Trieve: The All-in-One Platform for Search, RAG, and Analytics
Discover Trieve — a comprehensive platform combining semantic search, RAG, recommendations, and analytics into a single API. Self-hostable and developer-friendly.
Introducing Trieve: The All-in-One Search, RAG, and Analytics Platform
Trieve is a comprehensive platform that brings together semantic search, recommendation systems, Retrieval-Augmented Generation (RAG), and analytics into a single, unified API. Whether you're building a next-generation search engine, a content recommendation system, or an AI-powered Q&A bot, Trieve provides the infrastructure you need without the complexity of integrating multiple services.
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Docker configs, system requirements, and installation guides — all on one page.
View Trieve Tool Page →What Makes Trieve Stand Out?
Trieve is not just another search API — it's a complete retrieval infrastructure platform. Here are its core capabilities:
| 🔍 Semantic Dense Vector Search | Integrates with OpenAI or Jina embedding models and Qdrant for high-quality vector search |
| 📝 Typo-Tolerant Full-Text Search | Uses neural SPLADE sparse vectors for typo-tolerant quality search |
| 🔄 Hybrid Search + Re-ranking | Combines dense and sparse search with cross-encoder re-ranking (BAAI/bge-reranker-large) |
| 🤖 RAG API Routes | Fully-managed RAG with topic-based memory management via OpenRouter |
| ⭐ Recommendations | Find similar chunks or files for content recommendation systems |
| 🎯 Sub-Sentence Highlighting | Highlight matching words within chunks for better UX |
Self-Hosting Made Simple
Trieve is fully self-hostable in your own infrastructure. The platform provides detailed guides for Docker Compose, AWS, GCP, and Kubernetes deployments. With the official Docker image trieve/server:latest (over 31K pulls), you can be up and running in minutes. Trieve also supports bringing your own embedding models, SPLADE models, cross-encoder re-rankers, and LLMs.
Use Cases
- Enterprise Search: Build search engines with hybrid retrieval and recency biasing
- RAG Applications: Create Q&A systems with managed topic-based memory
- Content Recommendations: Power recommendation engines with similarity search
- Analytics: Use signals like clicks and citations for tunable merchandizing
Ready to try Trieve?
Get the system requirements, docker compose templates, and deployment guides.
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