swarmauri_vectorstore_tfidf 0.6.1

A Tfidf based Vector Store and Tfidf Based Embedding Model.

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TF-IDF Vector Store

A vector store implementation using TF-IDF (Term Frequency-Inverse Document Frequency) for document embedding and retrieval. This package provides efficient document storage and similarity-based retrieval using the TF-IDF algorithm.

Installation

pip install swarmauri_vectorstore_tfidf

Usage

Here's a basic example of how to use the TF-IDF Vector Store:

from swarmauri.vector_stores.TfidfVectorStore import TfidfVectorStore
from swarmauri.documents.Document import Document

# Initialize the vector store
vector_store = TfidfVectorStore()

# Add documents
documents = [
    Document(content="Machine learning basics"),
    Document(content="Python programming guide"),
    Document(content="Data science tutorial")
]
vector_store.add_documents(documents)

# Retrieve similar documents
results = vector_store.retrieve(query="python tutorial", top_k=2)

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