How to Add Memory to Your AI Chatbot Using Sessions or Vector Stores
6/29/20251 min read
In the world of AI, memory is everything. A chatbot that forgets your last question can feel robotic. But a chatbot that remembers context? That’s a game-changer. In this tutorial, we’ll show you how to give your OpenAI-powered chatbot memory — so it feels smarter, more personalized, and more human.
You’ll learn two approaches:
Short-term memory using Flask session
Long-term memory using a vector store like FAISS
Let’s get started.
Method 1: Add Short-Term Memory Using Flask Sessions
Step 1: Enable Flask Session
Install Flask-Session:
Import and configure it in app.py:
Step 2: Store Chat History in Session
Modify your /chat route to track conversation history:
This lets the chatbot remember the current session context (until the tab is closed or cleared).
Method 2: Add Long-Term Memory Using a Vector Store
If you want your chatbot to recall older conversations or documents across sessions, vector databases are key.
What is a Vector Store?
Vector stores (like FAISS, ChromaDB, or Pinecone) allow you to store text as embeddings and search them by meaning (semantic search).
Step 1: Install FAISS & Sentence Transformers
Step 2: Store User Messages as Embeddings
Step 3: Retrieve Relevant Context
Include this context in your OpenAI prompt.
Final Thoughts
Memory is what turns a basic chatbot into an intelligent assistant. Whether you use sessions for short-term context or a vector store for long-term recall, this enhancement will significantly improve user experience.