SANDI Solr is a hybrid search system that combines the battle-tested Apache Solr search engine with cutting-edge AI technologies: dense vector embeddings for semantic understanding, LLM-based query enhancement (spell correction, query expansion, and RAG answers), and cross-encoder reranking for maximum relevance. It leverages the latest large language model technologies to deliver intelligent, context-aware search that understands natural language — going far beyond traditional keyword matching.
Almost every system that serves end users needs good search functionality. Whether it's an internal knowledge base, customer support portal, e-commerce site, or document management system, users expect to find information quickly and easily.
This is why companies look to integrate search engines into their software. But modern users have changed what they expect from search.
Users don't want to learn special commands or complex query syntax. They want to ask a question in plain human language and get direct answer. This means search needs to go beyond simple keyword matching. It requires integration with AI to understand natural language and provide intelligent answers.
Building AI-powered search is not a simple task. It requires skills from multiple fields:
Each of these fields requires different skills and different technologies. You need:
This means that before you even start building your actual business application, you need to build a complex search system. This takes significant resources that not every company has, and it costs a lot of money.
This means learning almost everything from scratch.
This takes a lot of time, a lot of resources, and a lot of money. The result is often unknown, and many home-grown search systems don't provide expected results.
You can hire a company that specializes in search to build and integrate a solution for you. This usually produces better results than building from scratch. However, it will cost a significant amount. Licensing, development and customization costs for proprietary search software can easily run into hundreds of thousands of dollars. For complex enterprise systems, costs can reach millions.
And you'll always be dependent on that vendor. Need a change? Pay for it. Need support? Pay for it. Want to switch vendors? Start over.
Enterprise search has another challenge: it needs both semantic understanding AND precision.
You need semantic understanding so that searches for "car" also find "automobile" and "vehicle". But you also need precision for company-specific terms, abbreviations, and technical identifiers.
A search for "Project Phoenix Q4 deliverables" needs to understand that this is about a specific project, in a specific quarter, and find exact matches for those terms - not just documents generally about projects or quarterly reports.
This is where SANDI-Solr helps. It addresses both semantic understanding and precision by orchestrating a full AI-powered search pipeline on your behalf:
Every step in this pipeline — from understanding the query with NLP, converting it to dense vectors for semantic matching, executing hybrid search in Solr, reranking results with a cross-encoder, and generating a natural language answer via RAG — is already built, integrated, configured for you and available with the simplicity of a single installation command:
No months of development. No hiring specialized consultants. No expensive licensing fees.
You get:
Then you can configure and modify it to address your specific company needs:
Data Security and Privacy:
One of the biggest advantages of SANDI-Solr is that all AI processing happens on your own infrastructure. Your sensitive data never leaves your servers. You don't need to send documents to external companies like OpenAI, Google, or other third-party AI services for processing. Everything - from document indexing to embedding generation to answer generation - runs locally on your machines. This means complete data security and compliance with privacy regulations.
You own it. You control it. You can modify it. No licensing fees. No vendor lock-in. Your data stays secure.
SANDI Solr is a search and indexing API that combines Apache Solr with AI language models to provide semantic search, natural language processing, and answer generation. Built with Spring Boot 3 and Solr 9, it provides a complete containerized solution for AI-enhanced search.
The entire platform can be started with one command:
This starts all services including Solr cluster, Zookeeper, embedding services, language models, and NLP engine. No complex setup needed.
Multi-tenant architecture allows a single SANDI Solr deployment to serve multiple applications simultaneously, each with its own isolated search configuration. For each tenant (client) admin maintains independent settings for collections, field mappings, synonym sets, and AI service endpoints, ensuring that different applications never interfere with each other. By consolidating multiple search tasks into one deployment, multi-tenancy significantly reduces operational overhead, hardware costs, and maintenance complexity compared to running separate search instances for each application.
SANDI supports multiple clients with separate configurations:
The Docker setup includes:
SANDI combines keyword search with semantic vector search
This enables:
Built-in support for generating answers from search results
The system:
Results can be reordered by relevance
This two-stage approach (search then rerank) improves accuracy
The indexing module handles multiple formats:
The single-machine setup is intended for learning, evaluation, and development.
For production environments, SANDI-Solr should be deployed across multiple machines for optimal performance, scalability, and high availability.
Typical production deployment architecture:
For production, services should be distributed across multiple machines using Docker Swarm, Kubernetes, or manual deployment to ensure scalability, fault tolerance, and optimal resource utilization.
This distributed architecture allows you to scale individual components based on your workload - for example, adding more search API servers during peak query times or more GPU nodes for faster embedding generation.
SANDI Solr works well for:
The SANDI Java project has three modules:
SANDI Solr combines traditional search with AI to provide semantic search and answer generation. It provides a ready-to-use foundation for building AI-powered search applications. The Docker Compose deployment makes it easy to set up and run. Get started with one command.