For readers in a hurry

• RAG (Retrieval-Augmented Generation) improves Large Language Models (LLMs) by providing relevant information from an extensive text corpus. It is the "search engine" of artificial intelligence.
• RAG process: Through the vectorization, text data is converted into numerical vectors that capture the semantic meaning (tools: sentence converter, InferSent). With the subsequent query the vector database is searched for documents that are similar to the user query (tools: Pinecone, Weaviate). Via the extension, the retrieved documents are used to add context to the the original user query to provide a context that enables a more informative LLM response.
• A major advantage: RAG Solves the limitations of LLM and provides corporate information with traceable sources, reducing the falsification of information by LLMs.
• Vector databases are crucial to RAG's success due to Scalability (efficient handling of large amounts of data), Speed: (faster similarity search for relevant documents) and Accuracy (documents with the highest semantic similarity to the search query are found).
• Use cases: Information retrieval (e.g. chatbots), scientific research (e.g. finding similar research papers) and legal research (e.g. contract databases).

What is Retrieval Augmented Generation (RAG)?

RAG is a technique that improves the capabilities of Large Language Models (LLMs) by providing them with relevant information retrieved from a large amount of text data. In most cases, this is proprietary and protected data that is to be used in AI processes such as information search with speech. And this is how it works:

Vectorization

Text data is converted into numerical representations, so-called vectors. These vectors capture the semantic meaning of the text and enable an efficient similarity comparison. (e.g. Openai, Langchain, etc.). Vectors are easy to handle for LLMs; hence vector databases are referred to as the databases of the AI world.

Query

When a user request is received, the LLM first searches the vector database for documents that are most similar to the request. This search process is supported by the vector database's ability to perform fast and accurate similarity searches (e.g. Pinecone, Weaviate, etc.).

Extension

The retrieved documents are then used to expand on the original user query. This gives the LLM additional context, enabling it to provide more comprehensive and informative answers. Now the LLM processes the retrieved documents, e.g. summarizes them, searches for specific information, translates the document, etc.

Why Retrieval Augmented Generation (RAG)?

LLMs often suffer from two fundamental limitations:

• No sourceLLM responses often do not include a source for the information provided, making it difficult to verify the accuracy or trustworthiness of the information.
• Not up to dateLLMs are trained on huge datasets, but these datasets can become outdated over time. This can lead to LLMs generating answers that need to be more relevant or accurate.

RAG solves both problems by providing LLMs with access to a constantly updated data store. Retrieval Augmented Generation solves these problems in the following way:

• Fresh informationRAG retrieves relevant information from the vector database, ensuring that LLM responses are based on the latest and most accurate data. This eliminates the "missing source" problem by providing a traceable origin for the information.
• Fewer hallucinations and data leaksLLMs sometimes falsify information or leak training data in their answers, which is often referred to as "hallucination". By basing LLM responses on real data from the vector database, RAG significantly reduces the risk of these problems.

Vector database

The vector database is crucial to RAG's success. Unlike conventional databases, they are ideal for storing and searching for high-dimensional vector data. This enables:

• Scalability: Efficient processing of huge data sets with billions of documents.
• Speed: Lightning-fast similarity search to identify relevant documents in real time.
• Accuracy: Retrieve documents with the highest semantic similarity to the user request.

Use case

Information procurement: Chatbot powered by RAG

When a customer submits a question, the chatbot retrieves similar previous queries and solutions from the Vector database. This information is then incorporated into the chatbot's response to ensure it is relevant, accurate and addresses the customer's specific needs.

Scientific research

A researcher investigating a specific topic can use a RAG-supported system. The researcher enters a request outlining their research focus. The RAG system retrieves similar research papers and funding applications from an extensive database of scientific literature stored in the Vector database. This enables the researcher to discover relevant studies, identify potential collaboration partners and gain a comprehensive understanding of the existing research landscape.

The future of RAG and vector databases

The synergy between retrieval augmented generation and vector databases opens up new possibilities for LLMs. As these technologies evolve, we can expect to see even more sophisticated applications that change the way AI interacts with the world.

About Business Automatica GmbH:

Business Automatica reduces process costs by automating manual activities, increases the quality of data exchange in complex system architectures and connects on-premise systems with modern cloud and SaaS architectures. Applied artificial intelligence in the company is an integral part of this. Business Automatica also offers automation solutions from the cloud that are geared towards cyber security.