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참고/instructor-main/docs/concepts/parallel.md

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+---
+title: Parallel Tools
+description: Learn about parallel tools in OpenAI, Google, and Anthropic.
+---
+
+## See Also
+
+- [from_provider Guide](./from_provider.md#async-clients) - Async client setup
+- [Batch Processing](../examples/batch_job_oai.md) - Process multiple requests efficiently
+- [Iterable](./iterable.md) - Extract multiple objects
+- [Lists](./lists.md) - Working with collections
+
+# Parallel Tools
+
+Parallel Tool Calling is a feature that allows you to call multiple functions in a single request.
+
+!!! warning "Experimental Feature"
+
+    Parallel Tool Calling is supported by Google, OpenAI, and Anthropic. Make sure to use the equivalent parallel tool `mode` for your client.
+
+## Understanding Parallel Tool Calling
+
+Parallel Function Calling helps you to significantly reduce the latency of your application without having to build a parent schema as a wrapper around these tool calls.
+
+=== "OpenAI"
+
+    ```python hl_lines="20 32"
+    from __future__ import annotations
+
+    import instructor
+
+    from typing import Iterable, Literal
+    from pydantic import BaseModel
+
+
+    class Weather(BaseModel):
+        location: str
+        units: Literal["imperial", "metric"]
+
+
+    class GoogleSearch(BaseModel):
+        query: str
+
+
+    client = instructor.from_provider(
+        "openai/gpt-4.1-mini",
+        mode=instructor.Mode.PARALLEL_TOOLS,
+    )
+    function_calls = client.create(
+        messages=[
+            {"role": "system", "content": "You must always use tools"},
+            {
+                "role": "user",
+                "content": "What is the weather in toronto and dallas and who won the super bowl?",
+            },
+        ],
+        response_model=Iterable[Weather | GoogleSearch],
+    )
+
+    for fc in function_calls:
+        print(fc)
+        #> location='Toronto' units='metric'
+        #> location='Dallas' units='metric'
+        #> query='who won the super bowl 2023'
+    ```
+
+=== "Vertex AI"
+
+    ```python
+    from typing import Iterable, Literal
+
+    import instructor
+    from pydantic import BaseModel
+
+    try:
+        import vertexai
+        import vertexai.generative_models as gm
+        from instructor import from_vertexai
+    except ImportError:
+        vertexai = None
+        gm = None
+        from_vertexai = None
+
+
+    class Weather(BaseModel):
+        location: str
+        units: Literal["imperial", "metric"]
+
+
+    class GoogleSearch(BaseModel):
+        query: str
+
+
+    if from_vertexai is not None and vertexai is not None and gm is not None:
+        vertexai.init(project="your-project-id", location="us-central1")
+        client = from_vertexai(
+            gm.GenerativeModel("gemini-2.5-flash"),
+            mode=instructor.Mode.PARALLEL_TOOLS,
+        )
+        function_calls = client.create(
+            messages=[
+                {
+                    "role": "user",
+                    "content": "What is the weather in toronto and dallas and who won the super bowl?",
+                },
+            ],
+            response_model=Iterable[Weather | GoogleSearch],
+        )
+
+        for fc in function_calls:
+            print(fc)
+            #> location='Toronto' units='metric'
+            #> location='Dallas' units='imperial'
+            #> query='who won the super bowl'
+    ```
+
+=== "Anthropic"
+
+    ```python hl_lines="20 32"
+    import instructor
+    from typing import Iterable, Literal
+    from pydantic import BaseModel
+
+
+    class Weather(BaseModel):
+        location: str
+        units: Literal["imperial", "metric"]
+
+
+    class GoogleSearch(BaseModel):
+        query: str
+
+
+    client = instructor.from_provider(
+        "anthropic/claude-3-7-sonnet-latest",
+        mode=instructor.Mode.PARALLEL_TOOLS,
+    )
+    function_calls = client.create(
+        messages=[
+            {"role": "system", "content": "You must always use tools"},
+            {
+                "role": "user",
+                "content": "What is the weather in toronto and dallas and who won the super bowl?",
+            },
+        ],
+        response_model=Iterable[Weather | GoogleSearch],
+    )
+
+    for fc in function_calls:
+        print(fc)
+        #> location='Toronto' units='metric'
+    ```
+
+We need to set the response model to `Iterable[Weather | GoogleSearch]` to indicate that the response will be a list of `Weather` and `GoogleSearch` objects.
+
+This is necessary because the response will be a list of objects, and we need to specify the types of the objects in the list. This returns an iterable which you can then iterate over