Shaped CLI
CLI for interactions with the Shaped API.
For full documentation, see docs.shaped.ai.
Installing the Shaped CLI
pip install shaped
Initialize
shaped init --api-key <API_KEY> [--env <ENV>]
The --env option defaults to prod and can be set to other environments like dev or staging.
Engine API
Create Engine
shaped create-engine --file <PATH_TO_FILE>
Or pipe from stdin:
cat <PATH_TO_FILE> | shaped create-engine
Update Engine
shaped update-engine --file <PATH_TO_FILE>
Or pipe from stdin:
cat <PATH_TO_FILE> | shaped update-engine
List Engines
shaped list-engines
View Engine
shaped view-engine --engine-name <ENGINE_NAME>
Delete Engine
shaped delete-engine --engine-name <ENGINE_NAME>
Table API
Create Table
shaped create-table --file <PATH_TO_FILE>
Or pipe from stdin:
cat <PATH_TO_FILE> | shaped create-table
Create Table from URI
Create a table and automatically insert data from a file:
shaped create-table-from-uri --name <TABLE_NAME> --path <PATH_TO_FILE> --type <FILE_TYPE>
Supported file types: parquet, csv, tsv, json, jsonl
List Tables
shaped list-tables
Table Insert
Insert data into an existing table:
shaped table-insert --table-name <TABLE_NAME> --file <DATAFRAME_FILE> --type <FILE_TYPE>
Supported file types: parquet, csv, tsv, json, jsonl
View Table
shaped view-table --table-name <TABLE_NAME>
Update Table
shaped update-table --file <PATH_TO_FILE>
Or pipe from stdin:
cat <PATH_TO_FILE> | shaped update-table
Delete Table
shaped delete-table --table-name <TABLE_NAME>
View API
Create View
shaped create-view --file <PATH_TO_FILE>
Or pipe from stdin:
cat <PATH_TO_FILE> | shaped create-view
List Views
shaped list-views
View View
shaped view-view --view-name <VIEW_NAME>
Update View
shaped update-view --file <PATH_TO_FILE>
Or pipe from stdin:
cat <PATH_TO_FILE> | shaped update-view
Delete View
shaped delete-view --view-name <VIEW_NAME>
Query API
Execute Query
Execute an ad-hoc query against an engine. The query can be provided in three ways:
From a file:
shaped query --engine-name <ENGINE_NAME> --query-file <PATH_TO_FILE>
As a JSON string:
shaped query --engine-name <ENGINE_NAME> --query '{"sql": "SELECT * FROM table"}'
From stdin:
cat <PATH_TO_FILE> | shaped query --engine-name <ENGINE_NAME>
Execute Saved Query
Execute a previously saved query by name:
shaped execute-saved-query --engine-name <ENGINE_NAME> --query-name <QUERY_NAME>
List Saved Queries
List all saved queries for an engine:
shaped list-saved-queries --engine-name <ENGINE_NAME>
View Saved Query
View the definition of a saved query:
shaped view-saved-query --engine-name <ENGINE_NAME> --query-name <QUERY_NAME>
Python SDK (V2)
Installation
Pip Installation
pip install shaped
V2 Query API with Fluent Builders
The V2 SDK provides a fluent query builder API that leverages Shaped's declarative query language:
from shaped import (
Client,
RankQueryBuilder,
ColumnOrder,
TextSearch,
Ensemble,
Diversity,
)
client = Client(api_key="your-api-key")
# Build a query using the fluent builder with step objects
query = (RankQueryBuilder()
.from_entity('item')
.retrieve(
ColumnOrder(
[{'name': 'popularity', 'ascending': False}],
limit=1000
),
TextSearch(
'laptop',
mode={'type': 'vector', 'text_embedding_ref': 'text_emb'}
)
)
.filter(Expression('price < 1000')) filter
.score(Ensemble('lightgbm', input_user_id='$parameters.userId'))
.reorder(Diversity(diversity_attributes=['category']))
.limit(50)
.columns(['item_id', 'title', 'price'])
.build())
# Execute the query
result = client.execute_query(
'my_engine',
query,
parameters={'userId': '123'}
)
Available Step Factory Functions
The SDK provides convenient factory functions for creating step objects:
Retrieve Steps:
ColumnOrder(columns, limit=100, where=None, name=None)- Sort by columnsTextSearch(input_text_query, mode, limit=100, where=None, name=None)- Text searchSimilarity(embedding_ref, query_encoder, limit=100, where=None, name=None)- Similarity searchFilter(where=None, limit=100, name=None)- Filter without orderingCandidateIds(item_ids, limit=None, name=None)- Specific item IDsCandidateAttributes(item_attributes, limit=None, name=None)- Item attributes
Filter Steps:
Expression(expression, name=None)- DuckDB filter expressionTruncate(max_length=500, name=None)- Truncate results to max_lengthPrebuilt(filter_ref, input_user_id=None, name=None)- Reference to prebuilt filter
Score Configs:
Ensemble(value_model, input_user_id=None, ...)Passthrough(name=None)
Reorder Steps:
Diversity(diversity_attributes=None, strength=0.5, ...)Boosted(retriever, strength=0.5, name=None)Exploration(retriever, strength=0.5, name=None)
Execute Saved Query
result = client.execute_saved_query(
'my_engine',
'recommendations',
parameters={'userId': '123', 'limit': 20}
)
TypeScript/Node.js SDK (V2)
Installation
NPM Installation
npm install @shaped.ai/client
V2 Query API with Fluent Builders
The V2 SDK provides a fluent, type-safe query builder API:
import { Client, RankQueryBuilder } from '@shaped.ai/client';
const client = new Client({ apiKey: 'your-api-key' });
// Build a query using the fluent builder
const query = new RankQueryBuilder()
.from('item')
.retrieve(step => step
.columnOrder([{name: 'popularity', ascending: false}], {limit: 1000}))
.retrieve(step => step
.textSearch('laptop', {type: 'vector', textEmbeddingRef: 'text_emb'}))
.filter(step => step.expression('price < 1000'))
.score(step => step.ensemble('lightgbm', {inputUserId: '$parameters.userId'}))
.reorder(step => step.diversity({attribute: 'category'}))
.limit(50)
.columns(['item_id', 'title', 'price'])
.build();
// Execute the query
const result = await client.executeQuery(
'my_engine',
query,
{userId: '123'}
);
Execute Saved Query
const result = await client.executeSavedQuery(
'my_engine',
'recommendations',
{userId: '123', limit: 20}
);
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