Can Agents get nostalgic about the past?
Lesson 3: Unlocking Ava's memories
Talking to an Agent without memory is like dealing with the main character from “Memento” - you say your name, and one message later, it’s asking again …
Ava was no different. We needed a memory module that made her feel real - one that actually keeps up with the conversation and remembers every relevant detail about you. Whether you mentioned it two minutes ago or two months ago, she’s got it.
Ready for Lesson 3? Let’s give Ava a memory boost! 👇
This is the third lesson of “Ava: The Whatsapp Agent” course. This lesson builds on the theory and code covered in the previous ones, so be sure to check them out if you haven’t already!
Ava’s Memory Overview
Let’s start with a diagram to give you a big-picture view. As you can see, there are two main memory “blocks” - one stored in a SQLite database (left) and the other in a Qdrant collection (right).
If this isn’t clear yet, don’t worry! We’ll go through each memory module in detail in the next sections.
💠 Short-term memory
The block on the left represents the short-term memory, which is stored in the LangGraph state and then persisted in a SQLite database. LangGraph makes this process simple since it comes with a built-in checkpointer for handling database storage.
In the code, we simply use the AsyncSqliteSaver class when compiling the graph. This ensures that the LangGraph state checkpoint is continuously saved to SQLite. You can see this in action in the code below.
Ava’s state is a subclass of LangGraph’s MessageState, which means it inherits a messages property. This property holds the history of messages exchanged in the conversation - essentially, that’s what we call short-term memory!
Integrating this short-term memory into the response chain is straightforward. We can use LangChain’s MessagesPlaceholder class, allowing Ava to consider past interactions when generating responses. This keeps the conversation smooth and coherent.
Simple, right? Now, let’s get into the interesting part: the long-term memory.
💠 Long-term memory
Long-term memory isn’t just about saving every single message from a conversation - far from it 😅. That would be impractical and impossible to scale. Long-term memory works quite differently.
Think about when you meet someone new - you don’t remember every word they say, right? You only retain key details, like their name, profession, where they’re from, or shared interests.
That’s exactly what we wanted to replicate with Ava. How? 🤔
By using a Vector Database like Qdrant, that lets us store relevant information from conversations as embeddings. Let’s break this down in more detail.
🔶 Memory Extraction Node
Remember the other day when we talked about the different nodes in our LangGraph workflow? The first one was the memory_extraction_node, which is responsible for identifying and storing key details from the conversation.
That’s the first essential piece we need to get our long-term memory module up and running! 💪
🔶 Qdrant
As the conversation progresses, the memory_extraction_node will keep gathering more and more details about you.
If you check your Qdrant Cloud instance, you’ll see the collection gradually filling up with “memories”.
Don’t know how to configure Qdrant Cloud? There’s a detailed guide on setting it up here!
In the example above, you can see how Ava remembers I was watching a film named “Mystic River” (great movie, by the way). The memories are stored in Qdrant as embeddings by the memory_extraction_node using the all-MiniLM-L6-v2 model, as can be seen in our VectorStore class.
🔶 Memory Injection Node
Now that all the memories are stored in Qdrant, how do we let Ava use them in her conversations?
It’s simple! We just need one more node: the memory_injection_node.
This node uses the MemoryManager class to retrieve relevant memories from Qdrant - essentially performing a vector search to find the top-k similar embeddings. Then, it transforms those embeddings (vector representations) into text using the format_memories_for_prompt method.
Once that’s done, the formatted memories are stored in the memory_context property of the graph. This allows them to be parsed into the Character Card Prompt - the one that defines Ava’s personality and behaviour.
And that’s all for today!
Just a quick reminder - Lesson 4 will be available next Wednesday, February 26th. Plus, don’t forget there’s a complementary video lesson on Jesús Copado’s YouTube channel.
We strongly recommend checking out both resources (written lessons and video lessons) to maximize your learning experience! 🙂
And don’t worry. Ava will remember you until next Wednesday! 🤣