microsoft
Graphrag local search vs global search vs normal RAG
Description This pull request introduces a new Jupyter Notebook example that demonstrates how to compare the results from standard RAG, GraphRAG with local search, and GraphRAG with global search.
The goal is to offer users a clear, hands-on way to evaluate the effectiveness of different GraphRAG search strategies against a baseline RAG implementation for their own datasets and questions.
Proposed Changes Input: The notebook takes a path to the root directory and some questions for comparison. it generates answers for the provided questions using three distinct methods: Evaluation: The generated answers from all three methods are then passed to a Language Model (LLM) which is prompted to provide ratings based on completeness, directness, empowerment, and diversity. Output: The notebook produces: Four Parquet tables containing the questions, the generated answers from each method, and their corresponding LLM-generated scores. A plot that visually summarizes the scores, allowing for an easy comparison of the performance across the different RAG methods.
[Yes] I have tested these changes locally. [Yes] I have reviewed the code changes. [Yes] I have updated the documentation (if necessary, e.g., adding a mention of the new example). [No] I have added appropriate unit tests (if applicable, though less common for example notebooks).
