> ## Documentation Index > Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-opensw-1783454697-4d4e2b4.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # ChatSambanova integration > Integrate with the ChatSambanova chat model using LangChain Python. This will help you get started with SambaNova [chat models](/oss/python/langchain/models/). For detailed documentation of all `ChatSambaNova` features and configurations head to the [API reference](https://docs.sambanova.ai/cloud/docs/get-started/overview). **[SambaNova](https://sambanova.ai/)'s** [SambaCloud](http://cloud.sambanova.ai?utm_source=langchain\&utm_medium=external\&utm_campaign=cloud_signup) is a cloud platform for performing inference with open-source models ## Overview ### Integration details | Class | Package | Serializable | JS support | Downloads | Version | | :--------------------------------------------------------------------------- | :--------------------------------------------------------------------- | :----------: | :--------: | :--------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------: | | [`ChatSambaNova`](https://docs.sambanova.ai/cloud/docs/get-started/overview) | [`langchain-sambanova`](/oss/python/integrations/providers/sambanova/) | beta | ❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/langchain_sambanova?style=flat-square\&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/langchain_sambanova?style=flat-square\&label=%20) | ### Model features | [Tool calling](/oss/python/langchain/tools) | [Structured output](/oss/python/langchain/structured-output) | [Image input](/oss/python/langchain/messages#multimodal) | Audio input | Video input | [Token-level streaming](/oss/python/langchain/streaming/) | Native async | [Token usage](/oss/python/langchain/models#token-usage) | [Logprobs](/oss/python/langchain/models#log-probabilities) | | :-----------------------------------------: | :----------------------------------------------------------: | :------------------------------------------------------: | :---------: | :---------: | :-------------------------------------------------------: | :----------: | :-----------------------------------------------------: | :--------------------------------------------------------: | | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ## Setup To access SambaNova models you will need to create a [SambaCloud](http://cloud.sambanova.ai?utm_source=langchain\&utm_medium=external\&utm_campaign=cloud_signup) account, get an API key, install the `langchain_sambanova` integration package. ```bash theme={null} pip install langchain-sambanova ``` ### Credentials Get an API Key from [cloud.sambanova.ai](http://cloud.sambanova.ai/apis?utm_source=langchain\&utm_medium=external\&utm_campaign=cloud_signupapis) Once you've done this set the SAMBANOVA\_API\_KEY environment variable: ```python theme={null} import getpass import os if not os.getenv("SAMBANOVA_API_KEY"): os.environ["SAMBANOVA_API_KEY"] = getpass.getpass( "Enter your SambaNova API key: " ) ``` To enable automated tracing of your model calls, set your [LangSmith](/langsmith/observability) API key: ```python theme={null} os.environ["LANGSMITH_TRACING"] = "true" os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ") ``` ### Installation The LangChain **SambaNova** integration lives in the `langchain_sambanova` package: ```python theme={null} pip install -qU langchain-sambanova ``` ## Instantiation Now we can instantiate our model object and generate chat completions: ```python theme={null} from langchain_sambanova import ChatSambaNova llm = ChatSambaNova( model="Meta-Llama-3.3-70B-Instruct", max_tokens=1024, temperature=0.7, top_p=0.01, # other params... ) ``` ## Invocation ```python theme={null} 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 ``` ```text theme={null} AIMessage(content="J'adore la programmation.", additional_kwargs={}, response_metadata={'finish_reason': 'stop', 'usage': {'acceptance_rate': 7, 'completion_tokens': 8, 'completion_tokens_after_first_per_sec': 195.0204119588971, 'completion_tokens_after_first_per_sec_first_ten': 618.3422770734173, 'completion_tokens_per_sec': 53.25837044790076, 'end_time': 1731535338.1864908, 'is_last_response': True, 'prompt_tokens': 55, 'start_time': 1731535338.0133238, 'time_to_first_token': 0.13727331161499023, 'total_latency': 0.15021112986973353, 'total_tokens': 63, 'total_tokens_per_sec': 419.4096672772185}, 'model_name': 'Meta-Llama-3.1-70B-Instruct', 'system_fingerprint': 'fastcoe', 'created': 1731535338}, id='f04b7c2c-bc46-47e0-9c6b-19a002e8f390') ``` ```python theme={null} print(ai_msg.content) ``` ```text theme={null} J'adore la programmation. ``` ## Streaming ```python theme={null} 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) ``` ```text theme={null} Yer lookin' fer some knowledge about owls, eh? Alright then, matey, settle yerself down with a pint o' grog and listen close. Owls be a fascinatin' lot, with their big round eyes and silent wings. They be birds o' prey, which means they hunt other creatures fer food. There be over 220 species o' owls, rangin' in size from the tiny Elf Owl (which be smaller than a parrot) to the Great Grey Owl (which be as big as a small eagle). One o' the most interestin' things about owls be their eyes. They be huge, with some species havin' eyes that be as big as their brains! This lets 'em see in the dark, which be perfect fer nocturnal huntin'. They also have special feathers on their faces that help 'em hear better, and their ears be specially designed to pinpoint sounds. Owls be known fer their silent flight, which be due to the special shape o' their wings. They be able to fly without makin' a sound, which be perfect fer sneakin' up on prey. They also be very agile, with some species able to fly through tight spaces and make sharp turns. Some o' the most common species o' owls include: * Barn Owl: A medium-sized owl with a heart-shaped face and a screechin' call. * Tawny Owl: A large owl with a distinctive hootin' call and a reddish-brown plumage. * Great Horned Owl: A big owl with ear tufts and a deep hootin' call. * Snowy Owl: A white owl with a round face and a soft, hootin' call. Owls be found all over the world, in a variety o' habitats, from forests to deserts. They be an important part o' many ecosystems, helpin' to keep populations o' small mammals and birds under control. So there ye have it, matey! Owls be amazin' creatures, with their big eyes, silent wings, and sharp talons. Now go forth and spread the word about these fascinatin' birds! ``` ## Async ```python theme={null} prompt = ChatPromptTemplate.from_messages( [ ( "human", "what is the capital of {country}?", ) ] ) chain = prompt | llm await chain.ainvoke({"country": "France"}) ``` ```text theme={null} AIMessage(content='The capital of France is Paris.', additional_kwargs={}, response_metadata={'finish_reason': 'stop', 'usage': {'acceptance_rate': 1, 'completion_tokens': 7, 'completion_tokens_after_first_per_sec': 442.126212227688, 'completion_tokens_after_first_per_sec_first_ten': 0, 'completion_tokens_per_sec': 46.28540439646366, 'end_time': 1731535343.0321083, 'is_last_response': True, 'prompt_tokens': 42, 'start_time': 1731535342.8808727, 'time_to_first_token': 0.137664794921875, 'total_latency': 0.15123558044433594, 'total_tokens': 49, 'total_tokens_per_sec': 323.99783077524563}, 'model_name': 'Meta-Llama-3.1-70B-Instruct', 'system_fingerprint': 'fastcoe', 'created': 1731535342}, id='c4b8c714-df38-4206-9aa8-fc8231f7275a') ``` ## Async streaming ```python theme={null} 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) ``` ```text theme={null} Quantum computers use quantum bits (qubits) to process info, leveraging superposition and entanglement to perform calculations exponentially faster than classical computers for certain complex problems. ``` ## Tool calling ```python theme={null} from datetime import datetime from langchain.messages import HumanMessage, ToolMessage from langchain.tools import tool @tool def get_time(kind: str = "both") -> str: """Returns current date, current time or both. Args: kind(str): date, time or both Returns: str: current date, current time or both """ if kind == "date": date = datetime.now().strftime("%m/%d/%Y") return f"Current date: {date}" elif kind == "time": time = datetime.now().strftime("%H:%M:%S") return f"Current time: {time}" else: date = datetime.now().strftime("%m/%d/%Y") time = datetime.now().strftime("%H:%M:%S") return f"Current date: {date}, Current time: {time}" tools = [get_time] def invoke_tools(tool_calls, messages): available_functions = {tool.name: tool for tool in tools} for tool_call in tool_calls: selected_tool = available_functions[tool_call["name"]] tool_output = selected_tool.invoke(tool_call["args"]) print(f"Tool output: {tool_output}") messages.append(ToolMessage(tool_output, tool_call_id=tool_call["id"])) return messages ``` ```python theme={null} llm_with_tools = llm.bind_tools(tools=tools) messages = [ HumanMessage( content="I need to schedule a meeting for two weeks from today. " "Can you tell me the exact date of the meeting?" ) ] ``` ```python theme={null} response = llm_with_tools.invoke(messages) while len(response.tool_calls) > 0: print(f"Intermediate model response: {response.tool_calls}") messages.append(response) messages = invoke_tools(response.tool_calls, messages) response = llm_with_tools.invoke(messages) print(f"final response: {response.content}") ``` ```text theme={null} Intermediate model response: [{'name': 'get_time', 'args': {'kind': 'date'}, 'id': 'call_7352ce7a18e24a7c9d', 'type': 'tool_call'}] Tool output: Current date: 11/13/2024 final response: The meeting should be scheduled for two weeks from November 13th, 2024. ``` ## Structured outputs ```python theme={null} from pydantic import BaseModel, Field class Joke(BaseModel): """Joke to tell user.""" setup: str = Field(description="The setup of the joke") punchline: str = Field(description="The punchline to the joke") structured_llm = llm.with_structured_output(Joke) structured_llm.invoke("Tell me a joke about cats") ``` ```text theme={null} Joke(setup='Why did the cat join a band?', punchline='Because it wanted to be the purr-cussionist!') ``` ## Input image ```python theme={null} multimodal_llm = ChatSambaNova( model="Llama-4-Maverick-17B-128E-Instruct", max_tokens=1024, temperature=0.7, top_p=0.01, ) ``` ```python theme={null} import base64 import httpx image_url = ( "https://images.pexels.com/photos/147411/italy-mountains-dawn-daybreak-147411.jpeg" ) image_data = base64.b64encode(httpx.get(image_url).content).decode("utf-8") message = HumanMessage( content=[ {"type": "text", "text": "describe the weather in this image in 1 sentence"}, { "type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_data}"}, }, ], ) response = multimodal_llm.invoke([message]) print(response.content) ``` ```text theme={null} The weather in this image is a serene and peaceful atmosphere, with a blue sky and white clouds, suggesting a pleasant day with mild temperatures and gentle breezes. ``` *** ## API reference For detailed documentation of all `SambaNova` features and configurations head to the API reference: [docs.sambanova.ai/cloud/docs/get-started/overview](https://docs.sambanova.ai/cloud/docs/get-started/overview) ***
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