Introduction
Semantic search is a powerful approach to information retrieval that goes beyond traditional keyword-based search by understanding the meaning and context of the query and the content being searched. Weaviate, a vector database, is a tool that facilitates semantic search by representing data as vectors in a high-dimensional space, allowing for efficient similarity calculations and semantic understanding. In this article, we’ll explore how to implement semantic search with Weaviate, including coding examples, to demonstrate its capabilities.
Understanding Weaviate
Weaviate is an open-source vector database designed to store and search data based on their semantic meanings. It organizes data as vectors in a multi-dimensional space, where similar vectors represent similar concepts or entities. This enables Weaviate to perform semantic searches efficiently, returning results that are conceptually related to the query.
Setting Up Weaviate
Before diving into semantic search implementation, we need to set up Weaviate. We can install Weaviate using Docker, making the setup process straightforward. Once installed, we can start the Weaviate server and interact with it using its HTTP API or client libraries.
docker run -d --name weaviate semitechnologies/weaviate:latest
With Weaviate running, we can now proceed to create a schema and add data to it.
Creating a Schema
In Weaviate, a schema defines the structure of the data to be stored. We define classes and properties that represent the entities and their attributes. For example, if we’re building a semantic search engine for articles, our schema might include classes like “Article” with properties such as “title,” “content,” and “tags.”
{
"classes": [
{
"class": "Article",
"properties": [
{
"name": "title",
"dataType": ["string"]
},
{
"name": "content",
"dataType": ["text"]
},
{
"name": "tags",
"dataType": ["string"],
"cardinality": "atMostOne"
}
]
}
]
}
Adding Data to Weaviate
Once the schema is defined, we can add data to Weaviate. Weaviate automatically converts the data into vectors based on the defined schema, making it ready for semantic search.
curl -X POST "http://localhost:8080/v1/batch" -H "Content-Type: application/json" -d @data.json
Here, data.json contains the JSON representation of the data to be added.
Performing Semantic Search
With data indexed in Weaviate, we can now perform semantic search queries. Weaviate allows us to search for similar entities based on their semantic meaning. For example, to find articles similar to a given query, we can execute a semantic search query like:
{
"query": {
"class": "Article",
"properties": [
{
"name": "title",
"value": "Semantic Search",
"operator": "Equal",
"certainty": 0.9
}
],
"certainty": 0.8
}
}
This query instructs Weaviate to find articles with a title similar to “Semantic Search,” with a certainty level of at least 0.8.
Coding Example: Python Client for Semantic Search
Let’s demonstrate how to perform semantic search using Weaviate’s Python client library.
from weaviate import Client
# Initialize Weaviate client
client = Client(“http://localhost:8080”)
# Define search query
query = {
“query”: {
“class”: “Article”,
“properties”: [
{
“name”: “title”,
“value”: “Semantic Search”,
“operator”: “Equal”,
“certainty”: 0.9
}
],
“certainty”: 0.8
}
}
# Perform semantic search
results = client.query.search(body=query)
# Display search results
for result in results[‘data’][‘search’][‘things’]:
print(result[‘schema’])
This Python code sends a semantic search query to Weaviate and displays the search results.
Conclusion
Semantic search powered by Weaviate offers a sophisticated approach to information retrieval, allowing users to find conceptually related entities efficiently. By representing data as vectors in a high-dimensional space, Weaviate enables semantic understanding and similarity calculations, leading to more accurate search results. With the examples provided in this article, developers can leverage Weaviate to implement semantic search functionality in their applications, enhancing the user experience and enabling more intelligent information discovery. As the field of natural language processing and semantic understanding continues to evolve, tools like Weaviate will play a crucial role in advancing the capabilities of search and knowledge retrieval systems.