Full-Text Search Still Works. It Just Doesn’t Get You to an Answer Manticore Search introduced Conversational Search, a feature that combines full-text, vector, and hybrid search with a language model to answer complex product queries, as demonstrated in its Manticore Apparel Shop demo built on the ConvApparel dataset of 82,524 products. The feature addresses the growing expectation for search systems to handle multi-part questions, a trend highlighted by Google's report that AI Mode surpassed one billion monthly users in May 2026. Imagine a typical online shoe store. A shopper opens search and types: I need black waterproof running shoes for daily runs on wet pavement. What would you recommend? A few years ago, almost no one expected this from a search box. The query would have been shortened to something like: black waterproof running shoes Then the shopper would open several product cards, compare descriptions, materials, intended use, and price, and make the decision alone. Today, people increasingly expect search itself to do part of that work. Not because full-text search has become worse. It still performs very well with exact names, SKUs, product codes, brands, and keywords. What has changed is what people expect to be able to ask a search system. For example, Google reported in May 2026 that AI Mode had surpassed one billion monthly users. The company notes that people are asking longer, more complex questions that previously did not fit into conventional search. blog.google https://blog.google/products-and-platforms/products/search/search-io-2026/ The same thing happens in an online store. A query such as: I need black waterproof running shoes for daily runs on wet pavement. only looks like one sentence. For a search system, it contains several tasks. It needs to extract constraints such as color and waterproofing; understand that this is about running rather than walking; account for price, size, and availability; find suitable products; and, if the user asks “which are better,” explain the differences. No single algorithm solves all of that. Full-text search, vector search, filters, hybrid search, and a language model each solve different parts of the problem. It is far more effective to combine them than to choose between them. That is exactly why Manticore has Conversational Search. Word search, semantic search, and conversation are different tasks Start with a simple query: Nike Pegasus 41 black Here, the system barely needs to interpret the user’s intent. Full-text search handles it directly. Or something even simpler: SKU 123456 Semantic methods are not needed here. Now consider another example: light shoes for long summer walks A product card may not contain the words “summer” or “long walks,” but it may include details such as “breathable material” or “lightweight construction.” This is where vector search becomes useful. Real queries often fall between these extremes: black Gore-Tex shoes for everyday running Some parameters — black and Gore-Tex — need to be preserved. Everyday running describes the user’s intent rather than an exact attribute. For such cases, Manticore uses hybrid search, combining full-text and vector search through result ranking. But even hybrid search returns only a list of results. At that point, search considers its job done. The user usually does not. It does not answer questions such as: Which of these models are better suited to rain? And it certainly does not handle a follow-up such as: Which of those cost less than $120? That is a conversation. It needs another layer. What we built To test this in practice, we used ConvApparel https://arxiv.org/abs/2602.16938 , a dataset of conversations about choosing apparel. After cleanup, it contained 82,524 products: footwear, pants, tops, and outerwear. Each product has a description, category, images, and attributes. We built Manticore Apparel Shop on this data. For example, you can type: I need black waterproof running shoes for jogging The system first finds suitable products, then a language model generates an answer using them as context, while the interface shows the products themselves. Try the demo: Manticore Apparel Shop generates a random product and a query that should retrieve it, then demonstrates that the same query does retrieve that product through Manticore. It is important to keep the connection between the answer and the data. If the system claims that a model is suitable for rain, the user should be able to open the product and verify the source of that claim. In this approach, the language model does not replace search. It interprets its results. How it works Two main commands are used: CREATE CHAT MODEL and CALL CHAT ... First, you create a Conversational Search model and set the rules it follows. CREATE CHAT MODEL assistant model='openrouter:google/gemma-4-26b-a4b-it', timeout=60, retrieval limit=5, max document length=3000, custom prompt='You are a context-only shopping assistant. Answer using only the provided context. Do not use outside knowledge or unsupported assumptions. Recommend only products supported by the retrieved context. For every recommended product, briefly explain why it matches the request. End every recommendation with the corresponding context source ID in the format ref: