Retrieval Augmented Generation (RAG) with Genkit (DeepDive #2) (Genkit version 0.5) скачать в хорошем качестве

Retrieval Augmented Generation (RAG) with Genkit (DeepDive #2) (Genkit version 0.5)

Unleash the power of your PDFs: advanced search with vector stores and re-rankers In this Genkit tutorial, Pavel dives deep into how to implement RAG using Genkit. Learn how to efficiently parse PDFs, convert their content into searchable vectors using Genkit's local vector store, and implement a re-ranker to pinpoint the most relevant documents for your queries. Chapters: 0:00 - Introduction to retrieval augmented generation (RAG) 0:23 - Sample project: "Home.js" web framework 1:21 - Querying Gemini about Home.js (no context) 2:33 - Parsing PDF with pdf-parse for Gemini context 3:33 - Querying Gemini with full PDF context 5:23 - Chunking documents with vector stores 6:52 - Indexing the input data 8:36 - Adding retriever to Q&A flow 9:37 - Running the updated Q&A flow 10:06 - Examining the local index file 10:22 - Querying with chunked documentation 10:45 - Under the hood: How it works 11:45 - Usage stats overview 12:34 - Re-ranking Resources: Documentation for Retrieval-augmented generation (RAG) → https://goo.gle/3yWPmoh Documentation for Genkit flows → https://goo.gle/3SAStsz Watch more Firebase Genkit → https://goo.gle/Firebase-Genkit Subscribe to Firebase → https://goo.gle/Firebase #AI #Genkit #RAG #Firebase Speaker: Pavel Jbanov Products Mentioned: Firebase, Genkit