Notion MCP Server V2

# 🧠 Understanding Memory in LLMs By default, LLMs like GPT do not come with memory. But what does it really mean to **give memory** to a language model? Memory is not something built into the model itself. Instead, it's an external system — a combination of: - ✅ Tools - ✅ Databases - ✅ Strategies Together, these simulate memory by: - Deciding **what to store** - Figuring out **how to retrieve it later** - And most importantly, **how to insert the retrieved data into the model's prompt** to guide its behavior ## 📊 Visualizing the Memory System  In this schema, we break down how an intelligent, memory-aware system builds prompts dynamically. The LLM receives **structured input**, made up of several components: - **🧾 Instructions**: Rules that guide the model's tone, goals, and behavior - **🙋 User Info**: Pulled from databases, this may include preferences, personality, or history - **🧠 Chat History Summary**: Condensed summaries of older conversations - **💬 Chat History**: Recent messages from the user and assistant - **🔧 Tool Explanation**: Information about available tools the model can call - **📥 Function Call Results**: Outputs from previous tool interactions - **📚 Few-Shot Examples**: Examples to help guide the model's reasoning - **❓User's Latest Question**: The new input from the user This layered input is all within the LLM's context window and is updated continuously. --- ## 🗃️ External Databases as Memory Sources To populate the prompt, the system pulls from various external memory sources: - **🟣 Vector Database (VectorDB)**: Stores past interactions as embeddings and enables *semantic search* - **🟢 SQL Database**: Stores structured history, user settings, or tool logs - **🟠 Graph Database**: Stores structured relationships and user modeling (e.g., interests, friends, goals) These databases provide the "long-term memory" that the LLM itself lacks. --- ## 🧩 Smart Prompt Construction is Key The real value lies in **how** memory is used — not just where it's stored. A smart system will: - Retrieve only what's relevant - Inject it into the right part of the prompt - Update and adapt the memory based on feedback and actions The result is a chatbot that appears consistent, aware, and helpful across conversations, even though the model itself has no actual memory. --- ## 🧾Lost in the Middle [Paper](https://arxiv.org/pdf/2307.03172) <img src="../images/lost-in-the-middle.jpg" alt="Prompt Memory Layout Schema" width="1000"/> --- > ✅ Memory in LLMs is not magic — it's a system design. > The better your design, the smarter and more helpful your assistant becomes. ---