Add foundation model examples to databricks-app-python skill

README.md

@@ -26,7 +26,7 @@ AI-Driven Development (vibe coding) on Databricks just got a whole lot better. T
 - **Knowledge Assistants** (RAG-based document Q&A)
 - **MLflow Experiments** (evaluation, scoring, traces)
 - **Model Serving** (deploy ML models and AI agents to endpoints)
-- **Databricks Apps** (full-stack web applications)
+- **Databricks Apps** (full-stack web applications with foundation model integration)
 - ...and more
 
 ---

databricks-skills/README.md

@@ -63,7 +63,7 @@ cp -r ai-dev-kit/databricks-skills/databricks-agent-bricks .claude/skills/
 ### 🚀 Development & Deployment
 - **databricks-bundles** - DABs for multi-environment deployments
 - **databricks-app-apx** - Full-stack apps (FastAPI + React)
-- **databricks-app-python** - Python web apps (Dash, Streamlit, Flask)
+- **databricks-app-python** - Python web apps (Dash, Streamlit, Flask) with foundation model integration
 - **databricks-python-sdk** - Python SDK, Connect, CLI, REST API
 - **databricks-config** - Profile authentication setup
 - **databricks-lakebase-provisioned** - Managed PostgreSQL for OLTP workloads

databricks-skills/databricks-app-python/SKILL.md

@@ -1,6 +1,6 @@
 ---
 name: databricks-app-python
-description: "Builds Python-based Databricks applications using Dash, Streamlit, Gradio, Flask, FastAPI, or Reflex. Handles OAuth authorization (app and user auth), app resources, SQL warehouse and Lakebase connectivity, model serving integration, and deployment. Use when building Python web apps, dashboards, ML demos, or REST APIs for Databricks, or when the user mentions Streamlit, Dash, Gradio, Flask, FastAPI, Reflex, or Databricks app."
+description: "Builds Python-based Databricks applications using Dash, Streamlit, Gradio, Flask, FastAPI, or Reflex. Handles OAuth authorization (app and user auth), app resources, SQL warehouse and Lakebase connectivity, model serving integration, foundation model APIs, LLM integration, and deployment. Use when building Python web apps, dashboards, ML demos, or REST APIs for Databricks, or when the user mentions Streamlit, Dash, Gradio, Flask, FastAPI, Reflex, or Databricks app."
 ---
 
 # Databricks Python Application
@@ -74,6 +74,8 @@ Copy this checklist and verify each item:
 
 **MCP tools**: Use [6-mcp-approach.md](6-mcp-approach.md) for managing app lifecycle via MCP tools — covers creating, deploying, monitoring, and deleting apps programmatically. (Keywords: MCP, create app, deploy app, app logs)
 
+**Foundation Models**: See [examples/llm_config.py](examples/llm_config.py) for calling Databricks foundation model APIs — covers OAuth M2M auth, OpenAI-compatible client wiring, and token caching. (Keywords: foundation model, LLM, OpenAI client, chat completions)
+
 ---
 
 ## Workflow
@@ -86,6 +88,7 @@ Copy this checklist and verify each item:
    **Using Lakebase (PostgreSQL)?** → Read [5-lakebase.md](5-lakebase.md)
    **Deploying to Databricks?** → Read [4-deployment.md](4-deployment.md)
    **Using MCP tools?** → Read [6-mcp-approach.md](6-mcp-approach.md)
+   **Calling foundation model/LLM APIs?** → See [examples/llm_config.py](examples/llm_config.py)
 
 2. Follow the instructions in the relevant guide
 3. For full code examples, browse https://apps-cookbook.dev/

databricks-skills/databricks-app-python/examples/fm-minimal-chat.py

@@ -0,0 +1,182 @@
+"""
+Minimal Databricks Foundation Model Chat App
+
+A complete, deployable Streamlit app demonstrating Foundation Model API integration
+in Databricks Apps. This is a working example extracted from databricksters-check-and-pub.
+
+Features:
+- Validated dual-mode auth (OAuth M2M in Apps, PAT for local dev)
+- OpenAI SDK wired to Databricks serving endpoints
+- Token caching with expiry check
+- Multi-turn chat with conversation history
+- Viewer identity display
+- Latency tracking
+
+Local Development:
+    export DATABRICKS_TOKEN="dapi..."
+    export DATABRICKS_SERVING_BASE_URL="https://<workspace>/serving-endpoints"
+    export DATABRICKS_MODEL="<endpoint-name>"  # See databricks-model-serving
+    streamlit run 2-minimal-chat-app.py
+
+Databricks Apps Deployment:
+    1. Create app.yaml:
+       command: ["streamlit", "run", "2-minimal-chat-app.py"]
+       env:
+         - name: DATABRICKS_SERVING_BASE_URL
+           value: "https://<workspace>/serving-endpoints"
+         - name: DATABRICKS_MODEL
+           value: "<endpoint-name>"  # See databricks-model-serving
+
+    2. Create requirements.txt:
+       streamlit>=1.38,<2.0
+       openai>=1.30,<2.0
+       requests>=2.31,<3.0  # Needed for endpoint validation and OAuth fallback
+
+    3. Deploy:
+       databricks apps create foundation-chat --source-code-path .
+
+    4. Add service principal via UI for OAuth M2M auth
+"""
+
+import time
+from typing import Dict, List, Optional, Tuple
+
+import streamlit as st
+from openai import OpenAI
+
+from llm_config import create_foundation_model_client, get_model_name
+
+
+def _get_forwarded_headers() -> Dict[str, str]:
+    try:
+        return dict(getattr(st, "context").headers)
+    except Exception:
+        return {}
+
+
+def get_viewer_identity() -> Tuple[Optional[str], Optional[str]]:
+    headers = _get_forwarded_headers()
+    email = headers.get("X-Forwarded-Email") or headers.get("x-forwarded-email")
+    token = headers.get("X-Forwarded-Access-Token") or headers.get(
+        "x-forwarded-access-token"
+    )
+    return email, token
+
+
+# =============================================================================
+# LLM Helper
+# =============================================================================
+def llm_chat(
+    client: OpenAI,
+    *,
+    model: str,
+    messages: List[Dict[str, str]],
+    max_tokens: int = 1000,
+    temperature: float = 0.7,
+) -> Tuple[str, int]:
+    """Call foundation model and return (response, latency_ms)."""
+    t0 = time.perf_counter()
+    resp = client.chat.completions.create(
+        model=model,
+        messages=messages,
+        max_tokens=max_tokens,
+        temperature=temperature,
+    )
+    elapsed_ms = int((time.perf_counter() - t0) * 1000)
+    content = resp.choices[0].message.content or ""
+    return content, elapsed_ms
+
+
+# =============================================================================
+# Streamlit App
+# =============================================================================
+def main():
+    st.set_page_config(
+        page_title="Databricks Foundation Model Chat",
+        page_icon="💬",
+        layout="centered",
+    )
+
+    st.title("💬 Foundation Model Chat")
+    st.caption("Powered by Databricks Apps")
+
+    # Sidebar: viewer identity
+    viewer_email, _ = get_viewer_identity()
+    if viewer_email:
+        st.sidebar.success(f"Logged in as: {viewer_email}")
+    else:
+        st.sidebar.info("Local dev mode (no viewer identity)")
+
+    # Sidebar: model config
+    with st.sidebar:
+        st.subheader("Configuration")
+        st.code(f"Model: {get_model_name()}", language=None)
+
+        if st.button("🗑️ Clear Chat History"):
+            st.session_state.messages = []
+            st.rerun()
+
+        with st.expander("ℹ️ About"):
+            st.markdown(
+                """
+                This app demonstrates calling Databricks Foundation Model APIs
+                from a Streamlit app using:
+                - Shared dual-mode auth (PAT + OAuth M2M)
+                - Shared OpenAI client wiring
+                - Viewer identity extraction
+                """
+            )
+
+    # Initialize chat history
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+
+    # Display chat history
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+            if message.get("latency_ms"):
+                st.caption(f"⏱️ {message['latency_ms']}ms")
+
+    # Chat input
+    if prompt := st.chat_input("Ask me anything..."):
+        # Add user message to chat history
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        with st.chat_message("user"):
+            st.markdown(prompt)
+
+        # Generate assistant response
+        with st.chat_message("assistant"):
+            with st.spinner("Thinking..."):
+                try:
+                    client = create_foundation_model_client(cache=st.session_state)
+
+                    # Call foundation model
+                    response, latency_ms = llm_chat(
+                        client,
+                        model=get_model_name(),
+                        messages=st.session_state.messages,
+                        max_tokens=1000,
+                        temperature=0.7,
+                    )
+
+                    # Display response
+                    st.markdown(response)
+                    st.caption(f"⏱️ {latency_ms}ms")
+
+                    # Add to chat history
+                    st.session_state.messages.append(
+                        {
+                            "role": "assistant",
+                            "content": response,
+                            "latency_ms": latency_ms,
+                        }
+                    )
+
+                except Exception as e:
+                    st.error(f"Error calling foundation model: {e}")
+                    st.session_state.messages.pop()  # Remove failed user message
+
+
+if __name__ == "__main__":
+    main()