Implementing hybrid search typically involves combining different search methods, most commonly a sparse retrieval method (like keyword matching or TF-IDF/BM25) with a dense retrieval method (using vector embeddings from deep learning models). The process outlined below, based on the provided reference, details how to set up and query the sparse search component using TF-IDF embeddings within a vector search framework, which is a fundamental part of many hybrid search implementations.
This approach leverages the strengths of TF-IDF for handling keyword matches while integrating it into a modern vector search infrastructure capable of handling various embedding types.
Steps to Implement the Sparse Component for Hybrid Search
Here are the key steps involved in setting up a sparse search index using TF-IDF embeddings, as provided:
1. Prepare a Sample Dataset
The first step is to prepare a sample dataset. This dataset contains the text documents or passages that you want to make searchable. It should be formatted appropriately for processing, typically as a collection of text strings. The quality and relevance of your dataset are crucial for the performance of your search system.
2. Prepare a TF-IDF Vectorizer
Next, you need to prepare a TF-IDF vectorizer. TF-IDF (Term Frequency-Inverse Document Frequency) is a statistical measure used to evaluate how important a word is to a document in a collection or corpus. A TF-IDF vectorizer converts text documents into sparse numerical vectors where each dimension corresponds to a word in the vocabulary, and the value reflects the TF-IDF score of that word in the document.
- Process: This involves fitting the vectorizer to your dataset's text to build the vocabulary and learn the IDF scores.
3. Get a Sparse Embedding
Using the prepared TF-IDF vectorizer, you will then get a sparse embedding for each document in your dataset. This means applying the trained vectorizer to transform each text document into its corresponding TF-IDF vector representation. These vectors are typically sparse because most documents only contain a small fraction of the total vocabulary words, resulting in vectors with many zero values.
4. Create an Input Data File
After generating the sparse embeddings, the next step is to create an input data file. This file will contain the data ready for indexing in your vector search platform. It typically includes the original document identifier, the text content, and the generated sparse TF-IDF embedding for each document. The specific format will depend on the requirements of your chosen vector search system.
5. Create a Sparse Embedding Index in Vector Search
Now, you need to create a sparse embedding index in Vector Search. Using the input data file, you ingest the documents and their sparse TF-IDF embeddings into a vector search index specifically configured to handle sparse vectors. This index structure allows for efficient searching based on the sparse vector similarity.
- Indexing: The vector search system processes the input data file and builds an index structure (like an inverted index or a specialized sparse vector index) that enables fast lookups of documents based on their TF-IDF components.
6. Run a Query with a Sparse Embedding Index
Finally, to utilize the sparse search component, you run a query with a sparse embedding index. When a user submits a search query (a text string), you first process this query using the same TF-IDF vectorizer used for indexing the documents. This converts the query text into a sparse TF-IDF vector. This query vector is then used to search the sparse embedding index to find documents with similar TF-IDF vector representations, effectively performing a keyword-based search optimized by the TF-IDF weighting and the efficient index structure.
- Search Process: The vector search system uses the query vector to quickly find documents in the index whose sparse vectors are most similar (e.g., using dot product or cosine similarity on the non-zero dimensions) to the query vector.
To implement full hybrid search, the results from this sparse search component would typically be combined with results from a dense search component (using different vector embeddings and potentially a different index or the same index capable of handling multiple vector types) using techniques like reciprocal rank fusion (RRF) or other ranking fusion methods.
