chore: import upstream snapshot with attribution

examples/langchain/chain_as_code_driver.py

@@ -0,0 +1,39 @@
+# This is an example for logging a Langchain model from code using the
+# mlflow.langchain.log_model API. When a path to a valid Python script is submitted to the
+# lc_model argument, the model code itself is serialized instead of the model object.
+# Within the targeted script, the model implementation must be defined and set by
+# using the mlflow.models.set_model API.
+
+import mlflow
+
+input_example = {
+    "messages": [
+        {
+            "role": "user",
+            "content": "What is Retrieval-augmented Generation?",
+        }
+    ]
+}
+
+# Specify the path to the chain notebook
+chain_path = "chain_as_code.py"
+
+print(f"Chain path: {chain_path}")
+
+print("Logging model as code using Langchain log model API")
+with mlflow.start_run():
+    logged_chain_info = mlflow.langchain.log_model(
+        lc_model=chain_path,
+        name="chain",
+        input_example=input_example,
+    )
+
+print("Loading model using Langchain load model API")
+model = mlflow.langchain.load_model(logged_chain_info.model_uri)
+output = model.invoke(input_example)
+print(f"Output: {output}")
+
+print("Loading model using Pyfunc load model API")
+pyfunc_model = mlflow.pyfunc.load_model(logged_chain_info.model_uri)
+output = pyfunc_model.predict([input_example])
+print(f"Output: {output}")