Databricks¶

集成 Databricks LLMs API。

前提条件¶

• Databricks 个人访问令牌：用于查询和访问 Databricks 模型服务终端节点。

• 位于支持区域的 Databricks 工作区，以便使用按令牌付费的基础模型 API。

安装¶

如果您在 Colab 上打开此 Notebook，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_TOKEN=<your api key>
export DATABRICKS_SERVING_ENDPOINT=<your api serving endpoint>

或者，您也可以在初始化LLM时传入API密钥和服务端点：

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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/",
)

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

可用的 LLM 模型列表详见此处。

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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 方法¶

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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)

流式传输¶

使用 stream_complete 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="")