Découvrez la génération augmentée par recherche (RAG)

Table of Contents

1. Introduction
2. Retrieval Augmented Generation Explained
   • 2.1 What is Retrieval Augmented Generation?
   • 2.2 The Issue with Traditional Language Models (LLMs)
   • 2.3 The Problem of Outdated Information
   • 2.4 Connecting LLMs to Databases
   • 2.5 Using Vector Stores for Retrieval
3. How Retrieval Augmented Generation Works
   • 3.1 Technical Background of RAG
   • 3.2 Splitting Documents into Chunks
   • 3.3 Generating Embeddings from Documents
   • 3.4 Storing Embeddings in a Vector Store
   • 3.5 Retrieving Information from Vector Store
4. Advantages of Retrieval Augmented Generation
   • 4.1 Always Having Up-to-Date Information
   • 4.2 Providing the Source of Information
   • 4.3 Further Exploration: Longformer and ChromaDB
5. Conclusion
6. Additional Resources

Retrieval Augmented Generation: Enhancing Language Models with Up-to-Date Information

📖 Introduction

Bienvenue sur ma chaîne YouTube ! Dans cette vidéo, nous allons parler de la génération augmentée par recherche, également connue sous le nom de Retrieval Augmented Generation (RAG). Vous avez peut-être entendu parler de l'augmentation et de la génération, mais qu'est-ce que tout cela signifie réellement ? Dans les paragraphes suivants, nous explorerons ce concept en détail, en abordant les problèmes des modèles de langage traditionnels, l'obsolescence de l'information, l'utilisation des bases de données et des vecteurs d'encastrement, ainsi que les avantages de la génération augmentée par recherche.

💡 Retrieval Augmented Generation Explained

2.1 What is Retrieval Augmented Generation?

RAG, or Retrieval Augmented Generation, is a concept that aims to enhance traditional language models by providing them with up-to-date information. It combines the power of generative language models (LLMs) with the ability to retrieve relevant information from a database or vector store. By doing so, RAG improves the accuracy and relevance of the responses generated by the language model.

2.2 The Issue with Traditional Language Models (LLMs)

Traditional language models, such as GPT and BERT, are impressive in their ability to generate human-like text. However, they have limitations when it comes to providing accurate and up-to-date information. These models are trained on data from a specific time frame, and they do not have the capability to understand new information that may have emerged after their training.

2.3 The Problem of Outdated Information

Imagine you ask a traditional language model about the price of a Tesla Model X, and it provides you with an answer based on outdated information. This can be highly misleading, as the price of products is subject to change. Even if the model gives a precise answer, its response may be inaccurate or unreliable. Retrieving the most recent information is crucial to ensure the accuracy of the generated response.

2.4 Connecting LLMs to Databases

To address the issue of outdated information, one possible solution is to connect the language model to a database. By doing so, the model can retrieve the most up-to-date information and generate a response based on that information. However, there is a technical challenge: traditional language models do not understand natural languages like English or French, which makes direct connections to databases impractical.

2.5 Using Vector Stores for Retrieval

To overcome the technical limitations, RAG utilizes vector stores or vector databases. These stores consist of a collection of vectors that represent different information. Instead of storing information in natural language format, RAG converts the information into embeddings and stores them in the vector store. When a user asks a question, RAG calculates the vector distance between the user's question and the available vectors in the store. It then selects the vector with the closest relationship to the user's query, providing the most relevant and up-to-date information.

🔬 How Retrieval Augmented Generation Works

3.1 Technical Background of RAG

Let's delve into the technical aspect of retrieving augmented generation. We start with a collection of documents containing the latest information. These documents are split into smaller chunks using libraries like Longformer and Chroma DB. Each chunk is then transformed into an embedding using techniques such as BERT or word2vec. These embeddings are stored in a vector store or a vector database, like Chroma Files or Lines.

3.2 Splitting Documents into Chunks

Splitting documents into smaller chunks is important for efficient retrieval. Libraries like Longformer excel in handling long documents and breaking them down into manageable parts. This enables the vector store to store and index the embeddings of each chunk separately, ensuring faster retrieval based on user queries.

3.3 Generating Embeddings from Documents

To create meaningful embeddings, techniques like BERT or word2vec are deployed. These techniques analyze the content of the documents and convert them into numerical vector representations. These embeddings capture the semantic relationships between words and phrases, making them suitable for retrieval and generation tasks.

3.4 Storing Embeddings in a Vector Store

The generated embeddings are then stored in a vector store, which acts as a repository for the information contained in the documents. These vector stores enable quick and efficient retrieval based on vector distances. By utilizing vector storage, the retrieval process becomes faster and more accurate.

3.5 Retrieving Information from Vector Store

When a user poses a question, RAG takes the user's natural language query and converts it into an embedding. It then performs a similarity search in the vector store to identify the closest embeddings to the user's query. Once the relevant embedding is found, RAG retrieves the corresponding document chunk from the vector store.

🌟 Advantages of Retrieval Augmented Generation

4.1 Always Having Up-to-Date Information

One of the major advantages of using RAG is the ability to provide the latest and most accurate information to users. With a well-maintained vector store, RAG can always retrieve up-to-date information without the need for constantly retraining the language model. This saves valuable time and resources, ensuring that users receive reliable information.

4.2 Providing the Source of Information

Another advantage of RAG is the ability to attribute the source of the retrieved information. By connecting the vector store to trusted sources or databases, RAG can provide users with the necessary context and confidence in the generated response. This transparency promotes trust and credibility in the information shared.

4.3 Further Exploration: Longformer and ChromaDB

For a deeper understanding of RAG and its underlying technologies, further exploration of Longformer and ChromaDB is recommended. Longformer provides efficient handling of long documents, while ChromaDB offers powerful capabilities for storing and retrieving vector embeddings. These tools further enhance the performance and functionality of RAG.

🔚 Conclusion

In conclusion, retrieval augmented generation (RAG) is a powerful approach that combines the strengths of language models with the retrieval of up-to-date information. By utilizing vector stores and embedding techniques, RAG ensures accurate and relevant responses to user queries. With the ability to provide the latest information and attribute sources, RAG offers an enhanced user experience and builds trust in the generated responses. Stay tuned for more content on RAG and the fascinating world of long-chain models and vector databases!

📚 Additional Resources

• Twitter Note: Insert actual link to the Medium article in the final version