JSON mode support for TypeAdapter and not adding computed_field

llmclient/llms.py

@@ -109,13 +109,15 @@ def sum_logprobs(choice: litellm.utils.Choices) -> float | None:
 
 
 def validate_json_completion(
-    completion: litellm.ModelResponse, output_type: type[BaseModel] | JSONSchema
+    completion: litellm.ModelResponse,
+    output_type: type[BaseModel] | TypeAdapter | JSONSchema,
 ) -> None:
     """Validate a completion against a JSON schema.
 
     Args:
         completion: The completion to validate.
-        output_type: A JSON schema or a Pydantic model to validate the completion.
+        output_type: A Pydantic model, Pydantic type adapter, or a JSON schema to
+            validate the completion.
     """
     try:
         for choice in completion.choices:
@@ -131,6 +133,8 @@ def validate_json_completion(
                 litellm.litellm_core_utils.json_validation_rule.validate_schema(
                     schema=dict(output_type), response=choice.message.content
                 )
+            elif isinstance(output_type, TypeAdapter):
+                output_type.validate_json(choice.message.content)
             else:
                 output_type.model_validate_json(choice.message.content)
     except ValidationError as err:
@@ -737,7 +741,7 @@ async def call(  # noqa: C901, PLR0915
         self,
         messages: list[Message],
         callbacks: list[Callable] | None = None,
-        output_type: type[BaseModel] | JSONSchema | None = None,
+        output_type: type[BaseModel] | TypeAdapter | JSONSchema | None = None,
         tools: list[Tool] | None = None,
         tool_choice: Tool | str | None = TOOL_CHOICE_REQUIRED,
         **chat_kwargs,
@@ -798,7 +802,10 @@ async def call(  # noqa: C901, PLR0915
                 },
             }
         elif output_type is not None:  # Use JSON mode
-            schema = json.dumps(output_type.model_json_schema(mode="serialization"))
+            if isinstance(output_type, TypeAdapter):
+                schema: str = json.dumps(output_type.json_schema())
+            else:
+                schema = json.dumps(output_type.model_json_schema())
             schema_msg = f"Respond following this JSON schema:\n\n{schema}"
             # Get the system prompt and its index, or the index to add it
             i, system_prompt = next(
@@ -941,7 +948,7 @@ async def call_single(
         self,
         messages: list[Message],
         callbacks: list[Callable] | None = None,
-        output_type: type[BaseModel] | None = None,
+        output_type: type[BaseModel] | TypeAdapter | None = None,
         tools: list[Tool] | None = None,
         tool_choice: Tool | str | None = TOOL_CHOICE_REQUIRED,
         **chat_kwargs,

tests/cassettes/TestMultipleCompletionLLMModel.test_output_schema[json-mode].yaml → tests/cassettes/TestMultipleCompletionLLMModel.test_output_schema[json-mode-base-model].yaml

@@ -2,9 +2,9 @@ interactions:
   - request:
       body:
         '{"messages":[{"role":"system","content":"Respond following this JSON schema:\n\n{\"properties\":
-        {\"name\": {\"title\": \"Name\", \"type\": \"string\"}, \"age\": {\"title\":
-        \"Age\", \"type\": \"integer\"}}, \"required\": [\"name\", \"age\"], \"title\":
-        \"DummyOutputSchema\", \"type\": \"object\"}"},{"role":"user","content":"My
+        {\"name\": {\"title\": \"Name\", \"type\": \"string\"}, \"age\": {\"description\":
+        \"Age in years.\", \"title\": \"Age\", \"type\": \"integer\"}}, \"required\":
+        [\"name\", \"age\"], \"title\": \"DummyOutputSchema\", \"type\": \"object\"}"},{"role":"user","content":"My
         name is Claude and I am 1 year old. What is my name and age?"}],"model":"gpt-3.5-turbo","n":2,"response_format":{"type":"json_object"}}'
       headers:
         accept:
@@ -14,7 +14,7 @@ interactions:
         connection:
           - keep-alive
         content-length:
-          - "465"
+          - "501"
         content-type:
           - application/json
         host:
@@ -44,34 +44,34 @@ interactions:
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       body:
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@@ -84,7 +84,7 @@ interactions:
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         message: OK

tests/cassettes/TestMultipleCompletionLLMModel.test_output_schema[json-mode-type-adapter].yaml

@@ -0,0 +1,103 @@
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+        {\"name\": {\"title\": \"Name\", \"type\": \"string\"}, \"age\": {\"description\":
+        \"Age in years.\", \"title\": \"Age\", \"type\": \"integer\"}}, \"required\":
+        [\"name\", \"age\"], \"title\": \"DummyOutputSchema\", \"type\": \"object\"}"},{"role":"user","content":"My
+        name is Claude and I am 1 year old. What is my name and age?"}],"model":"gpt-4o","n":2,"response_format":{"type":"json_object"}}'
+      headers:
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tests/cassettes/TestMultipleCompletionLLMModel.test_output_schema[structured-outputs].yaml

@@ -2,7 +2,8 @@ interactions:
   - request:
       body:
         '{"messages":[{"role":"user","content":"My name is Claude and I am 1 year
-        old. What is my name and age?"}],"model":"gpt-4o","n":2,"response_format":{"type":"json_schema","json_schema":{"strict":true,"schema":{"properties":{"name":{"title":"Name","type":"string"},"age":{"title":"Age","type":"integer"}},"required":["name","age"],"title":"DummyOutputSchema","type":"object","additionalProperties":false},"name":"DummyOutputSchema"}}}'
+        old. What is my name and age?"}],"model":"gpt-4o","n":2,"response_format":{"type":"json_schema","json_schema":{"strict":true,"schema":{"properties":{"name":{"title":"Name","type":"string"},"age":{"description":"Age
+        in years.","title":"Age","type":"integer"}},"required":["name","age"],"title":"DummyOutputSchema","type":"object","additionalProperties":false},"name":"DummyOutputSchema"}}}'
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@@ -11,7 +12,7 @@ interactions:
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@@ -31,7 +32,7 @@ interactions:
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tests/test_llms.py

@@ -7,7 +7,7 @@
 import numpy as np
 import pytest
 from aviary.core import Message, Tool, ToolRequestMessage
-from pydantic import BaseModel
+from pydantic import BaseModel, Field, TypeAdapter, computed_field
 
 from llmclient.exceptions import JSONSchemaValidationError
 from llmclient.llms import (
@@ -261,7 +261,12 @@ def test_pickling(self, tmp_path: pathlib.Path) -> None:
 
 class DummyOutputSchema(BaseModel):
     name: str
-    age: int
+    age: int = Field(description="Age in years.")
+
+    @computed_field  # type: ignore[prop-decorator]
+    @property
+    def name_and_age(self) -> str:  # So we can test computed_field is not included
+        return f"{self.name}, {self.age}"
 
 
 class TestMultipleCompletionLLMModel:
@@ -364,7 +369,10 @@ def play(move: int | None) -> None:
     @pytest.mark.parametrize(
         ("model_name", "output_type"),
         [
-            pytest.param("gpt-3.5-turbo", DummyOutputSchema, id="json-mode"),
+            pytest.param("gpt-3.5-turbo", DummyOutputSchema, id="json-mode-base-model"),
+            pytest.param(
+                "gpt-4o", TypeAdapter(DummyOutputSchema), id="json-mode-type-adapter"
+            ),
             pytest.param(
                 "gpt-4o", DummyOutputSchema.model_json_schema(), id="structured-outputs"
             ),