Advanced RAG Techniques with LlamaIndex

2024-04-16 Unstructured Data Meetup

What are we talking about?

  • What is RAG?
  • What is LlamaIndex
  • The stages of RAG
    • Ingestion
    • Indexing
    • Storing
    • Querying
  • Advanced querying strategies x7
  • Getting into production

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

RAG challenges:

Faithfulness

RAG challenges:

Recency

RAG challenges:

Provenance

RAG challenges:

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

What is LlamaIndex?

llamaindex.ai

  • OSS libraries in Python and TypeScript
  • LlamaParse - PDF parsing as a service
  • LlamaCloud - managed ingestion service

Supported LLMs

5 line starter

documents = SimpleDirectoryReader("./data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("What's up?")
print(response)

LlamaHub

LlamaParse

part of LlamaCloud

from llama_parse import LlamaParse
from llama_index.core import SimpleDirectoryReader

parser = LlamaParse(
    result_type="markdown"
)

file_extractor = {".pdf": parser}
reader = SimpleDirectoryReader(
  "./data", 
  file_extractor=file_extractor
)
documents = reader.load_data()

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
  • BaiduVector DB
  • ChatGPT Retrieval Plugin
  • Chroma
  • DashVector
  • Databricks
  • Deeplake
  • DocArray
  • DuckDB
  • DynamoDB
  • Elasticsearch
  • FAISS
  • Jaguar
  • LanceDB
  • Lantern
  • Metal
  • MongoDB Atlas
  • MyScale
  • Milvus / Zilliz
  • Neo4jVector
  • OpenSearch
  • Pinecone
  • Postgres
  • pgvecto.rs
  • Qdrant
  • Redis
  • Rockset
  • Simple
  • SingleStore
  • Supabase
  • Tair
  • TiDB 
  • TencentVectorDB
  • Timescale
  • Typesense
  • Upstash
  • Weaviate

Advanced query strategies

SubQuestionQueryEngine

Problems with precision

Small-to-big retrieval

Precision through preprocessing

Metadata support

  • Apache Cassandra
  • Astra DB
  • Azure AI Search
  • BaiduVector DB
  • Chroma
  • DashVector
  • Databricks
  • Deeplake
  • DocArray
  • DuckDB
  • Elasticsearch
  • Qdrant
  • Redis
  • Simple
  • SingleStore
  • Supabase
  • Tair
  • TiDB
  • TencentVectorDB
  • Timescale
  • Typesense
  • Weaviate
  • Jaguar
  • LanceDB
  • Lantern
  • Metal
  • MongoDB Atlas
  • MyScale
  • Milvus / Zilliz
  • OpenSearch
  • Pinecone
  • Postgres
  • pgvecto.rs

Hybrid Search

Hybrid search support

  • Azure Cognitive Search
  • BaiduVector DB
  • DashVector
  • Elasticsearch
  • Jaguar
  • Lantern
  • MyScale
  • OpenSearch
  • Pinecone
  • Postgres
  • pgvecto.rs
  • Qdrant
  • TencentVectorDB
  • Weaviate

Text to SQL

Multi-document agents

SECinsights.ai

Composability

"2024 is the year of LlamaIndex in production"

– Shawn "swyx" Wang, Latent.Space podcast

npx create-llama

What next?

Follow me on Twitter: @seldo

Advanced RAG techniques lightning talk

By seldo

Advanced RAG techniques lightning talk

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