Merge branch 'main' into fix-stream-non-stream

llama_stack/providers/inline/agents/meta_reference/agent_instance.py

@@ -66,6 +66,7 @@ from llama_stack.apis.vector_io import VectorIO
 from llama_stack.providers.utils.kvstore import KVStore
 from llama_stack.providers.utils.memory.vector_store import concat_interleaved_content
 from llama_stack.providers.utils.telemetry import tracing
+
 from .persistence import AgentPersistence
 from .safety import SafetyException, ShieldRunnerMixin
 
@@ -476,9 +477,12 @@ class ChatAgent(ShieldRunnerMixin):
                 )
                 span.set_attribute("output", retrieved_context)
                 span.set_attribute("tool_name", MEMORY_QUERY_TOOL)
-                if retrieved_context:
-                    last_message = input_messages[-1]
-                    last_message.context = retrieved_context
+
+                # append retrieved_context to the last user message
+                for message in input_messages[::-1]:
+                    if isinstance(message, UserMessage):
+                        message.context = retrieved_context
+                        break
 
         output_attachments = []
 

tests/client-sdk/agents/test_agents.py

@@ -211,7 +211,7 @@ def test_code_interpreter_for_attachments(llama_stack_client, agent_config):
     }
 
     codex_agent = Agent(llama_stack_client, agent_config)
-    session_id = codex_agent.create_session("test-session")
+    session_id = codex_agent.create_session(f"test-session-{uuid4()}")
     inflation_doc = AgentDocument(
         content="https://raw.githubusercontent.com/meta-llama/llama-stack-apps/main/examples/resources/inflation.csv",
         mime_type="text/csv",
@@ -285,7 +285,8 @@ def test_rag_agent(llama_stack_client, agent_config):
     llama_stack_client.tool_runtime.rag_tool.insert(
         documents=documents,
         vector_db_id=vector_db_id,
-        chunk_size_in_tokens=512,
+        # small chunks help to get specific info out of the docs
+        chunk_size_in_tokens=128,
     )
     agent_config = {
         **agent_config,
@@ -299,11 +300,15 @@ def test_rag_agent(llama_stack_client, agent_config):
         ],
     }
     rag_agent = Agent(llama_stack_client, agent_config)
-    session_id = rag_agent.create_session("test-session")
+    session_id = rag_agent.create_session(f"test-session-{uuid4()}")
     user_prompts = [
-        "What are the top 5 topics that were explained? Only list succinct bullet points.",
+        (
+            "Instead of the standard multi-head attention, what attention type does Llama3-8B use?",
+            "grouped-query",
+        ),
+        ("What command to use to get access to Llama3-8B-Instruct ?", "tune download"),
     ]
-    for prompt in user_prompts:
+    for prompt, expected_kw in user_prompts:
         print(f"User> {prompt}")
         response = rag_agent.create_turn(
             messages=[{"role": "user", "content": prompt}],
@@ -312,3 +317,69 @@ def test_rag_agent(llama_stack_client, agent_config):
         logs = [str(log) for log in EventLogger().log(response) if log is not None]
         logs_str = "".join(logs)
         assert "Tool:query_from_memory" in logs_str
+        assert expected_kw in logs_str.lower()
+
+
+def test_rag_and_code_agent(llama_stack_client, agent_config):
+    urls = ["chat.rst"]
+    documents = [
+        Document(
+            document_id=f"num-{i}",
+            content=f"https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/{url}",
+            mime_type="text/plain",
+            metadata={},
+        )
+        for i, url in enumerate(urls)
+    ]
+    vector_db_id = "test-vector-db"
+    llama_stack_client.vector_dbs.register(
+        vector_db_id=vector_db_id,
+        embedding_model="all-MiniLM-L6-v2",
+        embedding_dimension=384,
+    )
+    llama_stack_client.tool_runtime.rag_tool.insert(
+        documents=documents,
+        vector_db_id=vector_db_id,
+        chunk_size_in_tokens=128,
+    )
+    agent_config = {
+        **agent_config,
+        "toolgroups": [
+            dict(
+                name="builtin::rag",
+                args={"vector_db_ids": [vector_db_id]},
+            ),
+            "builtin::code_interpreter",
+        ],
+    }
+    agent = Agent(llama_stack_client, agent_config)
+    inflation_doc = Document(
+        document_id="test_csv",
+        content="https://raw.githubusercontent.com/meta-llama/llama-stack-apps/main/examples/resources/inflation.csv",
+        mime_type="text/csv",
+        metadata={},
+    )
+    user_prompts = [
+        (
+            "Here is a csv file, can you describe it?",
+            [inflation_doc],
+            "code_interpreter",
+        ),
+        (
+            "What are the top 5 topics that were explained? Only list succinct bullet points.",
+            [],
+            "query_from_memory",
+        ),
+    ]
+
+    for prompt, docs, tool_name in user_prompts:
+        print(f"User> {prompt}")
+        session_id = agent.create_session(f"test-session-{uuid4()}")
+        response = agent.create_turn(
+            messages=[{"role": "user", "content": prompt}],
+            session_id=session_id,
+            documents=docs,
+        )
+        logs = [str(log) for log in EventLogger().log(response) if log is not None]
+        logs_str = "".join(logs)
+        assert f"Tool:{tool_name}" in logs_str

tests/client-sdk/inference/test_inference.py

@@ -5,7 +5,7 @@
 # the root directory of this source tree.
 
 import base64
-import os
+import pathlib
 
 import pytest
 from pydantic import BaseModel
@@ -57,13 +57,20 @@ def get_weather_tool_definition():
 
 
 @pytest.fixture
-def base64_image_url():
-    image_path = os.path.join(os.path.dirname(__file__), "dog.png")
-    with open(image_path, "rb") as image_file:
-        # Convert the image to base64
-        base64_string = base64.b64encode(image_file.read()).decode("utf-8")
-        base64_url = f"data:image/png;base64,{base64_string}"
-        return base64_url
+def image_path():
+    return pathlib.Path(__file__).parent / "dog.png"
+
+
+@pytest.fixture
+def base64_image_data(image_path):
+    # Convert the image to base64
+    return base64.b64encode(image_path.read_bytes()).decode("utf-8")
+
+
+@pytest.fixture
+def base64_image_url(base64_image_data, image_path):
+    # suffix includes the ., so we remove it
+    return f"data:image/{image_path.suffix[1:]};base64,{base64_image_data}"
 
 
 def test_text_completion_non_streaming(llama_stack_client, text_model_id):
@@ -371,20 +378,31 @@ def test_image_chat_completion_streaming(llama_stack_client, vision_model_id):
     assert any(expected in streamed_content for expected in {"dog", "puppy", "pup"})
 
 
-def test_image_chat_completion_base64_url(
-    llama_stack_client, vision_model_id, base64_image_url
+@pytest.mark.parametrize("type_", ["url", "data"])
+def test_image_chat_completion_base64(
+    llama_stack_client, vision_model_id, base64_image_data, base64_image_url, type_
 ):
+    image_spec = {
+        "url": {
+            "type": "image",
+            "image": {
+                "url": {
+                    "uri": base64_image_url,
+                },
+            },
+        },
+        "data": {
+            "type": "image",
+            "image": {
+                "data": base64_image_data,
+            },
+        },
+    }[type_]
+
     message = {
         "role": "user",
         "content": [
-            {
-                "type": "image",
-                "image": {
-                    "url": {
-                        "uri": base64_image_url,
-                    },
-                },
-            },
+            image_spec,
             {
                 "type": "text",
                 "text": "Describe what is in this image.",