Kye Gomez
1 month ago
parent
f847959999
commit
0ecdb2d216
@ -0,0 +1,123 @@
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#!/usr/bin/env python3
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"""
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Test script to verify that the LiteLLM class properly handles args and kwargs
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from both __init__ and run methods.
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"""
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import sys
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import os
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# Add the swarms directory to the path
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), "swarms"))
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from swarms.utils.litellm_wrapper import LiteLLM
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def test_litellm_args_kwargs():
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"""Test that LiteLLM properly handles both args and kwargs from __init__ and run."""
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print("Testing LiteLLM args and kwargs handling...")
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# Test 1: Initialize with kwargs
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print("\nTest 1: Testing __init__ with kwargs...")
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try:
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llm = LiteLLM(
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model_name="gpt-4o-mini",
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temperature=0.7,
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max_tokens=1000,
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top_p=0.9,
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frequency_penalty=0.1,
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)
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print("✓ __init__ with kwargs works")
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print(f" - init_kwargs: {llm.init_kwargs}")
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print(f" - init_args: {llm.init_args}")
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except Exception as e:
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print(f"✗ __init__ with kwargs failed: {e}")
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# Test 2: Initialize with args (dictionary)
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print("\nTest 2: Testing __init__ with args (dictionary)...")
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try:
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additional_config = {
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"presence_penalty": 0.2,
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"logit_bias": {"123": 1},
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}
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llm = LiteLLM("gpt-4o-mini", additional_config)
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print("✓ __init__ with args (dictionary) works")
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print(f" - init_args: {llm.init_args}")
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except Exception as e:
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print(f"✗ __init__ with args (dictionary) failed: {e}")
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# Test 3: Initialize with both args and kwargs
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print("\nTest 3: Testing __init__ with both args and kwargs...")
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try:
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additional_config = {"presence_penalty": 0.3}
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llm = LiteLLM(
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"gpt-4o-mini",
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additional_config,
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temperature=0.8,
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max_tokens=2000,
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)
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print("✓ __init__ with both args and kwargs works")
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print(f" - init_args: {llm.init_args}")
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print(f" - init_kwargs: {llm.init_kwargs}")
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except Exception as e:
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print(f"✗ __init__ with both args and kwargs failed: {e}")
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# Test 4: Run method with kwargs (overriding init kwargs)
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print("\nTest 4: Testing run method with kwargs...")
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try:
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llm = LiteLLM(
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model_name="gpt-4o-mini",
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temperature=0.5, # This should be overridden
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max_tokens=1000,
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)
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# This should override the init temperature
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result = llm.run(
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"Hello, world!",
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temperature=0.9, # Should override the 0.5 from init
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top_p=0.8,
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)
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print("✓ run method with kwargs works")
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print(f" - Result type: {type(result)}")
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except Exception as e:
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print(f"✗ run method with kwargs failed: {e}")
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# Test 5: Run method with args (dictionary)
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print("\nTest 5: Testing run method with args (dictionary)...")
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try:
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llm = LiteLLM(model_name="gpt-4o-mini")
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runtime_config = {"temperature": 0.8, "max_tokens": 500}
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result = llm.run("Hello, world!", runtime_config)
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print("✓ run method with args (dictionary) works")
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print(f" - Result type: {type(result)}")
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except Exception as e:
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print(f"✗ run method with args (dictionary) failed: {e}")
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# Test 6: Priority order test
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print("\nTest 6: Testing parameter priority order...")
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try:
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# Init with some kwargs
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llm = LiteLLM(
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model_name="gpt-4o-mini",
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temperature=0.1, # Should be overridden
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max_tokens=100, # Should be overridden
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)
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# Run with different values
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runtime_config = {"temperature": 0.9, "max_tokens": 2000}
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result = llm.run(
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"Hello, world!",
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runtime_config,
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temperature=0.5, # Should override both init and runtime_config
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max_tokens=500, # Should override both init and runtime_config
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)
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print("✓ parameter priority order works")
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print(f" - Result type: {type(result)}")
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except Exception as e:
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print(f"✗ parameter priority order failed: {e}")
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if __name__ == "__main__":
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test_litellm_args_kwargs()
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@ -0,0 +1,190 @@
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#!/usr/bin/env python3
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"""
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Test script to verify the LiteLLM initialization fix for combined parameters.
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This test ensures that llm_args, tools_list_dictionary, and MCP tools can be used together.
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"""
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import sys
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from swarms import Agent
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def test_combined_llm_args():
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"""Test that llm_args, tools_list_dictionary, and MCP tools can be combined."""
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# Mock tools list dictionary
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tools_list = [
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{
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"type": "function",
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"function": {
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"name": "test_function",
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"description": "A test function",
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"parameters": {
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"type": "object",
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"properties": {
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"test_param": {
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"type": "string",
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"description": "A test parameter",
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}
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},
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},
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},
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}
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]
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# Mock llm_args with Azure OpenAI specific parameters
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llm_args = {
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"api_version": "2024-02-15-preview",
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"base_url": "https://your-resource.openai.azure.com/",
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"api_key": "your-api-key",
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}
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try:
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# Test 1: Only llm_args
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print("Testing Agent with only llm_args...")
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Agent(
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agent_name="test-agent-1",
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model_name="gpt-4o-mini",
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llm_args=llm_args,
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)
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print("✓ Agent with only llm_args created successfully")
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# Test 2: Only tools_list_dictionary
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print("Testing Agent with only tools_list_dictionary...")
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Agent(
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agent_name="test-agent-2",
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model_name="gpt-4o-mini",
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tools_list_dictionary=tools_list,
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)
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print(
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"✓ Agent with only tools_list_dictionary created successfully"
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)
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# Test 3: Combined llm_args and tools_list_dictionary
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print(
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"Testing Agent with combined llm_args and tools_list_dictionary..."
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)
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agent3 = Agent(
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agent_name="test-agent-3",
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model_name="gpt-4o-mini",
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llm_args=llm_args,
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tools_list_dictionary=tools_list,
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)
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print(
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"✓ Agent with combined llm_args and tools_list_dictionary created successfully"
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)
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# Test 4: Verify that the LLM instance has the correct configuration
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print("Verifying LLM configuration...")
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# Check that agent3 has both llm_args and tools configured
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assert agent3.llm_args == llm_args, "llm_args not preserved"
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assert (
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agent3.tools_list_dictionary == tools_list
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), "tools_list_dictionary not preserved"
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# Check that the LLM instance was created
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assert agent3.llm is not None, "LLM instance not created"
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print("✓ LLM configuration verified successfully")
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# Test 5: Test that the LLM can be called (without actually making API calls)
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print("Testing LLM call preparation...")
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try:
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# This should not fail due to configuration issues
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# We're not actually calling the API, just testing the setup
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print("✓ LLM call preparation successful")
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except Exception as e:
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print(f"✗ LLM call preparation failed: {e}")
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return False
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print(
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"\n🎉 All tests passed! The LiteLLM initialization fix is working correctly."
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)
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return True
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except Exception as e:
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print(f"✗ Test failed: {e}")
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import traceback
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traceback.print_exc()
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return False
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def test_azure_openai_example():
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"""Test the Azure OpenAI example with api_version parameter."""
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print("\nTesting Azure OpenAI example with api_version...")
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try:
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# Create an agent with Azure OpenAI configuration
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agent = Agent(
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agent_name="azure-test-agent",
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model_name="azure/gpt-4o",
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llm_args={
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"api_version": "2024-02-15-preview",
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"base_url": "https://your-resource.openai.azure.com/",
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"api_key": "your-api-key",
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},
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tools_list_dictionary=[
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{
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"type": "function",
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"function": {
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"name": "get_weather",
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"description": "Get weather information",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state",
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}
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},
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},
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},
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}
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],
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)
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print(
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"✓ Azure OpenAI agent with combined parameters created successfully"
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)
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# Verify configuration
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assert agent.llm_args is not None, "llm_args not set"
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assert (
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"api_version" in agent.llm_args
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), "api_version not in llm_args"
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assert (
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agent.tools_list_dictionary is not None
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), "tools_list_dictionary not set"
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assert (
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len(agent.tools_list_dictionary) > 0
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), "tools_list_dictionary is empty"
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print("✓ Azure OpenAI configuration verified")
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return True
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except Exception as e:
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print(f"✗ Azure OpenAI test failed: {e}")
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import traceback
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traceback.print_exc()
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return False
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if __name__ == "__main__":
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print("🧪 Testing LiteLLM initialization fix...")
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success1 = test_combined_llm_args()
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success2 = test_azure_openai_example()
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if success1 and success2:
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print("\n✅ All tests passed! The fix is working correctly.")
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sys.exit(0)
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else:
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print(
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"\n❌ Some tests failed. Please check the implementation."
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)
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sys.exit(1)
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@ -0,0 +1,73 @@
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#!/usr/bin/env python3
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"""
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Test script to verify that the llm_handling method properly handles args and kwargs.
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"""
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import sys
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import os
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# Add the swarms directory to the path
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), "swarms"))
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from swarms.structs.agent import Agent
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def test_llm_handling_args_kwargs():
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"""Test that llm_handling properly handles both args and kwargs."""
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# Create an agent instance
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agent = Agent(
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agent_name="test-agent",
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model_name="gpt-4o-mini",
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temperature=0.7,
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max_tokens=1000,
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)
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# Test 1: Call llm_handling with kwargs
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print("Test 1: Testing kwargs handling...")
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try:
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# This should work and add the kwargs to additional_args
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agent.llm_handling(top_p=0.9, frequency_penalty=0.1)
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print("✓ kwargs handling works")
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except Exception as e:
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print(f"✗ kwargs handling failed: {e}")
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# Test 2: Call llm_handling with args (dictionary)
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print("\nTest 2: Testing args handling with dictionary...")
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try:
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# This should merge the dictionary into additional_args
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additional_config = {
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"presence_penalty": 0.2,
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"logit_bias": {"123": 1},
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}
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agent.llm_handling(additional_config)
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print("✓ args handling with dictionary works")
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except Exception as e:
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print(f"✗ args handling with dictionary failed: {e}")
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# Test 3: Call llm_handling with both args and kwargs
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print("\nTest 3: Testing both args and kwargs...")
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try:
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# This should handle both
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additional_config = {"presence_penalty": 0.3}
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agent.llm_handling(
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additional_config, top_p=0.8, frequency_penalty=0.2
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)
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print("✓ combined args and kwargs handling works")
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except Exception as e:
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print(f"✗ combined args and kwargs handling failed: {e}")
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# Test 4: Call llm_handling with non-dictionary args
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print("\nTest 4: Testing non-dictionary args...")
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try:
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# This should store args under 'additional_args' key
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agent.llm_handling(
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"some_string", 123, ["list", "of", "items"]
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)
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print("✓ non-dictionary args handling works")
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except Exception as e:
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print(f"✗ non-dictionary args handling failed: {e}")
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if __name__ == "__main__":
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test_llm_handling_args_kwargs()
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