Unverified50% confidenceSolutionExact time
Combining a structured external database with an LLM is typically achieved through two approaches: encapsulating database queries as Tools for the Agent to call on demand, or preprocessing data into vector embeddings stored in a vector database for RAG semantic retrieval.
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50%
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Long-term
Relevance
9/19/2026
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UnverifiedTraditional agent memory management solutions typically rely on vector databases for semantic retrieval, or dynamically fetch relevant information via RAG72% similarUnverifiedThe "query–analyze–generate–distribute" pipeline involves multiple coordinating modules, including a data ingestion layer, an LLM orchestration layer, a tool execution layer, and an output distribution layer.72% similarUnverified解决大模型实时数据问题有两类方案：通过RAG接入实时数据库，或为Agent配备可调用实时API的工具集72% similarUnverifiedLlamaIndex focuses on data connectivity and multi-level index management, while LangChain provides a more complete framework for chained calls and workflow orchestration.71% similar
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