Metadata-Version: 2.1
Name: fastembed
Version: 0.0.1a1
Summary: Fast embedding alternative to Sentence transformers
Home-page: https://github.com/qdrant/qdrant-client
License: Apache License
Keywords: vector,embedding,neural,search,qdrant
Author: NirantK
Author-email: nirant.bits@gmail.com
Requires-Python: >=3.8.0,<3.12
Classifier: License :: Other/Proprietary License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Dist: onnxruntime (>=1.15.1,<2.0.0)
Requires-Dist: onnxruntime-silicon (>=1.15.0,<2.0.0) ; sys_platform == "darwin"
Requires-Dist: sentence-transformers (>=2.2.2,<3.0.0)
Project-URL: Repository, https://github.com/qdrant/qdrant-client
Description-Content-Type: text/markdown

# FastVector Library

FastVector is a Python library that provides convenient methods for indexing and searching text documents using Qdrant, a high-dimensional vector indexing and search system.

## Features

- Batch document insertion with automatic embedding using SentenceTransformers. With support for OpenAI and custom embeddings.
- Efficient batch searching with support for filtering by metadata.
- Automatic generation of unique IDs for documents.
- Convenient alias methods for adding documents and performing queries.

## Installation

To install the FastVector library, we install Qdrant client as well with pip:

```bash
pip install fastvector qdrant-client
```

## Usage

Here's a simple usage example, which works as is:

```python
from qdrant_client import QdrantClient

# Initialize the client
client = QdrantClient(":memory:")  # or QdrantClient(path="path/to/db")

# Prepare your documents, metadata, and IDs
docs = ["Qdrant has Langchain integrations", "Qdrant also has Llama Index integrations"]
metadatas = [
    {"source": "Langchain-docs"},
    {"source": "Linkedin-docs"},
]
ids = [42, 2]

# Use the new add method
client.add(collection_name="demo_collection", docs={"documents": docs, "metadatas": metadatas, "ids": ids})

search_result = client.query(collection_name="demo_collection", query_texts=["This is a query document"])
print(search_result)
```

