TxtAI: Simplifying RAG, Semantic Search with an All-in-One Embeddings Database
AI Summary
- Introduction to Text AI capabilities
- All-in-one package for text vectorization, indexing, and semantic search
- SQL queries with natural language
- Creation of pipelines and workflows
- Setting up Text AI
- Create a Conda environment and install Text AI
- Write a Python script to import embeddings and index sample data
- Semantic Search
- Index data and perform semantic searches on topics like “Feelgood story” and “climate change”
- Updating and Deleting Embeddings
- Demonstrate updating the index with new data
- Show how to delete records from the index
- Saving and Loading Embeddings
- Save embeddings to a database
- Reset and reload saved embeddings to confirm persistence
- Keyword and Dense Vector Indexing
- Create embeddings with keyword and dense vector indexing
- Perform searches using different index types
- Hybrid Search
- Combine sparse and dense indexing for hybrid search
- Use hybrid search to find relevant documents on topics like “Public Health” and “War”
- Handling Large Data Sets
- Embed and save large amounts of data in a database
- Graph Indexing
- Create embeddings with graph indexing for automatic data categorization
- Using Large Language Models (LLMs)
- Import and use LLMs for answering questions
- Retrieve-And-Generate (RAG) Applications and Workflows
- Create a RAG-based application using Text AI pipelines
- Set up workflows for language model tasks
- Conclusion
- Text AI offers a comprehensive solution for embedding-based applications