Pinecone Spruces Up AI Knowledge Platform

Data is knowledge. However, many data channels are morphing into new structures, with some being driven by new approaches to data storage, data management and data retrieval. We still live in a world of databases, but many of our information resources are now feeding data lakes, data warehouses and data lakehouses as we start to form packaged data products. All of which data nomenclature means that we’re now able to talk about knowledge platforms and, more deeply, AI knowledge platforms.

What is an AI knowledge platform?

The birth of the AI knowledge platform runs very much in line with the wider adoption of artificial intelligence, with generative AI functions being a core part of the progression. The emergence of AI knowledge platforms represents a fundamental shift in how applications access and utilize information. Unlike traditional knowledge management systems that focus on human collaboration and document sharing, modern AI knowledge platforms provide the infrastructure for AI applications to effectively store, retrieve and use large amounts of information. These platforms combine intelligent methods of representing, accessing, and prioritizing knowledge to help AI applications understand and retrieve relevant information with high accuracy and semantic understanding.

As an AI knowledge platform company with a vector database at its core, Pinecone wants to explain how its technology helps build scalable AI with powerful integrated inference capabilities. These new capabilities focus on the process of using trained AI models to process new data – specifically through managed embedding and reranking models, along with a new approach to “sparse” embedding retrieval. The company says that it has combined these elements with its “dense retrieval” capabilities, to create a technology that offers hybrid retrieval as a new standard for AI. We’ll define these terms in just a moment.

Pinecone now becomes the first AI infrastructure company (yes, it’s kind of a new sub-category that the tech trade is enjoying talking about) to offer a managed and hosted solution that integrates embedding, reranking and vector search into a single application programming interface. This simplifies the previously complex process of building AI applications requiring vector search capabilities.

“Our goal at Pinecone has always been to make it as easy as possible for developers to build production-ready knowledgeable AI applications quickly and at scale,” said Edo Liberty, founder and CEO of Pinecone. “By adding built-in and fully-managed inference capabilities directly into our vector database, as well as new retrieval functionality, we’re not only simplifying the development process but also dramatically improving the performance and accuracy of AI-powered solutions.”

What Is Dense & Sparse Retrieval?

In the world of information management, dense retrieval makes use of continuous high-dimensional vector representations (relationships between data values) to build query responses based on a rich semantic meaning extracted from the information pool at hand. Most vectors in dense retrieval have non-zero values, meaning that they actually mean something. If we searched for “best steak restaurant near me” in a dense retrieval system, the search would incorporate the semantic meaning behind best (could be quality, could be value for money), steak (could be beef, could be pork, could be vegetarian roasts) and near (could mean ten minutes walk, could mean 30 mins drive) before it delivered its results.

In contrast, sparse retrieval systems use what is known as a traditional bag-of-words low-dimension set of vector representations, where matches are made based upon the existence of exact keywords. Where dense retrieval is computationally expensive, sparse retrieval is unsurprisingly cheaper and also faster. A sparse retrieval search for “best steak restaurant near me” would only return results with that exact phrasing and may not include location relevance in this example.

Why Retrieval Matters

High-quality retrieval is key to delivering the best user experience in AI search and retrieval-augmented generation applications. Pinecone’s research suggests that state-of-the-art performance in this field requires the combination of three key components: dense vector retrieval to capture deep semantic similarities; fast and precise sparse retrieval for keyword and entity search using a proprietary sparse indexing algorithm; and reranking models to combine dense and sparse results and maximize relevance.

By combining the sparse retrieval, dense retrieval and reranking capabilities within Pinecone, software developers will be able to create end-to-end retrieval systems that deliver better performance than dense or sparse retrieval alone, claims the company.

“Pinecone’s new integrated inference capabilities are a game-changer for us,” said Isaac Pohl-Zaretsky, CTO & co-founder at Pocus, a company that which helps sales teams track how customers are using software. “The ability to have embedding, reranking and retrieval all within the same environment not only streamlines our workflows but also powers our AI solutions with minimal latency, less technical debt and improved performance. Pinecone was already helping us deliver tremendous value with precise signals to power our customers’ go-to-market efforts, and now with their unique platform we’re thrilled to be able to deliver even more.”

With the release of Pinecone’s integrated inference capability, software and data engineers can now develop applications without the burden of managing model hosting, integration, or infrastructure. By offering these capabilities behind a single API, developers can access top embedding and reranking models hosted on Pinecone’s infrastructure, eliminating the need to worry about vectors or data being routed through multiple providers. This consolidation is said to simplify development and also enhance security and efficiency.

Mission-Critical Workloads

Pinecone’s AWS Generative AI Competency ranking acknowledges the company as a generative AI solution provider. Software teams can use Amazon Bedrock Knowledge Bases with Pinecone to build with AI and reduce operational complexity and costs. Specifically, Knowledge Bases for Amazon Bedrock provides one-click integration with Pinecone, fully automating the ingestion, embedding and querying of customer data as part of the LLM generation process.

This flow provides a foundation for AI, enabling faster time-to-value and more grounded, production-grade AI applications. Furthermore, customers using Amazon Bedrock Knowledge Bases with Pinecone can now run RAG evaluations natively in Amazon Bedrock instead of having to connect third-party tools.

As an AI infrastructure company that now provides a single platform for inference, retrieval and knowledge base management, Pinecone says it is setting a new standard in the industry. This integrated approach may soon be part of the way we talk about information management that sits in close proximity to our new notion of intelligence and indeed knowledge platforms as a whole.