docs/reference/query-languages/esql/esql-search-tutorial.md
This hands-on tutorial covers full-text search, semantic search, hybrid search, vector search, and AI-powered search capabilities using {{esql}}.
In this scenario, we're implementing search for a cooking blog. The blog contains recipes with various attributes including textual content, categorical data, and numerical ratings.
:::{note} This tutorial uses a small dataset for learning purposes. The goal is to demonstrate search concepts and {{esql}} syntax. :::
You need a running {{es}} cluster, together with {{kib}} to use the Dev Tools API Console. Refer to choose your deployment type for deployment options.
Want to get started quickly? Run the following command in your terminal to set up a single-node local cluster in Docker:
curl -fsSL https://elastic.co/start-local | sh
% NOTCONSOLE
In this tutorial, {{esql}} examples are displayed in the following format:
FROM cooking_blog
| WHERE description:"fluffy pancakes"
| LIMIT 1000
If you want to run these queries in the Dev Tools Console, you need to use the following syntax:
POST /_query?format=txt
{
"query": """
FROM cooking_blog
| WHERE description:"fluffy pancakes"
| LIMIT 1000
"""
}
If you'd prefer to use your favorite programming language, refer to Client libraries for a list of official and community-supported clients.
Create the cooking_blog index to get started:
PUT /cooking_blog
Now define the mappings for the index:
PUT /cooking_blog/_mapping
{
"properties": {
"title": {
"type": "text",
"analyzer": "standard", <1>
"fields": { <2>
"keyword": {
"type": "keyword",
"ignore_above": 256 <3>
}
}
},
"description": {
"type": "text" <1>
},
"author": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword"
}
}
},
"date": {
"type": "date",
"format": "yyyy-MM-dd"
},
"category": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword"
}
}
},
"tags": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword"
}
}
},
"rating": {
"type": "float"
}
}
}
standard analyzer is used by default for text fields. It's included here explicitly for demonstration purposes. To know more about analyzers, refer to Anatomy of an analyzer.title, author, category, and tags fields are declared with both text and keyword data types. The text version allows full-text search, while the keyword version enables exact filtering. If you use dynamic mapping, these multi-fields will be created automatically.ignore_above parameter prevents indexing values longer than 256 characters in the keyword field. This is the default value and it's included here for demonstration purposes. For more information, refer to the ignore_above parameter.keyword fields store exact values and are used for filtering, sorting, and aggregations. Unlike text fields, they are not analyzed. Use keyword when you need exact matching (e.g., filtering by category or finding a specific author name), but use text when you want to search within longer content like descriptions.::::{tip} Full-text search is powered by text analysis. Text analysis normalizes and standardizes text data so it can be efficiently stored in an inverted index and searched in near real-time. Analysis happens at both index and search time. This tutorial won't cover analysis in detail, but it's important to understand how text is processed to create effective search queries. ::::
Next, you’ll need to index some example blog posts using the Bulk API. Note that text fields are analyzed and multi-fields are generated at index time.
POST /cooking_blog/_bulk?refresh=wait_for
{"index":{"_id":"1"}}
{"title":"Perfect Pancakes: A Fluffy Breakfast Delight","description":"Learn the secrets to making the fluffiest pancakes, so amazing you won't believe your tastebuds. This recipe uses buttermilk and a special folding technique to create light, airy pancakes that are perfect for lazy Sunday mornings.","author":"Maria Rodriguez","date":"2023-05-01","category":"Breakfast","tags":["pancakes","breakfast","easy recipes"],"rating":4.8}
{"index":{"_id":"2"}}
{"title":"Spicy Thai Green Curry: A Vegetarian Adventure","description":"Dive into the flavors of Thailand with this vibrant green curry. Packed with vegetables and aromatic herbs, this dish is both healthy and satisfying. Don't worry about the heat - you can easily adjust the spice level to your liking.","author":"Liam Chen","date":"2023-05-05","category":"Main Course","tags":["thai","vegetarian","curry","spicy"],"rating":4.6}
{"index":{"_id":"3"}}
{"title":"Classic Beef Stroganoff: A Creamy Comfort Food","description":"Indulge in this rich and creamy beef stroganoff. Tender strips of beef in a savory mushroom sauce, served over a bed of egg noodles. It's the ultimate comfort food for chilly evenings.","author":"Emma Watson","date":"2023-05-10","category":"Main Course","tags":["beef","pasta","comfort food"],"rating":4.7}
{"index":{"_id":"4"}}
{"title":"Vegan Chocolate Avocado Mousse","description":"Discover the magic of avocado in this rich, vegan chocolate mousse. Creamy, indulgent, and secretly healthy, it's the perfect guilt-free dessert for chocolate lovers.","author":"Alex Green","date":"2023-05-15","category":"Dessert","tags":["vegan","chocolate","avocado","healthy dessert"],"rating":4.5}
{"index":{"_id":"5"}}
{"title":"Crispy Oven-Fried Chicken","description":"Get that perfect crunch without the deep fryer! This oven-fried chicken recipe delivers crispy, juicy results every time. A healthier take on the classic comfort food.","author":"Maria Rodriguez","date":"2023-05-20","category":"Main Course","tags":["chicken","oven-fried","healthy"],"rating":4.9}
Full-text search involves executing text-based queries across one or more document fields. In this section, you'll start with simple text matching and build up to understanding how search results are ranked.
{{esql}} provides multiple functions for full-text search, including MATCH, MATCH_PHRASE, and QSTR. For basic text matching, you can use either:
match(field, "search terms"):": field:"search terms"Both are equivalent for basic matching and can be used interchangeably. The compact syntax is more concise, while the function syntax allows for more configuration options. We use the compact syntax in most examples for brevity.
Refer to the MATCH function reference docs for advanced parameters available with the function syntax.
Let's start with the simplest possible search - looking for documents that contain specific words:
FROM cooking_blog
| WHERE description:"fluffy pancakes"
| LIMIT 1000
This query searches the description field for documents containing either "fluffy" OR "pancakes" (or both). By default, {{esql}} uses OR logic between search terms, so it matches documents that contain any of the specified words.
You can specify the exact fields to include in your results using the KEEP command:
FROM cooking_blog
| WHERE description:"fluffy pancakes"
| KEEP title, description, rating
| LIMIT 1000
This helps reduce the amount of data returned and focuses on the information you need.
Another option is to retrieve the original document source via the _source metadata field:
FROM cooking_blog METADATA _source
| WHERE description:"fluffy pancakes"
| KEEP _source
| LIMIT 1000
Using _source is useful when you want to retrieve most or all fields from a document.
The original document is stored together in _source, so fetching it can be more efficient than retrieving many individual fields separately.
It also returns the document exactly as indexed, preserving nested objects and arrays.
Selecting individual fields may be more efficient when you only need a small number of fields from each document. To learn more, refer to retrieving _source.
Search results can be ranked based on how well they match your query. To calculate and use relevance scores, you need to explicitly request the _score metadata:
FROM cooking_blog METADATA _score
| WHERE description:"fluffy pancakes"
| KEEP title, description, _score
| SORT _score DESC
| LIMIT 1000
Notice two important things:
METADATA _score tells {{esql}} to include relevance scores in the resultsSORT _score DESC orders results by relevance (highest scores first)If you don't include METADATA _score in your query, you won't see relevance scores in your results. This means you won't be able to sort by relevance or filter based on relevance scores.
Without explicit sorting, results aren't ordered by relevance even when scores are calculated. If you want the most relevant results first, you must sort by _score, by explicitly using SORT _score DESC or SORT _score ASC.
:::{tip}
When you include METADATA _score, search functions included in WHERE conditions contribute to the relevance score. Filtering operations (like range conditions and exact matches) don't affect the score.
:::
Sometimes you need exact matches rather than full-text search. Use the .keyword field for case-sensitive exact matching:
FROM cooking_blog
| WHERE category.keyword == "Breakfast" # Exact match (case-sensitive)
| KEEP title, category, rating
| SORT rating DESC
| LIMIT 1000
This is fundamentally different from full-text search - it's a binary yes/no filter that doesn't affect relevance scoring.
Now that you understand basic searching, explore how to control the precision of your text matches.
AND logic)By default, searches with match use OR logic between terms. To require ALL terms to match, use the function syntax with the operator parameter to specify AND logic:
FROM cooking_blog
| WHERE match(description, "fluffy pancakes", {"operator": "AND"})
| LIMIT 1000
This stricter search returns zero hits on our sample data, as no document contains both "fluffy" and "pancakes" in the description.
:::{note}
The MATCH function with AND logic doesn't require terms to be adjacent or in order. It only requires that all terms appear somewhere in the field. Use MATCH_PHRASE to search for exact phrases.
:::
Sometimes requiring all terms is too strict, but the default OR behavior is too lenient. You can specify a minimum number of terms that must match:
FROM cooking_blog
| WHERE match(title, "fluffy pancakes breakfast", {"minimum_should_match": 2})
| LIMIT 1000
This query searches the title field to match at least 2 of the 3 terms: "fluffy", "pancakes", or "breakfast".
When you need to find documents containing an exact sequence of words, use the MATCH_PHRASE function:
FROM cooking_blog
| WHERE MATCH_PHRASE(description, "rich and creamy")
| KEEP title, description
| LIMIT 1000
This query only matches documents where the words "rich and creamy" appear exactly in that order in the description field.
Once you're comfortable with basic search precision, learn how to search across multiple fields and use query string syntax for complex patterns.
The QSTR function enables powerful search patterns using a compact query language. It's ideal for when you need wildcards, fuzzy matching, and boolean logic in a single expression:
FROM cooking_blog
| WHERE QSTR("description: (fluffy AND pancak*) OR (creamy -vegan)")
| KEEP title, description
| LIMIT 1000
Query string syntax lets you:
AND, OR, - (NOT)pancak* matches "pancake" and "pancakes"pancake~1 for typo tolerance(thai AND curry) OR pasta"fluffy pancakes"QSTR("(title:pancake OR description:pancake) AND creamy")When users enter a search query, they often don't know (or care) whether their search terms appear in a specific field. You can search across multiple fields simultaneously:
FROM cooking_blog
| WHERE title:"vegetarian curry" OR description:"vegetarian curry" OR tags:"vegetarian curry"
| LIMIT 1000
This query searches for "vegetarian curry" across the title, description, and tags fields. Each field is treated with equal importance.
In many cases, matches in certain fields (like the title) might be more relevant than others. You can adjust the importance of each field using boost scoring:
FROM cooking_blog METADATA _score
| WHERE MATCH(title, "vegetarian curry", {"boost": 2.0}) # Title matches are twice as important
OR MATCH(description, "vegetarian curry")
OR MATCH(tags, "vegetarian curry")
| KEEP title, description, tags, _score
| SORT _score DESC
| LIMIT 1000
Filtering allows you to narrow down your search results based on exact criteria. Unlike full-text searches, filters are binary (yes/no) and do not affect the relevance score. Filters execute faster than queries because excluded results don't need to be scored.
FROM cooking_blog
| WHERE category.keyword == "Breakfast" # Exact match using keyword field
| KEEP title, author, rating, tags
| SORT rating DESC
| LIMIT 1000
Often users want to find content published within a specific time frame:
FROM cooking_blog
| WHERE date >= "2023-05-01" AND date <= "2023-05-31" # Inclusive date range filter
| KEEP title, author, date, rating
| LIMIT 1000
Filter by ratings or other numerical values:
FROM cooking_blog
| WHERE rating >= 4.5 # Only highly-rated recipes
| KEEP title, author, rating, tags
| SORT rating DESC
| LIMIT 1000
Find recipes by a specific author:
FROM cooking_blog
| WHERE author.keyword == "Maria Rodriguez" # Exact match on author
| KEEP title, author, rating, tags
| SORT rating DESC
| LIMIT 1000
Real-world search often requires combining multiple types of criteria. This section shows how to build sophisticated search experiences by combining the techniques you've learned.
Mix filters, full-text search, and custom scoring in a single query:
FROM cooking_blog METADATA _score
| WHERE rating >= 4.5 # Numerical filter
AND NOT category.keyword == "Dessert" # Exclusion filter
AND (title:"curry spicy" OR description:"curry spicy") # Full-text search in multiple fields
| SORT _score DESC
| KEEP title, author, rating, tags, description
| LIMIT 1000
For complex relevance scoring with combined criteria, you can use the EVAL command to calculate custom scores:
FROM cooking_blog METADATA _score
| WHERE NOT category.keyword == "Dessert"
| EVAL tags_concat = MV_CONCAT(tags.keyword, ",") # Convert multi-value field to string
| WHERE tags_concat LIKE "*vegetarian*" AND rating >= 4.5 # Wildcard pattern matching
| WHERE MATCH(title, "curry spicy", {"boost": 2.0}) OR MATCH(description, "curry spicy")
| EVAL category_boost = CASE(category.keyword == "Main Course", 1.0, 0.0) # Conditional boost
| EVAL date_boost = CASE(DATE_DIFF("month", date, NOW()) <= 1, 0.5, 0.0) # Boost recent content
| EVAL custom_score = _score + category_boost + date_boost # Combine scores
| WHERE custom_score > 0 # Filter based on custom score
| SORT custom_score DESC
| LIMIT 1000
{{es}} allows you to semantically search for documents based on the meaning of the text, rather than just the presence of specific keywords. This is useful when you want to find documents that are conceptually similar to a given query, even if they don't contain the exact search terms.
ES|QL supports semantic search when your mappings include fields of the semantic_text type. This example mapping update adds a new field called semantic_description with the type semantic_text:
PUT /cooking_blog/_mapping
{
"properties": {
"semantic_description": {
"type": "semantic_text"
}
}
}
Next, index a document with content into the new field:
POST /cooking_blog/_doc
{
"title": "Mediterranean Quinoa Bowl",
"semantic_description": "A protein-rich bowl with quinoa, chickpeas, fresh vegetables, and herbs. This nutritious Mediterranean-inspired dish is easy to prepare and perfect for a quick, healthy dinner.",
"author": "Jamie Oliver",
"date": "2023-06-01",
"category": "Main Course",
"tags": ["vegetarian", "healthy", "mediterranean", "quinoa"],
"rating": 4.7
}
Once the document has been processed by the underlying model running on the inference endpoint, you can perform semantic searches. Use the MATCH function (or the ":" operator shorthand) on semantic_text fields to perform semantic queries:
FROM cooking_blog METADATA _score
| WHERE semantic_description:"I need plant-based meals that aren't difficult to make"
| SORT _score DESC
| LIMIT 5
When you use MATCH or ":" on a semantic_text field, {{esql}} automatically performs a semantic search rather than a lexical search. This means the query finds documents based on semantic similarity, not just keyword matching.
:::{tip} If you'd like to test out the semantic search workflow against a large dataset, follow the semantic-search-tutorial. :::
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Hybrid search combines different search strategies (like lexical and semantic search) to leverage the strengths of each approach. Use FORK and FUSE to execute different search strategies in parallel, then merge and score the combined results.
FUSE supports different merging algorithms, namely Reciprocal Rank Fusion (RRF) and linear combination.
::::{tab-set}
:::{tab-item} RRF (default)
RRF is a ranking algorithm that combines rankings from multiple search strategies without needing to tune weights. It's effective when you want to blend different search approaches without manual score tuning.
FROM cooking_blog METADATA _id, _index, _score
| FORK (
WHERE title:"vegetarian curry" <1>
| SORT _score DESC
| LIMIT 5
) (
WHERE semantic_description:"easy vegetarian curry recipes" <2>
| SORT _score DESC
| LIMIT 5
)
| FUSE <3>
| KEEP title, description, rating, _score
| SORT _score DESC
| LIMIT 5
:::{tab-item} Linear combination
Linear combination allows you to specify explicit weights for each search strategy. This gives you fine-grained control over how much each strategy contributes to the final score.
FROM cooking_blog METADATA _id, _index, _score
| FORK (
WHERE title:"vegetarian curry" <1>
| SORT _score DESC
| LIMIT 5
) (
WHERE semantic_description:"easy vegetarian curry recipes" <2>
| SORT _score DESC
| LIMIT 5
)
| FUSE LINEAR WITH { "weights": { "fork1": 0.7, "fork2": 0.3 } } <3>
| KEEP title, description, rating, _score
| SORT _score DESC
| LIMIT 5
::::
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{{esql}} provides commands for AI-powered search enhancements and text generation tasks. You will need to set up an inference endpoint with appropriate models to follow along with these examples.
RERANKThe RERANK command re-scores search results using inference models for improved relevance. This is particularly useful for improving the ranking of initial search results by applying more sophisticated semantic understanding.
:::{tip}
RERANK requires an inference endpoint configured for the rerank task. Refer to the setup instructions.
:::
FROM cooking_blog METADATA _score
| WHERE description:"vegetarian recipes" <1>
| SORT _score DESC
| LIMIT 100 <2>
| RERANK "healthy quick meals" ON description <3>
| LIMIT 5 <4>
| KEEP title, description, _score
You can configure RERANK to use a specific reranking model. See the RERANK command documentation for configuration options.
COMPLETION:::{tip} COMPLETION requires a configured inference endpoint with an LLM. Refer to the Inference API documentation for setup instructions. :::
The COMPLETION command sends prompts to a Large Language Model (LLM) for text generation tasks like question answering, summarization, or translation.
FROM cooking_blog
| WHERE category.keyword == "Dessert"
| LIMIT 3
| EVAL prompt = CONCAT("Summarize this recipe: ", title, " - ", description)
| COMPLETION summary = prompt WITH { "inference_id": "my_llm_endpoint" }
| KEEP title, summary
This enables you to:
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The TOP_SNIPPETS function is like the CHUNK function with additional relevance ranking capabilities. TOP_SNIPPETS ranks chunks by relevance to your query and returns only the top matches.
This is very useful for context engineering with LLMs: instead of sending entire field values, you send only the most relevant portions. This reduces token costs, helps you stay within context limits, and avoids the "lost in the middle" problem where important information is overlooked in large blocks of text.
Here's the basic syntax for extracting top snippets from a text field:
FROM cooking_blog
| WHERE description:"vegetarian curry"
| EVAL snippets = TOP_SNIPPETS(description, "vegetarian curry")
| KEEP title, snippets
| LIMIT 5
This query searches for "vegetarian curry" and extracts the top matching snippets from the description field.
::::{dropdown} Example response :icon: code
title | snippets
----------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Spicy Thai Green Curry: A Vegetarian Adventure|Dive into the flavors of Thailand with this vibrant green curry. Packed with vegetables and aromatic herbs, this dish is both healthy and satisfying. Don't worry about the heat - you can easily adjust the spice level to your liking.
Notice how TOP_SNIPPETS extracted the full description as the most relevant snippet for the query "vegetarian curry".
::::
You can fine-tune the snippet extraction using named parameters:
FROM cooking_blog
| WHERE description:"healthy quick meals"
| EVAL snippets = TOP_SNIPPETS(description, "healthy quick meals", {"num_snippets": 3, "num_words": 25})
| KEEP title, snippets
| LIMIT 5
The parameters control:
num_snippets: Maximum number of matching snippets to return (useful when a query matches multiple parts of a document)num_words: Maximum number of words per snippet (helps control token usage for LLM inference). This parameter automatically configures sentence-based chunking; other custom chunking configurations are not currently supported.::::{dropdown} Example response :icon: code
title | snippets
----------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------
Spicy Thai Green Curry: A Vegetarian Adventure|Dive into the flavors of Thailand with this vibrant green curry. Packed with vegetables and aromatic herbs, this dish is both healthy and satisfying.
Vegan Chocolate Avocado Mousse |Discover the magic of avocado in this rich, vegan chocolate mousse. Creamy, indulgent, and secretly healthy, it's the perfect guilt-free dessert for chocolate lovers.
Notice how the snippets are now limited to approximately 25 words each, compared to the basic example which returned the full description. The num_words parameter helps you control snippet length for more precise LLM context.
Because TOP_SNIPPETS uses sentence-based chunking, it prioritizes breaking at the nearest sentence boundary rather than cutting off mid-sentence, which may result in a slightly lower word count than requested.
::::
COMPLETION for efficient LLM contextThe real power of TOP_SNIPPETS emerges when combined with COMPLETION. Instead of sending entire documents to an LLM, send only the most relevant snippets:
FROM cooking_blog METADATA _score
| WHERE semantic_description:"vegetarian recipes" <1>
| SORT _score DESC
| LIMIT 10 <2>
| EVAL snippets = TOP_SNIPPETS(semantic_description, "vegetarian recipes", {"num_snippets": 3, "num_words": 30}) <3>
| EVAL prompt = CONCAT("Based on these recipe snippets, suggest quick meal ideas: ", snippets) <4>
| COMPLETION answer = prompt WITH {"inference_id": "my_llm_endpoint"} <5>
| KEEP title, answer
This approach significantly reduces token costs compared to sending full field values, while maintaining high-quality context for the LLM.
RERANK for precisionYou can also use TOP_SNIPPETS with the RERANK command to rerank based on extracted snippets rather than entire field values:
FROM cooking_blog METADATA _score
| WHERE description:"healthy meals"
| SORT _score DESC
| LIMIT 100
| EVAL snippets = TOP_SNIPPETS(description, "quick healthy vegetarian", {"num_snippets": 3, "num_words": 25})
| RERANK "quick healthy vegetarian" ON snippets <1>
| LIMIT 5
| KEEP title, description, snippets, _score
This can improve reranking precision for models that work better with focused text rather than long documents.
:::{tip}
To learn more about TOP_SNIPPETS refer to this blog post. You can also learn about the equivalent chunk_rescorer parameter in the text_similarity_reranker retriever.
:::
:::{note}
This subsection is for advanced users who want explicit control over vector search. For most use cases, the semantic_text field type covered in Step 8 is recommended as it handles embeddings automatically.
:::
The KNN function finds the k nearest vectors to a query vector through approximate search on indexed dense_vector fields.
KNN with pre-computed vectorsFirst, add a dense_vector field and index a document with a pre-computed vector. This example uses a toy example for readability: the vector has only 3 dimensions.
PUT /cooking_blog/_mapping
{
"properties": {
"recipe_vector": {
"type": "dense_vector",
"dims": 3
}
}
}
POST /cooking_blog/_doc/6
{
"title": "Quick Vegan Stir-Fry",
"description": "A fast and healthy vegan stir-fry with tofu and vegetables",
"recipe_vector": [0.5, 0.8, 0.3],
"rating": 4.6
}
Then search using a query vector:
FROM cooking_blog METADATA _score
| WHERE KNN(recipe_vector, [0.5, 0.8, 0.3])
| SORT _score DESC
| LIMIT 5
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The TEXT_EMBEDDING function generates dense vector embeddings from text input at query time using an inference model.
:::{tip} You'll need to set up an inference endpoint with an embedding model to use this feature. Refer to the Inference API documentation for setup instructions. :::
FROM cooking_blog METADATA _score
| WHERE KNN(recipe_vector, TEXT_EMBEDDING("vegan recipe", "my_embedding_model"))
| SORT _score DESC
| LIMIT 5
This approach gives you full control over vector embeddings, unlike semantic_text which handles embeddings automatically.
:::{note}
The vectors in your index must be generated by the same embedding model you specify in TEXT_EMBEDDING, otherwise the similarity calculations will be meaningless and you won't get relevant results.
:::
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While KNN performs approximate nearest neighbor search, {{esql}} also provides vector similarity functions for exact vector search. These functions calculate similarity over all the query vectors, guaranteeing accurate results at the cost of slower performance.
:::{tip} When to use exact search instead of KNN
Use vector similarity functions when:
To learn more, read this blog: how to choose between exact and approximate kNN search in {{es}}. :::
Example: Cosine similarity with threshold filtering
FROM cooking_blog
| EVAL similarity = V_COSINE(recipe_vector, TEXT_EMBEDDING("vegan recipe", "my_embedding_model"))
| WHERE similarity > 0.8
| SORT similarity DESC
| LIMIT 10
This query:
Example: Combining exact vector search with other filters
FROM cooking_blog
| WHERE category.keyword == "Main Course" AND rating >= 4.5
| EVAL similarity = V_COSINE(recipe_vector, TEXT_EMBEDDING("vegan recipe", "my_embedding_model"))
| EVAL combined_score = similarity + (rating / 5.0)
| SORT combined_score DESC
| LIMIT 10
This advanced query combines:
This tutorial introduced search, filtering, and AI-powered capabilities in {{esql}}. Here are some resources once you're ready to dive deeper:
semantic_text workflow: Learn how to use the semantic_text field type for semantic search. This is the recommended approach for most users looking to perform semantic search in {{es}}, because it abstracts away the complexity of setting up inference endpoints and models.RERANK, COMPLETION, and TEXT_EMBEDDING commands.