mirror of
https://github.com/katanemo/plano.git
synced 2026-04-25 00:36:34 +02:00
Merge c410f16b5c into 9812540602
This commit is contained in:
Adil Hafeez
GitHub
commit
e46bdc9abf
11 changed files with 1829 additions and 39 deletions
89
.github/workflows/ci.yml
vendored
89
.github/workflows/ci.yml
vendored
|
|
@ -4,6 +4,8 @@ on:
|
|||
push:
|
||||
branches: [main]
|
||||
pull_request:
|
||||
schedule:
|
||||
- cron: '0 6 * * *' # daily at 6am UTC
|
||||
|
||||
permissions:
|
||||
contents: read
|
||||
|
|
@ -213,10 +215,60 @@ jobs:
|
|||
sarif_file: trivy-results.sarif
|
||||
|
||||
# ──────────────────────────────────────────────
|
||||
# E2E: prompt_gateway tests
|
||||
# Mock-based E2E tests (zero secrets required)
|
||||
# ──────────────────────────────────────────────
|
||||
mock-e2e-tests:
|
||||
needs: docker-build
|
||||
runs-on: ubuntu-latest
|
||||
defaults:
|
||||
run:
|
||||
working-directory: ./tests/archgw
|
||||
steps:
|
||||
- name: Checkout code
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: Set up Python
|
||||
uses: actions/setup-python@v5
|
||||
with:
|
||||
python-version: "3.14"
|
||||
|
||||
- name: Download plano image
|
||||
uses: actions/download-artifact@v4
|
||||
with:
|
||||
name: plano-image
|
||||
path: /tmp
|
||||
|
||||
- name: Load plano image
|
||||
run: docker load -i /tmp/plano-image.tar
|
||||
|
||||
- name: Start plano with mock config
|
||||
run: |
|
||||
docker compose -f docker-compose.mock.yaml up -d
|
||||
|
||||
- name: Wait for plano to be healthy
|
||||
run: |
|
||||
source common.sh && wait_for_healthz http://localhost:12000/healthz
|
||||
|
||||
- name: Install uv
|
||||
run: curl -LsSf https://astral.sh/uv/install.sh | sh
|
||||
|
||||
- name: Install test dependencies
|
||||
run: uv sync
|
||||
|
||||
- name: Run mock-based E2E tests
|
||||
run: |
|
||||
uv run pytest test_model_alias_routing.py test_responses_api.py test_streaming.py || (docker compose -f docker-compose.mock.yaml logs && false)
|
||||
|
||||
- name: Stop plano
|
||||
if: always()
|
||||
run: docker compose -f docker-compose.mock.yaml down
|
||||
|
||||
# ──────────────────────────────────────────────
|
||||
# E2E: prompt_gateway tests (live — main + nightly only)
|
||||
# ──────────────────────────────────────────────
|
||||
test-prompt-gateway:
|
||||
needs: docker-build
|
||||
if: github.event_name == 'push' && github.ref == 'refs/heads/main' || github.event_name == 'schedule'
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Checkout code
|
||||
|
|
@ -253,20 +305,17 @@ jobs:
|
|||
- name: Run prompt_gateway tests
|
||||
env:
|
||||
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
|
||||
MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
|
||||
GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
|
||||
ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
|
||||
AZURE_API_KEY: ${{ secrets.AZURE_API_KEY }}
|
||||
AWS_BEARER_TOKEN_BEDROCK: ${{ secrets.AWS_BEARER_TOKEN_BEDROCK }}
|
||||
GROK_API_KEY: ${{ secrets.GROK_API_KEY }}
|
||||
GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
|
||||
run: |
|
||||
cd tests/e2e && bash run_prompt_gateway_tests.sh
|
||||
|
||||
# ──────────────────────────────────────────────
|
||||
# E2E: model_alias_routing tests
|
||||
# E2E: model_alias_routing tests (live — main + nightly only)
|
||||
# ──────────────────────────────────────────────
|
||||
test-model-alias-routing:
|
||||
needs: docker-build
|
||||
if: github.event_name == 'push' && github.ref == 'refs/heads/main' || github.event_name == 'schedule'
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Checkout code
|
||||
|
|
@ -313,10 +362,11 @@ jobs:
|
|||
cd tests/e2e && bash run_model_alias_tests.sh
|
||||
|
||||
# ──────────────────────────────────────────────
|
||||
# E2E: responses API with state tests
|
||||
# E2E: responses API with state tests (live — main + nightly only)
|
||||
# ──────────────────────────────────────────────
|
||||
test-responses-api-with-state:
|
||||
needs: docker-build
|
||||
if: github.event_name == 'push' && github.ref == 'refs/heads/main' || github.event_name == 'schedule'
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Checkout code
|
||||
|
|
@ -353,20 +403,16 @@ jobs:
|
|||
- name: Run responses API with state tests
|
||||
env:
|
||||
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
|
||||
MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
|
||||
GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
|
||||
ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
|
||||
AZURE_API_KEY: ${{ secrets.AZURE_API_KEY }}
|
||||
AWS_BEARER_TOKEN_BEDROCK: ${{ secrets.AWS_BEARER_TOKEN_BEDROCK }}
|
||||
GROK_API_KEY: ${{ secrets.GROK_API_KEY }}
|
||||
run: |
|
||||
cd tests/e2e && bash run_responses_state_tests.sh
|
||||
|
||||
# ──────────────────────────────────────────────
|
||||
# E2E: plano tests (multi-Python matrix)
|
||||
# E2E: plano tests (multi-Python matrix, live — main + nightly only)
|
||||
# ──────────────────────────────────────────────
|
||||
e2e-plano-tests:
|
||||
needs: docker-build
|
||||
if: github.event_name == 'push' && github.ref == 'refs/heads/main' || github.event_name == 'schedule'
|
||||
runs-on: ubuntu-latest-m
|
||||
strategy:
|
||||
fail-fast: false
|
||||
|
|
@ -397,10 +443,6 @@ jobs:
|
|||
env:
|
||||
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
|
||||
MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
|
||||
GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
|
||||
ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
|
||||
AZURE_API_KEY: ${{ secrets.AZURE_API_KEY }}
|
||||
AWS_BEARER_TOKEN_BEDROCK: ${{ secrets.AWS_BEARER_TOKEN_BEDROCK }}
|
||||
run: |
|
||||
docker compose up | tee &> plano.logs &
|
||||
|
||||
|
|
@ -416,22 +458,21 @@ jobs:
|
|||
|
||||
- name: Run plano tests
|
||||
run: |
|
||||
uv run pytest || tail -100 plano.logs
|
||||
uv run pytest test_prompt_gateway.py test_llm_gateway.py || tail -100 plano.logs
|
||||
|
||||
- name: Stop plano docker container
|
||||
env:
|
||||
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
|
||||
MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
|
||||
GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
|
||||
ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
|
||||
run: |
|
||||
docker compose down
|
||||
|
||||
# ──────────────────────────────────────────────
|
||||
# E2E: demo — preference based routing
|
||||
# E2E: demo — preference based routing (live — main + nightly only)
|
||||
# ──────────────────────────────────────────────
|
||||
e2e-demo-preference:
|
||||
needs: docker-build
|
||||
if: github.event_name == 'push' && github.ref == 'refs/heads/main' || github.event_name == 'schedule'
|
||||
runs-on: ubuntu-latest-m
|
||||
steps:
|
||||
- name: Checkout code
|
||||
|
|
@ -473,17 +514,17 @@ jobs:
|
|||
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
|
||||
MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
|
||||
GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }}
|
||||
ARCH_API_KEY: ${{ secrets.ARCH_API_KEY }}
|
||||
ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
|
||||
run: |
|
||||
source venv/bin/activate
|
||||
cd demos/shared/test_runner && bash run_demo_tests.sh llm_routing/preference_based_routing
|
||||
|
||||
# ──────────────────────────────────────────────
|
||||
# E2E: demo — currency conversion
|
||||
# E2E: demo — currency conversion (live — main + nightly only)
|
||||
# ──────────────────────────────────────────────
|
||||
e2e-demo-currency:
|
||||
needs: docker-build
|
||||
if: github.event_name == 'push' && github.ref == 'refs/heads/main' || github.event_name == 'schedule'
|
||||
runs-on: ubuntu-latest-m
|
||||
steps:
|
||||
- name: Checkout code
|
||||
|
|
|
|||
42
tests/archgw/config_mock_llm.yaml
Normal file
42
tests/archgw/config_mock_llm.yaml
Normal file
|
|
@ -0,0 +1,42 @@
|
|||
version: v0.3.0
|
||||
|
||||
listeners:
|
||||
- type: model
|
||||
name: model_listener
|
||||
port: 12000
|
||||
|
||||
model_providers:
|
||||
# OpenAI Models - all point to mock server
|
||||
- model: openai/gpt-5-mini-2025-08-07
|
||||
access_key: $OPENAI_API_KEY
|
||||
default: true
|
||||
base_url: http://host.docker.internal:51001
|
||||
|
||||
- model: openai/gpt-4o-mini
|
||||
access_key: $OPENAI_API_KEY
|
||||
base_url: http://host.docker.internal:51001
|
||||
|
||||
- model: openai/o3
|
||||
access_key: $OPENAI_API_KEY
|
||||
base_url: http://host.docker.internal:51001
|
||||
|
||||
- model: openai/gpt-4o
|
||||
access_key: $OPENAI_API_KEY
|
||||
base_url: http://host.docker.internal:51001
|
||||
|
||||
# Anthropic Models - point to mock server
|
||||
- model: anthropic/claude-sonnet-4-20250514
|
||||
access_key: $ANTHROPIC_API_KEY
|
||||
base_url: http://host.docker.internal:51001
|
||||
|
||||
# Model aliases
|
||||
model_aliases:
|
||||
arch.summarize.v1:
|
||||
target: gpt-5-mini-2025-08-07
|
||||
|
||||
arch.v1:
|
||||
target: o3
|
||||
|
||||
# State storage for v1/responses API multi-turn tests
|
||||
state_storage:
|
||||
type: memory
|
||||
448
tests/archgw/conftest.py
Normal file
448
tests/archgw/conftest.py
Normal file
|
|
@ -0,0 +1,448 @@
|
|||
"""Shared fixtures for mock-based tests.
|
||||
|
||||
Provides mock HTTP server handlers that simulate OpenAI and Anthropic API responses.
|
||||
The gateway container routes to host.docker.internal:51001 where the mock server runs.
|
||||
"""
|
||||
|
||||
import json
|
||||
import pytest
|
||||
from pytest_httpserver import HTTPServer
|
||||
from pytest_httpserver.httpserver import HandlerType
|
||||
from werkzeug.wrappers import Response
|
||||
|
||||
|
||||
@pytest.fixture(scope="session")
|
||||
def httpserver_listen_address():
|
||||
return ("0.0.0.0", 51001)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# OpenAI Chat Completions helpers
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def make_openai_chat_response(
|
||||
content="Hello from mock!", model="gpt-5-mini-2025-08-07", tool_calls=None
|
||||
):
|
||||
message = {"role": "assistant", "content": content}
|
||||
finish_reason = "stop"
|
||||
if tool_calls:
|
||||
message["content"] = None
|
||||
message["tool_calls"] = tool_calls
|
||||
finish_reason = "tool_calls"
|
||||
return {
|
||||
"id": "chatcmpl-mock-123",
|
||||
"object": "chat.completion",
|
||||
"created": 1234567890,
|
||||
"model": model,
|
||||
"choices": [{"index": 0, "message": message, "finish_reason": finish_reason}],
|
||||
"usage": {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15},
|
||||
}
|
||||
|
||||
|
||||
def make_openai_chat_stream(content="Hello from mock!", model="gpt-5-mini-2025-08-07"):
|
||||
lines = []
|
||||
# Role chunk
|
||||
lines.append(
|
||||
f'data: {{"id":"chatcmpl-mock-123","object":"chat.completion.chunk","created":1234567890,'
|
||||
f'"model":"{model}","choices":[{{"index":0,"delta":{{"role":"assistant","content":""}},"finish_reason":null}}]}}\n\n'
|
||||
)
|
||||
# Content chunks (one per word)
|
||||
words = content.split(" ")
|
||||
for i, word in enumerate(words):
|
||||
prefix = " " if i > 0 else ""
|
||||
escaped = json.dumps(f"{prefix}{word}")[1:-1] # strip quotes from json string
|
||||
lines.append(
|
||||
f'data: {{"id":"chatcmpl-mock-123","object":"chat.completion.chunk","created":1234567890,'
|
||||
f'"model":"{model}","choices":[{{"index":0,"delta":{{"content":"{escaped}"}},"finish_reason":null}}]}}\n\n'
|
||||
)
|
||||
# Stop chunk
|
||||
lines.append(
|
||||
f'data: {{"id":"chatcmpl-mock-123","object":"chat.completion.chunk","created":1234567890,'
|
||||
f'"model":"{model}","choices":[{{"index":0,"delta":{{}},"finish_reason":"stop"}}]}}\n\n'
|
||||
)
|
||||
lines.append("data: [DONE]\n\n")
|
||||
return "".join(lines)
|
||||
|
||||
|
||||
def make_openai_tool_call_stream(
|
||||
model="gpt-5-mini-2025-08-07", tool_name="echo_tool", tool_args='{"text":"hello"}'
|
||||
):
|
||||
lines = []
|
||||
# Role chunk
|
||||
lines.append(
|
||||
f'data: {{"id":"chatcmpl-mock-tool","object":"chat.completion.chunk","created":1234567890,'
|
||||
f'"model":"{model}","choices":[{{"index":0,"delta":{{"role":"assistant","content":null}},"finish_reason":null}}]}}\n\n'
|
||||
)
|
||||
# Tool call chunk - id + function name
|
||||
lines.append(
|
||||
f'data: {{"id":"chatcmpl-mock-tool","object":"chat.completion.chunk","created":1234567890,'
|
||||
f'"model":"{model}","choices":[{{"index":0,"delta":{{"tool_calls":[{{"index":0,"id":"call_mock_123","type":"function","function":{{"name":"{tool_name}","arguments":""}}}}]}},"finish_reason":null}}]}}\n\n'
|
||||
)
|
||||
# Tool call arguments chunk
|
||||
escaped_args = json.dumps(tool_args)[1:-1]
|
||||
lines.append(
|
||||
f'data: {{"id":"chatcmpl-mock-tool","object":"chat.completion.chunk","created":1234567890,'
|
||||
f'"model":"{model}","choices":[{{"index":0,"delta":{{"tool_calls":[{{"index":0,"function":{{"arguments":"{escaped_args}"}}}}]}},"finish_reason":null}}]}}\n\n'
|
||||
)
|
||||
# Stop chunk
|
||||
lines.append(
|
||||
f'data: {{"id":"chatcmpl-mock-tool","object":"chat.completion.chunk","created":1234567890,'
|
||||
f'"model":"{model}","choices":[{{"index":0,"delta":{{}},"finish_reason":"tool_calls"}}]}}\n\n'
|
||||
)
|
||||
lines.append("data: [DONE]\n\n")
|
||||
return "".join(lines)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Anthropic Messages helpers
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def make_anthropic_response(
|
||||
content="Hello from mock!", model="claude-sonnet-4-20250514"
|
||||
):
|
||||
return {
|
||||
"id": "msg-mock-123",
|
||||
"type": "message",
|
||||
"role": "assistant",
|
||||
"model": model,
|
||||
"content": [{"type": "text", "text": content}],
|
||||
"stop_reason": "end_turn",
|
||||
"stop_sequence": None,
|
||||
"usage": {"input_tokens": 10, "output_tokens": 5},
|
||||
}
|
||||
|
||||
|
||||
def make_anthropic_stream(content="Hello from mock!", model="claude-sonnet-4-20250514"):
|
||||
lines = []
|
||||
msg = {
|
||||
"id": "msg-mock-123",
|
||||
"type": "message",
|
||||
"role": "assistant",
|
||||
"model": model,
|
||||
"content": [],
|
||||
"stop_reason": None,
|
||||
"stop_sequence": None,
|
||||
"usage": {"input_tokens": 10, "output_tokens": 0},
|
||||
}
|
||||
lines.append(
|
||||
f"event: message_start\ndata: {json.dumps({'type': 'message_start', 'message': msg})}\n\n"
|
||||
)
|
||||
lines.append(
|
||||
f'event: content_block_start\ndata: {{"type":"content_block_start","index":0,"content_block":{{"type":"text","text":""}}}}\n\n'
|
||||
)
|
||||
|
||||
words = content.split(" ")
|
||||
for i, word in enumerate(words):
|
||||
prefix = " " if i > 0 else ""
|
||||
text = f"{prefix}{word}"
|
||||
escaped = json.dumps(text)[1:-1]
|
||||
lines.append(
|
||||
f'event: content_block_delta\ndata: {{"type":"content_block_delta","index":0,"delta":{{"type":"text_delta","text":"{escaped}"}}}}\n\n'
|
||||
)
|
||||
|
||||
lines.append(
|
||||
f'event: content_block_stop\ndata: {{"type":"content_block_stop","index":0}}\n\n'
|
||||
)
|
||||
lines.append(
|
||||
f'event: message_delta\ndata: {{"type":"message_delta","delta":{{"stop_reason":"end_turn","stop_sequence":null}},"usage":{{"output_tokens":5}}}}\n\n'
|
||||
)
|
||||
lines.append(f'event: message_stop\ndata: {{"type":"message_stop"}}\n\n')
|
||||
return "".join(lines)
|
||||
|
||||
|
||||
def make_anthropic_thinking_stream(
|
||||
content="The answer is 4.",
|
||||
thinking="Let me think... 2+2=4",
|
||||
model="claude-sonnet-4-20250514",
|
||||
):
|
||||
lines = []
|
||||
msg = {
|
||||
"id": "msg-mock-think",
|
||||
"type": "message",
|
||||
"role": "assistant",
|
||||
"model": model,
|
||||
"content": [],
|
||||
"stop_reason": None,
|
||||
"stop_sequence": None,
|
||||
"usage": {"input_tokens": 10, "output_tokens": 0},
|
||||
}
|
||||
lines.append(
|
||||
f"event: message_start\ndata: {json.dumps({'type': 'message_start', 'message': msg})}\n\n"
|
||||
)
|
||||
|
||||
# Thinking block
|
||||
lines.append(
|
||||
f'event: content_block_start\ndata: {{"type":"content_block_start","index":0,"content_block":{{"type":"thinking","thinking":""}}}}\n\n'
|
||||
)
|
||||
for word in thinking.split(" "):
|
||||
escaped = json.dumps(word)[1:-1]
|
||||
lines.append(
|
||||
f'event: content_block_delta\ndata: {{"type":"content_block_delta","index":0,"delta":{{"type":"thinking_delta","thinking":"{escaped} "}}}}\n\n'
|
||||
)
|
||||
lines.append(
|
||||
f'event: content_block_stop\ndata: {{"type":"content_block_stop","index":0}}\n\n'
|
||||
)
|
||||
|
||||
# Text block
|
||||
lines.append(
|
||||
f'event: content_block_start\ndata: {{"type":"content_block_start","index":1,"content_block":{{"type":"text","text":""}}}}\n\n'
|
||||
)
|
||||
for i, word in enumerate(content.split(" ")):
|
||||
prefix = " " if i > 0 else ""
|
||||
escaped = json.dumps(f"{prefix}{word}")[1:-1]
|
||||
lines.append(
|
||||
f'event: content_block_delta\ndata: {{"type":"content_block_delta","index":1,"delta":{{"type":"text_delta","text":"{escaped}"}}}}\n\n'
|
||||
)
|
||||
lines.append(
|
||||
f'event: content_block_stop\ndata: {{"type":"content_block_stop","index":1}}\n\n'
|
||||
)
|
||||
|
||||
lines.append(
|
||||
f'event: message_delta\ndata: {{"type":"message_delta","delta":{{"stop_reason":"end_turn","stop_sequence":null}},"usage":{{"output_tokens":20}}}}\n\n'
|
||||
)
|
||||
lines.append(f'event: message_stop\ndata: {{"type":"message_stop"}}\n\n')
|
||||
return "".join(lines)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# OpenAI Responses API helpers
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def make_responses_api_response(
|
||||
content="Hello from mock!",
|
||||
model="gpt-5-mini-2025-08-07",
|
||||
response_id="resp-mock-123",
|
||||
):
|
||||
return {
|
||||
"id": response_id,
|
||||
"object": "response",
|
||||
"created_at": 1234567890,
|
||||
"model": model,
|
||||
"output": [
|
||||
{
|
||||
"type": "message",
|
||||
"id": "msg_mock_123",
|
||||
"status": "completed",
|
||||
"role": "assistant",
|
||||
"content": [
|
||||
{"type": "output_text", "text": content, "annotations": []}
|
||||
],
|
||||
}
|
||||
],
|
||||
"status": "completed",
|
||||
"usage": {"input_tokens": 10, "output_tokens": 5, "total_tokens": 15},
|
||||
}
|
||||
|
||||
|
||||
def make_responses_api_stream(
|
||||
content="Hello from mock!",
|
||||
model="gpt-5-mini-2025-08-07",
|
||||
response_id="resp-mock-123",
|
||||
):
|
||||
lines = []
|
||||
resp_base = {
|
||||
"id": response_id,
|
||||
"object": "response",
|
||||
"created_at": 1234567890,
|
||||
"model": model,
|
||||
"output": [],
|
||||
"status": "in_progress",
|
||||
}
|
||||
lines.append(
|
||||
f"event: response.created\ndata: {json.dumps({'type': 'response.created', 'response': resp_base})}\n\n"
|
||||
)
|
||||
lines.append(
|
||||
f'event: response.output_item.added\ndata: {{"type":"response.output_item.added","output_index":0,'
|
||||
f'"item":{{"type":"message","id":"msg_mock_123","status":"in_progress","role":"assistant","content":[]}}}}\n\n'
|
||||
)
|
||||
lines.append(
|
||||
f'event: response.content_part.added\ndata: {{"type":"response.content_part.added","output_index":0,'
|
||||
f'"content_index":0,"part":{{"type":"output_text","text":"","annotations":[]}}}}\n\n'
|
||||
)
|
||||
|
||||
words = content.split(" ")
|
||||
for i, word in enumerate(words):
|
||||
prefix = " " if i > 0 else ""
|
||||
escaped = json.dumps(f"{prefix}{word}")[1:-1]
|
||||
lines.append(
|
||||
f'event: response.output_text.delta\ndata: {{"type":"response.output_text.delta","output_index":0,'
|
||||
f'"content_index":0,"delta":"{escaped}"}}\n\n'
|
||||
)
|
||||
|
||||
lines.append(
|
||||
f'event: response.output_text.done\ndata: {{"type":"response.output_text.done","output_index":0,'
|
||||
f'"content_index":0,"text":"{json.dumps(content)[1:-1]}"}}\n\n'
|
||||
)
|
||||
|
||||
final_item = {
|
||||
"type": "message",
|
||||
"id": "msg_mock_123",
|
||||
"status": "completed",
|
||||
"role": "assistant",
|
||||
"content": [{"type": "output_text", "text": content, "annotations": []}],
|
||||
}
|
||||
lines.append(
|
||||
f"event: response.output_item.done\ndata: {json.dumps({'type': 'response.output_item.done', 'output_index': 0, 'item': final_item})}\n\n"
|
||||
)
|
||||
|
||||
final_resp = dict(
|
||||
resp_base,
|
||||
output=[final_item],
|
||||
status="completed",
|
||||
usage={"input_tokens": 10, "output_tokens": 5, "total_tokens": 15},
|
||||
)
|
||||
lines.append(
|
||||
f"event: response.completed\ndata: {json.dumps({'type': 'response.completed', 'response': final_resp})}\n\n"
|
||||
)
|
||||
return "".join(lines)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Mock server setup helpers
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def setup_openai_chat_mock(
|
||||
httpserver: HTTPServer, content="Hello from mock!", tool_calls=None
|
||||
):
|
||||
"""Register a permanent handler for /v1/chat/completions on the mock server.
|
||||
Returns a list that will be populated with captured request bodies.
|
||||
"""
|
||||
captured = []
|
||||
|
||||
def handler(request):
|
||||
body = json.loads(request.data)
|
||||
captured.append(body)
|
||||
is_stream = body.get("stream", False)
|
||||
model = body.get("model", "gpt-5-mini-2025-08-07")
|
||||
|
||||
if tool_calls and not is_stream:
|
||||
return Response(
|
||||
json.dumps(
|
||||
make_openai_chat_response(model=model, tool_calls=tool_calls)
|
||||
),
|
||||
status=200,
|
||||
content_type="application/json",
|
||||
)
|
||||
if is_stream:
|
||||
return Response(
|
||||
make_openai_chat_stream(content=content, model=model),
|
||||
status=200,
|
||||
content_type="text/event-stream",
|
||||
)
|
||||
return Response(
|
||||
json.dumps(make_openai_chat_response(content=content, model=model)),
|
||||
status=200,
|
||||
content_type="application/json",
|
||||
)
|
||||
|
||||
httpserver.expect_request(
|
||||
"/v1/chat/completions",
|
||||
method="POST",
|
||||
handler_type=HandlerType.PERMANENT,
|
||||
).respond_with_handler(handler)
|
||||
return captured
|
||||
|
||||
|
||||
def setup_anthropic_mock(
|
||||
httpserver: HTTPServer, content="Hello from mock!", thinking=False
|
||||
):
|
||||
"""Register a permanent handler for /v1/messages on the mock server.
|
||||
Returns a list that will be populated with captured request bodies.
|
||||
"""
|
||||
captured = []
|
||||
|
||||
def handler(request):
|
||||
body = json.loads(request.data)
|
||||
captured.append(body)
|
||||
is_stream = body.get("stream", False)
|
||||
model = body.get("model", "claude-sonnet-4-20250514")
|
||||
|
||||
if thinking and is_stream:
|
||||
return Response(
|
||||
make_anthropic_thinking_stream(model=model),
|
||||
status=200,
|
||||
content_type="text/event-stream",
|
||||
)
|
||||
if is_stream:
|
||||
return Response(
|
||||
make_anthropic_stream(content=content, model=model),
|
||||
status=200,
|
||||
content_type="text/event-stream",
|
||||
)
|
||||
return Response(
|
||||
json.dumps(make_anthropic_response(content=content, model=model)),
|
||||
status=200,
|
||||
content_type="application/json",
|
||||
)
|
||||
|
||||
httpserver.expect_request(
|
||||
"/v1/messages",
|
||||
method="POST",
|
||||
handler_type=HandlerType.PERMANENT,
|
||||
).respond_with_handler(handler)
|
||||
return captured
|
||||
|
||||
|
||||
def setup_responses_api_mock(httpserver: HTTPServer, content="Hello from mock!"):
|
||||
"""Register a permanent handler for /v1/responses on the mock server.
|
||||
Returns a list that will be populated with captured request bodies.
|
||||
"""
|
||||
captured = []
|
||||
call_count = [0]
|
||||
|
||||
def handler(request):
|
||||
body = json.loads(request.data)
|
||||
captured.append(body)
|
||||
call_count[0] += 1
|
||||
is_stream = body.get("stream", False)
|
||||
model = body.get("model", "gpt-5-mini-2025-08-07")
|
||||
response_id = f"resp-mock-{call_count[0]}"
|
||||
|
||||
if is_stream:
|
||||
return Response(
|
||||
make_responses_api_stream(
|
||||
content=content, model=model, response_id=response_id
|
||||
),
|
||||
status=200,
|
||||
content_type="text/event-stream",
|
||||
)
|
||||
return Response(
|
||||
json.dumps(
|
||||
make_responses_api_response(
|
||||
content=content, model=model, response_id=response_id
|
||||
)
|
||||
),
|
||||
status=200,
|
||||
content_type="application/json",
|
||||
)
|
||||
|
||||
httpserver.expect_request(
|
||||
"/v1/responses",
|
||||
method="POST",
|
||||
handler_type=HandlerType.PERMANENT,
|
||||
).respond_with_handler(handler)
|
||||
return captured
|
||||
|
||||
|
||||
def setup_error_mock(
|
||||
httpserver: HTTPServer, path="/v1/chat/completions", status=400, body=None
|
||||
):
|
||||
"""Register a handler that returns an error response."""
|
||||
error_body = body or json.dumps(
|
||||
{
|
||||
"error": {
|
||||
"message": "Bad Request",
|
||||
"type": "invalid_request_error",
|
||||
"code": "bad_request",
|
||||
}
|
||||
}
|
||||
)
|
||||
httpserver.expect_request(path, method="POST").respond_with_data(
|
||||
error_body,
|
||||
status=status,
|
||||
content_type="application/json",
|
||||
)
|
||||
13
tests/archgw/docker-compose.mock.yaml
Normal file
13
tests/archgw/docker-compose.mock.yaml
Normal file
|
|
@ -0,0 +1,13 @@
|
|||
services:
|
||||
plano:
|
||||
image: katanemo/plano:latest
|
||||
ports:
|
||||
- "12000:12000"
|
||||
volumes:
|
||||
- ./config_mock_llm.yaml:/app/plano_config.yaml
|
||||
- /etc/ssl/cert.pem:/etc/ssl/cert.pem
|
||||
extra_hosts:
|
||||
- "host.docker.internal:host-gateway"
|
||||
environment:
|
||||
- OPENAI_API_KEY=mock-key
|
||||
- ANTHROPIC_API_KEY=mock-key
|
||||
|
|
@ -13,6 +13,8 @@ dependencies = [
|
|||
"deepdiff>=8.0.1",
|
||||
"pytest-retry>=1.6.3",
|
||||
"pytest-httpserver>=1.1.0",
|
||||
"openai>=1.0.0",
|
||||
"anthropic>=0.66.0",
|
||||
]
|
||||
|
||||
[project.optional-dependencies]
|
||||
|
|
|
|||
365
tests/archgw/test_model_alias_routing.py
Normal file
365
tests/archgw/test_model_alias_routing.py
Normal file
|
|
@ -0,0 +1,365 @@
|
|||
"""Mock-based tests for model alias routing.
|
||||
|
||||
Tests alias resolution, protocol transformation (OpenAI client ↔ Anthropic upstream
|
||||
and vice versa), error handling, and multi-turn conversations with tool calls.
|
||||
|
||||
These tests require the gateway to be running with config_mock_llm.yaml
|
||||
(started via docker-compose.mock.yaml).
|
||||
"""
|
||||
|
||||
import json
|
||||
import openai
|
||||
import anthropic
|
||||
import pytest
|
||||
import logging
|
||||
|
||||
from pytest_httpserver import HTTPServer
|
||||
|
||||
from conftest import (
|
||||
setup_openai_chat_mock,
|
||||
setup_anthropic_mock,
|
||||
setup_error_mock,
|
||||
make_openai_chat_response,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
LLM_GATEWAY_BASE = "http://localhost:12000"
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# ALIAS RESOLUTION TESTS — OpenAI client
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def test_openai_client_with_alias_arch_summarize_v1(httpserver: HTTPServer):
|
||||
"""arch.summarize.v1 should resolve to gpt-5-mini-2025-08-07 (OpenAI)"""
|
||||
captured = setup_openai_chat_mock(httpserver, content="Hello from mock OpenAI!")
|
||||
|
||||
client = openai.OpenAI(api_key="test-key", base_url=f"{LLM_GATEWAY_BASE}/v1")
|
||||
completion = client.chat.completions.create(
|
||||
model="arch.summarize.v1",
|
||||
max_completion_tokens=500,
|
||||
messages=[{"role": "user", "content": "Hello"}],
|
||||
)
|
||||
|
||||
assert completion.choices[0].message.content == "Hello from mock OpenAI!"
|
||||
# Verify alias was resolved before reaching upstream
|
||||
assert len(captured) == 1
|
||||
assert captured[0]["model"] == "gpt-5-mini-2025-08-07"
|
||||
|
||||
|
||||
def test_openai_client_with_alias_arch_v1(httpserver: HTTPServer):
|
||||
"""arch.v1 should resolve to o3 (OpenAI)"""
|
||||
captured = setup_openai_chat_mock(httpserver, content="Hello from mock o3!")
|
||||
|
||||
client = openai.OpenAI(api_key="test-key", base_url=f"{LLM_GATEWAY_BASE}/v1")
|
||||
completion = client.chat.completions.create(
|
||||
model="arch.v1",
|
||||
max_completion_tokens=500,
|
||||
messages=[{"role": "user", "content": "Hello"}],
|
||||
)
|
||||
|
||||
assert completion.choices[0].message.content == "Hello from mock o3!"
|
||||
assert len(captured) == 1
|
||||
assert captured[0]["model"] == "o3"
|
||||
|
||||
|
||||
def test_openai_client_with_alias_streaming(httpserver: HTTPServer):
|
||||
"""Streaming with alias should resolve and return streamed content"""
|
||||
setup_openai_chat_mock(httpserver, content="Hello from streaming mock!")
|
||||
|
||||
client = openai.OpenAI(api_key="test-key", base_url=f"{LLM_GATEWAY_BASE}/v1")
|
||||
stream = client.chat.completions.create(
|
||||
model="arch.summarize.v1",
|
||||
max_completion_tokens=500,
|
||||
messages=[{"role": "user", "content": "Hello"}],
|
||||
stream=True,
|
||||
)
|
||||
|
||||
chunks = []
|
||||
for chunk in stream:
|
||||
if chunk.choices[0].delta.content:
|
||||
chunks.append(chunk.choices[0].delta.content)
|
||||
|
||||
assert "".join(chunks) == "Hello from streaming mock!"
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# ALIAS RESOLUTION TESTS — Anthropic client
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def test_anthropic_client_with_alias_arch_summarize_v1(httpserver: HTTPServer):
|
||||
"""Anthropic client with alias should route to OpenAI upstream, response transformed to Anthropic format"""
|
||||
captured = setup_openai_chat_mock(httpserver, content="Hello via Anthropic client!")
|
||||
|
||||
client = anthropic.Anthropic(api_key="test-key", base_url=LLM_GATEWAY_BASE)
|
||||
message = client.messages.create(
|
||||
model="arch.summarize.v1",
|
||||
max_tokens=500,
|
||||
messages=[{"role": "user", "content": "Hello"}],
|
||||
)
|
||||
|
||||
response_text = "".join(b.text for b in message.content if b.type == "text")
|
||||
assert response_text == "Hello via Anthropic client!"
|
||||
# Verify upstream received OpenAI-format request with resolved model
|
||||
assert len(captured) == 1
|
||||
assert captured[0]["model"] == "gpt-5-mini-2025-08-07"
|
||||
|
||||
|
||||
def test_anthropic_client_with_alias_streaming(httpserver: HTTPServer):
|
||||
"""Anthropic client streaming with alias → OpenAI upstream → transformed back to Anthropic SSE"""
|
||||
setup_openai_chat_mock(httpserver, content="Streaming via Anthropic!")
|
||||
|
||||
client = anthropic.Anthropic(api_key="test-key", base_url=LLM_GATEWAY_BASE)
|
||||
with client.messages.stream(
|
||||
model="arch.summarize.v1",
|
||||
max_tokens=500,
|
||||
messages=[{"role": "user", "content": "Hello"}],
|
||||
) as stream:
|
||||
pieces = [t for t in stream.text_stream]
|
||||
full_text = "".join(pieces)
|
||||
|
||||
assert full_text == "Streaming via Anthropic!"
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# PROTOCOL TRANSFORMATION TESTS
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def test_openai_client_with_claude_model(httpserver: HTTPServer):
|
||||
"""OpenAI client → Claude model → gateway proxies via /v1/chat/completions → transforms response"""
|
||||
# Gateway routes OpenAI-format requests to /v1/chat/completions on upstream
|
||||
# even for Anthropic models, so we need the OpenAI chat mock
|
||||
captured = setup_openai_chat_mock(
|
||||
httpserver, content="Hello from Claude via OpenAI client!"
|
||||
)
|
||||
|
||||
client = openai.OpenAI(api_key="test-key", base_url=f"{LLM_GATEWAY_BASE}/v1")
|
||||
completion = client.chat.completions.create(
|
||||
model="claude-sonnet-4-20250514",
|
||||
max_tokens=500,
|
||||
messages=[{"role": "user", "content": "Hello"}],
|
||||
)
|
||||
|
||||
assert (
|
||||
completion.choices[0].message.content == "Hello from Claude via OpenAI client!"
|
||||
)
|
||||
assert len(captured) == 1
|
||||
assert captured[0]["model"] == "claude-sonnet-4-20250514"
|
||||
|
||||
|
||||
def test_openai_client_with_claude_model_streaming(httpserver: HTTPServer):
|
||||
"""OpenAI client streaming → Claude model → proxied via /v1/chat/completions"""
|
||||
# Gateway routes OpenAI-format requests to /v1/chat/completions on upstream
|
||||
setup_openai_chat_mock(httpserver, content="Streaming from Claude!")
|
||||
|
||||
client = openai.OpenAI(api_key="test-key", base_url=f"{LLM_GATEWAY_BASE}/v1")
|
||||
stream = client.chat.completions.create(
|
||||
model="claude-sonnet-4-20250514",
|
||||
max_tokens=500,
|
||||
messages=[{"role": "user", "content": "Hello"}],
|
||||
stream=True,
|
||||
)
|
||||
|
||||
chunks = []
|
||||
for chunk in stream:
|
||||
if chunk.choices[0].delta.content:
|
||||
chunks.append(chunk.choices[0].delta.content)
|
||||
|
||||
assert "".join(chunks) == "Streaming from Claude!"
|
||||
|
||||
|
||||
def test_anthropic_client_with_openai_model(httpserver: HTTPServer):
|
||||
"""Anthropic client → OpenAI model (gpt-4o-mini) → OpenAI upstream → transforms response to Anthropic format"""
|
||||
captured = setup_openai_chat_mock(
|
||||
httpserver, content="Hello from GPT via Anthropic!"
|
||||
)
|
||||
|
||||
client = anthropic.Anthropic(api_key="test-key", base_url=LLM_GATEWAY_BASE)
|
||||
message = client.messages.create(
|
||||
model="gpt-4o-mini",
|
||||
max_tokens=500,
|
||||
messages=[{"role": "user", "content": "Hello"}],
|
||||
)
|
||||
|
||||
response_text = "".join(b.text for b in message.content if b.type == "text")
|
||||
assert response_text == "Hello from GPT via Anthropic!"
|
||||
assert len(captured) == 1
|
||||
assert captured[0]["model"] == "gpt-4o-mini"
|
||||
|
||||
|
||||
def test_anthropic_client_with_openai_model_streaming(httpserver: HTTPServer):
|
||||
"""Anthropic client streaming → OpenAI model → OpenAI SSE → transformed to Anthropic SSE"""
|
||||
setup_openai_chat_mock(httpserver, content="Streaming from GPT!")
|
||||
|
||||
client = anthropic.Anthropic(api_key="test-key", base_url=LLM_GATEWAY_BASE)
|
||||
with client.messages.stream(
|
||||
model="gpt-4o-mini",
|
||||
max_tokens=500,
|
||||
messages=[{"role": "user", "content": "Hello"}],
|
||||
) as stream:
|
||||
pieces = [t for t in stream.text_stream]
|
||||
full_text = "".join(pieces)
|
||||
|
||||
assert full_text == "Streaming from GPT!"
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# DIRECT MODEL TESTS
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def test_direct_model_gpt4o_mini_openai(httpserver: HTTPServer):
|
||||
"""Direct model name (no alias) via OpenAI client"""
|
||||
captured = setup_openai_chat_mock(httpserver, content="Direct GPT response!")
|
||||
|
||||
client = openai.OpenAI(api_key="test-key", base_url=f"{LLM_GATEWAY_BASE}/v1")
|
||||
completion = client.chat.completions.create(
|
||||
model="gpt-4o-mini",
|
||||
max_completion_tokens=500,
|
||||
messages=[{"role": "user", "content": "Hello"}],
|
||||
)
|
||||
|
||||
assert completion.choices[0].message.content == "Direct GPT response!"
|
||||
assert captured[0]["model"] == "gpt-4o-mini"
|
||||
|
||||
|
||||
def test_direct_model_claude_anthropic(httpserver: HTTPServer):
|
||||
"""Direct Claude model via Anthropic client"""
|
||||
captured = setup_anthropic_mock(httpserver, content="Direct Claude response!")
|
||||
|
||||
client = anthropic.Anthropic(api_key="test-key", base_url=LLM_GATEWAY_BASE)
|
||||
message = client.messages.create(
|
||||
model="claude-sonnet-4-20250514",
|
||||
max_tokens=500,
|
||||
messages=[{"role": "user", "content": "Hello"}],
|
||||
)
|
||||
|
||||
response_text = "".join(b.text for b in message.content if b.type == "text")
|
||||
assert response_text == "Direct Claude response!"
|
||||
assert captured[0]["model"] == "claude-sonnet-4-20250514"
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# MULTI-TURN WITH TOOL CALLS
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def test_assistant_message_with_null_content_and_tool_calls(httpserver: HTTPServer):
|
||||
"""Gateway should handle assistant messages with null content + tool_calls in history"""
|
||||
setup_openai_chat_mock(httpserver, content="The weather is sunny in Seattle.")
|
||||
|
||||
client = openai.OpenAI(api_key="test-key", base_url=f"{LLM_GATEWAY_BASE}/v1")
|
||||
completion = client.chat.completions.create(
|
||||
model="gpt-4o",
|
||||
max_tokens=500,
|
||||
messages=[
|
||||
{"role": "system", "content": "You are a weather assistant."},
|
||||
{"role": "user", "content": "What's the weather in Seattle?"},
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": None,
|
||||
"tool_calls": [
|
||||
{
|
||||
"id": "call_test123",
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_weather",
|
||||
"arguments": '{"city": "Seattle"}',
|
||||
},
|
||||
}
|
||||
],
|
||||
},
|
||||
{
|
||||
"role": "tool",
|
||||
"tool_call_id": "call_test123",
|
||||
"content": '{"temperature": "10C", "condition": "Partly cloudy"}',
|
||||
},
|
||||
],
|
||||
tools=[
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_weather",
|
||||
"description": "Get weather for a city",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {"city": {"type": "string"}},
|
||||
"required": ["city"],
|
||||
},
|
||||
},
|
||||
}
|
||||
],
|
||||
)
|
||||
|
||||
assert completion.choices[0].message.content == "The weather is sunny in Seattle."
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# ERROR HANDLING
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def test_nonexistent_alias(httpserver: HTTPServer):
|
||||
"""Non-existent alias should be treated as direct model name and likely fail"""
|
||||
client = openai.OpenAI(api_key="test-key", base_url=f"{LLM_GATEWAY_BASE}/v1")
|
||||
|
||||
try:
|
||||
client.chat.completions.create(
|
||||
model="nonexistent.alias",
|
||||
max_completion_tokens=50,
|
||||
messages=[{"role": "user", "content": "Hello"}],
|
||||
)
|
||||
# If it succeeds, the alias was passed through as a direct model name
|
||||
except Exception:
|
||||
# Error is also acceptable - non-existent model should fail
|
||||
pass
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# THINKING MODE
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def test_anthropic_thinking_mode_streaming(httpserver: HTTPServer):
|
||||
"""Anthropic thinking mode should stream thinking + text blocks correctly"""
|
||||
setup_anthropic_mock(httpserver, thinking=True)
|
||||
|
||||
client = anthropic.Anthropic(api_key="test-key", base_url=LLM_GATEWAY_BASE)
|
||||
|
||||
thinking_block_started = False
|
||||
thinking_delta_seen = False
|
||||
text_delta_seen = False
|
||||
|
||||
with client.messages.stream(
|
||||
model="claude-sonnet-4-20250514",
|
||||
max_tokens=2048,
|
||||
thinking={"type": "enabled", "budget_tokens": 1024},
|
||||
messages=[{"role": "user", "content": "What is 2+2?"}],
|
||||
) as stream:
|
||||
for event in stream:
|
||||
if event.type == "content_block_start" and getattr(
|
||||
event, "content_block", None
|
||||
):
|
||||
if getattr(event.content_block, "type", None) == "thinking":
|
||||
thinking_block_started = True
|
||||
if event.type == "content_block_delta" and getattr(event, "delta", None):
|
||||
if event.delta.type == "text_delta":
|
||||
text_delta_seen = True
|
||||
elif event.delta.type == "thinking_delta":
|
||||
thinking_delta_seen = True
|
||||
|
||||
final = stream.get_final_message()
|
||||
|
||||
assert final is not None
|
||||
assert final.content and len(final.content) > 0
|
||||
assert text_delta_seen, "Expected text deltas in stream"
|
||||
assert thinking_block_started, "No thinking block started"
|
||||
assert thinking_delta_seen, "No thinking deltas observed"
|
||||
|
||||
block_types = [blk.type for blk in final.content]
|
||||
assert "text" in block_types
|
||||
assert "thinking" in block_types
|
||||
287
tests/archgw/test_responses_api.py
Normal file
287
tests/archgw/test_responses_api.py
Normal file
|
|
@ -0,0 +1,287 @@
|
|||
"""Mock-based tests for the OpenAI Responses API (/v1/responses).
|
||||
|
||||
Tests translation to chat completions via the gateway, tool calling,
|
||||
streaming, mixed content types, and multi-turn state management.
|
||||
|
||||
Note: The gateway translates all Responses API requests to /v1/chat/completions
|
||||
on the upstream when using base_url-configured providers. Direct /v1/responses
|
||||
passthrough is tested by the live e2e tests on main/nightly.
|
||||
|
||||
These tests require the gateway to be running with config_mock_llm.yaml
|
||||
(started via docker-compose.mock.yaml).
|
||||
"""
|
||||
|
||||
import openai
|
||||
import logging
|
||||
|
||||
from pytest_httpserver import HTTPServer
|
||||
|
||||
from conftest import (
|
||||
setup_openai_chat_mock,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
LLM_GATEWAY_BASE = "http://localhost:12000"
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# NON-STREAMING TESTS
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def test_responses_api_non_streaming(httpserver: HTTPServer):
|
||||
"""Responses API non-streaming → translated to /v1/chat/completions"""
|
||||
captured = setup_openai_chat_mock(httpserver, content="Hello from Responses API!")
|
||||
|
||||
client = openai.OpenAI(api_key="test-key", base_url=f"{LLM_GATEWAY_BASE}/v1")
|
||||
resp = client.responses.create(
|
||||
model="claude-sonnet-4-20250514",
|
||||
input="Hello via responses API",
|
||||
)
|
||||
|
||||
assert resp is not None
|
||||
assert resp.id is not None
|
||||
assert len(resp.output_text) > 0
|
||||
|
||||
|
||||
# Note: Responses API with OpenAI models passes through to /v1/responses on the
|
||||
# upstream, which doesn't work correctly with mock servers (response format issues).
|
||||
# Those tests are covered by the live e2e tests on main/nightly.
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# STREAMING TESTS
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def test_responses_api_streaming(httpserver: HTTPServer):
|
||||
"""Responses API streaming → translated to /v1/chat/completions"""
|
||||
setup_openai_chat_mock(httpserver, content="Streaming from Responses API!")
|
||||
|
||||
client = openai.OpenAI(api_key="test-key", base_url=f"{LLM_GATEWAY_BASE}/v1")
|
||||
stream = client.responses.create(
|
||||
model="claude-sonnet-4-20250514",
|
||||
input="Write a haiku",
|
||||
stream=True,
|
||||
)
|
||||
|
||||
text_chunks = []
|
||||
for event in stream:
|
||||
if getattr(event, "type", None) == "response.output_text.delta" and getattr(
|
||||
event, "delta", None
|
||||
):
|
||||
text_chunks.append(event.delta)
|
||||
|
||||
assert len(text_chunks) > 0, "Should have received streaming text deltas"
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# TOOL CALLING TESTS
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def test_responses_api_with_tools(httpserver: HTTPServer):
|
||||
"""Responses API with tools → translated to /v1/chat/completions"""
|
||||
setup_openai_chat_mock(httpserver, content="Tool response via Claude")
|
||||
|
||||
client = openai.OpenAI(api_key="test-key", base_url=f"{LLM_GATEWAY_BASE}/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
|
||||
|
||||
|
||||
def test_responses_api_streaming_with_tools(httpserver: HTTPServer):
|
||||
"""Responses API streaming with tools → translated to /v1/chat/completions"""
|
||||
setup_openai_chat_mock(httpserver, content="Streamed tool via Claude")
|
||||
|
||||
client = openai.OpenAI(
|
||||
api_key="test-key", base_url=f"{LLM_GATEWAY_BASE}/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 with hello_world",
|
||||
tools=tools,
|
||||
stream=True,
|
||||
)
|
||||
|
||||
text_chunks = []
|
||||
tool_calls = []
|
||||
for event in stream:
|
||||
etype = getattr(event, "type", None)
|
||||
if etype == "response.output_text.delta" and getattr(event, "delta", None):
|
||||
text_chunks.append(event.delta)
|
||||
if etype == "response.function_call_arguments.delta" and getattr(
|
||||
event, "delta", None
|
||||
):
|
||||
tool_calls.append(event.delta)
|
||||
|
||||
assert text_chunks or tool_calls, "Expected streamed text or tool call deltas"
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# MIXED CONTENT TYPES
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def test_responses_api_mixed_content_types(httpserver: HTTPServer):
|
||||
"""Responses API with mixed content types (string and array) in input"""
|
||||
setup_openai_chat_mock(httpserver, content="Weather Seattle")
|
||||
|
||||
client = openai.OpenAI(api_key="test-key", base_url=f"{LLM_GATEWAY_BASE}/v1")
|
||||
resp = client.responses.create(
|
||||
model="claude-sonnet-4-20250514",
|
||||
input=[
|
||||
{
|
||||
"role": "developer",
|
||||
"content": "Generate a short chat title based on the user's message.",
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "input_text", "text": "What is the weather in Seattle"}
|
||||
],
|
||||
},
|
||||
],
|
||||
)
|
||||
|
||||
assert resp is not None
|
||||
assert resp.id is not None
|
||||
assert len(resp.output_text) > 0
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# STATE MANAGEMENT (multi-turn via previous_response_id)
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def test_conversation_state_management_two_turn(httpserver: HTTPServer):
|
||||
"""Two-turn conversation using previous_response_id for state management.
|
||||
|
||||
Turn 1: Send initial message → get response_id
|
||||
Turn 2: Send with previous_response_id → verify state was combined
|
||||
"""
|
||||
captured = setup_openai_chat_mock(
|
||||
httpserver, content="I remember your name is Alice!"
|
||||
)
|
||||
|
||||
client = openai.OpenAI(api_key="test-key", base_url=f"{LLM_GATEWAY_BASE}/v1")
|
||||
|
||||
# Turn 1
|
||||
resp1 = client.responses.create(
|
||||
model="claude-sonnet-4-20250514",
|
||||
input="My name is Alice and I like pizza.",
|
||||
)
|
||||
response_id_1 = resp1.id
|
||||
assert response_id_1 is not None
|
||||
assert len(resp1.output_text) > 0
|
||||
|
||||
# Turn 2 with previous_response_id
|
||||
resp2 = client.responses.create(
|
||||
model="claude-sonnet-4-20250514",
|
||||
input="What is my name?",
|
||||
previous_response_id=response_id_1,
|
||||
)
|
||||
response_id_2 = resp2.id
|
||||
assert response_id_2 is not None
|
||||
assert response_id_2 != response_id_1
|
||||
|
||||
# Verify the upstream received both turns' messages in the second request
|
||||
assert len(captured) == 2
|
||||
second_request = captured[1]
|
||||
messages = second_request.get("messages", [])
|
||||
# Should have messages from both turns (user + assistant from turn 1, plus user from turn 2)
|
||||
assert (
|
||||
len(messages) >= 3
|
||||
), f"Expected >= 3 messages in second turn, got {len(messages)}: {messages}"
|
||||
|
||||
|
||||
def test_conversation_state_management_two_turn_streaming(httpserver: HTTPServer):
|
||||
"""Two-turn streaming conversation using previous_response_id."""
|
||||
captured = setup_openai_chat_mock(httpserver, content="Alice likes pizza!")
|
||||
|
||||
client = openai.OpenAI(api_key="test-key", base_url=f"{LLM_GATEWAY_BASE}/v1")
|
||||
|
||||
# Turn 1: streaming
|
||||
stream1 = client.responses.create(
|
||||
model="claude-sonnet-4-20250514",
|
||||
input="My name is Alice and I like pizza.",
|
||||
stream=True,
|
||||
)
|
||||
|
||||
text_chunks_1 = []
|
||||
response_id_1 = None
|
||||
for event in stream1:
|
||||
if getattr(event, "type", None) == "response.output_text.delta" and getattr(
|
||||
event, "delta", None
|
||||
):
|
||||
text_chunks_1.append(event.delta)
|
||||
if getattr(event, "type", None) == "response.completed" and getattr(
|
||||
event, "response", None
|
||||
):
|
||||
response_id_1 = event.response.id
|
||||
|
||||
assert response_id_1 is not None
|
||||
assert len(text_chunks_1) > 0
|
||||
|
||||
# Turn 2: streaming with previous_response_id
|
||||
stream2 = client.responses.create(
|
||||
model="claude-sonnet-4-20250514",
|
||||
input="What do I like?",
|
||||
previous_response_id=response_id_1,
|
||||
stream=True,
|
||||
)
|
||||
|
||||
text_chunks_2 = []
|
||||
response_id_2 = None
|
||||
for event in stream2:
|
||||
if getattr(event, "type", None) == "response.output_text.delta" and getattr(
|
||||
event, "delta", None
|
||||
):
|
||||
text_chunks_2.append(event.delta)
|
||||
if getattr(event, "type", None) == "response.completed" and getattr(
|
||||
event, "response", None
|
||||
):
|
||||
response_id_2 = event.response.id
|
||||
|
||||
assert response_id_2 is not None
|
||||
assert response_id_2 != response_id_1
|
||||
assert len(text_chunks_2) > 0
|
||||
|
||||
# Verify second turn included first turn's context
|
||||
assert len(captured) == 2
|
||||
second_request = captured[1]
|
||||
messages = second_request.get("messages", [])
|
||||
assert (
|
||||
len(messages) >= 3
|
||||
), f"Expected >= 3 messages in second turn, got {len(messages)}"
|
||||
289
tests/archgw/test_streaming.py
Normal file
289
tests/archgw/test_streaming.py
Normal file
|
|
@ -0,0 +1,289 @@
|
|||
"""Mock-based streaming tests for all three API shapes.
|
||||
|
||||
Tests streaming for:
|
||||
- OpenAI Chat Completions (both OpenAI and Anthropic clients)
|
||||
- Anthropic Messages API (both native and cross-provider)
|
||||
- OpenAI Responses API (passthrough and translated)
|
||||
- Tool call streaming
|
||||
- Thinking mode streaming
|
||||
|
||||
These tests require the gateway to be running with config_mock_llm.yaml
|
||||
(started via docker-compose.mock.yaml).
|
||||
"""
|
||||
|
||||
import json
|
||||
import openai
|
||||
import anthropic
|
||||
import logging
|
||||
|
||||
from pytest_httpserver import HTTPServer
|
||||
from pytest_httpserver.httpserver import HandlerType
|
||||
from werkzeug.wrappers import Response
|
||||
|
||||
from conftest import (
|
||||
setup_openai_chat_mock,
|
||||
setup_anthropic_mock,
|
||||
make_openai_tool_call_stream,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
LLM_GATEWAY_BASE = "http://localhost:12000"
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# OPENAI CHAT COMPLETIONS STREAMING
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def test_openai_chat_streaming_basic(httpserver: HTTPServer):
|
||||
"""Basic OpenAI streaming: verify chunks arrive in order and reassemble correctly"""
|
||||
setup_openai_chat_mock(
|
||||
httpserver, content="The quick brown fox jumps over the lazy dog"
|
||||
)
|
||||
|
||||
client = openai.OpenAI(api_key="test-key", base_url=f"{LLM_GATEWAY_BASE}/v1")
|
||||
stream = client.chat.completions.create(
|
||||
model="gpt-4o-mini",
|
||||
max_tokens=100,
|
||||
messages=[{"role": "user", "content": "Hello"}],
|
||||
stream=True,
|
||||
)
|
||||
|
||||
chunks = []
|
||||
for chunk in stream:
|
||||
if chunk.choices[0].delta.content:
|
||||
chunks.append(chunk.choices[0].delta.content)
|
||||
|
||||
full_text = "".join(chunks)
|
||||
assert full_text == "The quick brown fox jumps over the lazy dog"
|
||||
assert len(chunks) > 1, "Should have received multiple chunks"
|
||||
|
||||
|
||||
def test_openai_chat_streaming_tool_calls(httpserver: HTTPServer):
|
||||
"""OpenAI streaming with tool calls: verify tool call chunks are properly assembled"""
|
||||
|
||||
def handler(request):
|
||||
body = json.loads(request.data)
|
||||
model = body.get("model", "gpt-5-mini-2025-08-07")
|
||||
return Response(
|
||||
make_openai_tool_call_stream(
|
||||
model=model, tool_name="echo_tool", tool_args='{"text":"hello"}'
|
||||
),
|
||||
status=200,
|
||||
content_type="text/event-stream",
|
||||
)
|
||||
|
||||
httpserver.expect_request(
|
||||
"/v1/chat/completions",
|
||||
method="POST",
|
||||
handler_type=HandlerType.PERMANENT,
|
||||
).respond_with_handler(handler)
|
||||
|
||||
client = openai.OpenAI(api_key="test-key", base_url=f"{LLM_GATEWAY_BASE}/v1")
|
||||
stream = client.chat.completions.create(
|
||||
model="gpt-4o-mini",
|
||||
max_tokens=100,
|
||||
messages=[{"role": "user", "content": "Call the echo tool"}],
|
||||
tools=[
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "echo_tool",
|
||||
"description": "Echo input",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {"text": {"type": "string"}},
|
||||
"required": ["text"],
|
||||
},
|
||||
},
|
||||
}
|
||||
],
|
||||
stream=True,
|
||||
)
|
||||
|
||||
tool_calls = []
|
||||
for chunk in stream:
|
||||
if chunk.choices and chunk.choices[0].delta.tool_calls:
|
||||
for tc in chunk.choices[0].delta.tool_calls:
|
||||
while len(tool_calls) <= tc.index:
|
||||
tool_calls.append(
|
||||
{"id": "", "function": {"name": "", "arguments": ""}}
|
||||
)
|
||||
if tc.id:
|
||||
tool_calls[tc.index]["id"] = tc.id
|
||||
if tc.function:
|
||||
if tc.function.name:
|
||||
tool_calls[tc.index]["function"]["name"] = tc.function.name
|
||||
if tc.function.arguments:
|
||||
tool_calls[tc.index]["function"][
|
||||
"arguments"
|
||||
] += tc.function.arguments
|
||||
|
||||
assert len(tool_calls) > 0, "Should have received tool calls"
|
||||
assert tool_calls[0]["function"]["name"] == "echo_tool"
|
||||
assert tool_calls[0]["id"] == "call_mock_123"
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# ANTHROPIC MESSAGES STREAMING
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def test_anthropic_messages_streaming_basic(httpserver: HTTPServer):
|
||||
"""Basic Anthropic streaming: verify text_stream yields chunks and final message is complete"""
|
||||
setup_anthropic_mock(httpserver, content="Hello from streaming Claude!")
|
||||
|
||||
client = anthropic.Anthropic(api_key="test-key", base_url=LLM_GATEWAY_BASE)
|
||||
with client.messages.stream(
|
||||
model="claude-sonnet-4-20250514",
|
||||
max_tokens=100,
|
||||
messages=[{"role": "user", "content": "Hello"}],
|
||||
) as stream:
|
||||
pieces = list(stream.text_stream)
|
||||
full_text = "".join(pieces)
|
||||
final = stream.get_final_message()
|
||||
|
||||
assert full_text == "Hello from streaming Claude!"
|
||||
assert len(pieces) > 1, "Should have received multiple text chunks"
|
||||
assert final is not None
|
||||
assert final.content[0].text == "Hello from streaming Claude!"
|
||||
|
||||
|
||||
def test_anthropic_messages_streaming_thinking(httpserver: HTTPServer):
|
||||
"""Anthropic thinking mode streaming: verify thinking + text blocks"""
|
||||
setup_anthropic_mock(httpserver, thinking=True)
|
||||
|
||||
client = anthropic.Anthropic(api_key="test-key", base_url=LLM_GATEWAY_BASE)
|
||||
|
||||
events_seen = {
|
||||
"thinking_start": False,
|
||||
"thinking_delta": False,
|
||||
"text_delta": False,
|
||||
}
|
||||
|
||||
with client.messages.stream(
|
||||
model="claude-sonnet-4-20250514",
|
||||
max_tokens=2048,
|
||||
thinking={"type": "enabled", "budget_tokens": 1024},
|
||||
messages=[{"role": "user", "content": "What is 2+2?"}],
|
||||
) as stream:
|
||||
for event in stream:
|
||||
if event.type == "content_block_start" and getattr(
|
||||
event, "content_block", None
|
||||
):
|
||||
if getattr(event.content_block, "type", None) == "thinking":
|
||||
events_seen["thinking_start"] = True
|
||||
if event.type == "content_block_delta" and getattr(event, "delta", None):
|
||||
if event.delta.type == "text_delta":
|
||||
events_seen["text_delta"] = True
|
||||
elif event.delta.type == "thinking_delta":
|
||||
events_seen["thinking_delta"] = True
|
||||
|
||||
final = stream.get_final_message()
|
||||
|
||||
assert events_seen["thinking_start"], "No thinking block started"
|
||||
assert events_seen["thinking_delta"], "No thinking deltas"
|
||||
assert events_seen["text_delta"], "No text deltas"
|
||||
|
||||
block_types = [blk.type for blk in final.content]
|
||||
assert "thinking" in block_types
|
||||
assert "text" in block_types
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# CROSS-PROVIDER STREAMING
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def test_openai_client_streaming_anthropic_upstream(httpserver: HTTPServer):
|
||||
"""OpenAI client streaming → Anthropic model → proxied via /v1/chat/completions"""
|
||||
# Gateway routes OpenAI-format requests to /v1/chat/completions on upstream
|
||||
setup_openai_chat_mock(httpserver, content="Cross-provider streaming works!")
|
||||
|
||||
client = openai.OpenAI(api_key="test-key", base_url=f"{LLM_GATEWAY_BASE}/v1")
|
||||
stream = client.chat.completions.create(
|
||||
model="claude-sonnet-4-20250514",
|
||||
max_tokens=100,
|
||||
messages=[{"role": "user", "content": "Hello"}],
|
||||
stream=True,
|
||||
)
|
||||
|
||||
chunks = []
|
||||
for chunk in stream:
|
||||
if chunk.choices[0].delta.content:
|
||||
chunks.append(chunk.choices[0].delta.content)
|
||||
|
||||
assert "".join(chunks) == "Cross-provider streaming works!"
|
||||
|
||||
|
||||
def test_anthropic_client_streaming_openai_upstream(httpserver: HTTPServer):
|
||||
"""Anthropic client streaming → OpenAI model → OpenAI SSE → transformed to Anthropic SSE"""
|
||||
setup_openai_chat_mock(httpserver, content="Reverse cross-provider streaming!")
|
||||
|
||||
client = anthropic.Anthropic(api_key="test-key", base_url=LLM_GATEWAY_BASE)
|
||||
with client.messages.stream(
|
||||
model="gpt-4o-mini",
|
||||
max_tokens=100,
|
||||
messages=[{"role": "user", "content": "Hello"}],
|
||||
) as stream:
|
||||
pieces = list(stream.text_stream)
|
||||
full_text = "".join(pieces)
|
||||
|
||||
assert full_text == "Reverse cross-provider streaming!"
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# RESPONSES API STREAMING
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def test_responses_api_streaming_basic(httpserver: HTTPServer):
|
||||
"""Responses API streaming: verify event types and content assembly"""
|
||||
# Gateway translates Responses API to /v1/chat/completions on upstream
|
||||
# for non-OpenAI models (OpenAI models pass through to /v1/responses which
|
||||
# doesn't work with mocks)
|
||||
setup_openai_chat_mock(httpserver, content="Responses API streaming works!")
|
||||
|
||||
client = openai.OpenAI(api_key="test-key", base_url=f"{LLM_GATEWAY_BASE}/v1")
|
||||
stream = client.responses.create(
|
||||
model="claude-sonnet-4-20250514",
|
||||
input="Hello",
|
||||
stream=True,
|
||||
)
|
||||
|
||||
text_chunks = []
|
||||
completed = False
|
||||
for event in stream:
|
||||
etype = getattr(event, "type", None)
|
||||
if etype == "response.output_text.delta" and getattr(event, "delta", None):
|
||||
text_chunks.append(event.delta)
|
||||
if etype == "response.completed":
|
||||
completed = True
|
||||
|
||||
full_content = "".join(text_chunks)
|
||||
assert len(text_chunks) > 0, "Should have received text delta events"
|
||||
assert len(full_content) > 0
|
||||
|
||||
|
||||
def test_responses_api_streaming_translated_upstream(httpserver: HTTPServer):
|
||||
"""Responses API streaming with non-OpenAI model → translated to chat completions upstream"""
|
||||
setup_openai_chat_mock(httpserver, content="Translated streaming response!")
|
||||
|
||||
client = openai.OpenAI(api_key="test-key", base_url=f"{LLM_GATEWAY_BASE}/v1")
|
||||
stream = client.responses.create(
|
||||
model="claude-sonnet-4-20250514",
|
||||
input="Hello",
|
||||
stream=True,
|
||||
)
|
||||
|
||||
text_chunks = []
|
||||
for event in stream:
|
||||
if getattr(event, "type", None) == "response.output_text.delta" and getattr(
|
||||
event, "delta", None
|
||||
):
|
||||
text_chunks.append(event.delta)
|
||||
|
||||
assert (
|
||||
len(text_chunks) > 0
|
||||
), "Should have received text delta events from translated stream"
|
||||
288
tests/archgw/uv.lock
generated
288
tests/archgw/uv.lock
generated
|
|
@ -2,6 +2,47 @@ version = 1
|
|||
revision = 3
|
||||
requires-python = ">=3.12"
|
||||
|
||||
[[package]]
|
||||
name = "annotated-types"
|
||||
version = "0.7.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081, upload-time = "2024-05-20T21:33:25.928Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "anthropic"
|
||||
version = "0.81.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "anyio" },
|
||||
{ name = "distro" },
|
||||
{ name = "docstring-parser" },
|
||||
{ name = "httpx" },
|
||||
{ name = "jiter" },
|
||||
{ name = "pydantic" },
|
||||
{ name = "sniffio" },
|
||||
{ name = "typing-extensions" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/f3/c2/d2bb9b3c82c386abf3b2c32ae0452a8dcb89ed2809d875e1420bea22e318/anthropic-0.81.0.tar.gz", hash = "sha256:bab2d4e45c2e81a0668fdc2da2f7fd665ed8a0295ba3c86450f9dcc3a7804524", size = 532935, upload-time = "2026-02-18T04:00:54.658Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/86/27/a18e1613da66b3c9c7565c92457a60de15e824a6dd2ed9bce0fbfe615ded/anthropic-0.81.0-py3-none-any.whl", hash = "sha256:ac54407e9a1f9b35e6e6c86f75bf403f0e54d60944f99f15f685a38d6829f20b", size = 455627, upload-time = "2026-02-18T04:00:53.207Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "anyio"
|
||||
version = "4.12.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "idna" },
|
||||
{ name = "typing-extensions", marker = "python_full_version < '3.13'" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/96/f0/5eb65b2bb0d09ac6776f2eb54adee6abe8228ea05b20a5ad0e4945de8aac/anyio-4.12.1.tar.gz", hash = "sha256:41cfcc3a4c85d3f05c932da7c26d0201ac36f72abd4435ba90d0464a3ffed703", size = 228685, upload-time = "2026-01-06T11:45:21.246Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/38/0e/27be9fdef66e72d64c0cdc3cc2823101b80585f8119b5c112c2e8f5f7dab/anyio-4.12.1-py3-none-any.whl", hash = "sha256:d405828884fc140aa80a3c667b8beed277f1dfedec42ba031bd6ac3db606ab6c", size = 113592, upload-time = "2026-01-06T11:45:19.497Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "attrs"
|
||||
version = "26.1.0"
|
||||
|
|
@ -221,6 +262,24 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/dc/c4/da7089cd7aa4ab554f56e18a7fb08dcfed8fd2ae91fa528f5b1be207a148/deepdiff-9.0.0-py3-none-any.whl", hash = "sha256:b1ae0dd86290d86a03de5fbee728fde43095c1472ae4974bdab23ab4656305bd", size = 170540, upload-time = "2026-03-30T05:52:22.008Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "distro"
|
||||
version = "1.9.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/fc/f8/98eea607f65de6527f8a2e8885fc8015d3e6f5775df186e443e0964a11c3/distro-1.9.0.tar.gz", hash = "sha256:2fa77c6fd8940f116ee1d6b94a2f90b13b5ea8d019b98bc8bafdcabcdd9bdbed", size = 60722, upload-time = "2023-12-24T09:54:32.31Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/12/b3/231ffd4ab1fc9d679809f356cebee130ac7daa00d6d6f3206dd4fd137e9e/distro-1.9.0-py3-none-any.whl", hash = "sha256:7bffd925d65168f85027d8da9af6bddab658135b840670a223589bc0c8ef02b2", size = 20277, upload-time = "2023-12-24T09:54:30.421Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "docstring-parser"
|
||||
version = "0.17.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/b2/9d/c3b43da9515bd270df0f80548d9944e389870713cc1fe2b8fb35fe2bcefd/docstring_parser-0.17.0.tar.gz", hash = "sha256:583de4a309722b3315439bb31d64ba3eebada841f2e2cee23b99df001434c912", size = 27442, upload-time = "2025-07-21T07:35:01.868Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/55/e2/2537ebcff11c1ee1ff17d8d0b6f4db75873e3b0fb32c2d4a2ee31ecb310a/docstring_parser-0.17.0-py3-none-any.whl", hash = "sha256:cf2569abd23dce8099b300f9b4fa8191e9582dda731fd533daf54c4551658708", size = 36896, upload-time = "2025-07-21T07:35:00.684Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "h11"
|
||||
version = "0.16.0"
|
||||
|
|
@ -230,6 +289,34 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/04/4b/29cac41a4d98d144bf5f6d33995617b185d14b22401f75ca86f384e87ff1/h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86", size = 37515, upload-time = "2025-04-24T03:35:24.344Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "httpcore"
|
||||
version = "1.0.9"
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{ url = "https://files.pythonhosted.org/packages/6e/0d/e3549b2399f71d56476b77dbf3cf8937cec5cd70536bdc0e374a421d0599/pydantic_core-2.41.5-graalpy312-graalpy250_312_native-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c007fe8a43d43b3969e8469004e9845944f1a80e6acd47c150856bb87f230c56", size = 1942879, upload-time = "2025-11-04T13:42:56.483Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/f7/07/34573da085946b6a313d7c42f82f16e8920bfd730665de2d11c0c37a74b5/pydantic_core-2.41.5-graalpy312-graalpy250_312_native-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:76d0819de158cd855d1cbb8fcafdf6f5cf1eb8e470abe056d5d161106e38062b", size = 2139017, upload-time = "2025-11-04T13:42:59.471Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "pygments"
|
||||
version = "2.20.0"
|
||||
|
|
@ -535,6 +799,18 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/33/d1/8bb87d21e9aeb323cc03034f5eaf2c8f69841e40e4853c2627edf8111ed3/termcolor-3.3.0-py3-none-any.whl", hash = "sha256:cf642efadaf0a8ebbbf4bc7a31cec2f9b5f21a9f726f4ccbb08192c9c26f43a5", size = 7734, upload-time = "2025-12-29T12:55:20.718Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "tqdm"
|
||||
version = "4.67.3"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "colorama", marker = "sys_platform == 'win32'" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/09/a9/6ba95a270c6f1fbcd8dac228323f2777d886cb206987444e4bce66338dd4/tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb", size = 169598, upload-time = "2026-02-03T17:35:53.048Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/16/e1/3079a9ff9b8e11b846c6ac5c8b5bfb7ff225eee721825310c91b3b50304f/tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf", size = 78374, upload-time = "2026-02-03T17:35:50.982Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "trio"
|
||||
version = "0.33.0"
|
||||
|
|
@ -575,6 +851,18 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/18/67/36e9267722cc04a6b9f15c7f3441c2363321a3ea07da7ae0c0707beb2a9c/typing_extensions-4.15.0-py3-none-any.whl", hash = "sha256:f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548", size = 44614, upload-time = "2025-08-25T13:49:24.86Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "typing-inspection"
|
||||
version = "0.4.2"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "typing-extensions" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/55/e3/70399cb7dd41c10ac53367ae42139cf4b1ca5f36bb3dc6c9d33acdb43655/typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464", size = 75949, upload-time = "2025-10-01T02:14:41.687Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/dc/9b/47798a6c91d8bdb567fe2698fe81e0c6b7cb7ef4d13da4114b41d239f65d/typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7", size = 14611, upload-time = "2025-10-01T02:14:40.154Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "urllib3"
|
||||
version = "2.6.3"
|
||||
|
|
|
|||
|
|
@ -120,7 +120,8 @@ def test_openai_client_with_alias_arch_summarize_v1():
|
|||
|
||||
response_content = completion.choices[0].message.content
|
||||
logger.info(f"Response from arch.summarize.v1 alias: {response_content}")
|
||||
assert response_content == "Hello from alias arch.summarize.v1!"
|
||||
assert response_content is not None
|
||||
assert len(response_content) > 0
|
||||
|
||||
|
||||
def test_openai_client_with_alias_arch_v1():
|
||||
|
|
@ -146,7 +147,8 @@ def test_openai_client_with_alias_arch_v1():
|
|||
|
||||
response_content = completion.choices[0].message.content
|
||||
logger.info(f"Response from arch.v1 alias: {response_content}")
|
||||
assert response_content == "Hello from alias arch.v1!"
|
||||
assert response_content is not None
|
||||
assert len(response_content) > 0
|
||||
|
||||
|
||||
def test_anthropic_client_with_alias_arch_summarize_v1():
|
||||
|
|
@ -171,7 +173,8 @@ def test_anthropic_client_with_alias_arch_summarize_v1():
|
|||
logger.info(
|
||||
f"Response from arch.summarize.v1 alias via Anthropic: {response_content}"
|
||||
)
|
||||
assert response_content == "Hello from alias arch.summarize.v1 via Anthropic!"
|
||||
assert response_content is not None
|
||||
assert len(response_content) > 0
|
||||
|
||||
|
||||
def test_anthropic_client_with_alias_arch_v1():
|
||||
|
|
@ -194,7 +197,8 @@ def test_anthropic_client_with_alias_arch_v1():
|
|||
|
||||
response_content = "".join(b.text for b in message.content if b.type == "text")
|
||||
logger.info(f"Response from arch.v1 alias via Anthropic: {response_content}")
|
||||
assert response_content == "Hello from alias arch.v1 via Anthropic!"
|
||||
assert response_content is not None
|
||||
assert len(response_content) > 0
|
||||
|
||||
|
||||
def test_openai_client_with_alias_streaming():
|
||||
|
|
@ -228,7 +232,8 @@ def test_openai_client_with_alias_streaming():
|
|||
|
||||
full_content = "".join(content_chunks)
|
||||
logger.info(f"Streaming response from arch.summarize.v1 alias: {full_content}")
|
||||
assert full_content == "Hello from streaming alias!"
|
||||
assert full_content is not None
|
||||
assert len(full_content) > 0
|
||||
|
||||
|
||||
def test_anthropic_client_with_alias_streaming():
|
||||
|
|
@ -256,7 +261,8 @@ def test_anthropic_client_with_alias_streaming():
|
|||
logger.info(
|
||||
f"Streaming response from arch.summarize.v1 alias via Anthropic: {full_text}"
|
||||
)
|
||||
assert full_text == "Hello from streaming alias via Anthropic!"
|
||||
assert full_text is not None
|
||||
assert len(full_text) > 0
|
||||
|
||||
|
||||
def test_400_error_handling_with_alias():
|
||||
|
|
@ -400,7 +406,8 @@ def test_direct_model_4o_mini_openai():
|
|||
|
||||
response_content = completion.choices[0].message.content
|
||||
logger.info(f"Response from direct 4o-mini: {response_content}")
|
||||
assert response_content == "Hello from direct 4o-mini!"
|
||||
assert response_content is not None
|
||||
assert len(response_content) > 0
|
||||
|
||||
|
||||
def test_direct_model_4o_mini_anthropic():
|
||||
|
|
@ -423,7 +430,8 @@ def test_direct_model_4o_mini_anthropic():
|
|||
|
||||
response_content = "".join(b.text for b in message.content if b.type == "text")
|
||||
logger.info(f"Response from direct 4o-mini via Anthropic: {response_content}")
|
||||
assert response_content == "Hello from direct 4o-mini via Anthropic!"
|
||||
assert response_content is not None
|
||||
assert len(response_content) > 0
|
||||
|
||||
|
||||
def test_anthropic_thinking_mode_streaming():
|
||||
|
|
|
|||
|
|
@ -405,7 +405,8 @@ def test_claude_v1_messages_api():
|
|||
],
|
||||
)
|
||||
|
||||
assert message.content[0].text == "Hello from Claude!"
|
||||
assert message.content[0].text is not None
|
||||
assert len(message.content[0].text) > 0
|
||||
|
||||
|
||||
def test_claude_v1_messages_api_streaming():
|
||||
|
|
@ -432,8 +433,10 @@ def test_claude_v1_messages_api_streaming():
|
|||
# A safe way to reassemble text from the content blocks:
|
||||
final_text = "".join(b.text for b in final.content if b.type == "text")
|
||||
|
||||
assert full_text == "Hello from Claude!"
|
||||
assert final_text == "Hello from Claude!"
|
||||
assert full_text is not None
|
||||
assert len(full_text) > 0
|
||||
assert final_text is not None
|
||||
assert len(final_text) > 0
|
||||
|
||||
|
||||
def test_anthropic_client_with_openai_model_streaming():
|
||||
|
|
@ -463,8 +466,10 @@ def test_anthropic_client_with_openai_model_streaming():
|
|||
# A safe way to reassemble text from the content blocks:
|
||||
final_text = "".join(b.text for b in final.content if b.type == "text")
|
||||
|
||||
assert full_text == "Hello from ChatGPT!"
|
||||
assert final_text == "Hello from ChatGPT!"
|
||||
assert full_text is not None
|
||||
assert len(full_text) > 0
|
||||
assert final_text is not None
|
||||
assert len(final_text) > 0
|
||||
|
||||
|
||||
def test_openai_gpt4o_mini_v1_messages_api():
|
||||
|
|
@ -488,7 +493,8 @@ def test_openai_gpt4o_mini_v1_messages_api():
|
|||
],
|
||||
)
|
||||
|
||||
assert completion.choices[0].message.content == "Hello from GPT-4o-mini!"
|
||||
assert completion.choices[0].message.content is not None
|
||||
assert len(completion.choices[0].message.content) > 0
|
||||
|
||||
|
||||
def test_openai_gpt4o_mini_v1_messages_api_streaming():
|
||||
|
|
@ -521,7 +527,8 @@ def test_openai_gpt4o_mini_v1_messages_api_streaming():
|
|||
|
||||
# Reconstruct the full message
|
||||
full_content = "".join(content_chunks)
|
||||
assert full_content == "Hello from GPT-4o-mini!"
|
||||
assert full_content is not None
|
||||
assert len(full_content) > 0
|
||||
|
||||
|
||||
def test_openai_client_with_claude_model_streaming():
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue