Toggle Menu
Prev Up Next

# `langchain_community.llms.koboldai`.KoboldApiLLM

class langchain_community.llms.koboldai.KoboldApiLLM

  Bases: `LLM`

  Kobold API language model.

  It includes several fields that can be used to control the text generation process.

  To use this class, instantiate it with the required parameters and call it with a prompt to generate text. For example:

  > kobold = KoboldApiLLM(endpoint=”http://localhost:5000”) result = kobold(“Write a story about a dragon.”)

  This will send a POST request to the Kobold API with the provided prompt and generate text.

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

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

  param cache: 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 endpoint: str [Required]

    The API endpoint to use for generating text.

  param max_context_length: Optional[int] = 1600

    Maximum number of tokens to send to the model.

    minimum: 1

  param max_length: Optional[int] = 80

    Number of tokens to generate.

    maximum: 512 minimum: 1

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

    Metadata to add to the run trace.

  param rep_pen: Optional[float] = 1.12

    Base repetition penalty value.

    minimum: 1

  param rep_pen_range: Optional[int] = 1024

    Repetition penalty range.

    minimum: 0

  param rep_pen_slope: Optional[float] = 0.9

    Repetition penalty slope.

    minimum: 0

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

    Tags to add to the run trace.

  param temperature: Optional[float] = 0.6

    Temperature value.

    exclusiveMinimum: 0

  param tfs: Optional[float] = 0.9

    Tail free sampling value.

    maximum: 1 minimum: 0

  param top_a: Optional[float] = 0.9

    Top-a sampling value.

    minimum: 0

  param top_k: Optional[int] = 0

    Top-k sampling value.

    minimum: 0

  param top_p: Optional[float] = 0.95

    Top-p sampling value.

    maximum: 1 minimum: 0

  param typical: Optional[float] = 0.5

    Typical sampling value.

    maximum: 1 minimum: 0

  param use_authors_note: Optional[bool] = False

    Whether to use the author’s note from the KoboldAI GUI when generating text.

    This has no effect unless use_story is also enabled.

  param use_memory: Optional[bool] = False

    Whether to use the memory from the KoboldAI GUI when generating text.

  param use_story: Optional[bool] = False

    Whether or not to use the story from the KoboldAI GUI when generating text.

  param use_world_info: Optional[bool] = False

    Whether to use the world info from the KoboldAI GUI when generating text.

  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

  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.

    For example, if the class is langchain.llms.openai.OpenAI, then the namespace is [“langchain”, “llms”, “openai”]

    Return type

      List[str]

  get_name(suffix: Optional[str] = None, *, name: Optional[str] = None) → str

    Get the name of the runnable.

    Parameters

      - suffix (Optional[str]) –

      - name (Optional[str]) –

    Return type

      str

  get_num_tokens(text: str) → int

    Get the number of tokens present in the text.

    Useful for checking if an input will fit in a model’s context window.

    Parameters

      text (str) – The string input to tokenize.

    Returns

      The integer number of tokens in the text.

    Return type

      int

  get_num_tokens_from_messages(messages: List[BaseMessage]) → int

    Get the number of tokens in the messages.

    Useful for checking if an input will fit in a model’s context window.

    Parameters

      messages (List[BaseMessage]) – The message inputs to tokenize.

    Returns

      The sum of the number of tokens across the messages.

    Return type

      int

  get_output_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]

    Get a pydantic model that can be used to validate output to the runnable.

    Runnables that leverage the configurable_fields and configurable_alternatives methods will have a dynamic output schema that depends on which configuration the runnable is invoked with.

    This method allows to get an output schema for a specific configuration.

    Parameters

      config (Optional[RunnableConfig]) – A config to use when generating the schema.

    Returns

      A pydantic model that can be used to validate output.

    Return type

      Type[BaseModel]

  get_prompts(config: Optional[RunnableConfig] = None) → List[BasePromptTemplate]
    Parameters

      config (Optional[RunnableConfig]) –

    Return type

      List[BasePromptTemplate]

  get_token_ids(text: str) → List[int]

    Return the ordered ids of the tokens in a text.

    Parameters

      text (str) – The string input to tokenize.

    Returns

      A list of ids corresponding to the tokens in the text, in order they occur

        in the text.

    Return type

      List[int]

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

    Is this class serializable?

    Return type

      bool

  json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode

    Generate a JSON representation of the model, include and exclude arguments as per dict().

    encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

    Parameters

      - include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

      - exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

      - by_alias (bool) –

      - skip_defaults (Optional[bool]) –

      - exclude_unset (bool) –

      - exclude_defaults (bool) –

      - exclude_none (bool) –

      - encoder (Optional[Callable[[Any], Any]]) –

      - models_as_dict (bool) –

      - dumps_kwargs (Any) –

    Return type

      unicode

  classmethod lc_id() → List[str]

    A unique identifier for this class for serialization purposes.

    The unique identifier is a list of strings that describes the path to the object.

    Return type

      List[str]

  map() → Runnable[List[Input], List[Output]]

    Return a new Runnable that maps a list of inputs to a list of outputs, by calling invoke() with each input.

    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]]

  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

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

    These attributes must be accepted by the constructor.

  property lc_secrets: Dict[str, str]

    A map of constructor argument names to secret ids.

    For example,

      {“openai_api_key”: “OPENAI_API_KEY”}

  name: Optional[str] = None

    The name of the runnable. Used for debugging and tracing.

  property output_schema: Type[BaseModel]

    The type of output this runnable produces specified as a pydantic model.

# Examples using KoboldApiLLM

- koboldai.md