ChatSambaNovaCloud
這將幫助您開始使用 SambaNovaCloud 聊天模型。如需所有 ChatSambaNovaCloud 功能和設定的詳細文件，請前往 API 參考。
SambaNova 的 SambaNova Cloud 是一個使用開放原始碼模型執行推論的平台
概觀
整合詳細資訊
| 類別 | 套件 | 本地 | 可序列化 | JS 支援 | 套件下載 | 套件最新版本 |
|---|---|---|---|---|---|---|
| ChatSambaNovaCloud | langchain-sambanova | ❌ | ❌ | ❌ |  |  |
模型功能
| 工具呼叫 | 結構化輸出 | JSON 模式 | 影像輸入 | 音訊輸入 | 視訊輸入 | Token 層級串流 | 原生非同步 | Token 使用量 | Logprobs |
|---|---|---|---|---|---|---|---|---|---|
| ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ |
設定
若要存取 ChatSambaNovaCloud 模型，您需要建立一個 SambaNovaCloud 帳戶、取得 API 金鑰，並安裝 langchain_sambanova 整合套件。
pip install langchain-sambanova
憑證
從 cloud.sambanova.ai 取得 API 金鑰，並將其新增至您的環境變數
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: "
)
如果您想要取得模型呼叫的自動追蹤，您也可以透過取消註解下方內容來設定您的 LangSmith API 金鑰
# os.environ["LANGSMITH_TRACING"] = "true"
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
安裝
LangChain SambaNovaCloud 整合位於 langchain_sambanova 套件中
%pip install -qU langchain-sambanova
例項化
現在我們可以例項化我們的模型物件並產生聊天完成內容
from langchain_sambanova import ChatSambaNovaCloud
llm = ChatSambaNovaCloud(
model="Meta-Llama-3.3-70B-Instruct",
max_tokens=1024,
temperature=0.7,
top_p=0.01,
)
調用
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.", 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')
print(ai_msg.content)
J'adore la programmation.
鏈結
我們可以像這樣使用提示範本鏈結我們的模型
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 參考：ChatPromptTemplate
AIMessage(content='Ich liebe das Programmieren.', additional_kwargs={}, response_metadata={'finish_reason': 'stop', 'usage': {'acceptance_rate': 2.3333333333333335, 'completion_tokens': 6, 'completion_tokens_after_first_per_sec': 106.06729752831038, 'completion_tokens_after_first_per_sec_first_ten': 204.92722183833433, 'completion_tokens_per_sec': 26.32497272023831, 'end_time': 1731535339.9997504, 'is_last_response': True, 'prompt_tokens': 50, 'start_time': 1731535339.7539687, 'time_to_first_token': 0.19864177703857422, 'total_latency': 0.22792046410696848, 'total_tokens': 56, 'total_tokens_per_sec': 245.6997453888909}, 'model_name': 'Meta-Llama-3.1-70B-Instruct', 'system_fingerprint': 'fastcoe', 'created': 1731535339}, id='dfe0bee6-b297-472e-ac9d-29906d162dcb')
串流
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 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!
非同步
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.', 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')
非同步串流
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 info, leveraging superposition and entanglement to perform calculations exponentially faster than classical computers for certain complex problems.
工具呼叫
from datetime import datetime
from langchain_core.messages import HumanMessage, ToolMessage
from langchain_core.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
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?"
)
]
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}")
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.
結構化輸出
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")
Joke(setup='Why did the cat join a band?', punchline='Because it wanted to be the purr-cussionist!')
輸入影像
multimodal_llm = ChatSambaNovaCloud(
model="Llama-3.2-11B-Vision-Instruct",
max_tokens=1024,
temperature=0.7,
top_p=0.01,
)
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)
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 參考
如需所有 SambaNovaCloud 功能和設定的詳細文件，請前往 API 參考：https://docs.sambanova.ai/cloud/docs/get-started/overview