Actualités

We were recently interviewed by the Fond de Dotation du Libre, which is producing a report on European cloud technologies for the DGE (Direction Générale des Entreprises). Here are our answers.

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Open source software is an essential tool for European digital sovereignty. Abilian is announcing its support for Europe's strategic autonomy in the cloud, with its Nua project.

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Abilian will be present to talk about open source and digital sovereignty at the "Euclidia Now!" event on 29 September in Brussels.

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One of the most promising recent advancements in AI is the concept of Retrieval-Augmented Generation (RAG). This innovative technique combines the best of both retrieval-based methods and generative models to create a more powerful, context-aware AI system. In this blog post, we delve into what RAG is, how it works, its advantages, and its applications in the real world.

What is RAG?

Retrieval-Augmented Generation (RAG) is a hybrid approach that leverages the strengths of both retrieval-based systems and generative models. The primary goal of RAG is to improve the accuracy and contextual relevance of generated text by incorporating external knowledge retrieved from a large corpus of documents.

Key Components of RAG

1. Retriever: This component is responsible for fetching relevant documents or passages from a pre-existing large dataset based on a given query. Advanced retrieval techniques such as Dense Passage Retrieval (DPR) are often used. DPR employs neural networks to encode queries and documents into dense vectors, enabling effective retrieval using vector similarity measures.

2. Generator: The generator, usually a Transformer-based model like GPT-3 or BART, takes the retrieved documents along with the original query to generate a response. This model is fine-tuned to produce coherent and contextually appropriate answers, using the information provided by the retriever.

How Does RAG Work?

1. Query Processing: When a query is input into the system, the retriever component searches through a vast corpus of documents to find the most relevant pieces of information.
2. Contextual Generation: The retrieved documents are then passed to the generator. This model uses the additional context to generate a more informed and accurate response.
3. Response Output: The final output is a synthesized response that integrates the knowledge retrieved with the generative capabilities of the model.

Advantages of RAG

• Enhanced Accuracy: By grounding the generative process in retrieved documents, RAG reduces the likelihood of producing incorrect or irrelevant information.
• Scalability: Retrieval models can efficiently handle and search through extensive datasets, making RAG scalable to large-scale applications.
• Contextual Awareness: The use of retrieved documents ensures that the generated responses are contextually aware and relevant to the query.

Applications of RAG

1. Open-Domain Question Answering: RAG is particularly effective in open-domain question-answering systems, where the model needs to provide accurate answers from a broad range of topics.
2. Customer Support: Automated customer support systems can leverage RAG to pull relevant information from knowledge bases, providing accurate and contextually appropriate responses to customer inquiries.
3. Content Generation: RAG can be used to generate content that requires factual accuracy, such as news articles, by retrieving and utilizing information from trusted sources.

Real-World Implementations

One notable implementation of RAG is by Facebook AI, which introduced a model combining BERT-based retrievers with BART-based generators. This model has shown significant improvements in tasks like open-domain question answering and conversational AI.

Technical Details

• Training: Typically, the retriever and generator components are trained separately. The retriever is trained to identify the most relevant documents, while the generator is fine-tuned to generate high-quality text using the retrieved context.
• Inference: During inference, the retriever first selects a set of documents based on the input query, which are then used by the generator to produce the final response.

Conclusion

Retrieval-Augmented Generation (RAG) represents a significant step forward in the development of AI systems that are both accurate and contextually aware. By combining retrieval and generation, RAG can provide more reliable and relevant responses, making it a powerful tool for a wide range of applications.

As AI continues to evolve, techniques like RAG will play a crucial role in enhancing the capabilities of language models, bringing us closer to truly intelligent and contextually aware AI systems.

References

Papers

• Retrieval Augmented Generation: Streamlining the creation of intelligent natural language processing models (Meta, 2020)
• Dense Passage Retrieval for Open-Domain Question Answering (arXiv, 2020)
• Combining Retrieval and Generation for Question Answering (arXiv, 2021)

Talks

(Added in 2024)

• Fundamentals of Retrieval Augmented Generation (EuroPython 2024)

Projects

(Updated in 2024)

• https://github.com/weaviate/Verba Retrieval Augmented Generation (RAG) chatbot powered by Weaviate
• https://github.com/IntelLabs/fastRAG Efficient Retrieval Augmentation and Generation Framework
• https://github.com/neuml/txtai All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
• https://github.com/infiniflow/ragflow RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding

Abilian, a partner in the Cython+ project, has published a series of articles presenting practical examples of how Cython+ can be used to optimise performance.

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Abilian has co-signed an open letter to the Vice-President of the European Commission on the anti-competitive practices of certain dominant American players in the European cloud market.

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Stefane Fermigier, CEO of Abilian, was on B-SMART TV yesterday to speak on behalf of the Euclidia Alliance, of which Abilian is a founding member.

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Abilian is a member of the Coalition for Competitive Digital Markets, which aims to tighten constraints on dominant players in the European digital market to prevent them from controlling consumer access to information and abusing their position to limit market access.

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We travel to La Rochelle this week for the second B-BOOST.

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Abilian is one of 23 independent European companies creating original cloud technologies that today announced the creation of the European Cloud Industrial Alliance to promote digital independence and strategic autonomy.

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