Uploaded November 2023 | Updated September 2026, 5 hours ago
Learn how to build a "retrieval augmented generation" (RAG) app with Langchain and OpenAI in Python.
You can use this to create chat-bots for your documents, books or files. You can also use it to build rich, interactive AI applications that use your data as a source.
👉 Links
🔗 Code: github.com/pixegami/langchain-rag-tutorial
📄 (Sample Data) AWS Docs: github.com/awsdocs/aws-lambda-developer-guide
📄 (Sample Data) Alice in Wonderland: gutenberg.org/ebooks/11
📚 Chapters
00:00 What is RAG?
01:36 Preparing the Data
05:05 Creating Chroma Database
06:36 What are Vector Embeddings?
09:38 Querying for Relevant Data
12:47 Crafting a Great Response
16:18 Wrapping Up
#pixegami #python
Learn how to build a "retrieval augmented generation" (RAG) app with Langchain and OpenAI in Python.
You can use this to create chat-bots for your documents, books or files. You can also use it to build rich, interactive AI applications that use your data as a source.
👉 Links
🔗 Code: github.com/pixegami/langchain-rag-tutorial
📄 (Sample Data) AWS Docs: github.com/awsdocs/aws-lambda-developer-guide
📄 (Sample Data) Alice in Wonderland: gutenberg.org/ebooks/11
📚 Chapters
00:00 What is RAG?
01:36 Preparing the Data
05:05 Creating Chroma Database
06:36 What are Vector Embeddings?
09:38 Querying for Relevant Data
12:47 Crafting a Great Response
16:18 Wrapping Up
#pixegami #python
