Congratulations!

Retrieval Augmented Generation (RAG) con LangChain

Meri Nova

Machine Learning Engineer

Chapter 1

Document chunks are stored.

Retrieval Augmented Generation (RAG) con LangChain

Chapter 1

The model output being passed to a parser.

Retrieval Augmented Generation (RAG) con LangChain

Chapter 2

 

Markdown files

  • UnstructuredMarkdownLoader

Python files

  • PythonLoader
  • language=Language.PYTHON

 

Token splitting → TokenTextSplitter

Semantic splitting → SemanticChunker

semantic3.jpg

Retrieval Augmented Generation (RAG) con LangChain

Chapter 2

A RAG workflow highlighting the processes that we're able to evaluate, including the retrieval process, LLM hallucination, answer relevance to the input question, and comparing the answer to a reference answer.

1 Image Credit: LangSmith
Retrieval Augmented Generation (RAG) con LangChain

Chapter 3

A graph showing connections between people, places, and interests.

Retrieval Augmented Generation (RAG) con LangChain

Chapter 3

A natural language to Cypher translator converts the natural language input into a Cypher query and the retrieved graph documents back into natural language.

Retrieval Augmented Generation (RAG) con LangChain

Let's practice!

Retrieval Augmented Generation (RAG) con LangChain

Preparing Video For Download...