mirror of
https://github.com/BerriAI/litellm.git
synced 2026-10-10 03:28:53 +00:00
chore: remove debug scripts and unused import
Remove 8 development scripts from scripts/ that were accidentally committed. Remove unused `import litellm` from responses_adapters/transformation.py.
This commit is contained in:
Chesars
parent
5c1e5c2510
commit
b74571214f
9 changed files with 0 additions and 601 deletions
|
|
@ -8,7 +8,6 @@ path used for OpenAI and Azure models.
|
|||
import json
|
||||
from typing import Any, Dict, List, Optional, Union, cast
|
||||
|
||||
import litellm
|
||||
from litellm.llms.anthropic.experimental_pass_through.utils import (
|
||||
is_default_reasoning_summary_disabled,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -1,77 +0,0 @@
|
|||
"""
|
||||
Repro script: verify that gpt-5.4 drops reasoning_effort when tools are present.
|
||||
Expected: the call succeeds (reasoning_effort is silently dropped).
|
||||
If the bug were still present, OpenAI would return an error like:
|
||||
"reasoning_effort is not supported with function calling"
|
||||
"""
|
||||
|
||||
import os
|
||||
from dotenv import load_dotenv
|
||||
|
||||
load_dotenv()
|
||||
|
||||
import litellm
|
||||
|
||||
litellm.set_verbose = True
|
||||
|
||||
tools = [
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_weather",
|
||||
"description": "Get current weather for a city",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"city": {"type": "string", "description": "City name"},
|
||||
},
|
||||
"required": ["city"],
|
||||
},
|
||||
},
|
||||
}
|
||||
]
|
||||
|
||||
print("=== Test: gpt-5.4 + reasoning_effort='medium' + tools ===")
|
||||
try:
|
||||
response = litellm.completion(
|
||||
model="gpt-5.4",
|
||||
messages=[{"role": "user", "content": "What's the weather in Buenos Aires?"}],
|
||||
reasoning_effort="medium",
|
||||
tools=tools,
|
||||
drop_params=True,
|
||||
)
|
||||
print(f"SUCCESS - model: {response.model}")
|
||||
print(f"Choice: {response.choices[0].message}")
|
||||
if response.choices[0].message.tool_calls:
|
||||
print(f"Tool calls: {response.choices[0].message.tool_calls}")
|
||||
print("\nreasoning_effort was correctly dropped (no error from OpenAI)")
|
||||
except Exception as e:
|
||||
print(f"FAILED: {e}")
|
||||
|
||||
print("\n=== Test: gpt-5.4 + reasoning_effort='high' + tools ===")
|
||||
try:
|
||||
response = litellm.completion(
|
||||
model="gpt-5.4",
|
||||
messages=[{"role": "user", "content": "What's 2+2?"}],
|
||||
reasoning_effort="high",
|
||||
tools=tools,
|
||||
drop_params=True,
|
||||
)
|
||||
print(f"SUCCESS - model: {response.model}")
|
||||
print(f"reasoning_effort was correctly dropped (no error from OpenAI)")
|
||||
except Exception as e:
|
||||
print(f"FAILED: {e}")
|
||||
|
||||
print("\n=== Test: gpt-5.4 + reasoning_effort='none' + tools (should KEEP reasoning_effort) ===")
|
||||
try:
|
||||
response = litellm.completion(
|
||||
model="gpt-5.4",
|
||||
messages=[{"role": "user", "content": "Say hello"}],
|
||||
reasoning_effort="none",
|
||||
tools=tools,
|
||||
drop_params=True,
|
||||
)
|
||||
print(f"SUCCESS - model: {response.model}")
|
||||
print(f"reasoning_effort='none' correctly kept (OpenAI allows this)")
|
||||
except Exception as e:
|
||||
print(f"FAILED: {e}")
|
||||
|
|
@ -1,83 +0,0 @@
|
|||
"""
|
||||
Simple regression test: call Perplexity through LiteLLM
|
||||
to verify chat completions and responses API both work.
|
||||
"""
|
||||
import os
|
||||
import sys
|
||||
from dotenv import load_dotenv
|
||||
|
||||
load_dotenv()
|
||||
|
||||
import litellm
|
||||
|
||||
# Show which branch we're on
|
||||
branch = os.popen("git rev-parse --abbrev-ref HEAD 2>/dev/null || echo unknown").read().strip()
|
||||
print(f"=== Branch: {branch} ===\n")
|
||||
|
||||
# 1. Chat completions
|
||||
print("--- Test 1: Chat Completions ---")
|
||||
try:
|
||||
resp = litellm.completion(
|
||||
model="perplexity/sonar",
|
||||
messages=[{"role": "user", "content": "Say hello in 3 words"}],
|
||||
max_tokens=20,
|
||||
)
|
||||
print(f"OK: {resp.choices[0].message.content[:80]}")
|
||||
print(f" model: {resp.model}")
|
||||
print(f" usage: {resp.usage}")
|
||||
except Exception as e:
|
||||
print(f"FAIL: {e}")
|
||||
|
||||
# 2. Responses API (string input)
|
||||
print("\n--- Test 2: Responses API (string input) ---")
|
||||
try:
|
||||
resp = litellm.responses(
|
||||
model="perplexity/sonar",
|
||||
input="Say hello in 3 words",
|
||||
max_output_tokens=20,
|
||||
)
|
||||
print(f"OK: {resp.output[0].content[0].text[:80]}")
|
||||
print(f" model: {resp.model}")
|
||||
except Exception as e:
|
||||
print(f"FAIL: {e}")
|
||||
|
||||
# 3. Responses API (list input - the _format_input concern)
|
||||
print("\n--- Test 3: Responses API (list input without type field) ---")
|
||||
try:
|
||||
resp = litellm.responses(
|
||||
model="perplexity/sonar",
|
||||
input=[{"role": "user", "content": "Say hello in 3 words"}],
|
||||
max_output_tokens=20,
|
||||
)
|
||||
print(f"OK: {resp.output[0].content[0].text[:80]}")
|
||||
except Exception as e:
|
||||
print(f"FAIL: {e}")
|
||||
|
||||
# 4. Check which config class is resolved for chat
|
||||
print("\n--- Test 4: Config class resolution ---")
|
||||
from litellm.utils import ProviderConfigManager
|
||||
from litellm.types.utils import LlmProviders
|
||||
|
||||
chat_config = ProviderConfigManager.get_provider_chat_config(
|
||||
model="perplexity/sonar", provider=LlmProviders.PERPLEXITY
|
||||
)
|
||||
print(f"Chat config class: {type(chat_config).__name__}")
|
||||
print(f" module: {type(chat_config).__module__}")
|
||||
|
||||
resp_config = ProviderConfigManager.get_provider_responses_api_config(
|
||||
provider=LlmProviders.PERPLEXITY
|
||||
)
|
||||
print(f"Responses config class: {type(resp_config).__name__}")
|
||||
print(f" module: {type(resp_config).__module__}")
|
||||
|
||||
# 5. Check supported params include preset/models for responses
|
||||
print("\n--- Test 5: Supported params ---")
|
||||
if resp_config:
|
||||
params = resp_config.get_supported_openai_params("sonar")
|
||||
print(f"Responses supported params: {params}")
|
||||
has_preset = "preset" in params
|
||||
has_models = "models" in params
|
||||
print(f" Has 'preset': {has_preset}")
|
||||
print(f" Has 'models': {has_models}")
|
||||
|
||||
print("\n=== Done ===")
|
||||
|
|
@ -1,179 +0,0 @@
|
|||
"""
|
||||
Live test: Perplexity Responses API via LiteLLM.
|
||||
Tests: non-streaming, streaming, preset models, models fallback param,
|
||||
chat completions (regression check), and cost dict→float parsing.
|
||||
|
||||
DO NOT COMMIT this file.
|
||||
"""
|
||||
|
||||
import os
|
||||
import traceback
|
||||
|
||||
from dotenv import load_dotenv
|
||||
|
||||
load_dotenv()
|
||||
|
||||
import litellm
|
||||
|
||||
# litellm.set_verbose = True
|
||||
|
||||
|
||||
def test_non_streaming_preset():
|
||||
"""Test non-streaming with preset model."""
|
||||
print("=" * 60)
|
||||
print("TEST 1: Non-streaming preset/pro-search")
|
||||
print("=" * 60)
|
||||
|
||||
response = litellm.responses(
|
||||
model="perplexity/preset/pro-search",
|
||||
input="What is 2 + 2? Answer in one word.",
|
||||
)
|
||||
|
||||
print(f" Response ID: {response.id}")
|
||||
print(f" Model: {response.model}")
|
||||
print(f" Status: {response.status}")
|
||||
|
||||
assert response.status == "completed", f"FAIL: status={response.status}"
|
||||
assert response.output, "FAIL: no output"
|
||||
print(" PASS: non-streaming preset works")
|
||||
|
||||
if response.usage and response.usage.cost is not None:
|
||||
assert isinstance(response.usage.cost, (int, float)), (
|
||||
f"FAIL: cost is {type(response.usage.cost)}: {response.usage.cost}"
|
||||
)
|
||||
print(f" PASS: cost={response.usage.cost} (float, not dict)")
|
||||
print()
|
||||
|
||||
|
||||
def test_streaming_preset():
|
||||
"""Test streaming with preset model."""
|
||||
print("=" * 60)
|
||||
print("TEST 2: Streaming preset/pro-search")
|
||||
print("=" * 60)
|
||||
|
||||
response = litellm.responses(
|
||||
model="perplexity/preset/pro-search",
|
||||
input="What is the capital of France? One word.",
|
||||
stream=True,
|
||||
)
|
||||
|
||||
chunks = 0
|
||||
completed = False
|
||||
for chunk in response:
|
||||
chunks += 1
|
||||
event_type = getattr(chunk, "type", "unknown")
|
||||
if event_type == "response.output_text.delta":
|
||||
print(f" delta: {chunk.delta}", end="", flush=True)
|
||||
elif event_type == "response.completed":
|
||||
completed = True
|
||||
print(f"\n [completed] model={chunk.response.model}")
|
||||
if chunk.response.usage and chunk.response.usage.cost is not None:
|
||||
cost = chunk.response.usage.cost
|
||||
assert isinstance(cost, (int, float)), (
|
||||
f"FAIL: streaming cost is {type(cost)}: {cost}"
|
||||
)
|
||||
print(f" PASS: streaming cost={cost} (float)")
|
||||
|
||||
assert chunks > 0, "FAIL: no chunks received"
|
||||
assert completed, "FAIL: never got response.completed event"
|
||||
print(f" Total chunks: {chunks}")
|
||||
print(" PASS: streaming preset works")
|
||||
print()
|
||||
|
||||
|
||||
def test_models_fallback_param():
|
||||
"""Test that 'models' param (Perplexity fallback chain) is forwarded."""
|
||||
print("=" * 60)
|
||||
print("TEST 3: models param (fallback chain)")
|
||||
print("=" * 60)
|
||||
|
||||
response = litellm.responses(
|
||||
model="perplexity/openai/gpt-5.1",
|
||||
input="Say 'hello' and nothing else.",
|
||||
models=["openai/gpt-5-mini", "openai/gpt-5.1"],
|
||||
)
|
||||
|
||||
print(f" Response ID: {response.id}")
|
||||
print(f" Model used: {response.model}")
|
||||
print(f" Status: {response.status}")
|
||||
|
||||
assert response.status == "completed", f"FAIL: status={response.status}"
|
||||
print(" PASS: models fallback param works")
|
||||
print()
|
||||
|
||||
|
||||
def test_chat_completions_not_broken():
|
||||
"""Regression: Perplexity chat completions must still use PerplexityChatConfig."""
|
||||
print("=" * 60)
|
||||
print("TEST 4: Chat completions regression check")
|
||||
print("=" * 60)
|
||||
|
||||
response = litellm.completion(
|
||||
model="perplexity/sonar",
|
||||
messages=[{"role": "user", "content": "Say 'hi' and nothing else."}],
|
||||
max_tokens=10,
|
||||
)
|
||||
|
||||
print(f" Model: {response.model}")
|
||||
print(f" Content: {response.choices[0].message.content[:50]}")
|
||||
|
||||
assert response.choices, "FAIL: no choices"
|
||||
assert response.choices[0].message.content, "FAIL: empty content"
|
||||
print(" PASS: chat completions still work (no regression)")
|
||||
print()
|
||||
|
||||
|
||||
def test_with_instructions():
|
||||
"""Test instructions param."""
|
||||
print("=" * 60)
|
||||
print("TEST 5: instructions param")
|
||||
print("=" * 60)
|
||||
|
||||
response = litellm.responses(
|
||||
model="perplexity/preset/pro-search",
|
||||
input="What is Python?",
|
||||
instructions="Answer in exactly 5 words.",
|
||||
)
|
||||
|
||||
print(f" Status: {response.status}")
|
||||
# Extract text from output
|
||||
for item in response.output:
|
||||
if hasattr(item, "content"):
|
||||
for c in item.content:
|
||||
if hasattr(c, "text"):
|
||||
print(f" Answer: {c.text}")
|
||||
break
|
||||
|
||||
assert response.status == "completed", f"FAIL: status={response.status}"
|
||||
print(" PASS: instructions param works")
|
||||
print()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
api_key = os.environ.get("PERPLEXITYAI_API_KEY", "NOT SET")
|
||||
print(f"Using PERPLEXITYAI_API_KEY: {api_key[:10]}...")
|
||||
print()
|
||||
|
||||
tests = [
|
||||
test_non_streaming_preset,
|
||||
test_streaming_preset,
|
||||
test_models_fallback_param,
|
||||
test_chat_completions_not_broken,
|
||||
test_with_instructions,
|
||||
]
|
||||
|
||||
passed = 0
|
||||
failed = 0
|
||||
for test in tests:
|
||||
try:
|
||||
test()
|
||||
passed += 1
|
||||
except Exception as e:
|
||||
failed += 1
|
||||
print(f" FAIL: {e}")
|
||||
traceback.print_exc()
|
||||
print()
|
||||
|
||||
print("=" * 60)
|
||||
print(f"Results: {passed} passed, {failed} failed out of {len(tests)}")
|
||||
print("=" * 60)
|
||||
|
|
@ -1,39 +0,0 @@
|
|||
"""
|
||||
Repro: gpt-5.4 + reasoning_effort='none' + tools
|
||||
Current behavior: reasoning_effort='none' is NOT dropped, but OpenAI rejects it.
|
||||
"""
|
||||
|
||||
import os
|
||||
from dotenv import load_dotenv
|
||||
|
||||
load_dotenv()
|
||||
|
||||
import litellm
|
||||
|
||||
tools = [
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_weather",
|
||||
"description": "Get current weather",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {"city": {"type": "string"}},
|
||||
"required": ["city"],
|
||||
},
|
||||
},
|
||||
}
|
||||
]
|
||||
|
||||
print("=== gpt-5.4 + reasoning_effort='none' + tools ===")
|
||||
try:
|
||||
response = litellm.completion(
|
||||
model="gpt-5.4",
|
||||
messages=[{"role": "user", "content": "What's the weather in Buenos Aires?"}],
|
||||
reasoning_effort="none",
|
||||
tools=tools,
|
||||
)
|
||||
print(f"SUCCESS - model: {response.model}")
|
||||
print(f"Choice: {response.choices[0].message}")
|
||||
except Exception as e:
|
||||
print(f"FAILED: {e}")
|
||||
|
|
@ -1,19 +0,0 @@
|
|||
"""Test gpt-5.4 with reasoning_effort + tools to see OpenAI's response."""
|
||||
import os
|
||||
from dotenv import load_dotenv
|
||||
load_dotenv()
|
||||
|
||||
import litellm
|
||||
|
||||
try:
|
||||
response = litellm.completion(
|
||||
model="gpt-5.4",
|
||||
messages=[{"role": "user", "content": "What's the weather in SF?"}],
|
||||
tools=[{"type": "function", "function": {"name": "get_weather", "description": "Get weather", "parameters": {"type": "object", "properties": {"city": {"type": "string"}}}}}],
|
||||
reasoning_effort="high",
|
||||
)
|
||||
print("SUCCESS:")
|
||||
print(response)
|
||||
except Exception as e:
|
||||
print(f"ERROR ({type(e).__name__}):")
|
||||
print(e)
|
||||
|
|
@ -1,45 +0,0 @@
|
|||
"""Post-fix verification for #23423: tool_choice with responses/ prefix."""
|
||||
import os
|
||||
from dotenv import load_dotenv
|
||||
load_dotenv()
|
||||
|
||||
import litellm
|
||||
|
||||
# Verify supports_tool_choice resolves correctly
|
||||
from litellm.utils import supports_tool_choice
|
||||
print("supports_tool_choice('gpt-5.4'):", supports_tool_choice("gpt-5.4"))
|
||||
print("supports_tool_choice('openai/responses/gpt-5.4'):", supports_tool_choice("openai/responses/gpt-5.4"))
|
||||
|
||||
# Verify tool_choice is in supported params
|
||||
params = litellm.get_supported_openai_params(model="openai/responses/gpt-5.4", custom_llm_provider="openai")
|
||||
print("tool_choice in supported params:", "tool_choice" in params)
|
||||
|
||||
# Real API call with tool_choice
|
||||
tools = [
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_weather",
|
||||
"description": "Get the weather for a city",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {"city": {"type": "string"}},
|
||||
"required": ["city"],
|
||||
},
|
||||
},
|
||||
}
|
||||
]
|
||||
|
||||
response = litellm.completion(
|
||||
model="openai/responses/gpt-4.1-nano", # cheaper model
|
||||
messages=[{"role": "user", "content": "What's the weather in Buenos Aires?"}],
|
||||
tools=tools,
|
||||
tool_choice="required",
|
||||
)
|
||||
|
||||
print("\nResponse:")
|
||||
print(" tool_calls:", response.choices[0].message.tool_calls)
|
||||
print(" finish_reason:", response.choices[0].finish_reason)
|
||||
|
||||
has_tool_call = response.choices[0].message.tool_calls is not None
|
||||
print("\nVERDICT:", "PASS - tool_choice works" if has_tool_call else "FAIL - tool_choice dropped")
|
||||
|
|
@ -1,57 +0,0 @@
|
|||
"""Test the Chat Completions Bridge tool search example from docs (line 856-887)"""
|
||||
import os
|
||||
from dotenv import load_dotenv
|
||||
load_dotenv()
|
||||
|
||||
import litellm
|
||||
|
||||
try:
|
||||
response = litellm.completion(
|
||||
model="openai/responses/gpt-5.4",
|
||||
messages=[{"role": "user", "content": "Look up invoice INV-2024-001"}],
|
||||
tools=[
|
||||
{"type": "tool_search"},
|
||||
{
|
||||
"type": "namespace",
|
||||
"name": "billing",
|
||||
"description": "Billing and invoicing tools",
|
||||
"tools": [
|
||||
{
|
||||
"type": "function",
|
||||
"name": "get_invoice",
|
||||
"description": "Get an invoice by ID",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {"invoice_id": {"type": "string"}},
|
||||
"required": ["invoice_id"],
|
||||
},
|
||||
"defer_loading": True,
|
||||
},
|
||||
],
|
||||
},
|
||||
],
|
||||
)
|
||||
|
||||
print("=== Raw response ===")
|
||||
print(f"tool_calls value: {response.choices[0].message.tool_calls}")
|
||||
print(f"tool_calls is None? {response.choices[0].message.tool_calls is None}")
|
||||
print()
|
||||
|
||||
# Test the docs code exactly as written
|
||||
print("=== Testing docs code (no None guard) ===")
|
||||
try:
|
||||
for tool_call in response.choices[0].message.tool_calls:
|
||||
print(f"Called: {tool_call.function.name}({tool_call.function.arguments})")
|
||||
except TypeError as e:
|
||||
print(f" !!! TypeError: {e}")
|
||||
print(f" Greptile was RIGHT - need 'or []' guard")
|
||||
|
||||
# Test with the fix
|
||||
print()
|
||||
print("=== Testing with fix (or [] guard) ===")
|
||||
for tool_call in (response.choices[0].message.tool_calls or []):
|
||||
print(f"Called: {tool_call.function.name}({tool_call.function.arguments})")
|
||||
print(" OK - no crash")
|
||||
|
||||
except Exception as e:
|
||||
print(f"API Error: {type(e).__name__}: {e}")
|
||||
|
|
@ -1,101 +0,0 @@
|
|||
"""Test the Responses API tool search example from docs (line 705-783)"""
|
||||
import os
|
||||
from dotenv import load_dotenv
|
||||
load_dotenv()
|
||||
|
||||
import litellm
|
||||
import json
|
||||
|
||||
# Define namespaces with deferred tools
|
||||
tools = [
|
||||
{"type": "tool_search"}, # Enable tool search
|
||||
{
|
||||
"type": "namespace",
|
||||
"name": "crm",
|
||||
"description": "CRM tools for customer management",
|
||||
"tools": [
|
||||
{
|
||||
"type": "function",
|
||||
"name": "get_customer",
|
||||
"description": "Get customer details by ID",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"customer_id": {"type": "string"}
|
||||
},
|
||||
"required": ["customer_id"],
|
||||
},
|
||||
"defer_loading": True,
|
||||
},
|
||||
{
|
||||
"type": "function",
|
||||
"name": "list_customers",
|
||||
"description": "List customers with optional filters",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"status": {"type": "string", "enum": ["active", "inactive"]},
|
||||
},
|
||||
},
|
||||
"defer_loading": True,
|
||||
},
|
||||
],
|
||||
},
|
||||
{
|
||||
"type": "namespace",
|
||||
"name": "billing",
|
||||
"description": "Billing and invoicing tools",
|
||||
"tools": [
|
||||
{
|
||||
"type": "function",
|
||||
"name": "get_invoice",
|
||||
"description": "Get an invoice by ID",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"invoice_id": {"type": "string"}
|
||||
},
|
||||
"required": ["invoice_id"],
|
||||
},
|
||||
"defer_loading": True,
|
||||
},
|
||||
],
|
||||
},
|
||||
]
|
||||
|
||||
try:
|
||||
response = litellm.responses(
|
||||
model="openai/gpt-5.4",
|
||||
input="Look up invoice INV-2024-001 from the billing system",
|
||||
tools=tools,
|
||||
)
|
||||
|
||||
print("=== Raw response.output ===")
|
||||
print(response.output)
|
||||
print()
|
||||
|
||||
# Test the parsing code from the docs
|
||||
print("=== Parsing output items ===")
|
||||
for item in response.output:
|
||||
print(f" item type: {type(item)}")
|
||||
if isinstance(item, dict):
|
||||
print(f" dict keys: {item.keys()}")
|
||||
if item["type"] == "tool_search_call":
|
||||
print(f"Searched namespaces: {item['arguments']['paths']}")
|
||||
elif item["type"] == "tool_search_output":
|
||||
print(f"Loaded {len(item['tools'])} tool(s)")
|
||||
elif item["type"] == "function_call":
|
||||
print(f"Called: {item.get('namespace', '')}.{item['name']}({item['arguments']})")
|
||||
else:
|
||||
print(f" object attrs: {dir(item)}")
|
||||
if item.type == "function_call":
|
||||
# Greptile says this will fail if namespace is missing
|
||||
print(f" Has 'namespace' attr? {hasattr(item, 'namespace')}")
|
||||
try:
|
||||
print(f"Called: {item.namespace}.{item.name}({item.arguments})")
|
||||
except AttributeError as e:
|
||||
print(f" !!! AttributeError: {e}")
|
||||
print(f" Greptile was RIGHT - need getattr fallback")
|
||||
|
||||
except Exception as e:
|
||||
print(f"API Error: {type(e).__name__}: {e}")
|
||||
Loading…
Add table
Reference in a new issue