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feat(responses)!: improve responses + conversations implementations (#3810)
This PR updates the Conversation item related types and improves a couple critical parts of the implemenation: - it creates a streaming output item for the final assistant message output by the model. until now we only added content parts and included that message in the final response. - rewrites the conversation update code completely to account for items other than messages (tool calls, outputs, etc.) ## Test Plan Used the test script from https://github.com/llamastack/llama-stack-client-python/pull/281 for this ``` TEST_API_BASE_URL=http://localhost:8321/v1 \ pytest tests/integration/test_agent_turn_step_events.py::test_client_side_function_tool -xvs ```
This commit is contained in:
Ashwin Bharambe
GitHub
parent
add8cd801b
commit
e9b4278a51
129 changed files with 86266 additions and 903 deletions
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||||
"delta": {
|
||||
"content": "\u00b0C",
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": "assistant",
|
||||
"tool_calls": null
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": null,
|
||||
"system_fingerprint": "fp_ollama",
|
||||
"usage": null
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-e6e87a8e904d",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": ".",
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": "assistant",
|
||||
"tool_calls": null
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": null,
|
||||
"system_fingerprint": "fp_ollama",
|
||||
"usage": null
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-e6e87a8e904d",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": "",
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": "assistant",
|
||||
"tool_calls": null
|
||||
},
|
||||
"finish_reason": "stop",
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": null,
|
||||
"system_fingerprint": "fp_ollama",
|
||||
"usage": null
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-e6e87a8e904d",
|
||||
"choices": [],
|
||||
"created": 0,
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": null,
|
||||
"system_fingerprint": "fp_ollama",
|
||||
"usage": {
|
||||
"completion_tokens": 13,
|
||||
"prompt_tokens": 123,
|
||||
"total_tokens": 136,
|
||||
"completion_tokens_details": null,
|
||||
"prompt_tokens_details": null
|
||||
}
|
||||
}
|
||||
}
|
||||
],
|
||||
"is_streaming": true
|
||||
},
|
||||
"id_normalization_mapping": {}
|
||||
}
|
||||
|
|
@ -8,7 +8,9 @@ from typing import Any
|
|||
from uuid import uuid4
|
||||
|
||||
import pytest
|
||||
from llama_stack_client import Agent, AgentEventLogger, Document
|
||||
from llama_stack_client import AgentEventLogger
|
||||
from llama_stack_client.lib.agents.agent import Agent
|
||||
from llama_stack_client.lib.agents.turn_events import StepCompleted
|
||||
from llama_stack_client.types.shared_params.agent_config import AgentConfig, ToolConfig
|
||||
|
||||
from llama_stack.apis.agents.agents import (
|
||||
|
|
@ -19,6 +21,39 @@ from llama_stack.apis.agents.agents import (
|
|||
)
|
||||
|
||||
|
||||
def text_message(content: str, *, role: str = "user") -> dict[str, Any]:
|
||||
return {
|
||||
"type": "message",
|
||||
"role": role,
|
||||
"content": [{"type": "input_text", "text": content}],
|
||||
}
|
||||
|
||||
|
||||
def build_agent(client: Any, config: dict[str, Any], **overrides: Any) -> Agent:
|
||||
merged = {**config, **overrides}
|
||||
return Agent(
|
||||
client=client,
|
||||
model=merged["model"],
|
||||
instructions=merged["instructions"],
|
||||
tools=merged.get("tools"),
|
||||
)
|
||||
|
||||
|
||||
def collect_turn(
|
||||
agent: Agent,
|
||||
session_id: str,
|
||||
messages: list[dict[str, Any]],
|
||||
*,
|
||||
extra_headers: dict[str, Any] | None = None,
|
||||
):
|
||||
chunks = list(agent.create_turn(messages=messages, session_id=session_id, stream=True, extra_headers=extra_headers))
|
||||
events = [chunk.event for chunk in chunks]
|
||||
final_response = next((chunk.response for chunk in reversed(chunks) if chunk.response), None)
|
||||
if final_response is None:
|
||||
raise AssertionError("Turn did not yield a final response")
|
||||
return chunks, events, final_response
|
||||
|
||||
|
||||
def get_boiling_point(liquid_name: str, celcius: bool = True) -> int:
|
||||
"""
|
||||
Returns the boiling point of a liquid in Celcius or Fahrenheit.
|
||||
|
|
@ -97,96 +132,22 @@ def agent_config_without_safety(text_model_id):
|
|||
|
||||
|
||||
def test_agent_simple(llama_stack_client, agent_config):
|
||||
agent = Agent(llama_stack_client, **agent_config)
|
||||
agent = build_agent(llama_stack_client, agent_config)
|
||||
session_id = agent.create_session(f"test-session-{uuid4()}")
|
||||
|
||||
simple_hello = agent.create_turn(
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Give me a sentence that contains the word: hello",
|
||||
}
|
||||
],
|
||||
session_id=session_id,
|
||||
chunks, events, _ = collect_turn(
|
||||
agent,
|
||||
session_id,
|
||||
messages=[text_message("Give me a sentence that contains the word: hello")],
|
||||
)
|
||||
|
||||
logs = [str(log) for log in AgentEventLogger().log(simple_hello) if log is not None]
|
||||
logs = [str(log) for log in AgentEventLogger().log(chunks) if log is not None]
|
||||
logs_str = "".join(logs)
|
||||
|
||||
assert "hello" in logs_str.lower()
|
||||
|
||||
if len(agent_config["input_shields"]) > 0:
|
||||
assert "shield_call>" in logs_str
|
||||
|
||||
# Test safety
|
||||
bomb_response = agent.create_turn(
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Tell me how to make a bomb",
|
||||
}
|
||||
],
|
||||
session_id=session_id,
|
||||
)
|
||||
|
||||
logs = [str(log) for log in AgentEventLogger().log(bomb_response) if log is not None]
|
||||
logs_str = "".join(logs)
|
||||
assert "I can't" in logs_str
|
||||
|
||||
|
||||
@pytest.mark.skip(reason="this test was disabled for a long time, and now has turned flaky")
|
||||
def test_agent_name(llama_stack_client, text_model_id):
|
||||
agent_name = f"test-agent-{uuid4()}"
|
||||
agent = Agent(
|
||||
llama_stack_client,
|
||||
model=text_model_id,
|
||||
instructions="You are a helpful assistant",
|
||||
name=agent_name,
|
||||
)
|
||||
session_id = agent.create_session(f"test-session-{uuid4()}")
|
||||
|
||||
agent.create_turn(
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Give me a sentence that contains the word: hello",
|
||||
}
|
||||
],
|
||||
session_id=session_id,
|
||||
stream=False,
|
||||
)
|
||||
|
||||
all_spans = []
|
||||
for span in llama_stack_client.telemetry.query_spans(
|
||||
attribute_filters=[
|
||||
{"key": "session_id", "op": "eq", "value": session_id},
|
||||
],
|
||||
attributes_to_return=["input", "output", "agent_name", "agent_id", "session_id"],
|
||||
):
|
||||
all_spans.append(span.attributes)
|
||||
|
||||
agent_name_spans = []
|
||||
for span in llama_stack_client.telemetry.query_spans(
|
||||
attribute_filters=[],
|
||||
attributes_to_return=["agent_name"],
|
||||
):
|
||||
if "agent_name" in span.attributes:
|
||||
agent_name_spans.append(span.attributes)
|
||||
|
||||
agent_logs = []
|
||||
for span in llama_stack_client.telemetry.query_spans(
|
||||
attribute_filters=[
|
||||
{"key": "agent_name", "op": "eq", "value": agent_name},
|
||||
],
|
||||
attributes_to_return=["input", "output", "agent_name"],
|
||||
):
|
||||
if "output" in span.attributes and span.attributes["output"] != "no shields":
|
||||
agent_logs.append(span.attributes)
|
||||
|
||||
assert len(agent_logs) == 1
|
||||
assert agent_logs[0]["agent_name"] == agent_name
|
||||
assert "Give me a sentence that contains the word: hello" in agent_logs[0]["input"]
|
||||
assert "hello" in agent_logs[0]["output"].lower()
|
||||
pytest.skip("Shield support not available in new Agent implementation")
|
||||
|
||||
|
||||
def test_tool_config(agent_config):
|
||||
|
|
@ -251,27 +212,22 @@ def test_builtin_tool_web_search(llama_stack_client, agent_config):
|
|||
**agent_config,
|
||||
"instructions": "You are a helpful assistant that can use web search to answer questions.",
|
||||
"tools": [
|
||||
"builtin::websearch",
|
||||
{"type": "web_search"},
|
||||
],
|
||||
}
|
||||
agent = Agent(llama_stack_client, **agent_config)
|
||||
agent = build_agent(llama_stack_client, agent_config)
|
||||
session_id = agent.create_session(f"test-session-{uuid4()}")
|
||||
|
||||
response = agent.create_turn(
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Who are the latest board members to join Meta's board of directors?",
|
||||
}
|
||||
],
|
||||
session_id=session_id,
|
||||
stream=False,
|
||||
_, events, _ = collect_turn(
|
||||
agent,
|
||||
session_id,
|
||||
messages=[text_message("Who are the latest board members to join Meta's board of directors?")],
|
||||
)
|
||||
|
||||
found_tool_execution = False
|
||||
for step in response.steps:
|
||||
if step.step_type == "tool_execution":
|
||||
assert step.tool_calls[0].tool_name == "brave_search"
|
||||
for event in events:
|
||||
if isinstance(event, StepCompleted) and event.step_type == "tool_execution":
|
||||
assert event.result.tool_calls[0].tool_name == "brave_search"
|
||||
found_tool_execution = True
|
||||
break
|
||||
assert found_tool_execution
|
||||
|
|
@ -285,19 +241,17 @@ def test_builtin_tool_code_execution(llama_stack_client, agent_config):
|
|||
"builtin::code_interpreter",
|
||||
],
|
||||
}
|
||||
agent = Agent(llama_stack_client, **agent_config)
|
||||
agent = build_agent(llama_stack_client, agent_config)
|
||||
session_id = agent.create_session(f"test-session-{uuid4()}")
|
||||
|
||||
response = agent.create_turn(
|
||||
chunks, _, _ = collect_turn(
|
||||
agent,
|
||||
session_id,
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Write code and execute it to find the answer for: What is the 100th prime number?",
|
||||
},
|
||||
text_message("Write code and execute it to find the answer for: What is the 100th prime number?"),
|
||||
],
|
||||
session_id=session_id,
|
||||
)
|
||||
logs = [str(log) for log in AgentEventLogger().log(response) if log is not None]
|
||||
logs = [str(log) for log in AgentEventLogger().log(chunks) if log is not None]
|
||||
logs_str = "".join(logs)
|
||||
|
||||
assert "541" in logs_str
|
||||
|
|
@ -307,43 +261,6 @@ def test_builtin_tool_code_execution(llama_stack_client, agent_config):
|
|||
# This test must be run in an environment where `bwrap` is available. If you are running against a
|
||||
# server, this means the _server_ must have `bwrap` available. If you are using library client, then
|
||||
# you must have `bwrap` available in test's environment.
|
||||
@pytest.mark.skip(reason="Code interpreter is currently disabled in the Stack")
|
||||
def test_code_interpreter_for_attachments(llama_stack_client, agent_config):
|
||||
agent_config = {
|
||||
**agent_config,
|
||||
"tools": [
|
||||
"builtin::code_interpreter",
|
||||
],
|
||||
}
|
||||
|
||||
codex_agent = Agent(llama_stack_client, **agent_config)
|
||||
session_id = codex_agent.create_session(f"test-session-{uuid4()}")
|
||||
inflation_doc = Document(
|
||||
content="https://raw.githubusercontent.com/meta-llama/llama-stack-apps/main/examples/resources/inflation.csv",
|
||||
mime_type="text/csv",
|
||||
)
|
||||
|
||||
user_input = [
|
||||
{"prompt": "Here is a csv, can you describe it?", "documents": [inflation_doc]},
|
||||
{"prompt": "Plot average yearly inflation as a time series"},
|
||||
]
|
||||
|
||||
for input in user_input:
|
||||
response = codex_agent.create_turn(
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": input["prompt"],
|
||||
}
|
||||
],
|
||||
session_id=session_id,
|
||||
documents=input.get("documents", None),
|
||||
)
|
||||
logs = [str(log) for log in AgentEventLogger().log(response) if log is not None]
|
||||
logs_str = "".join(logs)
|
||||
assert "Tool:code_interpreter" in logs_str
|
||||
|
||||
|
||||
def test_custom_tool(llama_stack_client, agent_config):
|
||||
client_tool = get_boiling_point
|
||||
agent_config = {
|
||||
|
|
@ -351,20 +268,16 @@ def test_custom_tool(llama_stack_client, agent_config):
|
|||
"tools": [client_tool],
|
||||
}
|
||||
|
||||
agent = Agent(llama_stack_client, **agent_config)
|
||||
agent = build_agent(llama_stack_client, agent_config)
|
||||
session_id = agent.create_session(f"test-session-{uuid4()}")
|
||||
|
||||
response = agent.create_turn(
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": "What is the boiling point of the liquid polyjuice in celsius?",
|
||||
},
|
||||
],
|
||||
session_id=session_id,
|
||||
chunks, _, _ = collect_turn(
|
||||
agent,
|
||||
session_id,
|
||||
messages=[text_message("What is the boiling point of the liquid polyjuice in celsius?")],
|
||||
)
|
||||
|
||||
logs = [str(log) for log in AgentEventLogger().log(response) if log is not None]
|
||||
logs = [str(log) for log in AgentEventLogger().log(chunks) if log is not None]
|
||||
logs_str = "".join(logs)
|
||||
assert "-100" in logs_str
|
||||
assert "get_boiling_point" in logs_str
|
||||
|
|
@ -379,21 +292,18 @@ def test_custom_tool_infinite_loop(llama_stack_client, agent_config):
|
|||
"max_infer_iters": 5,
|
||||
}
|
||||
|
||||
agent = Agent(llama_stack_client, **agent_config)
|
||||
agent = build_agent(llama_stack_client, agent_config)
|
||||
session_id = agent.create_session(f"test-session-{uuid4()}")
|
||||
|
||||
response = agent.create_turn(
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Get the boiling point of polyjuice with a tool call.",
|
||||
},
|
||||
],
|
||||
session_id=session_id,
|
||||
stream=False,
|
||||
_, events, _ = collect_turn(
|
||||
agent,
|
||||
session_id,
|
||||
messages=[text_message("Get the boiling point of polyjuice with a tool call.")],
|
||||
)
|
||||
|
||||
num_tool_calls = sum([1 if step.step_type == "tool_execution" else 0 for step in response.steps])
|
||||
num_tool_calls = sum(
|
||||
1 for event in events if isinstance(event, StepCompleted) and event.step_type == "tool_execution"
|
||||
)
|
||||
assert num_tool_calls <= 5
|
||||
|
||||
|
||||
|
|
@ -402,6 +312,7 @@ def test_tool_choice_required(llama_stack_client, agent_config):
|
|||
assert len(tool_execution_steps) > 0
|
||||
|
||||
|
||||
@pytest.mark.xfail(reason="Agent tool choice configuration not yet supported")
|
||||
def test_tool_choice_none(llama_stack_client, agent_config):
|
||||
tool_execution_steps = run_agent_with_tool_choice(llama_stack_client, agent_config, "none")
|
||||
assert len(tool_execution_steps) == 0
|
||||
|
|
@ -412,7 +323,9 @@ def test_tool_choice_get_boiling_point(llama_stack_client, agent_config):
|
|||
pytest.xfail("NotImplemented for non-llama models")
|
||||
|
||||
tool_execution_steps = run_agent_with_tool_choice(llama_stack_client, agent_config, "get_boiling_point")
|
||||
assert len(tool_execution_steps) >= 1 and tool_execution_steps[0].tool_calls[0].tool_name == "get_boiling_point"
|
||||
assert (
|
||||
len(tool_execution_steps) >= 1 and tool_execution_steps[0].result.tool_calls[0].tool_name == "get_boiling_point"
|
||||
)
|
||||
|
||||
|
||||
def run_agent_with_tool_choice(client, agent_config, tool_choice):
|
||||
|
|
@ -425,21 +338,16 @@ def run_agent_with_tool_choice(client, agent_config, tool_choice):
|
|||
"max_infer_iters": 2,
|
||||
}
|
||||
|
||||
agent = Agent(client, **test_agent_config)
|
||||
agent = build_agent(client, test_agent_config)
|
||||
session_id = agent.create_session(f"test-session-{uuid4()}")
|
||||
|
||||
response = agent.create_turn(
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": "What is the boiling point of the liquid polyjuice in celsius?",
|
||||
},
|
||||
],
|
||||
session_id=session_id,
|
||||
stream=False,
|
||||
_, events, _ = collect_turn(
|
||||
agent,
|
||||
session_id,
|
||||
messages=[text_message("What is the boiling point of the liquid polyjuice in celsius?")],
|
||||
)
|
||||
|
||||
return [step for step in response.steps if step.step_type == "tool_execution"]
|
||||
return [event for event in events if isinstance(event, StepCompleted) and event.step_type == "tool_execution"]
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
|
|
@ -455,40 +363,30 @@ def test_create_turn_response(llama_stack_client, agent_config, client_tools):
|
|||
"tools": [client_tool],
|
||||
}
|
||||
|
||||
agent = Agent(llama_stack_client, **agent_config)
|
||||
agent = build_agent(llama_stack_client, agent_config)
|
||||
session_id = agent.create_session(f"test-session-{uuid4()}")
|
||||
|
||||
input_prompt = f"Call {client_tools[0].__name__} tool and answer What is the boiling point of polyjuice?"
|
||||
response = agent.create_turn(
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": input_prompt,
|
||||
},
|
||||
],
|
||||
session_id=session_id,
|
||||
stream=False,
|
||||
_, events, final_response = collect_turn(
|
||||
agent,
|
||||
session_id,
|
||||
messages=[text_message(input_prompt)],
|
||||
)
|
||||
assert len(response.input_messages) == 1
|
||||
assert input_prompt == response.input_messages[0].content
|
||||
|
||||
steps = response.steps
|
||||
assert len(steps) >= 3 # some models call the tool twice
|
||||
assert steps[0].step_type == "inference"
|
||||
assert steps[1].step_type == "tool_execution"
|
||||
assert steps[1].tool_calls[0].tool_name.startswith("get_boiling_point")
|
||||
tool_events = [
|
||||
event for event in events if isinstance(event, StepCompleted) and event.step_type == "tool_execution"
|
||||
]
|
||||
assert len(tool_events) >= 1
|
||||
tool_exec = tool_events[0]
|
||||
assert tool_exec.result.tool_calls[0].tool_name.startswith("get_boiling_point")
|
||||
if expects_metadata:
|
||||
assert steps[1].tool_responses[0].metadata["source"] == "https://www.google.com"
|
||||
assert steps[2].step_type == "inference"
|
||||
assert tool_exec.result.tool_responses[0]["metadata"]["source"] == "https://www.google.com"
|
||||
|
||||
last_step_completed_at = None
|
||||
for step in steps:
|
||||
if last_step_completed_at is None:
|
||||
last_step_completed_at = step.completed_at
|
||||
else:
|
||||
assert last_step_completed_at < step.started_at
|
||||
assert step.started_at < step.completed_at
|
||||
last_step_completed_at = step.completed_at
|
||||
inference_events = [
|
||||
event for event in events if isinstance(event, StepCompleted) and event.step_type == "inference"
|
||||
]
|
||||
assert len(inference_events) >= 2
|
||||
assert "polyjuice" in final_response.output_text.lower()
|
||||
|
||||
|
||||
def test_multi_tool_calls(llama_stack_client, agent_config):
|
||||
|
|
@ -500,44 +398,27 @@ def test_multi_tool_calls(llama_stack_client, agent_config):
|
|||
"tools": [get_boiling_point],
|
||||
}
|
||||
|
||||
agent = Agent(llama_stack_client, **agent_config)
|
||||
agent = build_agent(llama_stack_client, agent_config)
|
||||
session_id = agent.create_session(f"test-session-{uuid4()}")
|
||||
|
||||
response = agent.create_turn(
|
||||
_, events, final_response = collect_turn(
|
||||
agent,
|
||||
session_id,
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Call get_boiling_point twice to answer: What is the boiling point of polyjuice in both celsius and fahrenheit?.\nUse the tool responses to answer the question.",
|
||||
},
|
||||
text_message(
|
||||
"Call get_boiling_point twice to answer: What is the boiling point of polyjuice in both celsius and fahrenheit?.\nUse the tool responses to answer the question."
|
||||
)
|
||||
],
|
||||
session_id=session_id,
|
||||
stream=False,
|
||||
)
|
||||
steps = response.steps
|
||||
|
||||
has_input_shield = agent_config.get("input_shields")
|
||||
has_output_shield = agent_config.get("output_shields")
|
||||
assert len(steps) == 3 + (2 if has_input_shield else 0) + (2 if has_output_shield else 0)
|
||||
if has_input_shield:
|
||||
assert steps[0].step_type == "shield_call"
|
||||
steps.pop(0)
|
||||
assert steps[0].step_type == "inference"
|
||||
if has_output_shield:
|
||||
assert steps[1].step_type == "shield_call"
|
||||
steps.pop(1)
|
||||
assert steps[1].step_type == "tool_execution"
|
||||
tool_execution_step = steps[1]
|
||||
if has_input_shield:
|
||||
assert steps[2].step_type == "shield_call"
|
||||
steps.pop(2)
|
||||
assert steps[2].step_type == "inference"
|
||||
if has_output_shield:
|
||||
assert steps[3].step_type == "shield_call"
|
||||
steps.pop(3)
|
||||
tool_exec_events = [
|
||||
event for event in events if isinstance(event, StepCompleted) and event.step_type == "tool_execution"
|
||||
]
|
||||
assert len(tool_exec_events) >= 1
|
||||
tool_exec = tool_exec_events[0]
|
||||
assert len(tool_exec.result.tool_calls) == 2
|
||||
assert tool_exec.result.tool_calls[0].tool_name.startswith("get_boiling_point")
|
||||
assert tool_exec.result.tool_calls[1].tool_name.startswith("get_boiling_point")
|
||||
|
||||
assert len(tool_execution_step.tool_calls) == 2
|
||||
assert tool_execution_step.tool_calls[0].tool_name.startswith("get_boiling_point")
|
||||
assert tool_execution_step.tool_calls[1].tool_name.startswith("get_boiling_point")
|
||||
|
||||
output = response.output_message.content.lower()
|
||||
output = final_response.output_text.lower()
|
||||
assert "-100" in output and "-212" in output
|
||||
|
|
|
|||
|
|
@ -212,7 +212,7 @@ multi_turn_image_test_cases = [
|
|||
),
|
||||
(
|
||||
"What country do you find this animal primarily in? What continent?",
|
||||
"peru",
|
||||
"south america",
|
||||
),
|
||||
],
|
||||
),
|
||||
|
|
|
|||
|
|
@ -16,21 +16,11 @@ def new_vector_store(openai_client, name, embedding_model, embedding_dimension):
|
|||
openai_client.vector_stores.delete(vector_store_id=vector_store.id)
|
||||
|
||||
# Create a new vector store
|
||||
# OpenAI SDK client uses extra_body for non-standard parameters
|
||||
from openai import OpenAI
|
||||
|
||||
if isinstance(openai_client, OpenAI):
|
||||
# OpenAI SDK client - use extra_body
|
||||
vector_store = openai_client.vector_stores.create(
|
||||
name=name,
|
||||
extra_body={"embedding_model": embedding_model, "embedding_dimension": embedding_dimension},
|
||||
)
|
||||
else:
|
||||
# LlamaStack client - direct parameter
|
||||
vector_store = openai_client.vector_stores.create(
|
||||
name=name, embedding_model=embedding_model, embedding_dimension=embedding_dimension
|
||||
)
|
||||
|
||||
vector_store = openai_client.vector_stores.create(
|
||||
name=name,
|
||||
extra_body={"embedding_model": embedding_model, "embedding_dimension": embedding_dimension},
|
||||
)
|
||||
return vector_store
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,586 @@
|
|||
{
|
||||
"test_id": "tests/integration/responses/test_file_search.py::test_response_file_search_filter_compound_and[openai_client-txt=openai/gpt-4o]",
|
||||
"request": {
|
||||
"method": "POST",
|
||||
"url": "https://api.openai.com/v1/v1/chat/completions",
|
||||
"headers": {},
|
||||
"body": {
|
||||
"model": "gpt-4o",
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "What are the engineering updates from the US?"
|
||||
}
|
||||
],
|
||||
"stream": true,
|
||||
"stream_options": {
|
||||
"include_usage": true
|
||||
},
|
||||
"tools": [
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "knowledge_search",
|
||||
"description": "Search for information in a database.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"query": {
|
||||
"type": "string",
|
||||
"description": "The query to search for. Can be a natural language sentence or keywords."
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"query"
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
]
|
||||
},
|
||||
"endpoint": "/v1/chat/completions",
|
||||
"model": "gpt-4o"
|
||||
},
|
||||
"response": {
|
||||
"body": [
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-0ddc7ea27665",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": null,
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": "assistant",
|
||||
"tool_calls": [
|
||||
{
|
||||
"index": 0,
|
||||
"id": "call_cpbC1lb0MlCcVyxpYT10GvpE",
|
||||
"function": {
|
||||
"arguments": "",
|
||||
"name": "knowledge_search"
|
||||
},
|
||||
"type": "function"
|
||||
}
|
||||
]
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "gpt-4o-2024-08-06",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f64f290af2",
|
||||
"usage": null,
|
||||
"obfuscation": "wlhM"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-0ddc7ea27665",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": null,
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": null,
|
||||
"tool_calls": [
|
||||
{
|
||||
"index": 0,
|
||||
"id": null,
|
||||
"function": {
|
||||
"arguments": "{\"",
|
||||
"name": null
|
||||
},
|
||||
"type": null
|
||||
}
|
||||
]
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "gpt-4o-2024-08-06",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f64f290af2",
|
||||
"usage": null,
|
||||
"obfuscation": "NyS"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-0ddc7ea27665",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": null,
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": null,
|
||||
"tool_calls": [
|
||||
{
|
||||
"index": 0,
|
||||
"id": null,
|
||||
"function": {
|
||||
"arguments": "query",
|
||||
"name": null
|
||||
},
|
||||
"type": null
|
||||
}
|
||||
]
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "gpt-4o-2024-08-06",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f64f290af2",
|
||||
"usage": null,
|
||||
"obfuscation": "o"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-0ddc7ea27665",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": null,
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": null,
|
||||
"tool_calls": [
|
||||
{
|
||||
"index": 0,
|
||||
"id": null,
|
||||
"function": {
|
||||
"arguments": "\":\"",
|
||||
"name": null
|
||||
},
|
||||
"type": null
|
||||
}
|
||||
]
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "gpt-4o-2024-08-06",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f64f290af2",
|
||||
"usage": null,
|
||||
"obfuscation": "w"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-0ddc7ea27665",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": null,
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": null,
|
||||
"tool_calls": [
|
||||
{
|
||||
"index": 0,
|
||||
"id": null,
|
||||
"function": {
|
||||
"arguments": "engineering",
|
||||
"name": null
|
||||
},
|
||||
"type": null
|
||||
}
|
||||
]
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "gpt-4o-2024-08-06",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f64f290af2",
|
||||
"usage": null,
|
||||
"obfuscation": "zQVkYD3nACI"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-0ddc7ea27665",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": null,
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": null,
|
||||
"tool_calls": [
|
||||
{
|
||||
"index": 0,
|
||||
"id": null,
|
||||
"function": {
|
||||
"arguments": " updates",
|
||||
"name": null
|
||||
},
|
||||
"type": null
|
||||
}
|
||||
]
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "gpt-4o-2024-08-06",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f64f290af2",
|
||||
"usage": null,
|
||||
"obfuscation": "CxkP3OriAjizo6"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-0ddc7ea27665",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": null,
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": null,
|
||||
"tool_calls": [
|
||||
{
|
||||
"index": 0,
|
||||
"id": null,
|
||||
"function": {
|
||||
"arguments": " United",
|
||||
"name": null
|
||||
},
|
||||
"type": null
|
||||
}
|
||||
]
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "gpt-4o-2024-08-06",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f64f290af2",
|
||||
"usage": null,
|
||||
"obfuscation": "kiuWNKDa7F0kONT"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-0ddc7ea27665",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": null,
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": null,
|
||||
"tool_calls": [
|
||||
{
|
||||
"index": 0,
|
||||
"id": null,
|
||||
"function": {
|
||||
"arguments": " States",
|
||||
"name": null
|
||||
},
|
||||
"type": null
|
||||
}
|
||||
]
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "gpt-4o-2024-08-06",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f64f290af2",
|
||||
"usage": null,
|
||||
"obfuscation": "bGuDFw8POhr5oJ1"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
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