Anthropic#
- class langchain_anthropic.llms.Anthropic[source]#
Bases:
AnthropicLLM
Deprecated since version 0.1.0: Use
AnthropicLLM
instead.Anthropic large language model.
Note
Anthropic implements the standard
Runnable Interface
. πThe
Runnable Interface
has additional methods that are available on runnables, such aswith_types
,with_retry
,assign
,bind
,get_graph
, and more.- param AI_PROMPT: str | None = None#
- param HUMAN_PROMPT: str | None = None#
- param anthropic_api_key: SecretStr [Optional] (alias 'api_key')#
Automatically read from env var ANTHROPIC_API_KEY if not provided.
- Constraints:
type = string
writeOnly = True
format = password
- param anthropic_api_url: str | None [Optional] (alias 'base_url')#
Base URL for API requests. Only specify if using a proxy or service emulator.
If a value isnβt passed in, will attempt to read the value from ANTHROPIC_API_URL. If not set, the default value of βhttps://api.anthropic.comβ will be used.
- param cache: BaseCache | bool | None = None#
Whether to cache the response.
If true, will use the global cache.
If false, will not use a cache
If None, will use the global cache if itβs set, otherwise no cache.
If instance of BaseCache, will use the provided cache.
Caching is not currently supported for streaming methods of models.
- param callback_manager: BaseCallbackManager | None = None#
[DEPRECATED]
- param callbacks: Callbacks = None#
Callbacks to add to the run trace.
- param count_tokens: Callable[[str], int] | None = None#
- param custom_get_token_ids: Callable[[str], List[int]] | None = None#
Optional encoder to use for counting tokens.
- param default_request_timeout: float | None = None#
Timeout for requests to Anthropic Completion API. Default is 600 seconds.
- param max_retries: int = 2#
Number of retries allowed for requests sent to the Anthropic Completion API.
- param max_tokens_to_sample: int = 1024 (alias 'max_tokens')#
Denotes the number of tokens to predict per generation.
- param metadata: Dict[str, Any] | None = None#
Metadata to add to the run trace.
- param model: str = 'claude-2' (alias 'model_name')#
Model name to use.
- param model_kwargs: Dict[str, Any] [Optional]#
- param streaming: bool = False#
Whether to stream the results.
- param tags: List[str] | None = None#
Tags to add to the run trace.
- param temperature: float | None = None#
A non-negative float that tunes the degree of randomness in generation.
- param top_k: int | None = None#
Number of most likely tokens to consider at each step.
- param top_p: float | None = None#
Total probability mass of tokens to consider at each step.
- param verbose: bool [Optional]#
Whether to print out response text.
- __call__(prompt: str, stop: List[str] | None = None, callbacks: List[BaseCallbackHandler] | BaseCallbackManager | None = None, *, tags: List[str] | None = None, metadata: Dict[str, Any] | None = None, **kwargs: Any) str #
Deprecated since version langchain-core==0.1.7: Use
invoke
instead.Check Cache and run the LLM on the given prompt and input.
- Parameters:
prompt (str) β The prompt to generate from.
stop (List[str] | None) β Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.
callbacks (List[BaseCallbackHandler] | BaseCallbackManager | None) β Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation.
tags (List[str] | None) β List of tags to associate with the prompt.
metadata (Dict[str, Any] | None) β Metadata to associate with the prompt.
**kwargs (Any) β Arbitrary additional keyword arguments. These are usually passed to the model provider API call.
- Returns:
The generated text.
- Raises:
ValueError β If the prompt is not a string.
- Return type:
str
- async abatch(inputs: List[PromptValue | str | Sequence[BaseMessage | List[str] | Tuple[str, str] | str | Dict[str, Any]]], config: RunnableConfig | List[RunnableConfig] | None = 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[PromptValue | str | Sequence[BaseMessage | List[str] | Tuple[str, str] | str | Dict[str, Any]]]) β A list of inputs to the Runnable.
config (RunnableConfig | List[RunnableConfig] | None) β A config to use when invoking the Runnable. The config supports standard keys like βtagsβ, βmetadataβ for tracing purposes, βmax_concurrencyβ for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details. Defaults to None.
return_exceptions (bool) β Whether to return exceptions instead of raising them. Defaults to False.
kwargs (Any) β Additional keyword arguments to pass to the Runnable.
- Returns:
A list of outputs from the Runnable.
- Return type:
List[str]
- async abatch_as_completed(inputs: Sequence[Input], config: RunnableConfig | Sequence[RunnableConfig] | None = None, *, return_exceptions: bool = False, **kwargs: Any | None) AsyncIterator[Tuple[int, Output | Exception]] #
Run ainvoke in parallel on a list of inputs, yielding results as they complete.
- Parameters:
inputs (Sequence[Input]) β A list of inputs to the Runnable.
config (RunnableConfig | Sequence[RunnableConfig] | None) β A config to use when invoking the Runnable. The config supports standard keys like βtagsβ, βmetadataβ for tracing purposes, βmax_concurrencyβ for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details. Defaults to None. Defaults to None.
return_exceptions (bool) β Whether to return exceptions instead of raising them. Defaults to False.
kwargs (Any | None) β Additional keyword arguments to pass to the Runnable.
- Yields:
A tuple of the index of the input and the output from the Runnable.
- Return type:
AsyncIterator[Tuple[int, Output | Exception]]
- async agenerate(prompts: List[str], stop: List[str] | None = None, callbacks: List[BaseCallbackHandler] | BaseCallbackManager | None | List[List[BaseCallbackHandler] | BaseCallbackManager | None] = None, *, tags: List[str] | List[List[str]] | None = None, metadata: Dict[str, Any] | List[Dict[str, Any]] | None = None, run_name: str | List[str] | None = None, run_id: UUID | List[UUID | None] | None = 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]) β List of string prompts.
stop (List[str] | None) β Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.
callbacks (List[BaseCallbackHandler] | BaseCallbackManager | None | List[List[BaseCallbackHandler] | BaseCallbackManager | None]) β Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation.
tags (List[str] | List[List[str]] | None) β List of tags to associate with each prompt. If provided, the length of the list must match the length of the prompts list.
metadata (Dict[str, Any] | List[Dict[str, Any]] | None) β List of metadata dictionaries to associate with each prompt. If provided, the length of the list must match the length of the prompts list.
run_name (str | List[str] | None) β List of run names to associate with each prompt. If provided, the length of the list must match the length of the prompts list.
run_id (UUID | List[UUID | None] | None) β List of run IDs to associate with each prompt. If provided, the length of the list must match the length of the prompts list.
**kwargs (Any) β 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 agenerate_prompt(prompts: List[PromptValue], stop: List[str] | None = None, callbacks: List[BaseCallbackHandler] | BaseCallbackManager | None | List[List[BaseCallbackHandler] | BaseCallbackManager | None] = 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]) β 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 (List[str] | None) β Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.
callbacks (List[BaseCallbackHandler] | BaseCallbackManager | None | List[List[BaseCallbackHandler] | BaseCallbackManager | None]) β Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation.
**kwargs (Any) β 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: PromptValue | str | Sequence[BaseMessage | List[str] | Tuple[str, str] | str | Dict[str, Any]], config: RunnableConfig | None = None, *, stop: List[str] | None = 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 (PromptValue | str | Sequence[BaseMessage | List[str] | Tuple[str, str] | str | Dict[str, Any]]) β
config (RunnableConfig | None) β
stop (List[str] | None) β
kwargs (Any) β
- Return type:
str
- async apredict(text: str, *, stop: Sequence[str] | None = None, **kwargs: Any) str #
Deprecated since version langchain-core==0.1.7: Use
ainvoke
instead.- Parameters:
text (str) β
stop (Sequence[str] | None) β
kwargs (Any) β
- Return type:
str
- async apredict_messages(messages: List[BaseMessage], *, stop: Sequence[str] | None = None, **kwargs: Any) BaseMessage #
Deprecated since version langchain-core==0.1.7: Use
ainvoke
instead.- Parameters:
messages (List[BaseMessage]) β
stop (Sequence[str] | None) β
kwargs (Any) β
- Return type:
- async astream(input: PromptValue | str | Sequence[BaseMessage | List[str] | Tuple[str, str] | str | Dict[str, Any]], config: RunnableConfig | None = None, *, stop: List[str] | None = None, **kwargs: Any) AsyncIterator[str] #
Default implementation of astream, which calls ainvoke. Subclasses should override this method if they support streaming output.
- Parameters:
input (PromptValue | str | Sequence[BaseMessage | List[str] | Tuple[str, str] | str | Dict[str, Any]]) β The input to the Runnable.
config (RunnableConfig | None) β The config to use for the Runnable. Defaults to None.
kwargs (Any) β Additional keyword arguments to pass to the Runnable.
stop (List[str] | None) β
- Yields:
The output of the Runnable.
- Return type:
AsyncIterator[str]
- astream_events(input: Any, config: RunnableConfig | None = None, *, version: Literal['v1', 'v2'], include_names: Sequence[str] | None = None, include_types: Sequence[str] | None = None, include_tags: Sequence[str] | None = None, exclude_names: Sequence[str] | None = None, exclude_types: Sequence[str] | None = None, exclude_tags: Sequence[str] | None = None, **kwargs: Any) AsyncIterator[StandardStreamEvent | CustomStreamEvent] #
Beta
This API is in beta and may change in the future.
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.
parent_ids
: List[str] - The IDs of the parent runnables thatgenerated the event. The root Runnable will have an empty list. The order of the parent IDs is from the root to the immediate parent. Only available for v2 version of the API. The v1 version of the API will return an empty list.
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.
ATTENTION This reference table is for the V2 version of the schema.
event
name
chunk
input
output
on_chat_model_start
[model name]
{βmessagesβ: [[SystemMessage, HumanMessage]]}
on_chat_model_stream
[model name]
AIMessageChunk(content=βhelloβ)
on_chat_model_end
[model name]
{βmessagesβ: [[SystemMessage, HumanMessage]]}
AIMessageChunk(content=βhello worldβ)
on_llm_start
[model name]
{βinputβ: βhelloβ}
on_llm_stream
[model name]
βHelloβ
on_llm_end
[model name]
βHello human!β
on_chain_start
format_docs
on_chain_stream
format_docs
βhello world!, goodbye world!β
on_chain_end
format_docs
[Document(β¦)]
βhello world!, goodbye world!β
on_tool_start
some_tool
{βxβ: 1, βyβ: β2β}
on_tool_end
some_tool
{βxβ: 1, βyβ: β2β}
on_retriever_start
[retriever name]
{βqueryβ: βhelloβ}
on_retriever_end
[retriever name]
{βqueryβ: βhelloβ}
[Document(β¦), ..]
on_prompt_start
[template_name]
{βquestionβ: βhelloβ}
on_prompt_end
[template_name]
{βquestionβ: βhelloβ}
ChatPromptValue(messages: [SystemMessage, β¦])
In addition to the standard events, users can also dispatch custom events (see example below).
Custom events will be only be surfaced with in the v2 version of the API!
A custom event has following format:
Attribute
Type
Description
name
str
A user defined name for the event.
data
Any
The data associated with the event. This can be anything, though we suggest making it JSON serializable.
Here are declarations associated with the standard 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="v2") ] # will produce the following events (run_id, and parent_ids # 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": [], }, ]
Example: Dispatch Custom Event
from langchain_core.callbacks.manager import ( adispatch_custom_event, ) from langchain_core.runnables import RunnableLambda, RunnableConfig import asyncio async def slow_thing(some_input: str, config: RunnableConfig) -> str: """Do something that takes a long time.""" await asyncio.sleep(1) # Placeholder for some slow operation await adispatch_custom_event( "progress_event", {"message": "Finished step 1 of 3"}, config=config # Must be included for python < 3.10 ) await asyncio.sleep(1) # Placeholder for some slow operation await adispatch_custom_event( "progress_event", {"message": "Finished step 2 of 3"}, config=config # Must be included for python < 3.10 ) await asyncio.sleep(1) # Placeholder for some slow operation return "Done" slow_thing = RunnableLambda(slow_thing) async for event in slow_thing.astream_events("some_input", version="v2"): print(event)
- Parameters:
input (Any) β The input to the Runnable.
config (RunnableConfig | None) β The config to use for the Runnable.
version (Literal['v1', 'v2']) β The version of the schema to use either v2 or v1. Users should use v2. v1 is for backwards compatibility and will be deprecated in 0.4.0. No default will be assigned until the API is stabilized. custom events will only be surfaced in v2.
include_names (Sequence[str] | None) β Only include events from runnables with matching names.
include_types (Sequence[str] | None) β Only include events from runnables with matching types.
include_tags (Sequence[str] | None) β Only include events from runnables with matching tags.
exclude_names (Sequence[str] | None) β Exclude events from runnables with matching names.
exclude_types (Sequence[str] | None) β Exclude events from runnables with matching types.
exclude_tags (Sequence[str] | None) β Exclude events from runnables with matching tags.
kwargs (Any) β 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.
- Yields:
An async stream of StreamEvents.
- Raises:
NotImplementedError β If the version is not v1 or v2.
- Return type:
AsyncIterator[StandardStreamEvent | CustomStreamEvent]
- batch(inputs: List[PromptValue | str | Sequence[BaseMessage | List[str] | Tuple[str, str] | str | Dict[str, Any]]], config: RunnableConfig | List[RunnableConfig] | None = 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[PromptValue | str | Sequence[BaseMessage | List[str] | Tuple[str, str] | str | Dict[str, Any]]]) β
config (RunnableConfig | List[RunnableConfig] | None) β
return_exceptions (bool) β
kwargs (Any) β
- Return type:
List[str]
- batch_as_completed(inputs: Sequence[Input], config: RunnableConfig | Sequence[RunnableConfig] | None = None, *, return_exceptions: bool = False, **kwargs: Any | None) Iterator[Tuple[int, Output | Exception]] #
Run invoke in parallel on a list of inputs, yielding results as they complete.
- Parameters:
inputs (Sequence[Input]) β
config (RunnableConfig | Sequence[RunnableConfig] | None) β
return_exceptions (bool) β
kwargs (Any | None) β
- Return type:
Iterator[Tuple[int, Output | Exception]]
- configurable_alternatives(which: ConfigurableField, *, default_key: str = 'default', prefix_keys: bool = False, **kwargs: Runnable[Input, Output] | Callable[[], Runnable[Input, Output]]) RunnableSerializable[Input, Output] #
Configure alternatives for Runnables that can be set at runtime.
- Parameters:
which (ConfigurableField) β The ConfigurableField instance that will be used to select the alternative.
default_key (str) β The default key to use if no alternative is selected. Defaults to βdefaultβ.
prefix_keys (bool) β Whether to prefix the keys with the ConfigurableField id. Defaults to False.
**kwargs (Runnable[Input, Output] | Callable[[], Runnable[Input, Output]]) β A dictionary of keys to Runnable instances or callables that return Runnable instances.
- Returns:
A new Runnable with the alternatives configured.
- Return type:
RunnableSerializable[Input, Output]
from langchain_anthropic import ChatAnthropic from langchain_core.runnables.utils import ConfigurableField from langchain_openai import ChatOpenAI model = ChatAnthropic( model_name="claude-3-sonnet-20240229" ).configurable_alternatives( ConfigurableField(id="llm"), default_key="anthropic", openai=ChatOpenAI() ) # uses the default model ChatAnthropic print(model.invoke("which organization created you?").content) # uses ChatOpenAI print( model.with_config( configurable={"llm": "openai"} ).invoke("which organization created you?").content )
- configurable_fields(**kwargs: ConfigurableField | ConfigurableFieldSingleOption | ConfigurableFieldMultiOption) RunnableSerializable[Input, Output] #
Configure particular Runnable fields at runtime.
- Parameters:
**kwargs (ConfigurableField | ConfigurableFieldSingleOption | ConfigurableFieldMultiOption) β A dictionary of ConfigurableField instances to configure.
- Returns:
A new Runnable with the fields configured.
- Return type:
RunnableSerializable[Input, Output]
from langchain_core.runnables import ConfigurableField from langchain_openai import ChatOpenAI model = ChatOpenAI(max_tokens=20).configurable_fields( max_tokens=ConfigurableField( id="output_token_number", name="Max tokens in the output", description="The maximum number of tokens in the output", ) ) # max_tokens = 20 print( "max_tokens_20: ", model.invoke("tell me something about chess").content ) # max_tokens = 200 print("max_tokens_200: ", model.with_config( configurable={"output_token_number": 200} ).invoke("tell me something about chess").content )
- convert_prompt(prompt: PromptValue) str #
- Parameters:
prompt (PromptValue) β
- Return type:
str
- generate(prompts: List[str], stop: List[str] | None = None, callbacks: List[BaseCallbackHandler] | BaseCallbackManager | None | List[List[BaseCallbackHandler] | BaseCallbackManager | None] = None, *, tags: List[str] | List[List[str]] | None = None, metadata: Dict[str, Any] | List[Dict[str, Any]] | None = None, run_name: str | List[str] | None = None, run_id: UUID | List[UUID | None] | None = 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]) β List of string prompts.
stop (List[str] | None) β Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.
callbacks (List[BaseCallbackHandler] | BaseCallbackManager | None | List[List[BaseCallbackHandler] | BaseCallbackManager | None]) β Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation.
tags (List[str] | List[List[str]] | None) β List of tags to associate with each prompt. If provided, the length of the list must match the length of the prompts list.
metadata (Dict[str, Any] | List[Dict[str, Any]] | None) β List of metadata dictionaries to associate with each prompt. If provided, the length of the list must match the length of the prompts list.
run_name (str | List[str] | None) β List of run names to associate with each prompt. If provided, the length of the list must match the length of the prompts list.
run_id (UUID | List[UUID | None] | None) β List of run IDs to associate with each prompt. If provided, the length of the list must match the length of the prompts list.
**kwargs (Any) β 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:
- generate_prompt(prompts: List[PromptValue], stop: List[str] | None = None, callbacks: List[BaseCallbackHandler] | BaseCallbackManager | None | List[List[BaseCallbackHandler] | BaseCallbackManager | None] = 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]) β 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 (List[str] | None) β Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.
callbacks (List[BaseCallbackHandler] | BaseCallbackManager | None | List[List[BaseCallbackHandler] | BaseCallbackManager | None]) β Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation.
**kwargs (Any) β 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_num_tokens(text: str) int #
Calculate number of tokens.
- Parameters:
text (str) β
- 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 fits in a modelβs context window.
- Parameters:
messages (List[BaseMessage]) β The message inputs to tokenize.
- Returns:
The sum of the number of tokens across the messages.
- Return type:
int
- get_token_ids(text: str) List[int] #
Return the ordered ids of the tokens in a text.
- Parameters:
text (str) β 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: PromptValue | str | Sequence[BaseMessage | List[str] | Tuple[str, str] | str | Dict[str, Any]], config: RunnableConfig | None = None, *, stop: List[str] | None = None, **kwargs: Any) str #
Transform a single input into an output. Override to implement.
- Parameters:
input (PromptValue | str | Sequence[BaseMessage | List[str] | Tuple[str, str] | str | Dict[str, Any]]) β The input to the Runnable.
config (RunnableConfig | None) β A config to use when invoking the Runnable. The config supports standard keys like βtagsβ, βmetadataβ for tracing purposes, βmax_concurrencyβ for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details.
stop (List[str] | None) β
kwargs (Any) β
- Returns:
The output of the Runnable.
- Return type:
str
- predict(text: str, *, stop: Sequence[str] | None = None, **kwargs: Any) str #
Deprecated since version langchain-core==0.1.7: Use
invoke
instead.- Parameters:
text (str) β
stop (Sequence[str] | None) β
kwargs (Any) β
- Return type:
str
- predict_messages(messages: List[BaseMessage], *, stop: Sequence[str] | None = None, **kwargs: Any) BaseMessage #
Deprecated since version langchain-core==0.1.7: Use
invoke
instead.- Parameters:
messages (List[BaseMessage]) β
stop (Sequence[str] | None) β
kwargs (Any) β
- Return type:
- save(file_path: Path | str) None #
Save the LLM.
- Parameters:
file_path (Path | str) β Path to file to save the LLM to.
- Raises:
ValueError β If the file path is not a string or Path object.
- Return type:
None
Example: .. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
- stream(input: PromptValue | str | Sequence[BaseMessage | List[str] | Tuple[str, str] | str | Dict[str, Any]], config: RunnableConfig | None = None, *, stop: List[str] | None = None, **kwargs: Any) Iterator[str] #
Default implementation of stream, which calls invoke. Subclasses should override this method if they support streaming output.
- Parameters:
input (PromptValue | str | Sequence[BaseMessage | List[str] | Tuple[str, str] | str | Dict[str, Any]]) β The input to the Runnable.
config (RunnableConfig | None) β The config to use for the Runnable. Defaults to None.
kwargs (Any) β Additional keyword arguments to pass to the Runnable.
stop (List[str] | None) β
- Yields:
The output of the Runnable.
- Return type:
Iterator[str]
- to_json() SerializedConstructor | SerializedNotImplemented #
Serialize the Runnable to JSON.
- Returns:
A JSON-serializable representation of the Runnable.
- Return type:
- with_structured_output(schema: Dict | Type[BaseModel], **kwargs: Any) Runnable[PromptValue | str | Sequence[BaseMessage | List[str] | Tuple[str, str] | str | Dict[str, Any]], Dict | BaseModel] #
Not implemented on this class.
- Parameters:
schema (Dict | Type[BaseModel]) β
kwargs (Any) β
- Return type:
Runnable[PromptValue | str | Sequence[BaseMessage | List[str] | Tuple[str, str] | str | Dict[str, Any]], Dict | BaseModel]