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:
npx create-llama
Create Llama Templates
SECinsights.ai
LlamaBot
LlamaHub (again)
"2024 is the year of LlamaIndex in production"
– Shawn "swyx" Wang, Latent.Space podcast
LlamaIndex in production
- Datastax
- OpenBB
- Springworks
- Gunderson Dettmer
- Jasper
- Replit
- Red Hat
- Clearbit
- Berkeley
- W&B
- Instabase
Case study:
Gunderson Dettmer
What's coming for LlamaIndex in 2024?
What's coming for the industry in 2024?
People
Recap
- What RAG is
- Why we do it
- How we do it
- The stages of RAG
- What's coming next
What now?
Follow me on twitter: @seldo
Enterprise RAG with LlamaIndex
By seldo
