Ollama Embedding - How to Feed Data to AI for Better Response?

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AI Summary

Summary: O Llama Embedding and RAG Application with Gradio UI

• Introduction to O Llama embedding for creating performant RAG applications.
• Ingest data from URLs, convert to embeddings, store in Vector database.
• Use Chroma DB, Nomic embedding, and MLess large language model.
• Gradio for user interface creation.

Steps to Create the Application:

1. Data Retrieval and Processing:
   • Ingest data from a list of URLs.
   • Use web-based loader to extract data.
   • Split data into chunks with overlap using character text splitter.
2. Embedding and Storage:
   • Initialize Chroma DB.
   • Define O Llama embedding with Nomic Embed Text model.
   • Store documents as embeddings in the database.
3. Retrieval-Augmented Generation (RAG):
   • Compare results before and after RAG.
   • Use chat prompt template and RAG chain.
   • Send prompts to large language model (Mistal).
   • Retrieve contextually relevant answers.
4. User Interface with Gradio:
   • Modify code to include Gradio.
   • Create a function to process input URLs and questions.
   • Set up Gradio interface with inputs for URLs and questions.
   • Launch the interface and interact with the RAG application locally.

Additional Information:

• Nomic Embed Text has a higher context length, outperforming OpenAI models.
• The application runs locally with zero cost.
• The author encourages subscribing to their YouTube channel for AI-related content.

Usage Instructions:

• Install necessary packages (Lang chain, Lang chain community, Lang chain core).
• Run the application using Python app.py.
• Use the Gradio UI to input URLs and ask questions.

Performance and Results:

• Embedding process is fast (approx. 219 milliseconds).
• RAG provides more accurate answers compared to pre-RAG.
• O Llama is a local AI model server for running large language models.

Conclusion:

• The video demonstrates the creation of a RAG application with a user-friendly interface.
• The author plans to create more similar content and encourages engagement with their channel.

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