LlamaIndex Vector_Stores Integration: Moorcheh

Welcome to the Moorcheh Vector Store that integrates Llama-Index.

This module introduces support for Moorcheh, a semantic vector database developed by EdgeAI Innovations. Moorcheh enables fast and intelligent document retrieval using hybrid scoring and generative answering capabilities. The integration is implemented in accordance with the standard vector store interface defined by LlamaIndex and supports all core methods including add, query, delete, and generate_answer.

To see the integration in action, refer to the demonstration notebook: Google Colab Demo.

Getting started

To begin using the Moorcheh vector store, make sure to install the necessary packages:

pip install llama_index
pip install moorcheh_sdk

Example Usage

Here is a simple example demonstrating how to use the Moorcheh integration with LlamaIndex:

from llama_index.core import VectorStoreIndex
from llama_index.llama_index_integrations.vector_stores.llama_index_vector_stores_moorcheh.llama_index.vector_stores-moorcheh import base, init, utils

api_key = os.environ["MOORCHEH_API_KEY"]

documents = SimpleDirectoryReader("./your-directory").load_data()
__all__ = ["MoorchehVectorStore"]

# Creates a Moorcheh Vector Store with the following parameters
# For text-based namespaces, set namespace_type to "text" and vector_dimension to None
# For vector-based namespaces, set namespace_type to "vector" and vector_dimension to the dimension of your uploaded vectors
vector_store = MoorchehVectorStore(api_key=api_key, namespace="llamaindex_moorcheh", namespace_type="text", vector_dimension=None, add_sparse_vector=False, batch_size=100)

storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)

query_engine = index.as_query_engine()
response = query_engine.query("Which company has had the highest revenue in 2025 and why?")

display(Markdown(f"<b>{response}</b>"))
print("\n\n================================\n\n", response, "\n\n================================\n\n")