ERAG with Ollama - RAG Tool with Lexical, Semantic, Knowledge Graph Searches
AI Summary
- Introduction to a rack tool for LLMs
- Rack tools provide context to LLMs about user data.
- Regular coverage of rack tools on the channel.
- Overview of the featured rack tool (EG)
- Offers lexical semantic text and knowledge craft searches.
- Includes conversation context.
- Types of searches explained
- Lexical search: Literal meaning analysis using string matching, tokenization, etc.
- Semantic search: Contextual meaning analysis using entity disambiguation, etc.
- Text search: Relevant text passage/document retrieval.
- Knowledge craft search: Graph-based knowledge retrieval.
- Conversation context in rack tools
- Considers dialogue history, user preferences, etc.
- Installation and usage of EG
- Sponsored by M compute with a discount on GPUs.
- Uses local model AMA for demonstration.
- Installation steps detailed, including creating a Conda environment.
- Issues with package compatibility and installation workarounds.
- Uploading and embedding documents in various formats.
- Configuration and management of chunking size, embedding models, etc.
- Creation of knowledge graphs from data.
- Interface for conversing with the rack system.
- Potential improvements: interface, installation, documentation.
- Conclusion
- Useful for testing rack parameters before production.
- Encouragement to share experiences with similar tools.
- Call to subscribe and share the channel’s content.