Скачать или смотреть RAG Basics: Pinecone Deep-Dive — Index, Chunk, Hybrid Search & Rerank | AI Bros EP 30

In EP 30, Nisaar and Rohan unpack Retrieval-Augmented Generation (RAG) and go hands-on with Pinecone as the vector DB. You’ll learn the end-to-end pipeline—ingestion, chunking, embeddings, namespaces, filtering, hybrid (dense+sparse) search, reranking, and evals—plus practical tips for latency, cost, and multi-tenant design. They also riff on AI’s impact on work culture (hello, longer weekends? 👀) before building queries that actually surface the right chunks and minimize hallucinations.

Timestamps
00:00 Intro: What is RAG & why Pinecone
05:00 AI + work weeks banter; “AI slowdown” hot takes
10:00 Ingestion pipeline: docs → chunks → embeddings
15:00 Retrieval basics: semantic/similarity & metadata filters
20:00 Embedding models, vector dims; PDFs/CSVs → vectors
25:00 Create Pinecone index: region, model, manual vs automated
30:00 POC vs production; uploaders; live-stream hiccups
35:00 Namespaces & multi-tenant design; OCR + categorization
40:00 Multiple indices vs one; ADK note; vector-dim demo
45:00 What embeddings look like; interactive vectors; start querying
50:00 Query flow: question → embed → vector search → top-K
55:00 Reranking: narrow candidates to top 10; config fields/metadata
1:00:00 Cleanup: delete index via Python
1:05:00 Sparse index vs normal; sparse vs dense representations
1:10:00 Chunked reports; ID-prefix namespaces; search modes (semantic/hybrid/filters)
1:15:00 Query exec: top-K & returned fields; direct ID lookups; async/parallel
1:20:00 Lexical vs semantic; interpreting scores; privacy via namespaces
1:25:00 One index vs many; indexing strategies; auto-rerank
1:30:00 Build dense + sparse; dedupe & merge; cross-index querying
1:35:00 Hybrid vector types; combine dense + sparse + metadata; pre-filters; disease example
1:40:00 Metadata filters recap; rerank models (Cohere/Pinecone); what’s next

Tags (comma-separated)
AIBROS, Nisaar, Rohan, Understanding RAG, Pinecone, Pinecone serverless, vector database, vector db, retrieval augmented generation, RAG tutorial, RAG pipeline, hybrid search, dense sparse hybrid, reranking, Cohere Rerank, metadata filtering, namespaces, embeddings, OpenAI embeddings, sentence transformers, BGE embeddings, Voyage AI, chunking strategies, chunk overlap, document ingestion, OCR, PDF to text, semantic search, similarity search, topK, latency optimization, cost optimization, evals, groundedness, hallucination reduction, production AI, enterprise search, document QA, Python SDK, async IO, FAISS vs Pinecone, index design, vector dimensions, MRR, NDCG, hit rate, multi-tenant, privacy, access control, keyword search, lexical search, pinecone sparse index, dense index, hybrid index, deduplication, rerank pipeline, AI podcast, tech livestream

Hashtags
#AIBROS #RAG #Pinecone #VectorDB #GenAI #LLM #SemanticSearch #Embeddings #ReRanking #MLOps #AItools #Developers