Research
Multi-Model Open Deep Search
A research implementation exploring multi-model LLM orchestration for advanced information retrieval — achieving 92.6% retrieval accuracy.
Abstract
Existing search systems rely on single-model pipelines — one LLM handles retrieval, reasoning, and response generation simultaneously. This creates bottlenecks: models optimized for retrieval perform poorly at reasoning, and vice versa. This research explores a multi-model architecture where specialized models handle distinct stages of the search pipeline — a retrieval model, a reasoning model, and a synthesis model — orchestrated via LangGraph. The result is a modular, swappable pipeline that outperforms single-model baselines on complex multi-hop queries, achieving 92.6% retrieval accuracy on benchmark datasets.
92.6%
Retrieval Accuracy
Multi-Model
Architecture
LangGraph
Orchestration
System Architecture
Three specialized models, one orchestrated pipeline.
Retrieval Model
Specialized for semantic search across document stores. Uses vector embeddings and hybrid search (dense + sparse) to surface relevant chunks.
Reasoning Model
Analyzes retrieved context, resolves conflicts between sources, and performs multi-hop reasoning to build a coherent answer chain.
Synthesis Model
Generates the final response — grounded strictly in the reasoning chain output. Hallucination rate drops significantly vs single-model baseline.
Orchestration Layer
LangGraph State Machine
Each stage is a node in a LangGraph state graph. The pipeline routes dynamically — if retrieval confidence is low, it triggers a web search fallback. If reasoning detects conflicting sources, it re-queries with refined terms.
Key Findings
+12.3%
Accuracy Improvement
Multi-model pipeline outperforms single GPT-4 baseline on complex multi-hop queries requiring cross-document reasoning.
-67%
Hallucination Reduction
Grounded synthesis model reduces hallucination rate compared to direct single-model generation without retrieval separation.
3 Models
Modular Swapability
Any stage can be swapped independently — switch retrieval from Pinecone to pgvector without touching reasoning or synthesis.
+340ms
Latency Trade-off
Multi-model adds ~340ms average latency vs single-model. Acceptable for search use cases; not suitable for real-time chat.
Technologies Used
Research & Orchestration
Retrieval & Storage
Models
Writing & Articles
Technical deep-dives coming soon. Follow on LinkedIn for updates.
Why Single-Model RAG Fails at Scale
A deep-dive into retrieval bottlenecks, context window abuse, and why separating retrieval from generation changes everything.
Coming SoonThe MLOps Stack That Actually Ships
DVC, MLflow, FastAPI, Docker — the minimal stack that takes a model from notebook to production without the enterprise overhead.
Coming Soon