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:

pip install fastvector qdrant-client

Usage

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

from qdrant_client import QdrantClient

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

# Prepare your documents, metadata, and IDs
docs = [
    "Qdrant has Langchain integrations",
    "Qdrant also has Llama Index integrations",
    # ...more documents...
]
metadatas = [
    {"source": "notion"},
    {"source": "google-docs"},
    # ...more metadata...
]
ids = [
    42,
    2,
]  # unique for each doc, if not mentioned, we'll generate random IDs, can lead to duplicates

# Use the new add method
client.add(
    collection_name="demo_collection",
    docs={"documents": docs, "metadatas": metadatas, "ids": ids},
    batch_size=512,  # Adjust as needed
    wait=True,  # Wait for the operation to complete
)

search_result = client.query(
    collection_name="demo_collection",
    query_texts=["This is a query document"],
    n_results=2,
)
print(search_result)