EASY Memory DB MCP Server Setup in Under 15 Minutes

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AI Summary

Summary of Video: Creating a Memory Database with OpenAI File Store

Overview:
The video demonstrates building a simple memory database using OpenAI’s file store to set up an MCP server, utilizing vector storage for efficient querying.

Step-by-Step Process:

1. Gathering Conversations:
   • Collect conversations to upload, including timestamps for organization.
   • Example conversations:
     • Conversation 1: (copied text with a date)
     • Conversation 2: (different conversation copied and dated)
2. Creating Vector Store:
   • Access the OpenAI dashboard and navigate to the ‘storage’ section.
   • Click on ‘CREATE’ and name it (e.g., “memory DB”).
   • Upload files into the vector store, setting appropriate chunk sizes (e.g., 2000 with 500 overlap).
3. Setting up MCP Server:
   • Gather documentation from model context protocol.io for server development.
   • Prepare prompts for creating the MCP server that utilizes the OpenAI file store and vector database.
   • Include necessary APIs and configurations (API keys and vector store IDs).
4. Running the Server:
   • Initialize server and dependencies, create required directories and configuration files (tsconfig.json, index.ts, types.ts).
   • Follow error instructions to debug and build successfully.
5. Uploading and Querying Data:
   • Use the upload function to add conversations into the vector store.
   • Perform searches and test functionality by querying for specific memory items.
   • Exchange conversation data through the MCP server.

Conclusion:

• The setup provides an easy way to manage a memory storage solution using OpenAI technologies.
• The process simplifies maintaining a historical database of conversations for further reference and analysis.