OpenAI#

class langchain_openai.llms.base.OpenAI[source]#

Bases: BaseOpenAI

OpenAI completion model integration.

Setup:

Install langchain-openai and set environment variable OPENAI_API_KEY.

pip install -U langchain-openai
export OPENAI_API_KEY="your-api-key"

Key init args — completion params:

model: str: Name of OpenAI model to use.
temperature: float: Sampling temperature.
max_tokens: Optional[int]: Max number of tokens to generate.
logprobs: Optional[bool]: Whether to return logprobs.
stream_options: Dict: Configure streaming outputs, like whether to return token usage when streaming ({"include_usage": True}).

Key init args — client params:

timeout: Union[float, Tuple[float, float], Any, None]: Timeout for requests.
max_retries: int: Max number of retries.
api_key: Optional[str]: OpenAI API key. If not passed in will be read from env var OPENAI_API_KEY.
base_url: Optional[str]: Base URL for API requests. Only specify if using a proxy or service emulator.
organization: Optional[str]: OpenAI organization ID. If not passed in will be read from env var OPENAI_ORG_ID.

See full list of supported init args and their descriptions in the params section.

Instantiate:

from langchain_openai import OpenAI

llm = OpenAI(
    model="gpt-3.5-turbo-instruct",
    temperature=0,
    max_retries=2,
    # api_key="...",
    # base_url="...",
    # organization="...",
    # other params...
)

Invoke:

input_text = "The meaning of life is "
llm.invoke(input_text)

"a philosophical question that has been debated by thinkers and scholars for centuries."

Stream:

for chunk in llm.stream(input_text):
    print(chunk, end="|")

a| philosophical| question| that| has| been| debated| by| thinkers| and| scholars| for| centuries|.

"".join(llm.stream(input_text))

"a philosophical question that has been debated by thinkers and scholars for centuries."

Async:

await llm.ainvoke(input_text)

# stream:
# async for chunk in (await llm.astream(input_text)):
#    print(chunk)

# batch:
# await llm.abatch([input_text])

"a philosophical question that has been debated by thinkers and scholars for centuries."

Note

OpenAI implements the standard Runnable Interface. 🏃

The Runnable Interface has additional methods that are available on runnables, such as with_types, with_retry, assign, bind, get_graph, and more.

param allowed_special: 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: 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 it’s 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 custom_get_token_ids: Callable[[str], List[int]] | None = None#: Optional encoder to use for counting tokens.

param default_headers: Mapping[str, str] | None = None#

param default_query: Mapping[str, object] | None = None#

param disallowed_special: Literal['all'] | Collection[str] = 'all'#: Set of special tokens that are not allowed。

param extra_body: Mapping[str, Any] | None = None#: Optional additional JSON properties to include in the request parameters when making requests to OpenAI compatible APIs, such as vLLM.

param frequency_penalty: float = 0#: Penalizes repeated tokens according to frequency.

param http_async_client: Any | None = None#: Optional httpx.AsyncClient. Only used for async invocations. Must specify http_client as well if you’d like a custom client for sync invocations.

param http_client: Any | None = None#: Optional httpx.Client. Only used for sync invocations. Must specify http_async_client as well if you’d like a custom client for async invocations.

param logit_bias: Dict[str, float] | None [Optional]#: Adjust the probability of specific tokens being generated.

param logprobs: int | None = None#: Include the log probabilities on the logprobs most likely output tokens, as well the chosen tokens.

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: Dict[str, Any] | None = 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 = 'gpt-3.5-turbo-instruct' (alias 'model')#: Model name to use.

param n: int = 1#: How many completions to generate for each prompt.

param openai_api_base: str | None [Optional] (alias 'base_url')#: Base URL path for API requests, leave blank if not using a proxy or service emulator.

param openai_api_key: SecretStr | None [Optional] (alias 'api_key')#

Automatically inferred from env var OPENAI_API_KEY if not provided.

Constraints:

type = string
writeOnly = True
format = password

param openai_organization: str | None [Optional] (alias 'organization')#: Automatically inferred from env var OPENAI_ORG_ID if not provided.

param openai_proxy: str | None [Optional]#

param presence_penalty: float = 0#: Penalizes repeated tokens.

param request_timeout: float | Tuple[float, float] | Any | None = None (alias 'timeout')#: Timeout for requests to OpenAI completion API. Can be float, httpx.Timeout or None.

param seed: int | None = None#: Seed for generation

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

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

param temperature: float = 0.7#: What sampling temperature to use.

param tiktoken_model_name: str | None = 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 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.

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) – The prompt to generate from.
stop (List[str] | None) – 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) – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation.
tags (List[str] | None) – List of tags to associate with the prompt.
metadata (Dict[str, Any] | None) – Metadata to associate with the prompt.
**kwargs (Any) – Arbitrary additional keyword arguments. These are usually passed to the model provider API call.

Returns:

The generated text.

Raises:

ValueError – If the prompt is not a string.

Return type:

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]]]) – A list of inputs to the Runnable.
config (RunnableConfig | List[RunnableConfig] | None) – 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. Defaults to None.
return_exceptions (bool) – Whether to return exceptions instead of raising them. Defaults to False.
kwargs (Any) – 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]) – A list of inputs to the Runnable.
config (RunnableConfig | Sequence[RunnableConfig] | None) – 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. Defaults to None. Defaults to None.
return_exceptions (bool) – Whether to return exceptions instead of raising them. Defaults to False.
kwargs (Any | None) – 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]]

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 (List[str] | None) – 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]) – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation.
tags (List[str] | List[List[str]] | None) – 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) – 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) – 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) – 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) – 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:

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 (List[str] | None) – 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]) – 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:

LLMResult

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]]) –
config (RunnableConfig | None) –
stop (List[str] | None) –
kwargs (Any) –

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) –
stop (Sequence[str] | None) –
kwargs (Any) –

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]) –
stop (Sequence[str] | None) –
kwargs (Any) –

Return type:

BaseMessage

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]]) – The input to the Runnable.
config (RunnableConfig | None) – The config to use for the Runnable. Defaults to None.
kwargs (Any) – Additional keyword arguments to pass to the Runnable.
stop (List[str] | None) –

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 the
format: 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 of
the 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 that
generated 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 generated
the event.
metadata: Optional[Dict[str, Any]] - The metadata of the Runnable
that 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]		{“messages”: [[SystemMessage, HumanMessage]]}
on_chat_model_stream	[model name]	AIMessageChunk(content=”hello”)
on_chat_model_end	[model name]		{“messages”: [[SystemMessage, HumanMessage]]}	AIMessageChunk(content=”hello world”)
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_end	some_tool			{“x”: 1, “y”: “2”}
on_retriever_start	[retriever name]		{“query”: “hello”}
on_retriever_end	[retriever name]		{“query”: “hello”}	[Document(…), ..]
on_prompt_start	[template_name]		{“question”: “hello”}
on_prompt_end	[template_name]		{“question”: “hello”}	ChatPromptValue(messages: [SystemMessage, …])

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) – The input to the Runnable.
config (RunnableConfig | None) – The config to use for the Runnable.
version (Literal['v1', 'v2']) – 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) – Only include events from runnables with matching names.
include_types (Sequence[str] | None) – Only include events from runnables with matching types.
include_tags (Sequence[str] | None) – Only include events from runnables with matching tags.
exclude_names (Sequence[str] | None) – Exclude events from runnables with matching names.
exclude_types (Sequence[str] | None) – Exclude events from runnables with matching types.
exclude_tags (Sequence[str] | None) – 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.

Yields:

An async stream of StreamEvents.

Raises:

NotImplementedError – If the version is not v1 or v2.

Return type:

AsyncIterator[StandardStreamEvent | CustomStreamEvent]

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]]]) –
config (RunnableConfig | List[RunnableConfig] | None) –
return_exceptions (bool) –
kwargs (Any) –

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]) –
config (RunnableConfig | Sequence[RunnableConfig] | None) –
return_exceptions (bool) –
kwargs (Any | None) –

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) – The ConfigurableField instance that will be used to select the alternative.
default_key (str) – The default key to use if no alternative is selected. Defaults to “default”.
prefix_keys (bool) – Whether to prefix the keys with the ConfigurableField id. Defaults to False.
**kwargs (Runnable[Input, Output] | Callable[[], Runnable[Input, Output]]) – 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) – 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
)

create_llm_result(choices: Any, prompts: List[str], params: Dict[str, Any], token_usage: Dict[str, int], *, system_fingerprint: str | None = None) → LLMResult#

Create the LLMResult from the choices and prompts.

Parameters:

choices (Any) –
prompts (List[str]) –
params (Dict[str, Any]) –
token_usage (Dict[str, int]) –
system_fingerprint (str | None) –

Return type:

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 (List[str] | None) – 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]) – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation.
tags (List[str] | List[List[str]] | None) – 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) – 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) – 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) – 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) – 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:

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 (List[str] | None) – 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]) – 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:

LLMResult

get_num_tokens(text: str) → int#

Get the number of tokens present in the text.

Useful for checking if an input fits 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 fits 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_sub_prompts(params: Dict[str, Any], prompts: List[str], stop: List[str] | None = None) → List[List[str]]#

Get the sub prompts for llm call.

Parameters:

params (Dict[str, Any]) –
prompts (List[str]) –
stop (List[str] | None) –

Return type:

List[List[str]]

get_token_ids(text: str) → List[int]#

Get the token IDs using the tiktoken package.

Parameters:: text (str) –
Return type:: List[int]

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]]) – The input to the Runnable.
config (RunnableConfig | None) – 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 (List[str] | None) –
kwargs (Any) –

Returns:

The output of the Runnable.

Return type:

str

max_tokens_for_prompt(prompt: str) → int#

Calculate the maximum number of tokens possible to generate for a prompt.

Parameters:: prompt (str) – The prompt to pass into the model.
Returns:: The maximum number of tokens to generate for a prompt.
Return type:: int

Example

max_tokens = openai.max_token_for_prompt("Tell me a joke.")

static modelname_to_contextsize(modelname: str) → int#

Calculate the maximum number of tokens possible to generate for a model.

Parameters:: modelname (str) – The modelname we want to know the context size for.
Returns:: The maximum context size
Return type:: int

Example

max_tokens = openai.modelname_to_contextsize("gpt-3.5-turbo-instruct")

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) –
stop (Sequence[str] | None) –
kwargs (Any) –

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]) –
stop (Sequence[str] | None) –
kwargs (Any) –

Return type:

BaseMessage

save(file_path: Path | str) → None#

Save the LLM.

Parameters:: file_path (Path | str) – Path to file to save the LLM to.
Raises:: ValueError – If the file path is not a string or Path object.
Return type:: None

Example: .. code-block:: python

llm.save(file_path=”path/llm.yaml”)

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]]) – The input to the Runnable.
config (RunnableConfig | None) – The config to use for the Runnable. Defaults to None.
kwargs (Any) – Additional keyword arguments to pass to the Runnable.
stop (List[str] | None) –

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:: SerializedConstructor | SerializedNotImplemented

Not implemented on this class.

Parameters:

schema (Dict | Type[BaseModel]) –
kwargs (Any) –

Return type:

property max_context_size: int#: Get max context size for this model.

Examples using OpenAI