Hybrid Search Architecture in RAG: Combining Vector and Metadata Filtering

When it comes to hybrid search architecture in Retrieval-Augmented Generation (RAG) models, combining vector and metadata filtering is a useful strategy. This involves a two-step process in which vector similarity search is used to retrieve relevant documents, after which metadata filters are applied to fine-tune the results.

Vector Search in RAG Models

Vector search, or more specifically, vector similarity search, is a method for determining related items based on their vector representations. In the context of RAG, this might be used to identify relevant documents based on the vectorized representation of a particular query.

For instance, when a user submits a question to a RAG model, the question is first transformed into a vector. This vector is compared to the vectors of all documents in the database in order to find matches that are most similar. This process is typically performed using a method like cosine similarity or nearest neighbour search.

Metadata Filtering in RAG Models

Following the vector similarity search, metadata filtering is then employed to further refine the results. Metadata might include information like document category, author, publication date, or any other additional data that provides context about the documents.

For example, if a user submits a question about a current news event, the RAG model might utilize metadata filtering to prioritize documents that were published recently, even if these documents are not the most similar to the query in terms of vector similarity.

Application of Vector and Metadata Filtering

Here's an illustration using a hypothetical example:

Suppose a RAG model is built to answer questions about scientific research. Now, the user submits a question about the latest findings on climate change.

• During the vector similarity search phase, documents having semantic similarity with the terms 'latest findings' and 'climate change' are found.
• Then metadata filtering is applied. Here, the RAG model might prioritize more recent documents, even if they're not exactly the most similar to the query, hence creating a balance between relevance and timeliness.

Benefits of Combining Vector and Metadata Filtering

The blend of both vector and metadata filtering provides a powerful tool for retrieving information that is not only contextually relevant but also meets certain specifications. It allows for a more nuanced, adaptable approach to information retrieval - one that takes into account the often multifaceted nature of many user queries.

While vector similarity search is capable of grasping the query's semantic essence, metadata filtering considers additional layers of relevance, like timeliness or category of information. Undergoing both these phases, a RAG hybrid search architecture can produce highly accurate and contextually nuanced responses.

Thus, combining vector and metadata filtering in hybrid search architecture provides a rich, detail-oriented, and context-aware avenue for handling complex search operations in RAG models.