How does VectorDB benefit applications like RAG?
In the world of RAG, where large-scale vector representations of documents are crucial for efficient question answering, VectorDB shines. It enables quick retrieval of relevant documents based on their similarity to a query, facilitating the retrieval step in RAG’s two-stage process.
In simplest way → Think of it like a super-smart librarian who organizes books based on how similar their covers look. When you ask for a book, it magically retrieves the ones with covers that match your description!
What are some key features of VectorDB?
VectorDB offers a range of features tailored to the needs of applications like RAG. These include:
- Efficient Storage: VectorDB efficiently stores high-dimensional vectors, optimizing storage space and retrieval speed.
- Fast Retrieval: Leveraging FAISS, VectorDB enables blazing-fast similarity search, crucial for real-time applications like RAG.
- Scalability: VectorDB is designed to scale effortlessly with growing datasets, making it suitable for applications handling massive amounts of data.
FAISS (Facebook AI Similarity Search):
- FAISS is a powerful open-source library for similarity search and clustering of dense vectors.
- It supports both CPU and GPU computation, making it suitable for large-scale applications.
- FAISS provides various indexing methods, including flat, IVF (Inverted File), and HNSW (Hierarchical Navigable Small World).
Let’s dive into practical implementation of FAISS:
In this blog, we’ll explore the practical implementation of a vector database using FAISS. We’ll delve into the step-by-step process of setting up and utilizing FAISS for building a vector database, enabling efficient storage and retrieval of high-dimensional data points.
1. Install libraries
!pip install faiss-cpu==1.7.4
2. Sample Dataset
3. Embeddings
4. Build vector index
5. Search embeddings for given query
Alternative Popular open-source vector databases:
1. ChromDB:
- ChromDB is an open-source vector database designed specifically for genomic data.
- It is optimized for efficiently storing and querying DNA sequences and other genomic vectors.
- ChromDB provides specialized indexing and query processing techniques tailored for genomic data, allowing researchers to perform similarity searches and analysis within large genomic datasets.
2. Milvus:
- Milvus is an open-source vector database designed for managing and searching massive collections of vectors.
- It supports various similarity search algorithms, including Euclidean distance and inner product.
- Milvus provides a RESTful API and SDKs for multiple programming languages.
3. Pinecone:
- Pinecone is a cloud-based vector database service that offers real-time similarity search as a managed service.
- It provides scalable infrastructure, allowing users to focus on application development without worrying about database management.
- Pinecone supports Python and provides integrations with popular libraries like TensorFlow and PyTorch.
Tags: faiss, vectordb, langchain, llm, embeddings, rag
