RAG

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Retrieval Augmented Generation (RAG)

Retrieval Augmented Generation (RAG) is a technique where a computer system generates text (like stories or answers) by combining two steps:

Layman's Explanation:

Imagine you're working on a project where you need to generate written content, like articles or answers to questions. Retrieval Augmented Generation (RAG) is a method that helps you create this content more efficiently and effectively.

Here's how it works:

1. Step 1: Gathering Relevant Information First, you have access to a large database filled with lots of information related to your project. It's like having a huge library at your fingertips! RAG starts by searching through this database to find the most relevant pieces of information for your task.

2. Step 2: Crafting the Content Once RAG has gathered the relevant information, it's time to start crafting the actual written content. Instead of starting from scratch, RAG uses the information it found to guide the writing process. It's like having a helpful assistant who provides suggestions and ideas to make your writing more accurate and engaging.

By combining the information from the database with your own input, RAG helps you create high-quality content that meets your project goals more efficiently than starting from scratch or relying solely on your own knowledge.

That's RAG! It's like having a knowledgeable assistant who helps you gather the right information and craft compelling content for your project.

Detailed Explanation:

Now, let's dive into the details of how Retrieval Augmented Generation works:

Retrieval Augmented Generation (RAG) is a technique where a computer system generates text (like stories or answers) by combining two steps:

I. Retrieval:

It first searches through a big database of information to find relevant details.

Creation of Vector Embeddings for our custom data Vector DB

To create a database for retrieval using vector embeddings, you typically follow these steps:

• Data Preprocessing: Prepare your text data by cleaning it, removing unnecessary information, and tokenizing it into smaller units like words or phrases.
• Feature Extraction: Use a technique like Word Embeddings (e.g., Word2Vec, GloVe) or Contextual Embeddings (e.g., BERT, GPT) to convert each word or phrase into a dense vector representation. This step captures the semantic meaning of the text.
• Vector Database Creation: Organize your data and their corresponding vector representations into a database structure that allows for efficient retrieval. This could be a key-value store, an inverted index, or another suitable data structure.
• Similarity Search: When you want to retrieve relevant information from the database, you use the vector representations of your query text and compare them with the vectors in the database. Techniques like cosine similarity or Euclidean distance are commonly used to measure similarity between vectors.
• Retrieval: Retrieve the data points (e.g., documents, paragraphs) with the highest similarity scores to your query vector. These are considered the most relevant matches to your query.
• Post-Processing: Depending on your application, you may perform additional post-processing steps such as filtering or ranking the retrieved results before presenting them to the user.

Retrieval of Embeddings based on user query

• Query Processing:
  • Tokenize and preprocess the query text, similar to how you preprocess your database entries.
  • Convert the query text into a vector representation using the same embedding technique used for generating the database.
• Similarity Calculation:
  • Calculate the similarity between the vector representation of the query and the vectors of entries in the database.
  • This can be done using techniques like cosine similarity, Euclidean distance, or other similarity measures depending on the nature of your vectors.
• Ranking:
  • Rank the database entries based on their similarity scores with the query vector. Entries with higher similarity scores are considered more relevant to the query.
  • You may choose to implement a threshold for similarity scores to filter out entries that are not sufficiently similar to the query.
• Retrieval of Relevant entries:
  • Retrieve the top-ranked database entries as the results of the query.

II. Generation:

After retrieving relevant information from the database, it's time to generate the final response using the Language Model (LLM). The LLM leverages the retrieved data to create text that is coherent and contextually relevant.

Integration with Language Model (LLM)

1. Input Data to LLM:

   • Send the retrieved data to the LLM for further processing. Depending on your specific goals, the LLM can perform tasks such as summarization, paraphrasing, or contextual expansion to enhance the retrieved results.

2. Process LLM Output:

   • Once the LLM processes the data, you may need to post-process the output based on your application needs. This could involve filtering out irrelevant information, formatting the output for presentation, or integrating it with other data sources.

3. Generate Final Response:

   • Use the processed output from the LLM to generate the final response to the user's query. This response may include refined and enhanced content produced by the LLM, along with any additional information or context deemed relevant.

Advantages of Retrieval Augmented Generation (RAG) over Model Fine-Tuning:

1. Broader Knowledge Incorporation:

   • RAG leverages a large database of information during the retrieval step, allowing it to access a wide range of knowledge beyond what is available in a single pre-trained model. This enables RAG to provide more diverse and comprehensive responses to user queries.

2. Dynamic Adaptability:

   • With RAG, the retrieval of relevant information is dynamic and adaptable to the specific context of each query. This flexibility ensures that the generated responses are tailored to the user's needs, even for niche or evolving topics.

3. Reduced Training Costs:

   • Unlike model fine-tuning, which requires retraining the entire model on new data, RAG utilizes a pre-existing database for retrieval. This significantly reduces the computational resources and time required for training, making it a more cost-effective solution.

4. Improved Performance Stability:

   • Model fine-tuning may lead to overfitting or performance degradation, especially when dealing with limited or noisy training data. In contrast, RAG's reliance on a pre-existing database helps maintain performance stability by leveraging a broader and more diverse set of information.

5. Scalability and Flexibility:

   • RAG's architecture allows for easy scalability and adaptation to different domains or tasks by simply updating or expanding the underlying database. This flexibility makes it suitable for various applications, from question answering to content generation.