Why elDoc Chooses MongoDB, Apache Solr & Qdrant for Intelligent Document Automation and AI Search?

Modern document environments demand far more than simple storage or basic keyword search. With the rise of Large Language Models (LLMs), AI-driven automation, and RAG-based document intelligence, organizations now require platforms that can deliver:

• High-speed access to massive document repositories
• Semantic understanding, not just text extraction
• Intelligent search that interprets context, meaning, and relationships
• AI-ready storage designed for embeddings, vectors, and machine reasoning
• Enterprise-grade reliability, scalability, and security across cloud and on-prem infrastructure

Today, businesses expect their Document Excellence Platform to work like a full AI assistant capable of answering questions, comparing documents, detecting inconsistencies, classifying content, and providing insights in real time. This level of intelligence requires a backend that is not only fast and fault-tolerant, but also designed to support LLM workloads, vector search, continuous indexing, and large-scale metadata handling.

elDoc is engineered specifically for this new era.

To deliver next-generation document intelligence, elDoc relies on a modern, AI-native technology stack built on industry-leading, enterprise-grade open-source technologies. Each component: MongoDB, Apache Solr, and Qdrant – was chosen for its ability to support the increasing demands of LLM processing, multimodal AI, semantic search, and intelligent document automation. This article explains why elDoc uses MongoDB, Apache Solr, and Qdrant as core engines, and how these technologies work together to power a secure, scalable, LLM-ready Document Excellence Platform designed for the future of AI-driven organizations.

MongoDB — The Backbone of elDoc’s Metadata & File Storage

MongoDB is the heart of elDoc’s storage architecture, powering both metadata storage and file storage (via GridFS).

Why elDoc Uses MongoDB

✔ Scales effortlessly
Its built-in vertical and horizontal scalability even in Community Edition means elDoc can handle deployments ranging from a few thousand documents to multi-terabyte repositories without redesign.

✔ High availability built in
Replica sets provide automatic failover, redundancy, and strong fault tolerance – critical for organizations requiring uptime and reliability.

✔ Flexible, schema-less modeling
Documents evolve. Formats change. Metadata grows.
MongoDB allows elDoc to store dynamic, complex, and evolving structures with zero downtime or risky migrations.

✔ Rich ecosystem & deep integration

• Official Java driver
• GridFS for large binary file storage
• Perfect compatibility with Jakarta EE services
• Optimized for Rocky Linux 9 (elDoc’s preferred OS)

✔ Advanced indexing for high-speed metadata queries
Including compound, partial, TTL, geospatial, and full-text indexes.

✔ Multi-tenant ready
Separate tenants using collections or databases with clean isolation.

✔ Runs everywhere
On-premise, in Docker, Kubernetes, hybrid environments, or cloud.

✔ Battle-tested under extreme workloads
Used globally for terabyte-scale workloads with predictable performance.

🧩 MongoDB forms the foundation of a reliable, scalable, and future-ready data layer within elDoc.

2) Apache Solr — Enterprise-Class Full-Text Search & Keyword Indexing

Where MongoDB handles structure, Apache Solr handles intelligence in text retrieval. Solr powers elDoc’s lightning-fast full-text search engine.

Why elDoc Uses Solr

✔ Automatic content extraction (via Apache Tika)
Solr extracts text from hundreds of document types including:
PDF, DOCX, XLSX, PPTX, emails, images (OCR), and more.

✔ Enterprise-grade search features

• Keyword search
• Stemming and lemmatization
• Boosting and relevancy ranking
• Synonyms
• Faceted search
• Filtering
• Highlighting
• Linguistic normalization

✔ High performance at massive scale
Index millions of files and return results instantly with low-latency search.

✔ Distributed & resilient
SolrCloud architecture supports sharding, replication, and horizontal scaling—ideal for global enterprise repositories.

✔ Trusted open-source technology
Used by Fortune 500 companies and countless government and enterprise systems.

✔ Simple and reliable integration
elDoc communicates with Solr through stable REST APIs, ensuring predictable performance in production environments.

🔍 Apache Solr ensures that elDoc users always get accurate, fast, and intelligent full-text search — no matter the size of the repository.

3) Qdrant — AI-Powered Vector Search for Semantic Understanding

When organizations need semantic search, LLM reasoning, or AI document understanding, keyword search alone is not enough.

This is where Qdrant becomes critical.

Why elDoc Uses Qdrant

✔ Built for RAG, embeddings & AI-native search
Qdrant was designed specifically for modern AI use cases, making it ideal for elDoc’s:

• LLM-powered document assistant
• Deep semantic search
• Cross-document reasoning
• Intelligent document clustering
• Similarity detection
• AI classification

✔ Hybrid search capabilities
Combine vectors + metadata filters for highly contextual, highly relevant results.

✔ Efficient and extremely scalable

• HNSW indexing
• Disk-based collections
• Handles millions or billions of embeddings
  Perfect for large-scale corporate archives.

✔ Open-source (with enterprise readiness)
Flexible licensing, community-driven innovation, and optional enterprise tier support.

✔ Easy integration with elDoc backend
Official Java client and simple API design ensure seamless performance.

✔ Fast-growing ecosystem
Strong documentation, active updates, and long-term viability.

🧠 Qdrant enables elDoc to deliver AI-powered document intelligence far beyond traditional search — true semantic understanding.

How These Three Engines Work Together Inside elDoc?

elDoc combines these technologies into a single, AI-native architecture:

• MongoDB stores and manages structured metadata, permissions, and large binary files.
• Apache Solr indexes extracted text for fast full-text and keyword search.
• Qdrant stores embeddings generated by LLMs for semantic search, similarity matching, and RAG workflows.

Together, they deliver:

✔ Ultra-fast search across all dimensions
Both traditional keyword search (powered by Solr) and deep semantic search (powered by Qdrant) work in harmony to provide instant, context-aware results — even across millions of documents.

✔ Reliable, high-performance storage for massive repositories
MongoDB ensures resilient, scalable storage capable of handling large volumes of files, complex metadata, and multi-tenant environments with ease.

✔ Enterprise-grade permissions, governance & auditability
Document access, versioning, user roles, and audit trails remain consistent, secure, and fully traceable across all components of the platform.

✔ AI-driven understanding, reasoning, and document intelligence
Qdrant and LLM-based processing allow elDoc to interpret meaning, detect relationships, compare documents, cluster content, and support advanced RAG workflows.

✔ Future-proof scalability across cloud, hybrid, and on-premise deployments
elDoc’s architecture is designed to grow with organizational needs — whether deployed in a secure datacenter, a private cloud, or a fully isolated on-prem environment with local LLMs.