Best Site Search Tools for IBM watsonx.data

Vectara offers LLM-powered search as-a-service. The platform offers a complete ML search process, from extraction and indexing to retrieval and re-ranking as well as calibration. API-addressable for every element of the platform. Developers can embed the most advanced NLP model for site and app search in minutes. Vectara automatically extracts text form PDF and Office to JSON HTML XML CommonMark, and many other formats. Use cutting-edge zero-shot models that use deep neural networks to understand language to encode at scale. Segment data into any number indexes that store vector encodings optimized to low latency and high recall. Use cutting-edge, zero shot neural network models to recall candidate results from millions upon millions of documents. Cross-attentional neural networks can increase the precision of retrieved answers. They can merge and reorder results. Focus on the likelihood that the retrieved answer is a probable answer to your query.

Flexible query-time sorting allows you to tailor your results to perfection. Pin records in a specific position to feature them or merchandize. If you make synonyms of the terms, they will be displayed as results for pants. Create API keys for each user to restrict access to their data. You can sort records by any field in your document. Sort by popularity, sort by price, etc. No duplicate indices needed. Grouping results can give you more variety in your results. All color variations of a shirt can be combined into one result. Only fetch records that match a particular filter. You can combine field values to get counts, min, maximum, and average values across all records. Sort results within a specified distance from a latitude/longitude, or within a polygon area. With a few simple steps, you can create a reliable production-grade search engine.

Qdrant is a vector database and similarity engine. It is an API service that allows you to search for the closest high-dimensional vectors. Qdrant allows embeddings and neural network encoders to be transformed into full-fledged apps for matching, searching, recommending, etc. This specification provides the OpenAPI version 3 specification to create a client library for almost any programming language. You can also use a ready-made client for Python, or other programming languages that has additional functionality. For Approximate Nearest Neighbor Search, you can make a custom modification to the HNSW algorithm. Search at a State of the Art speed and use search filters to maximize results. Additional payload can be associated with vectors. Allows you to store payload and filter results based upon payload values.

Open source, lightning fast, and highly relevant search engine that will enhance your search experience. MeiliSearch offers a wide range of customization options. These customization options are not mandatory, unlike other search engines. It works straight out of the box with a preset that meets most applications' needs. Because most developers are familiar with the RESTful API, communication is done using this API. The search experience is intuitive and simple. Search bars can make users feel that they need to learn a new language to get the best results. Or worse, that they must jump back and forth between Google and their search to find the correct spelling or product UID. MeiliSearch makes it easy to search and responsive so that users can focus on the results. MeiliSearch provides a natural query language search experience that is both typo-tolerant, and easy to use.