topk-sdk 0.1.22

Python SDK for topk.io

TopK Python SDK

Full documentation is available at docs.topk.io.

The TopK SDK provides a Python API for managing collections, inserting and deleting documents, and running powerful search queries. With automatic embeddings via semantic_index(), you can perform semantic search without needing to manage vector embeddings manually.

TopK’s query language is designed to be simple yet expressive, allowing you to search using semantic similarity, keyword matching, and filters—all in a single query.

Features

• Automatic embeddings—no need for external vector models.
• Create and manage collections with custom schemas.
• Perform hybrid search using semantic similarity, keyword search, and filters.
• Upsert and delete documents within collections.
• Automatic schema validation and easy collection management.
• Intuitive Python API for seamless integration.

1. Install the SDK

Install the TopK SDK via pip:

pip install topk-sdk

2. Create an API Key

To authenticate with TopK, you'll need an API key:

1. Go to the TopK Console.
2. Create an account and generate your API key.

Keep your API key secure—you'll need it for authentication.

3. Create a Collection

Collections are the primary data structure in TopK. They store documents and enable queries.

Initialize the Client

from topk_sdk import Client

client = Client(api_key="YOUR_TOPK_API_KEY", region="aws-us-east-1-elastica")

Define and Create a Collection

from topk_sdk.schema import text, semantic_index

client.collections().create(
  "books",
  schema={
    "title": text().required().index(semantic_index()),  # Semantic search enabled on title
  },
)

Note: Other fields can still be upserted even if they are not defined in the schema.

4. Add Documents

Now, add documents to the collection. Each document must have an _id field.

client.collection("books").upsert(
  [
    {"_id": "gatsby", "title": "The Great Gatsby"},
    {"_id": "1984", "title": "1984"},
    {"_id": "catcher", "title": "The Catcher in the Rye"}
  ],
)

5. Run a Search Query

Now, retrieve books using semantic search:

from topk_sdk.query import select, fn

results = client.collection("books").query(
  select(
    "title",
    title_similarity=fn.semantic_similarity("title", "classic American novel"), # Semantic search
  )
  .top_k(field("title_similarity"), 10) # Return top 10 most relevant results
)

6. (Optional) Delete a Collection

To remove the entire collection:

client.collections().delete("books")