Explore the importance of the document retrieval system in enhancing the efficiency of RAG in LLM model.
Retrieval Augmented Generation (RAG) is a powerful technique that combines the strengths of large language models (LLMs) with an external knowledge base to provide accurate and contextually relevant responses. Central to the effectiveness of a RAG system is the document retrieval system, which maintains a vast database of information that the LLM can query to answer specific questions.
Document retrieval system is crucial part of the RAG system, maintaining a database of information that LLM can query to answer a specific question. But how a document retrieval system is created, and how a document ingested into the RAG system?
After reading this blog, we hope you can understand more about GreenNode’s RAG and document retrieval in more detail.
Understanding the document embedding
At GreenNode’s RAG system, the documents are split into chunks, or we can count as paragraphs. After that, chunks will be converted to multidimensional vectors. In this blog, we can treat the embedding model as a black box AI and focus on the main concept of the document retrieval system.
Multidimensional vectors are another representation of documentations from a semantic point of view. But why is that? In natural language processing, a sentence embedding refers to the numeric representation of a sentence form of a vector of real numbers which encodes meaningful semantic information.
Image above represents a semantic concept in embedding: a1 is a vector representing a question about the population of Berlin, p1, p2, p3, etc. are vectors representing the information database. A document embedding system must ensure the distance between a query vector and relevant information vectors must be closer than irrelevant information. In figure above, the distance between question a and paragraph p1 is smaller than the others.
Why is document retrieval important?
“RAG is a hybrid model that merges the best of both worlds: the deep understanding of language from LLMs and the vast knowledge encoded in external data sources. By integrating these two elements, RAG-powered chatbots can deliver precise, relevant, and context-aware responses to users.“
Revolutionizing AI Conversations with GreenNode's Advanced RAG Technology
Every LLMs baseline model knowledge has a gap that is limited in its training data. Asking LLMs to write about the latest trending or information, LLMs will have no idea what you’re talking about, and the response will be crafted from the model's hallucinations and its worst. LLM’s problem comes from many key issues:
- Training data is out of date.
- Model tends to extrapolate when facts aren’t available (called hallucination).
- LLMs training is very expensive. According to Hackernoon statistic, OpenAI’s GPT-3 with over 175 billion parameters costs over $4.6 millions while Bloomberg’s BloomberGPT with 50 billion parameters costs $2.7 millions.
But LLMs is powerful when generating answers of context it has knowledge from (called Zero Shot Learning). And with embedding models to create embedding databases, we can utilize the dynamic of semantic embedding, retrieving relevant or custom knowledge we want LLMs to answer. With the cost of building a document embedding database is outperform the cost to fine tuning a LLM.
Understanding the document ingesting
Computers don’t understand the meaning of texts, all information in computers is represented as bits and bytes, signals and numbers. In GreenNode’s RAG system, document after split into chunks will be processed into embedding vectors.
Understanding the document retrieval
To retrieve an answer for a particular question, in the first step, we must encode or embed the question into embedding vectors. Once we have a question, we retrieve the nearest neighbors to represent the vector of the question. The advantage is we don’t really need to query specific words or concepts, we just need the idea and retrieve semantically similar vectors.
Conclusion
Document retrieval plays a crucial role in the GreenNode’s RAG system because it maintains data and context knowledge so LLMs can understand and return answers accurately.
Document retrieval in GreenNode’s advanced RAG solution represents a significant leap forward in the realm of conversational AI. By prioritizing accuracy, context-awareness, and user engagement, we're not just creating chatbots; we're creating digital conversationalists—knowledgeable, efficient, and highly intuitive. Join us on this exciting journey as we continue to redefine the boundaries of AI communications.