Retrieval-Augmented Generation (RAG) is a powerful technique used to make large language models (LLMs) more accurate and reliable. This method facilitates the integration of LLM with the organizationās proprietary data.
How it works?
Retrieval-Augmented Generation (RAG) enriches Large Language Models (LLM) responses by incorporating relevant external information into the generation process.
- Retrieval:Ā Retrieves pertinent information from external knowledge bases or data sources in response to a user query.
- Augmented:Ā Incorporates the retrieved context into the prompt, which is then fed into the LLM.
- Generation:Ā Produces a response from the LLM, utilizing the context-enhanced prompt for guidance.
Why is this important?
- Factual Consistency: LLMs are trained on huge amounts of text, so they’re great at producing text, but could generate incorrect or misleading information because they don’t always have a solid understanding of facts.
- Out of date Knowledge: LLM knowledge is fixed during training. They don’t automatically get new, updated information.
- Transparency: RAG can often show you the sources it used to generate an answer, making it easier to trust.
RAG Use Cases
- Question Answering: RAG-powered question-answering systems can handle complex questions that require specific knowledge from authoritative sources.
- Summarization: RAG can improve summarization by ensuring important facts from a long text are included in the summary.
- Content Creation: RAG can help LLMs generate more creative and factually consistent stories, articles, etc.
- Personalized Search: RAG can enhance search by dynamically retrieving and integrating user-specific information to generate more accurate and tailored search results.
- Real-time data: RAG can fetch and summarize data from API calls or databases in real-time, ensuring generated responses are informed by the most current information available.
Inner workings of RAG
Components of Data Ingestion
- Data Source: This includes various forms of data such as PDF files, web pages, and more.
- Data Cleanup/Preprocessing: The process of cleaning and preparing the data for further processing.
- Data Splitting: Segmenting the data into manageable parts or specific formats.
- Embedding Model: Utilized to generate embeddings from the processed data.
- Vector Database Storage: Storing the generated embeddings in a vector database for easy retrieval and use.
Text Generation Process with Simple Retrieval Flow
- User Question: The initial query or question posed by the user.
- Embedding Generation: Creating an embedding for the user’s question using the same model that generated the stored embeddings.
- Search: Retrieving the most relevant content based on the question embedding.
- Prompt Construction: Formulating a prompt based on the retrieved content.
- Large Language Model (LLM) Invocation: Making a call to a large language model with the constructed prompt.
- Answer Retrieval: Obtaining the answer generated by the LLM.
Types of Retrieval in RAG
- Rule-Based Retrieval: This involves keyword searches where the retrieval is based on specific, predefined rules or keywords.
- Structured Data Retrieval: This type involves querying databases or making API calls to retrieve structured data.
- Semantic Search: This method retrieves data by understanding the true meaning behind queries, such as linking “San Francisco” to related concepts like “Silicon Valley,” “Golden Gate Bridge,”, etc.
Embeddings
Embeddings are numerical vectors that represent texts, capturing the semantic relationships between words. For example, the name “NEEL” could be represented as a vector like [0.027, 0.045, …, -0.0078, …].
Models that generate these embeddings are known as embedding models. They are primarily used to compare the similarity between texts. Multilingual embedding models have the capability to understand meanings across different languages.
Furthermore, embeddings can be created not only for text but also for images, audio, and video, allowing for a wide range of applications in understanding and processing different types of data.
Vector Databases
Vector databases play a pivotal role here as they provide LLMs with access to real-time proprietary data, enabling the development of Retrieval Augmented Generation (RAG) applications.
At their core, vector databases rely on the use of embeddings to capture the meaning of data and gauge the similarity between different pairs of vectors and sift through extensive datasets, identifying the most similar vectors.
Thereās moreā¦
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