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Update 0-architecture.md (#961)
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@ -65,7 +65,7 @@ Our underlying dataframe technology may change over time, but our primary goal i
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### LLM Caching
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The GraphRAG library was designed with LLM interactions in mind, and a common setback when working with LLM APIs is various errors errors due to network latency, throttling, etc..
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The GraphRAG library was designed with LLM interactions in mind, and a common setback when working with LLM APIs is various errors due to network latency, throttling, etc..
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Because of these potential error cases, we've added a cache layer around LLM interactions.
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When completion requests are made using the same input set (prompt and tuning parameters), we return a cached result if one exists.
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This allows our indexer to be more resilient to network issues, to act idempotently, and to provide a more efficient end-user experience.
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