DataBricks¶

集成DataBricks LLMs API。

先决条件¶

• Databricks个人访问令牌用于查询和访问Databricks模型服务端点。

• 在支持的区域中拥有Databricks工作区，以便使用Foundation Model APIs按令牌付费。

设置¶

如果您在colab上打开这个笔记本，您可能需要安装LlamaIndex 🦙。

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% pip install llama-index-llms-databricks

% pip install llama-index-llms-databricks

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!pip install llama-index

!pip install llama-index

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from llama_index.llms.databricks import DataBricks

from llama_index.llms.databricks import DataBricks

None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.

export DATABRICKS_API_KEY=<你的API密钥>
export DATABRICKS_API_BASE=<你的API服务端点>

或者，你可以在初始化时将你的API密钥和服务端点传递给LLM：

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llm = DataBricks(
    model="databricks-dbrx-instruct",
    api_key="your_api_key",
    api_base="https://[your-work-space].cloud.databricks.com/serving-endpoints/[your-serving-endpoint]",
)

llm = DataBricks( model="databricks-dbrx-instruct", api_key="your_api_key", api_base="https://[your-work-space].cloud.databricks.com/serving-endpoints/[your-serving-endpoint]", )

可以在这里找到可用的LLM模型列表。

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response = llm.complete("Explain the importance of open source LLMs")

response = llm.complete("Explain the importance of open source LLMs")

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print(response)

print(response)

使用消息列表调用 chat¶

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from llama_index.core.llms import ChatMessage

messages = [
    ChatMessage(
        role="system", content="You are a pirate with a colorful personality"
    ),
    ChatMessage(role="user", content="What is your name"),
]
resp = llm.chat(messages)

from llama_index.core.llms import ChatMessage messages = [ ChatMessage( role="system", content="You are a pirate with a colorful personality" ), ChatMessage(role="user", content="What is your name"), ] resp = llm.chat(messages)

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print(resp)

print(resp)

Streaming¶

使用 stream_complete 终端点

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response = llm.stream_complete("Explain the importance of open source LLMs")

response = llm.stream_complete("Explain the importance of open source LLMs")

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for r in response:
    print(r.delta, end="")

for r in response: print(r.delta, end="")

使用 stream_chat 端点

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from llama_index.core.llms import ChatMessage

messages = [
    ChatMessage(
        role="system", content="You are a pirate with a colorful personality"
    ),
    ChatMessage(role="user", content="What is your name"),
]
resp = llm.stream_chat(messages)

from llama_index.core.llms import ChatMessage messages = [ ChatMessage( role="system", content="You are a pirate with a colorful personality" ), ChatMessage(role="user", content="What is your name"), ] resp = llm.stream_chat(messages)

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for r in resp:
    print(r.delta, end="")

for r in resp: print(r.delta, end="")