ChatCerebras 集成

pip install langchain-cerebras

export CEREBRAS_API_KEY="your-api-key-here"

import getpass
import os

if "CEREBRAS_API_KEY" not in os.environ:
    os.environ["CEREBRAS_API_KEY"] = getpass.getpass("Enter your Cerebras API key: ")

Enter your Cerebras API key:  ········

os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
os.environ["LANGSMITH_TRACING"] = "true"

pip install -qU langchain-cerebras

from langchain_cerebras import ChatCerebras

llm = ChatCerebras(
    model="llama-3.3-70b",
    # 其他参数...
)

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='Je adore le programmation.', response_metadata={'token_usage': {'completion_tokens': 7, 'prompt_tokens': 35, 'total_tokens': 42}, 'model_name': 'llama3-8b-8192', 'system_fingerprint': 'fp_be27ec77ff', 'finish_reason': 'stop'}, id='run-e5d66faf-019c-4ac6-9265-71093b13202d-0', usage_metadata={'input_tokens': 35, 'output_tokens': 7, 'total_tokens': 42})

from langchain_cerebras import ChatCerebras
from langchain_core.prompts import ChatPromptTemplate

llm = ChatCerebras(
    model="llama-3.3-70b",
    # 其他参数...
)

system = "You are an expert on animals who must answer questions in a manner that a 5 year old can understand."
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": "Lion"}):
    print(chunk.content, end="", flush=True)

OH BOY! Let me tell you all about LIONS!

Lions are the kings of the jungle! They're really big and have beautiful, fluffy manes around their necks. The mane is like a big, golden crown!

Lions live in groups called prides. A pride is like a big family, and the lionesses (that's what we call the female lions) take care of the babies. The lionesses are like the mommies, and they teach the babies how to hunt and play.

Lions are very good at hunting. They work together to catch their food, like zebras and antelopes. They're super fast and can run really, really fast!

But lions are also very sleepy. They like to take long naps in the sun, and they can sleep for up to 20 hours a day! Can you imagine sleeping that much?

Lions are also very loud. They roar really loudly to talk to each other. It's like they're saying, "ROAR! I'm the king of the jungle!"

And guess what? Lions are very social. They like to play and cuddle with each other. They're like big, furry teddy bears!

So, that's lions! Aren't they just the coolest?

from langchain_cerebras import ChatCerebras
from langchain_core.prompts import ChatPromptTemplate

llm = ChatCerebras(
    model="llama-3.3-70b",
    # 其他参数...
)

prompt = ChatPromptTemplate.from_messages(
    [
        (
            "human",
            "Let's play a game of opposites. What's the opposite of {topic}? Just give me the answer with no extra input.",
        )
    ]
)
chain = prompt | llm
await chain.ainvoke({"topic": "fire"})

AIMessage(content='Ice', response_metadata={'token_usage': {'completion_tokens': 2, 'prompt_tokens': 36, 'total_tokens': 38}, 'model_name': 'llama3-8b-8192', 'system_fingerprint': 'fp_be27ec77ff', 'finish_reason': 'stop'}, id='run-7434bdde-1bec-44cf-827b-8d978071dfe8-0', usage_metadata={'input_tokens': 36, 'output_tokens': 2, 'total_tokens': 38})

from langchain_cerebras import ChatCerebras
from langchain_core.prompts import ChatPromptTemplate

llm = ChatCerebras(
    model="llama-3.3-70b",
    # 其他参数...
)

prompt = ChatPromptTemplate.from_messages(
    [
        (
            "human",
            "Write a long convoluted story about {subject}. I want {num_paragraphs} paragraphs.",
        )
    ]
)
chain = prompt | llm

async for chunk in chain.astream({"num_paragraphs": 3, "subject": "blackholes"}):
    print(chunk.content, end="", flush=True)

In the distant reaches of the cosmos, there existed a peculiar phenomenon known as the "Eclipse of Eternity," a swirling vortex of darkness that had been shrouded in mystery for eons. It was said that this blackhole, born from the cataclysmic collision of two ancient stars, had been slowly devouring the fabric of space-time itself, warping the very essence of reality. As the celestial bodies of the galaxy danced around it, they began to notice a strange, almost imperceptible distortion in the fabric of space, as if the blackhole's gravitational pull was exerting an influence on the very course of events itself.

As the centuries passed, astronomers from across the galaxy became increasingly fascinated by the Eclipse of Eternity, pouring over ancient texts and scouring the cosmos for any hint of its secrets. One such scholar, a brilliant and reclusive astrophysicist named Dr. Elara Vex, became obsessed with unraveling the mysteries of the blackhole. She spent years pouring over ancient texts, deciphering cryptic messages and hidden codes that hinted at the existence of a long-lost civilization that had once thrived in the heart of the blackhole itself. According to legend, this ancient civilization had possessed knowledge of the cosmos that was beyond human comprehension, and had used their mastery of the universe to create the Eclipse of Eternity as a gateway to other dimensions.

As Dr. Vex delved deeper into her research, she began to experience strange and vivid dreams, visions that seemed to transport her to the very heart of the blackhole itself. In these dreams, she saw ancient beings, their faces twisted in agony as they were consumed by the void. She saw stars and galaxies, their light warped and distorted by the blackhole's gravitational pull. And she saw the Eclipse of Eternity itself, its swirling vortex of darkness pulsing with an otherworldly energy that seemed to be calling to her. As the dreams grew more vivid and more frequent, Dr. Vex became convinced that she was being drawn into the heart of the blackhole, and that the secrets of the universe lay waiting for her on the other side.