
# TopK Python SDK
**Full documentation is available at [docs.topk.io](https://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**:
```bash
pip install topk-sdk
```
## 2. Create an API Key
To authenticate with TopK, you'll need an **API key**:
1. Go to the <a href="https://console.topk.io" target="_blank">TopK Console</a>.
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**
```python
from topk_sdk import Client
client = Client(api_key="YOUR_TOPK_API_KEY", region="aws-us-east-1-elastica")
```
### **Define and Create a Collection**
```python
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**.
```python
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**:
```python
from topk_sdk.query import select, field, fn
results = client.collection("books").query(
select(
"title",
title_similarity=fn.semantic_similarity("title", "classic American novel"), # Semantic search
)
.topk(field("title_similarity"), 10) # Return top 10 most relevant results
)
```
## 6. (Optional) Delete a Collection
To remove the entire collection:
```
client.collections().delete("books")
```
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"description": "\n\n# TopK Python SDK\n\n**Full documentation is available at [docs.topk.io](https://docs.topk.io).**\n\nThe **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**.\n\nTopK\u2019s **query language** is designed to be simple yet expressive, allowing you to search using **semantic similarity, keyword matching, and filters\u2014all in a single query**.\n\n## Features\n\n- **Automatic embeddings**\u2014no need for external vector models.\n- **Create and manage collections** with custom schemas.\n- **Perform hybrid search** using **semantic similarity, keyword search, and filters**.\n- **Upsert and delete documents** within collections.\n- **Automatic schema validation** and easy collection management.\n- **Intuitive Python API** for seamless integration.\n\n## 1. Install the SDK\n\nInstall the TopK SDK via **pip**:\n\n```bash\npip install topk-sdk\n```\n\n## 2. Create an API Key\n\nTo authenticate with TopK, you'll need an **API key**:\n\n1. Go to the <a href=\"https://console.topk.io\" target=\"_blank\">TopK Console</a>.\n2. Create an account and generate your API key.\n\n> **Keep your API key secure**\u2014you'll need it for authentication.\n\n## 3. Create a Collection\n\nCollections are the primary data structure in TopK. They store documents and enable queries.\n\n### **Initialize the Client**\n\n```python\nfrom topk_sdk import Client\n\nclient = Client(api_key=\"YOUR_TOPK_API_KEY\", region=\"aws-us-east-1-elastica\")\n```\n\n### **Define and Create a Collection**\n\n```python\nfrom topk_sdk.schema import text, semantic_index\n\nclient.collections().create(\n \"books\",\n schema={\n \"title\": text().required().index(semantic_index()), # Semantic search enabled on title\n },\n)\n```\n\n> **Note:** Other fields can still be upserted even if they are not defined in the schema.\n\n## 4. Add Documents\n\nNow, add documents to the collection. **Each document must have an `_id` field**.\n\n```python\nclient.collection(\"books\").upsert(\n [\n {\"_id\": \"gatsby\", \"title\": \"The Great Gatsby\"},\n {\"_id\": \"1984\", \"title\": \"1984\"},\n {\"_id\": \"catcher\", \"title\": \"The Catcher in the Rye\"}\n ],\n)\n```\n\n## 5. Run a Search Query\n\nNow, **retrieve books using semantic search**:\n\n```python\nfrom topk_sdk.query import select, field, fn\n\nresults = client.collection(\"books\").query(\n select(\n \"title\",\n title_similarity=fn.semantic_similarity(\"title\", \"classic American novel\"), # Semantic search\n )\n .topk(field(\"title_similarity\"), 10) # Return top 10 most relevant results\n)\n```\n\n## 6. (Optional) Delete a Collection\n\nTo remove the entire collection:\n\n```\nclient.collections().delete(\"books\")\n```\n\n",
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