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World Congress 2025 • Aug 20, 2025 • Session details

RAG like a hero with Docling

Alex Soto , Markus Eisele

Are complex PDFs breaking your RAG pipeline? Learn how to flawlessly parse unstructured data using Docling. Secure downstream vector databases with distance-preserving encryption to prevent severe data leaks.

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#1 about 4 min

Enhancing language models with context engineering

Utilizing proprietary enterprise data to enrich execution matrices reduces the reliance on expensive model fine-tuning.

#2 about 2 min

Traditional architecture for retrieval-augmented generation pipelines

Implementing structural vector databases enables retrieval strategies to match token embeddings logically alongside user inquiries.

#3 about 1 min

Overcoming challenges in unstructured document layout ingestion

Resolving the complexities of extracting reliable token sequences from multi-column layouts prevents ingestion corruptions.

#4 about 2 min

Processing complex unstructured enterprise documents with Docling

Parsing erratic document layouts into uniformly structured schemas prevents context pollution during data injection workflows.

#5 about 3 min

Comparing structural tree generation with traditional parsers

Generating structured tree objects captures obscure relational formatting that basic serialization libraries consistently fail to identify.

#6 about 2 min

Architecting a reliable backend data ingestion pipeline

Connecting applications to intelligent document parsing interfaces streamlines the ongoing translation of file properties into Redis.

#7 about 6 min

Executing the vector embedding and indexing workflow

Executing chunked encoding processes against practical payloads constructs reliable embedded semantics within locally indexed databases.

#8 about 3 min

Preventing document pipeline exploitation and data poisoning

Scrubbing internal deposits against potential prompt injection payloads halts unchecked malicious execution capabilities.

#9 about 3 min

Defending against advanced vector database injection vulnerabilities

Protecting vector layers from targeted embedding inversion averts data theft and manipulated system responses.

#10 about 2 min

Securing semantic vectors using randomized dimensional shuffling

Disguising database parameters through custom dimensional shuffling safely protects privacy without disrupting mathematical search integrity.

#11 about 5 min

Anonymizing pipeline targets and validating digital signatures

Validating document configurations blocks spoofed material while automatically masking personally identifiable metrics before system ingestion.

Matching moments

3:16 min

Integrating vector stores and RAG capabilities in Spring AI

Sandra Ahlgrimm Sandra Ahlgrimm +1 · WWC 2024

3:50 min

Building local RAG architectures using the Anything LLM tool

Cedric Clyburn Cedric Clyburn +1 · WWC 2025

3:38 min

Enhancing domain context using retrieval augmented generation

Vijay Krishan Gupta +1 · LIVE

1:50 min

Simplifying generative AI deployments using the RagStack opinionated framework

David Leconte David Leconte +1 · WWC 2024

3:08 min

Addressing knowledge base threats in RAG architectures

Alex Soto Alex Soto · WWC 2025

1:57 min

Understanding overarching retrieval and generation steps in RAG architectures

Csenge Szabo Csenge Szabo · Europe 2026 Virtual

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