docs: small cleanup (#733)

docs/docs/tutorials/index.md

@@ -15,55 +15,47 @@ Learn from example implementations of graphs designed for specific scenarios and
 #### Chatbots
 
 - [Customer Support](customer-support/customer-support.ipynb): Build a customer support chatbot to manage flights, hotel reservations, car rentals, and other tasks
-- [Info Gathering](chatbots/information-gather-prompting.ipynb): Build an information gathering chatbot  
-- [Code Assistant](code_assistant/langgraph_code_assistant.ipynb): Building a code analysis and generation assistant
-
+- [Prompt Generation from User Requirements](chatbots/information-gather-prompting.ipynb): Build an information gathering chatbot
+- [Code Assistant](code_assistant/langgraph_code_assistant.ipynb): Build a code analysis and generation assistant
 
 #### Multi-Agent Systems
 
-- [Collaboration](multi_agent/multi-agent-collaboration.ipynb): Enabling two agents to collaborate on a task  
-- [Supervision](multi_agent/agent_supervisor.ipynb): Using an LLM to orchestrate and delegate to individual agents
-- [Hierarchical Teams](multi_agent/hierarchical_agent_teams.ipynb): Orchestrating nested teams of agents to solve problems
+- [Collaboration](multi_agent/multi-agent-collaboration.ipynb): Enable two agents to collaborate on a task
+- [Supervision](multi_agent/agent_supervisor.ipynb): Use an LLM to orchestrate and delegate to individual agents
+- [Hierarchical Teams](multi_agent/hierarchical_agent_teams.ipynb): Orchestrate nested teams of agents to solve problems
 
 #### RAG
 
 - [Adaptive RAG](rag/langgraph_adaptive_rag.ipynb)
-    - [Adaptive RAG using local models](rag/langgraph_adaptive_rag_local.ipynb)
-- [Agentic RAG.ipynb](rag/langgraph_agentic_rag.ipynb)
+    - [Adaptive RAG using local LLMs](rag/langgraph_adaptive_rag_local.ipynb)
+- [Agentic RAG](rag/langgraph_agentic_rag.ipynb)
 - [Corrective RAG](rag/langgraph_crag.ipynb)
-    - [Corrective RAG with local models](rag/langgraph_crag_local.ipynb)
+    - [Corrective RAG using local LLMs](rag/langgraph_crag_local.ipynb)
 - [Self-RAG](rag/langgraph_self_rag.ipynb)
-    - [Self-RAG with local models](rag/langgraph_self_rag_local.ipynb)
-- [Web Research (STORM)](storm/storm.ipynb): Generating Wikipedia-like articles via research and multi-perspective QA
-
+    - [Self-RAG using local LLMs](rag/langgraph_self_rag_local.ipynb)
+- [SQL Agent](sql-agent.ipynb)
 
 #### Planning Agents
 
-- [Plan-and-Execute](plan-and-execute/plan-and-execute.ipynb): Implementing a basic planning and execution agent  
-- [Reasoning without Observation](rewoo/rewoo.ipynb): Reducing re-planning by saving observations as variables 
-- [LLMCompiler](llm-compiler/LLMCompiler.ipynb): Streaming and eagerly executing a DAG of tasks from a planner
+- [Plan-and-Execute](plan-and-execute/plan-and-execute.ipynb): Implement a basic planning and execution agent
+- [Reasoning without Observation](rewoo/rewoo.ipynb): Reduce re-planning by saving observations as variables
+- [LLMCompiler](llm-compiler/LLMCompiler.ipynb): Stream and eagerly execute a DAG of tasks from a planner
 
 #### Reflection & Critique 
 
-- [Basic Reflection](reflection/reflection.ipynb): Prompting the agent to reflect on and revise its outputs
-- [Reflexion](reflexion/reflexion.ipynb): Critiquing missing and superfluous details to guide next steps
-- [Language Agent Tree Search](lats/lats.ipynb): Using reflection and rewards to drive a tree search over agents
-- [Self-Discovering Agent](self-discover/self-discover.ipynb): Analyzing an agent that learns about its own capabilities
+- [Basic Reflection](reflection/reflection.ipynb): Prompt the agent to reflect on and revise its outputs
+- [Reflexion](reflexion/reflexion.ipynb): Critique missing and superfluous details to guide next steps
+- [Language Agent Tree Search](lats/lats.ipynb): Use reflection and rewards to drive a tree search over agents
+- [Self-Discover Agent](self-discover/self-discover.ipynb): Analyze an agent that learns about its own capabilities
 
 #### Evaluation
 
-- [Agent-based](chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb): Evaluating chatbots via simulated user interactions
-- [Within LangSmith](chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb): Evaluating chatbots in LangSmith over a dialog dataset
+- [Agent-based](chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb): Evaluate chatbots via simulated user interactions
+- [In LangSmith](chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb): Evaluate chatbots in LangSmith over a dialog dataset
 
-#### Text Mining
+#### Experimental
 
-- [TNT-LLM](tnt-llm/tnt-llm.ipynb): learn to build rich, interpretable taxonomies of user intentand using the classification system developed by Microsoft for their Bing Copilot application.
-
-#### Competitive Programming
-
-- [Can Language Models Solve Olympiad Programming?](usaco/usaco.ipynb): Build an agent with few-shot "episodic memory" and human-in-the-loop collaboration to solve problems from the USA Computing Olympiad; adapted from the [paper of the same name](https://arxiv.org/abs/2404.10952v1) by Shi, Tang, Narasimhan, and Yao.
-
-
-#### Other Experimental Architectures
-
-- [Web Navigation](web-navigation/web_voyager.ipynb): Building an agent that can navigate and interact with websites
+- [Web Research (STORM)](storm/storm.ipynb): Generate Wikipedia-like articles via research and multi-perspective QA
+- [TNT-LLM](tnt-llm/tnt-llm.ipynb): Build rich, interpretable taxonomies of user intentand using the classification system developed by Microsoft for their Bing Copilot application.
+- [Web Navigation](web-navigation/web_voyager.ipynb): Build an agent that can navigate and interact with websites
+- [Competitive Programming](usaco/usaco.ipynb): Build an agent with few-shot "episodic memory" and human-in-the-loop collaboration to solve problems from the USA Computing Olympiad; adapted from the ["Can Language Models Solve Olympiad Programming?"](https://arxiv.org/abs/2404.10952v1) paper by Shi, Tang, Narasimhan, and Yao.