davidradl commented on code in PR #27243:
URL: https://github.com/apache/flink/pull/27243#discussion_r2533345534
##########
docs/content/docs/dev/table/tableApi.md:
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@@ -2735,6 +2735,114 @@ result = t.select(col('a'), col('c')) \
{{< query_state_warning >}}
+### Model Inference
+
+{{< label Streaming >}}
+
+The Table API supports model inference operations that allow you to integrate
machine learning models directly into your data processing pipelines. You can
create models with specific providers (like OpenAI) and use them to make
predictions on your data.
+
+#### Creating and Using Models
+
+Models are created using `ModelDescriptor` which specifies the provider,
input/output schemas, and configuration options. Once created, you can use the
model to make predictions on tables.
+
+{{< tabs "model-inference" >}}
+{{< tab "Java" >}}
+
+```java
+// 1. Set up the local environment
+EnvironmentSettings settings = EnvironmentSettings.inStreamingMode();
+TableEnvironment tEnv = TableEnvironment.create(settings);
+
+// 2. Create a source table from in-memory data
+Table myTable = tEnv.fromValues(
+ ROW(FIELD("text", STRING())),
+ row("Hello"),
+ row("Machine Learning"),
+ row("Good morning")
+);
+
+// 3. Create model
+tEnv.createModel(
+ "my_model",
+ ModelDescriptor.forProvider("openai")
+ .inputSchema(Schema.newBuilder().column("input", STRING()).build())
+ .outputSchema(Schema.newBuilder().column("output", STRING()).build())
+ .option("endpoint", "https://api.openai.com/v1/chat/completions";)
+ .option("model", "gpt-4.1")
+ .option("system-prompt", "translate to chinese")
+ .option("api-key", "<your-openai-api-key-here>")
+ .build()
+);
+
+Model model = tEnv.fromModel("my_model");
+
+// 4. Use the model to make predictions
Review Comment:
it would be good to give more context around the predictions - I think a
simple predication use case would help here.
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