ChatAnthropic#
- class langchain_anthropic.chat_models.ChatAnthropic[source]#
Bases:
BaseChatModel
Anthropic chat models.
See https://docs.anthropic.com/en/docs/models-overview for a list of the latest models.
- Setup:
Install
langchain-anthropic
and set environment variableANTHROPIC_API_KEY
.pip install -U langchain-anthropic export ANTHROPIC_API_KEY="your-api-key"
- Key init args β completion params:
- model: str
Name of Anthropic model to use. E.g. βclaude-3-sonnet-20240229β.
- temperature: float
Sampling temperature. Ranges from 0.0 to 1.0.
- max_tokens: Optional[int]
Max number of tokens to generate.
- Key init args β client params:
- timeout: Optional[float]
Timeout for requests.
- max_retries: int
Max number of retries if a request fails.
- api_key: Optional[str]
Anthropic API key. If not passed in will be read from env var ANTHROPIC_API_KEY.
- base_url: Optional[str]
Base URL for API requests. Only specify if using a proxy or service emulator.
See full list of supported init args and their descriptions in the params section.
- Instantiate:
from langchain_anthropic import ChatAnthropic llm = ChatAnthropic( model="claude-3-sonnet-20240229", temperature=0, max_tokens=1024, timeout=None, max_retries=2, # api_key="...", # base_url="...", # other params... )
NOTE: Any param which is not explicitly supported will be passed directly to the
anthropic.Anthropic.messages.create(...)
API every time to the model is invoked. For example:from langchain_anthropic import ChatAnthropic import anthropic ChatAnthropic(..., extra_headers={}).invoke(...) # results in underlying API call of: anthropic.Anthropic(..).messages.create(..., extra_headers={}) # which is also equivalent to: ChatAnthropic(...).invoke(..., extra_headers={})
- Invoke:
messages = [ ("system", "You are a helpful translator. Translate the user sentence to French."), ("human", "I love programming."), ] llm.invoke(messages)
AIMessage(content="J'aime la programmation.", response_metadata={'id': 'msg_01Trik66aiQ9Z1higrD5XFx3', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 25, 'output_tokens': 11}}, id='run-5886ac5f-3c2e-49f5-8a44-b1e92808c929-0', usage_metadata={'input_tokens': 25, 'output_tokens': 11, 'total_tokens': 36})
- Stream:
for chunk in llm.stream(messages): print(chunk)
AIMessageChunk(content='J', id='run-272ff5f9-8485-402c-b90d-eac8babc5b25') AIMessageChunk(content="'", id='run-272ff5f9-8485-402c-b90d-eac8babc5b25') AIMessageChunk(content='a', id='run-272ff5f9-8485-402c-b90d-eac8babc5b25') AIMessageChunk(content='ime', id='run-272ff5f9-8485-402c-b90d-eac8babc5b25') AIMessageChunk(content=' la', id='run-272ff5f9-8485-402c-b90d-eac8babc5b25') AIMessageChunk(content=' programm', id='run-272ff5f9-8485-402c-b90d-eac8babc5b25') AIMessageChunk(content='ation', id='run-272ff5f9-8485-402c-b90d-eac8babc5b25') AIMessageChunk(content='.', id='run-272ff5f9-8485-402c-b90d-eac8babc5b25')
stream = llm.stream(messages) full = next(stream) for chunk in stream: full += chunk full
AIMessageChunk(content="J'aime la programmation.", id='run-b34faef0-882f-4869-a19c-ed2b856e6361')
- Async:
await llm.ainvoke(messages) # stream: # async for chunk in (await llm.astream(messages)) # batch: # await llm.abatch([messages])
AIMessage(content="J'aime la programmation.", response_metadata={'id': 'msg_01Trik66aiQ9Z1higrD5XFx3', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 25, 'output_tokens': 11}}, id='run-5886ac5f-3c2e-49f5-8a44-b1e92808c929-0', usage_metadata={'input_tokens': 25, 'output_tokens': 11, 'total_tokens': 36})
- Tool calling:
from pydantic import BaseModel, Field class GetWeather(BaseModel): '''Get the current weather in a given location''' location: str = Field(..., description="The city and state, e.g. San Francisco, CA") class GetPopulation(BaseModel): '''Get the current population in a given location''' location: str = Field(..., description="The city and state, e.g. San Francisco, CA") llm_with_tools = llm.bind_tools([GetWeather, GetPopulation]) ai_msg = llm_with_tools.invoke("Which city is hotter today and which is bigger: LA or NY?") ai_msg.tool_calls
[{'name': 'GetWeather', 'args': {'location': 'Los Angeles, CA'}, 'id': 'toolu_01KzpPEAgzura7hpBqwHbWdo'}, {'name': 'GetWeather', 'args': {'location': 'New York, NY'}, 'id': 'toolu_01JtgbVGVJbiSwtZk3Uycezx'}, {'name': 'GetPopulation', 'args': {'location': 'Los Angeles, CA'}, 'id': 'toolu_01429aygngesudV9nTbCKGuw'}, {'name': 'GetPopulation', 'args': {'location': 'New York, NY'}, 'id': 'toolu_01JPktyd44tVMeBcPPnFSEJG'}]
See
ChatAnthropic.bind_tools()
method for more.- Structured output:
from typing import Optional from pydantic import BaseModel, Field class Joke(BaseModel): '''Joke to tell user.''' setup: str = Field(description="The setup of the joke") punchline: str = Field(description="The punchline to the joke") rating: Optional[int] = Field(description="How funny the joke is, from 1 to 10") structured_llm = llm.with_structured_output(Joke) structured_llm.invoke("Tell me a joke about cats")
Joke(setup='Why was the cat sitting on the computer?', punchline='To keep an eye on the mouse!', rating=None)
See
ChatAnthropic.with_structured_output()
for more.- Image input:
import base64 import httpx from langchain_core.messages import HumanMessage image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" image_data = base64.b64encode(httpx.get(image_url).content).decode("utf-8") message = HumanMessage( content=[ {"type": "text", "text": "describe the weather in this image"}, { "type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_data}"}, }, ], ) ai_msg = llm.invoke([message]) ai_msg.content
"The image depicts a sunny day with a partly cloudy sky. The sky is a brilliant blue color with scattered white clouds drifting across. The lighting and cloud patterns suggest pleasant, mild weather conditions. The scene shows a grassy field or meadow with a wooden boardwalk trail leading through it, indicating an outdoor setting on a nice day well-suited for enjoying nature."
- Token usage:
ai_msg = llm.invoke(messages) ai_msg.usage_metadata
{'input_tokens': 25, 'output_tokens': 11, 'total_tokens': 36}
Message chunks containing token usage will be included during streaming by default:
stream = llm.stream(messages) full = next(stream) for chunk in stream: full += chunk full.usage_metadata
{'input_tokens': 25, 'output_tokens': 11, 'total_tokens': 36}
These can be disabled by setting
stream_usage=False
in the stream method, or by settingstream_usage=False
when initializing ChatAnthropic.- Response metadata
ai_msg = llm.invoke(messages) ai_msg.response_metadata
{'id': 'msg_013xU6FHEGEq76aP4RgFerVT', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 25, 'output_tokens': 11}}
Note
ChatAnthropic implements the standard
Runnable Interface
. πThe
Runnable Interface
has additional methods that are available on runnables, such aswith_types
,with_retry
,assign
,bind
,get_graph
, and more.- param anthropic_api_key: SecretStr [Optional] (alias 'api_key')#
Automatically read from env var ANTHROPIC_API_KEY if not provided.
- param anthropic_api_url: str | None [Optional] (alias 'base_url')#
Base URL for API requests. Only specify if using a proxy or service emulator.
If a value isnβt passed in, will attempt to read the value first from ANTHROPIC_API_URL and if that is not set, ANTHROPIC_BASE_URL. If neither are set, the default value of βhttps://api.anthropic.comβ will be used.
- 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 itβs 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 custom_get_token_ids: Callable[[str], list[int]] | None = None#
Optional encoder to use for counting tokens.
- param default_headers: Mapping[str, str] | None = None#
Headers to pass to the Anthropic clients, will be used for every API call.
- param default_request_timeout: float | None = None (alias 'timeout')#
Timeout for requests to Anthropic Completion API.
- param disable_streaming: bool | Literal['tool_calling'] = False#
Whether to disable streaming for this model.
If streaming is bypassed, then
stream()/astream()
will defer toinvoke()/ainvoke()
.If True, will always bypass streaming case.
If βtool_callingβ, 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 max_retries: int = 2#
Number of retries allowed for requests sent to the Anthropic Completion API.
- param max_tokens: int = 1024 (alias 'max_tokens_to_sample')#
Denotes the number of tokens to predict per generation.
- param metadata: dict[str, Any] | None = None#
Metadata to add to the run trace.
- param model: str [Required] (alias 'model_name')#
Model name to use.
- param model_kwargs: Dict[str, Any] [Optional]#
- param rate_limiter: BaseRateLimiter | None = None#
An optional rate limiter to use for limiting the number of requests.
- param stop_sequences: List[str] | None = None (alias 'stop')#
Default stop sequences.
- param stream_usage: bool = True#
Whether to include usage metadata in streaming output. If True, additional message chunks will be generated during the stream including usage metadata.
- param streaming: bool = False#
Whether to use streaming or not.
- param tags: list[str] | None = None#
Tags to add to the run trace.
- param temperature: float | None = None#
A non-negative float that tunes the degree of randomness in generation.
- param top_k: int | None = None#
Number of most likely tokens to consider at each step.
- param top_p: float | None = None#
Total probability mass of tokens to consider at each step.
- 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:
- 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]) β A list of inputs to the Runnable.
config (RunnableConfig | list[RunnableConfig] | None) β A config to use when invoking the Runnable. The config supports standard keys like βtagsβ, βmetadataβ for tracing purposes, βmax_concurrencyβ 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) β Whether to return exceptions instead of raising them. Defaults to False.
kwargs (Any | None) β 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]) β A list of inputs to the Runnable.
config (RunnableConfig | Sequence[RunnableConfig] | None) β A config to use when invoking the Runnable. The config supports standard keys like βtagsβ, βmetadataβ for tracing purposes, βmax_concurrencyβ 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) β Whether to return exceptions instead of raising them. Defaults to False.
kwargs (Any | None) β 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: 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:
- 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) β The input to the Runnable.
config (Optional[RunnableConfig]) β The config to use for the Runnable. Defaults to None.
kwargs (Any) β Additional keyword arguments to pass to the Runnable.
stop (Optional[list[str]])
- Yields:
The output of the Runnable.
- Return type:
AsyncIterator[BaseMessageChunk]
- async 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] #
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 theformat: 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 ofthe 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 thatgenerated 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 generatedthe event.
metadata
: Optional[Dict[str, Any]] - The metadata of the Runnablethat 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]
{βmessagesβ: [[SystemMessage, HumanMessage]]}
on_chat_model_stream
[model name]
AIMessageChunk(content=βhelloβ)
on_chat_model_end
[model name]
{βmessagesβ: [[SystemMessage, HumanMessage]]}
AIMessageChunk(content=βhello worldβ)
on_llm_start
[model name]
{βinputβ: βhelloβ}
on_llm_stream
[model name]
βHelloβ
on_llm_end
[model name]
βHello human!β
on_chain_start
format_docs
on_chain_stream
format_docs
βhello world!, goodbye world!β
on_chain_end
format_docs
[Document(β¦)]
βhello world!, goodbye world!β
on_tool_start
some_tool
{βxβ: 1, βyβ: β2β}
on_tool_end
some_tool
{βxβ: 1, βyβ: β2β}
on_retriever_start
[retriever name]
{βqueryβ: βhelloβ}
on_retriever_end
[retriever name]
{βqueryβ: βhelloβ}
[Document(β¦), ..]
on_prompt_start
[template_name]
{βquestionβ: βhelloβ}
on_prompt_end
[template_name]
{βquestionβ: βhelloβ}
ChatPromptValue(messages: [SystemMessage, β¦])
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) β The input to the Runnable.
config (RunnableConfig | None) β The config to use for the Runnable.
version (Literal['v1', 'v2']) β 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) β Only include events from runnables with matching names.
include_types (Sequence[str] | None) β Only include events from runnables with matching types.
include_tags (Sequence[str] | None) β Only include events from runnables with matching tags.
exclude_names (Sequence[str] | None) β Exclude events from runnables with matching names.
exclude_types (Sequence[str] | None) β Exclude events from runnables with matching types.
exclude_tags (Sequence[str] | None) β Exclude events from runnables with matching tags.
kwargs (Any) β 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 β 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(**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.
- Parameters:
kwargs (Any) β The arguments to bind to the Runnable.
- Returns:
A new Runnable with the arguments bound.
- Return type:
Runnable[Input, Output]
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'
- bind_tools(tools: Sequence[Dict[str, Any] | Type | Callable | BaseTool], *, tool_choice: Dict[str, str] | Literal['any', 'auto'] | str | None = None, **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.
- Parameters:
tools (Sequence[Dict[str, Any] | Type | Callable | BaseTool]) β A list of tool definitions to bind to this chat model. Supports Anthropic format tool schemas and any tool definition handled by
convert_to_openai_tool()
.tool_choice (Dict[str, str] | Literal['any', 'auto'] | str | None) β
Which tool to require the model to call. Options are:
name of the tool as a string or as dict
{"type": "tool", "name": "<<tool_name>>"}
: calls corresponding tool;"auto"
,{"type: "auto"}
, or None: automatically selects a tool (including no tool);"any"
or{"type: "any"}
: force at least one tool to be called;
kwargs (Any) β Any additional parameters are passed directly to
bind()
.
- Return type:
Runnable[PromptValue | str | Sequence[BaseMessage | list[str] | tuple[str, str] | str | dict[str, Any]], BaseMessage]
Example
from langchain_anthropic import ChatAnthropic from pydantic import BaseModel, Field class GetWeather(BaseModel): '''Get the current weather in a given location''' location: str = Field(..., description="The city and state, e.g. San Francisco, CA") class GetPrice(BaseModel): '''Get the price of a specific product.''' product: str = Field(..., description="The product to look up.") llm = ChatAnthropic(model="claude-3-5-sonnet-20240620", temperature=0) llm_with_tools = llm.bind_tools([GetWeather, GetPrice]) llm_with_tools.invoke("what is the weather like in San Francisco",) # -> AIMessage( # content=[ # {'text': '<thinking>\nBased on the user\'s question, the relevant function to call is GetWeather, which requires the "location" parameter.\n\nThe user has directly specified the location as "San Francisco". Since San Francisco is a well known city, I can reasonably infer they mean San Francisco, CA without needing the state specified.\n\nAll the required parameters are provided, so I can proceed with the API call.\n</thinking>', 'type': 'text'}, # {'text': None, 'type': 'tool_use', 'id': 'toolu_01SCgExKzQ7eqSkMHfygvYuu', 'name': 'GetWeather', 'input': {'location': 'San Francisco, CA'}} # ], # response_metadata={'id': 'msg_01GM3zQtoFv8jGQMW7abLnhi', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 487, 'output_tokens': 145}}, # id='run-87b1331e-9251-4a68-acef-f0a018b639cc-0' # )
- Example β force tool call with tool_choice βanyβ:
from langchain_anthropic import ChatAnthropic from pydantic import BaseModel, Field class GetWeather(BaseModel): '''Get the current weather in a given location''' location: str = Field(..., description="The city and state, e.g. San Francisco, CA") class GetPrice(BaseModel): '''Get the price of a specific product.''' product: str = Field(..., description="The product to look up.") llm = ChatAnthropic(model="claude-3-5-sonnet-20240620", temperature=0) llm_with_tools = llm.bind_tools([GetWeather, GetPrice], tool_choice="any") llm_with_tools.invoke("what is the weather like in San Francisco",)
- Example β force specific tool call with tool_choice β<name_of_tool>β:
from langchain_anthropic import ChatAnthropic from pydantic import BaseModel, Field class GetWeather(BaseModel): '''Get the current weather in a given location''' location: str = Field(..., description="The city and state, e.g. San Francisco, CA") class GetPrice(BaseModel): '''Get the price of a specific product.''' product: str = Field(..., description="The product to look up.") llm = ChatAnthropic(model="claude-3-5-sonnet-20240620", temperature=0) llm_with_tools = llm.bind_tools([GetWeather, GetPrice], tool_choice="GetWeather") llm_with_tools.invoke("what is the weather like in San Francisco",)
- Example β cache specific tools:
from langchain_anthropic import ChatAnthropic, convert_to_anthropic_tool from pydantic import BaseModel, Field class GetWeather(BaseModel): '''Get the current weather in a given location''' location: str = Field(..., description="The city and state, e.g. San Francisco, CA") class GetPrice(BaseModel): '''Get the price of a specific product.''' product: str = Field(..., description="The product to look up.") # We'll convert our pydantic class to the anthropic tool format # before passing to bind_tools so that we can set the 'cache_control' # field on our tool. cached_price_tool = convert_to_anthropic_tool(GetPrice) # Currently the only supported "cache_control" value is # {"type": "ephemeral"}. cached_price_tool["cache_control"] = {"type": "ephemeral"} # We need to pass in extra headers to enable use of the beta cache # control API. llm = ChatAnthropic( model="claude-3-5-sonnet-20240620", temperature=0, extra_headers={"anthropic-beta": "prompt-caching-2024-07-31"} ) llm_with_tools = llm.bind_tools([GetWeather, cached_price_tool]) llm_with_tools.invoke("what is the weather like in San Francisco",)
This outputs:
AIMessage(content=[{'text': "Certainly! I can help you find out the current weather in San Francisco. To get this information, I'll use the GetWeather function. Let me fetch that data for you right away.", 'type': 'text'}, {'id': 'toolu_01TS5h8LNo7p5imcG7yRiaUM', 'input': {'location': 'San Francisco, CA'}, 'name': 'GetWeather', 'type': 'tool_use'}], response_metadata={'id': 'msg_01Xg7Wr5inFWgBxE5jH9rpRo', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 171, 'output_tokens': 96, 'cache_creation_input_tokens': 1470, 'cache_read_input_tokens': 0}}, id='run-b36a5b54-5d69-470e-a1b0-b932d00b089e-0', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'San Francisco, CA'}, 'id': 'toolu_01TS5h8LNo7p5imcG7yRiaUM', 'type': 'tool_call'}], usage_metadata={'input_tokens': 171, 'output_tokens': 96, 'total_tokens': 267})
If we invoke the tool again, we can see that the βusageβ information in the AIMessage.response_metadata shows that we had a cache hit:
AIMessage(content=[{'text': 'To get the current weather in San Francisco, I can use the GetWeather function. Let me check that for you.', 'type': 'text'}, {'id': 'toolu_01HtVtY1qhMFdPprx42qU2eA', 'input': {'location': 'San Francisco, CA'}, 'name': 'GetWeather', 'type': 'tool_use'}], response_metadata={'id': 'msg_016RfWHrRvW6DAGCdwB6Ac64', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 171, 'output_tokens': 82, 'cache_creation_input_tokens': 0, 'cache_read_input_tokens': 1470}}, id='run-88b1f825-dcb7-4277-ac27-53df55d22001-0', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'San Francisco, CA'}, 'id': 'toolu_01HtVtY1qhMFdPprx42qU2eA', 'type': 'tool_call'}], usage_metadata={'input_tokens': 171, 'output_tokens': 82, 'total_tokens': 253})
- configurable_alternatives(which: ConfigurableField, *, default_key: str = 'default', prefix_keys: bool = False, **kwargs: Runnable[Input, Output] | Callable[[], Runnable[Input, Output]]) RunnableSerializable #
Configure alternatives for Runnables that can be set at runtime.
- Parameters:
which (ConfigurableField) β The ConfigurableField instance that will be used to select the alternative.
default_key (str) β The default key to use if no alternative is selected. Defaults to βdefaultβ.
prefix_keys (bool) β Whether to prefix the keys with the ConfigurableField id. Defaults to False.
**kwargs (Runnable[Input, Output] | Callable[[], Runnable[Input, Output]]) β A dictionary of keys to Runnable instances or callables that return Runnable instances.
- Returns:
A new Runnable with the alternatives configured.
- Return type:
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 #
Configure particular Runnable fields at runtime.
- Parameters:
**kwargs (ConfigurableField | ConfigurableFieldSingleOption | ConfigurableFieldMultiOption) β A dictionary of ConfigurableField instances to configure.
- Returns:
A new Runnable with the fields configured.
- Return type:
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 )
- get_num_tokens(text: str) int #
Get the number of tokens present in the text.
Useful for checking if an input fits in a modelβs context window.
- Parameters:
text (str) β 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 modelβs context window.
- Parameters:
messages (list[BaseMessage]) β 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) β 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) β The input to the Runnable.
config (Optional[RunnableConfig]) β A config to use when invoking the Runnable. The config supports standard keys like βtagsβ, βmetadataβ for tracing purposes, βmax_concurrencyβ 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:
- 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) β The input to the Runnable.
config (Optional[RunnableConfig]) β The config to use for the Runnable. Defaults to None.
kwargs (Any) β Additional keyword arguments to pass to the Runnable.
stop (Optional[list[str]])
- Yields:
The output of the Runnable.
- Return type:
Iterator[BaseMessageChunk]
- with_alisteners(*, on_start: AsyncListener | None = None, on_end: AsyncListener | None = None, on_error: AsyncListener | None = None) Runnable[Input, Output] #
Bind asynchronous lifecycle listeners to a Runnable, returning a new Runnable.
on_start: Asynchronously called before the Runnable starts running. on_end: Asynchronously called after the Runnable finishes running. on_error: Asynchronously called if the Runnable throws an error.
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.
- Parameters:
on_start (Optional[AsyncListener]) β Asynchronously called before the Runnable starts running. Defaults to None.
on_end (Optional[AsyncListener]) β Asynchronously called after the Runnable finishes running. Defaults to None.
on_error (Optional[AsyncListener]) β Asynchronously called if the Runnable throws an error. Defaults to None.
- Returns:
A new Runnable with the listeners bound.
- Return type:
Runnable[Input, Output]
Example:
from langchain_core.runnables import RunnableLambda import time async def test_runnable(time_to_sleep : int): print(f"Runnable[{time_to_sleep}s]: starts at {format_t(time.time())}") await asyncio.sleep(time_to_sleep) print(f"Runnable[{time_to_sleep}s]: ends at {format_t(time.time())}") async def fn_start(run_obj : Runnable): print(f"on start callback starts at {format_t(time.time())} await asyncio.sleep(3) print(f"on start callback ends at {format_t(time.time())}") async def fn_end(run_obj : Runnable): print(f"on end callback starts at {format_t(time.time())} await asyncio.sleep(2) print(f"on end callback ends at {format_t(time.time())}") runnable = RunnableLambda(test_runnable).with_alisteners( on_start=fn_start, on_end=fn_end ) async def concurrent_runs(): await asyncio.gather(runnable.ainvoke(2), runnable.ainvoke(3)) asyncio.run(concurrent_runs()) Result: on start callback starts at 2024-05-16T14:20:29.637053+00:00 on start callback starts at 2024-05-16T14:20:29.637150+00:00 on start callback ends at 2024-05-16T14:20:32.638305+00:00 on start callback ends at 2024-05-16T14:20:32.638383+00:00 Runnable[3s]: starts at 2024-05-16T14:20:32.638849+00:00 Runnable[5s]: starts at 2024-05-16T14:20:32.638999+00:00 Runnable[3s]: ends at 2024-05-16T14:20:35.640016+00:00 on end callback starts at 2024-05-16T14:20:35.640534+00:00 Runnable[5s]: ends at 2024-05-16T14:20:37.640169+00:00 on end callback starts at 2024-05-16T14:20:37.640574+00:00 on end callback ends at 2024-05-16T14:20:37.640654+00:00 on end callback ends at 2024-05-16T14:20:39.641751+00:00
- with_config(config: RunnableConfig | None = None, **kwargs: Any) Runnable[Input, Output] #
Bind config to a Runnable, returning a new Runnable.
- Parameters:
config (RunnableConfig | None) β The config to bind to the Runnable.
kwargs (Any) β Additional keyword arguments to pass to the Runnable.
- Returns:
A new Runnable with the config bound.
- 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.
The new Runnable will try the original Runnable, and then each fallback in order, upon failures.
- Parameters:
fallbacks (Sequence[Runnable[Input, Output]]) β A sequence of runnables to try if the original Runnable fails.
exceptions_to_handle (tuple[type[BaseException], ...]) β A tuple of exception types to handle. Defaults to (Exception,).
exception_key (Optional[str]) β 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. Defaults to None.
- Returns:
A new Runnable that will try the original Runnable, and then each fallback in order, upon failures.
- Return type:
RunnableWithFallbacksT[Input, Output]
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]]) β A sequence of runnables to try if the original Runnable fails.
exceptions_to_handle (tuple[type[BaseException], ...]) β A tuple of exception types to handle.
exception_key (Optional[str]) β 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: Callable[[Run], None] | Callable[[Run, RunnableConfig], None] | None = None, on_end: Callable[[Run], None] | Callable[[Run, RunnableConfig], None] | None = None, on_error: Callable[[Run], None] | Callable[[Run, RunnableConfig], None] | None = 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.
- Parameters:
on_start (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]) β Called before the Runnable starts running. Defaults to None.
on_end (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]) β Called after the Runnable finishes running. Defaults to None.
on_error (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]) β Called if the Runnable throws an error. Defaults to None.
- Returns:
A new Runnable with the listeners bound.
- Return type:
Runnable[Input, Output]
Example:
from langchain_core.runnables import RunnableLambda from langchain_core.tracers.schemas import Run import time def test_runnable(time_to_sleep : int): time.sleep(time_to_sleep) def fn_start(run_obj: Run): print("start_time:", run_obj.start_time) def fn_end(run_obj: Run): print("end_time:", run_obj.end_time) chain = RunnableLambda(test_runnable).with_listeners( on_start=fn_start, on_end=fn_end ) chain.invoke(2)
- with_retry(*, retry_if_exception_type: tuple[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.
- Parameters:
retry_if_exception_type (tuple[type[BaseException], ...]) β A tuple of exception types to retry on. Defaults to (Exception,).
wait_exponential_jitter (bool) β Whether to add jitter to the wait time between retries. Defaults to True.
stop_after_attempt (int) β The maximum number of attempts to make before giving up. Defaults to 3.
- Returns:
A new Runnable that retries the original Runnable on exceptions.
- Return type:
Runnable[Input, Output]
Example:
from langchain_core.runnables import RunnableLambda count = 0 def _lambda(x: int) -> None: global count count = count + 1 if x == 1: raise ValueError("x is 1") else: pass runnable = RunnableLambda(_lambda) try: runnable.with_retry( stop_after_attempt=2, retry_if_exception_type=(ValueError,), ).invoke(1) except ValueError: pass assert (count == 2)
- Parameters:
retry_if_exception_type (tuple[type[BaseException], ...]) β A tuple of exception types to retry on
wait_exponential_jitter (bool) β Whether to add jitter to the wait time between retries
stop_after_attempt (int) β 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: Dict | Type[BaseModel], *, include_raw: bool = False, **kwargs: Any) Runnable[PromptValue | str | Sequence[BaseMessage | list[str] | tuple[str, str] | str | dict[str, Any]], Dict | BaseModel] [source]#
Model wrapper that returns outputs formatted to match the given schema.
- Parameters:
schema (Dict | Type[BaseModel]) β
The output schema. Can be passed in as:
an Anthropic tool schema,
an OpenAI function/tool schema,
a JSON Schema,
a TypedDict class,
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. Seeconvert_to_openai_tool()
for more on how to properly specify types and descriptions of schema fields when specifying a Pydantic or TypedDict class.include_raw (bool) β 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 βrawβ, βparsedβ, and βparsing_errorβ.
kwargs (Any) β Additional keyword arguments are ignored.
- Returns:
A Runnable that takes same inputs as a
BaseChatModel
.If
include_raw
is False andschema
is a Pydantic class, Runnable outputs an instance ofschema
(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 theschema
as described above."parsing_error"
: Optional[BaseException]
- If
- Return type:
Runnable[PromptValue | str | Sequence[BaseMessage | list[str] | tuple[str, str] | str | dict[str, Any]], Dict | BaseModel]
- Example: Pydantic schema (include_raw=False):
from langchain_anthropic import ChatAnthropic from pydantic import BaseModel class AnswerWithJustification(BaseModel): '''An answer to the user question along with justification for the answer.''' answer: str justification: str llm = ChatAnthropic(model="claude-3-5-sonnet-20240620", 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_anthropic import ChatAnthropic from pydantic import BaseModel class AnswerWithJustification(BaseModel): '''An answer to the user question along with justification for the answer.''' answer: str justification: str llm = ChatAnthropic(model="claude-3-5-sonnet-20240620", 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_anthropic import ChatAnthropic schema = { "name": "AnswerWithJustification", "description": "An answer to the user question along with justification for the answer.", "input_schema": { "type": "object", "properties": { "answer": {"type": "string"}, "justification": {"type": "string"}, }, "required": ["answer", "justification"] } } llm = ChatAnthropic(model="claude-3-5-sonnet-20240620", temperature=0) structured_llm = llm.with_structured_output(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.' # }
Changed in version 0.1.22: Added support for TypedDict class as schema.
- with_types(*, input_type: type[Input] | None = None, output_type: type[Output] | None = None) Runnable[Input, Output] #
Bind input and output types to a Runnable, returning a new Runnable.
- Parameters:
input_type (type[Input] | None) β The input type to bind to the Runnable. Defaults to None.
output_type (type[Output] | None) β The output type to bind to the Runnable. Defaults to None.
- Returns:
A new Runnable with the types bound.
- Return type:
Runnable[Input, Output]
Examples using ChatAnthropic