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90 lines
2.2 KiB
Markdown
90 lines
2.2 KiB
Markdown
---
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title: Databricks
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description: Guide to using instructor with Databricks models
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---
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# Structured outputs with Databricks, a complete guide w/ instructor
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[Databricks](https://www.databricks.com/) provides an AI platform with access to various models. This guide shows how to use instructor with Databricks to get structured outputs.
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## Quick Start
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First, install the required packages:
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```bash
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uv pip install instructor openai
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```
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Set your Databricks workspace URL and token as environment variables:
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```bash
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export DATABRICKS_TOKEN="your_personal_access_token"
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export DATABRICKS_HOST="https://your-workspace.cloud.databricks.com"
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```
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`DATABRICKS_API_KEY` and `DATABRICKS_WORKSPACE_URL` are also supported if you prefer those names. The provider appends `/serving-endpoints` automatically, so the host only needs the base workspace URL.
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## Basic Example
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Here's how to extract structured data from Databricks models:
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```python
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import instructor
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from pydantic import BaseModel
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# Initialize the client; host and token are read from the environment
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client = instructor.from_provider(
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"databricks/dbrx-instruct",
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mode=instructor.Mode.TOOLS,
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)
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# Define your data structure
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class UserExtract(BaseModel):
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name: str
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age: int
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# Extract structured data
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user = client.create(
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response_model=UserExtract,
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messages=[
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{"role": "user", "content": "Extract jason is 25 years old"},
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],
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)
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print(user)
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# Output: UserExtract(name='Jason', age=25)
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```
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If you need to point at a different workspace or testing endpoint, pass `base_url="https://alt-workspace.cloud.databricks.com/serving-endpoints"`. The helper will use that value as-is without adding another suffix.
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### Async Example
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```python
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async_client = instructor.from_provider(
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"databricks/dbrx-instruct",
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async_client=True,
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mode=instructor.Mode.TOOLS,
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)
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```
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## Supported Modes
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Databricks supports the same modes as OpenAI:
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- `Mode.TOOLS`
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- `Mode.JSON`
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- `Mode.FUNCTIONS`
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- `Mode.PARALLEL_TOOLS`
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- `Mode.MD_JSON`
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- `Mode.TOOLS_STRICT`
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- `Mode.JSON_O1`
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## Models
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Databricks provides access to various models depending on your setup, including:
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- Foundation models hosted on Databricks
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- Custom fine-tuned models
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- Open source models deployed on Databricks
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