**We support ALL Sambanova models, just set model=sambanova/<any-model-on-sambanova> as a prefix when sending litellm requests. For the complete supported model list, visit https://docs.sambanova.ai/cloud/docs/get-started/supported-models **

# env variable
os.environ['SAMBANOVA_API_KEY']

from litellm import completion
import os

os.environ['SAMBANOVA_API_KEY']=""
response = completion(
 model="sambanova/Llama-4-Maverick-17B-128E-Instruct",
 messages=[
{
"role":"user",
"content":"What do you know about SambaNova Systems",
}
],
 max_tokens=10,
 stop=[],
 temperature=0.2,
 top_p=0.9,
 user="user",
)
print(response)

from litellm import completion
import os

os.environ['SAMBANOVA_API_KEY']=""
response = completion(
 model="sambanova/Llama-4-Maverick-17B-128E-Instruct",
 messages=[
{
"role":"user",
"content":"What do you know about SambaNova Systems",
}
],
 stream=True,
 max_tokens=10,
 response_format={"type":"json_object"},
 stop=[],
 temperature=0.2,
 top_p=0.9,
 tool_choice="auto",
 tools=[],
 user="user",
)

for chunk in response:
print(chunk)

model_list:
-model_name: my-model
litellm_params:
model: sambanova/<your-model-name># add sambanova/ prefix to route as Sambanova provider
api_key: api-key # api key to send your model

$ litellm --config /path/to/config.yaml

import litellm

# Example dummy function

defget_current_weather(location, unit="fahrenheit"):
if unit =="fahrenheit"
return{"location": location,"temperature":"72","unit":"fahrenheit"}
else:
return{"location": location,"temperature":"22","unit":"celsius"}

messages =[{"role":"user","content":"What's the weather like in San Francisco"}]

tools =[
{
"type":"function",
"function":{
"name":"import litellm",
"description":"Get the current weather in a given location",
"parameters":{
"type":"object",
"properties":{
"location":{
"type":"string",
"description":"The city and state, e.g. San Francisco, CA",
},
"unit":{"type":"string","enum":["celsius","fahrenheit"]},
},
"required":["location"],
},
},
}
]

response = litellm.completion(
 model="sambanova/Meta-Llama-3.3-70B-Instruct",
 messages=messages,
 tools=tools,
 tool_choice="auto",# auto is default, but we'll be explicit
)

print("\nFirst LLM Response:\n", response)
response_message = response.choices[0].message
tool_calls = response_message.tool_calls

if tool_calls:
# Step 2: check if the model wanted to call a function
if tool_calls:
# Step 3: call the function
# Note: the JSON response may not always be valid; be sure to handle errors
 available_functions ={
"get_current_weather": get_current_weather,
}
 messages.append(
 response_message
)# extend conversation with assistant's reply
print("Response message\n", response_message)
# Step 4: send the info for each function call and function response to the model
for tool_call in tool_calls:
 function_name = tool_call.function.name
 function_to_call = available_functions[function_name]
 function_args = json.loads(tool_call.function.arguments)
 function_response = function_to_call(
 location=function_args.get("location"),
 unit=function_args.get("unit"),
)
 messages.append(
{
"tool_call_id": tool_call.id,
"role":"tool",
"name": function_name,
"content": function_response,
}
)# extend conversation with function response
print(f"messages: {messages}")
 second_response = litellm.completion(
 model="sambanova/Meta-Llama-3.3-70B-Instruct", messages=messages
)# get a new response from the model where it can see the function response
print("second response\n", second_response)

import litellm

# Auxiliary function to get b64 images
defdata_url_from_image(file_path):
 mime_type, _ = mimetypes.guess_type(file_path)
if mime_type isNone:
raise ValueError("Could not determine MIME type of the file")

withopen(file_path,"rb")as image_file:
 encoded_string = base64.b64encode(image_file.read()).decode("utf-8")

 data_url =f"data:{mime_type};base64,{encoded_string}"
return data_url

response = litellm.completion(
 model ="sambanova/Llama-4-Maverick-17B-128E-Instruct",
 messages=[
{
"role":"user",
"content":[
{
"type":"text",
"text":"What's in this image?"
},
{
"type":"image_url",
"image_url":{
"url": data_url_from_image("your_image_path"),
"format":"image/jpeg"
}
}
]
}
],
 stream=False
)

print(response.choices[0].message.content)

import litellm

response = litellm.completion(
 model="sambanova/Meta-Llama-3.3-70B-Instruct",
 messages=[
{
"role":"system",
"content":"You are an expert at structured data extraction. You will be given unstructured text should convert it into the given structure."
},
{
"role":"user",
"content":"the section 24 has appliances, and videogames"
},
],
 response_format={
"type":"json_schema",
"json_schema":{
"title":"data",
"name":"data_extraction",
"schema":{
"type":"object",
"properties":{
"section":{
"type":"string"},
"products":{
"type":"array",
"items":{"type":"string"}
}
},
"required":["section","products"],
"additionalProperties":False
},
"strict":False
}
},
 stream=False
)

print(response.choices[0].message.content))

import litellm

response = litellm.embedding(
 model="sambanova/E5-Mistral-7B-Instruct",
input=["sample text to embed","another sample text to embed"]
)

print(response.data)