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참고/instructor-main/docs/blog/posts/announcing-gemini-tool-calling-support.md

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+---
+authors:
+- ivanleomk
+categories:
+- LLM Techniques
+comments: true
+date: 2024-09-03
+description: Introducing structured outputs for Gemini tool calling support in the
+  instructor library, enhancing interactions with Gemini and VertexAI SDKs.
+draft: false
+tags:
+- Gemini
+- VertexAI
+- Tool Calling
+- Instructor Library
+- AI SDKs
+---
+
+# Structured Outputs for Gemini now supported
+
+We're excited to announce that `instructor` now supports structured outputs using tool calling for both the Gemini SDK and the VertexAI SDK.
+
+A special shoutout to [Sonal](https://x.com/sonalsaldanha) for his contributions to the Gemini Tool Calling support.
+
+Let's walk through a simple example of how to use these new features
+
+## Installation
+
+To get started, install the latest version of `instructor`. Depending on whether you're using Gemini or VertexAI, you should install the following:
+
+=== "Gemini"
+
+    ```bash
+    pip install "instructor[google-generativeai]"
+    ```
+
+=== "VertexAI"
+
+    ```bash
+    pip install "instructor[vertexai]"
+    ```
+
+This ensures that you have the necessary dependencies to use the Gemini or VertexAI SDKs with instructor.
+
+We recommend using the Gemini SDK over the VertexAI SDK for two main reasons.
+
+1. Compared to the VertexAI SDK, the Gemini SDK comes with a free daily quota of 1.5 billion tokens to use for developers.
+2. The Gemini SDK is significantly easier to setup, all you need is a `GOOGLE_API_KEY` that you can generate in your GCP console. THe VertexAI SDK on the other hand requires a credentials.json file or an OAuth integration to use.
+
+## Getting Started
+
+With our provider agnostic API, you can use the same interface to interact with both SDKs, the only thing that changes here is how we initialise the client itself.
+
+Before running the following code, you'll need to make sure that you have your Gemini API Key set in your shell under the alias `GOOGLE_API_KEY`.
+
+```python
+import instructor
+import google.generativeai as genai
+from pydantic import BaseModel
+
+
+class User(BaseModel):
+    name: str
+    age: int
+
+
+client = instructor.from_provider("google/gemini-2.5-flash")
+    )
+)
+
+resp = client.create(
+    messages=[
+        {
+            "role": "user",
+            "content": "Extract Jason is 25 years old.",
+        }
+    ],
+    response_model=User,
+)
+
+print(resp)
+#> name='Jason' age=25
+```
+
+1. Current Gemini models that support tool calling are `gemini-3-flash` and `gemini-1.5-pro-latest`.
+
+We can achieve a similar thing with the VertexAI SDK. For this to work, you'll need to authenticate to VertexAI.
+
+There are some instructions [here](https://cloud.google.com/vertex-ai/docs/authentication) but the easiest way I found was to simply download the GCloud cli and run `gcloud auth application-default login`.
+
+```python
+import instructor
+import vertexai  # type: ignore
+from vertexai.generative_models import GenerativeModel  # type: ignore
+from pydantic import BaseModel
+
+vertexai.init()
+
+
+class User(BaseModel):
+    name: str
+    age: int
+
+
+client = instructor.from_provider("google/gemini-2.5-flash", vertexai=True),  # (1)!
+)
+
+
+resp = client.create(
+    messages=[
+        {
+            "role": "user",
+            "content": "Extract Jason is 25 years old.",
+        }
+    ],
+    response_model=User,
+)
+
+print(resp)
+#> name='Jason' age=25
+```
+
+1. Current Gemini models that support tool calling are `gemini-3-flash` and `gemini-1.5-pro-latest`.