current time.
get_summary(force_refresh: bool = False) β str[source]#
Return a descriptive summary of the agent.
field last_refreshed: datetime.datetime [Optional]#
The last... | https://python.langchain.com/en/latest/reference/modules/experimental.html |
0e37f39b8177-3 | 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[f... | https://python.langchain.com/en/latest/reference/modules/experimental.html |
0e37f39b8177-4 | 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(... | https://python.langchain.com/en/latest/reference/modules/experimental.html |
e256c4b00fa3-0 | .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]) β None[source]#
Add texts to in memory dictionary.
search(search: s... | https://python.langchain.com/en/latest/reference/modules/docstore.html |
aa069beb2523-0 | .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 Β» callback_manager
set_verbose Β» verbose
validate_api_answer_prompt... | https://python.langchain.com/en/latest/reference/modules/chains.html |
aa069beb2523-1 | 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, parti... | https://python.langchain.com/en/latest/reference/modules/chains.html |
aa069beb2523-2 | pydantic model langchain.chains.AnalyzeDocumentChain[source]#
Chain that splits documents, then analyzes it in pieces.
Validators
set_callback_manager Β» callback_manager
set_verbose Β» verbose
field combine_docs_chain: langchain.chains.combine_documents.base.BaseCombineDocumentsChain [Required]#
field text_splitter: lan... | https://python.langchain.com/en/latest/reference/modules/chains.html |
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