This pipeline effectively sets up a local RAG Question Answering system using LLama 3.1, allowing users to retrieve relevant information and generate answers based on their queries.

Here's a summary of the pipeline for building a local Retrieval Augmented Generation (RAG) Question Answering application using LLama 3.1:

Pipeline Steps:
Setup and Installation:
Launch a Jupyter Notebook.
Access the notebook through the provided link and token.
Open a new terminal and a new notebook within Jupyter.

Pipeline Selection:
Visit CompuFlair to search for "I would like to build a local RAG application".
Select the appropriate pipeline using the drop-down menu.

Pipeline Summary:
Ask the chat AI to summarize the pipeline steps, which include installations, document loading and splitting, initializing the vector store, setting up the model, and retrieving the model.

Installations:
Copy and paste the installation commands into the notebook and run them.

Document Splitting:
Read content from a URL, split it into 500-character chunks, and store them in a Python list.

Vector Store Initialization:
Import necessary packages for embeddings.
Define local embeddings to convert text chunks into numeric vectors.
Build the Chroma vector store from these numeric vectors.
Resolve any errors (e.g., installing OLAMA and pulling the necessary models).

Vector Store Testing:
Perform a similarity search based on a question.
Retrieve and print the most similar document based on cosine similarity between vectors.

Model Setup:
Create the LLama 3.1 model and ensure it runs correctly.

Retriever and Chain Setup:
Build a retriever to turn the vector store into a retrievable format.
Construct a chain of operations for the question-answering process.
Define and import necessary components (e.g., format doc, runnable pass-through, rag prompt, chat prompt template).
Configure the question-answering process to retrieve relevant documents and answer questions based on the retrieved context.

Run and Test:
Run the complete chain of operations.
Test the question-answering process by asking questions and receiving context-based answers.