ERAG with Ollama - RAG Tool with Lexical, Semantic, Knowledge Graph Searches
AI Summary
Summary: Introduction to a RAG Tool
- Purpose of RAG Tools:
- Provide context to language models (LLMs) about user data.
- RAG tools are essential for feeding relevant data to LLMs.
- Features of the RAG Tool Discussed:
- Lexical semantic text and knowledge graph searches.
- Conversation context consideration.
- Types of Searches:
- Lexical Search:
- Literal meaning analysis using string matching, tokenization, etc.
- Semantic Search:
- Contextual meaning analysis using entity disambiguation, etc.
- Text Search:
- Relevant text passage retrieval using keyword extraction, etc.
- Knowledge Graph Search:
- Graph-based knowledge retrieval using graph traversal, etc.
- Installation and Setup:
- Sponsored by M compute with a discount on GPUs.
- Use of local model “olama” for demonstration.
- Creation of a conda environment and installation of dependencies.
- Downloading and setting up models for natural language processing.
- Usage:
- Embedding documents into numerical representations.
- Creating and managing knowledge graphs.
- Adjusting chunking size and overlap for document processing.
- Configuring RAG system parameters.
- Interface and Documentation:
- Retro-looking interface.
- Some areas for improvement in interface, installation, and documentation.
- Conclusion:
- Useful for testing RAG parameters before production.
- Encouragement for feedback and sharing experiences.
For a more detailed exploration and instructions, viewers are encouraged to watch the video and subscribe to the channel for more content.