TopK Python SDK

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

API key — sign in to console.topk.io and generate an API key.
Region — available regions are listed at docs.topk.io/regions.

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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Release history Release notifications | RSS feed

This release

0.15.0

Aug 14, 2026

0.14.0

Aug 5, 2026

0.13.0

Jul 30, 2026

0.12.0

Jul 20, 2026

0.11.0

Jun 3, 2026

0.10.0

May 21, 2026

0.9.3

May 11, 2026

0.9.2

May 8, 2026

0.9.1

May 1, 2026

0.9.0

May 1, 2026

0.8.1

Apr 27, 2026

0.8.0

Mar 23, 2026

0.7.7

Mar 10, 2026

0.7.6

Feb 24, 2026

0.7.5

Feb 4, 2026

0.7.4

Jan 28, 2026

0.7.3

Jan 15, 2026

0.7.2

Nov 20, 2025

0.7.1

Nov 4, 2025

0.7.0

Oct 29, 2025

0.6.9

Oct 21, 2025

0.6.8

Oct 10, 2025

0.6.7

Oct 9, 2025

0.6.6

Sep 26, 2025

0.6.5

Sep 25, 2025

0.6.4

Sep 24, 2025

0.6.3

Sep 23, 2025

0.6.2

Sep 19, 2025

0.6.1

Sep 16, 2025

0.6.0

Sep 10, 2025

0.5.0

Sep 1, 2025

0.4.10

Aug 28, 2025

0.4.9

Aug 27, 2025

0.4.8

Aug 27, 2025

0.4.7

Aug 12, 2025

0.4.6

Jul 24, 2025

0.4.5

Jul 9, 2025

0.4.4

Jul 5, 2025

0.4.3

Jul 4, 2025

0.4.2

Jul 4, 2025

0.4.1

Jul 2, 2025

0.4.0

Jul 1, 2025

0.3.7

Jun 18, 2025

0.3.6

Jun 18, 2025

0.3.5

Jun 3, 2025

0.3.4

May 27, 2025

0.3.3

May 22, 2025

0.3.1

May 21, 2025

0.3.0

May 21, 2025

0.2.2

May 20, 2025

0.2.1

May 15, 2025

0.2.0

Apr 28, 2025

0.1.22

Apr 26, 2025

0.1.21

Apr 25, 2025

0.1.20

Apr 25, 2025

0.1.19

Mar 31, 2025

0.1.18

Mar 29, 2025

0.1.17

Mar 6, 2025

0.1.16

Mar 5, 2025

0.1.15

Feb 23, 2025

0.1.14

Feb 14, 2025

0.1.13

Feb 14, 2025

0.1.12

Feb 13, 2025

0.1.11

Feb 13, 2025

0.1.10

Feb 6, 2025

0.1.8

Feb 5, 2025

0.1.6

Jan 27, 2025

0.1.5

Jan 27, 2025

topk-sdk 0.15.0

TopK Python SDK

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