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