Enterprise-level AI Document Search Platform: An Intelligent Knowledge Retrieval System Based on RAG and Vector Database

This article introduces the open-source project Enterprise Document Search Platform. Targeting the pain points of enterprise massive document management, this platform adopts the RAG architecture, vector database, and large language models. It supports semantic search for multi-format documents such as PDFs, Word files, and emails, as well as intelligent Q&A with source citations. The project is maintained by Kapil Chavan and open-sourced on GitHub (link: https://github.com/kapilchavan984/Enterprise-Document-Search-Platform). The current version is v1.0.0, and it follows an open-source license.

In the digital transformation process, enterprises face the challenge of managing massive document assets. Traditional keyword search cannot meet the needs of semantic understanding. Employees need an intelligent search experience that can understand the semantics of questions, provide accurate answers, and indicate sources. This project is an open-source solution designed to address this pain point.

The project core uses the RAG architecture, which is divided into two phases: indexing and querying.

• Indexing phase: Parse and chunk documents, convert them into vectors via an embedding model, and store them in a vector database.
• Querying phase: Convert user questions into vectors, perform similarity search to obtain relevant fragments, and call LLM with context to generate answers with sources. The vector database handles semantic similarity retrieval, and the LLM service supports flexible integration (local or third-party models), effectively reducing the risk of LLM hallucinations and leveraging the latest document content.

System components include the front-end layer (Web/Chat UI), API gateway, search service, RAG engine, embedding service, object storage, document processing pipeline, and monitoring stack. The tech stack covers:

• DevOps: Jenkins CI/CD, GitOps, Terraform infrastructure automation;
• Cloud-native: Docker containerization, Kubernetes deployment (supports multi-node high availability, RBAC, auto-scaling);
• Security: OAuth2 authentication, LDAP integration, key management, etc.

There are multiple deployment methods:

1. Quick start: Clone the repository, then build and deploy to Kubernetes via scripts;
2. Local deployment with Docker Compose: Suitable for development and testing;
3. AWS cloud deployment: Automatically create resources via Terraform. Usage scenario example: When a user asks "How does Kubernetes scheduling work?", the system generates an answer and cites the "Kubernetes Architecture Guide" and internal platform documents.

The roadmap includes v1.1 (enhanced RAG pipeline: reordering, multi-hop reasoning), v1.2 (multi-tenant support), v1.3 (Agentic AI search), and v2.0 (multi-cloud deployment). Project value:

• Reference architecture: The full-stack design provides a reference for enterprises to build AI search systems;
• Skill demonstration: Covers multi-domain skills such as AI/ML engineering, cloud-native development, and DevOps.

Limitations of the current v1.0 version: Concise documentation, test coverage needs improvement, production deployment needs optimization. Recommendations for enterprise adoption:

• Conduct POC testing first;
• Evaluate the complexity of integration with existing systems;
• Pay attention to data privacy compliance (e.g., LLM data cross-border transfer);
• Build operation and maintenance team capabilities to maintain the Kubernetes system.