implementing initial LLM class

tests/agent_test.py

@@ -51,6 +51,50 @@ def test_custom_llm_with_langchain():
     assert agent.llm == "gpt-4"
 
 
+def test_custom_llm_temperature_preservation():
+    from langchain_openai import ChatOpenAI
+
+    langchain_llm = ChatOpenAI(temperature=0.7, model="gpt-4")
+    agent = Agent(
+        role="temperature test role",
+        goal="temperature test goal",
+        backstory="temperature test backstory",
+        llm=langchain_llm,
+    )
+
+    assert isinstance(agent.llm, LLM)
+    assert agent.llm.model == "gpt-4"
+    assert agent.llm.temperature == 0.7
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_execute_task():
+    from langchain_openai import ChatOpenAI
+    from crewai import Task
+
+    agent = Agent(
+        role="Math Tutor",
+        goal="Solve math problems accurately",
+        backstory="You are an experienced math tutor with a knack for explaining complex concepts simply.",
+        llm=ChatOpenAI(temperature=0.7, model="gpt-4o-mini"),
+    )
+
+    task = Task(
+        description="Calculate the area of a circle with radius 5 cm.",
+        expected_output="The calculated area of the circle in square centimeters.",
+        agent=agent,
+    )
+
+    result = agent.execute_task(task)
+
+    assert result is not None
+    assert (
+        "The area of a circle with a radius of 5 cm is calculated using the formula A = πr^2, where A is the area and r is the...in the values, we get A = π*5^2 = 25π square centimeters. Therefore, the area of the circle is 25π square centimeters."
+        in result
+    )
+    assert "square centimeters" in result.lower()
+
+
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_agent_execution():
     agent = Agent(
@@ -67,7 +111,7 @@ def test_agent_execution():
     )
 
     output = agent.execute_task(task)
-    assert output == "1 + 1 = 2"
+    assert output == "The result of the math operation 1 + 1 is 2."
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -182,7 +226,7 @@ def test_cache_hitting():
         task = Task(
             description="What is 2 times 6? Ignore correctness and just return the result of the multiplication tool, you must use the tool.",
             agent=agent,
-            expected_output="The number that is the result of the multiplication.",
+            expected_output="The number that is the result of the multiplication tool.",
         )
         output = agent.execute_task(task)
         assert output == "0"
@@ -1102,6 +1146,88 @@ def test_agent_max_retry_limit():
         )
 
 
+def test_agent_with_llm():
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(model="gpt-3.5-turbo", temperature=0.7),
+    )
+
+    assert isinstance(agent.llm, LLM)
+    assert agent.llm.model == "gpt-3.5-turbo"
+    assert agent.llm.temperature == 0.7
+
+
+def test_agent_with_custom_stop_words():
+    stop_words = ["STOP", "END"]
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(model="gpt-3.5-turbo", stop=stop_words),
+    )
+
+    assert isinstance(agent.llm, LLM)
+    assert agent.llm.stop == stop_words
+
+
+def test_agent_with_callbacks():
+    def dummy_callback(response):
+        pass
+
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(model="gpt-3.5-turbo", callbacks=[dummy_callback]),
+    )
+
+    assert isinstance(agent.llm, LLM)
+    assert len(agent.llm.callbacks) == 1
+    assert agent.llm.callbacks[0] == dummy_callback
+
+
+def test_agent_with_additional_kwargs():
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(
+            model="gpt-3.5-turbo",
+            temperature=0.8,
+            top_p=0.9,
+            presence_penalty=0.1,
+            frequency_penalty=0.1,
+        ),
+    )
+
+    assert isinstance(agent.llm, LLM)
+    assert agent.llm.model == "gpt-3.5-turbo"
+    assert agent.llm.temperature == 0.8
+    assert agent.llm.top_p == 0.9
+    assert agent.llm.presence_penalty == 0.1
+    assert agent.llm.frequency_penalty == 0.1
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_llm_call():
+    llm = LLM(model="gpt-3.5-turbo")
+    messages = [{"role": "user", "content": "Say 'Hello, World!'"}]
+
+    response = llm.call(messages)
+    assert "Hello, World!" in response
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_llm_call_with_error():
+    llm = LLM(model="non-existent-model")
+    messages = [{"role": "user", "content": "This should fail"}]
+
+    with pytest.raises(Exception):
+        llm.call(messages)
+
+
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_handle_context_length_exceeds_limit():
     agent = Agent(
@@ -1172,3 +1298,213 @@ def test_handle_context_length_exceeds_limit_cli_no():
             CrewAgentExecutor, "_handle_context_length"
         ) as mock_handle_context:
             mock_handle_context.assert_not_called()
+
+
+def test_agent_with_all_llm_attributes():
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(
+            model="gpt-3.5-turbo",
+            timeout=10,
+            temperature=0.7,
+            top_p=0.9,
+            n=1,
+            stop=["STOP", "END"],
+            max_tokens=100,
+            presence_penalty=0.1,
+            frequency_penalty=0.1,
+            logit_bias={50256: -100},  # Example: bias against the EOT token
+            response_format={"type": "json_object"},
+            seed=42,
+            logprobs=True,
+            top_logprobs=5,
+            base_url="https://api.openai.com/v1",
+            api_version="2023-05-15",
+            api_key="sk-your-api-key-here",
+        ),
+    )
+
+    assert isinstance(agent.llm, LLM)
+    assert agent.llm.model == "gpt-3.5-turbo"
+    assert agent.llm.timeout == 10
+    assert agent.llm.temperature == 0.7
+    assert agent.llm.top_p == 0.9
+    assert agent.llm.n == 1
+    assert agent.llm.stop == ["STOP", "END"]
+    assert agent.llm.max_tokens == 100
+    assert agent.llm.presence_penalty == 0.1
+    assert agent.llm.frequency_penalty == 0.1
+    assert agent.llm.logit_bias == {50256: -100}
+    assert agent.llm.response_format == {"type": "json_object"}
+    assert agent.llm.seed == 42
+    assert agent.llm.logprobs
+    assert agent.llm.top_logprobs == 5
+    assert agent.llm.base_url == "https://api.openai.com/v1"
+    assert agent.llm.api_version == "2023-05-15"
+    assert agent.llm.api_key == "sk-your-api-key-here"
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_llm_call_with_all_attributes():
+    llm = LLM(
+        model="gpt-3.5-turbo",
+        temperature=0.7,
+        max_tokens=50,
+        stop=["STOP"],
+        presence_penalty=0.1,
+        frequency_penalty=0.1,
+    )
+    messages = [{"role": "user", "content": "Say 'Hello, World!' and then say STOP"}]
+
+    response = llm.call(messages)
+    assert "Hello, World!" in response
+    assert "STOP" not in response
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_with_ollama_gemma():
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(
+            model="ollama/gemma2:latest",
+            base_url="http://localhost:8080",
+        ),
+    )
+
+    assert isinstance(agent.llm, LLM)
+    assert agent.llm.model == "ollama/gemma2:latest"
+    assert agent.llm.base_url == "http://localhost:8080"
+
+    task = "Respond in 20 words. Who are you?"
+    response = agent.llm.call([{"role": "user", "content": task}])
+
+    assert response
+    assert len(response.split()) <= 25  # Allow a little flexibility in word count
+    assert "Gemma" in response or "AI" in response or "language model" in response
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_llm_call_with_ollama_gemma():
+    llm = LLM(
+        model="ollama/gemma2:latest",
+        base_url="http://localhost:8080",
+        temperature=0.7,
+        max_tokens=30,
+    )
+    messages = [{"role": "user", "content": "Respond in 20 words. Who are you?"}]
+
+    response = llm.call(messages)
+
+    assert response
+    assert len(response.split()) <= 25  # Allow a little flexibility in word count
+    assert "Gemma" in response or "AI" in response or "language model" in response
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_execute_task_basic():
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(model="gpt-3.5-turbo"),
+    )
+
+    task = Task(
+        description="Calculate 2 + 2",
+        expected_output="The result of the calculation",
+        agent=agent,
+    )
+
+    result = agent.execute_task(task)
+    assert "4" in result
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_execute_task_with_context():
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(model="gpt-3.5-turbo"),
+    )
+
+    task = Task(
+        description="Summarize the given context in one sentence",
+        expected_output="A one-sentence summary",
+        agent=agent,
+    )
+
+    context = "The quick brown fox jumps over the lazy dog. This sentence contains every letter of the alphabet."
+
+    result = agent.execute_task(task, context=context)
+    assert len(result.split(".")) == 3
+    assert "fox" in result.lower() and "dog" in result.lower()
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_execute_task_with_tool():
+    @tool
+    def dummy_tool(query: str) -> str:
+        """Useful for when you need to get a dummy result for a query."""
+        return f"Dummy result for: {query}"
+
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(model="gpt-3.5-turbo"),
+        tools=[dummy_tool],
+    )
+
+    task = Task(
+        description="Use the dummy tool to get a result for 'test query'",
+        expected_output="The result from the dummy tool",
+        agent=agent,
+    )
+
+    result = agent.execute_task(task)
+    assert "Dummy result for: test query" in result
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_execute_task_with_custom_llm():
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(model="gpt-3.5-turbo", temperature=0.7, max_tokens=50),
+    )
+
+    task = Task(
+        description="Write a haiku about AI",
+        expected_output="A haiku (3 lines, 5-7-5 syllable pattern) about AI",
+        agent=agent,
+    )
+
+    result = agent.execute_task(task)
+    assert len(result.split("\n")) == 3
+    assert "AI" in result or "artificial intelligence" in result.lower()
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_execute_task_with_ollama():
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        llm=LLM(model="ollama/gemma2:latest", base_url="http://localhost:8080"),
+    )
+
+    task = Task(
+        description="Explain what AI is in one sentence",
+        expected_output="A one-sentence explanation of AI",
+        agent=agent,
+    )
+
+    result = agent.execute_task(task)
+    assert len(result.split(".")) == 2
+    assert "AI" in result or "artificial intelligence" in result.lower()