Pinned

1. Improve-the-first-step-of-building-Retrieval-Augmented-RAG-using-Gensim-ChromaDB-and-Mistral-in- Improve-the-first-step-of-building-Retrieval-Augmented-RAG-using-Gensim-ChromaDB-and-Mistral-in- Public

   Here, we learn how to change the transformer for ChromaDB to something else, insert data into ChromaDB, and answer queries.

   Jupyter Notebook

2. ApacheSpark_ApacheZeppelin_SQL_Shell ApacheSpark_ApacheZeppelin_SQL_Shell Public

   Run your first analysis project on Apache Zeppelin using Scala (Spark), Shell, and SQL

   Scala

3. add_new_dimension_using_LLM add_new_dimension_using_LLM Public

   Transformation is one of the main essential parts of the ETL task. Nowadays, it is easy to build new dimensions for the data using LLMs

   Jupyter Notebook

4. Apache-Zeppelin-Installation-Linux- Apache-Zeppelin-Installation-Linux- Public

   Apache Zeppelin Installation (on remote server)

5. Chunk_Method_in_Python_LLM Chunk_Method_in_Python_LLM Public

   Here, you can find how to deal with a big text when feeding it to a Large Language Model. The provided code is efficient for the English language. BTW, it can work properly for other languages such…

   Python

6. second-step-of-building-the-Retrieval-Augmented-Generation-RAG-in-Python second-step-of-building-the-Retrieval-Augmented-Generation-RAG-in-Python Public

   we learn how we can feed the output of vector databases (in our story, we employed ChromaDB) to a Large Language Model to build RAG

   Jupyter Notebook