Semantic Search
Build search and retrieval-augmented generation systems with low-latency embeddings, fast long-context inference, and predictable costs—even across massive document collections and heavy query loads.
problem
Search & retrieval can break at scale
Embedding large corpora is expensive
Bulk indexing and continuous ingestion can drive substantial GPU costs without efficient batching and utilization.
Embedding latency slows retrieval
Slow query embedding generation adds latency to every search and RAG request.
Throughput collapses under concurrency
Large retrieved document sets can exceed practical context limits, degrading answer quality or causing truncation.
Long documents exhaust context for synthesis
Many providers truncate silently when synthesizing across large retrieved document sets, resulting in incomplete or incoherent answers.
solution
Built for high-performance search and retrieval workloads
Continuous batching maximizes embedding throughput
Process large indexing jobs and real-time query embeddings efficiently by dynamically batching requests across available GPU capacity.
Optimized inference reduces retrieval latency
Serve embeddings and generation requests with low latency to keep search and RAG pipelines responsive under load.
Efficient GPU utilization lowers cost per query
Maintain predictable economics at scale by maximizing hardware efficiency across concurrent workloads.
Memory-efficient serving supports long-context synthesis
Sustain large retrieved contexts during generation without truncation or degraded answer quality.
Open models for embedding, reranking, and generation
Access the world’s largest collection of 540,000 models through seamless Hugging Face integration. From text generation to computer vision, launch any model with a single click.
Have a custom or fine-tuned model?
We'll help you deploy it just as easily. Contact us to deploy your model.
How teams scale with FriendliAI
Learn how leading companies achieve unmatched performance, scalability, and reliability with FriendliAI
Our custom model API went live in about a day with enterprise-grade monitoring built in.
Scale to trillions of tokens with 50% fewer GPUs, thanks to FriendliAI.
Rock-solid reliability with ultra-low tail latency.
Cutting GPU costs accelerated our path to profitability.
Fluctuating traffic is no longer a concern because autoscaling just works.
Our custom model API went live in about a day with enterprise-grade monitoring built in.
Scale to trillions of tokens with 50% fewer GPUs, thanks to FriendliAI.
Rock-solid reliability with ultra-low tail latency.
Cutting GPU costs accelerated our path to profitability.
Fluctuating traffic is no longer a concern because autoscaling just works.
Resources
Docs, demos, and resources for semantic search and RAG applications.
Leveraging Milvus and Friendli Serverless Endpoints for Advanced RAG and Multi-Modal Queries
Building Your RAG Application on LlamaIndex with Friendli Inference: A Step-by-Step Guide
Retrieval Augmented Generation (RAG) with MongoDB and FriendliAI
RAG AI Agent with LangChain—Query Your Internal Documents
Leveraging Milvus and Friendli Serverless Endpoints for Advanced RAG and Multi-Modal Queries
Building Your RAG Application on LlamaIndex with Friendli Inference: A Step-by-Step Guide
Retrieval Augmented Generation (RAG) with MongoDB and FriendliAI