TavilySearchResults

class langchain_community.tools.tavily_search.tool.TavilySearchResults

Bases: BaseTool

Tool that queries the Tavily Search API and gets back json.

Setup:

Install langchain-openai and tavily-python, and set environment variable TAVILY_API_KEY.

pip install -U langchain-community tavily-python
export TAVILY_API_KEY="your-api-key"

Instantiate:

from langchain_community.tools import TavilySearchResults

tool = TavilySearchResults(
    max_results=5,
    include_answer=True,
    include_raw_content=True,
    include_images=True,
    # search_depth="advanced",
    # include_domains = []
    # exclude_domains = []
)

Invoke directly with args:

tool.invoke({'query': 'who won the last french open'})

'{

“url”: “https://www.nytimes.com…”, “content”: “Novak Djokovic won the last French Open by beating Casper Ruud …’

Invoke with tool call:

tool.invoke({"args": {'query': 'who won the last french open'}, "type": "tool_call", "id": "foo", "name": "tavily"})

ToolMessage(
    content='{

“url”: “https://www.nytimes.com…”, “content”: “Novak Djokovic won the last French Open by beating Casper Ruud …’,

artifact={

‘query’: ‘who won the last french open’, ‘follow_up_questions’: None, ‘answer’: ‘Novak …’, ‘images’: [

‘https://www.amny.com/wp-content/uploads/2023/06/AP23162622181176-1200x800.jpg’, … ],

‘results’: [

{

‘title’: ‘Djokovic …’, ‘url’: ‘https://www.nytimes.com…’, ‘content’: “Novak…”, ‘score’: 0.99505633, ‘raw_content’: ‘Tennis

Novak …’

], ‘response_time’: 2.92

}, tool_call_id=’1’, name=’tavily_search_results_json’,

)

Initialize the tool.

Note

TavilySearchResults 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 api_wrapper: TavilySearchAPIWrapper [Optional]

param args_schema: Type[BaseModel] = <class 'langchain_community.tools.tavily_search.tool.TavilyInput'>

The tool response format.

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 be called during tool execution.

param description: str = 'A search engine optimized for comprehensive, accurate, and trusted results. Useful for when you need to answer questions about current events. Input should be a search query.'

Used to tell the model how/when/why to use the tool.

You can provide few-shot examples as a part of the description.

param exclude_domains: List[str] = []

A list of domains to specifically exclude from the search results.

Default is None, which doesn’t exclude any domains.

New in version 0.2.5.

param handle_tool_error: bool | str | Callable[[ToolException], str] | None = False

Handle the content of the ToolException thrown.

param handle_validation_error: bool | str | Callable[[ValidationError], str] | None = False

Handle the content of the ValidationError thrown.

param include_answer: bool = False

Include a short answer to original query in the search results.

Default is False.

New in version 0.2.5.

param include_domains: List[str] = []

A list of domains to specifically include in the search results.

Default is None, which includes all domains.

New in version 0.2.5.

param include_images: bool = False

Include a list of query related images in the response.

Default is False.

New in version 0.2.5.

param include_raw_content: bool = False

Include cleaned and parsed HTML of each site search results.

Default is False.

New in version 0.2.5.

param max_results: int = 5

Max search results to return, default is 5

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

Optional metadata associated with the tool. Defaults to None. This metadata will be associated with each call to this tool, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a tool with its use case.

param response_format: Literal['content_and_artifact'] = 'content_and_artifact'

The tool response format. Defaults to ‘content’.

If “content” then the output of the tool is interpreted as the contents of a ToolMessage. If “content_and_artifact” then the output is expected to be a two-tuple corresponding to the (content, artifact) of a ToolMessage.

param return_direct: bool = False

Whether to return the tool’s output directly.

Setting this to True means that after the tool is called, the AgentExecutor will stop looping.

param search_depth: str = 'advanced'

The depth of the search. It can be “basic” or “advanced”

New in version 0.2.5.

param tags: List[str] | None = None

Optional list of tags associated with the tool. Defaults to None. These tags will be associated with each call to this tool, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a tool with its use case.

param verbose: bool = False

Whether to log the tool’s progress.

__call__(tool_input: str, callbacks: List[BaseCallbackHandler] | BaseCallbackManager | None = None) → str

Deprecated since version langchain-core==0.1.47: Use invoke instead.

Make tool callable.

Parameters:

• tool_input (str) –

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

Return type:

str

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 ainvoke(input: str | Dict | ToolCall, config: RunnableConfig | None = None, **kwargs: Any) → Any

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 (str | Dict | ToolCall) –

• config (RunnableConfig | None) –

• kwargs (Any) –

Return type:

Any

async arun(tool_input: str | Dict, verbose: bool | None = None, start_color: str | None = 'green', color: str | None = 'green', 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, config: RunnableConfig | None = None, tool_call_id: str | None = None, **kwargs: Any) → Any

Run the tool asynchronously.

Parameters:

• tool_input (str | Dict) – The input to the tool.

• verbose (bool | None) – Whether to log the tool’s progress. Defaults to None.

• start_color (str | None) – The color to use when starting the tool. Defaults to ‘green’.

• color (str | None) – The color to use when ending the tool. Defaults to ‘green’.

• callbacks (List[BaseCallbackHandler] | BaseCallbackManager | None) – Callbacks to be called during tool execution. Defaults to None.

• tags (List[str] | None) – Optional list of tags associated with the tool. Defaults to None.

• metadata (Dict[str, Any] | None) – Optional metadata associated with the tool. Defaults to None.

• run_name (str | None) – The name of the run. Defaults to None.

• run_id (UUID | None) – The id of the run. Defaults to None.

• config (RunnableConfig | None) – The configuration for the tool. Defaults to None.

• tool_call_id (str | None) – The id of the tool call. Defaults to None.

• kwargs (Any) – Additional arguments to pass to the tool

Returns:

The output of the tool.

Raises:

ToolException – If an error occurs during tool execution.

Return type:

Any

async astream(input: Input, config: RunnableConfig | None = None, **kwargs: Any | None) → AsyncIterator[Output]

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

Parameters:

• input (Input) – The input to the Runnable.

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

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

Yields:

The output of the Runnable.

Return type:

AsyncIterator[Output]

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]]

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
)

invoke(input: str | Dict | ToolCall, config: RunnableConfig | None = None, **kwargs: Any) → Any

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

Parameters:

• input (str | Dict | ToolCall) – The input to the Runnable.

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

• kwargs (Any) –

Returns:

The output of the Runnable.

Return type:

Any

run(tool_input: str | Dict[str, Any], verbose: bool | None = None, start_color: str | None = 'green', color: str | None = 'green', 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, config: RunnableConfig | None = None, tool_call_id: str | None = None, **kwargs: Any) → Any

Run the tool.

Parameters:

• tool_input (str | Dict[str, Any]) – The input to the tool.

• verbose (bool | None) – Whether to log the tool’s progress. Defaults to None.

• start_color (str | None) – The color to use when starting the tool. Defaults to ‘green’.

• color (str | None) – The color to use when ending the tool. Defaults to ‘green’.

• callbacks (List[BaseCallbackHandler] | BaseCallbackManager | None) – Callbacks to be called during tool execution. Defaults to None.

• tags (List[str] | None) – Optional list of tags associated with the tool. Defaults to None.

• metadata (Dict[str, Any] | None) – Optional metadata associated with the tool. Defaults to None.

• run_name (str | None) – The name of the run. Defaults to None.

• run_id (UUID | None) – The id of the run. Defaults to None.

• config (RunnableConfig | None) – The configuration for the tool. Defaults to None.

• tool_call_id (str | None) – The id of the tool call. Defaults to None.

• kwargs (Any) – Additional arguments to pass to the tool

Returns:

The output of the tool.

Raises:

ToolException – If an error occurs during tool execution.

Return type:

Any

stream(input: Input, config: RunnableConfig | None = None, **kwargs: Any | None) → Iterator[Output]

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

Parameters:

• input (Input) – The input to the Runnable.

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

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

Yields:

The output of the Runnable.

Return type:

Iterator[Output]

to_json() → SerializedConstructor | SerializedNotImplemented

Serialize the Runnable to JSON.

Returns:

A JSON-serializable representation of the Runnable.

Return type:

SerializedConstructor | SerializedNotImplemented

property args: dict

property is_single_input: bool

Whether the tool only accepts a single input.

property tool_call_schema: Type[BaseModel]

Examples using TavilySearchResults

• Build an Agent

• Build an Agent with AgentExecutor (Legacy)

• Cohere

• How to add tools to chatbots

• How to debug your LLM apps

• Tavily Search

• ZHIPU AI