Category Archives: AI

RAG vs Vector Database

Why Do I Need RAG if There is a Vector Database?

AI

While a vector database is excellent for storing and retrieving information, it doesn’t generate responses. This is where RAG comes in.

Why is the use of a vector database not sufficient?

  • No Response Generation: A vector database can retrieve information but cannot generate natural language responses.
  • Limited Context Understanding: It doesn’t understand the query context or retrieved documents.
  • No Conversational Ability: It cannot engage in a dialogue or provide nuanced answers.

Why Not Just Use RAG?

  • No Data Storage: RAG relies on a retrieval system (e.g., a vector database) to provide the necessary context.
  • Inefficient for Large Datasets: Retrieving relevant information from large datasets would be slow and inefficient without a vector database.
How They Work Together
  1. Vector Database: Stores and retrieves relevant information efficiently.
  2. RAG: Uses the retrieved information to generate accurate and context-aware responses

Vector Database + RAG = Retrieval + Generation

  • RAG uses the vector database to retrieve relevant information.
  • It then passes this information to an LLM, which generates a context-aware response.
  • This ensures that the response is not only accurate but also natural and conversational.

Key Differences

AspectVector DatabaseRAG
Role in WorkflowActs as a knowledge base for retrieval.Uses retrieval to enhance LLM responses
DependencyCan be used independentlyDepends on a retrieval system (e.g., vector database) and an LLM
InputVector embeddings (e.g., text, images).User queries and retrieved context.
OutputSimilarity search results (e.g., documents).Context-aware, generated responses
RAG VS VECTOR DATABASE
Example Usecase

User Query: “What are the symptoms of diabetes?”

Vector Database: Retrieves the most relevant document: “Symptoms of diabetes include frequent urination, excessive thirst, and unexplained weight loss.”

RAG:

  • Passes the retrieved document and the query to the LLM.
  • The LLM generates a response: “Common symptoms of diabetes include frequent urination, excessive thirst, and unexplained weight loss.”
    RAG and Vector Database in work

    Browse more https://vectorize.io/rag-vector-database-traps/

    Find more at https://twirltech.in/

    RAG

    What Does RAG Do

    AI

    RAG combines both retrieval and generation. rAG strengthens the LMMs by grounding their response in external, up-to-date, or domain-specific data. Such updates on these models enable the RAG to support applications such as question-answering systems, chatbots, and content generation that rely highly on accuracy, relevance, and context awareness.

    Retrieval—the act of searching for relevant information

    generation—using an LLM to produce a response

    How RAG Works

    Retrieval
    Augmentation
    Generation
    • When a user submits a query (e.g., “What are the symptoms of diabetes?”), the system searches a knowledge base (e.g., a vector database) for relevant information.
    • The knowledge base could include documents, FAQs, research papers, or other structured or unstructured data.
    • The retrieval process is often powered by vector embeddings and similarity search, which find the most semantically relevant information to the query.
    • The retrieved information (e.g., a medical guideline or research paper) is passed to the LLM as context.
    • This context helps the LLM understand the query better and generate a more accurate response.
    • The LLM uses the retrieved context and the user’s query to generate a natural language response.
    • The response is not only based on the LLM’s pre-trained knowledge but also on the specific, up-to-date information retrieved from the knowledge base.

    Key Components of RAG

    1. Retriever:
      • A system, for example, a vector database that retrieves relevant information from a knowledge base.
      • It often uses vector embeddings and similarity search.
    2. Generator:
      • An LLM (like GPT-4, DeepSeek) generates natural language responses based on the retrieved context and the user’s query.
    3. Knowledge Base:
      • A collection of documents, FAQs, or other data that the retriever searches through.
      • It can be stored in a vector database for efficient retrieval.

    Usecase – Healthcare Chatbot

    Let’s discuss how a healthcare chatbot using RAG will work.

    1. User Query: “What are the symptoms of diabetes?”
    2. Retrieval: The system searches a vector database of medical guidelines and retrieves the most relevant document: “Symptoms of diabetes include frequent urination, excessive thirst, and unexplained weight loss.”
    3. Augmentation: The retrieved document is passed to the LLM as context.
    4. Generation: The LLM generates a response: “Common symptoms of diabetes include frequent urination, excessive thirst, and unexplained weight loss.”
    RAG

    Reference: https://cloud.google.com/use-cases/retrieval-augmented-generation?hl=en

    Find more at https://twirltech.in/

    Vector Database - twirltech

    Why do we need Vector Database in AI Application

    AI,

    A vector database is a type of database that specifically stores, indexes, and retrieves high-dimensional vectors (arrays of numbers). Such vectors are generated by machine learning models, for instance LLMs (Large Language Models), to numerically represent data such as text, images, or audio. We will understand what a vector database is and why we need one to build an AI Application.

    Designed to handle vector embeddings, a vector database allows the numerical representation of data, i.e., words, sentences, images. These embeddings would capture the semantic meaning of the data to allow one to perform the following operations:

    • Similarity search: Find those items that are semantically similar to a given query.
    • Clustering: Group items together that are deemed to be similar.
    • Classification: assign labels to items based on their embeddings.

    Let us understand in layman’s language.

    Think of a vector database as a supersmart librarian for a special kind of library. In this library, instead of books being organized by titles or authors, they’re organized by their meaning or content. For example:

    • Books about “dogs” are grouped together.
    • Books about “space exploration” are in another section.
    • Books about “healthy eating” are in yet another section.

    Now, if you ask the librarian, “Can you find me books about pets?” The librarian won’t just look for the word “pets” in the titles. Instead, they’ll understand the meaning of your question and find books that are semantically related, like books about dogs, cats, or even exotic animals.

    A vector database is like that super-smart librarian. It helps you find things based on their meaning, not just keywords.

    How is it different?

    • Traditional databases can only search for exact matches (e.g., finding the word “dog” in a document).
    • A vector database understands the context and meaning of your query. For example:
      • If you search for “pets,” it will also find documents about “dogs” or “cats” because they’re semantically related.

    How it works with LLMs

    Modern AI models (like GPT, Gemini, DeepSeek ) can convert text, images, or audio into vector embeddings (a list of numbers representing meaning).

    • A vector database stores these embeddings and helps you search through them efficiently.
    Vector Database with LLM
    This diagram illustrates how a Vector Database works with an LLM. When a user submits a query (e.g., ‘What are the symptoms of diabetes?’), the LLM converts the query into a vector embedding. The Vector Database then performs a similarity search to retrieve the most relevant documents. The LLM uses this retrieved context to generate a context-aware response displayed to the user.

    Use case and How Vector Database helps to solve the problem

    You’re building a chatbot to answer medical questions. You have thousands of medical documents – guidelines, research papers, FAQs

    If you store these documents in your traditional database, your chatbot can only search based on the keyword we discussed above. Also, your search will not be efficient or performant.

    Here comes the Vector Database

    • Use an LLM to convert each document into a vector embedding (a numerical representation of its meaning) and store it in the vector database.
    • When the system gets a new user query, convert the query into a vector using the same or compatible model.
    • Marrying the Results with an LLM: Once you retrieve the top relevant snippets, you include them in the prompt sent to the LLM (often called “Retrieval-Augmented Generation”). This prompt typically contains the user’s question plus the relevant text snippets.
    • The LLM uses these snippets to produce a context-aware response grounded in the retrieved data.
    • The user asks, “What are the symptoms of diabetes?”
    • The system converts the question into a vector embedding and searches the vector database for the most relevant documents.
    • It finds documents about diabetes symptoms, even if they don’t explicitly mention the word “symptoms.”
    • Now, the user query is embedded with the Vector Database chunk.
    • Pass the retrieved documents and the query to the LLM.
    • The LLM generates a context-aware response.

    Find more blogs https://twirltech.in/

    References:

    https://www.pinecone.io/learn/vector-database/

    https://www.ibm.com/think/topics/vector-database