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L1_Embeddings.ipynb

L1_Embeddings.ipynb

 
 

L2_kNN.ipynb

L2_kNN.ipynb

 
 

L3_approximate_nearest_neighbours.ipynb

L3_approximate_nearest_neighbours.ipynb

 
 

L4_Objects_Vectors.ipynb

L4_Objects_Vectors.ipynb

 
 

L5_HybridSearch.ipynb

L5_HybridSearch.ipynb

 
 

L6_Multilingual_RAG_Search.ipynb

L6_Multilingual_RAG_Search.ipynb

 
 

README.md

README.md

 
 

README.md

Multilingual RAG Search

A multilingual search tool based on Wikipedia data, utilizing the RAG approach to prompt GPT.

Setup

You need to create a virtual env and install the packages listed in requirements.txt.

Follow these steps: How to Work with Python Virtual Environments, Jupyter Notebooks and VS Code.

You need to create a .env file with your OPENAI_API_KEY.

Features

  • obtaining vector representations of data.
  • searching for similar vectors.
  • looking for approximate nearest neighbors.
  • utilizing vector databases.
  • understanding sparse, dense, and hybrid search.
  • creating a multilingual search tool.

Based on Vector Databases: from Embeddings to Applications by Sebastian Witalec (2023).