make a streaming ollama chat with a gradio interface. for that use ChatInterface from Gradio 5.0
ChatInterface

gradio.ChatInterface(fn, type="messages", ···)

Description
ChatInterface is Gradio's high-level abstraction for creating chatbot UIs, and allows you to create a web-based demo around a chatbot model in a few lines of code. Only one parameter is required: fn, which takes a function that governs the response of the chatbot based on the user input and chat history. Additional parameters can be used to control the appearance and behavior of the demo.
Example Usage

Basic Example: A chatbot that echoes back the users’s message

import gradio as gr

def echo(message, history):
    return message

demo = gr.ChatInterface(fn=echo, type="messages", examples=["hello", "hola", "merhaba"], title="Echo Bot")
demo.launch()

Custom Chatbot: A gr.ChatInterface with a custom gr.Chatbot that includes a placeholder as well as upvote/downvote buttons. The upvote/downvote buttons are automatically added when a .like() event is attached to a gr.Chatbot. In order to attach event listeners to your custom chatbot, wrap the gr.Chatbot as well as the gr.ChatInterface inside of a gr.Blocks like this:

import gradio as gr

def yes(message, history):
    return "yes"

def vote(data: gr.LikeData):
    if data.liked:
        print("You upvoted this response: " + data.value["value"])
    else:
        print("You downvoted this response: " + data.value["value"])

with gr.Blocks() as demo:
    chatbot = gr.Chatbot(placeholder="<strong>Your Personal Yes-Man</strong><br>Ask Me Anything")
    chatbot.like(vote, None, None)
    gr.ChatInterface(fn=yes, type="messages", chatbot=chatbot)
    
demo.launch()

Initialization
Parameters

fn: Callable

multimodal: bool

type: Literal['messages', 'tuples']

chatbot: Chatbot | None

textbox: Textbox | MultimodalTextbox | None

additional_inputs: str | Component | list[str | Component] | None

additional_inputs_accordion: str | Accordion | None

examples: list[str] | list[MultimodalValue] | list[list] | None

example_labels: list[str] | None

example_icons: list[str] | None

cache_examples: bool | None

cache_mode: Literal['eager', 'lazy'] | None

title: str | None

description: str | None

theme: Theme | str | None

css: str | None

css_paths: str | Path | list[str | Path] | None

js: str | None

head: str | None

head_paths: str | Path | list[str | Path] | None

analytics_enabled: bool | None

autofocus: bool

autoscroll: bool

concurrency_limit: int | None | Literal['default']

fill_height: bool

delete_cache: tuple[int, int] | None

show_progress: Literal['full', 'minimal', 'hidden']

fill_width: bool

submit_btn: str | bool | None

stop_btn: str | bool | None




and please use ChatOllama from Langchain with the llama3.2-vision model.

ChatOllama
class langchain_ollama.chat_models.ChatOllama
[source]

Bases: BaseChatModel

Ollama chat model integration.

Install langchain-ollama and download any models you want to use from ollama.

ollama pull mistral:v0.3
pip install -U langchain-ollama

Key init args — completion params:

    model: str

        Name of Ollama model to use.
    temperature: float

        Sampling temperature. Ranges from 0.0 to 1.0.
    num_predict: Optional[int]

        Max number of tokens to generate.

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

Instantiate:

    from langchain_ollama import ChatOllama

    llm = ChatOllama(
        model = "llama3",
        temperature = 0.8,
        num_predict = 256,
        # other params ...
    )

Invoke:

    messages = [
        ("system", "You are a helpful translator. Translate the user sentence to French."),
        ("human", "I love programming."),
    ]
    llm.invoke(messages)

AIMessage(content='J'adore le programmation. (Note: "programming" can also refer to the act of writing code, so if you meant that, I could translate it as "J'adore programmer". But since you didn't specify, I assumed you were talking about the activity itself, which is what "le programmation" usually refers to.)', response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:37:50.182604Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 3576619666, 'load_duration': 788524916, 'prompt_eval_count': 32, 'prompt_eval_duration': 128125000, 'eval_count': 71, 'eval_duration': 2656556000}, id='run-ba48f958-6402-41a5-b461-5e250a4ebd36-0')

Stream:

    messages = [
        ("human", "Return the words Hello World!"),
    ]
    for chunk in llm.stream(messages):
        print(chunk)

content='Hello' id='run-327ff5ad-45c8-49fe-965c-0a93982e9be1'
content=' World' id='run-327ff5ad-45c8-49fe-965c-0a93982e9be1'
content='!' id='run-327ff5ad-45c8-49fe-965c-0a93982e9be1'
content='' response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:39:42.274449Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 411875125, 'load_duration': 1898166, 'prompt_eval_count': 14, 'prompt_eval_duration': 297320000, 'eval_count': 4, 'eval_duration': 111099000} id='run-327ff5ad-45c8-49fe-965c-0a93982e9be1'

stream = llm.stream(messages)
full = next(stream)
for chunk in stream:
    full += chunk
full

AIMessageChunk(content='Je adore le programmation.(Note: "programmation" is the formal way to say "programming" in French, but informally, people might use the phrase "le développement logiciel" or simply "le code")', response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:38:54.933154Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 1977300042, 'load_duration': 1345709, 'prompt_eval_duration': 159343000, 'eval_count': 47, 'eval_duration': 1815123000}, id='run-3c81a3ed-3e79-4dd3-a796-04064d804890')

Async:

    messages = [
        ("human", "Hello how are you!"),
    ]
    await llm.ainvoke(messages)

AIMessage(content="Hi there! I'm just an AI, so I don't have feelings or emotions like humans do. But I'm functioning properly and ready to help with any questions or tasks you may have! How can I assist you today?", response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:52:08.165478Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 2138492875, 'load_duration': 1364000, 'prompt_eval_count': 10, 'prompt_eval_duration': 297081000, 'eval_count': 47, 'eval_duration': 1838524000}, id='run-29c510ae-49a4-4cdd-8f23-b972bfab1c49-0')

messages = [
    ("human", "Say hello world!"),
]
async for chunk in llm.astream(messages):
    print(chunk.content)

HEL
LO
WORLD
!

messages = [
    ("human", "Say hello world!"),
    ("human","Say goodbye world!")
]
await llm.abatch(messages)

[AIMessage(content='HELLO, WORLD!', response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:55:07.315396Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 1696745458, 'load_duration': 1505000, 'prompt_eval_count': 8, 'prompt_eval_duration': 111627000, 'eval_count': 6, 'eval_duration': 185181000}, id='run-da6c7562-e25a-4a44-987a-2c83cd8c2686-0'),
AIMessage(content="It's been a blast chatting with you! Say goodbye to the world for me, and don't forget to come back and visit us again soon!", response_metadata={'model': 'llama3', 'created_at': '2024-07-04T03:55:07.018076Z', 'message': {'role': 'assistant', 'content': ''}, 'done_reason': 'stop', 'done': True, 'total_duration': 1399391083, 'load_duration': 1187417, 'prompt_eval_count': 20, 'prompt_eval_duration': 230349000, 'eval_count': 31, 'eval_duration': 1166047000}, id='run-96cad530-6f3e-4cf9-86b4-e0f8abba4cdb-0')]

JSON mode:

    json_llm = ChatOllama(format="json")
    messages = [
        ("human", "Return a query for the weather in a random location and time of day with two keys: location and time_of_day. Respond using JSON only."),
    ]
    llm.invoke(messages).content

'{"location": "Pune, India", "time_of_day": "morning"}'

Tool Calling:

    Warning

    Ollama currently does not support streaming for tools

    from langchain_ollama import ChatOllama
    from langchain_core.pydantic_v1 import BaseModel, Field

    class Multiply(BaseModel):
        a: int = Field(..., description="First integer")
        b: int = Field(..., description="Second integer")

    ans = await chat.invoke("What is 45*67")
    ans.tool_calls

[{'name': 'Multiply',
'args': {'a': 45, 'b': 67},
'id': '420c3f3b-df10-4188-945f-eb3abdb40622',
'type': 'tool_call'}]

Note

ChatOllama implements the standard Runnable Interface. 🏃

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

param base_url: str | None = None

    Base url the model is hosted under.

param cache: BaseCache | bool | None = None

    Whether to cache the response.

        If true, will use the global cache.

        If false, will not use a cache

        If None, will use the global cache if it’s set, otherwise no cache.

        If instance of BaseCache, will use the provided cache.

    Caching is not currently supported for streaming methods of models.

param callback_manager: BaseCallbackManager | None = None

    Deprecated since version 0.1.7: Use callbacks instead.

    Callback manager to add to the run trace.

param callbacks: Callbacks = None

    Callbacks to add to the run trace.

param client_kwargs: dict | None = {}

    Additional kwargs to pass to the httpx Client. For a full list of the params, see [this link](https://pydoc.dev/httpx/latest/httpx.Client.html)

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

    Optional encoder to use for counting tokens.

param disable_streaming: bool | Literal['tool_calling'] = False

    Whether to disable streaming for this model.

    If streaming is bypassed, then stream()/astream() will defer to invoke()/ainvoke().

        If True, will always bypass streaming case.

        If “tool_calling”, will bypass streaming case only when the model is called with a tools keyword argument.

        If False (default), will always use streaming case if available.

param format: Literal['', 'json'] = ''

    Specify the format of the output (options: json)

param keep_alive: int | str | None = None

    How long the model will stay loaded into memory.

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

    Metadata to add to the run trace.

param mirostat: int | None = None

    Enable Mirostat sampling for controlling perplexity. (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)

param mirostat_eta: float | None = None

    Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. (Default: 0.1)

param mirostat_tau: float | None = None

    Controls the balance between coherence and diversity of the output. A lower value will result in more focused and coherent text. (Default: 5.0)

param model: str [Required]

    Model name to use.

param num_ctx: int | None = None

    Sets the size of the context window used to generate the next token. (Default: 2048)

param num_gpu: int | None = None

    The number of GPUs to use. On macOS it defaults to 1 to enable metal support, 0 to disable.

param num_predict: int | None = None

    Maximum number of tokens to predict when generating text. (Default: 128, -1 = infinite generation, -2 = fill context)

param num_thread: int | None = None

    Sets the number of threads to use during computation. By default, Ollama will detect this for optimal performance. It is recommended to set this value to the number of physical CPU cores your system has (as opposed to the logical number of cores).

param rate_limiter: BaseRateLimiter | None = None

    An optional rate limiter to use for limiting the number of requests.

param repeat_last_n: int | None = None

    Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)

param repeat_penalty: float | None = None

    Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1)

param seed: int | None = None

    Sets the random number seed to use for generation. Setting this to a specific number will make the model generate the same text for the same prompt.

param stop: List[str] | None = None

    Sets the stop tokens to use.

param tags: List[str] | None = None

    Tags to add to the run trace.

param temperature: float | None = None

    The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8)

param tfs_z: float | None = None

    Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting. (default: 1)

param top_k: int | None = None

    Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40)

param top_p: float | None = None

    Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9)

param verbose: bool [Optional]

    Whether to print out response text.

__call__(messages: List[BaseMessage], stop: List[str] | None = None, callbacks: List[BaseCallbackHandler] | BaseCallbackManager | None = None, **kwargs: Any) → BaseMessage

    Deprecated since version langchain-core==0.1.7: Use invoke instead.

    Parameters:

            messages (List[BaseMessage]) –

            stop (List[str] | None) –

            callbacks (List[BaseCallbackHandler] | BaseCallbackManager | None) –

            kwargs (Any) –

    Return type:

        BaseMessage

async abatch(inputs: List[Input], config: RunnableConfig | List[RunnableConfig] | None = None, *, return_exceptions: bool = False, **kwargs: Any | None) → List[Output]

    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[Input]) – A list of inputs to the Runnable.

            config (RunnableConfig | List[RunnableConfig] | None) – A config to use when invoking the Runnable. The config supports standard keys like ‘tags’, ‘metadata’ for tracing purposes, ‘max_concurrency’ for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details. Defaults to None.

            return_exceptions (bool) – Whether to return exceptions instead of raising them. Defaults to False.

            kwargs (Any | None) – Additional keyword arguments to pass to the Runnable.

    Returns:

        A list of outputs from the Runnable.
    Return type:

        List[Output]

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

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

    Parameters:

            inputs (Sequence[Input]) – A list of inputs to the Runnable.

            config (RunnableConfig | Sequence[RunnableConfig] | None) – A config to use when invoking the Runnable. The config supports standard keys like ‘tags’, ‘metadata’ for tracing purposes, ‘max_concurrency’ for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details. Defaults to None. Defaults to None.

            return_exceptions (bool) – Whether to return exceptions instead of raising them. Defaults to False.

            kwargs (Any | None) – Additional keyword arguments to pass to the Runnable.

    Yields:

        A tuple of the index of the input and the output from the Runnable.
    Return type:

        AsyncIterator[Tuple[int, Output | Exception]]

async agenerate(messages: List[List[BaseMessage]], stop: List[str] | None = None, callbacks: List[BaseCallbackHandler] | BaseCallbackManager | None = None, *, tags: List[str] | None = None, metadata: Dict[str, Any] | None = None, run_name: str | None = None, run_id: UUID | None = None, **kwargs: Any) → LLMResult

    Asynchronously pass a sequence of prompts to a model and return generations.

    This method should make use of batched calls for models that expose a batched API.

    Use this method when you want to:

            take advantage of batched calls,

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

            are building chains that are agnostic to the underlying language model

                type (e.g., pure text completion models vs chat models).

    Parameters:

            messages (List[List[BaseMessage]]) – List of list of messages.

            stop (List[str] | None) – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.

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

            **kwargs (Any) – Arbitrary additional keyword arguments. These are usually passed to the model provider API call.

            tags (List[str] | None) –

            metadata (Dict[str, Any] | None) –

            run_name (str | None) –

            run_id (UUID | None) –

            **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: List[str] | None = None, callbacks: List[BaseCallbackHandler] | BaseCallbackManager | None = None, **kwargs: Any) → LLMResult

    Asynchronously pass a sequence of prompts and return model generations.

    This method should make use of batched calls for models that expose a batched API.

    Use this method when you want to:

            take advantage of batched calls,

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

            are building chains that are agnostic to the underlying language model

                type (e.g., pure text completion models vs chat models).

    Parameters:

            prompts (List[PromptValue]) – List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models).

            stop (List[str] | None) – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.

            callbacks (List[BaseCallbackHandler] | BaseCallbackManager | None) – 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: LanguageModelInput, config: RunnableConfig | None = None, *, stop: List[str] | None = None, **kwargs: Any) → BaseMessage

    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 (LanguageModelInput) –

            config (Optional[RunnableConfig]) –

            stop (Optional[List[str]]) –

            kwargs (Any) –

    Return type:

        BaseMessage

async apredict(text: str, *, stop: Sequence[str] | None = None, **kwargs: Any) → str

    Deprecated since version langchain-core==0.1.7: Use ainvoke instead.

    Parameters:

            text (str) –

            stop (Sequence[str] | None) –

            kwargs (Any) –

    Return type:

        str

async apredict_messages(messages: List[BaseMessage], *, stop: Sequence[str] | None = None, **kwargs: Any) → BaseMessage

    Deprecated since version langchain-core==0.1.7: Use ainvoke instead.

    Parameters:

            messages (List[BaseMessage]) –

            stop (Sequence[str] | None) –

            kwargs (Any) –

    Return type:

        BaseMessage

async astream(input: LanguageModelInput, config: RunnableConfig | None = None, *, stop: List[str] | None = None, **kwargs: Any) → AsyncIterator[BaseMessageChunk]

    Default implementation of astream, which calls ainvoke. Subclasses should override this method if they support streaming output.

    Parameters:

            input (LanguageModelInput) – The input to the Runnable.

            config (Optional[RunnableConfig]) – The config to use for the Runnable. Defaults to None.

            kwargs (Any) – Additional keyword arguments to pass to the Runnable.

            stop (Optional[List[str]]) –

    Yields:

        The output of the Runnable.
    Return type:

        AsyncIterator[BaseMessageChunk]

astream_events(input: Any, config: RunnableConfig | None = None, *, version: Literal['v1', 'v2'], include_names: Sequence[str] | None = None, include_types: Sequence[str] | None = None, include_tags: Sequence[str] | None = None, exclude_names: Sequence[str] | None = None, exclude_types: Sequence[str] | None = None, exclude_tags: Sequence[str] | None = None, **kwargs: Any) → AsyncIterator[StandardStreamEvent | CustomStreamEvent]

Beta

This API is in beta and may change in the future.

Generate a stream of events.

Use to create an iterator over StreamEvents that provide real-time information about the progress of the Runnable, including StreamEvents from intermediate results.

A StreamEvent is a dictionary with the following schema:

    event: str - Event names are of the

        format: on_[runnable_type]_(start|stream|end).

    name: str - The name of the Runnable that generated the event.

    run_id: str - randomly generated ID associated with the given execution of

        the Runnable that emitted the event. A child Runnable that gets invoked as part of the execution of a parent Runnable is assigned its own unique ID.

    parent_ids: List[str] - The IDs of the parent runnables that

        generated the event. The root Runnable will have an empty list. The order of the parent IDs is from the root to the immediate parent. Only available for v2 version of the API. The v1 version of the API will return an empty list.

    tags: Optional[List[str]] - The tags of the Runnable that generated

        the event.

    metadata: Optional[Dict[str, Any]] - The metadata of the Runnable

        that generated the event.

    data: Dict[str, Any]

Below is a table that illustrates some evens that might be emitted by various chains. Metadata fields have been omitted from the table for brevity. Chain definitions have been included after the table.

ATTENTION This reference table is for the V2 version of the schema.

event
	

name
	

chunk
	

input
	

output

on_chat_model_start
	

[model name]
		

{“messages”: [[SystemMessage, HumanMessage]]}
	

on_chat_model_stream
	

[model name]
	

AIMessageChunk(content=”hello”)
		

on_chat_model_end
	

[model name]
		

{“messages”: [[SystemMessage, HumanMessage]]}
	

AIMessageChunk(content=”hello world”)

on_llm_start
	

[model name]
		

{‘input’: ‘hello’}
	

on_llm_stream
	

[model name]
	

‘Hello’
		

on_llm_end
	

[model name]
		

‘Hello human!’
	

on_chain_start
	

format_docs
			

on_chain_stream
	

format_docs
	

“hello world!, goodbye world!”
		

on_chain_end
	

format_docs
		

[Document(…)]
	

“hello world!, goodbye world!”

on_tool_start
	

some_tool
		

{“x”: 1, “y”: “2”}
	

on_tool_end
	

some_tool
			

{“x”: 1, “y”: “2”}

on_retriever_start
	

[retriever name]
		

{“query”: “hello”}
	

on_retriever_end
	

[retriever name]
		

{“query”: “hello”}
	

[Document(…), ..]

on_prompt_start
	

[template_name]
		

{“question”: “hello”}
	

on_prompt_end
	

[template_name]
		

{“question”: “hello”}
	

ChatPromptValue(messages: [SystemMessage, …])

In addition to the standard events, users can also dispatch custom events (see example below).

Custom events will be only be surfaced with in the v2 version of the API!

A custom event has following format:

Attribute
	

Type
	

Description

name
	

str
	

A user defined name for the event.

data
	

Any
	

The data associated with the event. This can be anything, though we suggest making it JSON serializable.

Here are declarations associated with the standard events shown above:

format_docs:

def format_docs(docs: List[Document]) -> str:
    '''Format the docs.'''
    return ", ".join([doc.page_content for doc in docs])

format_docs = RunnableLambda(format_docs)

some_tool:

@tool
def some_tool(x: int, y: str) -> dict:
    '''Some_tool.'''
    return {"x": x, "y": y}

prompt:

template = ChatPromptTemplate.from_messages(
    [("system", "You are Cat Agent 007"), ("human", "{question}")]
).with_config({"run_name": "my_template", "tags": ["my_template"]})

Example:

from langchain_core.runnables import RunnableLambda

async def reverse(s: str) -> str:
    return s[::-1]

chain = RunnableLambda(func=reverse)

events = [
    event async for event in chain.astream_events("hello", version="v2")
]

# will produce the following events (run_id, and parent_ids
# has been omitted for brevity):
[
    {
        "data": {"input": "hello"},
        "event": "on_chain_start",
        "metadata": {},
        "name": "reverse",
        "tags": [],
    },
    {
        "data": {"chunk": "olleh"},
        "event": "on_chain_stream",
        "metadata": {},
        "name": "reverse",
        "tags": [],
    },
    {
        "data": {"output": "olleh"},
        "event": "on_chain_end",
        "metadata": {},
        "name": "reverse",
        "tags": [],
    },
]

Example: Dispatch Custom Event

from langchain_core.callbacks.manager import (
    adispatch_custom_event,
)
from langchain_core.runnables import RunnableLambda, RunnableConfig
import asyncio


async def slow_thing(some_input: str, config: RunnableConfig) -> str:
    """Do something that takes a long time."""
    await asyncio.sleep(1) # Placeholder for some slow operation
    await adispatch_custom_event(
        "progress_event",
        {"message": "Finished step 1 of 3"},
        config=config # Must be included for python < 3.10
    )
    await asyncio.sleep(1) # Placeholder for some slow operation
    await adispatch_custom_event(
        "progress_event",
        {"message": "Finished step 2 of 3"},
        config=config # Must be included for python < 3.10
    )
    await asyncio.sleep(1) # Placeholder for some slow operation
    return "Done"

slow_thing = RunnableLambda(slow_thing)

async for event in slow_thing.astream_events("some_input", version="v2"):
    print(event)

    Parameters:

            input (Any) – The input to the Runnable.

            config (RunnableConfig | None) – The config to use for the Runnable.

            version (Literal['v1', 'v2']) – The version of the schema to use either v2 or v1. Users should use v2. v1 is for backwards compatibility and will be deprecated in 0.4.0. No default will be assigned until the API is stabilized. custom events will only be surfaced in v2.

            include_names (Sequence[str] | None) – Only include events from runnables with matching names.

            include_types (Sequence[str] | None) – Only include events from runnables with matching types.

            include_tags (Sequence[str] | None) – Only include events from runnables with matching tags.

            exclude_names (Sequence[str] | None) – Exclude events from runnables with matching names.

            exclude_types (Sequence[str] | None) – Exclude events from runnables with matching types.

            exclude_tags (Sequence[str] | None) – Exclude events from runnables with matching tags.

            kwargs (Any) – Additional keyword arguments to pass to the Runnable. These will be passed to astream_log as this implementation of astream_events is built on top of astream_log.

    Yields:

        An async stream of StreamEvents.
    Raises:

        NotImplementedError – If the version is not v1 or v2.
    Return type:

        AsyncIterator[StandardStreamEvent | CustomStreamEvent]

batch(inputs: List[Input], config: RunnableConfig | List[RunnableConfig] | None = None, *, return_exceptions: bool = False, **kwargs: Any | None) → List[Output]

    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[Input]) –

            config (RunnableConfig | List[RunnableConfig] | None) –

            return_exceptions (bool) –

            kwargs (Any | None) –

    Return type:

        List[Output]

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

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

    Parameters:

            inputs (Sequence[Input]) –

            config (RunnableConfig | Sequence[RunnableConfig] | None) –

            return_exceptions (bool) –

            kwargs (Any | None) –

    Return type:

        Iterator[Tuple[int, Output | Exception]]

bind_tools(tools: Sequence[Dict[str, Any] | Type | Callable | BaseTool], **kwargs: Any) → Runnable[PromptValue | str | Sequence[BaseMessage | List[str] | Tuple[str, str] | str | Dict[str, Any]], BaseMessage]
[source]

    Bind tool-like objects to this chat model.

    Assumes model is compatible with OpenAI tool-calling API.

    Parameters:

            tools (Sequence[Dict[str, Any] | Type | Callable | BaseTool]) – A list of tool definitions to bind to this chat model. Supports any tool definition handled by langchain_core.utils.function_calling.convert_to_openai_tool().

            kwargs (Any) – Any additional parameters are passed directly to self.bind(**kwargs).

    Return type:

        Runnable[PromptValue | str | Sequence[BaseMessage | List[str] | Tuple[str, str] | str | Dict[str, Any]], BaseMessage]

call_as_llm(message: str, stop: List[str] | None = None, **kwargs: Any) → str

    Deprecated since version langchain-core==0.1.7: Use invoke instead.

    Parameters:

            message (str) –

            stop (List[str] | None) –

            kwargs (Any) –

    Return type:

        str

configurable_alternatives(which: ConfigurableField, *, default_key: str = 'default', prefix_keys: bool = False, **kwargs: Runnable[Input, Output] | Callable[[], Runnable[Input, Output]]) → RunnableSerializable[Input, Output]

Configure alternatives for Runnables that can be set at runtime.

Parameters:

        which (ConfigurableField) – The ConfigurableField instance that will be used to select the alternative.

        default_key (str) – The default key to use if no alternative is selected. Defaults to “default”.

        prefix_keys (bool) – Whether to prefix the keys with the ConfigurableField id. Defaults to False.

        **kwargs (Runnable[Input, Output] | Callable[[], Runnable[Input, Output]]) – A dictionary of keys to Runnable instances or callables that return Runnable instances.

Returns:

    A new Runnable with the alternatives configured.
Return type:

    RunnableSerializable[Input, Output]

from langchain_anthropic import ChatAnthropic
from langchain_core.runnables.utils import ConfigurableField
from langchain_openai import ChatOpenAI

model = ChatAnthropic(
    model_name="claude-3-sonnet-20240229"
).configurable_alternatives(
    ConfigurableField(id="llm"),
    default_key="anthropic",
    openai=ChatOpenAI()
)

# uses the default model ChatAnthropic
print(model.invoke("which organization created you?").content)

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

configurable_fields(**kwargs: ConfigurableField | ConfigurableFieldSingleOption | ConfigurableFieldMultiOption) → RunnableSerializable[Input, Output]

Configure particular Runnable fields at runtime.

Parameters:

    **kwargs (ConfigurableField | ConfigurableFieldSingleOption | ConfigurableFieldMultiOption) – A dictionary of ConfigurableField instances to configure.
Returns:

    A new Runnable with the fields configured.
Return type:

    RunnableSerializable[Input, Output]

from langchain_core.runnables import ConfigurableField
from langchain_openai import ChatOpenAI

model = ChatOpenAI(max_tokens=20).configurable_fields(
    max_tokens=ConfigurableField(
        id="output_token_number",
        name="Max tokens in the output",
        description="The maximum number of tokens in the output",
    )
)

# max_tokens = 20
print(
    "max_tokens_20: ",
    model.invoke("tell me something about chess").content
)

# max_tokens = 200
print("max_tokens_200: ", model.with_config(
    configurable={"output_token_number": 200}
    ).invoke("tell me something about chess").content
)

generate(messages: List[List[BaseMessage]], stop: List[str] | None = None, callbacks: List[BaseCallbackHandler] | BaseCallbackManager | None = None, *, tags: List[str] | None = None, metadata: Dict[str, Any] | None = None, run_name: str | None = None, run_id: UUID | None = None, **kwargs: Any) → LLMResult

    Pass a sequence of prompts to the model and return model generations.

    This method should make use of batched calls for models that expose a batched API.

    Use this method when you want to:

            take advantage of batched calls,

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

            are building chains that are agnostic to the underlying language model

                type (e.g., pure text completion models vs chat models).

    Parameters:

            messages (List[List[BaseMessage]]) – List of list of messages.

            stop (List[str] | None) – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.

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

            **kwargs (Any) – Arbitrary additional keyword arguments. These are usually passed to the model provider API call.

            tags (List[str] | None) –

            metadata (Dict[str, Any] | None) –

            run_name (str | None) –

            run_id (UUID | None) –

            **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: List[str] | None = None, callbacks: List[BaseCallbackHandler] | BaseCallbackManager | None = None, **kwargs: Any) → LLMResult

    Pass a sequence of prompts to the model and return model generations.

    This method should make use of batched calls for models that expose a batched API.

    Use this method when you want to:

            take advantage of batched calls,

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

            are building chains that are agnostic to the underlying language model

                type (e.g., pure text completion models vs chat models).

    Parameters:

            prompts (List[PromptValue]) – List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models).

            stop (List[str] | None) – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.

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

            **kwargs (Any) – Arbitrary additional keyword arguments. These are usually passed to the model provider API call.

    Returns:

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

            prompt and additional model provider-specific output.

    Return type:

        LLMResult

get_num_tokens(text: str) → int

    Get the number of tokens present in the text.

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

    Parameters:

        text (str) – The string input to tokenize.
    Returns:

        The integer number of tokens in the text.
    Return type:

        int

get_num_tokens_from_messages(messages: List[BaseMessage]) → int

    Get the number of tokens in the messages.

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

    Parameters:

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

        The sum of the number of tokens across the messages.
    Return type:

        int

get_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: LanguageModelInput, config: RunnableConfig | None = None, *, stop: List[str] | None = None, **kwargs: Any) → BaseMessage

    Transform a single input into an output. Override to implement.

    Parameters:

            input (LanguageModelInput) – 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:

        BaseMessage

predict(text: str, *, stop: Sequence[str] | None = None, **kwargs: Any) → str

    Deprecated since version langchain-core==0.1.7: Use invoke instead.

    Parameters:

            text (str) –

            stop (Sequence[str] | None) –

            kwargs (Any) –

    Return type:

        str

predict_messages(messages: List[BaseMessage], *, stop: Sequence[str] | None = None, **kwargs: Any) → BaseMessage

    Deprecated since version langchain-core==0.1.7: Use invoke instead.

    Parameters:

            messages (List[BaseMessage]) –

            stop (Sequence[str] | None) –

            kwargs (Any) –

    Return type:

        BaseMessage

stream(input: LanguageModelInput, config: RunnableConfig | None = None, *, stop: List[str] | None = None, **kwargs: Any) → Iterator[BaseMessageChunk]

    Default implementation of stream, which calls invoke. Subclasses should override this method if they support streaming output.

    Parameters:

            input (LanguageModelInput) – The input to the Runnable.

            config (Optional[RunnableConfig]) – The config to use for the Runnable. Defaults to None.

            kwargs (Any) – Additional keyword arguments to pass to the Runnable.

            stop (Optional[List[str]]) –

    Yields:

        The output of the Runnable.
    Return type:

        Iterator[BaseMessageChunk]

to_json() → SerializedConstructor | SerializedNotImplemented

    Serialize the Runnable to JSON.

    Returns:

        A JSON-serializable representation of the Runnable.
    Return type:

        SerializedConstructor | SerializedNotImplemented

with_structured_output(schema: Dict | Type, *, include_raw: bool = False, **kwargs: Any) → Runnable[LanguageModelInput, Dict | BaseModel]

Model wrapper that returns outputs formatted to match the given schema.

Parameters:

        schema (Union[Dict, Type]) –

        The output schema. Can be passed in as:

                an OpenAI function/tool schema,

                a JSON Schema,

                a TypedDict class (support added in 0.2.26),

                or a Pydantic class.

        If schema is a Pydantic class then the model output will be a Pydantic instance of that class, and the model-generated fields will be validated by the Pydantic class. Otherwise the model output will be a dict and will not be validated. See langchain_core.utils.function_calling.convert_to_openai_tool() for more on how to properly specify types and descriptions of schema fields when specifying a Pydantic or TypedDict class.

        Changed in version 0.2.26: Added support for TypedDict class.

        include_raw (bool) – If False then only the parsed structured output is returned. If an error occurs during model output parsing it will be raised. If True then both the raw model response (a BaseMessage) and the parsed model response will be returned. If an error occurs during output parsing it will be caught and returned as well. The final output is always a dict with keys “raw”, “parsed”, and “parsing_error”.

        kwargs (Any) –

Returns:

    A Runnable that takes same inputs as a langchain_core.language_models.chat.BaseChatModel.

    If include_raw is False and schema is a Pydantic class, Runnable outputs an instance of schema (i.e., a Pydantic object).

    Otherwise, if include_raw is False then Runnable outputs a dict.

    If
    include_raw
    is True, then Runnable outputs a dict with keys:

            "raw": BaseMessage

            "parsed": None if there was a parsing error, otherwise the type depends on the schema as described above.

            "parsing_error": Optional[BaseException]

Return type:

    Runnable[LanguageModelInput, Union[Dict, BaseModel]]

Example: Pydantic schema (include_raw=False):

    from langchain_core.pydantic_v1 import BaseModel

    class AnswerWithJustification(BaseModel):
        '''An answer to the user question along with justification for the answer.'''
        answer: str
        justification: str

    llm = ChatModel(model="model-name", temperature=0)
    structured_llm = llm.with_structured_output(AnswerWithJustification)

    structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")

    # -> AnswerWithJustification(
    #     answer='They weigh the same',
    #     justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
    # )

Example: Pydantic schema (include_raw=True):

    from langchain_core.pydantic_v1 import BaseModel

    class AnswerWithJustification(BaseModel):
        '''An answer to the user question along with justification for the answer.'''
        answer: str
        justification: str

    llm = ChatModel(model="model-name", temperature=0)
    structured_llm = llm.with_structured_output(AnswerWithJustification, include_raw=True)

    structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
    # -> {
    #     'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}),
    #     'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'),
    #     'parsing_error': None
    # }

Example: Dict schema (include_raw=False):

    from langchain_core.pydantic_v1 import BaseModel
    from langchain_core.utils.function_calling import convert_to_openai_tool

    class AnswerWithJustification(BaseModel):
        '''An answer to the user question along with justification for the answer.'''
        answer: str
        justification: str

    dict_schema = convert_to_openai_tool(AnswerWithJustification)
    llm = ChatModel(model="model-name", temperature=0)
    structured_llm = llm.with_structured_output(dict_schema)

    structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
    # -> {
    #     'answer': 'They weigh the same',
    #     'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
    # }





ouput the whole code