langchain_community.llms.vllm
.VLLM¶
- class langchain_community.llms.vllm.VLLM[source]¶
Bases:
BaseLLM
VLLM language model.
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 best_of: Optional[int] = None¶
Number of output sequences that are generated from the prompt.
- param cache: Optional[bool] = None¶
Whether to cache the response.
- param callback_manager: Optional[BaseCallbackManager] = None¶
[DEPRECATED]
- param callbacks: Callbacks = None¶
Callbacks to add to the run trace.
- param download_dir: Optional[str] = None¶
Directory to download and load the weights. (Default to the default cache dir of huggingface)
- param dtype: str = 'auto'¶
The data type for the model weights and activations.
- param frequency_penalty: float = 0.0¶
Float that penalizes new tokens based on their frequency in the generated text so far
- param ignore_eos: bool = False¶
Whether to ignore the EOS token and continue generating tokens after the EOS token is generated.
- param logprobs: Optional[int] = None¶
Number of log probabilities to return per output token.
- param max_new_tokens: int = 512¶
Maximum number of tokens to generate per output sequence.
- param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
- param model: str = ''¶
The name or path of a HuggingFace Transformers model.
- param n: int = 1¶
Number of output sequences to return for the given prompt.
- param presence_penalty: float = 0.0¶
Float that penalizes new tokens based on whether they appear in the generated text so far
- param stop: Optional[List[str]] = None¶
List of strings that stop the generation when they are generated.
- param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
- param temperature: float = 1.0¶
Float that controls the randomness of the sampling.
- param tensor_parallel_size: Optional[int] = 1¶
The number of GPUs to use for distributed execution with tensor parallelism.
- param top_k: int = -1¶
Integer that controls the number of top tokens to consider.
- param top_p: float = 1.0¶
Float that controls the cumulative probability of the top tokens to consider.
- param trust_remote_code: Optional[bool] = False¶
Trust remote code (e.g., from HuggingFace) when downloading the model and tokenizer.
- param use_beam_search: bool = False¶
Whether to use beam search instead of sampling.
- param verbose: bool [Optional]¶
Whether to print out response text.
- param vllm_kwargs: Dict[str, Any] [Optional]¶
Holds any model parameters valid for vllm.LLM call not explicitly specified.
- __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 ¶
[Deprecated] Check Cache and run the LLM on the given prompt and input.
Notes
Deprecated since version 0.1.7: Use invoke instead.
- Parameters
prompt (str) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) –
tags (Optional[List[str]]) –
metadata (Optional[Dict[str, Any]]) –
kwargs (Any) –
- Return type
str
- async abatch(inputs: List[Union[PromptValue, str, Sequence[Union[BaseMessage, Tuple[str, str], str, Dict[str, Any]]]]], 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.
- Parameters
inputs (List[Union[PromptValue, str, Sequence[Union[BaseMessage, Tuple[str, str], str, Dict[str, Any]]]]]) –
config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) –
return_exceptions (bool) –
kwargs (Any) –
- Return type
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, run_name: Optional[Union[str, List[str]]] = None, **kwargs: Any) LLMResult ¶
Asynchronously pass a sequence of prompts to a model and return 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[str]) – List of string prompts.
stop (Optional[List[str]]) – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.
callbacks (Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]]) – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation.
**kwargs (Any) – Arbitrary additional keyword arguments. These are usually passed to the model provider API call.
tags (Optional[Union[List[str], List[List[str]]]]) –
metadata (Optional[Union[Dict[str, Any], List[Dict[str, Any]]]]) –
run_name (Optional[Union[str, List[str]]]) –
**kwargs –
- Returns
- An LLMResult, which contains a list of candidate Generations for each input
prompt and additional model provider-specific output.
- Return type
- 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[PromptValue]) – 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 (Optional[List[str]]) – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.
callbacks (Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]]) – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation.
**kwargs (Any) – 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.
- Return type
- async ainvoke(input: Union[PromptValue, str, Sequence[Union[BaseMessage, Tuple[str, str], str, Dict[str, Any]]]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) str ¶
Default implementation of ainvoke, calls invoke from a thread.
The default implementation allows usage of async code even if the runnable did not implement a native async version of invoke.
Subclasses should override this method if they can run asynchronously.
- Parameters
input (Union[PromptValue, str, Sequence[Union[BaseMessage, Tuple[str, str], str, Dict[str, Any]]]]) –
config (Optional[RunnableConfig]) –
stop (Optional[List[str]]) –
kwargs (Any) –
- Return type
str
- async apredict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) str ¶
[Deprecated]
Notes
Deprecated since version 0.1.7: Use ainvoke instead.
- Parameters
text (str) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
- Return type
str
- async apredict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) BaseMessage ¶
[Deprecated]
Notes
Deprecated since version 0.1.7: Use ainvoke instead.
- Parameters
messages (List[BaseMessage]) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
- Return type
- assign(**kwargs: Union[Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any], Mapping[str, Union[Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any]]]]) RunnableSerializable[Any, Any] ¶
Assigns new fields to the dict output of this runnable. Returns a new runnable.
- Parameters
kwargs (Union[Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any], Mapping[str, Union[Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any]]]]) –
- Return type
RunnableSerializable[Any, Any]
- async astream(input: Union[PromptValue, str, Sequence[Union[BaseMessage, Tuple[str, str], str, Dict[str, Any]]]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) AsyncIterator[str] ¶
Default implementation of astream, which calls ainvoke. Subclasses should override this method if they support streaming output.
- Parameters
input (Union[PromptValue, str, Sequence[Union[BaseMessage, Tuple[str, str], str, Dict[str, Any]]]]) –
config (Optional[RunnableConfig]) –
stop (Optional[List[str]]) –
kwargs (Any) –
- Return type
AsyncIterator[str]
- astream_events(input: Any, config: Optional[RunnableConfig] = None, *, version: Literal['v1'], include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Any) AsyncIterator[StreamEvent] ¶
[Beta] Generate a stream of events.
Use to create an iterator over StreamEvents that provide real-time information about the progress of the runnable, including StreamEvents from intermediate results.
A StreamEvent is a dictionary with the following schema:
event
: str - Event names are of theformat: on_[runnable_type]_(start|stream|end).
name
: str - The name of the runnable that generated the event.run_id
: str - randomly generated ID associated with the given execution ofthe runnable that emitted the event. A child runnable that gets invoked as part of the execution of a parent runnable is assigned its own unique ID.
tags
: Optional[List[str]] - The tags of the runnable that generatedthe event.
metadata
: Optional[Dict[str, Any]] - The metadata of the runnablethat generated the event.
data
: Dict[str, Any]
Below is a table that illustrates some evens that might be emitted by various chains. Metadata fields have been omitted from the table for brevity. Chain definitions have been included after the table.
event
name
chunk
input
output
on_chat_model_start
[model name]
{“messages”: [[SystemMessage, HumanMessage]]}
on_chat_model_stream
[model name]
AIMessageChunk(content=”hello”)
on_chat_model_end
[model name]
{“messages”: [[SystemMessage, HumanMessage]]}
{“generations”: […], “llm_output”: None, …}
on_llm_start
[model name]
{‘input’: ‘hello’}
on_llm_stream
[model name]
‘Hello’
on_llm_end
[model name]
‘Hello human!’
on_chain_start
format_docs
on_chain_stream
format_docs
“hello world!, goodbye world!”
on_chain_end
format_docs
[Document(…)]
“hello world!, goodbye world!”
on_tool_start
some_tool
{“x”: 1, “y”: “2”}
on_tool_stream
some_tool
{“x”: 1, “y”: “2”}
on_tool_end
some_tool
{“x”: 1, “y”: “2”}
on_retriever_start
[retriever name]
{“query”: “hello”}
on_retriever_chunk
[retriever name]
{documents: […]}
on_retriever_end
[retriever name]
{“query”: “hello”}
{documents: […]}
on_prompt_start
[template_name]
{“question”: “hello”}
on_prompt_end
[template_name]
{“question”: “hello”}
ChatPromptValue(messages: [SystemMessage, …])
Here are declarations associated with the events shown above:
format_docs:
def format_docs(docs: List[Document]) -> str: '''Format the docs.''' return ", ".join([doc.page_content for doc in docs]) format_docs = RunnableLambda(format_docs)
some_tool:
@tool def some_tool(x: int, y: str) -> dict: '''Some_tool.''' return {"x": x, "y": y}
prompt:
template = ChatPromptTemplate.from_messages( [("system", "You are Cat Agent 007"), ("human", "{question}")] ).with_config({"run_name": "my_template", "tags": ["my_template"]})
Example:
from langchain_core.runnables import RunnableLambda async def reverse(s: str) -> str: return s[::-1] chain = RunnableLambda(func=reverse) events = [ event async for event in chain.astream_events("hello", version="v1") ] # will produce the following events (run_id has been omitted for brevity): [ { "data": {"input": "hello"}, "event": "on_chain_start", "metadata": {}, "name": "reverse", "tags": [], }, { "data": {"chunk": "olleh"}, "event": "on_chain_stream", "metadata": {}, "name": "reverse", "tags": [], }, { "data": {"output": "olleh"}, "event": "on_chain_end", "metadata": {}, "name": "reverse", "tags": [], }, ]
- Parameters
input (Any) – The input to the runnable.
config (Optional[RunnableConfig]) – The config to use for the runnable.
version (Literal['v1']) – The version of the schema to use. Currently only version 1 is available. No default will be assigned until the API is stabilized.
include_names (Optional[Sequence[str]]) – Only include events from runnables with matching names.
include_types (Optional[Sequence[str]]) – Only include events from runnables with matching types.
include_tags (Optional[Sequence[str]]) – Only include events from runnables with matching tags.
exclude_names (Optional[Sequence[str]]) – Exclude events from runnables with matching names.
exclude_types (Optional[Sequence[str]]) – Exclude events from runnables with matching types.
exclude_tags (Optional[Sequence[str]]) – Exclude events from runnables with matching tags.
kwargs (Any) – Additional keyword arguments to pass to the runnable. These will be passed to astream_log as this implementation of astream_events is built on top of astream_log.
- Returns
An async stream of StreamEvents.
- Return type
AsyncIterator[StreamEvent]
Notes
- async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, diff: bool = True, with_streamed_output_list: bool = True, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Any) Union[AsyncIterator[RunLogPatch], AsyncIterator[RunLog]] ¶
Stream all output from a runnable, as reported to the callback system. This includes all inner runs of LLMs, Retrievers, Tools, etc.
Output is streamed as Log objects, which include a list of jsonpatch ops that describe how the state of the run has changed in each step, and the final state of the run.
The jsonpatch ops can be applied in order to construct state.
- Parameters
input (Any) – The input to the runnable.
config (Optional[RunnableConfig]) – The config to use for the runnable.
diff (bool) – Whether to yield diffs between each step, or the current state.
with_streamed_output_list (bool) – Whether to yield the streamed_output list.
include_names (Optional[Sequence[str]]) – Only include logs with these names.
include_types (Optional[Sequence[str]]) – Only include logs with these types.
include_tags (Optional[Sequence[str]]) – Only include logs with these tags.
exclude_names (Optional[Sequence[str]]) – Exclude logs with these names.
exclude_types (Optional[Sequence[str]]) – Exclude logs with these types.
exclude_tags (Optional[Sequence[str]]) – Exclude logs with these tags.
kwargs (Any) –
- Return type
Union[AsyncIterator[RunLogPatch], AsyncIterator[RunLog]]
- async atransform(input: AsyncIterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) AsyncIterator[Output] ¶
Default implementation of atransform, which buffers input and calls astream. Subclasses should override this method if they can start producing output while input is still being generated.
- Parameters
input (AsyncIterator[Input]) –
config (Optional[RunnableConfig]) –
kwargs (Optional[Any]) –
- Return type
AsyncIterator[Output]
- batch(inputs: List[Union[PromptValue, str, Sequence[Union[BaseMessage, Tuple[str, str], str, Dict[str, Any]]]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Any) List[str] ¶
Default implementation runs invoke in parallel using a thread pool executor.
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.
- Parameters
inputs (List[Union[PromptValue, str, Sequence[Union[BaseMessage, Tuple[str, str], str, Dict[str, Any]]]]]) –
config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) –
return_exceptions (bool) –
kwargs (Any) –
- Return type
List[str]
- bind(**kwargs: Any) Runnable[Input, Output] ¶
Bind arguments to a Runnable, returning a new Runnable.
- Parameters
kwargs (Any) –
- Return type
Runnable[Input, Output]
- config_schema(*, include: Optional[Sequence[str]] = None) Type[BaseModel] ¶
The type of config this runnable accepts specified as a pydantic model.
To mark a field as configurable, see the configurable_fields and configurable_alternatives methods.
- Parameters
include (Optional[Sequence[str]]) – A list of fields to include in the config schema.
- Returns
A pydantic model that can be used to validate config.
- Return type
Type[BaseModel]
- configurable_alternatives(which: ConfigurableField, *, default_key: str = 'default', prefix_keys: bool = False, **kwargs: Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) RunnableSerializable[Input, Output] ¶
- Parameters
which (ConfigurableField) –
default_key (str) –
prefix_keys (bool) –
kwargs (Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) –
- Return type
RunnableSerializable[Input, Output]
- configurable_fields(**kwargs: Union[ConfigurableField, ConfigurableFieldSingleOption, ConfigurableFieldMultiOption]) RunnableSerializable[Input, Output] ¶
- Parameters
kwargs (Union[ConfigurableField, ConfigurableFieldSingleOption, ConfigurableFieldMultiOption]) –
- Return type
RunnableSerializable[Input, Output]
- 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
- Parameters
_fields_set (Optional[SetStr]) –
values (Any) –
- Return type
Model
- 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 (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – 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 (bool) – set to True to make a deep copy of the model
self (Model) –
- Returns
new model instance
- Return type
Model
- dict(**kwargs: Any) Dict ¶
Return a dictionary of the LLM.
- Parameters
kwargs (Any) –
- Return type
Dict
- classmethod from_orm(obj: Any) Model ¶
- Parameters
obj (Any) –
- Return type
Model
- 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, run_name: Optional[Union[str, List[str]]] = None, **kwargs: Any) LLMResult ¶
Pass a sequence of prompts to a model and return 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[str]) – List of string prompts.
stop (Optional[List[str]]) – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.
callbacks (Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]]) – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation.
**kwargs (Any) – Arbitrary additional keyword arguments. These are usually passed to the model provider API call.
tags (Optional[Union[List[str], List[List[str]]]]) –
metadata (Optional[Union[Dict[str, Any], List[Dict[str, Any]]]]) –
run_name (Optional[Union[str, List[str]]]) –
**kwargs –
- Returns
- An LLMResult, which contains a list of candidate Generations for each input
prompt and additional model provider-specific output.
- Return type
- 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[PromptValue]) – 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 (Optional[List[str]]) – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.
callbacks (Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]]) – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation.
**kwargs (Any) – 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.
- Return type
- get_graph(config: Optional[RunnableConfig] = None) Graph ¶
Return a graph representation of this runnable.
- Parameters
config (Optional[RunnableConfig]) –
- Return type
- get_input_schema(config: Optional[RunnableConfig] = None) Type[BaseModel] ¶
Get a pydantic model that can be used to validate input to the runnable.
Runnables that leverage the configurable_fields and configurable_alternatives methods will have a dynamic input schema that depends on which configuration the runnable is invoked with.
This method allows to get an input schema for a specific configuration.
- Parameters
config (Optional[RunnableConfig]) – A config to use when generating the schema.
- Returns
A pydantic model that can be used to validate input.
- Return type
Type[BaseModel]
- classmethod get_lc_namespace() List[str] ¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the namespace is [“langchain”, “llms”, “openai”]
- Return type
List[str]
- get_name(suffix: Optional[str] = None, *, name: Optional[str] = None) str ¶
Get the name of the runnable.
- Parameters
suffix (Optional[str]) –
name (Optional[str]) –
- Return type
str
- 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 (str) – The string input to tokenize.
- Returns
The integer number of tokens in the text.
- Return type
int
- 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 (List[BaseMessage]) – The message inputs to tokenize.
- Returns
The sum of the number of tokens across the messages.
- Return type
int
- get_output_schema(config: Optional[RunnableConfig] = None) Type[BaseModel] ¶
Get a pydantic model that can be used to validate output to the runnable.
Runnables that leverage the configurable_fields and configurable_alternatives methods will have a dynamic output schema that depends on which configuration the runnable is invoked with.
This method allows to get an output schema for a specific configuration.
- Parameters
config (Optional[RunnableConfig]) – A config to use when generating the schema.
- Returns
A pydantic model that can be used to validate output.
- Return type
Type[BaseModel]
- get_prompts(config: Optional[RunnableConfig] = None) List[BasePromptTemplate] ¶
- Parameters
config (Optional[RunnableConfig]) –
- Return type
List[BasePromptTemplate]
- get_token_ids(text: str) List[int] ¶
Return the ordered ids of the tokens in a text.
- Parameters
text (str) – The string input to tokenize.
- Returns
- A list of ids corresponding to the tokens in the text, in order they occur
in the text.
- Return type
List[int]
- invoke(input: Union[PromptValue, str, Sequence[Union[BaseMessage, Tuple[str, str], str, Dict[str, Any]]]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) str ¶
Transform a single input into an output. Override to implement.
- Parameters
input (Union[PromptValue, str, Sequence[Union[BaseMessage, Tuple[str, str], str, Dict[str, Any]]]]) – The input to the runnable.
config (Optional[RunnableConfig]) – A config to use when invoking the runnable. The config supports standard keys like ‘tags’, ‘metadata’ for tracing purposes, ‘max_concurrency’ for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details.
stop (Optional[List[str]]) –
kwargs (Any) –
- Returns
The output of the runnable.
- Return type
str
- classmethod is_lc_serializable() bool ¶
Is this class serializable?
- Return type
bool
- 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().
- Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
encoder (Optional[Callable[[Any], Any]]) –
models_as_dict (bool) –
dumps_kwargs (Any) –
- Return type
unicode
- classmethod lc_id() List[str] ¶
A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path to the object.
- Return type
List[str]
- map() Runnable[List[Input], List[Output]] ¶
Return a new Runnable that maps a list of inputs to a list of outputs, by calling invoke() with each input.
- Return type
Runnable[List[Input], List[Output]]
- classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) Model ¶
- Parameters
path (Union[str, Path]) –
content_type (unicode) –
encoding (unicode) –
proto (Protocol) –
allow_pickle (bool) –
- Return type
Model
- classmethod parse_obj(obj: Any) Model ¶
- Parameters
obj (Any) –
- Return type
Model
- classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) Model ¶
- Parameters
b (Union[str, bytes]) –
content_type (unicode) –
encoding (unicode) –
proto (Protocol) –
allow_pickle (bool) –
- Return type
Model
- pick(keys: Union[str, List[str]]) RunnableSerializable[Any, Any] ¶
Pick keys from the dict output of this runnable. Returns a new runnable.
- Parameters
keys (Union[str, List[str]]) –
- Return type
RunnableSerializable[Any, Any]
- pipe(*others: Union[Runnable[Any, Other], Callable[[Any], Other]], name: Optional[str] = None) RunnableSerializable[Input, Other] ¶
Compose this runnable with another object to create a RunnableSequence.
- Parameters
others (Union[Runnable[Any, Other], Callable[[Any], Other]]) –
name (Optional[str]) –
- Return type
RunnableSerializable[Input, Other]
- predict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) str ¶
[Deprecated]
Notes
Deprecated since version 0.1.7: Use invoke instead.
- Parameters
text (str) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
- Return type
str
- predict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) BaseMessage ¶
[Deprecated]
Notes
Deprecated since version 0.1.7: Use invoke instead.
- Parameters
messages (List[BaseMessage]) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
- Return type
- save(file_path: Union[Path, str]) None ¶
Save the LLM.
- Parameters
file_path (Union[Path, str]) – Path to file to save the LLM to.
- Return type
None
Example: .. code-block:: python
llm.save(file_path=”path/llm.yaml”)
- classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') DictStrAny ¶
- Parameters
by_alias (bool) –
ref_template (unicode) –
- Return type
DictStrAny
- classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) unicode ¶
- Parameters
by_alias (bool) –
ref_template (unicode) –
dumps_kwargs (Any) –
- Return type
unicode
- stream(input: Union[PromptValue, str, Sequence[Union[BaseMessage, Tuple[str, str], str, Dict[str, Any]]]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) Iterator[str] ¶
Default implementation of stream, which calls invoke. Subclasses should override this method if they support streaming output.
- Parameters
input (Union[PromptValue, str, Sequence[Union[BaseMessage, Tuple[str, str], str, Dict[str, Any]]]]) –
config (Optional[RunnableConfig]) –
stop (Optional[List[str]]) –
kwargs (Any) –
- Return type
Iterator[str]
- to_json() Union[SerializedConstructor, SerializedNotImplemented] ¶
Serialize the runnable to JSON.
- Return type
- to_json_not_implemented() SerializedNotImplemented ¶
- Return type
- transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) Iterator[Output] ¶
Default implementation of transform, which buffers input and then calls stream. Subclasses should override this method if they can start producing output while input is still being generated.
- Parameters
input (Iterator[Input]) –
config (Optional[RunnableConfig]) –
kwargs (Optional[Any]) –
- Return type
Iterator[Output]
- classmethod update_forward_refs(**localns: Any) None ¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
- Parameters
localns (Any) –
- Return type
None
- classmethod validate(value: Any) Model ¶
- Parameters
value (Any) –
- Return type
Model
- with_config(config: Optional[RunnableConfig] = None, **kwargs: Any) Runnable[Input, Output] ¶
Bind config to a Runnable, returning a new Runnable.
- Parameters
config (Optional[RunnableConfig]) –
kwargs (Any) –
- Return type
Runnable[Input, Output]
- with_fallbacks(fallbacks: Sequence[Runnable[Input, Output]], *, exceptions_to_handle: Tuple[Type[BaseException], ...] = (<class 'Exception'>,), exception_key: Optional[str] = None) RunnableWithFallbacksT[Input, Output] ¶
Add fallbacks to a runnable, returning a new Runnable.
- Parameters
fallbacks (Sequence[Runnable[Input, Output]]) – A sequence of runnables to try if the original runnable fails.
exceptions_to_handle (Tuple[Type[BaseException], ...]) – A tuple of exception types to handle.
exception_key (Optional[str]) – If string is specified then handled exceptions will be passed to fallbacks as part of the input under the specified key. If None, exceptions will not be passed to fallbacks. If used, the base runnable and its fallbacks must accept a dictionary as input.
- Returns
A new Runnable that will try the original runnable, and then each fallback in order, upon failures.
- Return type
RunnableWithFallbacksT[Input, Output]
- with_listeners(*, on_start: Optional[Listener] = None, on_end: Optional[Listener] = None, on_error: Optional[Listener] = None) Runnable[Input, Output] ¶
Bind lifecycle listeners to a Runnable, returning a new Runnable.
on_start: Called before the runnable starts running, with the Run object. on_end: Called after the runnable finishes running, with the Run object. on_error: Called if the runnable throws an error, with the Run object.
The Run object contains information about the run, including its id, type, input, output, error, start_time, end_time, and any tags or metadata added to the run.
- Parameters
on_start (Optional[Listener]) –
on_end (Optional[Listener]) –
on_error (Optional[Listener]) –
- Return type
Runnable[Input, Output]
- with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, stop_after_attempt: int = 3) Runnable[Input, Output] ¶
Create a new Runnable that retries the original runnable on exceptions.
- Parameters
retry_if_exception_type (Tuple[Type[BaseException], ...]) – A tuple of exception types to retry on
wait_exponential_jitter (bool) – Whether to add jitter to the wait time between retries
stop_after_attempt (int) – The maximum number of attempts to make before giving up
- Returns
A new Runnable that retries the original runnable on exceptions.
- Return type
Runnable[Input, Output]
- with_structured_output(schema: Union[Dict, Type[BaseModel]], **kwargs: Any) Runnable[Union[PromptValue, str, Sequence[Union[BaseMessage, Tuple[str, str], str, Dict[str, Any]]]], Union[Dict, BaseModel]] ¶
[Beta] Implement this if there is a way of steering the model to generate responses that match a given schema.
Notes
- Parameters
schema (Union[Dict, Type[BaseModel]]) –
kwargs (Any) –
- Return type
Runnable[Union[PromptValue, str, Sequence[Union[BaseMessage, Tuple[str, str], str, Dict[str, Any]]]], Union[Dict, BaseModel]]
- 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.
- Parameters
input_type (Optional[Type[Input]]) –
output_type (Optional[Type[Output]]) –
- Return type
Runnable[Input, Output]
- 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[ConfigurableFieldSpec]¶
List configurable fields for this runnable.
- property input_schema: Type[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”}
- name: Optional[str] = None¶
The name of the runnable. Used for debugging and tracing.
- property output_schema: Type[BaseModel]¶
The type of output this runnable produces specified as a pydantic model.