Merge release-1.11.0 into feat/a2a

Author: erichare

src/backend/base/langflow/initial_setup/starter_projects/Document Q&A.json

@@ -700,7 +700,7 @@
                   },
                   {
                     "name": "cryptography",
-                    "version": "49.0.0"
+                    "version": "48.0.1"
                   },
                   {
                     "name": "langchain_chroma",

src/backend/base/langflow/initial_setup/starter_projects/Knowledge Retrieval.json

@@ -706,7 +706,7 @@
                   },
                   {
                     "name": "cryptography",
-                    "version": "49.0.0"
+                    "version": "48.0.1"
                   },
                   {
                     "name": "langchain_chroma",

src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json

@@ -1169,7 +1169,7 @@
                   },
                   {
                     "name": "cryptography",
-                    "version": "49.0.0"
+                    "version": "48.0.1"
                   },
                   {
                     "name": "langchain_chroma",

src/backend/base/langflow/locales/en.json

@@ -6290,6 +6290,39 @@
   "components.urlcomponent.inputs.use_async.info.5d52d50c": "If enabled, uses asynchronous loading which can be significantly faster but might use more system resources.",
   "components.urlcomponent.outputs.page_results.display_name.02d3e1ea": "Extracted Pages",
   "components.urlcomponent.outputs.raw_results.display_name.960c8537": "Raw Content",
+  "components.valkey.description.b20bd825": "Implementation of Vector Store using Valkey",
+  "components.valkey.display_name.2392ad6b": "Valkey",
+  "components.valkey.inputs.embedding.display_name.4c1a0852": "Embedding",
+  "components.valkey.inputs.ingest_data.display_name.b8c4a377": "Ingest Data",
+  "components.valkey.inputs.number_of_results.display_name.e8f2e1bb": "Number of Results",
+  "components.valkey.inputs.number_of_results.info.5232e01a": "Number of results to return.",
+  "components.valkey.inputs.search_query.display_name.3ad6e0f4": "Search Query",
+  "components.valkey.inputs.search_query.info.d9f2d336": "Enter a query to run a similarity search.",
+  "components.valkey.inputs.search_query.placeholder.2a5ab7eb": "Enter a query...",
+  "components.valkey.inputs.should_cache_vector_store.display_name.2291db58": "Cache Vector Store",
+  "components.valkey.inputs.should_cache_vector_store.info.371ea81a": "If True, the vector store will be cached for the current build of the component. This is useful for components that have multiple output methods and want to share the same vector store.",
+  "components.valkey.inputs.valkey_index_name.display_name.e9f53690": "Valkey Index",
+  "components.valkey.inputs.valkey_server_url.display_name.08153eab": "Valkey Server Connection String",
+  "components.valkey.outputs.dataframe.display_name.16d1c905": "Table",
+  "components.valkey.outputs.search_results.display_name.39414102": "Search Results",
+  "components.valkey.outputs.search_results.info.c3d7164d": "Performs semantic similarity search in the vector store to find relevant documents/files. Input: A natural language search query (str). Returns: A list of Data objects (dicts). Each result has structure: {'data': {'text': '...', ...}}. The 'text' field always contains the matched document content. Other metadata fields depend on what was indexed (may include: 'file_path', 'source', 'page', etc.). IMPORTANT: To extract metadata like file_path, use: result['data'].get('file_path') or result.data.get('file_path'). Check what fields are available by inspecting result['data'].keys() or the first search result.",
+  "components.valkeychatmemory.description.561f6f53": "Retrieves and stores chat messages from Valkey.",
+  "components.valkeychatmemory.display_name.ecf133e7": "Valkey Chat Memory",
+  "components.valkeychatmemory.inputs.database.display_name.3549b002": "Database",
+  "components.valkeychatmemory.inputs.database.info.83c1cbb2": "Valkey database.",
+  "components.valkeychatmemory.inputs.host.display_name.7063dece": "Hostname",
+  "components.valkeychatmemory.inputs.host.info.e9e5c338": "IP address or hostname.",
+  "components.valkeychatmemory.inputs.key_prefix.display_name.ac467d48": "Key prefix",
+  "components.valkeychatmemory.inputs.key_prefix.info.0d48bfe7": "Key prefix.",
+  "components.valkeychatmemory.inputs.password.display_name.7ca8e190": "Valkey Password",
+  "components.valkeychatmemory.inputs.password.info.11256ab8": "Password for the specified username.",
+  "components.valkeychatmemory.inputs.port.display_name.f8d397a3": "Port",
+  "components.valkeychatmemory.inputs.port.info.b8b389d9": "Valkey Port Number.",
+  "components.valkeychatmemory.inputs.session_id.display_name.cb9ac5c5": "Session ID",
+  "components.valkeychatmemory.inputs.session_id.info.997ef643": "Session ID for the message.",
+  "components.valkeychatmemory.inputs.username.display_name.e3b89e9d": "Username",
+  "components.valkeychatmemory.inputs.username.info.024d2d8f": "The Valkey user name.",
+  "components.valkeychatmemory.outputs.memory.display_name.c3963aed": "Memory",
   "components.vectara.description.7cbc0e3e": "Vectara Vector Store with search capabilities",
   "components.vectara.display_name.80b2c20a": "Vectara",
   "components.vectara.inputs.embedding.display_name.4c1a0852": "Embedding",

src/lfx/src/lfx/base/models/aws_constants.py

@@ -12,7 +12,29 @@
     create_model_metadata(
         provider="Amazon Bedrock", name="amazon.titan-text-premier-v1:0", icon="Amazon", tool_calling=True
     ),
-    # Anthropic Models
+    # Anthropic Models - Claude 4.x
+    create_model_metadata(
+        provider="Amazon Bedrock", name="anthropic.claude-opus-4-7", icon="Amazon", tool_calling=True
+    ),
+    create_model_metadata(
+        provider="Amazon Bedrock", name="anthropic.claude-opus-4-6-v1", icon="Amazon", tool_calling=True
+    ),
+    create_model_metadata(
+        provider="Amazon Bedrock", name="anthropic.claude-sonnet-4-6", icon="Amazon", tool_calling=True
+    ),
+    create_model_metadata(
+        provider="Amazon Bedrock", name="anthropic.claude-sonnet-4-5-20250929-v1:0", icon="Amazon", tool_calling=True
+    ),
+    create_model_metadata(
+        provider="Amazon Bedrock", name="anthropic.claude-opus-4-5-20251101-v1:0", icon="Amazon", tool_calling=True
+    ),
+    create_model_metadata(
+        provider="Amazon Bedrock", name="anthropic.claude-haiku-4-5-20251001-v1:0", icon="Amazon", tool_calling=True
+    ),
+    # Anthropic Models - Claude 3.x (legacy)
+    create_model_metadata(
+        provider="Amazon Bedrock", name="anthropic.claude-3-7-sonnet-20250219-v1:0", icon="Amazon", tool_calling=True
+    ),
     create_model_metadata(provider="Amazon Bedrock", name="anthropic.claude-v2", icon="Amazon", tool_calling=True),
     create_model_metadata(provider="Amazon Bedrock", name="anthropic.claude-v2:1", icon="Amazon", tool_calling=True),
     create_model_metadata(