Introducing Search Toolkit

Today, we're releasing Search Toolkit in public preview. Search Toolkit is a composable framework for building production search pipelines for AI applications. We built it because teams building search infrastructure still spend too much engineering time on plumbing. Most stitch together separate tools for ingestion, retrieval, and evaluation, each with its own interface and its own assumptions about data. Search Toolkit brings all three into a single framework with a shared interface, so teams spend their time improving search quality instead of maintaining integrations. Search Toolkit is open source and runs wherever your infrastructure does. Cloud, on-premises, edge.

Search infrastructure is still harder than it should be.

Most teams building retrieval systems spend more time assembling infrastructure than improving search quality. Ingestion requires one set of tools. Retrieval requires another. Evaluation, if it happens at all, is bolted on with a separate framework and separate assumptions about data shape.

Teams report weeks of integration work before they can run a single query against their own data. Measuring whether the retriever is returning the right results often requires yet another toolchain. For organisations building RAG workflows or internal knowledge systems, that overhead multiplies at every layer.

Where it fits.

Domain-specific retrieval. Legal filings, medical records, codebases, financial disclosures. Off-the-shelf retrievers are trained on general-purpose text and tend to struggle with specialised terminology, document structures, and relevance criteria that differ from web search. Teams that need domain-tuned retrieval often end up building custom retrieval infrastructure from scratch, which is expensive to maintain and hard to evaluate.