added more docs

source/openapi.html

@@ -3318,27 +3318,29 @@
     ],
     "tags": [
         {
-            "name": "Inference",
+            "name": "AgenticSystem",
-            "x-displayName": "Set of methods that can be called on the inference service."
+            "description": "Multi-step tool-use concretely helps address many common problems with LLMs that users may \n    face:\n    1. Finding accurate and up-to-date information. LLMs are limited to training data and knowledge cut off date. \n    2. Current LLMs are limited in their understanding and reasoning abilities for solving more complex math problems, processing and analyzing data. Tools like code-execution or APIs like Wolfram can help bridge the gap.\n    3. Users may need help with a task that requires multiple tools to execute or a task that has multiple steps (e.g., graph plotting, etc.)\n    4. Our current LLMs are not able to generate other modalities (images, voice, video) directly. \n\nFinally, we want the underlying LLM to remain broadly steerable and adaptable to use cases which \nneed varying levels of safety protection. To enable this, we want to shift safety into a two-tiered \nsystem: \n    1. a set of \"always on\" safety checks are always performed at the model level, and\n    2. a set of configurable safety checks which can be run at the overall system level.",
-        },
+            "x-displayName": "The Llama 3 models released by Meta in July should not just be seen as a model,      but really as a system starting the transition towards an entity capable of performing      \"agentic\" tasks. By that we mean the following specific capabilities:      1. Ability to act as the central planner -- break a task down and perform multi-step reasoning.      2. Ability to perceive multimodal inputs -- text, images, files and eventually speech and video in later iterations.     3. Ability to use tools -         a. built-in: the model has built-in knowledge of tools like search or code interpreter         b. zero-shot: the model can learn to call tools using previously unseen, in-context tool definitions"
-        {
-            "name": "RewardScoring"
-        },
-        {
-            "name": "AgenticSystem"
         },
         {
             "name": "SyntheticDataGeneration"
         },
-        {
-            "name": "PostTraining"
-        },
         {
             "name": "Datasets"
         },
         {
             "name": "MemoryBanks"
         },
+        {
+            "name": "Inference",
+            "x-displayName": "Set of methods that can be called on the inference service."
+        },
+        {
+            "name": "PostTraining"
+        },
+        {
+            "name": "RewardScoring"
+        },
         {
             "name": "ShieldConfig",
             "description": "<SchemaDefinition schemaRef=\"#/components/schemas/ShieldConfig\" />"

source/openapi.yaml

@@ -2015,14 +2015,37 @@ security:
 servers:
 - url: http://any-hosted-llama-stack.com
 tags:
-- name: Inference
+- description: "Multi-step tool-use concretely helps address many common problems\
-  x-displayName: Set of methods that can be called on the inference service.
+    \ with LLMs that users may \n    face:\n    1. Finding accurate and up-to-date\
-- name: RewardScoring
+    \ information. LLMs are limited to training data and knowledge cut off date. \n\
-- name: AgenticSystem
+    \    2. Current LLMs are limited in their understanding and reasoning abilities\
+    \ for solving more complex math problems, processing and analyzing data. Tools\
+    \ like code-execution or APIs like Wolfram can help bridge the gap.\n    3. Users\
+    \ may need help with a task that requires multiple tools to execute or a task\
+    \ that has multiple steps (e.g., graph plotting, etc.)\n    4. Our current LLMs\
+    \ are not able to generate other modalities (images, voice, video) directly. \n\
+    \nFinally, we want the underlying LLM to remain broadly steerable and adaptable\
+    \ to use cases which \nneed varying levels of safety protection. To enable this,\
+    \ we want to shift safety into a two-tiered \nsystem: \n    1. a set of \"always\
+    \ on\" safety checks are always performed at the model level, and\n    2. a set\
+    \ of configurable safety checks which can be run at the overall system level."
+  name: AgenticSystem
+  x-displayName: 'The Llama 3 models released by Meta in July should not just be seen
+    as a model,      but really as a system starting the transition towards an entity
+    capable of performing      "agentic" tasks. By that we mean the following specific
+    capabilities:      1. Ability to act as the central planner -- break a task down
+    and perform multi-step reasoning.      2. Ability to perceive multimodal inputs
+    -- text, images, files and eventually speech and video in later iterations.     3.
+    Ability to use tools -         a. built-in: the model has built-in knowledge of
+    tools like search or code interpreter         b. zero-shot: the model can learn
+    to call tools using previously unseen, in-context tool definitions'
 - name: SyntheticDataGeneration
-- name: PostTraining
 - name: Datasets
 - name: MemoryBanks
+- name: Inference
+  x-displayName: Set of methods that can be called on the inference service.
+- name: PostTraining
+- name: RewardScoring
 - description: <SchemaDefinition schemaRef="#/components/schemas/ShieldConfig" />
   name: ShieldConfig
 - description: <SchemaDefinition schemaRef="#/components/schemas/AgenticSystemCreateRequest"