Conversational Memory with SQLite in LangChain

Hello there! 👋 This example demonstrates how to create a conversational AI system with memory persistence using SQLite in Go with the LangChain library. Let's break down what this exciting code does!

What Does This Example Do?

1. Sets up an OpenAI Language Model: It initializes an OpenAI language model to power our conversational AI.

2. Creates a SQLite Database: The code sets up a SQLite database to store conversation history.

3. Implements Conversation Memory: It uses SQLite to maintain a persistent memory of the conversation, allowing the AI to remember previous interactions.

4. Prepares Sample Data: If the database is empty, it inserts a sample message to kickstart the conversation.

5. Runs a Conversation: The example runs a conversation chain, asking the AI a question that requires memory of previous interactions.

Key Components

• SQLite Chat Message History: Uses sqlite3.NewSqliteChatMessageHistory to create a chat history stored in SQLite.
• Conversation Buffer: Implements memory.NewConversationBuffer to manage the conversation memory.
• Conversation Chain: Creates a chains.NewConversation to handle the flow of the conversation.

How It Works

1. The code first checks if there's any existing data in the SQLite database.
2. If empty, it inserts a sample message: "Hi there, my name is Murilo!"
3. It then asks the AI: "What's my name? How many times did I ask this?"
4. The AI responds based on the conversation history stored in the SQLite database.

This example showcases how to create a conversational AI system with persistent memory, allowing for more context-aware and personalized interactions over time!

Feel free to run this example and experiment with different questions to see how the AI remembers and uses previous conversation context! 🚀🤖