add pydantic ai example

Author: khuyentran1401

.github/workflows/publish-marimo.yml

@@ -4,7 +4,7 @@ on:
   push:
     branches: [master]
     paths:
-      - 'data_science_tools/polars_vs_pandas.py'
+      - '*.py'
       - '.github/workflows/publish-marimo.yml'
 
 jobs:
@@ -20,11 +20,12 @@ jobs:
           python-version: '3.11'
 
       - name: Install Marimo
-        run: pip install marimo numpy pandas polars seaborn matplotlib
+        run: pip install marimo 
 
       - name: Export Marimo Notebook
         run: |
-          marimo export html-wasm data_science_tools/polars_vs_pandas.py -o build/data_science_tools/polars_vs_pandas --mode run --show-code
+          marimo export html-wasm data_science_tools/polars_vs_pandas.py -o build/data_science_tools/polars_vs_pandas --show-code
+          marimo export html-wasm llm/pydantic_ai_examples.py -o build/llm/pydantic_ai_examples --show-code
 
       - name: Upload Pages Artifact
         uses: actions/upload-pages-artifact@v3

data_science_tools/marimo_examples/dashboard.py

@@ -0,0 +1,6 @@
+# /// script
+# requires-python = ">=3.11"
+# dependencies = [
+#     "marimo",
+# ]
+# ///

data_science_tools/polars_vs_pandas.py

@@ -1,3 +1,12 @@
+# /// script
+# requires-python = ">=3.11"
+# dependencies = [
+#     "marimo",
+#     "numpy==2.2.5",
+#     "pandas==2.2.3",
+#     "polars==1.27.1",
+# ]
+# ///
 import marimo
 
 __generated_with = "0.13.0"

llm/pydantic_ai_examples.py

@@ -0,0 +1,163 @@
+# /// script
+# requires-python = ">=3.11"
+# dependencies = [
+#     "marimo",
+#     "nest-asyncio==1.6.0",
+#     "openai==1.76.0",
+#     "pandas==2.2.3",
+#     "pydantic==2.11.3",
+#     "pydantic-ai==0.1.4",
+#     "pydantic-ai-slim[duckduckgo]==0.1.4",
+# ]
+# ///
+
+import marimo
+
+__generated_with = "0.13.0"
+app = marimo.App(width="medium")
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(r"""# Introduction""")
+    return
+
+
+@app.cell
+def _():
+    import marimo as mo
+
+    return (mo,)
+
+
+@app.cell
+def _():
+    import os
+
+    from openai import OpenAI
+
+    client = OpenAI(
+        api_key=os.environ.get("OPENAI_API_KEY"),
+    )
+
+    response = client.responses.create(
+        model="gpt-4o-mini-2024-07-18",
+        instructions="Extract first name, last name, years of experience, and primary skill from the job applicant description.",
+        input="Khuyen Tran is a data scientist with 5 years of experience, skilled in Python and machine learning.",
+    )
+
+    print(response.output_text)
+    return
+
+
+@app.cell(hide_code=True)
+def _():
+    # Core Workflow
+    return
+
+
+@app.cell(hide_code=True)
+def _():
+    import nest_asyncio
+
+    nest_asyncio.apply()
+    return
+
+
+@app.cell
+def _():
+    from typing import List
+
+    from pydantic import BaseModel
+    from pydantic_ai import Agent
+
+    return Agent, BaseModel, List
+
+
+@app.cell
+def _(BaseModel, List):
+    class ApplicantProfile(BaseModel):
+        first_name: str
+        last_name: str
+        experience_years: int
+        primary_skill: List[str]
+
+    return (ApplicantProfile,)
+
+
+@app.cell
+def _(Agent, ApplicantProfile):
+    agent = Agent(
+        "gpt-4o-mini-2024-07-18",
+        system_prompt="Extract name, years of experience, and primary skill from the job applicant description.",
+        output_type=ApplicantProfile,
+    )
+
+    result = agent.run_sync(
+        "Khuyen Tran is a data scientist with 5 years of experience, skilled in Python and machine learning."
+    )
+    print(result.output)
+    return (result,)
+
+
+@app.cell
+def _(result):
+    result.output.model_dump()
+    return
+
+
+@app.cell
+def _(result):
+    import pandas as pd
+
+    df = pd.DataFrame(result.output.model_dump())
+    df
+    return (pd,)
+
+
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(r"""# Using the DuckDuckGo Search Tool""")
+    return
+
+
+@app.cell
+def _(BaseModel, List):
+    class UnemploymentDataSource(BaseModel):
+        title: List[str]
+        description: List[str]
+        url: List[str]
+
+    return (UnemploymentDataSource,)
+
+
+@app.cell
+def _(Agent, UnemploymentDataSource):
+    from pydantic_ai.common_tools.duckduckgo import duckduckgo_search_tool
+
+    # Define the agent with DuckDuckGo search tool
+    search_agent = Agent(
+        "gpt-4o-mini-2024-07-18",
+        tools=[duckduckgo_search_tool()],
+        system_prompt="Search DuckDuckGo and return links or resources that match the query.",
+        output_type=UnemploymentDataSource,
+    )
+
+    # Run a search for unemployment rate dataset
+    unemployment_result = search_agent.run_sync(
+        "Monthly unemployment rate dataset for US from 2018 to 2024"
+    )
+
+    print(unemployment_result.output)
+    return (unemployment_result,)
+
+
+@app.cell
+def _(pd, unemployment_result):
+    unemployment_df = pd.DataFrame(unemployment_result.output.model_dump())
+    unemployment_df
+    return
+
+
+if __name__ == "__main__":
+    app.run()