Building a Vector Search Assistant: What I Learned from Module 2

1. 1Build a vector search index using a library like Faiss or Annoy
2. 2Configure a retrieval model to query the index
3. 3Test the search assistant with sample queries
4. 4Apply fine-tuning techniques to improve search results
5. 5Compare the performance of different retrieval models

NLP engineers and data scientists can benefit from this knowledge to improve their search and generation models

💡 Vector search is a crucial component of Retrieval-Augmented Generation and can be improved with fine-tuning and model selection

💡 Build a vector search assistant to improve your Retrieval-Augmented Generation skills #RAG #vectorsearch