Mini Project Category

Artificial Intelligence

MemoryMaker

In Beta

RAG application using vector embeddings for intelligent knowledge retrieval and search.

Date

2024-04

Duration

6 weeks

Team

solo

Difficulty

hard

MemoryMaker explores Retrieval-Augmented Generation by turning private documents into a searchable knowledge base.

OpenAI embeddings and FAISS provide the retrieval layer, while prompt orchestration improves answer grounding and relevance.

PythonOpenAI EmbeddingsFAISSVector SearchRAG

Document indexing pipeline

Semantic retrieval

Natural-language querying

Context-aware answers

Batch processing flow

Vector index lifecycle management

Retrieval quality tuning

Document preprocessing complexity

Compute budget constraints

Chunking strategy strongly affects retrieval quality

RAG improves trust for private-domain answers

FAISS gives strong performance-to-complexity ratio

Evaluation loops are needed for relevance tuning

Resume helper focused on showcasing AI skills

Outfit assistant using image generation models. Uses MacBook Air M1 instead of GPU.

Speech-to-Markdown using LLM and Whisper

If you want to build a practical AI feature like this in your product, reach out and I can help with architecture, prototyping, and delivery.