MemoryMaker
In Beta
RAG application using vector embeddings for intelligent knowledge retrieval and search.
Date
2024-04
Duration
6 weeks
Team
solo
Difficulty
hard
Project Story
MemoryMaker explores the capabilities of Retrieval-Augmented Generation (RAG) systems by creating a knowledge base from documents that can be intelligently searched and queried using natural language.
Interface showing document upload and query capabilities
The system uses OpenAI embeddings for vector storage and FAISS for efficient similarity search, allowing users to ask questions about their documents and receive contextually relevant answers.
Technical Details
Tech Stack
Python OpenAI Embeddings FAISS Vector Database RAG
Key Features
✓ Document indexing
✓ Semantic search
✓ Natural language queries
✓ Context-aware responses
✓ Batch processing
✓ Memory management
Challenges Faced
⚠ VeCtor database management
⚠ Retrieval accuracy vs. response quality
⚠ Computing resource requirements
⚠ Document preprocessing complexity
Key Learnings
💡 Vector embeddings are powerful for semantic search
💡 RAG systems bridge gap between LLMs and private data
💡 FAISS provides excellent performance for vector search
💡 Document chunking affects retrieval quality
💡 Context relevance is key for useful answers