From Keywords to Vector Embeddings: Weaviate's Disruptive Approach to Scalable Search

In the new era of LLMs and genAI nothing is more important than good data, and Weaviate, an Intel Liftoff member, has the unique ability to bring structure to data and gather premium insights. Traditional search engines often rely on keyword matching, which can lead to irrelevant or misleading results. Fortunately, Weaviate is redefining the search landscape with its open-source, cloud-native vector database.

Vector Embeddings

Weaviate uses a groundbreaking approach to search – scalable indexing using vector embeddings. It translates data into vector embeddings, which Sebastian Witalec, Head of DevRel at Weaviate, calls "the secret sauce behind ML-based search." Unlike traditional search methods that rely on keyword matching, Weaviate focuses on the semantic meaning of queries. This means you get results that are contextually relevant, not just a mishmash of keyword hits.

Powering Up with Intel's oneAPI

To make this happen, Weaviate relies on dedicated hardware to vectorize data. That's where Intel's oneAPI comes in. By using the power and acceleration provided by oneAPI, Weaviate can process data on an unprecedented scale. This collaboration is not just about hardware; it's about reimagining what's possible for data search and management.

Multimodality: The Next Frontier

Weaviate doesn't stop at text. Its capabilities extend into multimodality, allowing users to search through images, audio, and even video. Imagine asking a query and receiving a comprehensive set of results that include relevant text documents, images, audio clips, and videos. This is not a distant dream but a reality that Weaviate is already demonstrating.

These images provide hands-on examples of the Weaviate solution's functionality:

Speeding Up Production

Transitioning from a proof-of-concept to a full-blown production environment can be a challenging task, especially in the AI and machine learning space. Weaviate's new tool simplifies this process, enabling businesses to scale up quickly. The focus here is on enhancing the developer experience, making it not just efficient but downright pleasant.

Collaboration in Action: Weaviate and Beewant

Weaviate's scalability has already proven beneficial for companies like Beewant, which focuses on video search. Before collaborating with Weaviate, Beewant was limited to handling around 100,000 vectors. Now, they can scale to billions, thanks to Weaviate's robust infrastructure.

Beyond Traditional Limitations with Retrieval Augmented Generation (RAG)

Weaviate's RAG feature allows for the integration of internal, company-specific data into search queries. This means you can ask about your company's vacation policy and get an answer that's not just accurate but also up-to-date, bypassing the limitations of pureplay LLMs.

The innovations spearheaded by Weaviate and Intel are not just about making jobs easier; they're about enhancing them. The future of AI, as seen through the lens of Weaviate, is one where technology serves as a powerful support system, making expert knowledge more accessible and enabling us to move faster and more efficiently.

Exciting Use Cases on the Horizon

From legal firms being able to quickly find precedents for cases to enterprises effortlessly searching through millions of documents, the applications are endless. Weaviate even envisions a "feedback loop" where chatbots remember past interactions, making each conversation more meaningful than the last.

In summary, Weaviate is not just another cool tool – it changes how we think about and interact with data. By focusing on semantic meaning and leveraging the power of Intel's oneAPI, Weaviate is setting the stage for a future where search is not just accurate but also contextually rich and incredibly fast.

If you're a startup looking to gain exposure and elevate your technical capabilities, Intel® Liftoff for Startups is the right place for you. Apply today and start your journey to new heights of success.