U brzom razvoju generativne umjetne inteligencije (Gen AI), modeli velikih jezika (LLM) kao što su OpenAI GPT-4, Google Gemma, Meta LLaMA 3, Mistral.AI, Falcon i drugi AI alati postaju nezamjenjivi poslovni resursi.
Jedan od napretka koji najviše obećava u ovoj domeni je Retrieval Augmented Generation (RAG). Ali šta je zapravo RAG i kako se može integrisati sa vašim poslovnim dokumentima i znanjem?
Šta je RAG?
RAG je pristup koji kombinuje Gen AI LLM sa tehnikama pronalaženja informacija. U suštini, RAG omogućava LLM-ima pristup eksternom znanju pohranjenom u bazama podataka, dokumentima i drugim repozitorijumima informacija, poboljšavajući njihovu sposobnost da generišu tačne i kontekstualno relevantne odgovore.
Kako je objasnio Maxime Vermeir, viši direktor AI strategije u ABBYY-u, vodećoj kompaniji za obradu dokumenata i AI rješenja: „RAG vam omogućava da kombinujete svoju vektorsku trgovinu sa samim LLM. Ova kombinacija omogućava LLM-u da zaključi ne samo o svom već postojećem znanju, već i o stvarnom znanju koje dajete kroz posebne upute. Ovaj proces rezultira preciznijim i kontekstualno relevantnijim odgovorima.”
Također: Napravite mjesta za RAG: Kako se ravnoteža snaga Gen AI mijenja
Ova mogućnost je posebno ključna za preduzeća koja trebaju izvući i koristiti specifično znanje iz ogromnih, nestrukturiranih izvora podataka, kao što su PDF-ovi, Word dokumenti i drugi formati datoteka. Kako Vermeir navodi u svom blogu, RAG ovlašćuje organizacije da iskoriste puni potencijal svojih podataka, pružajući efikasniji i precizniji način interakcije sa rješenjima vođenim umjetnom inteligencijom.
Depiction of how a typical RAG data pipeline works.
Intel/LFAI & Data Foundation
At the heart of RAG is the concept of vector databases. A vector database stores data in vectors, which are numerical data representations. These vectors are created through a process known as embedding, where chunks of data (for example, text from documents) are transformed into mathematical representations that the LLM can understand and retrieve when needed.
Maxime elaborated: “Using a vector database begins with ingesting and structuring your data. This involves taking your structured data, documents, and other information and transforming it into numerical embeddings. These embeddings represent the data, allowing the LLM to retrieve relevant information when processing a query accurately.”
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This process allows the LLM to access specific data relevant to a query rather than relying solely on its general training data. As a result, the responses generated by the LLM are more accurate and contextually relevant, reducing the likelihood of “hallucinations” – a term used to describe AI-generated content that is factually incorrect or misleading.
Practical steps to integrate RAG into your organization
Assess your data landscape: Evaluate the documents and data your organization generates and stores. Identify the key sources of knowledge that are most critical for your business operations.
Choose the right tools: Depending on your existing infrastructure, you may opt for cloud-based RAG solutions offered by providers like AWS, Google, Azure, or Oracle. Alternatively, you can explore open-source tools and frameworks that allow for more customized implementations.
Data preparation and structuring: Before feeding your data into a vector database, ensure it is properly formatted and structured. This might involve converting PDFs, images, and other unstructured data into an easily embedded format.
Implement vector databases: Set up a vector database to store your data’s embedded representations. This database will serve as the backbone of your RAG system, enabling efficient and accurate information retrieval.
Integrate with LLMs: Connect your vector database to an LLM that supports RAG. Depending on your security and performance requirements, this could be a cloud-based LLM service or an on-premises solution.
Test and optimize: Once your RAG system is in place, conduct thorough testing to ensure it meets your business needs. Monitor performance, accuracy, and the occurrence of any hallucinations, and make adjustments as needed.
Continuous learning and improvement: RAG systems are dynamic and should be continually updated as your business evolves. Regularly update your vector database with new data and re-train your LLM to ensure it remains relevant and effective.
Implementing RAG with open-source tools
Several open-source tools can help you implement RAG effectively within your organization:
LangChain is a versatile tool that enhances LLMs by integrating retrieval steps into conversational models. LangChain supports dynamic information retrieval from databases and document collections, making LLM responses more accurate and contextually relevant.
LlamaIndex is an advanced toolkit that allows developers to query and retrieve information from various data sources, enabling LLMs to access, understand, and synthesize information effectively. LlamaIndex supports complex queries and integrates seamlessly with other AI components.
Haystack is a comprehensive framework for building customizable, production-ready RAG applications. Haystack connects models, vector databases, and file converters into pipelines that can interact with your data, supporting use cases like question-answering, semantic search, and conversational agents.
Verba is an open-source RAG chatbot that simplifies exploring datasets and extracting insights. It supports local deployments and integration with LLM providers like OpenAI, Cohere, and HuggingFace. Verba’s core features include seamless data import, advanced query resolution, and accelerated queries through semantic caching, making it ideal for creating sophisticated RAG applications.
Phoenix focuses on AI observability and evaluation. It offers tools like LLM Traces for understanding and troubleshooting LLM applications and LLM Evals for assessing applications’ relevance and toxicity. Phoenix supports embedding, RAG, and structured data analysis for A/B testing and drift analysis, making it a robust tool for improving RAG pipelines.
MongoDB is a powerful NoSQL database designed for scalability and performance. Its document-oriented approach supports data structures similar to JSON, making it a popular choice for managing large volumes of dynamic data. MongoDB is well-suited for web applications and real-time analytics, and it integrates with RAG models to provide robust, scalable solutions.
Nvidia offers a range of tools that support RAG implementations, including the NeMo framework for building and fine-tuning AI models and NeMo Guardrails for adding programmable controls to conversational AI systems. NVIDIA Merlin enhances data processing and recommendation systems, which can be adapted for RAG, while Triton Inference Server provides scalable model deployment capabilities. NVIDIA’s DGX platform and Rapids software libraries also offer the necessary computational power and acceleration for handling large datasets and embedding operations, making them valuable components in a robust RAG setup.
IBM has released its Granite 3.0 LLM and its derivative Granite-3.0-8B-Instruct, which has built-in retrieval capabilities for agentic AI. It’s also released Docling, an MIT-licensed document conversion system that simplifies the process of converting unstructured documents into JSON and Markdown files, making them easier for LLMs and other foundation models to process.
Implementing RAG with major cloud providers
The hyperscale cloud providers offer multiple tools and services that allow businesses to develop, deploy, and scale RAG systems efficiently.
Amazon Web Services (AWS)
Amazon Bedrock–> je potpuno upravljana usluga koja pruža visokoučinkovite temeljne modele (FM) sa mogućnostima za izgradnju generativnih AI aplikacija. Bedrock automatizuje vektorske konverzije, pronalaženje dokumenata i generisanje izlaza.
Amazon Kendra