12 Retrieval Augmented Generation (RAG) Tools / Software in '24
Generative AI stats show that Gen AI tools and models like (ChatGPT) can automate knowledge intensive NLP tasks that make up 60% to 70% of employees’ time. Yet, 56% of business leaders consider AI-generated content biased or inaccurate, lowering the adoption rate of LLMs.
Retrieval-augmented generation (RAG) is an AI framework that aims to improve LLM response quality. It helps by connecting the AI to outside information to improve its answers. When we use RAG in a question-answering system with AI, two things happen:
- The AI gets the newest and most reliable facts.
- Users can see where the AI gets its information, ensuring it’s correct and trustworthy.
However, business leaders may not be aware of the term, as RAG is a recently emerging area (See Figure 1).
Therefore, we aim to explore what RAG is, how it operates, its benefits, and available RAG models and tools in the LLM market landscape.
What are available RAG models and tools?
RAG models and tools can be divided into three category. The first category covers LLMs that already employ RAG to improve their output accuracy and quality. The second one refers to RAG libraries and frameworks that can be applied to LLMs. The final category includes models and libraries that can be combined with each other or LLMs to build RAG models.
RAG in LLMs
LLMs obtain plugins and adapters to optimize their model. LLMs that provide RAG include:
- Azure machine learning: Azure Machine Learning allows you to incorporate RAG in your AI using the Azure AI Studio or using code with Azure Machine Learning pipelines.
- ChatGPT Retrieval Plugin: OpenAI offers a retrieval plugin to combine ChatGPT with a retrieval-based system to enhance its responses. You can set up a database of documents and use retrieval algorithms to find relevant information to include in ChatGPT’s responses.
- HuggingFace Transformer plugin: HuggingFace provides a transformer to generate RAG models.
- IBM Watsonx.ai: The model can deploy RAG pattern to generate factually accurate output.
- Meta AI: Meta AI Research (Former Facebook Research) directly combines retrieval and generation within a single framework. It’s designed for tasks that require both retrieving information from a large corpus and generating coherent responses.
RAG libraries and frameworks
- Haystack: End-to-end RAG framework for document search provided by Deepset
- REALM: Retrieval Augmented Language Model (REALM) training is a Google toolkit for open-domain question answering with RAG.
Integration frameworks (e.g. Langhcain and Dust) simplify the development of context-aware and reasoning-enabled applications powered by language models. These frameworks provide modular components and pre-configured chains to meet specific application requirements while customizing models. Users can combine these frameworks with vector database to employ RAG in their LLMs.
Vector Databases (VDs) are designed to store multi-dimensional data, including information related to patients, such as their symptoms, blood test outcomes, behaviors, and overall health status. Certain VD software applications, like Deep Lake, offer support for Large Language Model (LLM) operations, making it easier to work with this type of data.
Other retrieval models
Since RAG is based on sequence-to-sequence and DPR models, ML/ LLM teams can combine these two models to ensure retrieval augmented generation. Some of these models include:
- BART with Retrieval
- ColBERT Model
- DPR (Document Passage Retrieval) Model.
What is retrieval augmented generation?
In 2020, Meta Research introduced RAG models to precisely manipulate knowledge. Lewis and colleagues refer to RAG as a general-purpose fine-tuning approach that can combine pre-trained parametric-memory generation models with a non-parametric memory.
In simple terms, Retrieval-augmented generation (RAG) is a natural language processing (NLP) approach that combines elements of both retrieval and generation models to improve the quality and relevance of generated content. It’s a hybrid approach that leverages the strengths of both techniques to address the limitations of purely generative or purely retrieval-based methods. Here is a brief video about RAG:
How do RAG models work?
RAG system operates in two phases: Retrieval and content generation.
In the retrieval phase:
Algorithms actively search for and retrieve relevant snippets of information based on the user’s prompt or question using techniques like BM25. This retrieved information is the basis for generating coherent and contextually relevant responses.
- In open-domain consumer settings, these facts can be sourced from indexed documents on the internet. In closed-domain enterprise settings, a more restricted set of sources is typically used to enhance the security and reliability of internal knowledge. For example, RAG system can look for:
- Current contextual factors, such as real-time weather updates and the user’s precise location
- User-centric details, their previous orders on the website, their interactions with the website, and their current account status
- Relevant factual data in retrieved documents that are either private or were updated after the LLM’s training process.
In the content generation phase:
- After retrieving the relevant embeddings, a generative language model, such as a transformer-based model like GPT, takes over. It uses the retrieved context to generate natural language responses. The generated text can be further conditioned or fine-tuned based on the retrieved content to ensure that it aligns with the context and is contextually accurate. The system may include links or references to the sources it consulted for transparency and verification purposes.
RAG LLMs use two systems to obtain external data:
- Vector database: Vector databases help find relevant documents using similarity searches. They can either work independently or be part of the LLM application.
- Feature stores: These are systems or platforms to manage and store structured data features used in machine learning and AI applications. They provide organized and accessible data for training and inference processes in machine learning models like LLMs.
What is retrieval augmented generation in large language models?
RAG models generate solutions that can address challenges faced by Large language models (LLMs). These main problems include:
- Limited knowledge access and manipulation: LLMs struggle with keeping their world knowledge up-to-date since their training dataset updates are infeasible. Also, they have limitations in precisely manipulating knowledge. This limitation affects their performance on knowledge intensive tasks, often causing them to fall behind task-specific architectures. For example, LLMs lack domain-specific knowledge as they are trained for generalized tasks.
- Lack of transparency: LLMs struggle to provide transparent information about how they make decisions. It is difficult to trace how and why they arrive at specific conclusions or answers, so they are often considered “black boxes”.
- Hallucinations in answers: Language models can answer questions that appear to be accurate or coherent but that are entirely fabricated or inaccurate. Addressing and reducing hallucinations is a crucial challenge in improving the reliability and trustworthiness of LLM-generated content.
What are the benefits of retrieval augmented generation?
RAG formulations can be applied to various NLP applications, including chatbots, question-answering systems, and content generation, where correct information retrieval and natural language generation are critical. The key advantages RAG provides include:
- Improved relevance and accuracy: By incorporating a retrieval component, RAG models can access external knowledge sources, ensuring the generated text is grounded in accurate and up-to-date information. This leads to more contextually relevant and accurate responses, reducing hallucinations in question answering and content generation.
- Contextual coherence: Retrieval-based models provide context for the generation process, making generating coherent and contextually appropriate text easier. This leads to more cohesive and understandable responses, as the generation component can build upon the retrieved information.
- Handling open-domain queries: RAG models excel in taking open-domain questions where the required information may not be in the training data. The retrieval component can fetch relevant information from a vast knowledge base, allowing the model to provide answers or generate content on various topics.
- Reduced generation bias: Incorporating retrieval can help mitigate some inherent biases in purely generative models. By relying on existing information from a diverse range of sources, RAG models can generate less biased and more objective responses.
- Efficient computation: Retrieval-based models can be computationally efficient for tasks where the knowledge base is already available and structured. Instead of generating responses from scratch, they can retrieve and adapt existing information, reducing the computational cost.
- Multi-modal capabilities: RAG models can be extended to work with multiple modalities, such as text and images. This allows them to generate contextually relevant text to textual and visual content, opening up possibilities for applications in image captioning, content summarization, and more.
- Customization and fine-tuning: RAG models can be customized for specific domains or use cases. This adaptability makes them suitable for various applications, including domain-specific chatbots, customer support, and information retrieval systems.
- Human-AI Collaboration: RAG models can assist humans in information retrieval tasks by quickly summarizing and presenting relevant information from a knowledge base, reducing the time and effort required for manual search.
Fine-Tuning vs. Retrieval-Augmented Generation
Typically, A foundation model can acquire new knowledge through two primary methods:
- Fine tuning: This process requires adjusting pre trained models based on a training set and model weights.
- RAG: This method introduces knowledge through model inputs or inserts information into a context window.
Fine-tuning has been a common approach. Yet, it is generally not recommended to enhance factual recall but rather to refine its performance on specialized tasks. Here is a comprehensive comparison between the two approaches:
|Combines retrieval and content generation
|Adapts pre-trained models to create content
|Retrieves external information as needed
|Limited to knowledge within the pre-trained model.
|Can incorporate the latest information
|Knowledge is static, challenging to update.
|Suitable for knowledge-intensive tasks
|Often used for specific, task-driven applications.
|Transparent due to sourced information
|May lack transparency in decision-making.
|May require significant computational resources
|Can be more resource-efficient.
|Can adapt to various domains and sources
|Must be fine-tuned for specific domains.
RAG is an emerging field, which is why there are few sources that can categorize these tools and frameworks. Therefore, AIMultiple relied on public vendor statements for such categorization. AIMultiple will improve this vendor list and categorization as the market grows.
RAG models and libraries listed above are sorted alphabetically on this page since AIMultiple doesn’t currently have access to more relevant metrics to rank these companies.
The vendor lists are not comprehensive.
Discover recent developments on LLMs and LLMOps by checking out:
- LLMOPs vs MLOPs: Discover the Best Choice for You
- Comparing 10+ LLMOps Tools: A Comprehensive Vendor Benchmark
- Compare Top 20+ AI Governance Tools: A Vendor Benchmark
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