The world is going through a digital revolution. Companies are increasingly embracing the idea of helping nontechnical staff members — those who have deep business-area expertise — learn to directly automate mundane repetivie processes that eat up their time, by providing them off the shelp AI tools to improve productions. Many tech companies have launched generative AI tools in their organization via the buy implementation pattern from popular GenAI providers.Such companies have noticed a 40% improvement in skilled worker productivity vs those that don’t use it.1
While off-the-shelf AI tools (buy implementation pattern) with customized simple prompting techniques that citizen developers and highly skilled workers can follow are often good enough for proof of concepts, real-world projects, and data vary a lot. Therefore, more advanced strategies may be required.
Others with technically savvy leadership are taking a two pronged approach using AI tools (buy) as well as building their customized AI solutions (build ad boost), either by leveraging the hands-on-keyboard engineers in their employment or using third party consulting firms to build such solutions..
Other organizations that are relatively advanced in their AI journey, reached level IV or level V in their organizational Analtyics FitnessTM (still novices in GenAI), have experience with advanced analytics (predictive, prescriptive), and have implemented multiple ML models in production will experiment with building AI solutions using build-and-boost implementation patterns. 2 This can be achieved by taking the base LLM model and fine-tuning it via vi opensource or commercial providers. AI solutions leveraging fine-tuned LLMs have achieved impressive results with improved accuracy. In various natural language processing (NLP) tasks, such as answering questions, summarization, translation, and dialogue to
But customizing open source LLM (base) or commercial LLM (base) by fine tuning can be complex, time-consuming, and resource intensive, and not every organization has data scientist and machine learning engineers on staff with the skill set to take on this challenging endeavor. Another way is to use the base LLM models to generate up-to-data, personalized responses and cost-effective customization technic that boost accuracy and relevance while taking advantage of a resource most organizations already have in abundance: data. Maintaining up-to-date information to feed LLM models via retrieval augment generation (RAG) systems is essential to accurately reflect document modifications, additions, or deletions in the stored vectors.
Let us build on the concepts and advantages we introduced, figure out how to handle the challenges associated with RAG, and dive deeper into concepts relevant to storing data in Vector Stores in RAG pipelines.
Listed below are the few benefits of Using RAG
- Up-to-Date and Accurate Responses: Enhances the LLM’s responses with current external data, improving accuracy and relevance by grounding the LLM’s output in relevant knowledge,
- Domain-Specific Responses: Delivers contextually relevant responses tailored to an organization’s proprietary data.
- Efficiency and Cost-Effectiveness: Offers a cost-effective method for customizing LLMs without extensive model fine-tuning.
RAG challenges and considerations:
Skill set: Fine-tuning and customizing an LLM model requires experience with natural language processing (NLP), deep learning (DL), model configuration, data reprocessing and evaluation, and technologies under the purview of data scientist/ML engineers. Customizing a model with RAG requires software engineering, python programming, and architectural skills. Compared to traditional fine-tuning methods, RAG provides a more accessible and straightforward way to get feedback, troubleshoot, and fix applications. The RAG framework using Langchain and LLama index lowers the barrier to entry as well.
Fine-tuning a model requires experience with natural language processing (NLP), DL, model configuration, data reprocessing, and evaluation. Overall, it can be more technical and time-consuming. In Serving RAG 1. User Experience: Ensuring rapid response times suitable for real-time applications. 2. Cost Efficiency: Managing the costs of serving millions of responses. 3. Accuracy: Ensuring outputs are accurate to avoid misinformation. 4. Recency and Relevance: Keeping responses and content current with the latest data. 5. Business Context Awareness: Aligning LLM responses with specific business contexts. 6. Service Scalability: Managing increased capacity while controlling costs. 7. Security and Governance: Implementing data security, privacy, and governance protocols.
Cost: Traditionally, fine-tuning is a DL technique requiring much data and computational resources. Historically, to inform a model with fine-tuning, you need to label data and run training on costly, high-end hardware. Additionally, the performance of the fine-tuned model depends on the quality of your data, and obtaining high-quality data can be expensive.
Comparatively, RAG tends to be more cost-efficient than fine-tuning. To set up RAG, you build data pipeline systems to connect your data to your LLM. This direct connection cuts down on resource costs by using existing data to inform your LLM.3
Data Governance: Using an organization’s proprietary data implies that RAG systems should adhere to strict data management rules.s Ensuring that the system abides by applicable laws, regulations, and ethical standards is essential to mitigate these risks. It enhances the system’s reliability and trustworthiness, which are key to its successful deployment.
Now, let us focus on vector databases and relevant terms in the context of RAG pipelines. Familiarity with the terms is suffice to have a productive conversation with the engineers and architects in your organization. The very first step in a RAG pipeline is data indexing.
Data Indexing: Organise data efficiently for quick retrieval. This involves processing, chunking, and storing data in a vector database using indexing strategies.
Vector database is specifically designed to operate on embedding vectors. In the simplest terms, vector embedding is a numerical representation of text data that encapsulates its semantic content while discarding irrelevant details in a way that machines can process and understand. As the popularity of LLMs and generative AI has grown recently, so has the use of embeddings to encode unstructured data. They offer the capabilities of both vector indexes and traditional databases, such as optimized storage, scalability, flexibility, and query language support. They allow users to find and retrieve similar or relevant data based on their semantic or contextual meaning.
Vector databases are specialized databases that keep and manage embeddings. They efficiently store, find, and study large amounts of complex data. By turning data into embeddings, vector databases enable searches based on meaning and similarity, which is better than just matching keywords. Vector databases have emerged as an effective solution for enterprises to deliver and scale GenAI use cases. Manage, secure, and scale embeddings in a production environment. Pinecone, ChromaDB, and Redis are popular vector databases in use today. 4 Vector databases can help RAG models quickly find the most similar documents or passages to a given query and use them as additional context for the LLM. Finding similarity in documents is made using vector index
A vector index is a data structure in a vector database designed to enhance the efficiency of processing, and it is particularly suited for the high-dimensional vector data encountered with LLMs. Its function is to streamline the search and retrieval processes within the database. By implementing a vector index, the system can conduct quick similarity searches, identifying vectors that closely match or are most similar to a given input vector. Essentially, vector indexes are designed to enable rapid and precise similarity search, facilitating the recovery of vector embeddings.
Once the data is converted to embeddings, vector databases can quickly find similar items because similar items are represented by vectors close to each other in the vector space, which we refer to as a vector store (storing our vectors). Semantic search, which searches within the vector stores, understands the meaning of a query by comparing its embedding with the embeddings of the stored data. This ensures that the search results are relevant and match the intended meaning, regardless of the specific words used in the query or the type of data being searched.
They organize the vectors using techniques such as hashing, clustering, or tree-based methods to make finding the most similar ones easy based on their distance or similarity metrics. For example, FAISS (Facebook AI Similarity Search) is a popular vector index that efficiently handles billions of vectors.
To create vector indexes for your embeddings, there are many options, such as exact or approximate nearest neighbor algorithms (e.g., HNSW or IVF), different distance metrics (e.g., cosine or Euclidean), or various compression techniques (e.g., quantization or pruning). Your index method depends on balancing speed, accuracy, and memory consumption. We can use different mathematical approaches to compare how similar two vector embeddings are—these are useful when searching and matching different embeddings.
Vector search is used to find the most relevant documents or passages to the query based on the similarity between the query vector and the document vectors in the index. A vector search is a query operation that finds the vectors most similar to a given query vector based on a similarity metric. In an LLM RAG pattern, a vector index stores the documents’ embeddings or passages that the LLM can retrieve as context for generating responses.
Similarity measures are mathematical methods that compare and compute a distance value between two vectors. This distance value indicates how dissimilar or similar the two vectors are in their semantic meaning. The distance can be based on multiple criteria, such as the length of the line segment between two points, the angle between two directions, or the number of mismatched elements in two arrays. Similarity measures are helpful for ML tasks involving grouping or classifying data objects, especially for vector or semantic search. For example, if we want to find words similar to “puppy,” we can generate a vector embedding for this word and look for other words with close vector embeddings, such as “dog.”
In this blog edition, we explored the advantages, challenges, and things to consider when using the RAG system. We also picked up terminology relevant to storing searching and storing vectors in an RAG system.
Embracing GenAI in business means being open to radical change, questioning existing business processes without fear of disrupting the status quo, and being dauntless in throwing out the rulebook and starting anew to achieve better business outcomes. Trailblazers, innovators, and those who are curious and on the lookout for technological developments that lie around the corner will reap the greatest benefit from GenAI. AI will not replace the role of humans in critical functions, but those incapable of embracing AI technologies will find themselves at a disadvantage, unable to partner and collaborate with AI practitioners within their organizations and beyond.
References
- Analytics for Business Success, A Guide To Analytics FitnessTM, Hema Seshadri, Ph.D. https://a.co/d/e8haiUR
- https://mitsloan.mit.edu/ideas-made-to-matter/how-generative-ai-can-boost-highly-skilled-workers-productivity
- https://www.redhat.com/en/topics/ai/rag-vs-fine-tuning
- https://www.techtarget.com/searchdatamanagement/tip/Top-vector-database-options-for-similarity-searches
