Unverified50% confidenceFactExact time
The mainstream approaches to dealing with the statelessness of large models fall into three categories: directly concatenating conversation history into the context window, encoding memories as embedding vectors in a vector database for retrieval, and having the model autonomously decide which information is worth writing to persistent storage and reading it back on demand.
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9/18/2026
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VerifiedVector memory databases convert historical conversation content into high-dimensional semantic vectors, and during new conversations dynamically retrieve relevant memory fragments via similarity search and inject them into the context window.75% similarUnverifiedMainstream solutions for addressing the limited context window of large language models include vector database retrieval-augmented generation (RAG), summary compression, and external memory storage.71% similarUnverified在 LLM 场景下，黑板系统思路以共享内存或向量数据库形式复现，使用 Pinecone、Weaviate 等向量数据库存储中间知识片段供语义检索71% similarUnverified向量数据库是支撑Agent长期记忆的关键基础设施，文本通过嵌入模型转换为高维向量，语义相近的文本在高维空间中距离更近70% similarUnverifiedPersistent memory typically relies on vector databases for semantic retrieval, converting information into high-dimensional dense vectors via embedding models and storing them in systems such as Qdrant, Pinecone, Weaviate, and Chroma.69% similar
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