A simple introduction to Retrieval Augmented Generation (RAG) Systems

In the rapidly evolving domain of data engineering and artificial intelligence, enabling swift and accurate querying of internal documents is crucial for sustaining competitive advantage. Our approach, meticulously crafted for SMEs, leverages the cutting-edge capabilities of Open Source Software (OSS) Large Language Models (LLMs), ensuring cost-effective and scalable solutions. For an in-depth understanding of our process, the PromptZenAI team has created a detailed video explanation.

Here’s an outline of our specialized process:

1. Knowledge Decomposition: We begin by segmenting the entirety of your knowledge base into manageable units. Each unit is designed to represent a discrete piece of context, facilitating queries from diverse sources such as Confluence documentation and PDF reports.
2. Embedding Transformation: Through the application of sophisticated Embedding Models, we transform these textual segments into vector embeddings. This step is pivotal in enabling efficient and precise information retrieval.
3. Vector Embedding Storage: The vector embeddings are then stored in a Vector Database, engineered for rapid data access, enhancing the speed and accuracy of query responses.
4. Contextual Mapping: In tandem, we catalogue the textual representation of each embedding, complete with pointers for quick retrieval. This meticulous mapping ensures a robust foundation for query processing.

Advanced Query Processing and Cost-Efficient Answer Construction:

1. Query Embedding: Queries are embedded using the same model that processed the initial knowledge base, ensuring uniformity and high relevance in search results.
2. Database Querying: Leveraging the query’s vector embedding, we conduct a search within the Vector Database, selecting an optimal number of vectors that align with the query’s context. This targeted selection is instrumental in refining the scope of responses.
3. Retrieval and Matching: Utilizing Approximate Nearest Neighbour (ANN) search techniques, the database identifies the most relevant context vectors, drawing from the rich, embedded information.
4. Context Integration: The process continues by associating the selected vector embeddings with their textual counterparts, setting the stage for precise answer formulation.
5. LLM Processing with OSS Advantage: Our utilization of Open Source Software (OSS) LLMs in this phase underscores our commitment to cost-efficiency without compromising on quality. The query and associated context are processed through an OSS LLM, benefiting from extensive prompt engineering to ensure responses are accurately bounded within the relevant context, avoiding unwarranted extrapolation.

User Interface Integration for Enhanced Accessibility:

Our solution transcends conventional query processing by incorporating a user-friendly Web UI. This interface serves as a conversational agent, facilitating user queries and delivering responses generated through our advanced, cost-effective methodology. This integration not only embodies the pinnacle of chatbot technology but also reflects our dedication to leveraging OSS innovations to reduce operational costs for our SME clients.

As we continue to refine our Retrieval-Augmented Generation Systems, we remain dedicated to exploring the nuances of cost-effective AI implementations, ensuring our SME clients benefit from high-quality, scalable, and economically efficient solutions.