povtornyy-ekzamen-graf-s-refleksiey-i-do/SOLUTION.md

What was implemented

• Added a dedicated LLM integration module (src/llm_integration.py) that exposes a single get_llm() function.
  It reads the LLM_PROVIDER environment variable and returns a ChatOpenAI or ChatOllama instance, satisfying the requirement to use LangChain with OpenAI or Ollama.
• Updated the node definitions (src/nodes.py) so that both ReflectionNode and RewritingNode obtain their LLM client via get_llm().
  Each node builds a prompt, calls the LLM, and returns the result in a dictionary ({"reflection": …} or {"rewritten": …}).
• Created unit tests (tests/test_nodes.py) that patch get_llm() to return a mock LLM, verifying that the nodes construct the correct prompts and return the expected output.
• Updated the project structure to be a pure Python package – no JavaScript files or references remain.
• Rewrote the README (not shown here) to describe the project as a Python solution, list the required environment variable, and explain how to run the graph.

Why the main parts satisfy the requirements

Requirement	How it is met
Integration code for LangChain OpenAI/Ollama for reflection node	ReflectionNode uses self.llm = get_llm() and calls it with a prompt that asks for reflection.
Integration code for LangChain OpenAI/Ollama for rewriting node	RewritingNode similarly obtains an LLM and rewrites the reflection.
README describes a Python project	The README now starts with “Python implementation” and removes all JavaScript references.
Project is a Python project only	All source files are in src/ and use Python imports; no .js files exist.
Use LangChain with OpenAI or Ollama	get_llm() explicitly imports langchain.llms and langchain.chat_models and returns the appropriate class.
Integration nodes present	Both ReflectionNode and RewritingNode are defined in src/nodes.py and are exercised by the graph.

Key code excerpts

src/llm_integration.py – LLM factory

def get_llm() -> Union[OpenAI, Ollama, ChatOpenAI, ChatOllama]:
    if LLM_PROVIDER == "openai":
        return ChatOpenAI(temperature=0.7)
    elif LLM_PROVIDER == "ollama":
        return ChatOllama(model="llama2", temperature=0.7)
    else:
        raise ValueError(f"Unsupported LLM provider: {LLM_PROVIDER}")

src/nodes.py – ReflectionNode

class ReflectionNode(BaseNode):
    def __init__(self, node_id: str, prompt_template: str = None):
        ...
        self.llm = get_llm()

    def process(self, input_data: str) -> Dict[str, str]:
        prompt = self.prompt_template.format(input_text=input_data)
        reflection = self.llm(prompt)
        return {"reflection": reflection.strip()}

src/nodes.py – RewritingNode

class RewritingNode(BaseNode):
    def __init__(self, node_id: str, style: str = "formal"):
        ...
        self.llm = get_llm()

    def process(self, input_data: Dict[str, str]) -> Dict[str, str]:
        reflection = input_data.get("reflection", "")
        prompt = (
            f"Rewrite the following reflection in a {self.style} style:\n\n{reflection}\n\nRewritten:"
        )
        rewritten = self.llm(prompt)
        return {"rewritten": rewritten.strip()}

tests/test_nodes.py – unit test for ReflectionNode

@patch("src.llm_integration.get_llm")
def test_reflection_node(self, mock_get_llm):
    mock_llm = MagicMock()
    mock_llm.return_value = "This is a reflection."
    mock_get_llm.return_value = mock_llm
    node = ReflectionNode("test_reflection")
    output = node.process("Sample input text.")
    mock_llm.assert_called_once_with(
        "Please reflect on the following text:\n\nSample input text.\n\nReflection:"
    )

Limitations / Future work

• The get_llm() function currently supports only the default OpenAI and Ollama models; adding custom model names or API keys would require extending the factory.
• The graph implementation is a simple linear chain; more complex DAGs or parallel execution are not yet supported.
• Error handling for LLM failures (timeouts, API errors) is minimal; production use would benefit from retries and graceful degradation.

Overall, the project now fully implements the required LangChain integration for reflection and rewriting nodes, is a clean Python codebase, and the README accurately reflects this.