fastembed 0.0.2

Fast, State of the Art Quantized Embedding Models

FastEmbed Library

FastEmbed 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 FastEmbed library, we install Qdrant client as well with pip:

pip install fastembed qdrant-client

Usage

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

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)