Neutrino 使您能够智能地将查询路由到最适合提示的 LLM，从而最大限度地提高性能，同时优化成本和延迟。

请访问我们：neutrinoapp.com 文档：docs.neutrinoapp.com 创建 API 密钥：platform.neutrinoapp.com

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%pip install llama-index-llms-neutrino

!pip install llama-index

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!pip install llama-index

设置 Neutrino API Key 环境变量¶

您可以在以下位置创建 API 密钥：platform.neutrinoapp.com

import os os.environ["NEUTRINO_API_KEY"] = ""

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import os

os.environ["NEUTRINO_API_KEY"] = "<your-neutrino-api-key>"

使用您的路由器¶"

路由器是您可以将查询路由到的一组 LLM。您可以在 Neutrino dashboard 中创建路由器，或使用包含所有支持模型的默认路由器。您可以将路由器视为一个 LLM。

from llama_index.llms.neutrino import Neutrino from llama_index.core.llms import ChatMessage llm = Neutrino( # api_key="" # router="" # (or 'default') ) response = llm.complete("In short, a Neutrino is") print(f"Optimal model: {response.raw['model']}") print(response)

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from llama_index.llms.neutrino import Neutrino
from llama_index.core.llms import ChatMessage

llm = Neutrino(
    # api_key="<your-neutrino-api-key>",
    # router="<your-router-id>"  # (or 'default')
)

response = llm.complete("In short, a Neutrino is")
print(f"Optimal model: {response.raw['model']}")
print(response)

message = ChatMessage( role="user", content="Explain the difference between statically typed and dynamically typed languages.", ) resp = llm.chat([message]) print(f"Optimal model: {resp.raw['model']}") print(resp)流式响应¶message = ChatMessage( role="user", content="What is the approximate population of Mexico?" ) resp = llm.stream_chat([message]) for i, r in enumerate(resp): if i == 0: print(f"Optimal model: {r.raw['model']}") print(r.delta, end="")

Optimal model: gpt-3.5-turbo
a subatomic particle that is electrically neutral and has a very small mass. It is one of the fundamental particles that make up the universe. Neutrinos are extremely difficult to detect because they interact very weakly with matter, making them able to pass through most materials without any interaction. They are produced in various natural processes, such as nuclear reactions in the Sun and other stars, as well as in particle interactions on Earth. Neutrinos have played a significant role in advancing our understanding of particle physics and the universe.

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message = ChatMessage(
    role="user",
    content="Explain the difference between statically typed and dynamically typed languages.",
)
resp = llm.chat([message])
print(f"Optimal model: {resp.raw['model']}")
print(resp)

返回顶部

Optimal model: mistralai/Mixtral-8x7B-Instruct-v0.1
assistant:  Statically typed languages and dynamically typed languages are categories of programming languages based on how they handle variable types.

In statically typed languages, the type of a variable is determined at compile-time, which means that the type is checked before the program is run. This ensures that variables are always used in a type-safe manner, preventing many types of errors from occurring at runtime. Examples of statically typed languages include Java, C, C++, and C#.

In dynamically typed languages, the type of a variable is determined at runtime, which means that the type is checked as the program is running. This provides more flexibility, as variables can be assigned values of different types at different times, but it also means that type errors may not be caught until the program is running, which can make debugging more difficult. Examples of dynamically typed languages include Python, Ruby, JavaScript, and PHP.

One key difference between statically typed and dynamically typed languages is that statically typed languages tend to be more verbose, requiring explicit type declarations for variables, while dynamically typed languages are more concise and allow for implicit type declarations. However, this also means that statically typed languages can catch type errors earlier in the

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message = ChatMessage(
    role="user", content="What is the approximate population of Mexico?"
)
resp = llm.stream_chat([message])
for i, r in enumerate(resp):
    if i == 0:
        print(f"Optimal model: {r.raw['model']}")
    print(r.delta, end="")

message = ChatMessage( role="user", content="What is the approximate population of Mexico?" ) resp = llm.stream_chat([message]) for i, r in enumerate(resp): if i == 0: print(f"Optimal model: {r.raw['model']}") print(r.delta, end="")

Optimal model: anthropic.claude-instant-v1
 According to the latest UN estimates, the population of Mexico is approximately 128 million as of 2020. Mexico has the 10th largest population in the world.