di37/langchain-rag-basic-to-advanced-tutorials

It includes the concepts for RAG application from basics till advanced using LangChain library.

14
/ 100
Experimental

These tutorials help you build applications that can answer questions using your own documents, rather than just general knowledge. You'll learn how to feed your specific information into a system and get accurate, context-aware answers out. This is for anyone creating intelligent applications that need to understand and retrieve information from custom datasets.

No commits in the last 6 months.

Use this if you need to build an application that can accurately answer questions based on a specific set of documents or data you provide.

Not ideal if you're looking for a ready-to-use application rather than guidance on how to build one yourself.

AI application development custom knowledge bases information retrieval systems contextual search language model integration
No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 6 / 25
Maturity 8 / 25
Community 0 / 25

How are scores calculated?

Stars

16

Forks

Language

Jupyter Notebook

License

Last pushed

Mar 31, 2024

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/rag/di37/langchain-rag-basic-to-advanced-tutorials"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.