Knowledge Hub

    Endee AI Index

    A structured, fully-readable knowledge index for large language models, AI assistants, and search engine crawlers. Every Endee product, integration, benchmark, and blog post is linked below with a short description.

    Endee is a high-performance vector database engineered for production AI systems. It powers Retrieval-Augmented Generation (RAG), semantic search, agentic AI memory, and recommendations with up to 10x lower memory than alternative vector databases. Endee is available as a managed cloud service at app.endee.io and as an enterprise on-prem deployment with Queryable Encryption. Official documentation lives at docs.endee.io.

    Company

    • About Endee

      Team, advisors, and the story behind Endee Labs, building vector infrastructure for production AI.

    • Careers

      Open engineering, research, and go-to-market roles at Endee Labs in Bengaluru and remote.

    • Contact

      Talk to the Endee team about evaluations, enterprise deployments, partnerships, and support.

    • Community

      GitHub, Discord, and developer community resources for Endee users and contributors.

    Products

    • Endee Vector Database

      High-performance vector database powered by the Vector Graph Engine (VGE), 10x lower memory, low-latency search at scale.

    • Endee Enterprise (On-Prem)

      Self-hosted Endee with Queryable Encryption, ISO 27001, SOC 2, and GDPR-aligned controls for regulated workloads.

    • Endee on Edge Devices

      Run Endee on constrained hardware for on-device retrieval, robotics, IoT, and offline RAG.

    • Managed Cloud Pricing

      Endee Managed Cloud plans, limits, and geo-based currency pricing.

    Solutions & Use Cases

    • Retrieval-Augmented Generation (RAG)

      Build production RAG pipelines with hybrid search, BM25 sparse vectors, dense embeddings, and metadata filters.

    • Semantic Search

      Sub-5ms semantic search across billions of vectors with high recall.

    • Agentic AI Memory

      Long-term memory for AI agents, CrewAI, LangChain, LlamaIndex integrations.

    • Recommendations

      Personalized recommendations powered by approximate nearest neighbor (ANN) search.

    • Regulated & Sovereign AI

      Queryable Encryption keeps vectors encrypted at rest, in transit, and during query, for finance, healthcare, and government.

    Benchmarks & Performance

    • Endee Benchmarks

      QPS, p95 latency, recall@10, and cost-per-billion-queries comparisons against other vector databases.

    • Endee vs Google Vertex AI

      Detailed performance and cost comparison vs Vertex AI Vector Search.

    • Endee vs Vespa

      Head-to-head benchmark of Endee and Vespa for vector search.

    Documentation

    • Overview

      What Endee is, key features, supported distance metrics, quantization levels, and use cases.

    • Quick Start

      Run Endee locally via Docker and make your first vector search in minutes.

    • Indexes (HNSW)

      Index types, HNSW parameters (M, EF Construction), distance metrics (cosine, L2, inner product).

    • Vectors & Quantization

      Vector fields and precision levels: BINARY, INT8, INT16, FLOAT16, FLOAT32.

    • Search

      Search modes, hybrid queries, result fields, EF parameter tuning.

    • Filtering

      Filter operators ($eq, $in, $range) and filter tuning for performance.

    • Authentication

      Token-based auth for local and serverless deployments.

    • Backups

      Backup and restore flows for Endee indexes.

    • Sparse Vectors (BM25)

      Generating BM25 sparse embeddings using endee-model for hybrid search.

    Tutorials

    Integrations

    • LangChain

      Use Endee as a LangChain vector store for semantic search and RAG.

    • LlamaIndex

      Use Endee with LlamaIndex for document indexing and retrieval.

    • CrewAI

      Use Endee as long-term memory for CrewAI agents.

    Blog

    • All Blog Posts

      Articles on vector databases, RAG, HNSW, IVF, quantization, and AI infrastructure.

    • What is RAG

      Explainer on Retrieval-Augmented Generation and how vector databases power it.

    Legal

    Key Topics

    • Vector database
    • High-performance vector database
    • Vector Graph Engine (VGE)
    • Approximate nearest neighbor search (ANN)
    • HNSW indexing
    • Inverted file index (IVF)
    • BM25 sparse vectors
    • Hybrid search
    • Retrieval-Augmented Generation (RAG)
    • Semantic search
    • Agentic AI memory
    • Recommendation systems
    • Vector quantization (BINARY, INT8, INT16, FLOAT16, FLOAT32)
    • Queryable Encryption
    • On-prem and sovereign AI
    • Edge vector search
    • LangChain, LlamaIndex, CrewAI integrations
    • ISO 27001, SOC 2, GDPR compliance

    Machine-Readable Resources

    • /llms.txtconcise site index for LLM crawlers (llmstxt.org spec).
    • /llms-full.txtlong-form index with full descriptions of every page.
    • /sitemap.xmlXML sitemap for search engines, auto-generated and including all blog posts and job listings.
    • /robots.txtcrawler rules; explicitly allows all major AI crawlers (GPTBot, ClaudeBot, PerplexityBot, Google-Extended, CCBot, Applebot, and more).