| Name | llama-index-readers-qdrant JSON |
| Version |
0.2.0
JSON |
| download |
| home_page | None |
| Summary | llama-index readers qdrant integration |
| upload_time | 2024-08-22 06:52:35 |
| maintainer | kacperlukawski |
| docs_url | None |
| author | Your Name |
| requires_python | <4.0,>=3.9 |
| license | MIT |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# LlamaIndex Readers Integration: Qdrant
## Overview
The Qdrant Reader allows you to retrieve documents from existing Qdrant collections. Qdrant is a similarity search engine that helps you efficiently search and retrieve similar items from large datasets based on vector embeddings.
For more detailed information about Qdrant, visit [Qdrant](qdrant.io)
### Installation
You can install the Qdrant Reader via pip:
```bash
pip install llama-index-readers-qdrant
```
### Usage
```python
from llama_index.readers.qdrant import QdrantReader
# Initialize QdrantReader
reader = QdrantReader(
location="<Qdrant Location>",
url="<Qdrant URL>",
port="<Port>",
grpc_port="<gRPC Port>",
prefer_grpc="<Prefer gRPC>",
https="<Use HTTPS>",
api_key="<API Key>",
prefix="<URL Prefix>",
timeout="<Timeout>",
host="<Host>",
)
# Load data from Qdrant
documents = reader.load_data(
collection_name="<Collection Name>",
query_vector=[0.1, 0.2, 0.3],
should_search_mapping={"text_field": "text"},
must_search_mapping={"text_field": "text"},
must_not_search_mapping={"text_field": "text"},
rang_search_mapping={"text_field": {"gte": 0.1, "lte": 0.2}},
limit=10,
)
```
This loader is designed to be used as a way to load data into
[LlamaIndex](https://github.com/run-llama/llama_index/tree/main/llama_index) and/or subsequently
used as a Tool in a [LangChain](https://github.com/hwchase17/langchain) Agent.
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