chore: remove unused descriptions

Author: elisalimli

libs/ui/config/site.ts

@@ -428,7 +428,6 @@ export const siteConfig = {
     },
     {
       disabled: false,
-      formDescription: "Please enter your Azure OpenAI API key.",
       provider: "AZURE_OPENAI",
       name: "Azure OpenAI",
       metadata: [
@@ -460,9 +459,6 @@ export const siteConfig = {
       provider: VectorDbProvider[VectorDbProvider.PINECONE],
       name: "Pinecone",
       logo: "/pinecone.png",
-      description:
-        "Cloud-based database for storing and searching vectors, enabling fast similarity comparisons. Scales well for large datasets.",
-      formDescription: "Please enter your Pinecone credentials.",
       metadata: [
         {
           key: "PINECONE_API_KEY",
@@ -485,9 +481,6 @@ export const siteConfig = {
       provider: VectorDbProvider[VectorDbProvider.QDRANT],
       name: "Qdrant",
       logo: "/qdrant.png",
-      description:
-        "Open-source database optimized for efficient vector search and filtering. Handles large datasets effectively while requiring minimal resources.",
-      formDescription: "Please enter your Qdrant credentials.",
       metadata: [
         {
           key: "QDRANT_API_KEY",
@@ -510,9 +503,6 @@ export const siteConfig = {
       provider: VectorDbProvider[VectorDbProvider.ASTRA_DB],
       name: "Astra DB",
       logo: "/datastax.jpeg",
-      description:
-        "Serverless database built on Cassandra, offering integration with Pinecone for vector similarity search.",
-      formDescription: "Please enter your Astra DB credentials",
       metadata: [
         {
           key: "ASTRA_DB_ID",
@@ -545,9 +535,6 @@ export const siteConfig = {
       provider: VectorDbProvider[VectorDbProvider.WEAVIATE],
       name: "Weaviate",
       logo: "/weaviate.png",
-      description:
-        "Semantic vector database with schema-based organization. Supports both vector search and connections between data points like a graph.",
-      formDescription: "Please enter your Weaviate credentials.",
       metadata: [
         {
           key: "WEAVIATE_API_KEY",
@@ -570,9 +557,6 @@ export const siteConfig = {
       provider: VectorDbProvider[VectorDbProvider.SUPABASE],
       name: "Supabase",
       logo: "/supabase.png",
-      description:
-        "The pgvector extension is particularly useful for tasks such as vector similarity search, retrieval, generation, and clustering",
-      formDescription: "Please enter your Supabase Pgvector credentials.",
       metadata: [
         {
           key: "SUPABASE_DB_URL",
@@ -598,34 +582,28 @@ export const siteConfig = {
       provider: MemoryDbProvider[MemoryDbProvider.MOTORHEAD],
       name: "Motorhead",
       logo: "/motorhead.png",
-      description:
-        "Cloud-based database for storing and searching vectors, enabling fast similarity comparisons. Scales well for large datasets.",
-      formDescription: "Please enter your Motorhead api URL.",
       metadata: [
         {
           key: "MEMORY_API_URL",
           type: "input",
-          label: "Memory api URL",
+          label: "Memory API URL",
         },
       ],
     },
     {
       provider: MemoryDbProvider[MemoryDbProvider.REDIS],
       name: "Redis",
       logo: "/redis.png",
-      description:
-        "Open-source database optimized for efficient vector search and filtering. Handles large datasets effectively while requiring minimal resources.",
-      formDescription: "Please enter your Redis options.",
       metadata: [
         {
           key: "REDIS_MEMORY_URL",
           type: "input",
-          label: "Redis memory URL",
+          label: "Redis URL",
         },
         {
           key: "REDIS_MEMORY_WINDOW",
           type: "input",
-          label: "Redis memory window size",
+          label: "Redis Memory Window Size",
         },
       ],
     },