hanjiale/Temporal-GraphRAG
Official code for ''RAG Meets Temporal Graphs: Time-Sensitive Modeling and Retrieval for Evolving Knowledge''.
This project helps financial analysts, market researchers, or business strategists understand how information evolves over time within large document sets like earnings call transcripts. It processes your text documents (like reports or articles) and extracts key information, organizing it into a 'time-aware' knowledge graph. The output is more accurate, context-rich answers to your questions, especially those related to specific time periods or trends, enabling you to track how financial concepts, companies, or events change over time.
Use this if you need to ask precise, time-sensitive questions about a constantly updated body of documents and require answers that reflect the most current or relevant information for a given period.
Not ideal if your documents contain static information that rarely changes or if you only need simple fact retrieval without any temporal context.
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Language
Python
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Last pushed
Feb 25, 2026
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