Sign up to save your podcasts
Or
Share Exploring ONNX, Embedding Models, and Retrieval Augmented Generation (RAG) with Langchain4j
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
Exploring ONNX, Embedding Models, and Retrieval Augmented Generation (RAG) with Langchain4j
An airhacks.fm conversation with Dmytro Liubarskyi (@langchain4j) about:
Dmytro previously on "#285 How LangChain4j Happened",
discussion about ONNX format and runtime for running neural network models in Java,
using langchain4j library for seamless integration and data handling,
embedding models for converting text into vector representations,
strategies for handling longer text inputs by splitting and averaging embeddings,
overview of the retrieval augmented generation (RAG) pipeline and its components,
using embeddings for query transformation, routing, and data source selection in RAG,
integrating Langchain4j with quarkus and CDI for building AI-powered applications,
Langchain4j provides pre-packaged ONNX models as Maven dependencies,
embedding models are faster and smaller compared to full language models,
possibilities of using embeddings for query expansion, summarization, and data source selection,
cross-checking model outputs using embeddings or another language model,
decomposing complex AI services into smaller,
specialized sub-modules,
injecting the right tools and data based on query classification
Dmytro Liubarskyi on twitter: @langchain4j
View all episodes
By Adam Bien
5
77 ratings
An airhacks.fm conversation with Dmytro Liubarskyi (@langchain4j) about:
Dmytro previously on "#285 How LangChain4j Happened",
discussion about ONNX format and runtime for running neural network models in Java,
using langchain4j library for seamless integration and data handling,
embedding models for converting text into vector representations,
strategies for handling longer text inputs by splitting and averaging embeddings,
overview of the retrieval augmented generation (RAG) pipeline and its components,
using embeddings for query transformation, routing, and data source selection in RAG,
integrating Langchain4j with quarkus and CDI for building AI-powered applications,
Langchain4j provides pre-packaged ONNX models as Maven dependencies,
embedding models are faster and smaller compared to full language models,
possibilities of using embeddings for query expansion, summarization, and data source selection,
cross-checking model outputs using embeddings or another language model,
decomposing complex AI services into smaller,
specialized sub-modules,
injecting the right tools and data based on query classification
Dmytro Liubarskyi on twitter: @langchain4j
More shows like airhacks.fm podcast with adam bien
View all
The Changelog: Software Development, Open Source
289 Listeners
WSJ Tech News Briefing
1,644 Listeners
The a16z Show
1,085 Listeners
Software Engineering Daily
624 Listeners
Talk Python To Me
585 Listeners
Bits und so
25 Listeners
c’t uplink - der IT-Podcast aus Nerdistan
6 Listeners
heiseshow
2 Listeners
REWORK
210 Listeners
AWS Podcast
203 Listeners
The Real Python Podcast
142 Listeners
LANZ & PRECHT
312 Listeners
Der KI-Podcast
19 Listeners
Foojay.io, the Friends Of OpenJDK!
0 Listeners
The Economics Show
148 Listeners