LLM infrastructure
AI Core
The LLM stack, layer by layer.
Tokenization with BPE, semantic chunking, multimodal document parsing and an annotation studio for RLHF/DPO data; contrastive embeddings, an HNSW vector database, knowledge-graph extraction, hybrid dense + BM25 retrieval and cross-encoder reranking.
On top: an inference server with token streaming and dynamic batching, a semantic model router, bidirectional guardrails, ReAct agents with structured memory and a sandboxed tool runtime — and the MLOps loop around them: synthetic data, dataset versioning, QLoRA fine-tuning, an eval harness and tracing. A hub orchestrates all of it.
- Repositories
- 22
- Lines
- 25,716
- Commits
- 255
- Test files
- 43
- Built
- Jun 2026 – Jul 2026
- Mostly
- Python
L1 Data & input
L2 Representation & retrieval
L3 Inference & control
L4 Agents
L5 MLOps & quality
↳ Flagship
↳ Flagship
Where it all comes together
L1
Data & input
L2
Representation & retrieval
L3
Inference & control
L4
Agents
L5