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README.md

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@@ -7,7 +7,7 @@ It is intended that the plugins and skills provided in this repository, are adap
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## Components
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- `./text_2_sql` contains an three Multi-Shot implementations for Text2SQL generation and querying which can be used to answer questions backed by a database as a knowledge base. A **prompt based** and **vector based** approach are shown, both of which exhibit great performance in answering sql queries. Additionally, a further iteration on the vector based approach is shown which uses a **query cache** to further speed up generation. With these plugins, your RAG application can now access and pull data from any SQL table exposed to it to answer questions.
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- `./adi_function_app` contains code for linking **Azure Document Intelligence** with AI Search to process complex documents with charts and images, and uses **multi-modal models (gpt4o)** to interpret and understand these. With this custom skill, the RAG application can **draw insights from complex charts** and images during the vector search.
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- `./adi_function_app` contains code for linking **Azure Document Intelligence** with AI Search to process complex documents with charts and images, and uses **multi-modal models (gpt4o)** to interpret and understand these. With this custom skill, the RAG application can **draw insights from complex charts** and images during the vector search. This function app also contains a **Semantic Text Chunking** method that aims to intelligently group similar sentences, retaining figures and tables together, whilst separating out distinct sentences.
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- `./deploy_ai_search` provides an easy Python based utility for deploying an index, indexer and corresponding skillset for AI Search and for Text2SQL.
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The above components have been successfully used on production RAG projects to increase the quality of responses.

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