import examples in CI, and other tweaks (#24)

.github/workflows/ci.yml

@@ -58,6 +58,8 @@ jobs:
         env:
           COVERAGE_FILE: coverage/.coverage.${{ runner.os }}-py${{ matrix.python-version }}-all-extras
 
+      - run: uv run --frozen --all-extras --python ${{ matrix.python-version }} python tests/import_examples.py
+
       - name: store coverage files
         uses: actions/upload-artifact@v4
         with:

Makefile

@@ -41,16 +41,11 @@ test:
 
 .PHONY: test-all-python  # Run tests on Python 3.9 to 3.13
 test-all-python:
-	UV_PROJECT_ENVIRONMENT=.venv39 uv run --python 3.9 coverage run -m pytest
-	@mv .coverage .coverage.3.9
-	UV_PROJECT_ENVIRONMENT=.venv310 uv run --python 3.10 coverage run -m pytest
-	@mv .coverage .coverage.3.10
-	UV_PROJECT_ENVIRONMENT=.venv311 uv run --python 3.11 coverage run -m pytest
-	@mv .coverage .coverage.3.11
-	UV_PROJECT_ENVIRONMENT=.venv312 uv run --python 3.12 coverage run -m pytest
-	@mv .coverage .coverage.3.12
-	UV_PROJECT_ENVIRONMENT=.venv313 uv run --python 3.13 coverage run -m pytest
-	@mv .coverage .coverage.3.13
+	UV_PROJECT_ENVIRONMENT=.venv39 uv run --python 3.9 coverage run -p -m pytest
+	UV_PROJECT_ENVIRONMENT=.venv310 uv run --python 3.10 coverage run -p -m pytest
+	UV_PROJECT_ENVIRONMENT=.venv311 uv run --python 3.11 coverage run -p -m pytest
+	UV_PROJECT_ENVIRONMENT=.venv312 uv run --python 3.12 coverage run -p -m pytest
+	UV_PROJECT_ENVIRONMENT=.venv313 uv run --python 3.13 coverage run -p -m pytest
 	@uv run coverage combine
 	@uv run coverage report
 

examples/README.md

@@ -84,19 +84,17 @@ the search tool as a retriever with the Pydantic AI agent.
 Logic for extracting sections from markdown files and a JSON file with that data is available in
 [this gist](https://gist.github.com/samuelcolvin/4b5bb9bb163b1122ff17e29e48c10992).
 
-[PostgreSQL with pgvector](https://github.com/pgvector/pgvector) is used as the search database.
-
-The easiest way to download and run pgvector is using Docker:
+[PostgreSQL with pgvector](https://github.com/pgvector/pgvector) is used as the search database, the easiest way to download and run pgvector is using Docker:
 
 ```bash
 mkdir postgres-data
 docker run --rm -e POSTGRES_PASSWORD=postgres -p 54320:5432 -v `pwd`/postgres-data:/var/lib/postgresql/data pgvector/pgvector:pg17
 ```
 
-We run postgres port `54320` to avoid conflicts with any other postgres instances you may have running.
+We run postgres on port `54320` to avoid conflicts with any other postgres instances you may have running.
 We also mount the postgresql `data` directory locally to persist the data if you need to stop and restart the container.
 
-Wit that running, we can then build the search database with (**WARNING**: this requires `OPENAI_API_KEY` and will calling the OpenAI embedding API around 300 times to generate embeddings for each section of the documentation):
+With that running, we can build the search database with (**WARNING**: this requires the `OPENAI_API_KEY` env variable and will calling the OpenAI embedding API around 300 times to generate embeddings for each section of the documentation):
 
 ```bash
 uv run --extra examples -m examples.rag build

examples/rag.py

@@ -38,7 +38,7 @@
 from pydantic_ai.agent import Agent
 
 # 'if-token-present' means nothing will be sent (and the example wil work) if you don't have logfire set up
-logfire.configure()
+logfire.configure(send_to_logfire='if-token-present')
 logfire.instrument_asyncpg()
 
 

examples/sql_gen.py

@@ -23,7 +23,7 @@
 from pydantic_ai import Agent, CallContext, ModelRetry
 
 # 'if-token-present' means nothing will be sent (and the example wil work) if you don't have logfire set up
-logfire.configure()
+logfire.configure(send_to_logfire='if-token-present')
 logfire.instrument_asyncpg()
 
 DB_SCHEMA = """

examples/weather.py

@@ -9,6 +9,8 @@
     uv run --extra examples -m examples.weather
 """
 
+from __future__ import annotations as _annotations
+
 import asyncio
 import os
 from dataclasses import dataclass
@@ -116,7 +118,7 @@ async def get_weather(ctx: CallContext[Deps], lat: float, lng: float) -> dict[st
         8000: 'Thunderstorm',
     }
     return {
-        'temperature': f'{values['temperatureApparent']:0.0f}°C',
+        'temperature': f'{values["temperatureApparent"]:0.0f}°C',
         'description': code_lookup.get(values['weatherCode'], 'Unknown'),
     }
 

pydantic_ai/_pydantic.py

@@ -14,7 +14,6 @@
 from pydantic import ConfigDict, TypeAdapter
 from pydantic._internal import _decorators, _generate_schema, _typing_extra
 from pydantic._internal._config import ConfigWrapper
-from pydantic._internal._typing_extra import origin_is_union
 from pydantic.fields import FieldInfo
 from pydantic.json_schema import GenerateJsonSchema
 from pydantic.plugin._schema_validator import create_schema_validator
@@ -32,7 +31,7 @@
 
 def is_union(tp: Any) -> bool:
     origin = get_origin(tp)
-    return origin_is_union(origin)
+    return _typing_extra.origin_is_union(origin)
 
 
 class FunctionSchema(TypedDict):

tests/import_examples.py

@@ -0,0 +1,14 @@
+"""
+Used to check that examples are at least valid syntax and can be imported without errors.
+
+Called in CI.
+"""
+
+import os
+from pathlib import Path
+
+os.environ.update(OPENAI_API_KEY='fake-key', GEMINI_API_KEY='fake-key')
+
+examples_dir = Path(__file__).parent.parent / 'examples'
+for example in examples_dir.glob('*.py'):
+    __import__(f'examples.{example.stem}')