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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.

HelixQL is deprecated in HelixDB v2. Queries are now written with the Rust DSL and dispatched as JSON — see the Querying guide. This section is kept as a reference for legacy HelixQL projects.
For the complete documentation index optimized for AI agents, see llms.txt.

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Embed Text using Embed

Automatically embed text into vectors using your configured embedding model. 
AddV<Type>(Embed(text))
AddV<Type>(Embed(text), {properties})
SearchV<Type>(Embed(text), limit)
The text is automatically embedded with an embedding model of your choice (can be defined in your config.hx.json file). The default embedding model is text-embedding-ada-002 from OpenAI.
Make sure to set your OPENAI_API_KEY environment variable with your API key in the same location as the queries.hx, schema.hx and config.hx.json files. When using the SDKs or curling the endpoint, the query name must match what is defined in the queries.hx file exactly.
All vectors in a vector type must have the same dimensions. If you change your embedding model (e.g., switching from text-embedding-ada-002 to a different model), the new vectors will have different dimensions and will cause an error. Ensure you use the same embedding model consistently for all vectors.

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Example 1: Creating a vector from text

QUERY InsertTextAsVector (content: String, created_at: Date) =>
    document <- AddV<Document>(Embed(content), { content: content, created_at: created_at })
    RETURN document
Here’s how to run the query using the SDKs or curl 
from datetime import datetime, timezone
from helix.client import Client

client = Client(local=True, port=6969)

print(client.query("InsertTextAsVector", {
    "content": "Machine learning is transforming the way we work.",
    "created_at": datetime.now(timezone.utc).isoformat(),
}))

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Example 2: Searching with text embeddings

QUERY SearchWithText (query: String, limit: I64) =>
    documents <- SearchV<Document>(Embed(query), limit)
    RETURN documents

QUERY InsertTextAsVector (content: String, created_at: Date) =>
    document <- AddV<Document>(Embed(content), { content: content, created_at: created_at })
    RETURN document
Here’s how to run the query using the SDKs or curl 
from datetime import datetime, timezone
from helix.client import Client

client = Client(local=True, port=6969)

sample_texts = [
    "Artificial intelligence is revolutionizing automation",
    "Machine learning algorithms for predictive analytics",
    "Deep learning applications in computer vision"
]

for text in sample_texts:
    client.query("InsertTextAsVector", {
        "content": text,
        "created_at": datetime.now(timezone.utc).isoformat(),
    })

result = client.query("SearchWithText", {
    "query": "artificial intelligence and automation",
    "limit": 5,
})

print(result)

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Example 3: Creating a vector and connecting it to a user

QUERY CreateUserDocument (user_id: ID, content: String, created_at: Date) =>
    document <- AddV<Document>(Embed(content), { content: content, created_at: created_at })
    edge <- AddE<User_to_Document_Embedding>::From(user_id)::To(document)
    RETURN document

QUERY CreateUser (name: String, email: String) =>
    user <- AddN<User>({
        name: name,
        email: email
    })
    RETURN user
Here’s how to run the query using the SDKs or curl 
from datetime import datetime, timezone
from helix.client import Client

client = Client(local=True, port=6969)

user = client.query("CreateUser", {
    "name": "Alice Johnson",
    "email": "alice@example.com",
})
user_id = user[0]["user"]["id"]

result = client.query("CreateUserDocument", {
    "user_id": user_id,
    "content": "This is my personal note about project planning.",
    "created_at": datetime.now(timezone.utc).isoformat(),
})

print(result)

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Example 4: Semantic search with postfiltering

QUERY SearchRecentNotes (query: String, limit: I64, cutoff_date: Date) =>
    documents <- SearchV<Document>(Embed(query), limit)
                    ::WHERE(_::{created_at}::GTE(cutoff_date))
    RETURN documents

QUERY InsertTextAsVector (content: String, created_at: Date) =>
    document <- AddV<Document>(Embed(content), { content: content, created_at: created_at })
    RETURN document
Here’s how to run the query using the SDKs or curl 
from datetime import datetime, timezone, timedelta
from helix.client import Client

client = Client(local=True, port=6969)

recent_date = datetime.now(timezone.utc).isoformat()
old_date = (datetime.now(timezone.utc) - timedelta(days=10)).isoformat()

client.query("InsertTextAsVector", {
    "content": "Project milestone review scheduled for next week",
    "created_at": recent_date,
})

client.query("InsertTextAsVector", {
    "content": "Weekly status report from last month",
    "created_at": old_date,
})

cutoff_date = (datetime.now(timezone.utc) - timedelta(days=7)).isoformat()

result = client.query("SearchRecentNotes", {
    "query": "project deadlines and milestones",
    "limit": 10,
    "cutoff_date": cutoff_date,
})

print(result)