13 Oct Vector embeddings in RAG and its process

We saw Vector embeddings as the 3^rd step in the previous lessons that covered the process and example of RAG. It is one of the key steps for RAG. Let us see what are vector embeddings and their complete process, that digs further into the RAG process.

Vector embeddings in Retrieval-Augmented Generation (RAG) refer to dense, low-dimensional representations of data. These embeddings are a bridge between retrieving relevant information and generating a meaningful response.

Process of Vector Embeddings

Let us bifurcate the process of vector embeddings further with examples in each step:

1. Input Query: The user inputs a query (e.g., “Tell me about the history of the Taj Mahal”).
2. Document Retrieval:
   The system searches a large dataset and retrieves relevant documents or information based on the input query. Example: Retrieving historical documents or articles about the Taj Mahal.
3. Encoding:
   Query Encoding: The input query is converted into a dense vector representation.
   Document Encoding: The retrieved documents are also converted into dense vector representations.
   This step uses models like BERT or other transformers to encode the text into vectors that capture the semantic
   meaning of the words.
   Example: The query “History of the Taj Mahal” might be encoded into a vector like [0.1, 0.2, 0.3, …].
4. Similarity Calculation:
   Calculate the similarity between the query vector and each document vector. A common method for this is
   cosine similarity.
   Example: The similarity between the query vector and the document vectors is calculated to find the most
   relevant documents.
5. Top-k Selection:
   Based on the similarity scores, select the top-k most relevant documents. The top-k documents or chunks with the highest similarity score are selected by the system.
   Example: Selecting the top 5 documents that are most like the query.
6. Context Encoding:
   Encode the selected documents and the query together to form a context vector.
   Example: Combining the top 5 document vectors with the query vector to create a rich context representation.
7. Attention Mechanism:
   The model uses an attention mechanism to focus on the most relevant parts of the context during the
   generation phase.
   Example: Focusing more on the sections of the documents that detail the construction and significance of the
   Taj Mahal.
8. Response Generation:
   A generative model (often based on transformers like GPT) uses the context vector to generate a coherent and
   contextually appropriate response.
   Example: Generating a detailed and accurate response about the history of the Taj Mahal based on the
   encoded context.
9. Output:
   The final generated response is provided to the user.
   Example: “The Taj Mahal was constructed in 1632 by Mughal Emperor Shah Jahan in memory of his wife
   Mumtaz Mahal. It took 22 years to complete and is renowned for its stunning white marble architecture.”

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