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Quickstart

This guide demonstrates how to generate SQL queries from natural language using the Text2Everything SDK.

Prerequisites​

Before you begin, ensure you have:

  • A Text2Everything API endpoint (default value works for most installations)
  • Access to your platform and access token
  • An H2OGPTe API Key
  • Workspace Owner permission for your workspace
  • Python 3.9 or higher installed

Installation​

pip install h2o-text-2-everything

Generate Your First Query​

from text2everything_sdk import Text2EverythingClient

# Simple initialization
client = Text2EverythingClient(
    access_token="your-access-token",
    workspace_name="workspaces/my-workspace",
)

# 1) Create a project
project = client.projects.create(name="Quickstart Project")

# 2) Add minimal context
client.contexts.create(
    project_id=project.id,
    name="Business Rules",
    content="Active customers have status = 'active'",
    is_always_displayed=True,
)

# 3) Add a minimal table schema
schema = client.schema_metadata.create(
    project_id=project.id,
    name="Customers Table",
    schema_data={
        "table": {
            "name": "customers",
            "columns": [
                {"name": "id", "type": "INTEGER"},
                {"name": "name", "type": "VARCHAR(100)"},
                {"name": "status", "type": "VARCHAR(32)"},
            ]
        }
    },
)

# 4) Add a golden example for better SQL quality
client.golden_examples.create(
    project_id=project.id,
    user_query="How many active customers do we have?",
    sql_query="SELECT COUNT(*) FROM customers WHERE status = 'active';",
    description="Count of active customers",
    is_always_displayed=True,
)

# 5) Start a chat session
session = client.chat_sessions.create(project_id=project.id)

# 6) Ask a question to generate SQL
resp = client.chat.chat_to_sql(
    project_id=project.id,
    chat_session_id=session.id,
    query="Count active customers",
)

print("Generated SQL:", resp.sql_query)

Next Steps​

To improve your implementation:

  • Add more golden examples to improve SQL generation quality
  • Connect your database using connectors to execute queries with chat_to_answer
  • Review the Guides section for detailed information on each resource type
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