如何使用 NVIDIA AI-Q 和 LangChain 为企业搜索构建深度智能体

cp deploy/.env.example deploy/.env

# Required
NVIDIA_API_KEY=nvapi-...
TAVILY_API_KEY=tvly-...

# Optional: enables trace monitoring (covered later in this post)
LANGSMITH_API_KEY=lsv2-...

docker compose -f deploy/compose/docker-compose.yaml up --build

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'

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

general: 
 telemetry:
   tracing:
     langsmith:
       _type: langsmith
       project: aiq-gtc-demo
       api_key: ${LANGSMITH_API_KEY}

What is the deepest place on earth?

Analyze the current 2026 scientific consensus on the deepest points on Earth, comparing the Challenger Deep in the Mariana Trench to terrestrial extremes such as the Veryovkina Cave and the Kola Superdeep Borehole. Include the latest bathymetric and geodetic measurements, an assessment of measurement uncertainties (including gravity and pressure sensor corrections), and a summary of recent deep-sea expeditions from 2020–2026 that have updated our understanding of the Hadal zone's topography and biological life.

from deepagents import create_deep_agent

return create_deep_agent(
   model=self.llm_provider.get(LLMRole.ORCHESTRATOR),
   system_prompt=orchestrator_prompt,
   tools=self.tools,
   subagents=self.subagents,
   middleware=custom_middleware,
   skills=self.skills,
).with_config({"recursion_limit": 1000})

self.subagents = [
   {
       "name": "planner-agent",
       "system_prompt": render_prompt_template(
           self._prompts["planner"], tools=self.tools_info,
       ),
       "tools": self.tools,
       "model": self.llm_provider.get(LLMRole.PLANNER),
   },
   {
       "name": "researcher-agent",
       "system_prompt": render_prompt_template(
           self._prompts["researcher"], tools=self.tools_info,
       ),
       "tools": self.tools,
       "model": self.llm_provider.get(LLMRole.RESEARCHER),
   },
]

{
 "report_title": "RAG vs Long-Context Models for Enterprise Search",
 "report_toc": [
   {
     "id": "1",
     "title": "Architectural Foundations",
     "subsections": [
       {"id": "1.1", "title": "Retrieval-Augmented Generation Pipeline"},
       {"id": "1.2", "title": "Long-Context Transformer Architectures"}
     ]
   },
   {
     "id": "2",
     "title": "Performance and Accuracy Trade-offs",
     "subsections": [
       {"id": "2.1", "title": "Factual Accuracy and Hallucination Rates"},
       {"id": "2.2", "title": "Latency and Throughput Benchmarks"}
     ]
   }
 ],
 "queries": [
   {
     "id": "q1",
     "query": "RAG retrieval-augmented generation architecture components ...",
     "target_sections": ["Architectural Foundations"],
     "rationale": "Establishes baseline understanding of RAG pipelines"
   }
 ]
}

# sources/internal_kb/src/register.py
from pydantic import Field, SecretStr
from nat.builder.builder import Builder
from nat.builder.function_info import FunctionInfo
from nat.cli.register_workflow import register_function
from nat.data_models.function import FunctionBaseConfig


class InternalKBConfig(FunctionBaseConfig, name="internal_kb"):
   """Search tool for the internal knowledge base."""
   api_url: str = Field(description="Knowledge base API endpoint")
   api_key: SecretStr = Field(description="Authentication key")
   max_results: int = Field(default=5)


@register_function(config_type=InternalKBConfig)
async def internal_kb(config: InternalKBConfig, builder: Builder):
   async def search(query: str) -> str:
       """Search the internal knowledge base for relevant documents."""
       results = await call_kb_api(config.api_url, query, config.max_results)
       return format_results(results)

   yield FunctionInfo.from_fn(search, description=search.__doc__)

functions:
 internal_kb_tool:
   _type: internal_kb
   api_url: "https://kb.internal.company.com/api/v1"
   api_key: ${INTERNAL_KB_API_KEY}
   max_results: 10

 shallow_research_agent:
   _type: shallow_research_agent
   llm: nemotron_llm
   tools:
     - web_search_tool
     - internal_kb_tool

 deep_research_agent:
   _type: deep_research_agent
   orchestrator_llm: gpt-5
   planner_llm: nemotron_llm
   researcher_llm: nemotron_llm
   tools:
     - advanced_web_search_tool
     - internal_kb_tool

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