flexvector 0.1.0rc1

A simple and intuitive vector database abstraction layer

Flex Vector

A simple and intuitive vector database abstraction layer supporting multiple vector stores.

Features

• Unified interface for multiple vector databases
  • Chroma
  • Qdrant
  • Weaviate
  • PGVector
• Seamless integration with LangChain and LlamaIndex
• Flexible data loading from files, direct data, or URIs
• Async support for all operations
• Command-line interface for common operations

Installation

pip install flexvector

Quick Start

Using the Python API

from flexvector import VectorDBFactory
from flexvector.config import VectorDBSettings
from flexvector.core import Document

# Initialize client with configuration
config = VectorDBSettings()
client = VectorDBFactory.get("chroma", config)

# Load documents from file or directory
docs = client.load(collection_name="my_collection", path="path/to/document.txt")

# Or create and add documents directly
from langchain_core.documents import Document

doc = Document(page_content="Hello world", metadata={"source": "example"})
client.from_langchain("my_collection", [doc])

# Search
results = client.search(
    collection_name="my_collection",
    query="hello",
    top_k=5
)

# Delete collection
client.remove_collection("my_collection")

# Delete documents

Using the Command Line Interface

Load documents from a file:

python cli.py load --input-file examples/files/data.txt --collection my_documents

Load documents from a directory:

python cli.py load --input-dir examples/files --collection research_papers

Search for documents:

python cli.py search --query "What is vector database?" --collection my_documents --top-k 5

Delete a collection:

python cli.py delete --collection my_documents

Documentation

For more usage info, see docs.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Projects using FlexVextor

• VectorDB UI

More Use Cases

This package aims to be a versatile tool for various AI applications, including but not limited to:

RAG Pipeline Integration

Build robust Retrieval Augmented Generation (RAG) systems with a database-agnostic approach:

• ETL Workflows: Create efficient extract-transform-load pipelines that process documents and store embeddings without locking into a specific vector database
• Multi-modal RAG: Store and retrieve text, images, and other data types with the same consistent interface
• Hybrid Search Systems: Combine semantic search with traditional keyword search for improved retrieval quality

Research and Development

• Prototyping: Quickly test different vector databases without changing your application code
• A/B Testing: Compare performance across different vector stores for your specific use case
• Academic Research: Study vector search behavior with a standardized interface