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82 lines
2.6 KiB
82 lines
2.6 KiB
# Qdrant Client Library
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## Overview
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The Qdrant Client Library is designed for interacting with the Qdrant vector database, allowing efficient storage and retrieval of high-dimensional vector data. It integrates with machine learning models for embedding and is particularly suited for search and recommendation systems.
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## Installation
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```python
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pip install qdrant-client sentence-transformers httpx
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```
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## Class Definition: Qdrant
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```python
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class Qdrant:
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def __init__(self, api_key: str, host: str, port: int = 6333, collection_name: str = "qdrant", model_name: str = "BAAI/bge-small-en-v1.5", https: bool = True):
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...
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```
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### Constructor Parameters
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| Parameter | Type | Description | Default Value |
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|-----------------|---------|--------------------------------------------------|-----------------------|
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| api_key | str | API key for authentication. | - |
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| host | str | Host address of the Qdrant server. | - |
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| port | int | Port number for the Qdrant server. | 6333 |
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| collection_name | str | Name of the collection to be used or created. | "qdrant" |
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| model_name | str | Name of the sentence transformer model. | "BAAI/bge-small-en-v1.5" |
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| https | bool | Flag to use HTTPS for connection. | True |
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### Methods
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#### `_load_embedding_model(model_name: str)`
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Loads the sentence embedding model.
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#### `_setup_collection()`
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Checks if the specified collection exists in Qdrant; if not, creates it.
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#### `add_vectors(docs: List[dict]) -> OperationResponse`
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Adds vectors to the Qdrant collection.
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#### `search_vectors(query: str, limit: int = 3) -> SearchResult`
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Searches the Qdrant collection for vectors similar to the query vector.
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## Usage Examples
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### Example 1: Setting Up the Qdrant Client
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```python
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from qdrant_client import Qdrant
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qdrant_client = Qdrant(api_key="your_api_key", host="localhost", port=6333)
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```
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### Example 2: Adding Vectors to a Collection
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```python
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documents = [
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{"page_content": "Sample text 1"},
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{"page_content": "Sample text 2"}
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]
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operation_info = qdrant_client.add_vectors(documents)
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print(operation_info)
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```
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### Example 3: Searching for Vectors
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```python
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search_result = qdrant_client.search_vectors("Sample search query")
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print(search_result)
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```
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## Further Information
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Refer to the [Qdrant Documentation](https://qdrant.tech/docs) for more details on the Qdrant vector database.
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