RAG with Citation
RAG-Buddy provides services to enhance the capabilities of the Retrieval Augmented Generation with Citation (RAG+C) system. This section provides an overview of Retrieval Augmented Generation with Citation (RAG+C), its purpose, and its mechanics.
What is RAG+C?
Retrieval Augmented Generation with Citation (RAG+C) is an advanced method that combines the power of Large Language Models (LLMs) with a retrieval mechanism and citation capabilities. An LLM is an AI model capable of generating human-like text based on the data it was trained on. A retrieval mechanism is a component capable of fetching specific data or context from various sources such as the internet, documents, or databases. The citation capability allows the model to provide references for the information it uses to generate responses.
Essentially, RAG+C acts as a bridge between the LLM and a vast reservoir of information, enabling the AI to generate contextually relevant and precise responses by extracting information from extensive knowledge sources or structured databases, and citing the sources of the information used.
What is the Purpose of RAG+C?
The primary purpose of RAG+C is to address knowledge-intensive tasks that require more than just a general-purpose language model. It is designed to provide more accurate, up-to-date, and verifiable text by leveraging the strengths of both LLMs and retrieval models. This approach is highly effective for tasks requiring access to real-time or organization-specific information, and where the source of the information is important.
For instance, in a corporate chatbot scenario, the static nature of the training data used by the LLM can lead to outdated or incorrect responses. RAG+C comes to the rescue by merging the generative capabilities of LLMs with real-time, targeted information retrieval, and citation capabilities, without altering the underlying model. This fusion allows the AI system to provide responses that are not only contextually apt but also based on the most current data, and with references to the sources of the information used.
How Does RAG+C Work?
The process of RAG+C can be broken down into four main steps: ingestion, retrieval, citation, and generation.
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Ingestion: This is the initial step where data from various sources is ingested and processed. This data could come from databases, documents, or any other type of data repository. The goal here is to continually update the system’s knowledge base with the most recent and relevant information.
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Retrieval: Once the data is ingested, the RAG+C system uses a vector search to select the most relevant information from the knowledge base. Vector search is a method that enables the discovery of analogous items based on shared data characteristics. It is invaluable in applications like text similarity searches, image association, recommendation systems, and anomaly detection.
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Citation: After the most relevant data has been retrieved, the RAG+C system identifies and records the sources of the information. This allows the system to provide references for the information used in the generated responses.
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Generation: After the most relevant data has been retrieved and the sources cited, the RAG+C system uses a Large Language Model to generate text based on this data. The LLM takes the retrieved data and uses it to generate a response that is not only contextually accurate but also rich in information, and with citations to the sources of the information used.
In conclusion, RAG+C is a powerful tool for generating contextually accurate, information-rich, and verifiable text. By combining the strengths of LLMs, vector search, and citation capabilities, RAG+C is able to provide a more accurate, up-to-date, and verifiable text generation solution.