`langchain.llms.gpt4all`.GPT4All¶

class langchain.llms.gpt4all.GPT4All(*, cache: Optional[bool] = None, verbose: bool = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, model: str, backend: Optional[str] = None, max_tokens: int = 200, n_parts: int = - 1, seed: int = 0, f16_kv: bool = False, logits_all: bool = False, vocab_only: bool = False, use_mlock: bool = False, embedding: bool = False, n_threads: Optional[int] = 4, n_predict: Optional[int] = 256, temp: Optional[float] = 0.7, top_p: Optional[float] = 0.1, top_k: Optional[int] = 40, echo: Optional[bool] = False, stop: Optional[List[str]] = [], repeat_last_n: Optional[int] = 64, repeat_penalty: Optional[float] = 1.18, n_batch: int = 8, streaming: bool = False, allow_download: bool = False, client: Any = None)[source]¶

Bases: LLM

GPT4All language models.

To use, you should have the gpt4all python package installed, the pre-trained model file, and the model’s config information.

Example

from langchain.llms import GPT4All
model = GPT4All(model="./models/gpt4all-model.bin", n_threads=8)

# Simplest invocation
response = model("Once upon a time, ")

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

param allow_download: bool = False¶: If model does not exist in ~/.cache/gpt4all/, download it.

param backend: Optional[str] = None¶

param cache: Optional[bool] = None¶

param callback_manager: Optional[BaseCallbackManager] = None¶

param callbacks: Callbacks = None¶

param echo: Optional[bool] = False¶: Whether to echo the prompt.

param embedding: bool = False¶: Use embedding mode only.

param f16_kv: bool = False¶: Use half-precision for key/value cache.

param logits_all: bool = False¶: Return logits for all tokens, not just the last token.

param max_tokens: int = 200¶: Token context window.

param metadata: Optional[Dict[str, Any]] = None¶: Metadata to add to the run trace.

param model: str [Required]¶: Path to the pre-trained GPT4All model file.

param n_batch: int = 8¶: Batch size for prompt processing.

param n_parts: int = -1¶: Number of parts to split the model into. If -1, the number of parts is automatically determined.

param n_predict: Optional[int] = 256¶: The maximum number of tokens to generate.

param n_threads: Optional[int] = 4¶: Number of threads to use.

param repeat_last_n: Optional[int] = 64¶: Last n tokens to penalize

param repeat_penalty: Optional[float] = 1.18¶: The penalty to apply to repeated tokens.

param seed: int = 0¶: Seed. If -1, a random seed is used.

param stop: Optional[List[str]] = []¶: A list of strings to stop generation when encountered.

param streaming: bool = False¶: Whether to stream the results or not.

param tags: Optional[List[str]] = None¶: Tags to add to the run trace.

param temp: Optional[float] = 0.7¶: The temperature to use for sampling.

param top_k: Optional[int] = 40¶: The top-k value to use for sampling.

param top_p: Optional[float] = 0.1¶: The top-p value to use for sampling.

param use_mlock: bool = False¶: Force system to keep model in RAM.

param verbose: bool [Optional]¶: Whether to print out response text.

param vocab_only: bool = False¶: Only load the vocabulary, no weights.

__call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → str¶: Check Cache and run the LLM on the given prompt and input.

async abatch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, max_concurrency: Optional[int] = None, **kwargs: Any) → List[str]¶

async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, **kwargs: Any) → LLMResult¶: Run the LLM on the given prompt and input.

async agenerate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, **kwargs: Any) → LLMResult¶

Asynchronously pass a sequence of prompts and return model generations.

This method should make use of batched calls for models that expose a batched API.

Use this method when you want to:

take advantage of batched calls,
need more output from the model than just the top generated value,
are building chains that are agnostic to the underlying language model
type (e.g., pure text completion models vs chat models).

Parameters

prompts – List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models).
stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.
callbacks – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation.
**kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call.

Returns

An LLMResult, which contains a list of candidate Generations for each input: prompt and additional model provider-specific output.

async ainvoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶

async apredict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str¶

Asynchronously pass a string to the model and return a string prediction.

Use this method when calling pure text generation models and only the top: candidate generation is needed.

Parameters

text – String input to pass to the model.
stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.
**kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call.

Returns

Top model prediction as a string.

async apredict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage¶

Asynchronously pass messages to the model and return a message prediction.

Use this method when calling chat models and only the top: candidate generation is needed.

Parameters

messages – A sequence of chat messages corresponding to a single model input.
stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.
**kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call.

Returns

Top model prediction as a message.

async astream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → AsyncIterator[str]¶

batch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, max_concurrency: Optional[int] = None, **kwargs: Any) → List[str]¶

dict(**kwargs: Any) → Dict¶: Return a dictionary of the LLM.

generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, **kwargs: Any) → LLMResult¶: Run the LLM on the given prompt and input.

generate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, **kwargs: Any) → LLMResult¶

Pass a sequence of prompts to the model and return model generations.

This method should make use of batched calls for models that expose a batched API.

Use this method when you want to:

take advantage of batched calls,
need more output from the model than just the top generated value,
are building chains that are agnostic to the underlying language model
type (e.g., pure text completion models vs chat models).

Parameters

prompts – List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models).
stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.
callbacks – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation.
**kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call.

Returns

An LLMResult, which contains a list of candidate Generations for each input: prompt and additional model provider-specific output.

get_num_tokens(text: str) → int¶

Get the number of tokens present in the text.

Useful for checking if an input will fit in a model’s context window.

Parameters: text – The string input to tokenize.
Returns: The integer number of tokens in the text.

get_num_tokens_from_messages(messages: List[BaseMessage]) → int¶

Get the number of tokens in the messages.

Useful for checking if an input will fit in a model’s context window.

Parameters: messages – The message inputs to tokenize.
Returns: The sum of the number of tokens across the messages.

get_token_ids(text: str) → List[int]¶

Return the ordered ids of the tokens in a text.

Parameters

text – The string input to tokenize.

Returns

A list of ids corresponding to the tokens in the text, in order they occur: in the text.

invoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶

predict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str¶

Pass a single string input to the model and return a string prediction.

Use this method when passing in raw text. If you want to pass in specific
types of chat messages, use predict_messages.

Parameters

text – String input to pass to the model.
stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.
**kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call.

Returns

Top model prediction as a string.

predict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage¶

Pass a message sequence to the model and return a message prediction.

Use this method when passing in chat messages. If you want to pass in raw text,: use predict.

Parameters

messages – A sequence of chat messages corresponding to a single model input.
stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.
**kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call.

Returns

Top model prediction as a message.

validator raise_deprecation » all fields¶: Raise deprecation warning if callback_manager is used.

save(file_path: Union[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”)

validator set_verbose » verbose¶

If verbose is None, set it.

This allows users to pass in None as verbose to access the global setting.

stream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → Iterator[str]¶

to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶

to_json_not_implemented() → SerializedNotImplemented¶

validator validate_environment » all fields[source]¶: Validate that the python package exists in the environment.

property lc_attributes: Dict¶: Return a list of attribute names that should be included in the serialized kwargs. These attributes must be accepted by the constructor.

property lc_namespace: List[str]¶: Return the namespace of the langchain object. eg. [“langchain”, “llms”, “openai”]

property lc_secrets: Dict[str, str]¶: Return a map of constructor argument names to secret ids. eg. {“openai_api_key”: “OPENAI_API_KEY”}

property lc_serializable: bool¶: Return whether or not the class is serializable.

model Config[source]¶

Bases: object

Configuration for this pydantic object.

extra = 'forbid'¶

langchain.llms.gpt4all.GPT4All¶

Examples using GPT4All¶

`langchain.llms.gpt4all`.GPT4All¶