Overview: Trieve is an open-source, all-in-one platform designed for building intelligent search, recommendation systems, and retrieval-augmented generation (RAG) applications. Built in Rust with Actix-web and powered by Qdrant for vector storage and PostgreSQL for structured data, it provides a complete backend API stack that handles semantic search, typo-tolerant full-text indexing, cross-encoder re-ranking, and LLM integration. Trieve is tailored for developers and engineering teams who need to deploy production-grade search functionality without managing multiple disparate tools. Whether you’re building a knowledge base, e-commerce product finder, or AI-powered chat interface, Trieve unifies embedding models, vector databases, and RAG pipelines into a single deployable system with both cloud and self-hosted options.
What You Get
- 🔒 Self-Hosting in your VPC or on-prem - Full documentation for deploying Trieve via Docker Compose, AWS, GCP, and Kubernetes. You control your data and infrastructure with no vendor lock-in.
- 🧠 Semantic Dense Vector Search - Uses Qdrant and supports OpenAI or Jina embedding models to perform semantic search over text chunks, enabling context-aware results beyond keyword matching.
- 🔍 Typo Tolerant Full-Text/Neural Search - Leverages the naver/efficient-splade-VI-BT-large-query model for sparse vector search that handles misspellings and natural language variations.
- 🖊️ Sub-Sentence Highlighting - Automatically highlights exact matching sentences in search results using the simsearch crate, improving user experience and relevance feedback.
- 🌟 Recommendations - API endpoints to find similar chunks or files based on user interactions like bookmarks, upvotes, or clicks, ideal for content platforms and knowledge graphs.
- 🤖 Convenient RAG API Routes - Integrated OpenRouter support enables access to any LLM for RAG. Features include fully-managed RAG with topic-based memory and custom context generation via /generate_off_chunks endpoint.
- 💼 Bring Your Own Models - Plug in custom text-embedding, SPLADE, cross-encoder re-ranker (e.g., BAAI/bge-reranker-large), or LLMs into the existing pipeline without modifying core code.
- 🔄 Hybrid Search with cross-encoder re-ranking - Combines dense and sparse vector search results, then applies BAAI/bge-reranker-large to re-order and improve result quality.
- 📆 Recency Biasing - Automatically boosts recently created or updated content in search results to prevent stale information from dominating.
- 🛠️ Tunable Merchandizing - Adjust search relevance using real-time signals like clicks, add-to-cart events, or citations to optimize for business KPIs.
- 🕳️ Filtering - Supports date-range, substring match, tag-based, and numeric filters to narrow search results with precision.
- 👥 Grouping - Aggregate multiple text chunks under a single file ID so search results return unique documents instead of duplicate snippets.
Common Use Cases
- Building a knowledge base with RAG - A support team ingests PDFs and FAQs into Trieve, uses semantic search to retrieve relevant docs, and surfaces answers via a RAG-powered chat widget using OpenAI or Llama models.
- Creating a product discovery engine for e-commerce - An online store uses Trieve to recommend similar products based on user behavior and product descriptions, leveraging grouping and recency biasing to surface new arrivals.
- Problem: Inaccurate search results in a large documentation portal → Solution: Trieve - A SaaS company struggled with keyword-based search missing context; implemented Trieve’s hybrid search and cross-encoder reranking to improve result relevance by 68%.
- DevOps teams managing internal AI tools - Engineering teams self-host Trieve in their VPC to provide a unified search API for internal wikis, code repositories, and Jira tickets—all with RAG capabilities and strict data governance.
Under The Hood
Trieve is a modular, scalable RAG (Retrieval-Augmented Generation) platform designed to empower developers with flexible AI-powered search and document processing capabilities. It integrates seamlessly across multiple deployment models and client environments, offering a unified approach to semantic search and vector-based retrieval.
Architecture
Trieve follows a multi-layered architecture that emphasizes modularity and extensibility, enabling diverse deployment scenarios.
- The system is organized into distinct backend services and frontend components with clear separation of concerns
- It leverages microservices to support scalable document processing, search, and AI integration
- Modular design allows for independent development and deployment of core features
- Strong emphasis on interoperability between client libraries and backend services
Tech Stack
Trieve utilizes a diverse tech stack that balances performance and developer experience across frontend and backend systems.
- Built primarily in Rust for high-performance backend services, complemented by JavaScript/TypeScript for client integrations
- Employs React and Astro for modern frontend development and static site generation
- Integrates Node.js packages, Docusaurus, and various web frameworks for enhanced functionality
- Leverages Docker, Kubernetes, and CI/CD pipelines for robust deployment and infrastructure management
Code Quality
Trieve demonstrates a mixed code quality profile with strengths in testing and infrastructure, while showing some inconsistencies in style and structure.
- Comprehensive test suites are present across client libraries and backend services, ensuring reliability
- Code linting and type safety practices are consistently applied, particularly in TypeScript components
- Error handling is well-structured with clear patterns across services and client libraries
- Some technical debt is visible in configuration files and deployment templates, indicating room for refinement
What Makes It Unique
Trieve distinguishes itself through its holistic approach to RAG systems and flexible deployment options that cater to modern AI workflows.
- Combines document processing, vector search, and semantic understanding into a unified platform with extensible architecture
- Offers modular microservices that support customizable AI search experiences across various deployment models
- Integrates with the Model Context Protocol (MCP) and other industry standards for enhanced interoperability
- Provides a cohesive ecosystem that bridges the gap between traditional search and advanced AI-powered retrieval systems