Alex Soto & Markus Eisele
RAG like a hero with Docling
#1about 3 minutes
Using RAG to enrich LLMs with proprietary data
Retrieval-augmented generation (RAG) is the key to making large language models useful for enterprises by providing them with up-to-date, proprietary information.
#2about 4 minutes
The challenge of parsing complex document structures
Simple document parsers can misinterpret layouts like multi-column text, leading to corrupted data and incorrect outputs from the language model.
#3about 3 minutes
Using Docling to convert documents into structured formats
Docling is an open-source tool that acts like an advanced OCR service, converting various binary document formats into a structured, parsable tree.
#4about 7 minutes
Demo of a basic RAG ingestion pipeline
A live demonstration shows how a Quarkus application uses Docling to ingest a PDF, generate embeddings, and store the resulting chunks and vectors in Redis.
#5about 3 minutes
Securing RAG against data poisoning and leaks
To prevent data poisoning and sensitive data leaks, it is crucial to sanitize documents, verify their signatures, and use tools for PII masking.
#6about 4 minutes
Mitigating vector store attacks and encryption challenges
Vector stores are vulnerable to attacks like close vector modification and reversal, and standard encryption breaks vector distance, requiring specialized solutions.
#7about 5 minutes
Demo of a secure ingestion pipeline in action
A final demonstration showcases a secure pipeline that verifies document signatures, anonymizes sensitive data, and encrypts vectors before storing them.
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Demo: Implementing RAG with LangChain4J and a vector database
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Addressing unique security risks in RAG systems
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21:19 MIN
Using RAG for secure enterprise data integration
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23:59 MIN
A deep dive into retrieval-augmented generation
Lies, Damned Lies and Large Language Models
15:49 MIN
Understanding retrieval-augmented generation (RAG)
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Visualizing the end-to-end RAG architecture
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Code walkthrough for building a RAG-based chatbot
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