feat: LlamaStackChatGenerator update tools param to ToolsType

.github/workflows/llama_stack.yml

@@ -82,13 +82,30 @@ jobs:
           OLLAMA_URL: http://localhost:11434
         shell: bash
         run: |
-          pip install uv
-          uv run --with llama-stack llama stack build --distro starter --image-type venv --run < /dev/null > server.log 2>&1 &
-          sleep 120
-          # Verify it's running
-          curl -f http://localhost:8321/v1/models || { cat server.log; exit 1; }
-
-          echo "Llama Stack Server started successfully."
+          set -euo pipefail
+          pip install -q uv
+
+          # Install the starter distro's deps into the uv environment
+          uv run --with llama-stack bash -lc 'llama stack list-deps starter | xargs -L1 uv pip install'
+
+          # Start Llama Stack (no more --image-type flag)
+          uv run --with llama-stack llama stack run starter > server.log 2>&1 &
+          SERVER_PID=$!
+
+          # Wait up to ~120s for health; fail fast if process dies
+          for i in {1..60}; do
+            if curl -fsS http://localhost:8321/v1/models >/dev/null; then
+              echo "Llama Stack Server started successfully."
+              break
+            fi
+            if ! kill -0 "$SERVER_PID" 2>/dev/null; then
+              echo "Server exited early. Logs:"; cat server.log; exit 1
+            fi
+            sleep 2
+          done
+
+          # Final health check
+          curl -fsS http://localhost:8321/v1/models || { echo "Health check failed. Logs:"; cat server.log; exit 1; }
 
       - name: Install Hatch
         run: pip install --upgrade hatch

integrations/llama_stack/examples/llama-stack-with-tools.py

@@ -34,7 +34,7 @@ def weather(city: str):
 tool_invoker = ToolInvoker(tools=[weather_tool])
 
 client = LlamaStackChatGenerator(
-    model="llama3.2:3b",  # model name varies depending on the inference provider used for the Llama Stack Server.
+    model="ollama/llama3.2:3b",  # model depends on the inference provider used for the Llama Stack Server.
     api_base_url="http://localhost:8321/v1/openai/v1",
 )
 messages = [ChatMessage.from_user("What's the weather in Tokyo?")]

integrations/llama_stack/pyproject.toml

@@ -20,7 +20,7 @@ classifiers = [
   "Programming Language :: Python :: Implementation :: CPython",
   "Programming Language :: Python :: Implementation :: PyPy",
 ]
-dependencies = ["haystack-ai>=2.14", "llama-stack>=0.2.17"]
+dependencies = ["haystack-ai>=2.19.0", "llama-stack>=0.2.17"]
 
 [project.urls]
 Documentation = "https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/llama-stack#readme"

integrations/llama_stack/src/haystack_integrations/components/generators/llama_stack/chat/chat_generator.py

@@ -2,12 +2,12 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Dict, Optional
 
 from haystack import component, default_from_dict, default_to_dict, logging
 from haystack.components.generators.chat import OpenAIChatGenerator
 from haystack.dataclasses import StreamingCallbackT
-from haystack.tools import Tool, Toolset, deserialize_tools_or_toolset_inplace, serialize_tools_or_toolset
+from haystack.tools import ToolsType, deserialize_tools_or_toolset_inplace, serialize_tools_or_toolset
 from haystack.utils import deserialize_callable, serialize_callable
 from haystack.utils.auth import Secret
 
@@ -41,15 +41,15 @@ class LlamaStackChatGenerator(OpenAIChatGenerator):
 
     messages = [ChatMessage.from_user("What's Natural Language Processing?")]
 
-    client = LlamaStackChatGenerator(model="llama3.2:3b")
+    client = LlamaStackChatGenerator(model="ollama/llama3.2:3b")
     response = client.run(messages)
     print(response)
 
     >>{'replies': [ChatMessage(_content=[TextContent(text='Natural Language Processing (NLP)
     is a branch of artificial intelligence
     >>that focuses on enabling computers to understand, interpret, and generate human language in a way that is
     >>meaningful and useful.')], _role=<ChatRole.ASSISTANT: 'assistant'>, _name=None,
-    >>_meta={'model': 'llama3.2:3b', 'index': 0, 'finish_reason': 'stop',
+    >>_meta={'model': 'ollama/llama3.2:3b', 'index': 0, 'finish_reason': 'stop',
     >>'usage': {'prompt_tokens': 15, 'completion_tokens': 36, 'total_tokens': 51}})]}
     """
 
@@ -62,7 +62,7 @@ def __init__(
         streaming_callback: Optional[StreamingCallbackT] = None,
         generation_kwargs: Optional[Dict[str, Any]] = None,
         timeout: Optional[int] = None,
-        tools: Optional[Union[List[Tool], Toolset]] = None,
+        tools: Optional[ToolsType] = None,
         tools_strict: bool = False,
         max_retries: Optional[int] = None,
         http_client_kwargs: Optional[Dict[str, Any]] = None,
@@ -98,8 +98,8 @@ def __init__(
             Timeout for client calls using OpenAI API. If not set, it defaults to either the
             `OPENAI_TIMEOUT` environment variable, or 30 seconds.
         :param tools:
-            A list of tools or a Toolset for which the model can prepare calls. This parameter can accept either a
-            list of `Tool` objects or a `Toolset` instance.
+            A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls.
+            Each tool should have a unique name.
         :param tools_strict:
             Whether to enable strict schema adherence for tool calls. If set to `True`, the model will follow exactly
             the schema provided in the `parameters` field of the tool definition, but this may increase latency.

integrations/llama_stack/tests/test_llama_stack_chat_generator.py

@@ -5,7 +5,7 @@
 import pytz
 from haystack.components.generators.utils import print_streaming_chunk
 from haystack.dataclasses import ChatMessage, ChatRole, StreamingChunk, ToolCall
-from haystack.tools import Tool
+from haystack.tools import Tool, Toolset
 from openai.types.chat import ChatCompletion, ChatCompletionMessage
 from openai.types.chat.chat_completion import Choice
 
@@ -25,6 +25,11 @@ def weather(city: str):
     return f"The weather in {city} is sunny and 32°C"
 
 
+def population(city: str):
+    """Get population for a given city."""
+    return f"The population of {city} is 2.2 million"
+
+
 @pytest.fixture
 def tools():
     tool_parameters = {"type": "object", "properties": {"city": {"type": "string"}}, "required": ["city"]}
@@ -38,6 +43,25 @@ def tools():
     return [tool]
 
 
+@pytest.fixture
+def mixed_tools():
+    """Fixture that returns a mixed list of Tool and Toolset."""
+    weather_tool = Tool(
+        name="weather",
+        description="useful to determine the weather in a given location",
+        parameters={"type": "object", "properties": {"city": {"type": "string"}}, "required": ["city"]},
+        function=weather,
+    )
+    population_tool = Tool(
+        name="population",
+        description="useful to determine the population of a given location",
+        parameters={"type": "object", "properties": {"city": {"type": "string"}}, "required": ["city"]},
+        function=population,
+    )
+    toolset = Toolset([population_tool])
+    return [weather_tool, toolset]
+
+
 @pytest.fixture
 def mock_chat_completion():
     """
@@ -46,7 +70,7 @@ def mock_chat_completion():
     with patch("openai.resources.chat.completions.Completions.create") as mock_chat_completion_create:
         completion = ChatCompletion(
             id="foo",
-            model="llama3.2:3b",
+            model="ollama/llama3.2:3b",
             object="chat.completion",
             choices=[
                 Choice(
@@ -66,27 +90,27 @@ def mock_chat_completion():
 
 class TestLlamaStackChatGenerator:
     def test_init_default(self):
-        component = LlamaStackChatGenerator(model="llama3.2:3b")
-        assert component.model == "llama3.2:3b"
+        component = LlamaStackChatGenerator(model="ollama/llama3.2:3b")
+        assert component.model == "ollama/llama3.2:3b"
         assert component.api_base_url == "http://localhost:8321/v1/openai/v1"
         assert component.streaming_callback is None
         assert not component.generation_kwargs
 
     def test_init_with_parameters(self):
         component = LlamaStackChatGenerator(
-            model="llama3.2:3b",
+            model="ollama/llama3.2:3b",
             streaming_callback=print_streaming_chunk,
             api_base_url="test-base-url",
             generation_kwargs={"max_tokens": 10, "some_test_param": "test-params"},
         )
-        assert component.model == "llama3.2:3b"
+        assert component.model == "ollama/llama3.2:3b"
         assert component.streaming_callback is print_streaming_chunk
         assert component.generation_kwargs == {"max_tokens": 10, "some_test_param": "test-params"}
 
     def test_to_dict_default(
         self,
     ):
-        component = LlamaStackChatGenerator(model="llama3.2:3b")
+        component = LlamaStackChatGenerator(model="ollama/llama3.2:3b")
         data = component.to_dict()
 
         assert (
@@ -95,7 +119,7 @@ def test_to_dict_default(
         )
 
         expected_params = {
-            "model": "llama3.2:3b",
+            "model": "ollama/llama3.2:3b",
             "streaming_callback": None,
             "api_base_url": "http://localhost:8321/v1/openai/v1",
             "generation_kwargs": {},
@@ -113,7 +137,7 @@ def test_to_dict_with_parameters(
         self,
     ):
         component = LlamaStackChatGenerator(
-            model="llama3.2:3b",
+            model="ollama/llama3.2:3b",
             streaming_callback=print_streaming_chunk,
             api_base_url="test-base-url",
             generation_kwargs={"max_tokens": 10, "some_test_param": "test-params"},
@@ -131,7 +155,7 @@ def test_to_dict_with_parameters(
         )
 
         expected_params = {
-            "model": "llama3.2:3b",
+            "model": "ollama/llama3.2:3b",
             "api_base_url": "test-base-url",
             "streaming_callback": "haystack.components.generators.utils.print_streaming_chunk",
             "generation_kwargs": {"max_tokens": 10, "some_test_param": "test-params"},
@@ -153,7 +177,7 @@ def test_from_dict(
                 "haystack_integrations.components.generators.llama_stack.chat.chat_generator.LlamaStackChatGenerator"
             ),
             "init_parameters": {
-                "model": "llama3.2:3b",
+                "model": "ollama/llama3.2:3b",
                 "api_base_url": "test-base-url",
                 "streaming_callback": "haystack.components.generators.utils.print_streaming_chunk",
                 "generation_kwargs": {"max_tokens": 10, "some_test_param": "test-params"},
@@ -165,7 +189,7 @@ def test_from_dict(
             },
         }
         component = LlamaStackChatGenerator.from_dict(data)
-        assert component.model == "llama3.2:3b"
+        assert component.model == "ollama/llama3.2:3b"
         assert component.streaming_callback is print_streaming_chunk
         assert component.api_base_url == "test-base-url"
         assert component.generation_kwargs == {"max_tokens": 10, "some_test_param": "test-params"}
@@ -175,8 +199,33 @@ def test_from_dict(
         assert component.max_retries == 10
         assert not component.tools_strict
 
+    def test_init_with_mixed_tools(self):
+        def tool_fn(city: str) -> str:
+            return city
+
+        weather_tool = Tool(
+            name="weather",
+            description="Weather lookup",
+            parameters={"type": "object", "properties": {"city": {"type": "string"}}, "required": ["city"]},
+            function=tool_fn,
+        )
+        population_tool = Tool(
+            name="population",
+            description="Population lookup",
+            parameters={"type": "object", "properties": {"city": {"type": "string"}}, "required": ["city"]},
+            function=tool_fn,
+        )
+        toolset = Toolset([population_tool])
+
+        generator = LlamaStackChatGenerator(
+            model="ollama/llama3.2:3b",
+            tools=[weather_tool, toolset],
+        )
+
+        assert generator.tools == [weather_tool, toolset]
+
     def test_run(self, chat_messages, mock_chat_completion):  # noqa: ARG002
-        component = LlamaStackChatGenerator(model="llama3.2:3b")
+        component = LlamaStackChatGenerator(model="ollama/llama3.2:3b")
         response = component.run(chat_messages)
 
         # check that the component returns the correct ChatMessage response
@@ -188,7 +237,7 @@ def test_run(self, chat_messages, mock_chat_completion):  # noqa: ARG002
 
     def test_run_with_params(self, chat_messages, mock_chat_completion):
         component = LlamaStackChatGenerator(
-            model="llama3.2:3b",
+            model="ollama/llama3.2:3b",
             generation_kwargs={"max_tokens": 10, "temperature": 0.5},
         )
         response = component.run(chat_messages)
@@ -208,7 +257,7 @@ def test_run_with_params(self, chat_messages, mock_chat_completion):
     @pytest.mark.integration
     def test_live_run(self):
         chat_messages = [ChatMessage.from_user("What's the capital of France")]
-        component = LlamaStackChatGenerator(model="llama3.2:3b")
+        component = LlamaStackChatGenerator(model="ollama/llama3.2:3b")
         results = component.run(chat_messages)
         assert len(results["replies"]) == 1
         message: ChatMessage = results["replies"][0]
@@ -228,7 +277,7 @@ def __call__(self, chunk: StreamingChunk) -> None:
                 self.responses += chunk.content if chunk.content else ""
 
         callback = Callback()
-        component = LlamaStackChatGenerator(model="llama3.2:3b", streaming_callback=callback)
+        component = LlamaStackChatGenerator(model="ollama/llama3.2:3b", streaming_callback=callback)
         results = component.run([ChatMessage.from_user("What's the capital of France?")])
 
         assert len(results["replies"]) == 1
@@ -244,7 +293,7 @@ def __call__(self, chunk: StreamingChunk) -> None:
     @pytest.mark.integration
     def test_live_run_with_tools(self, tools):
         chat_messages = [ChatMessage.from_user("What's the weather like in Paris?")]
-        component = LlamaStackChatGenerator(model="llama3.2:3b", tools=tools)
+        component = LlamaStackChatGenerator(model="ollama/llama3.2:3b", tools=tools)
         results = component.run(chat_messages)
         assert len(results["replies"]) == 1
         message = results["replies"][0]
@@ -263,7 +312,7 @@ def test_live_run_with_tools_and_response(self, tools):
         Integration test that the LlamaStackChatGenerator component can run with tools and get a response.
         """
         initial_messages = [ChatMessage.from_user("What's the weather like in Paris?")]
-        component = LlamaStackChatGenerator(model="llama3.2:3b", tools=tools)
+        component = LlamaStackChatGenerator(model="ollama/llama3.2:3b", tools=tools)
         results = component.run(messages=initial_messages, generation_kwargs={"tool_choice": "auto"})
 
         assert len(results["replies"]) > 0, "No replies received"
@@ -298,3 +347,63 @@ def test_live_run_with_tools_and_response(self, tools):
         assert not final_message.tool_call
         assert len(final_message.text) > 0
         assert "paris" in final_message.text.lower()
+
+    @pytest.mark.integration
+    def test_live_run_with_mixed_tools(self, mixed_tools):
+        """
+        Integration test that verifies LlamaStackChatGenerator works with mixed Tool and Toolset.
+        This tests that the LLM can correctly invoke tools from both a standalone Tool and a Toolset.
+        """
+        initial_messages = [
+            ChatMessage.from_user("What's the weather like in Paris and what is the population of Berlin?")
+        ]
+        component = LlamaStackChatGenerator(model="ollama/llama3.2:3b", tools=mixed_tools)
+        results = component.run(messages=initial_messages)
+
+        assert len(results["replies"]) > 0, "No replies received"
+
+        # Find the message with tool calls
+        tool_call_message = None
+        for message in results["replies"]:
+            if message.tool_calls:
+                tool_call_message = message
+                break
+
+        assert tool_call_message is not None, "No message with tool call found"
+        assert isinstance(tool_call_message, ChatMessage), "Tool message is not a ChatMessage instance"
+        assert ChatMessage.is_from(tool_call_message, ChatRole.ASSISTANT), "Tool message is not from the assistant"
+
+        tool_calls = tool_call_message.tool_calls
+        assert len(tool_calls) == 2, f"Expected 2 tool calls, got {len(tool_calls)}"
+
+        # Verify we got calls to both weather and population tools
+        tool_names = {tc.tool_name for tc in tool_calls}
+        assert "weather" in tool_names, "Expected 'weather' tool call"
+        assert "population" in tool_names, "Expected 'population' tool call"
+
+        # Verify tool call details
+        for tool_call in tool_calls:
+            assert tool_call.id, "Tool call does not contain value for 'id' key"
+            assert tool_call.tool_name in ["weather", "population"]
+            assert "city" in tool_call.arguments
+            assert tool_call.arguments["city"] in ["Paris", "Berlin"]
+            assert tool_call_message.meta["finish_reason"] == "tool_calls"
+
+        # Mock the response we'd get from ToolInvoker
+        tool_result_messages = []
+        for tool_call in tool_calls:
+            if tool_call.tool_name == "weather":
+                result = "The weather in Paris is sunny and 32°C"
+            else:  # population
+                result = "The population of Berlin is 2.2 million"
+            tool_result_messages.append(ChatMessage.from_tool(tool_result=result, origin=tool_call))
+
+        new_messages = [*initial_messages, tool_call_message, *tool_result_messages]
+        results = component.run(new_messages)
+
+        assert len(results["replies"]) == 1
+        final_message = results["replies"][0]
+        assert not final_message.tool_call
+        assert len(final_message.text) > 0
+        assert "paris" in final_message.text.lower()
+        assert "berlin" in final_message.text.lower()