Hotfix v5.4.1 into Main

VERSION

@@ -1 +1 @@
-5.4.0
+5.4.1

lib/api-base/fastApiContainer.ts

@@ -81,8 +81,8 @@ export class FastApiContainer extends Construct {
         };
 
         // Add build config overrides if provided
-        if (config.restApiConfig.buildConfig?.NODEENV_CACHE_DIR) {
-            buildArgs.NODEENV_CACHE_DIR = config.restApiConfig.buildConfig.NODEENV_CACHE_DIR;
+        if (config.restApiConfig.buildConfig?.PRISMA_CACHE_DIR) {
+            buildArgs.PRISMA_CACHE_DIR = config.restApiConfig.buildConfig.PRISMA_CACHE_DIR;
         }
 
         // Add MCP Workbench build config overrides if provided

lib/docs/admin/deploy.md

@@ -294,10 +294,10 @@ npmConfig:
 # Use ADC-accessible base images for LISA-Serve and Batch Ingestion
 baseImage: <adc-registry>/python:3.11
 
-# Configure offline build dependencies for REST API (nodeenv for prisma-client-py)
+# Configure offline build dependencies for REST API (prisma-client-py dependencies)
 restApiConfig:
   buildConfig:
-    NODEENV_CACHE_DIR: "./nodeenv-cache"  # Path relative to lib/serve/rest-api/
+    PRISMA_CACHE_DIR: "./PRISMA_CACHE"  # Path relative to lib/serve/rest-api/
 
 # Configure offline build dependencies for MCP Workbench (S6 Overlay and rclone)
 mcpWorkbenchBuildConfig:
@@ -311,12 +311,33 @@ You'll also want any model hosting base containers available, e.g. vllm/vllm-ope
 
 For environments without internet access during Docker builds, you can pre-cache required dependencies:
 
-**REST API nodeenv cache** (required by prisma-client-py):
+**REST API Prisma cache** (required by prisma-client-py):
+
+The `prisma-client-py` package requires platform-specific binaries and a Node.js environment to function. When Prisma runs for the first time, it downloads these dependencies to `~/.cache/prisma/` and `~/.cache/prisma-python/`. For offline deployments, you need to pre-populate this cache.
+
+Below is an example workflow using an Amazon Linux 2023 instance with Python 3.12:
+
 ```bash
-# Create the cache directory in the REST API build context
-python -m nodeenv lib/serve/rest-api/nodeenv-cache
+# Ensure Pip is up-to-date
+pip3 install --upgrade pip
+
+# Install Prisma Python package
+pip3 install prisma
+
+# Trigger Prisma to download all required binaries and create its Node.js environment
+# This populates ~/.cache/prisma/ and ~/.cache/prisma-python/
+prisma version
+
+# Copy the complete Prisma cache to your build context
+# The wildcard captures both 'prisma' and 'prisma-python' directories
+cp -r ~/.cache/prisma* lib/serve/rest-api/PRISMA_CACHE/
 ```
 
+**Important Notes:**
+- The cache is platform-specific. Generate it on a system matching your Docker base image (e.g., for `python:3.13-slim` which is Debian-based, so you may want to use a Debian-based system)
+- The `prisma version` command downloads binaries for your current platform
+- Both `prisma/` and `prisma-python/` directories are required for offline operation
+
 **MCP Workbench dependencies** (S6 Overlay and rclone):
 ```bash
 # Download S6 Overlay files

lib/schema/configSchema.ts

@@ -711,7 +711,7 @@ const FastApiContainerConfigSchema = z.object({
     sslCertIamArn: z.string().nullish().default(null).describe('ARN of the self-signed cert to be used throughout the system'),
     imageConfig: ImageAssetSchema.optional().describe('Override image configuration for ECS FastAPI Containers'),
     buildConfig: z.object({
-        NODEENV_CACHE_DIR: z.string().optional().describe('Override with a path relative to the build directory for a pre-cached nodeenv directory. Defaults to NODEENV_CACHE. For offline environments, populate using: python -m nodeenv PATH')
+        PRISMA_CACHE_DIR: z.string().optional().describe('Override with a path relative to the build directory for a pre-cached prisma directory. Defaults to PRISMA_CACHE.')
     }).default({}),
     rdsConfig: RdsInstanceConfig
         .default({

lib/serve/rest-api/Dockerfile

@@ -1,8 +1,8 @@
 ARG BASE_IMAGE=python:3.11
 FROM ${BASE_IMAGE}
 
-ARG NODEENV_CACHE_DIR=NODEENV_CACHE
-ENV NODEENV_CACHE_DIR=$NODEENV_CACHE_DIR
+ARG PRISMA_CACHE_DIR=PRISMA_CACHE
+ENV PRISMA_CACHE_DIR=$PRISMA_CACHE_DIR
 
 # Install build dependencies for madoka package
 RUN apt-get update && apt-get install -y \
@@ -28,20 +28,20 @@ WORKDIR /app
 COPY src/requirements.txt .
 RUN pip install --no-cache-dir --upgrade -r requirements.txt
 
-# Copy nodeenv cache directory (always exists, may be empty or populated)
-COPY ${NODEENV_CACHE_DIR} /tmp/nodeenv-cache/
+# Copy prisma cache directory (always exists, may be empty or populated)
+COPY ${PRISMA_CACHE_DIR} /tmp/prisma-cache/
 
-# Pre-cache nodeenv for prisma-client-py
+# Pre-cache prisma for prisma-client-py
 # If the copied directory has content, use it (for offline environments)
 # Otherwise, download it during build (requires internet)
-RUN mkdir -p /root/.cache/prisma-python && \
-    if [ -d "/tmp/nodeenv-cache" ] && [ -n "$(ls /tmp/nodeenv-cache 2>/dev/null)" ]; then \
-        echo "Using pre-cached nodeenv from host" && \
-        cp -r /tmp/nodeenv-cache /root/.cache/prisma-python/nodeenv && \
-        rm -rf /tmp/nodeenv-cache; \
+RUN mkdir -p /root/.cache && \
+    if [ -d "/tmp/prisma-cache" ] && [ -n "$(ls /tmp/prisma-cache 2>/dev/null)" ]; then \
+        echo "Using pre-cached Prisma dependencies from host" && \
+        cp -r /tmp/prisma-cache/prisma* /root/.cache && \
+        rm -rf /tmp/prisma-cache; \
     else \
-        echo "Downloading nodeenv (requires internet)" && \
-        python -m nodeenv /root/.cache/prisma-python/nodeenv; \
+        echo "Fetching Prisma Dependencies (requires internet)" && \
+        prisma version; \
     fi
 
 # Copy the source code into the container

lib/serve/rest-api/PRISMA_CACHE/.gitkeep

@@ -0,0 +1,5 @@
+# Placeholder to ensure PRISMA_CACHE directory exists in build context
+# For offline builds, populate this directory by copying the complete Prisma cache:
+#   1. Install prisma: pip3 install prisma
+#   2. Generate cache: prisma version
+#   3. Copy cache: cp -r ~/.cache/prisma* lib/serve/rest-api/PRISMA_CACHE/

lib/user-interface/react/package.json

@@ -1,7 +1,7 @@
 {
   "name": "lisa-web",
   "private": true,
-  "version": "5.4.0",
+  "version": "5.4.1",
   "type": "module",
   "scripts": {
     "dev": "vite",

lib/user-interface/react/src/shared/reducers/configuration.reducer.ts

@@ -23,7 +23,7 @@ export const configurationApi = createApi({
     baseQuery: lisaBaseQuery(),
     tagTypes: ['configuration'],
     refetchOnFocus: true,
-    refetchOnReconnect: true,
+    refetchOnMountOrArgChange: true,
     endpoints: (builder) => ({
         getConfiguration: builder.query<IConfiguration[], string>({
             query: (configScope) => ({

lib/user-interface/react/src/shared/reducers/mcp-server.reducer.ts

@@ -62,7 +62,7 @@ export const mcpServerApi = createApi({
     baseQuery: lisaBaseQuery(),
     tagTypes: ['mcpServers'],
     refetchOnFocus: true,
-    refetchOnReconnect: true,
+    refetchOnMountOrArgChange: true,
     endpoints: (builder) => ({
         createMcpServer: builder.mutation<McpServer, NewMcpServer>({
             query: (mcpServer) => ({

lib/user-interface/react/src/shared/reducers/mcp-tools.reducer.ts

@@ -31,7 +31,7 @@ export const mcpToolsApi = createApi({
     baseQuery: lisaBaseQuery(),
     tagTypes: ['mcpTools'],
     refetchOnFocus: true,
-    refetchOnReconnect: true,
+    refetchOnMountOrArgChange: true,
     endpoints: (builder) => ({
         listMcpTools: builder.query<IMcpTool[], void>({
             query: () => ({

lib/user-interface/react/src/shared/reducers/model-management.reducer.ts

@@ -23,7 +23,7 @@ export const modelManagementApi = createApi({
     baseQuery: lisaBaseQuery(),
     tagTypes: ['models'],
     refetchOnFocus: true,
-    refetchOnReconnect: true,
+    refetchOnMountOrArgChange: true,
     endpoints: (builder) => ({
         getAllModels: builder.query<IModelListResponse['models'], void>({
             query: () => ({

lib/user-interface/react/src/shared/reducers/prompt-templates.reducer.ts

@@ -54,7 +54,7 @@ export const promptTemplateApi = createApi({
     baseQuery: lisaBaseQuery(),
     tagTypes: ['promptTemplates'],
     refetchOnFocus: true,
-    refetchOnReconnect: true,
+    refetchOnMountOrArgChange: true,
     endpoints: (builder) => ({
         createPromptTemplate: builder.mutation<PromptTemplate, NewPromptTemplate>({
             query: (promptTemplate) => ({

lib/user-interface/react/src/shared/reducers/rag.reducer.ts

@@ -101,7 +101,7 @@ export const ragApi = createApi({
     baseQuery: lisaBaseQuery(),
     tagTypes: ['repositories', 'docs', 'repository-status', 'jobs'],
     refetchOnFocus: true,
-    refetchOnReconnect: true,
+    refetchOnMountOrArgChange: true,
     endpoints: (builder) => ({
         listRagRepositories: builder.query<RagRepositoryConfig[], void>({
             query: () => ({

lib/user-interface/react/src/shared/reducers/session.reducer.ts

@@ -29,7 +29,7 @@ export const sessionApi = createApi({
     baseQuery: lisaBaseQuery(),
     tagTypes: ['sessions'],
     refetchOnFocus: true,
-    refetchOnReconnect: true,
+    refetchOnMountOrArgChange: true,
     endpoints: (builder) => ({
         getSessionById: builder.query<LisaChatSession, string>({
             query: (sessionId: string) => ({

lib/user-interface/react/src/shared/reducers/user-preferences.reducer.ts

@@ -50,7 +50,7 @@ export const userPreferencesApi = createApi({
     baseQuery: lisaBaseQuery(),
     tagTypes: ['user-preferences'],
     refetchOnFocus: true,
-    refetchOnReconnect: true,
+    refetchOnMountOrArgChange: true,
     endpoints: (builder) => ({
         updateUserPreferences: builder.mutation<UserPreferences, UserPreferences>({
             query: (userPreferences) => ({

lisa-sdk/pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "lisapy"
-version = "5.4.0"
+version = "5.4.1"
 description = "A simple SDK to help you interact with LISA. LISA is an LLM hosting solution for AWS dedicated clouds or ADCs."
 readme = "README.md"
 requires-python = ">=3.11"
@@ -15,7 +15,7 @@ dependencies = [
 
 [tool.poetry]
 name = "lisapy"
-version = "5.4.0"
+version = "5.4.1"
 description = "A simple SDK to help you interact with LISA. LISA is an LLM hosting solution for AWS dedicated clouds or ADCs."
 authors = ["Steve Goley <sgoley@amazon.com>"]
 readme = "README.md"

package.json

@@ -1,6 +1,6 @@
 {
   "name": "@awslabs/lisa",
-  "version": "5.4.0",
+  "version": "5.4.1",
   "description": "A scalable infrastructure-as-code solution for self-hosting and orchestrating LLM inference with RAG capabilities, providing low-latency access to generative AI and embedding models across multiple providers.",
   "homepage": "https://awslabs.github.io/LISA/",
   "license": "Apache-2.0",