Mongo Vector Search Util

What is Mongo Vector Search Util ?

Mongo Vector Search Util is a powerful tool designed for document analysis and vector-based search. It enables users to search documents using vector embeddings, making it ideal for applications that require semantic similarity searches or neural network-based queries. By leveraging vector embeddings, it allows for more advanced and nuanced document retrieval compared to traditional keyword searches.

Features

• Vector Embedding Search: Utilize vector embeddings to find semantically similar documents.
• Document Similarity: Identify documents with similar content based on vector representations.
• Efficient Indexing: Supports efficient indexing of high-dimensional vector data for fast query performance.
• Integration with MongoDB: Seamlessly integrates with MongoDB collections for scalable document analysis.
• Approximate Nearest Neighbor (ANN) Search: Enables fast and accurate ANN queries for vector data.
• Flexible Data Support: Works with various data types, including text, images, and more, as long as they can be converted to vector embeddings.

How to use Mongo Vector Search Util ?

1. Install the Package: Run pip install mongo-vector-search-util to install the package.
2. Initialize MongoDB Collection: Connect to a MongoDB collection and initialize the vector search utility.
3. Add Documents with Vector Embeddings: Insert documents into your MongoDB collection with their corresponding vector embeddings.
4. Perform Vector Search: Use the utility to search for documents based on vector similarity.

Example code snippet:

from mongo_vector_search_util import VectorSearch

# Initialize vector search
vector_search = VectorSearch(mongo_collection)

# Add document with vector embedding
document = {"content": "example text", "vector": [0.1, 0.2, 0.3]}
vector_search.add_document(document)

# Search for similar documents
results = vector_search.query_vector([0.15, 0.25, 0.35])

Frequently Asked Questions

What is vector search?
Vector search is a technique used to find documents or data points that are semantically similar to a given query. It uses vector embeddings to represent documents in a high-dimensional space, enabling more accurate and nuanced search results compared to traditional methods.

How do I generate vector embeddings for my documents?
Vector embeddings can be generated using various machine learning models or libraries, such as sentence-transformers for text or image-embeddings for images. The specific method depends on the type of data you are working with.

What is the difference between vector search and keyword search?
Vector search focuses on semantic similarity, meaning it finds documents that are contextually related to the query, even if they don’t share exact keywords. Keyword search, on the other hand, matches documents based on exact keyword presence, which can be less flexible and less accurate for nuanced queries.