Here’s a rewritten version of the video transcript, formatted as an SEO-optimized blog post:

Building a RAG Application with MongoDB Vector Search

Hello everyone, my name is Kash Nayak, and welcome to my YouTube channel! In this video, we’re diving into the exciting world of Retrieval Augmented Generation (RAG) applications and how to build one using MongoDB’s vector search capabilities. We’ll explore how MongoDB’s vector search database can be used to create a RAG application step by step, and we’ll discuss the architecture involved.

A big thank you to MongoDB for sponsoring this video!

Getting Started with MongoDB

First, let’s get set up with MongoDB.

1. Go to www.mongodb.com.
2. Click on “Sign In” if you already have an account, or “Get Started” if you’re new.
3. If you don’t have an account, sign up using your preferred method. I’ll use my Google account for this demonstration, and will use my official email address.
4. Verify your email address if prompted.

Once signed up, you’ll be directed to your MongoDB account dashboard. You’ll find organization details and a default project here.

RAG Application Architecture

Let’s take a look at the architecture of the RAG application we’re building.

We’re focusing on these three key components:

• Injection
• Retrieval
• Generation

Injection

Injection involves getting data from a specific source and transforming it into a format suitable for vector storage. This process typically involves the following steps:

1. Data Ingestion: Data is extracted from various sources, such as documents, databases, or APIs.
2. Data Chunking: Large documents are split into smaller, more manageable chunks.
3. Text Embedding: Each chunk of text is converted into a vector embedding using a model. This embedding represents the semantic meaning of the text in a high-dimensional space.
4. Vector Storage: The vector embeddings, along with the original text chunks, are stored in MongoDB. The text embedding model will process your data and create vector embeddings which are numerical representations that capture the semantic meaning of your text. These embeddings are then stored in a vector database like MongoDB alongside the original text chunks.

Retrieval

Retrieval is the process of finding the most relevant data chunks in response to a user query. This process typically involves the following steps:

1. Query Embedding: The user’s query is converted into a vector embedding using the same text embedding model used during injection.
2. Similarity Search: The query embedding is compared to the vector embeddings in MongoDB using a similarity metric. The most similar embeddings are retrieved.
3. Context Extraction: The text chunks associated with the retrieved embeddings are extracted and used as context for the generation step. A user poses a query, which is then converted into a vector embedding using the same model. This query embedding is used to perform a similarity search in MongoDB, retrieving the most relevant text chunks based on their vector embeddings. These chunks are then extracted and used as context for the next step.

Generation

Generation is the process of creating a response to the user query using the retrieved context. This process typically involves the following steps:

1. Prompt Engineering: A prompt is created that includes the user query and the retrieved context. The prompt is carefully designed to guide the language model to generate a relevant and informative response.
2. Response Generation: A large language model (LLM) is used to generate a response based on the prompt. The LLM uses its knowledge and understanding of the language to create a coherent and natural-sounding response.
3. Post-Processing: The generated response is post-processed to improve its quality and readability. This may involve removing redundant information, correcting grammatical errors, and ensuring that the response is relevant to the user query. The retrieved context is combined with the original query to create a prompt for a large language model (LLM). The LLM then generates a response based on the prompt and its knowledge of the language. The response is then post-processed to ensure quality and relevance.

#Build #Advanced #RetrievalAugmented #Generation #RAG #MongoDB #Vector #Search
Thanks for reaching. Please let us know your thoughts and ideas in the comment section.

Source link

About The Author