The Core of RAG: Embeddings
For RAG (Retrieval-Augmented Generation) to function effectively, it needs a mechanism capable of intelligently comparing text, even if phrased differently. This is where embeddings come into play.
What is an embedding? An embedding is a mathematical representation of text generated by an artificial intelligence model.
In practical terms, each sentence, paragraph, or document is transformed into a vector (a series of numbers) in a multidimensional space.
Thanks to this transformation: • Two texts discussing the same subject will have embeddings that are close to each other, even if they use different words. • This enables the retrieval of the most relevant content from a database without being restricted to exact keywords.
Embeddings and Information Retrieval In a RAG system, embeddings are used as follows:
- Your company’s internal documents are preprocessed: each portion is converted into embeddings.
- When a user asks a question, it is also transformed into an embedding.
- The system compares vectors to find the most semantically similar passages.
- These results are then passed to a generative model (LLM), which uses them to produce a clear, reliable, and contextualized response.
Why it’s a strategic advantage
- Deep Language Understanding: Instead of relying on keywords, the system grasps the meaning.
- Compatible with Your Private Data: Embeddings can be applied to your internal documents without external exposure.
- Rapid Response Time: Once embeddings are calculated, searches become extremely fast.
- Scalable: Easily enrich your document base without the need for manual requalification.
At COGNIWAVE DYNAMICS, we have mastered the generation, optimization, and management of embeddings, leveraging models such as OpenAI, HuggingFace, LLaMA, or open-source solutions.
We implement robust pipelines to ensure optimal performance of your RAG systems, whether deployed on-premise or in the cloud.
