Editorial illustration for Guide: Deploy Three LLMs, Including Nemotron-3-Super-120B, for Enterprise Search
Enterprise Search: 3 LLMs Powered by NVIDIA AI Stack
Guide: Deploy Three LLMs, Including Nemotron-3-Super-120B, for Enterprise Search
Enterprise search is getting a practical playbook. This guide walks you through wiring three large language models into a single pipeline, showing how NVIDIA’s AI‑Q stack can be paired with LangChain to handle distinct tasks without a single model shouldering everything. While the tech is impressive, the real question is how you configure each model so the system stays predictable and cost‑effective.
The example that follows does exactly that: it names a “non‑thinking” Nemotron‑3‑Super‑120B instance, sets a modest temperature of 0.7, and caps output at 8,192 tokens, then adds a second Nemotron‑3‑Super‑120B with the default thinking mode enabled. By separating roles at the configuration level, you can tailor response style and resource use to the needs of your search workload. Here’s the snippet that lays out those three LLM definitions and the parameters that distinguish them.
The following example declares three LLMs with different roles: llms: nemotron_llm_non_thinking: _type: nim model_name: nvidia/nemotron-3-super-120b-a12b temperature: 0.7 max_tokens: 8192 chat_template_kwargs: enable_thinking: false nemotron_llm: _type: nim model_name: nvidia/nemotron-3-super-120b-a12b temperature: 1.0 max_tokens: 100000 chat_template_kwargs: enable_thinking: true gpt-5-2: _type: openai model_name: 'gpt-5.2' nemotron_llm_non_thinking handles fast responses where chain-of-thought adds latency without benefit. nemotron_llm enables thinking mode with a 100K context window for the agents that need multi-step reasoning. The blueprint consists of both a shallow and deep research agent. The following configuration shows both: functions: shallow_research_agent: _type: shallow_research_agent llm: nemotron_llm tools: - web_search_tool max_llm_turns: 10 max_tool_calls: 5 deep_research_agent: _type: deep_research_agent orchestrator_llm: gpt-5 planner_llm: nemotron_llm researcher_llm: nemotron_llm max_loops: 2 tools: - advanced_web_search_tool The shallow research agent runs a bounded tool-calling loop--up to 10 LLM turns and 5 tool calls--then returns a concise answer with citations.
The guide walks developers through deploying three distinct LLMs, two instances of NVIDIA’s Nemotron‑3‑Super‑120B and a non‑thinking variant, within an enterprise search agent built on LangChain. Its open‑source AI‑Q blueprint claims to bridge the gap between consumer‑grade AI and fragmented workplace data. The tutorial, packaged as an NVIDIA launchable, shows how to configure temperature, token limits and chat templates for each model.
By turning off the “thinking” flag, the non‑thinking model can act as a deterministic filter, while the standard Nemotron instance handles richer generation. LangChain’s new enterprise agent platform underpins the setup, promising scalability for production workloads. Yet the article does not provide performance metrics, so it remains uncertain whether the approach delivers consistent context across disparate data sources.
The example configuration is clear, but real‑world integration challenges may surface when moving beyond the demo environment. Developers interested in deep research agents now have a concrete starting point, though further testing will be needed to validate the claimed benefits.
Further Reading
- LangChain Partners with NVIDIA to Build Enterprise AI Agent Platform - MEXC
- LangChain Announces Enterprise Agentic AI Platform Built with NVIDIA - PR Newswire
- LangChain Announces Enterprise Agentic AI Platform Built with NVIDIA - LangChain Blog
- Build an AI Agent for Enterprise Research Blueprint by NVIDIA - NVIDIA
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Common Questions Answered
How does the non-thinking Nemotron-3-Super-120B model differ from the standard Nemotron-3-Super-120B in this enterprise search configuration?
The non-thinking Nemotron-3-Super-120B variant has the 'enable_thinking' flag set to false, which changes its response generation approach. This model is configured with a lower temperature of 0.7 and is designed to handle fast responses with more predictable output compared to the standard thinking model.
What are the key configuration parameters for the LLMs in this enterprise search pipeline?
The LLM configurations include model name (nvidia/nemotron-3-super-120b-a12b), temperature settings (0.7 for non-thinking and 1.0 for standard), and maximum token limits (8192 and 100000 respectively). Additionally, the chat template allows for enabling or disabling the 'thinking' mode for different model instances.
How does this NVIDIA AI-Q stack with LangChain address enterprise search challenges?
The AI-Q blueprint aims to bridge the gap between consumer-grade AI and fragmented workplace data by deploying multiple LLMs with distinct roles in a single pipeline. By configuring different model instances with specific parameters, the system can handle various search tasks more efficiently and predictably.