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- Setting Up the TxtAI Embeddings Database
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Classroom Contents
TxtAI - Simplifying RAG and Semantic Search with an All-in-One Embeddings Database
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- 1 - Introduction to TxtAI
- 2 - Setting Up the TxtAI Embeddings Database
- 3 - Understanding SPSE and DSE Indexing in TxtAI
- 4 - Text Vectorization and Indexing Techniques in TxtAI
- 5 - Overview of the Tutorial Content
- 6 - Creating a Conda Environment for TxtAI
- 7 - Installing and Configuring TxtAI
- 8 - Conducting Semantic Search with TxtAI
- 9 - Saving and Managing Embeddings in TxtAI
- 10 - Keyword Search and Dense Vector Indexing in TxtAI
- 11 - Hybrid Search: Combining Sparse and Dense Indexes in TxtAI
- 12 - Using LLM for Advanced Text Queries in TxtAI
- 13 - Creating a RAG Based Application with TxtAI
- 14 - Setting Up Language Model Workflows in TxtAI
- 15 - Conclusion and Final Thoughts
