Retrieval Augmented Generation (RAG) with Azure AI Search

In the realm of Artificial Intelligence, generative AI models like Large Language Models (LLMs) have captured significant attention for their ability to produce human-quality text formats. However, their reliance on training data can limit their understanding of specific contexts. Retrieval Augmented Generation (RAG) emerges as a powerful approach to address this limitation.

What is RAG?

RAG integrates information retrieval with LLMs. Here's the core functionality:

1. Retrieval: An information retrieval system, like Azure AI Search, identifies relevant information (grounding data) based on a user query or prompt.
2. Augmentation: This retrieved information is presented to an LLM, enriching its understanding of the context.
3. Generation: Leveraging the enriched context, the LLM generates a response tailored to the specific prompt.

Large Language Models (LLMs) are impressive feats of engineering, capable of generating human-quality text formats. However, they have a significant limitation: their understanding of the world is primarily based on the massive datasets they are trained on. This training data might not always encompass the specific context of a user query. As a result, LLM responses can sometimes be generic or irrelevant.

Retrieval-Augmented Generation (RAG) addresses this limitation by introducing an information retrieval system into the LLM response generation process. Here's a closer look at the core functionalities of RAG:

1. Retrieval: Bridging the Gap between Prompt and Knowledge
2. Augmentation: Enriching the LLM's Context
3. Generation: Leveraging Knowledge for Informed Responses

Benefits of RAG: Its More Than Just Grounding

Beyond simply providing context, RAG offers several advantages:

• Control over Grounding Data: Unlike pre-trained LLMs, RAG allows you to curate the information retrieval system's data sources. This ensures that the LLM's responses are grounded in your specific domain knowledge and adhere to your organization's policies.
• Improved Relevance and Accuracy: By incorporating relevant information from the retrieval system, RAG solutions generate more accurate and targeted responses to user queries. This leads to a more satisfying user experience.
• Flexibility for Diverse Content: Modern information retrieval systems like Azure AI Search can handle various content types (text, images, code). This flexibility allows RAG to be applied to a broader range of use cases within your organization.

Benefits of RAG with Azure AI Search:

• Enhanced Control: Unlike pre-trained LLMs, RAG with Azure AI Search allows you to restrict the grounding data to your enterprise content. This ensures responses align with your specific domain and knowledge base.
• Improved Relevance: By incorporating relevant information from Azure AI Search, RAG solutions generate more accurate and targeted responses to user queries.
• Flexibility: Azure AI Search supports various content types (text, images) with different indexing options. This flexibility caters to diverse use cases within your organization.

Building a RAG Solution with Azure AI Search:

While Azure AI Search provides the retrieval foundation, a custom solution requires additional components:

1. LLM Integration: Code needs to be written to integrate your chosen LLM (e.g., Azure OpenAI) with the retrieval and response generation pipeline.
2. Web Frontend: A user interface is needed for users to interact with the RAG system and submit queries.
3. Vector Encoding (Optional): For complex content like images, vector encoding might be required to represent the content mathematically for similarity search within Azure AI Search.

Core Components of a RAG Solution with Azure AI Search:

1. App UX (Web App): Provides the user interface for interaction.
2. App Server/Orchestrator: Coordinates the handoff between information retrieval and the LLM. Tools like LangChain can simplify this process.
3. Azure AI Search: Acts as the information retrieval system, providing searchable indexes and query capabilities.
4. LLM (e.g., Azure OpenAI): Generates the final response based on the prompt and retrieved information.

Content Indexing in Azure AI Search:

• Search indexes in Azure AI Search store content for fast retrieval with millisecond response times.
• Indexes contain indexed content (extracted data) and unaltered text for specific use cases.
• Indexing features cater to various content types:Text: Indexed as tokens and unaltered text. Analyzers and normalizers can modify text during indexing.Images: Can be processed for text recognition or image characteristics using Azure AI Vision skills. Extracted information is then indexed. Alternatively, images can be vectorized externally for similarity search.

Content Retrieval in Azure AI Search:

• Azure AI Search offers various query capabilities to retrieve relevant information: Simple or Full Lucene Syntax: Ideal for exact matches on text and non-vector numeric content.Filters and Facets: Narrow down the search surface based on specific criteria. Semantic Ranking: Re-ranks search results based on semantic models, generating short summaries suitable for LLM input.Vector Search: Enables similarity search for content represented as vectors (e.g., vectorized images).Hybrid Search: Combines any or all of the above techniques for optimal results.

Structuring the Query Response:

The quality of search results directly impacts the LLM's response generation. Here's what defines the response structure:

• Fields: Determine which parts of the index are included in the response. Only "retrievable" fields are returned.
• Rows: Represent matches from the index, ranked by relevance or similarity. The default limit is 50 for full-text search and k-nearest neighbors for vector search.

Optimizing Search Results for Effective RAG:

• Relevance Tuning: Since retrieved information directly influences the LLM's response, ensuring highly relevant search results is crucial. Azure AI Search offers features like scoring profiles and semantic ranking to improve relevance tuning.

RAG with Azure AI Search presents a compelling approach for building robust generative AI applications. By leveraging Azure AI Search's information retrieval capabilities and the power of LLMs, you can generate more targeted, relevant, and informative responses to user queries within the context of your specific domain knowledge.

https://medium.com/@manasranjanrath/retrieval-augmented-generation-rag-with-azure-ai-search-71b3b8e5f140