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MapReduce Chains in LLM Applications
In the context of LLM Applications, MapReduce streamlines document processing and analysis. It operates on the principle of dividing a task into smaller chunks, processing these chunks in parallel (the Map step), and then combining the results into a unified output (the Reduce step).
The LLM chain consists of a sequence of LLMs performing various natural language tasks like extraction, summarization, and translation. The combine documents chain merges multiple documents into one cohesive output.
The process begins by taking an initial document as input, applying the LLM chain to perform specified tasks, and generating mapped documents. These mapped documents undergo optional compression to fit into the combine documents chain. Finally, the combine documents chain integrates the mapped documents to produce a single output document containing summaries, synthesis, or recommendations based on the input.
Let’s be a little repetitive and go pointwise:
- In the Map step, an LLM chain is applied to each sub-document individually. The LLM chain is a sequence of LLMs that perform different natural language tasks, such as extraction, summarization, translation, etc.
- Each sub document is processed in parallel using…
