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This notebook covers how to get started with Nebula - Symbl.ai’s chat model.

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Integration details

Head to the API reference for detailed documentation.

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Model features: TODO

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Setup

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Credentials

To get started, request a Nebula API key and set the NEBULA_API_KEY environment variable: 
import getpass
import os

os.environ["NEBULA_API_KEY"] = getpass.getpass()

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Installation

The integration is set up in the langchain-community package.

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Instantiation

from langchain_community.chat_models.symblai_nebula import ChatNebula
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage

chat = ChatNebula(max_tokens=1024, temperature=0.5)

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Invocation

messages = [
    SystemMessage(
        content="You are a helpful assistant that answers general knowledge questions."
    ),
    HumanMessage(content="What is the capital of France?"),
]
chat.invoke(messages)

AIMessage(content=[{'role': 'human', 'text': 'What is the capital of France?'}, {'role': 'assistant', 'text': 'The capital of France is Paris.'}])

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Async

await chat.ainvoke(messages)

AIMessage(content=[{'role': 'human', 'text': 'What is the capital of France?'}, {'role': 'assistant', 'text': 'The capital of France is Paris.'}])

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Streaming

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

 The capital of France is Paris.

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Batch

chat.batch([messages])

[AIMessage(content=[{'role': 'human', 'text': 'What is the capital of France?'}, {'role': 'assistant', 'text': 'The capital of France is Paris.'}])]

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Chaining

from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_template("Tell me a joke about {topic}")
chain = prompt | chat

chain.invoke({"topic": "cows"})

AIMessage(content=[{'role': 'human', 'text': 'Tell me a joke about cows'}, {'role': 'assistant', 'text': "Sure, here's a joke about cows:\n\nWhy did the cow cross the road?\n\nTo get to the udder side!"}])

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API reference

Check out the API reference for more detail.