Keep the inference layer as one portable artifact: the same Docker image, Helm chart, and SDK calls on any Kubernetes cluster, from a laptop to any cloud.

SIE handles routing in a stateless gateway, batching in worker pods, model configuration in a single-writer control plane, and LoRA adapters as a per-request option.

Serve every model from one SIE deployment with on-demand loading and LRU eviction on shared GPUs. Add new models with a config write instead of a new release.

If the inference you need is embeddings, reranking, and extraction rather than text generation, SIE is the best fit: many small models on shared GPUs behind one API.

For embedding, reranking, and extraction inside an agent workload, the strongest self-hosted alternative to a metered API is SIE: no per-token cost and no data leaving your cloud.

Routing AI agent tasks to the right model means matching each step to a specialist model. Learn the two layers of routing and how to serve every model from one endpoint.

Route each agent task to a purpose-built model by naming the model per request against one SIE endpoint, using encode, score, and extract.

Run embedding, reranking, extraction, and document-parsing work on one open-source stack (SIE), and let your LLM handle generation and tool-call reasoning beside it.

Small open-source models in the 100M to 1B parameter range already handle most of the inference an agent runs around its main LLM: embeddings, reranking, and more.

SIE embeddings and Qdrant retrieval behind a GPT-4 router: cross-encoder reranking, hard filters, and five agent tools for natural language real estate search.

How hierarchical cluster-embedding chunking with RAPTOR improves RAG retrieval over vanilla chunking, with a step-by-step implementation and a note on serving embeddings in production with SIE.

Part two of our RAG evaluation series: building synthetic eval datasets with RAGAS, interpreting faithfulness and retrieval metrics, and mapping results to inference and serving concerns.

We benchmarked LlamaIndex and LangChain chunkers, MTEB embedding models, ColBERT v2, and rerankers on HotpotQA, SQUAD, and QuAC—and what the results mean for inference-heavy retrieval stacks.

Explore semantic chunking for RAG: embedding similarity, hierarchical clustering, and LLM-based methods, with code, HotpotQA and SQUAD evaluation, and BAAI/bge-small-en-v1.5.

Key considerations and trade-offs for picking a vector database that fits your architecture, scale, and operational limits.

How combining keyword search, vector search, and semantic reranking improves RAG retrieval precision and recall.

Build AI apps that generate and compare vector embeddings directly in your browser using TensorFlow.js. No backend required.