ChatBedrockConverse

Setup:

To use Amazon Bedrock make sure you've gone through all the steps described here: https://docs.aws.amazon.com/bedrock/latest/userguide/setting-up.html

Once that's completed, install the LangChain integration:

pip install -U langchain-aws

Key init args — completion params: model: str Name of BedrockConverse model to use. temperature: float Sampling temperature. max_tokens: Optional[int] Max number of tokens to generate.

Key init args — client params: region_name: Optional[str] AWS region to use, e.g. 'us-west-2'. base_url: Optional[str] Bedrock endpoint to use. Needed if you don't want to default to us-east- 1 endpoint. credentials_profile_name: Optional[str] The name of the profile in the ~/.aws/credentials or ~/.aws/config files.

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

Instantiate:

from langchain_aws import ChatBedrockConverse

model = ChatBedrockConverse(
    model="anthropic.claude-3-sonnet-20240229-v1:0",
    temperature=0,
    max_tokens=None,
    # other params...
)

Invoke:

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

AIMessage(content=[{'type': 'text', 'text': "J'aime la programmation."}], response_metadata={'ResponseMetadata': {'RequestId': '9ef1e313-a4c1-4f79-b631-171f658d3c0e', 'HTTPStatusCode': 200, 'HTTPHeaders': {'date': 'Sat, 15 Jun 2024 01:19:24 GMT', 'content-type': 'application/json', 'content-length': '205', 'connection': 'keep-alive', 'x-amzn-requestid': '9ef1e313-a4c1-4f79-b631-171f658d3c0e'}, 'RetryAttempts': 0}, 'stopReason': 'end_turn', 'metrics': {'latencyMs': 609}}, id='run-754e152b-2b41-4784-9538-d40d71a5c3bc-0', usage_metadata={'input_tokens': 25, 'output_tokens': 11, 'total_tokens': 36})

Stream:

for chunk in model.stream(messages):
    print(chunk)

AIMessageChunk(content=[], id='run-da3c2606-4792-440a-ac66-72e0d1f6d117')
AIMessageChunk(content=[{'type': 'text', 'text': 'J', 'index': 0}], id='run-da3c2606-4792-440a-ac66-72e0d1f6d117')
AIMessageChunk(content=[{'text': "'", 'index': 0}], id='run-da3c2606-4792-440a-ac66-72e0d1f6d117')
AIMessageChunk(content=[{'text': 'a', 'index': 0}], id='run-da3c2606-4792-440a-ac66-72e0d1f6d117')
AIMessageChunk(content=[{'text': 'ime', 'index': 0}], id='run-da3c2606-4792-440a-ac66-72e0d1f6d117')
AIMessageChunk(content=[{'text': ' la', 'index': 0}], id='run-da3c2606-4792-440a-ac66-72e0d1f6d117')
AIMessageChunk(content=[{'text': ' programm', 'index': 0}], id='run-da3c2606-4792-440a-ac66-72e0d1f6d117')
AIMessageChunk(content=[{'text': 'ation', 'index': 0}], id='run-da3c2606-4792-440a-ac66-72e0d1f6d117')
AIMessageChunk(content=[{'text': '.', 'index': 0}], id='run-da3c2606-4792-440a-ac66-72e0d1f6d117')
AIMessageChunk(content=[{'index': 0}], id='run-da3c2606-4792-440a-ac66-72e0d1f6d117')
AIMessageChunk(content=[], response_metadata={'stopReason': 'end_turn'}, id='run-da3c2606-4792-440a-ac66-72e0d1f6d117')
AIMessageChunk(content=[], response_metadata={'metrics': {'latencyMs': 581}}, id='run-da3c2606-4792-440a-ac66-72e0d1f6d117', usage_metadata={'input_tokens': 25, 'output_tokens': 11, 'total_tokens': 36})

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

AIMessageChunk(content=[{'type': 'text', 'text': "J'aime la programmation.", 'index': 0}], response_metadata={'stopReason': 'end_turn', 'metrics': {'latencyMs': 554}}, id='run-56a5a5e0-de86-412b-9835-624652dc3539', 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")

model_with_tools = model.bind_tools([GetWeather, GetPopulation])
ai_msg = model_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': 'tooluse_Mspi2igUTQygp-xbX6XGVw'},
 {'name': 'GetWeather',
  'args': {'location': 'New York, NY'},
  'id': 'tooluse_tOPHiDhvR2m0xF5_5tyqWg'},
 {'name': 'GetPopulation',
  'args': {'location': 'Los Angeles, CA'},
  'id': 'tooluse__gcY_klbSC-GqB-bF_pxNg'},
 {'name': 'GetPopulation',
  'args': {'location': 'New York, NY'},
  'id': 'tooluse_-1HSoGX0TQCSaIg7cdFy8Q'}]

See ChatBedrockConverse.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_model = model.with_structured_output(Joke)
structured_model.invoke("Tell me a joke about cats")

Joke(setup='What do you call a cat that gets all dressed up?', punchline='A purrfessional!', rating=7)

See ChatBedrockConverse.with_structured_output() for more.

Extended thinking:

Some models, such as Claude 3.7 Sonnet, support an extended thinking feature that outputs the step-by-step reasoning process that led to an answer.

To use it, specify the thinking parameter when initializing ChatBedrockConverse as shown below.

You will need to specify a token budget to use this feature. See usage example:

from langchain_aws import ChatBedrockConverse

thinking_params= {
    "thinking": {
        "type": "enabled",
        "budget_tokens": 2000
    }
}

model = ChatBedrockConverse(
    model="us.anthropic.claude-sonnet-4-5-20250929-v1:0",
    max_tokens=5000,
    region_name="us-west-2",
    additional_model_request_fields=thinking_params,
)

response = model.invoke("What is the cube root of 50.653?")
print(response.content)

[
    {'type': 'reasoning_content', 'reasoning_content': {'type': 'text', 'text': 'I need to calculate the cube root of... ', 'signature': '...'}},
    {'type': 'text', 'text': 'The cube root of 50.653 is...'}
]

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",
            "source": {"type": "base64", "media_type": "image/jpeg", "data": image_data},
        },
    ],
)
ai_msg = model.invoke([message])
ai_msg.content

[{'type': 'text',
  'text': '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 an open grassy field or meadow, indicating warm temperatures conducive for vegetation growth. Overall, the weather portrayed in this scenic outdoor image appears to be sunny with some clouds, likely representing a nice, comfortable day.'}]

Token usage:

ai_msg = model.invoke(messages)
ai_msg.usage_metadata

{'input_tokens': 25, 'output_tokens': 11, 'total_tokens': 36}

Response metadata:

ai_msg = model.invoke(messages)
ai_msg.response_metadata

{'ResponseMetadata': {'RequestId': '776a2a26-5946-45ae-859e-82dc5f12017c',
  'HTTPStatusCode': 200,
  'HTTPHeaders': {'date': 'Mon, 17 Jun 2024 01:37:05 GMT',
   'content-type': 'application/json',
   'content-length': '206',
   'connection': 'keep-alive',
   'x-amzn-requestid': '776a2a26-5946-45ae-859e-82dc5f12017c'},
  'RetryAttempts': 0},
 'stopReason': 'end_turn',
 'metrics': {'latencyMs': 1290}}