viruPVS/langchain-docs huggingface dataset:
Sequences when completion generation will stop.
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param temperature: Optional[float] = None¶
What sampling temperature to use.
param top_p: Optional[float] = None¶
Total probability mass of tokens to consider at each step.
param verbose: bool [Optional]¶
Whether to print out response text.
param writer_api_key: Optional[str] = None¶
Writer API key.
param writer_org_id: Optional[str] = None¶
Writer organization ID.
__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, *, return_exceptions: bool = False, **kwargs: Any) → List[str]¶
Default implementation runs ainvoke in parallel using asyncio.gather.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode.
property config_specs: List[langchain_core.runnables.utils.ConfigurableFieldSpec]¶
List configurable fields for this runnable.
property input_schema: Type[pydantic.main.BaseModel]¶
The type of input this runnable accepts specified as a pydantic model.
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_schema: Type[pydantic.main.BaseModel]¶
The type of output this runnable produces specified as a pydantic model.
Examples using Writer¶
Writer
langchain_community.llms.gooseai.GooseAI¶
class langchain_community.llms.gooseai.GooseAI[source]¶
Bases: LLM
GooseAI large language models.
To use, you should have the openai python package installed, and the
environment variable GOOSEAI_API_KEY set with your API key.
Any parameters that are valid to be passed to the openai.create call can be passed
in, even if not explicitly saved on this class.
Example
from langchain_community.llms import GooseAI
gooseai = GooseAI(model_name="gpt-neo-20b")
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 cache: Optional[bool] = None¶
param callback_manager: Optional[BaseCallbackManager] = None¶
param callbacks: Callbacks = None¶
param client: Any = None¶
param frequency_penalty: float = 0¶
Penalizes repeated tokens according to frequency.
param gooseai_api_key: Optional[pydantic.types.SecretStr] = None¶
Constraints
type = string
writeOnly = True
format = password
param logit_bias: Optional[Dict[str, float]] [Optional]¶
Adjust the probability of specific tokens being generated.
param max_tokens: int = 256¶
The maximum number of tokens to generate in the completion.
-1 returns as many tokens as possible given the prompt and
the models maximal context size.
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
param min_tokens: int = 1¶
The minimum number of tokens to generate in the completion.
param model_kwargs: Dict[str, Any] [Optional]¶
param model_kwargs: Dict[str, Any] [Optional]¶
Holds any model parameters valid for create call not explicitly specified.
param model_name: str = 'gpt-neo-20b'¶
Model name to use
param n: int = 1¶
How many completions to generate for each prompt.
param presence_penalty: float = 0¶
Penalizes repeated tokens.
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param temperature: float = 0.7¶
What sampling temperature to use
param top_p: float = 1¶
Total probability mass of tokens to consider at each step.
param verbose: bool [Optional]¶
Whether to print out response text.
__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, *, return_exceptions: bool = False, **kwargs: Any) → List[str]¶
Default implementation runs ainvoke in parallel using asyncio.gather.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode.
property config_specs: List[langchain_core.runnables.utils.ConfigurableFieldSpec]¶
List configurable fields for this runnable.
property input_schema: Type[pydantic.main.BaseModel]¶
The type of input this runnable accepts specified as a pydantic model.
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_schema: Type[pydantic.main.BaseModel]¶
The type of output this runnable produces specified as a pydantic model.
Examples using GooseAI¶
GooseAI
langchain_community.llms.ai21.AI21PenaltyData¶
class langchain_community.llms.ai21.AI21PenaltyData[source]¶
Bases: BaseModel
Parameters for AI21 penalty data.
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 applyToEmojis: bool = True¶
param applyToNumbers: bool = True¶
param applyToPunctuations: bool = True¶
param applyToStopwords: bool = True¶
param applyToWhitespaces: bool = True¶
param scale: int = 0¶
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
langchain_community.llms.pipelineai.PipelineAI¶
class langchain_community.llms.pipelineai.PipelineAI[source]¶
Bases: LLM, BaseModel
PipelineAI large language models.
To use, you should have the pipeline-ai python package installed,
and the environment variable PIPELINE_API_KEY set with your API key.
Any parameters that are valid to be passed to the call can be passed
in, even if not explicitly saved on this class.
Example
from langchain_community.llms import PipelineAI
pipeline = PipelineAI(pipeline_key="")
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 cache: Optional[bool] = None¶
param callback_manager: Optional[BaseCallbackManager] = None¶
param callbacks: Callbacks = None¶
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
param pipeline_api_key: Optional[str] = None¶
param pipeline_key: str = ''¶
The id or tag of the target pipeline
param pipeline_kwargs: Dict[str, Any] [Optional]¶
Holds any pipeline parameters valid for create call not
explicitly specified.
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param verbose: bool [Optional]¶
Whether to print out response text.
__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.
property config_specs: List[langchain_core.runnables.utils.ConfigurableFieldSpec]¶
List configurable fields for this runnable.
property input_schema: Type[pydantic.main.BaseModel]¶
The type of input this runnable accepts specified as a pydantic model.
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_schema: Type[pydantic.main.BaseModel]¶
The type of output this runnable produces specified as a pydantic model.
Examples using PipelineAI¶
PipelineAI
langchain_community.llms.ctransformers.CTransformers¶
class langchain_community.llms.ctransformers.CTransformers[source]¶
Bases: LLM
C Transformers LLM models.
To use, you should have the ctransformers python package installed.
See https://github.com/marella/ctransformers
Example
from langchain_community.llms import CTransformers
llm = CTransformers(model="/path/to/ggml-gpt-2.bin", model_type="gpt2")
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 cache: Optional[bool] = None¶
param callback_manager: Optional[BaseCallbackManager] = None¶
param callbacks: Callbacks = None¶
param config: Optional[Dict[str, Any]] = None¶
The config parameters.
See https://github.com/marella/ctransformers#config
param lib: Optional[str] = None¶
The path to a shared library or one of avx2, avx, basic.
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
param model: str [Required]¶
The path to a model file or directory or the name of a Hugging Face Hub
model repo.
param model_file: Optional[str] = None¶
The name of the model file in repo or directory.
param model_type: Optional[str] = None¶
The model type.
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param verbose: bool [Optional]¶
Whether to print out response text.
between retries
stop_after_attempt – The maximum number of attempts to make before giving up
Returns
A new Runnable that retries the original runnable on exceptions.
with_types(*, input_type: Optional[Type[Input]] = None, output_type: Optional[Type[Output]] = None) → Runnable[Input, Output]¶
Bind input and output types to a Runnable, returning a new Runnable.
property InputType: TypeAlias¶
Get the input type for this runnable.
property OutputType: Type[str]¶
Get the input type for this runnable.
property config_specs: List[langchain_core.runnables.utils.ConfigurableFieldSpec]¶
List configurable fields for this runnable.
property input_schema: Type[pydantic.main.BaseModel]¶
The type of input this runnable accepts specified as a pydantic model.
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_schema: Type[pydantic.main.BaseModel]¶
The type of output this runnable produces specified as a pydantic model.
Examples using CTransformers¶
C Transformers
langchain_community.llms.azureml_endpoint.AzureMLOnlineEndpoint¶
class langchain_community.llms.azureml_endpoint.AzureMLOnlineEndpoint[source]¶
Bases: LLM, BaseModel
Azure ML Online Endpoint models.
Example
azure_llm = AzureMLOnlineEndpoint(
    endpoint_url="https://<your-endpoint>.<your_region>.inference.ml.azure.com/score",
    endpoint_api_key="my-api-key",
    content_formatter=content_formatter,
)
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 cache: Optional[bool] = None¶
param callback_manager: Optional[BaseCallbackManager] = None¶
param callbacks: Callbacks = None¶
param content_formatter: Any = None¶
The content formatter that provides an input and output
transform function to handle formats between the LLM and
the endpoint
param deployment_name: str = ''¶
Deployment Name for Endpoint. NOT REQUIRED to call endpoint. Should be passed
to constructor or specified as env var AZUREML_DEPLOYMENT_NAME.
param endpoint_api_key: str = ''¶
Authentication Key for Endpoint. Should be passed to constructor or specified as
env var AZUREML_ENDPOINT_API_KEY.
param endpoint_url: str = ''¶
URL of pre-existing Endpoint. Should be passed to constructor or specified as
env var AZUREML_ENDPOINT_URL.
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
param model_kwargs: Optional[dict] = None¶
Keyword arguments to pass to the model.
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param verbose: bool [Optional]¶
Whether to print out response text.
between retries
stop_after_attempt – The maximum number of attempts to make before giving up
Returns
A new Runnable that retries the original runnable on exceptions.
with_types(*, input_type: Optional[Type[Input]] = None, output_type: Optional[Type[Output]] = None) → Runnable[Input, Output]¶
Bind input and output types to a Runnable, returning a new Runnable.
property InputType: TypeAlias¶
Get the input type for this runnable.
property OutputType: Type[str]¶
Get the input type for this runnable.
property config_specs: List[langchain_core.runnables.utils.ConfigurableFieldSpec]¶
List configurable fields for this runnable.
property input_schema: Type[pydantic.main.BaseModel]¶
The type of input this runnable accepts specified as a pydantic model.
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_schema: Type[pydantic.main.BaseModel]¶
The type of output this runnable produces specified as a pydantic model.
Examples using AzureMLOnlineEndpoint¶
Azure ML
langchain_community.llms.azureml_endpoint.LlamaContentFormatter¶
class langchain_community.llms.azureml_endpoint.LlamaContentFormatter[source]¶
Content formatter for LLaMa
Attributes
accepts
The MIME type of the response data returned from the endpoint
content_type
The MIME type of the input data passed to the endpoint
Methods
__init__()
escape_special_characters(prompt)
Escapes any special characters in prompt
format_request_payload(prompt, model_kwargs)
Formats the request according to the chosen api
format_response_payload(output)
Formats response
__init__()¶
static escape_special_characters(prompt: str) → str¶
Escapes any special characters in prompt
format_request_payload(prompt: str, model_kwargs: Dict) → bytes[source]¶
Formats the request according to the chosen api
format_response_payload(output: bytes) → str[source]¶
Formats response
Examples using LlamaContentFormatter¶
AzureML Chat Online Endpoint
langchain_community.llms.google_palm.completion_with_retry¶
langchain_community.llms.google_palm.completion_with_retry(llm: GooglePalm, prompt: Union[PromptValue, str, List[BaseMessage]], is_gemini: bool = False, stream: bool = False, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any) → Any[source]¶
Use tenacity to retry the completion call.
langchain_experimental.llms.ollama_functions.OllamaFunctions¶
class langchain_experimental.llms.ollama_functions.OllamaFunctions[source]¶
Bases: BaseChatModel
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 cache: Optional[bool] = None¶
Whether to cache the response.
param callback_manager: Optional[BaseCallbackManager] = None¶
Callback manager to add to the run trace.
param callbacks: Callbacks = None¶
Callbacks to add to the run trace.
param llm: langchain_community.chat_models.ollama.ChatOllama [Required]¶
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param tool_system_prompt_template: str [Required]¶
param verbose: bool [Optional]¶
Whether to print out response text.
__call__(messages: List[BaseMessage], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → BaseMessage¶
Call self as a function.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation runs ainvoke in parallel using asyncio.gather.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode.
langchain_community.llms.vllm.VLLMOpenAI¶
class langchain_community.llms.vllm.VLLMOpenAI[source]¶
Bases: BaseOpenAI
vLLM OpenAI-compatible API client
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param allowed_special: Union[Literal['all'], AbstractSet[str]] = {}¶
Set of special tokens that are allowed。
param batch_size: int = 20¶
Batch size to use when passing multiple documents to generate.
param best_of: int = 1¶
Generates best_of completions server-side and returns the “best”.
param cache: Optional[bool] = None¶
param callback_manager: Optional[BaseCallbackManager] = None¶
param callbacks: Callbacks = None¶
param default_headers: Union[Mapping[str, str], None] = None¶
param default_query: Union[Mapping[str, object], None] = None¶
param disallowed_special: Union[Literal['all'], Collection[str]] = 'all'¶
Set of special tokens that are not allowed。
param frequency_penalty: float = 0¶
Penalizes repeated tokens according to frequency.
param http_client: Union[Any, None] = None¶
Optional httpx.Client.
param logit_bias: Optional[Dict[str, float]] [Optional]¶
Adjust the probability of specific tokens being generated.
param max_retries: int = 2¶
Maximum number of retries to make when generating.
param max_tokens: int = 256¶
The maximum number of tokens to generate in the completion.
-1 returns as many tokens as possible given the prompt and
the models maximal context size.
param metadata: Optional[Dict[str, Any]] = None¶
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
param model_kwargs: Dict[str, Any] [Optional]¶
Holds any model parameters valid for create call not explicitly specified.
param model_name: str = 'text-davinci-003' (alias 'model')¶
Model name to use.
param n: int = 1¶
How many completions to generate for each prompt.
param openai_api_base: Optional[str] = None (alias 'base_url')¶
Base URL path for API requests, leave blank if not using a proxy or service
emulator.
param openai_api_key: Optional[str] = None (alias 'api_key')¶
Automatically inferred from env var OPENAI_API_KEY if not provided.
param openai_organization: Optional[str] = None (alias 'organization')¶
Automatically inferred from env var OPENAI_ORG_ID if not provided.
param openai_proxy: Optional[str] = None¶
param presence_penalty: float = 0¶
Penalizes repeated tokens.
param request_timeout: Union[float, Tuple[float, float], Any, None] = None (alias 'timeout')¶
Timeout for requests to OpenAI completion API. Can be float, httpx.Timeout or
None.
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 temperature: float = 0.7¶
What sampling temperature to use.
param tiktoken_model_name: Optional[str] = None¶
The model name to pass to tiktoken when using this class.
Tiktoken is used to count the number of tokens in documents to constrain
them to be under a certain limit. By default, when set to None, this will
them to be under a certain limit. By default, when set to None, this will
be the same as the embedding model name. However, there are some cases
where you may want to use this Embedding class with a model name not
supported by tiktoken. This can include when using Azure embeddings or
when using one of the many model providers that expose an OpenAI-like
API but with different models. In those cases, in order to avoid erroring
when tiktoken is called, you can specify a model name to use here.
param top_p: float = 1¶
Total probability mass of tokens to consider at each step.
param verbose: bool [Optional]¶
Whether to print out response text.
__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, *, return_exceptions: bool = False, **kwargs: Any) → List[str]¶
Default implementation runs ainvoke in parallel using asyncio.gather.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode.
between retries
stop_after_attempt – The maximum number of attempts to make before giving up
Returns
A new Runnable that retries the original runnable on exceptions.
with_types(*, input_type: Optional[Type[Input]] = None, output_type: Optional[Type[Output]] = None) → Runnable[Input, Output]¶
Bind input and output types to a Runnable, returning a new Runnable.
property InputType: TypeAlias¶
Get the input type for this runnable.
property OutputType: Type[str]¶
Get the input type for this runnable.
property config_specs: List[langchain_core.runnables.utils.ConfigurableFieldSpec]¶
List configurable fields for this runnable.
property input_schema: Type[pydantic.main.BaseModel]¶
The type of input this runnable accepts specified as a pydantic model.
property lc_attributes: Dict[str, Any]¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
property max_context_size: int¶
Get max context size for this model.
property output_schema: Type[pydantic.main.BaseModel]¶
The type of output this runnable produces specified as a pydantic model.
Examples using VLLMOpenAI¶
vLLM