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ChatSambaNovaCloud

This will help you getting started with SambaNovaCloud chat models. For detailed documentation of all ChatSambaNovaCloud features and configurations head to the API reference.

SambaNova's SambaNova Cloud is a platform for performing inference with open-source models

Overview​

Integration details​

ClassPackageLocalSerializableJS supportPackage downloadsPackage latest
ChatSambaNovaCloudlangchain-community❌❌❌PyPI - DownloadsPyPI - Version

Model features​

Tool callingStructured outputJSON modeImage inputAudio inputVideo inputToken-level streamingNative asyncToken usageLogprobs
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Setup​

To access ChatSambaNovaCloud models you will need to create a SambaNovaCloud account, get an API key, install the langchain_community integration package, and install the SSEClient Package.

pip install langchain-community
pip install sseclient-py

Credentials​

Get an API Key from cloud.sambanova.ai and add it to your environment variables:

export SAMBANOVA_API_KEY="your-api-key-here"
import getpass
import os

if not os.getenv("SAMBANOVA_API_KEY"):
    os.environ["SAMBANOVA_API_KEY"] = getpass.getpass(
        "Enter your SambaNova Cloud API key: "
    )

If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below:

# os.environ["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")

Installation​

The LangChain SambaNovaCloud integration lives in the langchain_community package:

%pip install -qU langchain-community
%pip install -qu sseclient-py

Instantiation​

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

from langchain_community.chat_models.sambanova import ChatSambaNovaCloud

llm = ChatSambaNovaCloud(
    model="llama3-405b", max_tokens=1024, temperature=0.7, top_k=1, top_p=0.01
)
API Reference:ChatSambaNovaCloud

Invocation​

messages = [
    (
        "system",
        "You are a helpful assistant that translates English to French. Translate the user sentence.",
    ),
    ("human", "I love programming."),
]
ai_msg = llm.invoke(messages)
ai_msg
AIMessage(content="J'adore la programmation.", response_metadata={'finish_reason': 'stop', 'usage': {'acceptance_rate': 11, 'completion_tokens': 9, 'completion_tokens_after_first_per_sec': 97.07042823956884, 'completion_tokens_after_first_per_sec_first_ten': 276.3343994441849, 'completion_tokens_per_sec': 23.775192800224037, 'end_time': 1726158364.7954874, 'is_last_response': True, 'prompt_tokens': 56, 'start_time': 1726158364.3670964, 'time_to_first_token': 0.3459765911102295, 'total_latency': 0.3785458261316473, 'total_tokens': 65, 'total_tokens_per_sec': 171.70972577939582}, 'model_name': 'Meta-Llama-3.1-405B-Instruct', 'system_fingerprint': 'fastcoe', 'created': 1726158364}, id='7154b676-9d5a-4b1a-a425-73bbe69f28fc')
print(ai_msg.content)
J'adore la programmation.

Chaining​

We can chain our model with a prompt template like so:

from langchain_core.prompts import ChatPromptTemplate

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

chain = prompt | llm
chain.invoke(
    {
        "input_language": "English",
        "output_language": "German",
        "input": "I love programming.",
    }
)
API Reference:ChatPromptTemplate
AIMessage(content='Ich liebe Programmieren.', response_metadata={'finish_reason': 'stop', 'usage': {'acceptance_rate': 11, 'completion_tokens': 6, 'completion_tokens_after_first_per_sec': 47.80258530102961, 'completion_tokens_after_first_per_sec_first_ten': 215.59002827036753, 'completion_tokens_per_sec': 5.263977583489829, 'end_time': 1726158506.3777263, 'is_last_response': True, 'prompt_tokens': 51, 'start_time': 1726158505.1611376, 'time_to_first_token': 1.1119918823242188, 'total_latency': 1.1398224830627441, 'total_tokens': 57, 'total_tokens_per_sec': 50.00778704315337}, 'model_name': 'Meta-Llama-3.1-405B-Instruct', 'system_fingerprint': 'fastcoe', 'created': 1726158505}, id='226471ac-8c52-44bb-baa7-f9d2f8c54477')

Streaming​

system = "You are a helpful assistant with pirate accent."
human = "I want to learn more about this animal: {animal}"
prompt = ChatPromptTemplate.from_messages([("system", system), ("human", human)])

chain = prompt | llm

for chunk in chain.stream({"animal": "owl"}):
    print(chunk.content, end="", flush=True)
Yer lookin' fer some info on owls, eh? Alright then, matey, settle yerself down with a pint o' grog and listen close.

Owls be nocturnal birds o' prey, meanin' they do most o' their huntin' at night. They got big, round eyes that be perfect fer seein' in the dark, like a trusty lantern on a dark sea. Their ears be sharp as a cutlass, too, helpin' 'em pinpoint the slightest sound o' a scurvy rodent scurryin' through the underbrush.

These birds be known fer their silent flight, like a ghost ship sailin' through the night. Their feathers be special, with a soft, fringed edge that helps 'em sneak up on their prey. And when they strike, it be swift and deadly, like a pirate's sword.

Owls be found all over the world, from the frozen tundras o' the north to the scorching deserts o' the south. They come in all shapes and sizes, from the tiny elf owl to the great grey owl, which be as big as a small dog.

Now, I know what ye be thinkin', "Pirate, what about their hootin'?" Aye, owls be famous fer their hoots, which be a form o' communication. They use different hoots to warn off predators, attract a mate, or even just to say, "Shiver me timbers, I be happy to be alive!"

So there ye have it, me hearty. Owls be fascinatin' creatures, and I hope ye found this info as interestin' as a chest overflowin' with gold doubloons. Fair winds and following seas!

Async​

prompt = ChatPromptTemplate.from_messages(
    [
        (
            "human",
            "what is the capital of {country}?",
        )
    ]
)

chain = prompt | llm
await chain.ainvoke({"country": "France"})
AIMessage(content='The capital of France is Paris.', response_metadata={'finish_reason': 'stop', 'usage': {'acceptance_rate': 13, 'completion_tokens': 8, 'completion_tokens_after_first_per_sec': 86.00726488715989, 'completion_tokens_after_first_per_sec_first_ten': 326.92555640828857, 'completion_tokens_per_sec': 21.74539360394493, 'end_time': 1726159287.9987085, 'is_last_response': True, 'prompt_tokens': 43, 'start_time': 1726159287.5738964, 'time_to_first_token': 0.34342360496520996, 'total_latency': 0.36789400760944074, 'total_tokens': 51, 'total_tokens_per_sec': 138.62688422514893}, 'model_name': 'Meta-Llama-3.1-405B-Instruct', 'system_fingerprint': 'fastcoe', 'created': 1726159287}, id='9b4ef015-50a2-434b-b980-29f8aa90c3e8')

Async Streaming​

prompt = ChatPromptTemplate.from_messages(
    [
        (
            "human",
            "in less than {num_words} words explain me {topic} ",
        )
    ]
)
chain = prompt | llm

async for chunk in chain.astream({"num_words": 30, "topic": "quantum computers"}):
    print(chunk.content, end="", flush=True)
Quantum computers use quantum bits (qubits) to process vast amounts of data simultaneously, leveraging quantum mechanics to solve complex problems exponentially faster than classical computers.

API reference​

For detailed documentation of all ChatSambaNovaCloud features and configurations head to the API reference: https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.sambanova.ChatSambaNovaCloud.html

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