HelixQL Language Reference

Documentation Index

Fetch the complete documentation index at: https://docs.helix-db.com/llms.txt

Use this file to discover all available pages before exploring further.

For the complete documentation index optimized for AI agents, see llms.txt.

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Query Structure

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QUERY

QUERY GetUser(user_id: ID) =>
QUERY CreateUser(name: String, age: U8) =>
QUERY GetAllUsers() =>

1. A query with one typed parameter.
2. A query with multiple typed parameters.
3. A query with no parameters.

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RETURN

RETURN users
RETURN user, posts
RETURN users::{name, age}
RETURN users::ID
RETURN "ok"
RETURN NONE

1. Return a single binding.
2. Return multiple bindings.
3. Return projected fields.
4. Return only IDs.
5. Return a string literal.
6. Return no payload.

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<- (Assignment)

user <- N<User>(user_id)
users <- N<User>::WHERE(_::{age}::GT(18))
count <- N<User>::COUNT

1. Bind a node lookup to user.
2. Bind a filtered traversal to users.
3. Bind a count result to count.

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Comments (//)

// This is a comment
user <- N<User>(user_id) // inline comment

1. A standalone comment line.
2. A comment at the end of a statement.

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Types

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Scalar Types

name: String
is_active: Boolean
age: U8
count: I64
score: F64

1. Text data.
2. True/false value.
3. 8-bit unsigned integer. Also available: U16, U32, U64, U128.
4. 64-bit signed integer. Also available: I8, I16, I32.
5. 64-bit floating point. Also available: F32.

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ID

QUERY GetUser(user_id: ID) =>
user <- N<User>(user_id)
RETURN user::{userID: ID}

1. Accept an ID as a query parameter.
2. Access an element’s unique identifier in a property selection.

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Date

created_at: Date
created_at: Date DEFAULT NOW

1. A date field in a schema.
2. A date field with automatic timestamp default.

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[T] (Arrays)

QUERY Search(vector: [F64]) =>
QUERY CreateUsers(user_data: [{name: String, age: U8}]) =>
tags: [String]

1. An array of 64-bit floats as a parameter.
2. An array of objects as a parameter.
3. An array of strings as a schema field.

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Schema Definition

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N:: (Node Schema)

N::User {
    name: String,
    age: U8,
    email: String,
}

1. Define a User node type with three properties.

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E:: (Edge Schema)

E::Follows {
    From: User,
    To: User,
}
E::Friends {
    From: User,
    To: User,
    Properties: {
        since: Date,
        strength: F64
    }
}

1. An edge with no properties connecting User to User.
2. An edge with properties connecting User to User.

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V:: (Vector Schema)

V::Document {
    content: String,
    created_at: Date
}
V::Embedding {}

1. A vector type with metadata properties.
2. A vector type with no extra properties.

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INDEX

N::User {
    INDEX email: String,
    name: String,
}

1. Index the email field on User nodes for fast lookups via N<User>({email: email}).

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UNIQUE INDEX

N::User {
    UNIQUE INDEX email: String,
    name: String,
}
E::BestFriend UNIQUE {
    From: User,
    To: User,
}

1. Enforce uniqueness on the email field.
2. Enforce that only one BestFriend edge can exist from a given user.

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DEFAULT

N::Post {
    title: String,
    view_count: I32 DEFAULT 0,
    status: String DEFAULT "draft",
    created_at: Date DEFAULT NOW,
    is_published: Boolean DEFAULT false,
    archived_at: Date DEFAULT NONE,
}

1. A schema with defaults for numbers, strings, timestamps, booleans, and NONE.

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Node Operations

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AddN

AddN<User>
AddN<User>({name: "Alice", age: 25, email: "alice@example.com"})

1. Create a User node with no properties.
2. Create a User node with a properties object.

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N

N<User>
N<User>(user_id)
N<User>({email: email})

1. Select all User nodes.
2. Select a User by ID.
3. Select a User by an indexed property value.

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UpsertN

existing <- N<Person>::WHERE(_::{name}::EQ(name))
person <- existing::UpsertN({name: "Alice", age: 30})

1. Find existing nodes matching a condition.
2. Upsert with the given properties — updates if found, creates if not.

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Edge Operations

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AddE

AddE<Follows>::From(user1_id)::To(user2_id)
AddE<Friends>({since: "2024-01-15", strength: 0.85})::From(user1_id)::To(user2_id)

1. Create a Follows edge between two nodes.
2. Create a Friends edge with properties.

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E

E<Follows>
E<Follows>(edge_id)

1. Select all Follows edges.
2. Select a specific Follows edge by ID.

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UpsertE

existing <- E<Knows>
edge <- existing::UpsertE({since: "2024-06-01"})::From(user1_id)::To(user2_id)

1. Select the edge type to operate on.
2. Upsert with properties between two nodes — updates if found, creates if not.

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Vector Operations

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AddV

AddV<Document>(vector)
AddV<Document>(vector, {content: "Hello world", created_at: created_at})
AddV<Document>(Embed(content), {content: content})

1. Create a vector with no metadata.
2. Create a vector with metadata properties.
3. Create a vector using the built-in Embed function.

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V

V<Document>
V<Document>(vector_id)

1. Select all Document vectors.
2. Select a specific vector by ID.

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UpsertV

existing <- V<Document>::WHERE(_::{content}::EQ(content))
doc <- existing::UpsertV(Embed(content), {content: content})

1. Find existing vectors matching a condition.
2. Upsert with new vector data and properties.

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Embed

AddV<Document>(Embed(content), {content: content})
SearchV<Document>(Embed(query), 10)

1. Embed text and store it as a vector.
2. Embed a search query for vector similarity search.

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Traversal Steps (from Nodes)

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::Out

N<User>(user_id)::Out<Follows>
N<User>(user_id)::Out<Follows>::Out<Follows>

1. Get all users that user_id follows.
2. Chain traversals to get followers-of-followers.

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::In

N<User>(user_id)::In<Follows>

1. Get all users that follow user_id.

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::OutE

N<User>(user_id)::OutE<Follows>

1. Get the outgoing Follows edge objects from a user.

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::InE

N<User>(user_id)::InE<Follows>

1. Get the incoming Follows edge objects pointing at a user.

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Traversal Steps (from Edges)

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::FromN

E<Creates>(edge_id)::FromN

1. Get the node that the Creates edge originates from.

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::ToN

E<Creates>(edge_id)::ToN

1. Get the node that the Creates edge points to.

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::FromV

E<HasEmbedding>(edge_id)::FromV

1. Get the vector that the edge originates from.

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::ToV

E<HasEmbedding>(edge_id)::ToV

1. Get the vector that the edge points to.

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Filtering & Conditions

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::WHERE

N<User>::WHERE(_::{age}::GT(18))
N<User>::WHERE(EXISTS(_::In<Follows>))
N<User>::WHERE(AND(_::{age}::GT(18), _::{age}::LT(30)))

1. Filter users where age is greater than 18.
2. Filter users that have at least one follower.
3. Combine multiple conditions with AND.

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::INTERSECT

N<Tag>::WHERE(_::{name}::IS_IN(tag_names))::INTERSECT(_::In<HasTag>)
N<Tag>::WHERE(_::{name}::IS_IN(tag_names))::INTERSECT(_::In<HasTag>)::WHERE(_::{title}::EQ(title))

1. INTERSECT takes an anonymous traversal expression, such as _::In<HasTag>.
2. Helix runs that sub-traversal for each upstream element, then keeps only IDs shared across all sub-results.
3. If the upstream set is empty (or there is no overlap), the final result is empty.

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::EQ

::WHERE(_::{status}::EQ("active"))
::WHERE(_::{name}::EQ(name))

1. Filter where status equals the string "active".
2. Filter where name equals a query parameter.

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::NEQ

::WHERE(_::{role}::NEQ("admin"))

1. Filter where role is not "admin".

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::GT

::WHERE(_::{age}::GT(18))

1. Filter where age is greater than 18.

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::GTE

::WHERE(_::{rating}::GTE(4.5))

1. Filter where rating is at least 4.5.

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::LT

::WHERE(_::{age}::LT(30))

1. Filter where age is less than 30.

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::LTE

::WHERE(_::{priority}::LTE(2))

1. Filter where priority is at most 2.

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::CONTAINS

::WHERE(_::{name}::CONTAINS("john"))
::WHERE(_::Out<Follows>::CONTAINS(user))

1. Filter where name contains the substring "john".
2. Filter where the outgoing Follows set contains user.

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::IS_IN

::WHERE(_::{status}::IS_IN(["active", "pending"]))

1. Filter where status is one of "active" or "pending".

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EXISTS

::WHERE(EXISTS(_::In<Follows>))
::WHERE(!EXISTS(_::In<Follows>))

1. Filter for elements that have incoming Follows edges.
2. Filter for elements that have no incoming Follows edges (negated with !).

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Multiple Conditions (AND / OR / !)

::WHERE(AND(_::{age}::GT(18), _::{age}::LT(30)))
::WHERE(OR(_::{role}::EQ("admin"), _::{role}::EQ("moderator")))
::WHERE(!AND(_::{is_active}::EQ(true), _::{age}::GT(18)))

1. Match when all conditions are true.
2. Match when any condition is true.
3. Negate an AND/OR block by prefixing !.

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Result Operations

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::FIRST

N<User>::FIRST
N<User>(user_id)::In<Follows>::FIRST

1. Get the first User node.
2. Get the first follower of a user.

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::COUNT

N<User>::COUNT
N<User>(user_id)::In<Follows>::COUNT

1. Count all User nodes.
2. Count how many users follow user_id.

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::RANGE

N<User>::RANGE(0, 10)
N<User>::RANGE(start, end)

1. Get the first 10 users (elements 0 through 9).
2. Get a dynamic page of users using parameters.

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::ORDER

N<User>::ORDER<Desc>(_::{age})
N<User>::ORDER<Asc>(_::{age})

1. Sort users by age descending (oldest first).
2. Sort users by age ascending (youngest first).

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Aggregation

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::GROUP_BY

N<User>::GROUP_BY(age)
N<User>::GROUP_BY(department, role)

1. Group users by age, returning [{age: 25, count: 3}, ...].
2. Group users by multiple properties.

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::AGGREGATE_BY

N<User>::AGGREGATE_BY(department)
N<User>::AGGREGATE_BY(department, role)

1. Aggregate users by department, returning counts and full user objects per group.
2. Aggregate by multiple properties.

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Property Operations

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::{} (Property Selection)

users::{name, age}
users::{name, age, email}

1. Return only name and age from each user.
2. Return three specific fields.

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::ID

users::ID
users::{userID: ID, name}

1. Return only the IDs of users.
2. Map the ID to a custom field name alongside other properties.

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::!{} (Property Exclusion)

users::!{email, location}

1. Return all user properties except email and location.

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Property Remapping

users::{displayName: name, userAge: age}
users::{userID: ID, displayName: name}

1. Rename name to displayName and age to userAge.
2. Map the ID to userID and name to displayName.

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Spread Operator (..)

users::{
    userID: ID,
    ..
}

1. Include all schema properties plus a computed userID field.

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Nested Mappings (::|name|{})

user::|usr|{
    posts: posts::{
        postID: ID,
        creatorID: usr::ID,
        creatorName: usr::name,
        ..
    }
}

1. Scope usr as a reference to user, then use it inside nested property selections.

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Computed Properties

users::{
    userID: ID,
    followerCount: _::In<Follows>::COUNT
}

1. Add a followerCount computed from the incoming Follows edge count.

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Modification Operations

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::UPDATE

N<Person>(user_id)::UPDATE({name: "Alice Johnson", age: 26})

1. Update the name and age of a person. Omitted properties stay unchanged.

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DROP

DROP N<User>(user_id)
DROP N<User>(user_id)::Out<Follows>
DROP N<User>(user_id)::OutE<Follows>

1. Delete a user node and all its connected edges.
2. Delete all nodes that the user follows (and connecting edges).
3. Delete only the outgoing Follows edges without touching neighbor nodes.

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Search

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SearchV

SearchV<Document>(vector, 10)
SearchV<Document>(Embed(query), limit)

1. Search for the 10 most similar Document vectors to a raw vector.
2. Search using an embedded text query with a dynamic limit.

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SearchBM25

SearchBM25<Document>(keywords, 10)
SearchBM25<Document>(query, limit)

1. Search Document nodes for keywords, returning up to 10 results.
2. Search with dynamic query text and limit.

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Reranking

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::RerankRRF

::RerankRRF
::RerankRRF(k: 60.0)

1. Apply RRF with the default k=60.
2. Apply RRF with a custom k parameter.

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::RerankMMR

::RerankMMR(lambda: 0.7)
::RerankMMR(lambda: 0.5, distance: "euclidean")
::RerankMMR(lambda: 0.6, distance: "dotproduct")

1. Apply MMR with cosine distance (default).
2. Apply MMR with euclidean distance.
3. Apply MMR with dot product distance.

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Shortest Path Algorithms

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::ShortestPath

N<City>(start_id)::ShortestPath<Road>::To(end_id)

1. Find the shortest unweighted path via Road edges.

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::ShortestPathBFS

N<City>(start_id)::ShortestPathBFS<Road>::To(end_id)

1. Find the shortest unweighted path from one city to another via Road edges.

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::ShortestPathDijkstras

N<City>(start_id)::ShortestPathDijkstras<Road>(_::{distance})::To(end_id)

1. Find the shortest weighted path using the distance edge property as the weight.

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::ShortestPathAStar

N<City>(start_id)::ShortestPathAStar<Road>(_::{distance}, "heuristic")::To(end_id)

1. Find the shortest path using edge distance as weight and a node heuristic property for guidance.

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Custom Weight Expressions

_::{distance}
_::FromN::{population}
_::ToN::{capacity}
ADD(_::{distance}, _::ToN::{traffic})

1. Use an edge property as weight.
2. Reference the source node’s property.
3. Reference the target node’s property.
4. Combine edge and node properties with arithmetic.

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Control Flow

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FOR ... IN

FOR user_name IN user_names {
    AddN<User>({name: user_name})
}

1. Loop through a collection of names and create a node for each element.

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FOR with Destructuring

FOR {name, age, email} IN user_data {
    AddN<User>({name: name, age: age, email: email})
}

1. Destructure name, age, and email from each element and use them directly.

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Math Functions

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Arithmetic

ADD(price, tax)
SUB(total, discount)
MUL(quantity, unit_price)
DIV(total, count)
POW(base, exponent)
MOD(value, divisor)

1. Addition: price + tax.
2. Subtraction: total - discount.
3. Multiplication: quantity * unit_price.
4. Division: total / count.
5. Power: base ^ exponent.
6. Modulo: value % divisor.

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Unary Math

ABS(value)
SQRT(value)
LN(value)
LOG10(value)
LOG(value, base)
EXP(value)
CEIL(value)
FLOOR(value)
ROUND(value)

1. Absolute value.
2. Square root.
3. Natural logarithm.
4. Base-10 logarithm.
5. Logarithm with custom base.
6. Exponential (e^value).
7. Round up to nearest integer.
8. Round down to nearest integer.
9. Round to nearest integer.

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Trigonometry

SIN(angle)
COS(angle)
TAN(angle)
ASIN(value)
ACOS(value)
ATAN(value)
ATAN2(y, x)

1. Sine of an angle.
2. Cosine of an angle.
3. Tangent of an angle.
4. Inverse sine (arc sine).
5. Inverse cosine (arc cosine).
6. Inverse tangent (arc tangent).
7. Two-argument inverse tangent.

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Constants

PI()
E()

1. Returns the value of pi (~3.14159).
2. Returns the value of Euler’s number (~2.71828).

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Aggregate Functions

MIN(ages)
MAX(ages)
SUM(prices)
AVG(scores)
COUNT(users)

1. Minimum value in a collection.
2. Maximum value in a collection.
3. Sum of all values.
4. Average of all values.
5. Number of elements.

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Macros

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#[model]

#[model("gemini:gemini-embedding-001:RETRIEVAL_DOCUMENT")]
QUERY StoreDocument(content: String) =>
    doc <- AddV<Document>(Embed(content), {content: content})
    RETURN doc

1. Override the default embedding model for this query using the #[model] macro.

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#[mcp]

#[mcp]
QUERY GetDocument(doc_id: ID) =>
    doc <- N<Document>(doc_id)
    RETURN doc

1. Mark a query as an MCP tool, making it accessible to LLM applications.

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Output

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Output Values

RETURN "Success"
RETURN users::{name, age}
RETURN user, posts
RETURN users::!{email}
RETURN NONE

1. Return a string literal.
2. Return projected properties.
3. Return multiple bindings.
4. Return all properties except email.
5. Return no payload.