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TopK Python SDK

PyPI version

The TopK Python library provides convenient access to the TopK API from any Python 3.9+ application. The library includes type definitions for all request params and response fields, and features both synchronous and asynchronous clients.

Documentation

The full documentation can be found at docs.topk.io.

The Python SDK reference can be found at docs.topk.io/sdk/topk-py.

Installation

pip install topk-sdk

# or

uv add topk-sdk

Prerequisites

Usage

Hybrid Search

import os
from topk_sdk import Client
from topk_sdk.schema import text, keyword_index, semantic_index
from topk_sdk.query import select, field, fn

client = Client(
    api_key=os.environ["TOPK_API_KEY"],
    region="aws-us-east-1-elastica",
)

# Create a collection
client.collections().create(
    "books",
    schema={
        "title": text().required().index(keyword_index()),
        "content": text().index(semantic_index()),
    },
)

# Upsert documents
client.collection("books").upsert([
    {
        "_id": "1",
        "title": "Catcher in the Rye",
        "content": "IF YOU REALLY WANT TO HEAR about it, the first thing you'll probably want to know is ...",
        "author": "J.D. Salinger",
        "rating": 3.8,
    },
    {
        "_id": "2",
        "title": "1984",
        "content": "It was a bright cold day in April, and the clocks were striking thirteen. Winston Smith, ...",
        "author": "George Orwell",
        "rating": 4.7,
    },
])

# Query with hybrid search
results = client.collection("books").query(
    select(
        # Select document fields to return
        "_id", "title", "author",
        # Compute semantic similarity of content field with the query
        similarity_score=fn.semantic_similarity(
            "content",
            "What is the meaning of life?",
        ),
    )
    # Filter documents by metadata
    .filter(field("rating") >= 3.0)
    # Rank using the computed similarity score and rating
    .sort(field("rating") * field("similarity_score"), asc=False)
    # Get top 10 highest ranked documents
    .limit(10)
)

Vector Search

import os
from topk_sdk import Client
from topk_sdk.schema import text, f32_vector, vector_index
from topk_sdk.query import select, field, fn

client = Client(
    api_key=os.environ["TOPK_API_KEY"],
    region="aws-us-east-1-elastica",
)

# Create a collection with a vector field (dimension must match your embedding model's output size)
client.collections().create(
    "books",
    schema={
        "title": text().required(),
        "embedding": f32_vector(dimension=1536).required().index(vector_index(metric="dot_product")),
    },
)

# Upsert documents with embeddings
client.collection("books").upsert([
    {"_id": "1", "title": "Catcher in the Rye", "embedding": [0.1, 0.2, ...]},
    {"_id": "2", "title": "1984",               "embedding": [0.9, 0.8, ...]},
])

# Query the nearest neighbors to a query vector
results = client.collection("books").query(
    select(
        "_id", "title",
        distance=fn.vector_distance("embedding", [0.8, 0.9, ...]),
    )
    # Return the 10 closest documents (ascending = closest first)
    .sort(field("distance"), asc=True)
    .limit(10)
)

File Search

import os
from topk_sdk import Client

client = Client(
    api_key=os.environ.get("TOPK_API_KEY"),
    region="aws-us-east-1-elastica",
)

# Create a dataset
client.datasets().create("my-dataset")

# Upload a file
handle = client.dataset("my-dataset").upsert_file(
    "doc-1",                                               # document ID
    input="/path/to/document.pdf",                         # path to file
    metadata={"kind": "report", "department": "finance"},  # optional metadata
)

# Wait for the file to process (optional)
client.dataset("my-dataset").wait_for_handle(handle)

# Ask a question
for message in client.ask(
    "What was the total net income of Bank of America in 2024?",
    datasets=["my-dataset"],
):
    print(message)

Async usage

Simply import AsyncClient instead of Client and use async for / await with each API call:

import os
import asyncio
from topk_sdk import AsyncClient
from topk_sdk.schema import text, keyword_index, semantic_index
from topk_sdk.query import select, field, fn

client = AsyncClient(
    api_key=os.environ["TOPK_API_KEY"],
    region="aws-us-east-1-elastica",
)

async def main() -> None:
    # Hybrid search
    await client.collections().create(
        "books",
        schema={
            "title": text().required().index(keyword_index()),
            "content": text().index(semantic_index()),
        },
    )

    await client.collection("books").upsert([
        {"_id": "1", "title": "Catcher in the Rye", "content": "...", "rating": 3.8},
        {"_id": "2", "title": "1984", "content": "...", "rating": 4.7},
    ])

    results = await client.collection("books").query(
        select(
            # Select document fields to return
            "_id", "title",
            # Compute semantic similarity of content field with the query
            similarity_score=fn.semantic_similarity("content", "What is the meaning of life?"),
        )
        # Filter documents by metadata
        .filter(field("rating") >= 3.0)
        # Rank using the computed similarity score and rating
        .sort(field("rating") * field("similarity_score"), asc=False)
        # Get top 10 highest ranked documents
        .limit(10)
    )

    # Document search
    await client.datasets().create("my-dataset")

    handle = await client.dataset("my-dataset").upsert_file(
        "doc-1",
        input="/path/to/document.pdf",
        metadata={"kind": "report", "department": "finance"},
    )
    await client.dataset("my-dataset").wait_for_handle(handle)

    async for message in client.ask(
        "What was the total net income of Bank of America in 2024?",
        datasets=["my-dataset"],
    ):
        print(message)

asyncio.run(main())

Handling errors

from topk_sdk.error import (
    DatasetNotFoundError,
    PermissionDeniedError,
    QuotaExceededError,
    SlowDownError,
)

try:
    for message in client.ask(
        "What was the total net income of Bank of America in 2024?",
        datasets=["my-dataset"],
    ):
        print(message)
except DatasetNotFoundError:
    print("Dataset does not exist")
except PermissionDeniedError:
    print("Check your API key")
except QuotaExceededError:
    print("Usage quota exceeded")
except SlowDownError:
    print("Rate limited — the client will retry automatically")
Error Description
CollectionNotFoundError Collection does not exist
PartitionNotFoundError Partition does not exist
CollectionAlreadyExistsError Collection with this name already exists
CollectionValidationError Invalid collection name or schema
DatasetNotFoundError Dataset does not exist
DatasetAlreadyExistsError Dataset with this name already exists
DocumentValidationError Invalid document
SchemaValidationError Invalid schema
PermissionDeniedError Invalid or missing API key
QuotaExceededError Usage quota exceeded
RequestTooLargeError Request payload too large
SlowDownError Rate limited by the server (retried automatically)
QueryLsnTimeoutError Timed out waiting for write consistency

Retries

The client automatically retries on SlowDownError and on LSN consistency timeouts. Retry behaviour can be configured via RetryConfig:

from topk_sdk import Client, RetryConfig, BackoffConfig

client = Client(
    api_key=os.environ.get("TOPK_API_KEY"),
    region="aws-us-east-1-elastica",
    retry_config=RetryConfig(
        max_retries=5,        # default: 3
        timeout=60_000,       # total retry chain timeout in ms, default: 30,000
        backoff=BackoffConfig(
            init_backoff=200, # default: 100 ms
            max_backoff=5_000, # default: 10,000 ms
        ),
    ),
)

Requirements

Python 3.9 or higher.

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