> ## Documentation Index > Fetch the complete documentation index at: https://docs.jelou.ai/llms.txt > Use this file to discover all available pages before exploring further. # Product search > How Jelou Shop's two search engines work and when to use each one. Jelou Shop ships with two product search engines. Both receive what the user types (for example, from the [webview](/en/guides/integraciones/e-commerce/webview) or the AI Agent tools) and return the most relevant products, but they solve the problem in different ways: | Engine | Approach | | ----------------- | ------------------------------------------------------- | | **Hybrid search** | Understands the meaning of the query, beyond the words. | | **Text search** | Looks up the query words directly in the catalog. | ## Hybrid search Uses artificial intelligence to understand the **meaning** of what the user types and compare it with the meaning of each product in the catalog. In parallel it also searches by keywords, and merges both results into a single ranking. Technically, the meaning is represented with semantic vectors (*embeddings*). Hybrid search is enabled by default in every store. ### How it works ```mermaid theme={null} flowchart LR Q["User
query"] -->|"embeddings"| S["Semantic
search"] Q -->|"FTS"| K["Keyword
search"] S --> M["Merge & rank
results"] K --> M M --> R["Relevant
products"] ``` By understanding the *meaning* of the query, it finds products even when the user doesn't use the exact words in the catalog: "something for the cold" can return jackets and coats even if none of them mention "cold". ### Advantages * Understands natural language and purchase intent, not just exact words. * Finds products even when the user's vocabulary differs from the catalog's. * Reduces empty results for general or descriptive queries. ### Limitations * It's slower: each query goes through artificial intelligence services before returning results. * It has an additional cost per query (AI services). * It's less precise with exact codes, such as SKUs. * The ranking is less predictable: two similar queries may rank differently. ## Text search Searches directly over the catalog information, without artificial intelligence services. Before searching, it cleans and prepares the query, then matches it against each product's name, SKU, categories, and description. Technically, it relies on full-text search (FTS). Enable it for your store with the **Enable smart search V2** option in the [Shop Admin settings](/en/guides/integraciones/e-commerce/shop-admin#settings). The change applies immediately to the webview and the AI Agent tools. Enable smart search V2 toggle in the Shop Admin settings ### How it works ```mermaid theme={null} flowchart LR Q["User
query"] --> P subgraph P["Prepare the query"] direction TB A["Accents
and casing"] ~~~ S["Synonyms"] W["Words
to ignore"] ~~~ M["Brands"] end P -->|"FTS"| I["Search the index
name · SKU · categories"] I --> R["Products ranked
by relevance"] ``` Highlights: * **Ignores accents and casing**: `cafe` matches `café`. * **Understands singular and plural**: `t-shirt` matches `t-shirts`. * **All terms must match**: searching `red t-shirt` doesn't return everything red or every t-shirt — only red t-shirts. * **Category aware**: searching `t-shirt` finds products in the "T-shirts" category even if the product name doesn't mention it. * **Your store's own synonyms**: you can teach it that `tv` = `television`. * **Words to ignore**: it discards words that only add noise to the search, such as "want" or "cheap". * **Recognizes your store's brands**: you can register your brands so it detects them in the query and uses them to filter the results. * **Typo tolerance** (optional): `Xioami` matches `Xiaomi`. ### Advantages * Very fast and low cost: it doesn't depend on external services. * Precise: excellent for SKUs, product names, and catalogs with technical vocabulary. * Consistent results: the same search always returns the same products. * Customizable per store: synonyms, words to ignore, and brands. ### Limitations * It only finds what is written: if the term doesn't appear in the product's name, SKU, category, or description (nor in the configured synonyms), there are no results. * It doesn't understand descriptive queries like "something for the cold" or "a gift for mom". * Your customers' particular vocabulary may require setting up synonyms. ## Comparison | | Hybrid search | Text search | | -------------------------------- | ------------------------- | ---------------------- | | **How it searches** | By meaning and keywords | By the catalog's words | | **Understands natural language** | Yes | No | | **Synonyms** | Automatic | Store-configurable | | **Accents and plurals** | Yes | Yes | | **Typos** | Tolerant | Optional tolerance | | **Exact SKU / code** | Fair | Excellent | | **Speed** | Slower (uses AI services) | Very fast | | **Cost per search** | Additional (AI services) | No additional cost | | **Result consistency** | Medium | High | ## Which one should you use? | Scenario | Recommended engine | | --------------------------------------------------------------- | ------------------ | | The user searches by SKU, code, or exact name | Text search | | Technical catalog: spare parts, hardware, pharmacy | Text search | | Speed and cost are the priority | Text search | | Long conversational queries from a chatbot | Hybrid search | | Descriptive queries: "something for the cold", "a gift for mom" | Hybrid search | | The user's vocabulary differs a lot from the catalog | Hybrid search | If you're not sure which one fits your catalog, contact the Jelou team.