ChatAmazonNova integration

This guide provides a quick overview for getting started with Amazon Nova chat models. Amazon Nova models are OpenAI-compatible and accessed via the OpenAI SDK pointed at Nova’s endpoint, providing seamless integration with LangChain’s standard interfaces. The Amazon Nova API is free tier with rate limits. For production deployments requiring higher throughput and enterprise features, consider using Amazon Nova models via Amazon Bedrock. You can find information about Amazon Nova’s models, their features, and API details in the Amazon Nova documentation.

API ReferenceFor detailed documentation of all ChatAmazonNova features and configuration options, head to the ChatAmazonNova API reference.For Amazon Nova model details and capabilities, see the Amazon Nova documentation.

Overview

Integration details

Class	Package	Serializable	JS/TS Support	Downloads	Latest Version
`ChatAmazonNova`	`langchain-amazon-nova`	beta	❌

Model features

Tool calling	Structured output	Image input	Audio input	Video input	Token-level streaming	Native async	Token usage	Logprobs
✅	✅	Model-dependent	❌	Model-dependent (Nova 2)	✅	✅	✅	❌

Setup

To access Amazon Nova models, you’ll need to obtain API credentials and install the langchain-amazon-nova integration package.

Installation

pip install -U langchain-amazon-nova

Credentials

Set your Nova API credentials as environment variables:

import getpass
import os

if "NOVA_API_KEY" not in os.environ:
    os.environ["NOVA_API_KEY"] = getpass.getpass("Enter your Nova API key: ")

if "NOVA_BASE_URL" not in os.environ:
    os.environ["NOVA_BASE_URL"] = getpass.getpass("Enter your Nova base URL: ")

To enable automated tracing of your model calls, set your LangSmith API key:

os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
os.environ["LANGSMITH_TRACING"] = "true"

Instantiation

Now we can instantiate our model object and generate chat completions:

from langchain_amazon_nova import ChatAmazonNova

model = ChatAmazonNova(
    model="nova-2-lite-v1",
    temperature=0.7,
    max_tokens=2048,
    timeout=None,
    max_retries=2,
    # other params...
)

For a complete list of supported parameters and their descriptions, see the Amazon Nova documentation.

Invocation

messages = [
    (
        "system",
        "You are a helpful assistant that translates English to French. Translate the user sentence.",
    ),
    ("user", "I love programming."),
]
ai_msg = model.invoke(messages)
ai_msg

AIMessage(content="J'adore la programmation.", response_metadata={'model': 'nova-2-lite-v1', 'finish_reason': 'stop'}, id='run-12345678-1234-1234-1234-123456789abc', usage_metadata={'input_tokens': 29, 'output_tokens': 8, 'total_tokens': 37})

print(ai_msg.content)

J'adore la programmation.

Content blocks

Amazon Nova messages can contain either a single string or a list of content blocks. You can access standardized content blocks using the content_blocks property:

ai_msg.content_blocks

Using content_blocks will render the content in a standard format that is consistent across other model providers. Read more about content blocks.

Streaming

Amazon Nova supports token-level streaming for real-time response generation:

for chunk in model.stream(messages):
    print(chunk.content, end="", flush=True)

J'adore la programmation.

Async streaming

For async applications, use astream:

import asyncio

async def main():
    async for chunk in model.astream(messages):
        print(chunk.content, end="", flush=True)

asyncio.run(main())

Tool calling

Amazon Nova supports tool calling (function calling) on compatible models. You can check if a model supports tool calling using LangChain model profiles.

For details on Nova’s tool calling implementation and available parameters, see the tool calling documentation.

Basic tool usage

Bind tools to the model using Pydantic models or LangChain @tool:

from pydantic import BaseModel, Field

class GetWeather(BaseModel):
    """Get the weather for a location."""

    location: str = Field(description="City name")

model_with_tools = model.bind_tools([GetWeather])
response = model_with_tools.invoke("What's the weather in Paris?")
print(response.tool_calls)

[{'name': 'GetWeather', 'args': {'location': 'Paris'}, 'id': 'call_abc123', 'type': 'tool_call'}]

You can also access tool calls specifically in a standard format using the tool_calls attribute:

response.tool_calls

[{'name': 'GetWeather',
  'args': {'location': 'Paris'},
  'id': 'call_abc123',
  'type': 'tool_call'}]

Using LangChain tools

You can also use standard LangChain tools:

from langchain.tools import tool

@tool
def get_weather(location: str) -> str:
    """Get the weather for a location."""
    return f"Weather in {location}: Sunny, 72°F"

model_with_tools = model.bind_tools([get_weather])
response = model_with_tools.invoke("What's the weather in San Francisco?")

Controlling tool choice

Amazon Nova supports controlling when the model should use tools via the tool_choice parameter:

# Model decides whether to call tools (default)
model_auto = model.bind_tools([get_weather], tool_choice="auto")

# Model must call a tool
model_required = model.bind_tools([get_weather], tool_choice="required")

# Model cannot call tools
model_none = model.bind_tools([get_weather], tool_choice="none")

Nova’s tool_choice valuesAmazon Nova supports tool_choice values of "auto", "required", and "none". Unlike some other providers, Nova does not support tool_choice="any" or specifying a specific tool name as the choice value.

The tool_choice="required" option is particularly useful for ensuring the model always uses tools, such as in structured output scenarios.

System tools

Amazon Nova provides built-in system tools that enhance the model’s capabilities with integrated functionality. These tools are enabled by passing them to the model initialization or as invocation parameters.

Available system tools

Amazon Nova supports the following built-in tools:

Web grounding (nova_grounding)

The grounding tool allows the model to search the web and ground its responses with real-time information from external sources.

from langchain_amazon_nova import ChatAmazonNova

model_with_grounding = ChatAmazonNova(
    model="nova-2-lite-v1",
    system_tools=["nova_grounding"],
)

response = model_with_grounding.invoke("What are the latest developments in AI?")

The grounding tool will automatically search for relevant information and include citations in the response.

Code interpreter (nova_code_interpreter)

The code interpreter tool enables the model to write and execute Python code in a sandboxed environment, useful for mathematical computations, data analysis, and code generation tasks.

from langchain_amazon_nova import ChatAmazonNova

model_with_code = ChatAmazonNova(
    model="nova-2-lite-v1",
    system_tools=["nova_code_interpreter"],
)

response = model_with_code.invoke("Calculate the fibonacci sequence up to the 10th number")

The code interpreter executes code securely and returns both the code and its output.

Combining system tools

You can enable multiple system tools simultaneously:

from langchain_amazon_nova import ChatAmazonNova

model_with_tools = ChatAmazonNova(
    model="nova-2-lite-v1",
    system_tools=["nova_grounding", "nova_code_interpreter"],
)

response = model_with_tools.invoke(
    "Search for the current price of Bitcoin and calculate its 7-day moving average"
)

The model will automatically determine which tool(s) to use based on the query.

System tools as invocation parameters

You can also specify system tools at invocation time instead of during initialization:

from langchain_amazon_nova import ChatAmazonNova

model = ChatAmazonNova(model="nova-2-lite-v1")

# Enable grounding for this specific call
response = model.invoke(
    "What's the weather today?",
    system_tools=["nova_grounding"]
)

This approach is useful when you want to use different system tools for different queries with the same model instance.

Tool outputs and citationsWhen using system tools, the model’s response will include:

The main text response
Citations or references (for grounding tool)
Code execution results (for code interpreter)

These outputs are included in the message’s response_metadata and can be accessed for displaying sources or debugging.

For complete details on system tools, their parameters, and capabilities, see the Amazon Nova documentation.

Structured output

Amazon Nova supports structured output through the with_structured_output() method, enabling you to get LLM responses in structured formats using Pydantic models or JSON schemas.

Basic usage with Pydantic

You can constrain LLM responses to match a specific structure using Pydantic models:

from pydantic import BaseModel, Field
from langchain_amazon_nova import ChatAmazonNova

class Person(BaseModel):
    """Information about a person."""
    name: str = Field(description="The person's name")
    age: int = Field(description="The person's age")

model = ChatAmazonNova(model="nova-pro-v1")
structured_model = model.with_structured_output(Person)

result = structured_model.invoke("John is 30 years old")
print(result)

Person(name='John', age=30)

JSON schema support

You can also provide JSON schemas directly:

json_schema = {
    "type": "object",
    "properties": {
        "name": {"type": "string"},
        "age": {"type": "integer"}
    },
    "required": ["name", "age"]
}

structured_model = model.with_structured_output(json_schema)
result = structured_model.invoke("Sarah is 28 years old")
print(result)

{'name': 'Sarah', 'age': 28}

Streaming structured output

Structured output works with streaming. The parsed object is returned once the complete response arrives:

from pydantic import BaseModel, Field

class Person(BaseModel):
    """Information about a person."""
    name: str = Field(description="The person's name")
    age: int = Field(description="The person's age")

structured_model = model.with_structured_output(Person)

for chunk in structured_model.stream("Michael is 35 years old"):
    print(chunk)

Person(name='Michael', age=35)

Accessing raw messages

The include_raw parameter allows access to both the parsed output and the raw AIMessage:

structured_model = model.with_structured_output(Person, include_raw=True)
result = structured_model.invoke("John is 30 years old")

print(f"Parsed: {result['parsed']}")
print(f"Raw message: {result['raw']}")

Parsed: Person(name='John', age=30)
Raw message: AIMessage(content='', additional_kwargs={'tool_calls': [...]}, ...)

This is useful for debugging, accessing metadata, or handling edge cases where parsing might fail.

Nested and complex schemas

You can use nested Pydantic models for complex data structures:

from pydantic import BaseModel, Field
from typing import List

class Address(BaseModel):
    """A physical address."""
    street: str
    city: str
    country: str

class Person(BaseModel):
    """Information about a person."""
    name: str
    age: int
    addresses: List[Address] = Field(description="List of addresses")

structured_model = model.with_structured_output(Person)
result = structured_model.invoke(
    "John is 30 years old. He lives at 123 Main St in Seattle, USA "
    "and has a vacation home at 456 Beach Rd in Miami, USA."
)
print(result)

Implementation detailsStructured output uses Nova’s tool calling capabilities under the hood with tool_choice='required' to ensure consistent structured responses. The schema is converted to a tool definition, and the tool call response is parsed back into the requested format.

Model profile

Amazon Nova provides different models with varying capabilities. It includes support for LangChain model profiles.

Model capabilities vary by modelSome Amazon Nova models support vision inputs while others do not. Always check model capabilities before using multimodal features.For a complete list of available models and their capabilities, see the Amazon Nova documentation.

Async operations

For production applications requiring high throughput, use native async operations:

import asyncio

async def main():
    messages = [
        ("system", "You are a helpful assistant."),
        ("human", "What is the capital of France?"),
    ]
    response = await model.ainvoke(messages)
    print(response.content)

asyncio.run(main())

The capital of France is Paris.

Chaining

Amazon Nova models work seamlessly with LangChain’s LCEL (LangChain Expression Language) for building chains:

from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant that translates {input_language} to {output_language}."),
    ("human", "{text}"),
])

chain = prompt | model | StrOutputParser()

result = chain.invoke({
    "input_language": "English",
    "output_language": "Spanish",
    "text": "Hello, how are you?"
})
print(result)

Hola, ¿cómo estás?

Error handling

The model includes built-in retry logic with configurable parameters:

model = ChatAmazonNova(
    model="nova-2-lite-v1",
    max_retries=3,  # Number of retries on failure
    timeout=60.0,   # Request timeout in seconds
)

For additional control over retries, use the with_retry method:

model_with_custom_retry = model.with_retry(
    stop_after_attempt=5,
    wait_exponential_jitter=True,
)

Troubleshooting

Connection issues

If you encounter connection errors, verify your environment variables are set correctly:

import os
print(f"API Key set: {'NOVA_API_KEY' in os.environ}")
print(f"Base URL: {os.environ.get('NOVA_BASE_URL', 'Not set')}")

For authentication and connection issues, refer to the Amazon Nova documentation.

Compression errors

The ChatAmazonNova client automatically disables compression to avoid potential decompression issues.

If you need to customize HTTP client behavior, you can access the underlying OpenAI client:

# The client is automatically configured with no compression
model = ChatAmazonNova(model="nova-2-lite-v1")
# model.client is the configured OpenAI client

Tool calling validation errors

If you receive a validation error when binding tools, ensure the model supports tool calling.

API reference

For detailed documentation of all ChatAmazonNova features and configurations, head to the ChatAmazonNova API reference. For Amazon Nova-specific features, model details, and API specifications, see the Amazon Nova documentation.

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Popular Providers

Integrations by component

Overview

Integration details

Model features

Setup

Installation

Credentials

Instantiation

Invocation

Content blocks

Streaming

Async streaming

Tool calling

Basic tool usage

Using LangChain tools

Controlling tool choice

System tools

Available system tools

Web grounding (nova_grounding)

Code interpreter (nova_code_interpreter)

Combining system tools

System tools as invocation parameters

Structured output

Basic usage with Pydantic

JSON schema support

Streaming structured output

Accessing raw messages

Nested and complex schemas

Model profile

Async operations

Chaining

Error handling

Troubleshooting

Connection issues

Compression errors

Tool calling validation errors

API reference

Popular Providers

Integrations by component

Documentation Index

​Overview

​Integration details

​Model features

​Setup

​Installation

​Credentials

​Instantiation

​Invocation

​Content blocks

​Streaming

​Async streaming

​Tool calling

​Basic tool usage

​Using LangChain tools

​Controlling tool choice

​System tools

​Available system tools

​Web grounding (nova_grounding)

​Code interpreter (nova_code_interpreter)

​Combining system tools

​System tools as invocation parameters

​Structured output

​Basic usage with Pydantic

​JSON schema support

​Streaming structured output

​Accessing raw messages

​Nested and complex schemas

​Model profile

​Async operations

​Chaining

​Error handling

​Troubleshooting

​Connection issues

​Compression errors

​Tool calling validation errors

​API reference

Overview

Integration details

Model features

Setup

Installation

Credentials

Instantiation

Invocation

Content blocks

Streaming

Async streaming

Tool calling

Basic tool usage

Using LangChain tools

Controlling tool choice

System tools

Available system tools

Web grounding (nova_grounding)

Code interpreter (nova_code_interpreter)

Combining system tools

System tools as invocation parameters

Structured output

Basic usage with Pydantic

JSON schema support

Streaming structured output

Accessing raw messages

Nested and complex schemas

Model profile

Async operations

Chaining

Error handling

Troubleshooting

Connection issues

Compression errors

Tool calling validation errors

API reference