Python RAG Tutorial (with Local LLMs): AI For Your PDFs

Step-by-Step Tutorial: Building an AI Application for PDFs Using Python RAG Tutorial

1. Setting Up the Environment

• Install or update the necessary libraries mentioned in the video, such as Langchain, Ollama, and AWS Bedrock.
• Download the PDFs you want to use for the project and store them in a folder.

2. Loading and Preprocessing PDF Documents

• Create a Python script to load the documents from the folder using the provided code snippet.
• Use the Langchain documentation to explore different document loaders for various document types.
• Split the PDF documents into smaller chunks using the recursive text splitter function.

3. Creating and Storing Embeddings

• Implement an embedding function to convert the text chunks into embeddings for storage.
• Store the embeddings in a vector database for efficient retrieval of relevant information.

4. Setting Up the LLM Model

• Install and run an Ollama server on your computer to serve as a local LLM model.
• Choose the appropriate embedding function for generating responses based on the data source.
• Ensure the quality of the embeddings by testing different models or services like AWS Bedrock.

5. Building the RAG Application

• Create a Python script or function that takes user queries as input.
• Retrieve the most relevant chunks from the database based on the query.
• Generate a prompt using the context and the original question text for the LLM model.

6. Evaluating Response Quality

• Write sample questions and expected answers to validate the responses.
• Use an LLM to judge the equivalence of the expected and actual responses.
• Implement unit tests to evaluate the performance of the RAG application.

7. Testing and Refining the Application

• Run the unit tests to check the accuracy of the responses.
• Analyze the results to ensure the application provides correct and relevant information.
• Make necessary adjustments to improve the quality of responses based on the test outcomes.

8. Deployment and Further Development

• Consider deploying the application to the cloud for wider accessibility.
• Explore using different LLM models to enhance the capabilities of the AI application.
• Continuously test and refine the application to ensure optimal performance and accuracy.

Additional Resources

• Check out the GitHub link provided in the video description for the full code implementation and project details.
• Experiment with different settings, models, and data sources to customize the AI application based on your requirements.

By following these steps, you can create an AI application that leverages natural language processing to provide insightful responses to user queries based on PDF documents.