Llama 3 RAG: How to Create AI App using Ollama?

Step-by-Step Tutorial: Creating an AI App using Llama 3 RAG

Step 1: Downloading and Installing Required Packages

1. Open your terminal.
2. Run the following command to download and install the necessary packages:

   pip install olama langchain beautifulsoup4 chromadb gradio
   

3. Press Enter to proceed.

Step 2: Downloading Llama 3 Model

1. In the terminal, run the command:

   olama pull llama3
   

2. Press Enter to automatically download the Llama 3 Model with 8 billion parameters to your computer.

Step 3: Importing Libraries

1. Import the required libraries by running the command:

   import olama
   import beautifulsoup4
   import recursivecharactersplitter
   import webbaseloader
   import chromadb
   import olamaembeddings
   import stringoutputparser
   import runnablepassthrough
   

Step 4: Loading Data from a URL and Preprocessing

1. Use the webbaseloader to load data from a specific URL.
2. The data is split into smaller chunks by the recursive character text splitter.
3. Convert the data into embeddings using olamaembeddings.
4. Store the embeddings in chromadb.
5. Pass the output using stringoutputparser.

Step 5: Creating Embeddings and Saving in Chroma DB

1. Initialize the olamaembeddings function.
2. Initialize the chromadb to convert and save the data from the URL into embeddings in the database.

Step 6: Calling the Llama 3 Language Model

1. Create a function to call the Llama 3 language model using ol.chat.
2. Provide the relevant information from the webpage for semantic searching using embeddings.

Step 7: Setting up the RAG (Retrieval-Augmented Generation)

1. Initialize the retriever to search for relevant information based on the question.
2. Create a function called combine_docs to merge the relevant sections identified by the retriever.
3. Use the retriever to retrieve and combine the relevant portions from the webpage.
4. Send the combined documents to the large language model for generating the final answer.

Step 8: Asking Questions and Getting Responses

1. In the terminal, run the command:

   python RP.py
   

2. Enter the question "What is Task decomposition?" and press Enter to get the response from the Llama 3 model.

Step 9: Using Nomic Embed Text Model

1. Download the Nomic Embed Text model by running the command:

   pull nomicembedtext
   

2. Modify the code to use the Nomic Embed Text model for embeddings.

Step 10: Testing the Application

1. Run the Python script to open the application.
2. Input a URL and a question to retrieve relevant information from the webpage using the Nomic Embed Text model.

Conclusion:

By following these steps, you can create an AI application using Llama 3 RAG for semantic searching and generating responses based on the provided URL and questions. Experiment with different models and approaches to enhance the capabilities of your AI application.