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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
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore the powerful capabilities of TxtAI, an all-in-one solution for advanced text processing and analysis, in this comprehensive 13-minute tutorial. Learn to leverage TxtAI for semantic search, language model workflows, and various text analysis tasks. Discover how to create embeddings, conduct semantic searches, and perform SQL queries. Delve into advanced features like keyword search, dense vector indexing, and hybrid search techniques. Gain practical skills in setting up TxtAI, managing embeddings, and creating RAG-based applications. Perfect for beginners and advanced users alike, this tutorial provides valuable insights into enhancing text processing capabilities using TxtAI's versatile platform.

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

Mervin Praison
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#Computer Science #Machine Learning #Semantic Search #Programming #Programming Languages #Python #Domain-Specific Languages (DSL) #SQL #Vector Search #Topic Modeling #Embeddings #Retrieval Augmented Generation
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- Introduction to TxtAI