Fixed library mode request format

Author: asimurka

src/app/endpoints/conversations_v3.py

@@ -108,46 +108,59 @@ def simplify_conversation_items(items: list[dict]) -> list[dict[str, Any]]:
 
     Args:
         items: The full conversation items list from llama-stack Conversations API
+            (in reverse chronological order, newest first)
 
     Returns:
         Simplified items with only essential message and tool call information
+        (in chronological order, oldest first, grouped by turns)
     """
-    chat_history = []
-
-    # Group items by turns (user message -> assistant response)
-    current_turn: dict[str, Any] = {"messages": []}
-    for item in items:
-        item_type = item.get("type")
-        item_role = item.get("role")
+    # Filter only message type items
+    message_items = [item for item in items if item.get("type") == "message"]
 
-        # Handle message items
-        if item_type == "message":
-            content = item.get("content", [])
+    # Process from bottom up (reverse to get chronological order)
+    # Assume items are grouped correctly: user input followed by assistant output
+    reversed_messages = list(reversed(message_items))
 
-            # Extract text content from content array
-            text_content = ""
-            for content_part in content:
+    chat_history = []
+    i = 0
+    while i < len(reversed_messages):
+        # Extract text content from user message
+        user_item = reversed_messages[i]
+        user_content = user_item.get("content", [])
+        user_text = ""
+        for content_part in user_content:
+            if isinstance(content_part, dict):
+                content_type = content_part.get("type")
+                if content_type == "input_text":
+                    user_text += content_part.get("text", "")
+            elif isinstance(content_part, str):
+                user_text += content_part
+
+        # Extract text content from assistant message (next item)
+        assistant_text = ""
+        if i + 1 < len(reversed_messages):
+            assistant_item = reversed_messages[i + 1]
+            assistant_content = assistant_item.get("content", [])
+            for content_part in assistant_content:
                 if isinstance(content_part, dict):
                     content_type = content_part.get("type")
-                    if content_type in ("input_text", "output_text", "text"):
-                        text_content += content_part.get("text", "")
+                    if content_type == "output_text":
+                        assistant_text += content_part.get("text", "")
                 elif isinstance(content_part, str):
-                    text_content += content_part
-
-            message = {
-                "content": text_content,
-                "type": item_role,
+                    assistant_text += content_part
+
+        # Create turn with user message first, then assistant message
+        chat_history.append(
+            {
+                "messages": [
+                    {"content": user_text, "type": "user"},
+                    {"content": assistant_text, "type": "assistant"},
+                ]
             }
-            current_turn["messages"].append(message)
-
-            # If this is an assistant message, it marks the end of a turn
-            if item_role == "assistant" and current_turn["messages"]:
-                chat_history.append(current_turn)
-                current_turn = {"messages": []}
+        )
 
-    # Add any remaining turn
-    if current_turn["messages"]:
-        chat_history.append(current_turn)
+        # Move to next pair (skip both user and assistant)
+        i += 2
 
     return chat_history
 
@@ -319,10 +332,10 @@ async def get_conversation_endpoint_handler(
         # Use Conversations API to retrieve conversation items
         conversation_items_response = await client.conversations.items.list(
             conversation_id=llama_stack_conv_id,
-            after=NOT_GIVEN,  # No pagination cursor
-            include=NOT_GIVEN,  # Include all available data
-            limit=1000,  # Max items to retrieve
-            order="asc",  # Get items in chronological order
+            after=NOT_GIVEN,
+            include=NOT_GIVEN,
+            limit=NOT_GIVEN,
+            order=NOT_GIVEN,
         )
         items = (
             conversation_items_response.data
@@ -340,7 +353,6 @@ async def get_conversation_endpoint_handler(
             len(items_dicts),
             conversation_id,
         )
-
         # Simplify the conversation items to include only essential information
         chat_history = simplify_conversation_items(items_dicts)
 

tests/e2e/features/info.feature

@@ -16,7 +16,7 @@ Feature: Info tests
      When I access REST API endpoint "info" using HTTP GET method
      Then The status code of the response is 200
       And The body of the response has proper name Lightspeed Core Service (LCS) and version 0.3.0
-      And The body of the response has llama-stack version 0.3.0rc3+rhai0
+      And The body of the response has llama-stack version 0.3.0
 
   @skip-in-library-mode
   Scenario: Check if info endpoint reports error when llama-stack connection is not working

tests/e2e/features/steps/info.py

@@ -1,6 +1,7 @@
 """Implementation of common test steps."""
 
 import json
+import re
 from behave import then  # pyright: ignore[reportAttributeAccessIssue]
 from behave.runner import Context
 
@@ -23,9 +24,15 @@ def check_llama_version(context: Context, llama_version: str) -> None:
     response_json = context.response.json()
     assert response_json is not None, "Response is not valid JSON"
 
+    version_pattern = r"\d+\.\d+\.\d+"
+    llama_stack_version = response_json["llama_stack_version"]
+    match = re.search(version_pattern, llama_stack_version)
+    assert match is not None, f"Could not extract version from {llama_stack_version}"
+    extracted_version = match.group(0)
+
     assert (
-        response_json["llama_stack_version"] == llama_version
-    ), f"llama-stack version is {response_json["llama_stack_version"]}"
+        extracted_version == llama_version
+    ), f"llama-stack version is {extracted_version}, expected {llama_version}"
 
 
 @then("The body of the response has proper model structure")