Enterprise Retrieval-Augmented Generation with LlamaIndex

2024-02-08 Streamlit at GitHub

What are we talking about?

  • RAG recap
  • Enterprise challenges of RAG
  • How do we RAG?
  • The stages of RAG
  • How LlamaIndex helps
  • Looking forward

RAG recap

  • Retrieve most relevant data
  • Augment query with context
  • Generate response

A solution to limited context windows

You have to be selective

and that's tricky

Accuracy

Enterprise challenges of RAG:

Faithfulness

Enterprise challenges of RAG:

Recency

Enterprise challenges of RAG:

Provenance

Enterprise challenges of RAG:

How do we do RAG?

1. Keyword search

How do we do RAG?

2. Structured queries

How do we do RAG?

3. Vector search

Vector embeddings

Turning words into numbers

Search by meaning

Hybrid approaches

Supported LLMs

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

Agentic strategies

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

Recap

Retrieve, Augment, Generate

  • Challenges:
    • Accuracy
    • Faithfulness
    • Recency
    • Provenance
  • The stages of RAG:
    • Loading
    • Indexing
    • Storing
    • Retrieval
    • Synthesis
    • Processing

What now?

Follow me on twitter: @seldo

Enterprise RAG with LlamaIndex

By seldo

Enterprise RAG with LlamaIndex

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