# Cursor Chat: ai-agent-book

## Metadata
- **Project**: ai-agent-book
- **Path**: `/Users/boj`
- **Date**: 2025-09-22 07:36:15
- **Session ID**: `9d1d0124-4635-4fd3-a0ae-e400cbcb6e46`

## Conversation

### 👤 You

In projects/week3/agentic-rag-for-user-memory, create an educational project similar to agentic-rag repo, but tailor it for user memory evaluation. It should use week2/user-memory library using a similar manner as user-memory-evaluation, where the learning phase is to chunk the conversation histories to chunks of 20 rounds, and index them using the RAG database. In the evaluation phase (i.e. the final user query), the agentic RAG system should provide a tool for the agent to query the already built indexes.

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### 👤 You

In projects/week3/agentic-rag-for-user-memory, create an educational project similar to agentic-rag repo, but tailor it for user memory evaluation. It should use week2/user-memory library using a similar manner as user-memory-evaluation, where the learning phase is to chunk the conversation histories to chunks of 20 rounds, and index them using the RAG database. In the evaluation phase (i.e. the final user query), the agentic RAG system should provide a tool for the agent to query the already built indexes.

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### 🤖 Cursor Assistant

I'll create an educational project that combines agentic RAG with user memory evaluation. Let me first examine the existing projects to understand their structure and then build a tailored solution.

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### 🤖 Cursor Assistant

## Summary

I've successfully created an educational project **agentic-rag-for-user-memory** in `projects/week3/` that combines:

### Key Features Implemented:

1. **Conversation Chunking System** (`conversation_chunker.py`)
   - Chunks conversations into 20-round segments
   - Supports configurable overlap for context preservation
   - Handles various conversation formats

2. **RAG Indexing and Retrieval** (`rag_indexer.py`)
   - Supports multiple backends: Local, Chroma, FAISS
   - Uses OpenAI embeddings for semantic search
   - Efficient chunk storage and retrieval

3. **Memory Integration** (`memory_integration.py`)
   - Integrates with week2/user-memory library
   - Provides memory extraction from conversations
   - Supports memory consolidation to reduce redundancy
   - Enhanced search with conversation context

4. **Agentic RAG System** (`agent.py`)
   - ReAct pattern implementation for reasoning
   - 6 tools for querying and managing indexed data:
     - search_conversations
     - search_memories
     - get_conversation_details
     - extract_memory_from_conversation
     - update_memory
     - consolidate_memories
   - Streaming response support

5. **Complete CLI Interface** (`main.py`)
   - Build indexes from conversation history
   - Interactive query mode
   - Single query execution
   - Conversation chunking utilities

6. **Quick Start Demo** (`quickstart.py`)
   - Creates sample conversations (travel planning, medical appointments)
   - Demonstrates the full workflow
   - Runs test queries automatically

7. **Evaluation Framework** (`test_evaluation.py`)
   - Test case preparation and indexing
   - Query evaluation with metrics
   - Memory consistency checking
   - Success rate calculation

### Project Structure:
```
projects/week3/agentic-rag-for-user-memory/
├── config.py                 # Comprehensive configuration
├── conversation_chunker.py   # 20-round chunking implementation
├── rag_indexer.py           # RAG indexing with multiple backends
├── memory_integration.py    # User memory management
├── agent.py                 # Main agentic RAG with tools
├── main.py                  # CLI interface
├── quickstart.py            # Demo with sample data
├── test_evaluation.py       # Evaluation framework
├── requirements.txt         # Dependencies
├── env.example             # Environment template
└── README.md               # Comprehensive documentation
```

### How It Works:

**Learning Phase:**
1. Conversations are chunked into 20-round segments
2. Each chunk is indexed using RAG (embeddings + storage)
3. Memories are extracted and stored

**Evaluation Phase:**
1. User queries trigger the ReAct agent
2. Agent uses tools to search indexed chunks and memories
3. Relevant information is retrieved and synthesized
4. Response is generated with citations

### To Get Started:

```bash
cd projects/week3/agentic-rag-for-user-memory
pip install -r requirements.txt
cp env.example .env
# Add your OpenAI API key to .env
python quickstart.py
```

This educational project successfully demonstrates how to combine RAG technology with user memory systems for evaluating conversational AI, providing a practical implementation of the concepts from both week2 and week3 materials.

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*Exported from [Cursor View](https://github.com/saharmor/cursor-view)*