iotadb 0.0.15

Minimal implementation of a local embedding database

iota - a minimal local embedding database.

Motivation

WIP

[!IMPORTANT] This project is by no means scalable, but should suffice for smaller projects.

Installation

Install the package via PyPI:

pip install iotadb

Usage

Here is a very simple example:

from iotadb import IotaDB, Document

# Define a list of documents
docs = [
    Document(text="That is a happy dog"),
    Document(text="That is a very happy person"),
    Document(text="Today is a sunny day")
]

# Create a collection
db = IotaDB()
db.create_collection(name="my_collection", documents=docs)

# Query documents within your collection
results = db.search("That is a happy person", return_similarities=True)

for doc, score in results:
    print(f"Text: {doc.text}")
    print(f"similarity: {score:.3f}\n")

More examples can be found in the /examples directory.

Features

• Simple interface: Easy-to-use API for database operations.
• Lightweight implementation: Minimal resource utilization.
• Local storage: Stores embeddings locally for fast and retrieval.
• Fast Indexing: Efficient embedding indexing for storage and retrieval.

Use cases

• Query with Natural Language: Search for relevant documents using simple natural language queries.
• Contextual Summarization: Integrate documents into LLM contexts like GPT-3 for data-augmented tasks.
• Similarity Search: Find similar items/documents based on their embeddings.

Contributing

Interested in contributing? Head over to the Contribution Guide for more details.