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Retrieval Augmented Generation (RAG) systems are revolutionizing AI by enhancing pre-trained language models (LLMs) with external knowledge. Leveraging vector databases, organizations are crafting RAG systems tailored to internal data sources, amplifying LLM capabilities. This fusion is reshaping how AI interprets user queries, delivering contextually relevant responses across domains.
As the name suggests, RAG augments the pre-trained knowledge of LLMs with enterprise or external knowledge to generate context-aware domain specific responses. To derive higher business value from large language foundation models, many organizations are leveraging vector databases for building RAG systems with enterprise internal data sources.
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RAG systems extend the capabilities of LLMs by integrating enterprise data sources dynamically with information during the inference phase. By definition, RAG includes the following:
- Retriever retrieves relevant context from data sources
- The Augment process integrates the retrieved data with user query
- The generation process generates relevant responses to user queries based on the integrated context.
RAG is an increasingly significant area in the field of natural language processing (NLP) and GenAI to provide enriched responses to customer queries with domain-specific information in chatbots and conversational systems. AlloyDB from Google, CosmosDB from Microsoft, Amazon DocumentDB, MongoDB in Atlas, Weaviate, Qdrant, and Pinecone all provide vector database functionality to serve as a platform for organizations to build RAG systems.
How RAG can help
The benefits of RAG can be classified into the following categories.
1. Bridging Knowledge Gaps: No matter how big the size of the LLM, and how well and how long the model is trained, it still lacks the domain-specific information and new information after it has last been trained. RAG helps to bridge these knowledge gaps, making the model equipped with additional information and capable of handling and responding to domain-specific queries.
2. Reduced Hallucination: By accessing and interpreting relevant information from external sources like PDFs and webpages, RAG systems can provide answers that are not made up but are based on real-world data and facts. This is crucial for tasks that require accuracy and up-to-date knowledge.
3. Efficiency: RAG systems can be more efficient in certain applications because they leverage existing knowledge bases, which reduces the need for the model to retrain, build and store all that information internally.
4. Improved Relevance: RAG systems can tailor their responses more specifically to the user’s prompt by fetching relevant information. This means the answers you get are more likely to be on point and useful.
Design elements of RAG systems
Identifying the purpose and goals of the RAG project is critical, whether it’s developed for marketing to generate content, customer support for question & answering, finance for billing details extraction, and so on. Second, selecting relevant data sources are fundamental steps in building a successful RAG system.
Capturing relevant information from these external documents involves breaking down this data into meaningful chunks or segments – known as chunking. Using SpaCY or NLTK libraries provides context-aware chunking via named entity recognition and dependency parsing.
Converting chunked information to vector format to represent data in a high-dimensional vector space involves placing semantically similar text next to each other. Langchain and LlamaIndex are frameworks that provide techniques for generating embeddings along with LLM models tailored to enterprise-specific needs, such as context-aware embeddings or embeddings optimized for retrieval tasks.
Once the data is converted into embeddings, the next step is storing them in an efficient database that supports vector functionality for retrieval. Selecting the vector database is critical based on vector search performance, functionality, and its cost, based on open source or commercial. Vector databases can be classified as follows:
- Native Vector Databases: Purpose-built for vector search on dense embeddings e.g. Weaviate, Pinecone, FAISS.
- NoSQL Databases: Key-Value Stores like Redis, Aerospike etc. and MongoDB – and AstraDB and Graph oriented databases for building knowledge graphs using Neo4
- General Purpose SQL Databases with Vector Functionality: Extending traditional SQL/NoSQL DBs like PostgreSQL with vector extensions, and AlloyDB from Google. Key Considerations
Both RAG and LLMs are resource-intensive models, requiring significant computational power, memory and storage to operate efficiently. Deploying these models in production environments can be challenging due to their high resource requirements.
Storing large amounts of data can incur significant costs, especially when using cloud-based storage solutions. Organizations must carefully consider the trade-offs between storage costs, performance, and accessibility when designing their storage infrastructure for RAG applications.
Managing the cost of serving queries in RAG systems requires a combination of optimizing resource utilization, minimizing data transfer costs, and implementing cost-effective infrastructure and computational strategies.
To improve search latency in RAG systems, indexing needs to be optimized for fast retrieval, caching mechanisms should be deployed to store frequently accessed data, and parallel processing and asynchronous techniques should be used for efficient query handling. Additionally, load balancing, data partitioning, and hardware acceleration to distribute workload and accelerate computation will result in faster query responses.
Another RAG deployment element is the overall cost of deployment, which needs to be carefully evaluated to meet business and budget goals, including:
- Cost of Embeddings: Certain data sources need high-quality embeddings, which increases the cost of embeddings generated by the LLM models.
- Cost of Serving Queries: The expense associated with handling queries in the RAG system is determined by the frequency of queries – whether per minute, hour, or day – and the complexity of the data involved. This cost is commonly calculated as dollars per query per hour ($/QPH).
- Storage Cost: Storage expenses are influenced by the number and complexity (dataset dimensionality) of data sources. As the complexity of these datasets increases, the cost of storage rises accordingly. Costs are typically calculated in dollars per terabyte.
- Search Latency: As a business, what is the SLA for response time for these vector queries in RAG systems? For example, a customer support RAG system must be highly responsive for superior customer experience. How many concurrent users need to be supported to deliver quality of service is also critical.
- The maintenance window for periodical updates to data sources.
- Cost of LLM Models: Using proprietary language models such as Gemini, OpenAI, and Mistral incurs extra charges based on the number of tokens processed for input and output.
Despite these potential challenges, RAG remains a critical component of the Generative AI strategy for enterprises, enabling the development of smarter applications that deliver contextually relevant and coherent responses grounded in real-world knowledge.
Conclusion
RAG systems represent a pivotal advancement in reshaping the AI landscape by seamlessly integrating enterprise data with LLMs to deliver contextually rich responses. From bridging knowledge gaps and reducing hallucination to enhancing efficiency and relevance in responses – RAG offers a multitude of benefits. However, the deployment of RAG systems comes with its own set of challenges, including resource-intensive computational requirements, managing costs, and optimizing search latency. By addressing these challenges and leveraging the capabilities of RAG, enterprises can unlock intelligent applications grounded in real-world knowledge – and a future where AI-driven interactions are more contextually relevant and coherent than ever before.
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