diff --git a/web/containers/CardSidebar/index.tsx b/web/containers/CardSidebar/index.tsx index cb9942087..f500141f0 100644 --- a/web/containers/CardSidebar/index.tsx +++ b/web/containers/CardSidebar/index.tsx @@ -31,7 +31,7 @@ export default function CardSidebar({ rightAction, hideMoreVerticalAction, }: Props) { - const [show, setShow] = useState(true) + const [show, setShow] = useState(false) const [more, setMore] = useState(false) const [menu, setMenu] = useState(null) const [toggle, setToggle] = useState(null) diff --git a/web/screens/Chat/Sidebar/index.tsx b/web/screens/Chat/Sidebar/index.tsx index f9e30aec8..1ad87128f 100644 --- a/web/screens/Chat/Sidebar/index.tsx +++ b/web/screens/Chat/Sidebar/index.tsx @@ -156,21 +156,87 @@ const Sidebar: React.FC = () => {
{activeThread?.assistants[0]?.tools && componentDataAssistantSetting.length > 0 && ( -
- -
-
+ +
+
+
+ +
-
+
+
+ {activeThread?.assistants[0]?.tools[0].enabled && ( +
+
+
+ @@ -179,140 +245,70 @@ const Sidebar: React.FC = () => { className="max-w-[240px]" > - Retrieval helps the assistant use - information from files you send to it. Once - you share a file, the assistant - automatically fetches the relevant content - based on your request. + Embedding model is crucial for understanding + and processing the input text effectively by + converting text to numerical + representations. Align the model choice with + your task, evaluate its performance, and + consider factors like resource availability. + Experiment to find the best fit for your + specific use case. - +
- { - if (activeThread) - updateThreadMetadata({ - ...activeThread, - assistants: [ - { - ...activeThread.assistants[0], - tools: [ - { - type: 'retrieval', - enabled: e, - settings: - (activeThread.assistants[0] - .tools && - activeThread.assistants[0] - .tools[0]?.settings) ?? - {}, - }, - ], - }, - ], - }) - }} - /> +
+
+
+ + + + + + + + + Vector Database is crucial for efficient + storage and retrieval of embeddings. + Consider your specific task, available + resources, and language requirements. + Experiment to find the best fit for your + specific use case. + + + + + +
+ +
+ +
+
+
- {activeThread?.assistants[0]?.tools[0].enabled && ( -
-
-
- - - - - - - - - Embedding model is crucial for - understanding and processing the input - text effectively by converting text to - numerical representations. Align the model - choice with your task, evaluate its - performance, and consider factors like - resource availability. Experiment to find - the best fit for your specific use case. - - - - - -
- -
- -
-
-
-
- - - - - - - - - Vector Database is crucial for efficient - storage and retrieval of embeddings. - Consider your specific task, available - resources, and language requirements. - Experiment to find the best fit for your - specific use case. - - - - - -
- -
- -
-
- -
- )} -
-
-
+ )} +
+
)}
)}