LangChain

pip install "langchain-sambanova>=1.1,<2"

python -c "import langchain_sambanova; print(langchain_sambanova.__version__)"

# Expected output
1.1.1

export SAMBANOVA_API_KEY="your-sambanova-api-key"

import getpass
import os

os.environ["SAMBANOVA_API_KEY"] = getpass.getpass("SambaNova API key: ")

from langchain_sambanova import ChatSambaNova

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

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)

# Expected output
J'adore la programmation.

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

# Expected output
J'adore la programmation.

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)

# Expected output
Ich liebe das Programmieren.

from langchain_core.tools import tool


@tool
def get_weather(city: str) -> str:
    """Return the current weather for a city."""
    return f"It is sunny in {city}."


llm_with_tools = llm.bind_tools([get_weather])
response = llm_with_tools.invoke("What is the weather in Palo Alto?")
print(response.tool_calls)

# Expected output
[{'name': 'get_weather', 'args': {'city': 'Palo Alto'}, 'id': 'call_1', 'type': 'tool_call'}]

from langchain_core.messages import HumanMessage

messages = [HumanMessage("What is the weather in Palo Alto?")]
messages.append(response)

for call in response.tool_calls:
    result = get_weather.invoke(call)
    messages.append(result)

print(llm_with_tools.invoke(messages).content)

from typing import Literal, Optional

from pydantic import BaseModel, Field


class Ticket(BaseModel):
    """A support ticket extracted from a message."""

    title: str = Field(description="One-line summary")
    priority: Literal["low", "medium", "high"] = Field(description="Urgency")
    owner: Optional[str] = Field(default=None, description="Assignee if named")


structured_llm = llm.with_structured_output(Ticket)
ticket = structured_llm.invoke("Ada needs the quarterly review finished, it is urgent.")
print(ticket)

# Expected output
title='Quarterly review' priority='high' owner='Ada'