Hybrid Retrieval: Combining Metadata and Vector Search

Welcome back! In the previous lesson, we explored the concept of similarity search using cosine similarity to measure the similarity between text embeddings. This foundational knowledge is crucial as we delve into more advanced techniques. Now, we will focus on hybrid retrieval, a powerful approach that combines metadata and vector search to enhance search results. This technique allows us to leverage both the semantic meaning captured in vector embeddings and the structured information available in metadata. By the end of this lesson, you will understand how to implement hybrid retrieval using Pinecone, a vector database that excels in handling such tasks.

Hybrid retrieval leverages the strengths of both metadata and vector search. Metadata provides structured information that can refine search queries, while vector search uses embeddings to understand the semantic meaning of text. By combining these approaches, we can achieve more precise and relevant search outcomes. Let's explore how this works in practice.

Before we dive into the implementation, let's briefly revisit the concepts of metadata and vector search. Metadata refers to structured information that describes the content of a document, such as categories, tags, or author names. It allows us to filter and refine search queries based on specific attributes.

Vector search, on the other hand, uses embeddings to capture the semantic meaning of text. By representing text as vectors, we can measure the similarity between different pieces of text, enabling us to perform semantic searches that go beyond simple keyword matching.

Combining metadata and vector search allows us to leverage the strengths of both approaches. Metadata helps us narrow down the search space, while vector search ensures that the results are semantically relevant. This synergy is what makes hybrid retrieval a powerful tool in semantic search systems.

Let's set up some sample data using Pinecone. This will help us understand how the data is structured and how it can be queried.

from sentence_transformers import SentenceTransformer
from scripts.pinecone_loader import initialize_pinecone_index

# Config
index_name = "pinecone-hybrid-demo"
namespace = "example-namespace"
file_path = "./data/corpus.json"

# Load the embedding model
model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")

# Initialize Pinecone index
index, documents, pc = initialize_pinecone_index(index_name, namespace, file_path)

In this setup, we load documents from a JSON file and initialize a Pinecone index. The initialize_pinecone_index function loads the corpus, generates embeddings, and upserts the data into the index, including metadata such as title, category, tags, and date. This data will be used in our hybrid retrieval examples.