mirror of
https://github.com/katanemo/plano.git
synced 2026-04-25 08:46:24 +02:00
Add support for v1/responses API (#622)
* making first commit. still need to work on streaming respones * making first commit. still need to work on streaming respones * stream buffer implementation with tests * adding grok API keys to workflow * fixed changes based on code review * adding support for bedrock models * fixed issues with translation to claude code --------- Co-authored-by: Salman Paracha <salmanparacha@MacBook-Pro-342.local>
This commit is contained in:
Salman Paracha
GitHub
parent
b01a81927d
commit
a448c6e9cb
38 changed files with 7015 additions and 2955 deletions
630
tests/e2e/test_openai_responses_api_client.py
Normal file
630
tests/e2e/test_openai_responses_api_client.py
Normal file
|
|
@ -0,0 +1,630 @@
|
|||
import openai
|
||||
import pytest
|
||||
import os
|
||||
import logging
|
||||
import sys
|
||||
|
||||
# Set up logging
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
|
||||
handlers=[logging.StreamHandler(sys.stdout)],
|
||||
)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
LLM_GATEWAY_ENDPOINT = os.getenv(
|
||||
"LLM_GATEWAY_ENDPOINT", "http://localhost:12000/v1/chat/completions"
|
||||
)
|
||||
|
||||
|
||||
# -----------------------
|
||||
# v1/responses API tests
|
||||
# -----------------------
|
||||
def test_openai_responses_api_non_streaming_passthrough():
|
||||
"""Build a v1/responses API request (pass-through) and ensure gateway accepts it"""
|
||||
base_url = LLM_GATEWAY_ENDPOINT.replace("/v1/chat/completions", "")
|
||||
client = openai.OpenAI(api_key="test-key", base_url=f"{base_url}/v1")
|
||||
|
||||
# Simple responses API request using a direct model (pass-through)
|
||||
resp = client.responses.create(
|
||||
model="gpt-4o", input="Hello via responses passthrough"
|
||||
)
|
||||
|
||||
# Print the response content - handle both responses format and chat completions format
|
||||
print(f"\n{'='*80}")
|
||||
print(f"Model: {resp.model}")
|
||||
print(f"Output: {resp.output_text}")
|
||||
print(f"{'='*80}\n")
|
||||
|
||||
# Minimal sanity checks
|
||||
assert resp is not None
|
||||
assert (
|
||||
getattr(resp, "id", None) is not None
|
||||
or getattr(resp, "output", None) is not None
|
||||
)
|
||||
|
||||
|
||||
def test_openai_responses_api_with_streaming_passthrough():
|
||||
"""Build a v1/responses API streaming request (pass-through) and ensure gateway accepts it"""
|
||||
base_url = LLM_GATEWAY_ENDPOINT.replace("/v1/chat/completions", "")
|
||||
client = openai.OpenAI(api_key="test-key", base_url=f"{base_url}/v1")
|
||||
|
||||
# Simple streaming responses API request using a direct model (pass-through)
|
||||
stream = client.responses.create(
|
||||
model="gpt-4o",
|
||||
input="Write a short haiku about coding",
|
||||
stream=True,
|
||||
)
|
||||
|
||||
# Collect streamed content using the official Responses API streaming shape
|
||||
text_chunks = []
|
||||
final_message = None
|
||||
|
||||
for event in stream:
|
||||
# The Python SDK surfaces a high-level Responses streaming interface.
|
||||
# We rely on its typed helpers instead of digging into model_extra.
|
||||
if getattr(event, "type", None) == "response.output_text.delta" and getattr(
|
||||
event, "delta", None
|
||||
):
|
||||
# Each delta contains a text fragment
|
||||
text_chunks.append(event.delta)
|
||||
|
||||
# Track the final response message if provided by the SDK
|
||||
if getattr(event, "type", None) == "response.completed" and getattr(
|
||||
event, "response", None
|
||||
):
|
||||
final_message = event.response
|
||||
|
||||
full_content = "".join(text_chunks)
|
||||
|
||||
# Print the streaming response
|
||||
print(f"\n{'='*80}")
|
||||
print(
|
||||
f"Model: {getattr(final_message, 'model', 'unknown') if final_message else 'unknown'}"
|
||||
)
|
||||
print(f"Streamed Output: {full_content}")
|
||||
print(f"{'='*80}\n")
|
||||
|
||||
assert len(text_chunks) > 0, "Should have received streaming text deltas"
|
||||
assert len(full_content) > 0, "Should have received content"
|
||||
|
||||
|
||||
def test_openai_responses_api_non_streaming_with_tools_passthrough():
|
||||
"""Responses API with a function/tool definition (pass-through)"""
|
||||
base_url = LLM_GATEWAY_ENDPOINT.replace("/v1/chat/completions", "")
|
||||
client = openai.OpenAI(api_key="test-key", base_url=f"{base_url}/v1", max_retries=0)
|
||||
|
||||
# Define a simple tool/function for the Responses API
|
||||
tools = [
|
||||
{
|
||||
"type": "function",
|
||||
"name": "echo_tool",
|
||||
"description": "Echo back the provided input",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {"text": {"type": "string"}},
|
||||
"required": ["text"],
|
||||
},
|
||||
}
|
||||
]
|
||||
|
||||
resp = client.responses.create(
|
||||
model="gpt-5",
|
||||
input="Call the echo tool",
|
||||
tools=tools,
|
||||
)
|
||||
|
||||
assert resp is not None
|
||||
assert (
|
||||
getattr(resp, "id", None) is not None
|
||||
or getattr(resp, "output", None) is not None
|
||||
)
|
||||
|
||||
|
||||
def test_openai_responses_api_with_streaming_with_tools_passthrough():
|
||||
"""Responses API with a function/tool definition (streaming, pass-through)"""
|
||||
base_url = LLM_GATEWAY_ENDPOINT.replace("/v1/chat/completions", "")
|
||||
client = openai.OpenAI(api_key="test-key", base_url=f"{base_url}/v1", max_retries=0)
|
||||
|
||||
tools = [
|
||||
{
|
||||
"type": "function",
|
||||
"name": "echo_tool",
|
||||
"description": "Echo back the provided input",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {"text": {"type": "string"}},
|
||||
"required": ["text"],
|
||||
},
|
||||
}
|
||||
]
|
||||
|
||||
stream = client.responses.create(
|
||||
model="gpt-5",
|
||||
input="Call the echo tool",
|
||||
tools=tools,
|
||||
stream=True,
|
||||
)
|
||||
|
||||
text_chunks = []
|
||||
tool_calls = []
|
||||
|
||||
for event in stream:
|
||||
etype = getattr(event, "type", None)
|
||||
|
||||
# Collect streamed text output
|
||||
if etype == "response.output_text.delta" and getattr(event, "delta", None):
|
||||
text_chunks.append(event.delta)
|
||||
|
||||
# Collect streamed tool call arguments
|
||||
if etype == "response.function_call_arguments.delta" and getattr(
|
||||
event, "delta", None
|
||||
):
|
||||
tool_calls.append(event.delta)
|
||||
|
||||
full_text = "".join(text_chunks)
|
||||
|
||||
print(f"\n{'='*80}")
|
||||
print("Responses tools streaming test")
|
||||
print(f"Streamed text: {full_text}")
|
||||
print(f"Tool call argument chunks: {len(tool_calls)}")
|
||||
print(f"{'='*80}\n")
|
||||
|
||||
# We expect either streamed text output or streamed tool-call arguments
|
||||
assert (
|
||||
full_text or tool_calls
|
||||
), "Expected streamed text or tool call argument deltas from Responses tools stream"
|
||||
|
||||
|
||||
def test_openai_responses_api_non_streaming_upstream_chat_completions():
|
||||
"""Send a v1/responses request using the grok alias to verify translation/routing"""
|
||||
base_url = LLM_GATEWAY_ENDPOINT.replace("/v1/chat/completions", "")
|
||||
client = openai.OpenAI(api_key="test-key", base_url=f"{base_url}/v1")
|
||||
|
||||
resp = client.responses.create(
|
||||
model="arch.grok.v1", input="Hello, translate this via grok alias"
|
||||
)
|
||||
|
||||
# Print the response content - handle both responses format and chat completions format
|
||||
print(f"\n{'='*80}")
|
||||
print(f"Model: {resp.model}")
|
||||
print(f"Output: {resp.output_text}")
|
||||
print(f"{'='*80}\n")
|
||||
|
||||
assert resp is not None
|
||||
assert resp.id is not None
|
||||
|
||||
|
||||
def test_openai_responses_api_with_streaming_upstream_chat_completions():
|
||||
"""Build a v1/responses API streaming request (pass-through) and ensure gateway accepts it"""
|
||||
base_url = LLM_GATEWAY_ENDPOINT.replace("/v1/chat/completions", "")
|
||||
client = openai.OpenAI(api_key="test-key", base_url=f"{base_url}/v1")
|
||||
|
||||
# Simple streaming responses API request using a direct model (pass-through)
|
||||
stream = client.responses.create(
|
||||
model="arch.grok.v1",
|
||||
input="Write a short haiku about coding",
|
||||
stream=True,
|
||||
)
|
||||
|
||||
# Collect streamed content using the official Responses API streaming shape
|
||||
text_chunks = []
|
||||
final_message = None
|
||||
|
||||
for event in stream:
|
||||
# The Python SDK surfaces a high-level Responses streaming interface.
|
||||
# We rely on its typed helpers instead of digging into model_extra.
|
||||
if getattr(event, "type", None) == "response.output_text.delta" and getattr(
|
||||
event, "delta", None
|
||||
):
|
||||
# Each delta contains a text fragment
|
||||
text_chunks.append(event.delta)
|
||||
|
||||
# Track the final response message if provided by the SDK
|
||||
if getattr(event, "type", None) == "response.completed" and getattr(
|
||||
event, "response", None
|
||||
):
|
||||
final_message = event.response
|
||||
|
||||
full_content = "".join(text_chunks)
|
||||
|
||||
# Print the streaming response
|
||||
print(f"\n{'='*80}")
|
||||
print(
|
||||
f"Model: {getattr(final_message, 'model', 'unknown') if final_message else 'unknown'}"
|
||||
)
|
||||
print(f"Streamed Output: {full_content}")
|
||||
print(f"{'='*80}\n")
|
||||
|
||||
assert len(text_chunks) > 0, "Should have received streaming text deltas"
|
||||
assert len(full_content) > 0, "Should have received content"
|
||||
|
||||
|
||||
def test_openai_responses_api_non_streaming_with_tools_upstream_chat_completions():
|
||||
"""Responses API wioutputling routed to grok via alias"""
|
||||
base_url = LLM_GATEWAY_ENDPOINT.replace("/v1/chat/completions", "")
|
||||
client = openai.OpenAI(api_key="test-key", base_url=f"{base_url}/v1")
|
||||
|
||||
tools = [
|
||||
{
|
||||
"type": "function",
|
||||
"name": "echo_tool",
|
||||
"description": "Echo back the provided input",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {"text": {"type": "string"}},
|
||||
"required": ["text"],
|
||||
},
|
||||
}
|
||||
]
|
||||
|
||||
resp = client.responses.create(
|
||||
model="arch.grok.v1",
|
||||
input="Call the echo tool",
|
||||
tools=tools,
|
||||
)
|
||||
|
||||
assert resp.id is not None
|
||||
|
||||
print(f"\n{'='*80}")
|
||||
print(f"Model: {resp.model}")
|
||||
print(f"Output: {resp.output_text}")
|
||||
print(f"{'='*80}\n")
|
||||
|
||||
|
||||
def test_openai_responses_api_streaming_with_tools_upstream_chat_completions():
|
||||
"""Responses API with a function/tool definition (streaming, pass-through)"""
|
||||
base_url = LLM_GATEWAY_ENDPOINT.replace("/v1/chat/completions", "")
|
||||
client = openai.OpenAI(api_key="test-key", base_url=f"{base_url}/v1", max_retries=0)
|
||||
|
||||
tools = [
|
||||
{
|
||||
"type": "function",
|
||||
"name": "echo_tool",
|
||||
"description": "Echo back the provided input",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {"text": {"type": "string"}},
|
||||
"required": ["text"],
|
||||
},
|
||||
}
|
||||
]
|
||||
|
||||
stream = client.responses.create(
|
||||
model="arch.grok.v1",
|
||||
input="Call the echo tool",
|
||||
tools=tools,
|
||||
stream=True,
|
||||
)
|
||||
|
||||
text_chunks = []
|
||||
tool_calls = []
|
||||
|
||||
for event in stream:
|
||||
etype = getattr(event, "type", None)
|
||||
|
||||
# Collect streamed text output
|
||||
if etype == "response.output_text.delta" and getattr(event, "delta", None):
|
||||
text_chunks.append(event.delta)
|
||||
|
||||
# Collect streamed tool call arguments
|
||||
if etype == "response.function_call_arguments.delta" and getattr(
|
||||
event, "delta", None
|
||||
):
|
||||
tool_calls.append(event.delta)
|
||||
|
||||
full_text = "".join(text_chunks)
|
||||
|
||||
print(f"\n{'='*80}")
|
||||
print("Responses tools streaming test")
|
||||
print(f"Streamed text: {full_text}")
|
||||
print(f"Tool call argument chunks: {len(tool_calls)}")
|
||||
print(f"{'='*80}\n")
|
||||
|
||||
# We expect either streamed text output or streamed tool-call arguments
|
||||
assert (
|
||||
full_text or tool_calls
|
||||
), "Expected streamed text or tool call argument deltas from Responses tools stream"
|
||||
|
||||
|
||||
def test_openai_responses_api_non_streaming_upstream_bedrock():
|
||||
"""Send a v1/responses request using the coding-model alias to verify Bedrock translation/routing"""
|
||||
base_url = LLM_GATEWAY_ENDPOINT.replace("/v1/chat/completions", "")
|
||||
client = openai.OpenAI(api_key="test-key", base_url=f"{base_url}/v1")
|
||||
|
||||
resp = client.responses.create(
|
||||
model="coding-model",
|
||||
input="Hello, translate this via coding-model alias to Bedrock",
|
||||
)
|
||||
|
||||
# Print the response content - handle both responses format and chat completions format
|
||||
print(f"\n{'='*80}")
|
||||
print(f"Model: {resp.model}")
|
||||
print(f"Output: {resp.output_text}")
|
||||
print(f"{'='*80}\n")
|
||||
|
||||
assert resp is not None
|
||||
assert resp.id is not None
|
||||
|
||||
|
||||
def test_openai_responses_api_with_streaming_upstream_bedrock():
|
||||
"""Build a v1/responses API streaming request routed to Bedrock via coding-model alias"""
|
||||
base_url = LLM_GATEWAY_ENDPOINT.replace("/v1/chat/completions", "")
|
||||
client = openai.OpenAI(api_key="test-key", base_url=f"{base_url}/v1")
|
||||
|
||||
# Simple streaming responses API request using coding-model alias
|
||||
stream = client.responses.create(
|
||||
model="coding-model",
|
||||
input="Write a short haiku about coding",
|
||||
stream=True,
|
||||
)
|
||||
|
||||
# Collect streamed content using the official Responses API streaming shape
|
||||
text_chunks = []
|
||||
final_message = None
|
||||
|
||||
for event in stream:
|
||||
# The Python SDK surfaces a high-level Responses streaming interface.
|
||||
# We rely on its typed helpers instead of digging into model_extra.
|
||||
if getattr(event, "type", None) == "response.output_text.delta" and getattr(
|
||||
event, "delta", None
|
||||
):
|
||||
# Each delta contains a text fragment
|
||||
text_chunks.append(event.delta)
|
||||
|
||||
# Track the final response message if provided by the SDK
|
||||
if getattr(event, "type", None) == "response.completed" and getattr(
|
||||
event, "response", None
|
||||
):
|
||||
final_message = event.response
|
||||
|
||||
full_content = "".join(text_chunks)
|
||||
|
||||
# Print the streaming response
|
||||
print(f"\n{'='*80}")
|
||||
print(
|
||||
f"Model: {getattr(final_message, 'model', 'unknown') if final_message else 'unknown'}"
|
||||
)
|
||||
print(f"Streamed Output: {full_content}")
|
||||
print(f"{'='*80}\n")
|
||||
|
||||
assert len(text_chunks) > 0, "Should have received streaming text deltas"
|
||||
assert len(full_content) > 0, "Should have received content"
|
||||
|
||||
|
||||
def test_openai_responses_api_non_streaming_with_tools_upstream_bedrock():
|
||||
"""Responses API with tools routed to Bedrock via coding-model alias"""
|
||||
base_url = LLM_GATEWAY_ENDPOINT.replace("/v1/chat/completions", "")
|
||||
client = openai.OpenAI(api_key="test-key", base_url=f"{base_url}/v1")
|
||||
|
||||
tools = [
|
||||
{
|
||||
"type": "function",
|
||||
"name": "echo_tool",
|
||||
"description": "Echo back the provided input",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {"text": {"type": "string"}},
|
||||
"required": ["text"],
|
||||
},
|
||||
}
|
||||
]
|
||||
|
||||
resp = client.responses.create(
|
||||
model="coding-model",
|
||||
input="Call the echo tool",
|
||||
tools=tools,
|
||||
)
|
||||
|
||||
assert resp.id is not None
|
||||
|
||||
print(f"\n{'='*80}")
|
||||
print(f"Model: {resp.model}")
|
||||
print(f"Output: {resp.output_text}")
|
||||
print(f"{'='*80}\n")
|
||||
|
||||
|
||||
def test_openai_responses_api_streaming_with_tools_upstream_bedrock():
|
||||
"""Responses API with a function/tool definition streaming to Bedrock via coding-model alias"""
|
||||
base_url = LLM_GATEWAY_ENDPOINT.replace("/v1/chat/completions", "")
|
||||
client = openai.OpenAI(api_key="test-key", base_url=f"{base_url}/v1", max_retries=0)
|
||||
|
||||
tools = [
|
||||
{
|
||||
"type": "function",
|
||||
"name": "echo_tool",
|
||||
"description": "Echo back the provided input",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {"text": {"type": "string"}},
|
||||
"required": ["text"],
|
||||
},
|
||||
}
|
||||
]
|
||||
|
||||
stream = client.responses.create(
|
||||
model="coding-model",
|
||||
input="Call the echo tool",
|
||||
tools=tools,
|
||||
stream=True,
|
||||
)
|
||||
|
||||
text_chunks = []
|
||||
tool_calls = []
|
||||
|
||||
for event in stream:
|
||||
etype = getattr(event, "type", None)
|
||||
|
||||
# Collect streamed text output
|
||||
if etype == "response.output_text.delta" and getattr(event, "delta", None):
|
||||
text_chunks.append(event.delta)
|
||||
|
||||
# Collect streamed tool call arguments
|
||||
if etype == "response.function_call_arguments.delta" and getattr(
|
||||
event, "delta", None
|
||||
):
|
||||
tool_calls.append(event.delta)
|
||||
|
||||
full_text = "".join(text_chunks)
|
||||
|
||||
print(f"\n{'='*80}")
|
||||
print("Responses tools streaming test (Bedrock)")
|
||||
print(f"Streamed text: {full_text}")
|
||||
print(f"Tool call argument chunks: {len(tool_calls)}")
|
||||
print(f"{'='*80}\n")
|
||||
|
||||
# We expect either streamed text output or streamed tool-call arguments
|
||||
assert (
|
||||
full_text or tool_calls
|
||||
), "Expected streamed text or tool call argument deltas from Responses tools stream"
|
||||
|
||||
|
||||
def test_openai_responses_api_non_streaming_upstream_anthropic():
|
||||
"""Send a v1/responses request using the grok alias to verify translation/routing"""
|
||||
base_url = LLM_GATEWAY_ENDPOINT.replace("/v1/chat/completions", "")
|
||||
client = openai.OpenAI(api_key="test-key", base_url=f"{base_url}/v1")
|
||||
|
||||
resp = client.responses.create(
|
||||
model="claude-sonnet-4-20250514", input="Hello, translate this via grok alias"
|
||||
)
|
||||
|
||||
# Print the response content - handle both responses format and chat completions format
|
||||
print(f"\n{'='*80}")
|
||||
print(f"Model: {resp.model}")
|
||||
print(f"Output: {resp.output_text}")
|
||||
print(f"{'='*80}\n")
|
||||
|
||||
assert resp is not None
|
||||
assert resp.id is not None
|
||||
|
||||
|
||||
def test_openai_responses_api_with_streaming_upstream_anthropic():
|
||||
"""Build a v1/responses API streaming request (pass-through) and ensure gateway accepts it"""
|
||||
base_url = LLM_GATEWAY_ENDPOINT.replace("/v1/chat/completions", "")
|
||||
client = openai.OpenAI(api_key="test-key", base_url=f"{base_url}/v1")
|
||||
|
||||
# Simple streaming responses API request using a direct model (pass-through)
|
||||
stream = client.responses.create(
|
||||
model="claude-sonnet-4-20250514",
|
||||
input="Write a short haiku about coding",
|
||||
stream=True,
|
||||
)
|
||||
|
||||
# Collect streamed content using the official Responses API streaming shape
|
||||
text_chunks = []
|
||||
final_message = None
|
||||
|
||||
for event in stream:
|
||||
# The Python SDK surfaces a high-level Responses streaming interface.
|
||||
# We rely on its typed helpers instead of digging into model_extra.
|
||||
if getattr(event, "type", None) == "response.output_text.delta" and getattr(
|
||||
event, "delta", None
|
||||
):
|
||||
# Each delta contains a text fragment
|
||||
text_chunks.append(event.delta)
|
||||
|
||||
# Track the final response message if provided by the SDK
|
||||
if getattr(event, "type", None) == "response.completed" and getattr(
|
||||
event, "response", None
|
||||
):
|
||||
final_message = event.response
|
||||
|
||||
full_content = "".join(text_chunks)
|
||||
|
||||
# Print the streaming response
|
||||
print(f"\n{'='*80}")
|
||||
print(
|
||||
f"Model: {getattr(final_message, 'model', 'unknown') if final_message else 'unknown'}"
|
||||
)
|
||||
print(f"Streamed Output: {full_content}")
|
||||
print(f"{'='*80}\n")
|
||||
|
||||
assert len(text_chunks) > 0, "Should have received streaming text deltas"
|
||||
assert len(full_content) > 0, "Should have received content"
|
||||
|
||||
|
||||
def test_openai_responses_api_non_streaming_with_tools_upstream_anthropic():
|
||||
"""Responses API with tools routed to grok via alias"""
|
||||
base_url = LLM_GATEWAY_ENDPOINT.replace("/v1/chat/completions", "")
|
||||
client = openai.OpenAI(api_key="test-key", base_url=f"{base_url}/v1")
|
||||
|
||||
tools = [
|
||||
{
|
||||
"type": "function",
|
||||
"name": "echo_tool",
|
||||
"description": "Echo back the provided input: hello_world",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {"text": {"type": "string"}},
|
||||
"required": ["text"],
|
||||
},
|
||||
}
|
||||
]
|
||||
|
||||
resp = client.responses.create(
|
||||
model="claude-sonnet-4-20250514",
|
||||
input="Call the echo tool",
|
||||
tools=tools,
|
||||
)
|
||||
|
||||
assert resp.id is not None
|
||||
|
||||
print(f"\n{'='*80}")
|
||||
print(f"Model: {resp.model}")
|
||||
print(f"Output: {resp.output_text}")
|
||||
print(f"{'='*80}\n")
|
||||
|
||||
|
||||
def test_openai_responses_api_streaming_with_tools_upstream_anthropic():
|
||||
"""Responses API with a function/tool definition (streaming, pass-through)"""
|
||||
base_url = LLM_GATEWAY_ENDPOINT.replace("/v1/chat/completions", "")
|
||||
client = openai.OpenAI(api_key="test-key", base_url=f"{base_url}/v1", max_retries=0)
|
||||
|
||||
tools = [
|
||||
{
|
||||
"type": "function",
|
||||
"name": "echo_tool",
|
||||
"description": "Echo back the provided input: hello_world",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {"text": {"type": "string"}},
|
||||
"required": ["text"],
|
||||
},
|
||||
}
|
||||
]
|
||||
|
||||
stream = client.responses.create(
|
||||
model="claude-sonnet-4-20250514",
|
||||
input="Call the echo tool",
|
||||
tools=tools,
|
||||
stream=True,
|
||||
)
|
||||
|
||||
text_chunks = []
|
||||
tool_calls = []
|
||||
|
||||
for event in stream:
|
||||
etype = getattr(event, "type", None)
|
||||
|
||||
# Collect streamed text output
|
||||
if etype == "response.output_text.delta" and getattr(event, "delta", None):
|
||||
text_chunks.append(event.delta)
|
||||
|
||||
# Collect streamed tool call arguments
|
||||
if etype == "response.function_call_arguments.delta" and getattr(
|
||||
event, "delta", None
|
||||
):
|
||||
tool_calls.append(event.delta)
|
||||
|
||||
full_text = "".join(text_chunks)
|
||||
|
||||
print(f"\n{'='*80}")
|
||||
print("Responses tools streaming test")
|
||||
print(f"Streamed text: {full_text}")
|
||||
print(f"Tool call argument chunks: {len(tool_calls)}")
|
||||
print(f"{'='*80}\n")
|
||||
|
||||
# We expect either streamed text output or streamed tool-call arguments
|
||||
assert (
|
||||
full_text or tool_calls
|
||||
), "Expected streamed text or tool call argument deltas from Responses tools stream"
|
||||
Loading…
Add table
Add a link
Reference in a new issue