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Cosdata Documentation

Introduction to Cosdata

Cosdata is a cutting-edge vector database designed to tackle the complex challenges of modern search and retrieval. By combining dense, sparse, and full-text search capabilities with advanced AI technologies, Cosdata delivers a powerful platform for building intelligent data applications.

Open Source: Cosdata is fully open-source and available on GitHub. We welcome contributions and feedback from the community!

New to vector databases? Read our Vector Databases 101 guide for a foundational overview and links to our educational blog series.

Tackling Modern Search Challenges

In today’s data-rich environment, traditional keyword-based search methods are no longer sufficient. Organizations face several key challenges:

  • Data Explosion: Managing and searching through massive amounts of structured and unstructured data
  • Context Understanding: Moving beyond simple keyword matching to understand the meaning behind queries
  • Performance at Scale: Maintaining speed and accuracy as data volumes grow exponentially
  • Integration Complexity: Seamlessly connecting search capabilities with existing ML pipelines

Cosdata addresses these challenges with a next-generation vector database specifically designed for precision, speed, and scalability.

Industry-Leading Performance

Independent benchmarks demonstrate Cosdata’s exceptional performance characteristics:

  • Dense Vector Search: Industry-leading 1,758+ QPS on 1M record datasets with 1536-dimensional vectors

  • 42% faster than Qdrant, 54% faster than Weaviate, 146% faster than ElasticSearch

  • Consistent 97% precision across challenging search tasks

  • Significantly faster indexing than ElasticSearch while maintaining superior query performance

  • Full-Text Search (BM25): Cosdata’s custom BM25 implementation achieves up to 151x faster QPS than ElasticSearch on the scifact dataset, with ~44x average improvement across all datasets

  • Similar ranking quality (NDCG) to ElasticSearch while delivering superior performance

  • Index creation is up to 12x faster on large datasets

  • Lower latency at both p50 and p95 percentiles across all tested datasets

On standard hardware configurations, Cosdata consistently outperforms other vector databases in throughput while maintaining high search accuracy.

For detailed benchmark information, see our Benchmarks page.

Core Capabilities

1. Hybrid Search: Dense, Sparse, and Full-Text

Cosdata elevates search precision and recall by combining:

  • Dense Vector Search: Captures semantic meaning through embeddings
  • Sparse Vector Search: Maintains keyword importance for traditional and hybrid search
  • Full-Text Search: Supports fast, scalable keyword and phrase queries

This hybrid approach delivers more relevant, context-rich results even for complex queries, making Cosdata ideal for powering advanced retrieval augmented generation (RAG) pipelines and enterprise search.

2. Lightning-Fast Performance

When dealing with millions of queries or massive datasets, speed is critical. Cosdata delivers exceptional performance through:

  • HNSW Indexing: Hierarchical Navigable Small World algorithms for efficient indexing of high-dimensional vector data
  • Smart Quantization: Advanced compression techniques that maintain accuracy while reducing storage requirements
  • Parallel Processing: Multi-threading and SIMD instructions for maximized performance

These optimizations ensure that Cosdata can handle high-throughput search operations with minimal latency, even at scale.

3. Streamlined Setup and Integration

Cosdata simplifies deployment and integration with:

  • Auto-configuration: Automatic fine-tuning of search parameters for optimal performance
  • Intuitive APIs: Simple RESTful APIs and client libraries for easy interaction
  • Cost Efficiency: Minimized resource consumption without compromising performance

Key Features

  • Hybrid Search: Combine dense, sparse, and full-text (BM25) search for maximum relevance
  • Semantic Search: Leverage embedding-based search to deliver deep semantic analysis
  • Real-Time Search at Scale: Execute real-time search with unmatched scalability and throughput
  • ML Pipeline Integration: Seamlessly integrate with your existing machine learning workflows
  • Transactional Guarantees: ACID-compliant operations for data consistency

Use Cases

Cosdata excels in a variety of applications:

Retrieval Augmented Generation (RAG)

Power AI-generated content with contextually relevant data retrieved in real-time, enhancing the accuracy and reliability of large language models.

Healthcare Information Retrieval

Enable doctors to quickly access precise information from vast pools of patient records, research papers, and medical knowledge bases.

E-commerce Product Discovery

Deliver fast, accurate product recommendations that understand customer intent beyond simple keyword matching.

Financial Analysis

Process and analyze complex financial documents, extracting insights and relationships that drive better investment decisions.

Knowledge Management

Create intelligent knowledge bases that understand the semantic relationships between documents and concepts.

Getting Started

Ready to explore Cosdata? Continue to the Installation & Quick Start Guide to set up your environment and see Cosdata in action.

For a deeper dive into Cosdata’s capabilities, explore our API documentation.

Community Resources

  • GitHub Repository: Explore the code, report issues, or contribute at github.com/cosdata/cosdata
  • Discord Community: Join our Discord server to connect with other users, get help, and stay updated on the latest developments
  • Documentation: This comprehensive documentation will help you make the most of Cosdata’s capabilities