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Chat Memory App

Complete Application

This is a complete, runnable application demonstrating Hindsight integration. View source on GitHub →

A demo chat application that uses Groq's qwen/qwen3-32b model with Hindsight for persistent per-user memory.

Features​

  • 🧠 Persistent Memory: Each user gets their own memory bank that remembers conversations
  • 🚀 Fast AI: Powered by Groq's high-speed inference
  • 🎯 Per-User Context: Isolated memory per user with automatic context retrieval
  • 💬 Real-time Chat: Instant responses with memory-augmented context

Setup​

1. Start Hindsight API​

First, start the Hindsight API server using Docker:

export GROQ_API_KEY=your_groq_api_key_here

# Start Hindsight with Groq as the LLM provider
docker run --rm -it --pull always -p 8888:8888 -p 9999:9999 \
  -e HINDSIGHT_API_LLM_PROVIDER=groq \
  -e HINDSIGHT_API_LLM_API_KEY=$GROQ_API_KEY \
  -e HINDSIGHT_API_LLM_MODEL="openai/gpt-oss-20b" \
  -v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
  ghcr.io/vectorize-io/hindsight:latest

2. Configure Environment​

Copy your Groq API key to the environment file:

# Update .env.local with your Groq API key
echo "GROQ_API_KEY=your_groq_api_key_here" > .env.local
echo "HINDSIGHT_API_URL=http://localhost:8888" >> .env.local

If you don't have one, you can get a free Groq API key here: https://console.groq.com/home

3. Install Dependencies​

npm install

4. Run the App​

npm run dev

Open http://localhost:3000 in your browser.

How It Works​

  1. User Identity: Each browser session gets a unique user ID
  2. Memory Bank Creation: First message creates a personal memory bank in Hindsight
  3. Context Retrieval: Before responding, relevant memories are retrieved
  4. Memory Augmented Response: Groq generates responses with memory context
  5. Conversation Storage: Each conversation is stored for future context

Architecture​

User Message
     ↓
Next.js API Route (/api/chat)
     ↓
Hindsight.recall() → Get relevant memories
     ↓
Groq API → Generate response with memory context
     ↓
Hindsight.retain() → Store conversation
     ↓
Response to User

Memory Bank Structure​

Each user gets their own isolated memory bank with:

  • Name: "Chat Memory for [userId]"
  • Background: Conversational AI assistant context
  • Disposition: Empathetic (4), Low Skepticism (2), Balanced Literalism (3)

Try It Out​

  1. First Conversation: Tell the assistant about yourself

    • "Hi! I'm a software engineer from San Francisco. I love Python and machine learning."

  2. Second Conversation: Ask what it remembers

    • "What do you know about me?"
    • "What programming languages do I like?"

  3. Context Building: Continue sharing preferences

    • "I prefer VS Code over other editors"
    • "I'm working on a React project"

  4. Memory Verification: Visit the Hindsight Control Plane at http://localhost:9999 to see stored memories

Development​

  • Groq Model: Uses qwen/qwen3-32b for fast, high-quality responses
  • Memory Storage: Automatic conversation retention with context categorization
  • Memory Retrieval: Semantic search with 2048 token budget for relevant context