docs: add end-to-end example showing full OCI application flow

Demonstrates embeddings → vector search → grounded chat → tool use
(multi-turn) → vision → streaming in a single runnable script.
All steps verified against live OCI Generative AI.

Author: fede-kamel

fern/pages/deployment-options/oracle-cloud-infrastructure-oci.mdx

@@ -345,6 +345,144 @@ for i, embedding in enumerate(response.embeddings.float_):
 | `tokenize` | Offline only |
 | `detokenize` | Offline only |
 
+## End-to-End Example
+
+The following example demonstrates a complete application flow on OCI Generative AI: embedding documents for a knowledge base, retrieving relevant context, using tool calling for live data, processing images with vision, and streaming a final response.
+
+```python
+import cohere
+import base64
+
+# Initialize V2 client for Command A models
+client = cohere.OciClientV2(
+    oci_region="us-chicago-1",
+    oci_compartment_id="ocid1.compartment.oc1...",
+)
+
+# --- Step 1: Build a knowledge base with embeddings ---
+
+documents = [
+    "Oracle Cloud Infrastructure provides enterprise-grade AI services.",
+    "Cohere Command A is a 111B parameter model with 256K context window.",
+    "OCI Generative AI is FedRAMP High and DISA IL5 authorized.",
+]
+
+doc_embeddings = client.embed(
+    model="embed-english-v3.0",
+    texts=documents,
+    input_type="search_document",
+).embeddings.float_
+
+query_embedding = client.embed(
+    model="embed-english-v3.0",
+    texts=["What security certifications does OCI have?"],
+    input_type="search_query",
+).embeddings.float_[0]
+
+# Find the most relevant document (cosine similarity)
+best_idx = max(
+    range(len(documents)),
+    key=lambda i: sum(a * b for a, b in zip(query_embedding, doc_embeddings[i])),
+)
+print(f"Best match: {documents[best_idx]}")
+
+# --- Step 2: Grounded chat with retrieved context ---
+
+response = client.chat(
+    model="command-a-03-2025",
+    messages=[
+        {"role": "system", "content": "Answer based on the provided context only."},
+        {"role": "user", "content": f"Context: {documents[best_idx]}\n\nWhat certifications does OCI have?"},
+    ],
+    temperature=0.3,
+)
+print(f"Answer: {response.message.content[0].text}")
+
+# --- Step 3: Tool use — call an external API ---
+
+response = client.chat(
+    model="command-a-03-2025",
+    messages=[{"role": "user", "content": "What's the current stock price of ORCL?"}],
+    tools=[{
+        "type": "function",
+        "function": {
+            "name": "get_stock_price",
+            "description": "Get the current stock price for a ticker symbol",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "ticker": {"type": "string", "description": "Stock ticker symbol"}
+                },
+                "required": ["ticker"],
+            },
+        },
+    }],
+)
+
+# Model returns a tool call
+tool_call = response.message.tool_calls[0]
+print(f"Tool call: {tool_call.function.name}({tool_call.function.arguments})")
+
+# Send the tool result back
+final = client.chat(
+    model="command-a-03-2025",
+    messages=[
+        {"role": "user", "content": "What's the current stock price of ORCL?"},
+        {
+            "role": "assistant",
+            "tool_calls": [{"id": tool_call.id, "type": "function", "function": {"name": tool_call.function.name, "arguments": tool_call.function.arguments}}],
+            "tool_plan": response.message.tool_plan,
+        },
+        {
+            "role": "tool",
+            "tool_call_id": tool_call.id,
+            "content": [{"type": "text", "text": '{"ticker": "ORCL", "price": 187.42, "currency": "USD"}'}],
+        },
+    ],
+    tools=[{
+        "type": "function",
+        "function": {
+            "name": "get_stock_price",
+            "description": "Get the current stock price for a ticker symbol",
+            "parameters": {
+                "type": "object",
+                "properties": {"ticker": {"type": "string"}},
+                "required": ["ticker"],
+            },
+        },
+    }],
+)
+print(f"Final answer: {final.message.content[0].text}")
+
+# --- Step 4: Vision — analyze an image ---
+
+with open("chart.png", "rb") as f:
+    img_b64 = base64.b64encode(f.read()).decode()
+
+response = client.chat(
+    model="command-a-vision",
+    messages=[{
+        "role": "user",
+        "content": [
+            {"type": "text", "text": "Describe the trend shown in this chart."},
+            {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{img_b64}"}},
+        ],
+    }],
+)
+print(f"Vision: {response.message.content[0].text}")
+
+# --- Step 5: Stream a response in real time ---
+
+print("Streaming: ", end="")
+for event in client.chat_stream(
+    model="command-a-03-2025",
+    messages=[{"role": "user", "content": "Summarize why enterprises choose OCI for AI."}],
+):
+    if event.type == "content-delta":
+        print(event.delta.message.content.text, end="")
+print()
+```
+
 ## Additional Resources
 
 - [Cohere Python SDK on GitHub](https://github.com/cohere-ai/cohere-python)