LangChain is a software framework for developing applications powered by (large language models) LLMs. This document describes how to use LangChain to interact with SambaNova models.

View the following resources for more information:

Follow the example below to see how you can directly instantiate a ChatSambaNovaCloud model from LangChain and prompt it.

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Setup

Do the following to access ChatSambaNovaCloud models:

  1. Create a SambaNovaCloud account and get an API key.

  2. Run the command below to install the langchain-sambanova integration package.

pip install langchain-sambanova

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Credentials

Run the commands below to register the API key you received from cloud.sambanova.ai as an environment variable.

import os

sambanova_api_key = "<your-api-key>"
os.environ["SAMBANOVA_API_KEY"] = sambanova_api_key

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Instantiation

Now you can instantiate a ChatSambaNovaCloud model object and generate chat completions, as shown in the example below. The example below uses Meta’s Llama 3.1 70B.

from langchain_sambanova import ChatSambaNovaCloud

llm = ChatSambaNovaCloud(
    model="Meta-Llama-3.3-70B-Instruct",
    max_tokens=1024,
    temperature=0.7,
    top_p=0.01,
)

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Invocation

You can invoke the model using the command below.

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)
print(ai_msg.content)

You should see an output similar to the one below.

J'adore la programmation.

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Chaining

You can chain the model with a prompt template, as shown in the example below.

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
response = chain.invoke(
    {
        "input_language": "English",
        "output_language": "German",
        "input": "I love programming.",
    }
)
print(response.content)

You should see an output similar to the one below.

Ich liebe das Programmieren.
LlamaIndexContinue