Map Reduce

MapReduce is a design pattern suitable when you have either:

• Large input data (e.g., multiple files to process), or
• Large output data (e.g., multiple forms to fill)

and there is a logical way to break the task into smaller, ideally independent parts.

You first break down the task using BatchNode in the map phase, followed by aggregation in the reduce phase.

Example: Document Summarization

class SummarizeAllFiles(BatchNode):
    def prep(self, shared):
        files_dict = shared["files"]  # e.g. 10 files
        return list(files_dict.items())  # [("file1.txt", "aaa..."), ("file2.txt", "bbb..."), ...]

    def exec(self, one_file):
        filename, file_content = one_file
        summary_text = call_llm(f"Summarize the following file:\n{file_content}")
        return (filename, summary_text)

    def post(self, shared, prep_res, exec_res_list):
        shared["file_summaries"] = dict(exec_res_list)

class CombineSummaries(Node):
    def prep(self, shared):
        return shared["file_summaries"]

    def exec(self, file_summaries):
        # format as: "File1: summary\nFile2: summary...\n"
        text_list = []
        for fname, summ in file_summaries.items():
            text_list.append(f"{fname} summary:\n{summ}\n")
        big_text = "\n---\n".join(text_list)

        return call_llm(f"Combine these file summaries into one final summary:\n{big_text}")

    def post(self, shared, prep_res, final_summary):
        shared["all_files_summary"] = final_summary

batch_node = SummarizeAllFiles()
combine_node = CombineSummaries()
batch_node >> combine_node

flow = Flow(start=batch_node)

shared = {
    "files": {
        "file1.txt": "Alice was beginning to get very tired of sitting by her sister...",
        "file2.txt": "Some other interesting text ...",
        # ...
    }
}
flow.run(shared)
print("Individual Summaries:", shared["file_summaries"])
print("\nFinal Summary:\n", shared["all_files_summary"])

Performance Tip: The example above works sequentially. You can speed up the map phase by running it in parallel. See (Advanced) Parallel for more details.