Skip to content

0_RAG_Overview.md

Latest commit

 

History

History
152 lines (123 loc) · 10.3 KB

0_RAG_Overview.md

File metadata and controls

152 lines (123 loc) · 10.3 KB

Retrieval-Augmented Generation (RAG) pattern - Overview

Costa Rica

GitHub Cloud2BR OSS - Learning Hub

Last updated: 2026-04-06

List of References (Click to expand)

Overview

Step Definition Implementation with Azure
Retrieval Retrieval involves searching and extracting relevant documents or data from a knowledge base or external data source based on the input query. Use Azure AI Search to index and query documents stored in Azure Storage Blob Containers. Configure the search index to perform semantic search and return the most relevant results.
Augmentation Augmentation involves enhancing the input query with the retrieved information to provide additional context and details. Use Azure AI Search skillsets to preprocess the retrieved data, extracting key phrases, entities, and contextual information. This augmented input is then used to inform the generative model.
Generation Generation involves using a generative model to process the augmented input and produce a coherent and contextually relevant response. Deploy a generative model like GPT-4 on Azure OpenAI Service. Use an Azure Function App to orchestrate the data flow, calling the Azure OpenAI API to generate responses based on the augmented input.

Implementing RAG Pattern with Azure AI:

graph LR
    A[Set Up a Knowledge Base] --> B[Configure Retrieval System] --> C[Integrate with a GenModel]
Loading
  1. Set Up a Knowledge Base: Store your documents in Azure Storage Blob Containers or another accessible data source.
  2. Configure a Retrieval System: Use Azure AI Search to index and retrieve relevant documents based on user queries.
  3. Integrate with a Generative Model: Use a generative model like GPT-4 to process the retrieved documents and generate responses.

Traditional methods and the Retrieval-Augmented Generation (RAG) pattern:

Aspect Traditional Methods RAG Pattern
Model Type Static, pre-trained models that rely on historical data. These models do not update dynamically and can become outdated. Dynamic integration of retrieval and generative models, allowing for real-time data updates, keeping responses current and relevant.
Data Freshness Relies on pre-trained data, which may not reflect the latest information. Retrieves the most recent data from external sources, ensuring up-to-date information.
Context Understanding Often lacks the ability to fully understand the context of a query, leading to less accurate results. Enhances context understanding by incorporating real-time information retrieval, providing richer context for responses.
Retrieval Techniques Uses keyword matching techniques like BM25 and TF-IDF, which may not capture the semantic meaning of queries. Employs advanced semantic search techniques that better understand the intent behind queries, leading to more relevant results.
Accuracy May struggle with understanding context and semantic meaning, resulting in less accurate responses. Improves accuracy by grounding responses in verified external knowledge, reducing the likelihood of errors.
Risk of Hallucinations Higher risk of generating incorrect information as responses are based solely on training data. Reduces this risk by grounding responses in real-time, verified information from external sources.
Flexibility Limited to specific data types and formats, which can restrict their applicability. Capable of handling various data types, including text, images, and videos, making it more versatile.
Adaptability Requires extensive retraining to incorporate new information, which can be time-consuming and costly. More adaptable as it integrates real-time data without the need for frequent retraining.
Cost Efficiency Can be resource-intensive due to the need for frequent retraining and large labeled datasets. More cost-effective as it minimizes the need for extensive retraining and leverages existing data sources.
Applications Suitable for basic search and static content generation. Ideal for complex applications such as healthcare, customer support, and content creation, where up-to-date and contextually relevant information is crucial.

Applications of RAG Pattern

 graph TD
    A[RAG Pattern]
    A --> B[Retrieval]
    B --> C[Knowledge Base]
    B --> D[External Data Source]
    A --> E[Augmentation]
    E --> F[Contextual Info]
    E --> G[Enhanced Query]
    A --> H[Generation]
    H --> I[LLM: e.g GPT-4]
    H --> J[Coherent Response]
    A --> K[Applications]
    K --> L[Question Answering]
    L --> M[Definition]
    L --> N[Implementation]
    K --> O[Document Summarization]
    O --> P[Definition]
    O --> Q[Implementation]
    K --> R[Conversational AI]
    R --> S[Definition]
    R --> T[Implementation]
Loading
Question Answering

Providing accurate answers by retrieving relevant documents and generating responses based on them.

  • Implementation:
    • Retrieval:
      • Use Azure AI Search to index a large corpus of documents, such as research papers, articles, or FAQs.
      • Perform semantic search to retrieve the most relevant documents based on the query.
    • Augmentation: Extract key information from the retrieved documents using Azure AI Search skillsets (key phrase extraction, entity recognition, language detection).
    • Generation:
      • Use Azure OpenAI Service to generate a coherent and contextually relevant answer by processing the augmented input.
      • Orchestrate the data flow using Azure Function App.
Document Summarization

Summarizing documents by retrieving key sections and generating concise summaries.

  • Implementation:
    • Retrieval:
      • Use Azure AI Search to index documents such as reports, articles, and books.
      • Retrieve the most relevant sections of the document based on the summary request.
    • Augmentation: Identify key sentences, paragraphs, and sections using Azure AI Search skillsets.
    • Generation:
      • Use Azure OpenAI Service to generate a concise summary by processing the augmented input.
      • Orchestrate the data flow using Azure Function App.
Conversational AI

Enhancing chatbot responses with up-to-date information from external sources.

  • Implementation:
    • Retrieval:
      • Use Azure AI Search to index a knowledge base containing FAQs, support articles, and user manuals.
      • Retrieve the most relevant documents based on the conversation.
    • Augmentation: Extract key information from the retrieved documents using Azure AI Search skillsets (answers to common questions, troubleshooting steps, product details).
    • Generation:
      • Use Azure OpenAI Service to generate coherent and contextually relevant chatbot responses by processing the augmented input.
      • Orchestrate the data flow using Azure Function App.
Total views

Refresh Date: 2026-04-06