Enterprise Retrieval-Augmented Generation with LlamaIndex

2024-02-06 Rockset webinar

What are we talking about?

RAG recap
Why we do RAG?
How do we RAG?
The stages of RAG
How LlamaIndex helps
Advanced querying strategies
Looking forward

RAG recap

Why RAG?

Retrieve most relevant data
Augment query with context
Generate response

A solution to limited context windows

Benefits of RAG: accuracy

Benefits of RAG: faithfulness

Benefits of RAG: recency

Benefits of RAG: provenance

How do we RAG?

Vector search
Keyword search
Structured queries

LlamaHub

Ingestion pipeline

Supported embedding models

OpenAI
Langchain
CohereAI
Qdrant FastEmbed
Gradient
Azure OpenAI

Elasticsearch
Clarifai
LLMRails
Google PaLM
Jina
Voyage

...plus everything on Hugging Face!

Supported Vector databases

Apache Cassandra
Astra DB
Azure Cognitive Search
Azure CosmosDB
ChatGPT Retrieval Plugin
Chroma
DashVector
Deeplake
DocArray
DynamoDB
Elasticsearch

FAISS
LanceDB
Lantern
Metal
MongoDB Atlas
MyScale
Milvus / Zilliz
Neo4jVector
OpenSearch
Pinecone
Postgres

pgvecto.rs
Qdrant
Redis
Rockset
SingleStore
Supabase
Tair
TencentVectorDB
Timescale
Typesense
Weaviate

Retrieval

Retrieval:

metadata filtering

Retrieval:

hybrid search

Retrieval:

text-to-SQL

Retrieval:

text-to-Pandas

Postprocessing

Agentic strategies

SubQuestionQueryEngine

Multi-document agents

Recursive retrieval

Composability

Supported LLMs

That's a lot of stuff!

from llama_index import VectorStoreIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")
print(response)

Let's do it in 5 lines of code: