Build a Talking Fully Local RAG with Llama 3, Ollama, LangChain, ChromaDB & ElevenLabs - Nvidia Stock

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AI Summary

Video Summary: Building a Local RAG with AI Tools

Introduction

• Presenter: Maryam with 20+ years in AI across 12 industries.
• Focus: Building a local RAG (Retrieval-Augmented Generation) pipeline using various AI tools.

Step 1: Parsing PDFs

• Tools used: Langchain community’s PyPDFloader and PDF Plumberloader.
• Issues addressed: Handling complex PDFs with tables, graphs, and scanned invoices.
• Tip: Optimize chunk size for better context in retrieval.

Step 2: Splitting Text

• Tool used: Langchain’s recursivecharactertextsplitter.
• Process: Splitting pages into chunks with overlap to avoid cutting words in half.
• Metadata: Adding metadata like title, author, or date if needed.

Step 3: Embedding

• Speed option: Fast embed from Langchain community.
• Quality option: NOMIC embedding using Ollama.
• Process: Download Ollama from ollama.com, install, and pull NOMIC embedding.
• Tip: Use embeddings to store in a vector database for the next step.

Step 4: Vector Databases

• Focus: Chroma DB, Quadrant, and FAISS for storing embeddings.
• Improvement: Using persist to speed up storing in ChromaDB.
• Tip: Preprocess data to remove NANs and empties for Quadrant.

Step 5: Multi Query Retriever

• Tool: Ollama for pulling LLMs like Lama 3.
• Process: Using Chate Ollama and multi query retriever with prompts to retrieve relevant documents.

Step 6: Chatting with RAG

• Importance: Quality of embedding and chunking for context-rich responses.
• Example: Asking for financial advice on Nvidia and receiving detailed responses.

Step 7: Adding Audio with ElevenLabs

• Tool: ElevenLabs for converting text to audio.
• Process: Obtain API key from elevenlabs.io, use elevenlabs play and stream libraries.
• Languages: English and multi-language support with various models.

Additional Tips

• Hugging Face: A framework for sentiment analysis, text summarization, translation, etc.

Conclusion

• The video demonstrates how to build a local RAG using AI tools for chatting and talking capabilities.
• Emphasizes the importance of each step in the pipeline for achieving quality results.
• Introduces ElevenLabs for audio conversion and Hugging Face for additional AI functionalities.