Method prep

nodes.py:85–116

(self, shared)

class IdentifyAbstractions(Node):
    def prep(self, shared):
        files_data = shared["files"]
        project_name = shared["project_name"]  # Get project name
        language = shared.get("language", "english")  # Get language
        use_cache = shared.get("use_cache", True)  # Get use_cache flag, default to True
        max_abstraction_num = shared.get("max_abstraction_num", 10)  # Get max_abstraction_num, default to 10

        # Helper to create context from files, respecting limits (basic example)
        def create_llm_context(files_data):
            context = ""
            file_info = []  # Store tuples of (index, path)
            for i, (path, content) in enumerate(files_data):
                entry = f"--- File Index {i}: {path} ---\n{content}\n\n"
                context += entry
                file_info.append((i, path))

            return context, file_info  # file_info is list of (index, path)

        context, file_info = create_llm_context(files_data)
        # Format file info for the prompt (comment is just a hint for LLM)
        file_listing_for_prompt = "\n".join(
            [f"- {idx} # {path}" for idx, path in file_info]
        )
        return (
            context,
            file_listing_for_prompt,
            len(files_data),
            project_name,
            language,
            use_cache,
            max_abstraction_num,
        )  # Return all parameters

    def exec(self, prep_res):
        (