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WeAreDevelopers LIVE • Nov 17, 2023

Develop AI-powered Applications with OpenAI Embeddings and Azure Search

Rainer Stropek

Stop splitting text by arbitrary character counts. Master the RAG pattern using OpenAI embeddings and Azure Search. Build fast, context-aware AI applications that cite their own sources.

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#1 about 3 min

Understanding embedding vectors and multi-dimensional spaces

Analogies of personality traits illustrate how text is converted into multi-dimensional numerical values representing meaning.

#2 about 3 min

Generating embeddings using the OpenAI API

Calling the embeddings endpoint via Microsoft Azure facilitates adherence to European GDPR requirements while processing data.

#3 about 5 min

Comparing vectors with cosine similarity and dot products

Calculating the dot product of normalized vectors determines the semantic similarity between different texts.

#4 about 5 min

Overview of the OpenAI API and state management

Building targeted frontend applications requires passing complete chat histories or utilizing newer thread APIs for state management.

#5 about 3 min

Understanding the core retrieval augmented generation pattern

Injecting current private facts into model prompts enables accurate answers without retraining the underlying language model.

#6 about 4 min

Defining the use case for a custom search assistant

Searching a complex institutional wiki requires a customized extraction and ingestion pipeline built with cross-platform frameworks.

#7 about 6 min

Extracting and preprocessing HTML data into markdown files

Custom crawlers navigate institutional databases to strip formatting elements and parse raw internet pages into clean markdown files.

#8 about 3 min

Splitting large texts into token-limited chunks for embeddings

Breaking documents into smaller pieces ensures text inputs remain below API limits during the embedding generation process.

#9 about 6 min

Storing calculated embeddings in Azure Cognitive Search

Writing pre-calculated embedding vectors into specialized databases optimizes retrieval speed during live semantic searches.

#10 about 5 min

Implementing query flows with vector searches and completions

Triggering nearest neighbor searches provides relevant document fragments which are streamed back directly as engineered prompts.

#11 about 5 min

Demonstrating the working console application and localized responses

Running the complete system via command-line effectively retrieves knowledge source materials and answers nuanced regional protocol questions.

#12 about 3 min

Addressing embedding calculations and model hallucination risks

Retaining source links in graphical interfaces helps users verify AI-generated answers and mitigate factual discrepancies.

#13 about 11 min

Navigating ethical development and selecting integration frameworks

Implementing content filters and tracking framework updates helps developers manage inevitable behavioral biases in rapid deployment cycles.

Matching moments

1:44 min

Understanding basic retrieval-augmented generation architectures in chatbots

Stan Girard Stan Girard · WWC 2024

3:49 min

Implementing text chunking and retrieval augmented generation

Damir Damir · WWC 2025

1:24 min

Expanding AI capabilities using retrieval-augmented generation

Cedric Clyburn Cedric Clyburn · WWC 2024

1:28 min

Reviewing the basic retrieval-augmented generation pipeline

Carl Lapierre Carl Lapierre · WWC 2024

1:37 min

Exploring common use cases for modern generative AI

Sandra Ahlgrimm Sandra Ahlgrimm +1 · WWC 2024

3:37 min

Demonstrating semantic latency reductions using Spring AI configurations

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