fix: remove inference.completion from docs

docs/docs/references/python_sdk_reference/index.md

@@ -217,7 +217,6 @@ from llama_stack_client.types import (
 Methods:
 
 - <code title="post /v1/inference/chat-completion">client.inference.<a href="https://github.com/meta-llama/llama-stack-client-python/tree/main/src/llama_stack_client/resources/inference.py">chat_completion</a>(\*\*<a href="https://github.com/meta-llama/llama-stack-client-python/tree/main/src/llama_stack_client/types/inference_chat_completion_params.py">params</a>) -> <a href="https://github.com/meta-llama/llama-stack-client-python/tree/main/src/llama_stack_client/types/inference_chat_completion_response.py">InferenceChatCompletionResponse</a></code>
-- <code title="post /v1/inference/completion">client.inference.<a href="https://github.com/meta-llama/llama-stack-client-python/tree/main/src/llama_stack_client/resources/inference.py">completion</a>(\*\*<a href="https://github.com/meta-llama/llama-stack-client-python/tree/main/src/llama_stack_client/types/inference_completion_params.py">params</a>) -> <a href="https://github.com/meta-llama/llama-stack-client-python/tree/main/src/llama_stack_client/types/inference_completion_response.py">InferenceCompletionResponse</a></code>
 - <code title="post /v1/inference/embeddings">client.inference.<a href="https://github.com/meta-llama/llama-stack-client-python/tree/main/src/llama_stack_client/resources/inference.py">embeddings</a>(\*\*<a href="https://github.com/meta-llama/llama-stack-client-python/tree/main/src/llama_stack_client/types/inference_embeddings_params.py">params</a>) -> <a href="https://github.com/meta-llama/llama-stack-client-python/tree/main/src/llama_stack_client/types/embeddings_response.py">EmbeddingsResponse</a></code>
 
 ## VectorIo

docs/getting_started.ipynb

@@ -824,16 +824,10 @@
         "\n",
         "\n",
         "user_input = \"Michael Jordan was born in 1963. He played basketball for the Chicago Bulls. He retired in 2003. Extract this information into JSON for me. \"\n",
-        "response = client.inference.completion(\n",
+        "response = client.chat.completions.create(\n",
-        "    model_id=\"meta-llama/Llama-3.1-8B-Instruct\",\n",
+        "    model=\"meta-llama/Llama-3.1-8B-Instruct\",\n",
-        "    content=user_input,\n",
+        "    messages=[{\"role\": \"user\", \"content\": user_input}],\n",
-        "    stream=False,\n",
+        "    max_tokens=50,\n",
-        "    sampling_params={\n",
-        "        \"strategy\": {\n",
-        "            \"type\": \"greedy\",\n",
-        "        },\n",
-        "        \"max_tokens\": 50,\n",
-        "    },\n",
         "    response_format={\n",
         "        \"type\": \"json_schema\",\n",
         "        \"json_schema\": Output.model_json_schema(),\n",

docs/notebooks/nvidia/beginner_e2e/Llama_Stack_NVIDIA_E2E_Flow.ipynb

@@ -706,20 +706,15 @@
     "    provider_id=\"nvidia\",\n",
     ")\n",
     "\n",
-    "response = client.inference.completion(\n",
+    "response = client.completions.create(\n",
-    "    content=\"Complete the sentence using one word: Roses are red, violets are \",\n",
+    "    prompt=\"Complete the sentence using one word: Roses are red, violets are \",\n",
     "    stream=False,\n",
-    "    model_id=CUSTOMIZED_MODEL_DIR,\n",
+    "    model=CUSTOMIZED_MODEL_DIR,\n",
-    "    sampling_params={\n",
+    "    temperature=0.7,\n",
-    "        \"strategy\": {\n",
+    "    top_p=0.9,\n",
-    "            \"type\": \"top_p\",\n",
+    "    max_tokens=20,\n",
-    "            \"temperature\": 0.7,\n",
-    "            \"top_p\": 0.9\n",
-    "        },\n",
-    "        \"max_tokens\": 20,\n",
-    "    },\n",
     ")\n",
-    "print(f\"Inference response: {response.content}\")"
+    "print(f\"Inference response: {response.choices[0].text}\")"
    ]
   },
   {
@@ -1233,20 +1228,15 @@
     "    provider_id=\"nvidia\",\n",
     ")\n",
     "\n",
-    "response = client.inference.completion(\n",
+    "response = client.completions.create(\n",
-    "    content=\"Complete the sentence using one word: Roses are red, violets are \",\n",
+    "    prompt=\"Complete the sentence using one word: Roses are red, violets are \",\n",
     "    stream=False,\n",
-    "    model_id=customized_chat_model_dir,\n",
+    "    model=customized_chat_model_dir,\n",
-    "    sampling_params={\n",
+    "    temperature=0.7,\n",
-    "        \"strategy\": {\n",
+    "    top_p=0.9,\n",
-    "            \"type\": \"top_p\",\n",
+    "    max_tokens=20,\n",
-    "            \"temperature\": 0.7,\n",
-    "            \"top_p\": 0.9\n",
-    "        },\n",
-    "        \"max_tokens\": 20,\n",
-    "    },\n",
     ")\n",
-    "print(f\"Inference response: {response.content}\")"
+    "print(f\"Inference response: {response.choices[0].text}\")"
    ]
   },
   {

llama_stack/providers/remote/inference/nvidia/NVIDIA.md

@@ -39,25 +39,6 @@ client = LlamaStackAsLibraryClient("nvidia")
 client.initialize()
 ```
 
-### Create Completion
-
-The following example shows how to create a completion for an NVIDIA NIM.
-
-> [!NOTE]
-> The hosted NVIDIA Llama NIMs (for example ```meta-llama/Llama-3.1-8B-Instruct```) that have ```NVIDIA_BASE_URL="https://integrate.api.nvidia.com"``` do not support the ```completion``` method, while locally deployed NIMs do.
-
-```python
-response = client.inference.completion(
-    model_id="meta-llama/Llama-3.1-8B-Instruct",
-    content="Complete the sentence using one word: Roses are red, violets are :",
-    stream=False,
-    sampling_params={
-        "max_tokens": 50,
-    },
-)
-print(f"Response: {response.content}")
-```
-
 ### Create Chat Completion
 
 The following example shows how to create a chat completion for an NVIDIA NIM.

llama_stack/providers/remote/post_training/nvidia/README.md

@@ -140,13 +140,11 @@ client.models.register(
 #### 2. Inference with the fine-tuned model
 
 ```python
-response = client.inference.completion(
+response = client.completions.create(
-    content="Complete the sentence using one word: Roses are red, violets are ",
+    prompt="Complete the sentence using one word: Roses are red, violets are ",
     stream=False,
-    model_id="test-example-model@v1",
+    model="test-example-model@v1",
-    sampling_params={
+    max_tokens=50,
-        "max_tokens": 50,
-    },
 )
-print(response.content)
+print(response.choices[0].text)
 ```

tests/integration/README.md

@@ -178,10 +178,10 @@ Note that when re-recording tests, you must use a Stack pointing to a server (i.
 
 ### Basic Test Pattern
 ```python
-def test_basic_completion(llama_stack_client, text_model_id):
+def test_basic_chat_completion(llama_stack_client, text_model_id):
-    response = llama_stack_client.inference.completion(
+    response = llama_stack_client.inference.chat_completion(
         model_id=text_model_id,
-        content=CompletionMessage(role="user", content="Hello"),
+        messages=[{"role": "user", "content": "Hello"}],
     )
 
     # Test structure, not AI output quality