langchain_openai.llms.base.OpenAI

class langchain_openai.llms.base.OpenAI

Bases: BaseOpenAI

OpenAI large language models.

To use, you should have the environment variable OPENAI_API_KEY set with your API key, or pass it as a named parameter to the constructor.

Any parameters that are valid to be passed to the openai.create call can be passed in, even if not explicitly saved on this class.

Example

from langchain_openai import OpenAI

model = OpenAI(model_name="gpt-3.5-turbo-instruct")

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

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

param allowed_special: Union[Literal['all'], AbstractSet[str]] = {}

Set of special tokens that are allowed。

param batch_size: int = 20

Batch size to use when passing multiple documents to generate.

param best_of: int = 1

Generates best_of completions server-side and returns the “best”.

param cache: Union[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: Optional[BaseCallbackManager] = None

[DEPRECATED]

param callbacks: Callbacks = None

Callbacks to add to the run trace.

param custom_get_token_ids: Optional[Callable[[str], List[int]]] = None

Optional encoder to use for counting tokens.

param default_headers: Union[Mapping[str, str], None] = None

param default_query: Union[Mapping[str, object], None] = None

param disallowed_special: Union[Literal['all'], Collection[str]] = 'all'

Set of special tokens that are not allowed。

param frequency_penalty: float = 0

Penalizes repeated tokens according to frequency.

param http_async_client: Union[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: Union[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: Optional[Dict[str, float]] [Optional]

Adjust the probability of specific tokens being generated.

param max_retries: int = 2

Maximum number of retries to make when generating.

param max_tokens: int = 256

The maximum number of tokens to generate in the completion. -1 returns as many tokens as possible given the prompt and the models maximal context size.

param metadata: Optional[Dict[str, Any]] = None

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: Optional[str] = None (alias 'base_url')

Base URL path for API requests, leave blank if not using a proxy or service emulator.

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

Automatically inferred from env var OPENAI_API_KEY if not provided.

Constraints

• type = string

• writeOnly = True

• format = password

param openai_organization: Optional[str] = None (alias 'organization')

Automatically inferred from env var OPENAI_ORG_ID if not provided.

param openai_proxy: Optional[str] = None

param presence_penalty: float = 0

Penalizes repeated tokens.

param request_timeout: Union[float, Tuple[float, float], Any, None] = None (alias 'timeout')

Timeout for requests to OpenAI completion API. Can be float, httpx.Timeout or None.

param streaming: bool = False

Whether to stream the results or not.

param tags: Optional[List[str]] = None

Tags to add to the run trace.

param temperature: float = 0.7

What sampling temperature to use.

param tiktoken_model_name: Optional[str] = None

The model name to pass to tiktoken when using this class. Tiktoken is used to count the number of tokens in documents to constrain them to be under a certain limit. By default, when set to None, this will be the same as the embedding model name. However, there are some cases where you may want to use this Embedding class with a model name not supported by tiktoken. This can include when using Azure embeddings or when using one of the many model providers that expose an OpenAI-like API but with different models. In those cases, in order to avoid erroring when tiktoken is called, you can specify a model name to use here.

param top_p: float = 1

Total probability mass of tokens to consider at each step.

param verbose: bool [Optional]

Whether to print out response text.

__call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → str

[Deprecated] Check Cache and run the LLM on the given prompt and input.

Notes

Deprecated since version langchain-core==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, List[str], 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, List[str], Tuple[str, str], str, Dict[str, Any]]]]]) –

• config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) –

• return_exceptions (bool) –

• kwargs (Any) –

Return type

List[str]

async abatch_as_completed(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → AsyncIterator[Tuple[int, Union[Output, Exception]]]

Run ainvoke in parallel on a list of inputs, yielding results as they complete.

Parameters

• inputs (List[Input]) –

• config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) –

• return_exceptions (bool) –

• kwargs (Optional[Any]) –

Return type

AsyncIterator[Tuple[int, Union[Output, Exception]]]

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, run_id: Optional[Union[UUID, List[Optional[UUID]]]] = 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:

1. take advantage of batched calls,

2. need more output from the model than just the top generated value,

3. 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]]]) –

• run_id (Optional[Union[UUID, List[Optional[UUID]]]]) –

• **kwargs –

Returns

An LLMResult, which contains a list of candidate Generations for each input

prompt and additional model provider-specific output.

Return type

LLMResult

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:

1. take advantage of batched calls,

2. need more output from the model than just the top generated value,

3. 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

LLMResult

async ainvoke(input: Union[PromptValue, str, Sequence[Union[BaseMessage, List[str], 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, List[str], 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 langchain-core==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 langchain-core==0.1.7: Use ainvoke instead.

Parameters

• messages (List[BaseMessage]) –

• stop (Optional[Sequence[str]]) –

• kwargs (Any) –

Return type

BaseMessage

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.

from langchain_community.llms.fake import FakeStreamingListLLM
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import SystemMessagePromptTemplate
from langchain_core.runnables import Runnable
from operator import itemgetter

prompt = (
    SystemMessagePromptTemplate.from_template("You are a nice assistant.")
    + "{question}"
)
llm = FakeStreamingListLLM(responses=["foo-lish"])

chain: Runnable = prompt | llm | {"str": StrOutputParser()}

chain_with_assign = chain.assign(hello=itemgetter("str") | llm)

print(chain_with_assign.input_schema.schema())
# {'title': 'PromptInput', 'type': 'object', 'properties':
{'question': {'title': 'Question', 'type': 'string'}}}
print(chain_with_assign.output_schema.schema()) #
{'title': 'RunnableSequenceOutput', 'type': 'object', 'properties':
{'str': {'title': 'Str',
'type': 'string'}, 'hello': {'title': 'Hello', 'type': 'string'}}}

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, List[str], 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, List[str], 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 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.

• 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.

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, List[str], 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, List[str], Tuple[str, str], str, Dict[str, Any]]]]]) –

• config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) –

• return_exceptions (bool) –

• kwargs (Any) –

Return type

List[str]

batch_as_completed(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → Iterator[Tuple[int, Union[Output, Exception]]]

Run invoke in parallel on a list of inputs, yielding results as they complete.

Parameters

• inputs (List[Input]) –

• config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) –

• return_exceptions (bool) –

• kwargs (Optional[Any]) –

Return type

Iterator[Tuple[int, Union[Output, Exception]]]

bind(**kwargs: Any) → Runnable[Input, Output]

Bind arguments to a Runnable, returning a new Runnable.

Useful when a runnable in a chain requires an argument that is not in the output of the previous runnable or included in the user input.

Example:

from langchain_community.chat_models import ChatOllama
from langchain_core.output_parsers import StrOutputParser

llm = ChatOllama(model='llama2')

# Without bind.
chain = (
    llm
    | StrOutputParser()
)

chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
# Output is 'One two three four five.'

# With bind.
chain = (
    llm.bind(stop=["three"])
    | StrOutputParser()
)

chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
# Output is 'One two'

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]

Configure alternatives for runnables that can be set at runtime.

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 ChatOpenaAI
print(
    model.with_config(
        configurable={"llm": "openai"}
    ).invoke("which organization created you?").content
)

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]

Configure particular runnable fields at runtime.

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
)

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

create_llm_result(choices: Any, prompts: List[str], params: Dict[str, Any], token_usage: Dict[str, int], *, system_fingerprint: Optional[str] = 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 (Optional[str]) –

Return type

LLMResult

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, run_id: Optional[Union[UUID, List[Optional[UUID]]]] = 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:

1. take advantage of batched calls,

2. need more output from the model than just the top generated value,

3. 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]]]) –

• run_id (Optional[Union[UUID, List[Optional[UUID]]]]) –

• **kwargs –

Returns

An LLMResult, which contains a list of candidate Generations for each input

prompt and additional model provider-specific output.

Return type

LLMResult

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:

1. take advantage of batched calls,

2. need more output from the model than just the top generated value,

3. 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

LLMResult

get_graph(config: Optional[RunnableConfig] = None) → Graph

Return a graph representation of this runnable.

Parameters

config (Optional[RunnableConfig]) –

Return type

Graph

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.

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_sub_prompts(params: Dict[str, Any], prompts: List[str], stop: Optional[List[str]] = None) → List[List[str]]

Get the sub prompts for llm call.

Parameters

• params (Dict[str, Any]) –

• prompts (List[str]) –

• stop (Optional[List[str]]) –

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]

invoke(input: Union[PromptValue, str, Sequence[Union[BaseMessage, List[str], 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, List[str], 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

Return whether this model can be serialized by Langchain.

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.

Example

from langchain_core.runnables import RunnableLambda

def _lambda(x: int) -> int:
    return x + 1

runnable = RunnableLambda(_lambda)
print(runnable.map().invoke([1, 2, 3])) # [2, 3, 4]

Return type

Runnable[List[Input], List[Output]]

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")

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.

Pick single key:

import json

from langchain_core.runnables import RunnableLambda, RunnableMap

as_str = RunnableLambda(str)
as_json = RunnableLambda(json.loads)
chain = RunnableMap(str=as_str, json=as_json)

chain.invoke("[1, 2, 3]")
# -> {"str": "[1, 2, 3]", "json": [1, 2, 3]}

json_only_chain = chain.pick("json")
json_only_chain.invoke("[1, 2, 3]")
# -> [1, 2, 3]

Pick list of keys:

from typing import Any

import json

from langchain_core.runnables import RunnableLambda, RunnableMap

as_str = RunnableLambda(str)
as_json = RunnableLambda(json.loads)
def as_bytes(x: Any) -> bytes:
    return bytes(x, "utf-8")

chain = RunnableMap(
    str=as_str,
    json=as_json,
    bytes=RunnableLambda(as_bytes)
)

chain.invoke("[1, 2, 3]")
# -> {"str": "[1, 2, 3]", "json": [1, 2, 3], "bytes": b"[1, 2, 3]"}

json_and_bytes_chain = chain.pick(["json", "bytes"])
json_and_bytes_chain.invoke("[1, 2, 3]")
# -> {"json": [1, 2, 3], "bytes": b"[1, 2, 3]"}

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 Runnable-like objects to make a RunnableSequence.

Equivalent to RunnableSequence(self, *others) or self | others[0] | …

Example

from langchain_core.runnables import RunnableLambda

def add_one(x: int) -> int:
    return x + 1

def mul_two(x: int) -> int:
    return x * 2

runnable_1 = RunnableLambda(add_one)
runnable_2 = RunnableLambda(mul_two)
sequence = runnable_1.pipe(runnable_2)
# Or equivalently:
# sequence = runnable_1 | runnable_2
# sequence = RunnableSequence(first=runnable_1, last=runnable_2)
sequence.invoke(1)
await sequence.ainvoke(1)
# -> 4

sequence.batch([1, 2, 3])
await sequence.abatch([1, 2, 3])
# -> [4, 6, 8]

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 langchain-core==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 langchain-core==0.1.7: Use invoke instead.

Parameters

• messages (List[BaseMessage]) –

• stop (Optional[Sequence[str]]) –

• kwargs (Any) –

Return type

BaseMessage

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, List[str], 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, List[str], 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

Union[SerializedConstructor, SerializedNotImplemented]

to_json_not_implemented() → SerializedNotImplemented

Return type

SerializedNotImplemented

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.

Example

from typing import Iterator

from langchain_core.runnables import RunnableGenerator


def _generate_immediate_error(input: Iterator) -> Iterator[str]:
    raise ValueError()
    yield ""


def _generate(input: Iterator) -> Iterator[str]:
    yield from "foo bar"


runnable = RunnableGenerator(_generate_immediate_error).with_fallbacks(
    [RunnableGenerator(_generate)]
    )
print(''.join(runnable.stream({}))) #foo bar

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.

Example:

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.

Example:

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, List[str], Tuple[str, str], str, Dict[str, Any]]]], Union[Dict, BaseModel]]

Implement this if there is a way of steering the model to generate responses that match a given schema.

Parameters

• schema (Union[Dict, Type[BaseModel]]) –

• kwargs (Any) –

Return type

Runnable[Union[PromptValue, str, Sequence[Union[BaseMessage, List[str], 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[str, Any]

List of attribute names that should be included in the serialized kwargs.

These attributes must be accepted by the constructor.

property lc_secrets: Dict[str, str]

A map of constructor argument names to secret ids.

For example,

{“openai_api_key”: “OPENAI_API_KEY”}

property max_context_size: int

Get max context size for this model.

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.

Examples using OpenAI

• !pip3 install rebuff openai -U

• !python -m spacy download en_core_web_lg

• 0: Import ray serve and request from starlette

• > ChatPromptValue(messages=[HumanMessage(content=’tell me a short joke about ice cream’)])

• Answer with ‘Zhu’

• Backed by a Vector Store

• Based on ReAct Agent

• Callbacks

• Combined

• Connect to Comet if no API Key is set

• Constitutional chain

• Conversation Buffer

• Conversation Buffer Window

• Conversation Summary

• Copilot Sandbox

• Create a dataframe

• Define your desired data structure.

• DocumentDB connection string

• Entity

• Fiddler project and model names, used for model registration

• First, define custom callback handler implementations

• For Windows/Linux

• From LangChain, import standard modules for prompting.

• Get embeddings.

• Get openAI api key by reading local .env file

• Get the prompt to use - you can modify this!

• Helicone

• Helper function for printing docs

• Here it is by default set to “AI”

• Here we want to make sure the answer is minimally relevant

• Install necessary dependencies.

• LLM Hyperparameters

• LLMonitor

• LangChain Decorators ✨

• Layerup Security

• Log10

• Logical Fallacy chain

• Model I/O

• NOTE: In this example. We must set `allow_dangerous_request=True` to enable the OpenAPI Agent to automatically use the Request Tool.

• Needed if you would like to display images in the notebook

• Note that we set max_retries = 0 to avoid retrying on RateLimits, etc

• OR (depending on Python version)

• OpenAI

• Pip install necessary packages

• Quick Start {#quick-start}

• Quickstart

• Retrievers

• SCENARIO 1 - LLM

• SearchApi

• Select the LLM to use. Here, we use gpt-3.5-turbo-instruct

• Self-querying

• Serper - Google Search API

• Set env var OPENAI_API_KEY or load from a .env file:

• Set environmental variables here

• Set this to your Zep server URL

• Set your environment variables using os.environ

• Setup and use the ClearML Callback

• Setup {#setup}

• Shale Protocol

• Streamlit

• This example only specifies a relevant query

• To make the caching really obvious, lets use a slower model.

• Uncomment this to install psychicapi if you don’t already have it installed

• Using https://huggingface.co/laiyer/deberta-v3-base-prompt-injection

• We can see here that the buffer is updated

• We can see here that there is a summary of the conversation and then some previous interactions

• You can kick off concurrent runs from within the context manager

• You can store your OPENAI_API_KEY in a .env file as well

• You need the dgml-utils package to use the DocugamiLoader (run pip install directly without “poetry run” if you are not using poetry)

• [Beta] Memory

• adding_memory.md

• adding_memory_chain_multiple_inputs.md

• agent_with_memory.md

• agent_with_memory_in_db.md

• argilla.md

• awslambda.md

• azure_ai_services.md

• batch_configurable_chain([“ice cream”, “spaghetti”, “dumplings”])

• cosine: distance metric

• create new index

• datetime.md

• eleven_labs_tts.md

• folder_id=’1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5’

• get a token: https://platform.openai.com/account/api-keys

• get from https://platform.openai.com/

• google_finance.md

• google_serper.md

• gradio_tools.md

• graphql.md

• import os

• in apache-spark root directory. (tested here with “spark-3.4.0-bin-hadoop3 and later”)

• in case if some queries fail consider installing libdeeplake manually

• initialize MongoDB python client

• initialize marqo

• install package

• install the opaqueprompts and langchain packages

• jira.md

• json.md

• kg.md

• labelstudio.md

• lemonai.md

• load text splitter and split docs into snippets of text

• loads previous state from Motörhead 🤘

• moderation.md

• nasa.md

• networkx.md

• openweathermap.md

• or install latest:

• os.environ[“OPENAI_API_KEY”] = “”

• os.environ[“OPENAI_ORGANIZATION”] = “…”

• pandas.md

• promptlayer.md

• retry.md

• scenario 1 - LLM

• sceneXplain.md

• search_tools.md

• searchapi.md

• send data into the chain

• steam.md

• streaming_llm.md

• this chain will both print to stdout (because verbose=True) and write to ‘output.log’

• token_usage_tracking.md

• trubrics.md

• use it with langchain {#use-it-with-langchain}

• wandb documentation to configure wandb using env variables

• xorbits.md

• you don’t need to call close to write profiles to WhyLabs, upload will occur periodically, but to demo let’s not wait.