TxtAI - Simplifying RAG and Semantic Search with an All-in-One Embeddings Database

TxtAI - Simplifying RAG and Semantic Search with an All-in-One Embeddings Database

Mervin Praison via YouTube Direct link

- Introduction to TxtAI

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1 of 15

- Introduction to TxtAI

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TxtAI - Simplifying RAG and Semantic Search with an All-in-One Embeddings Database

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

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