docs: fix warnings in links (#4672)

docs/docs/tutorials/index.md

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-# Tutorials
-
-New to LangGraph or LLM app development? Read this material to get up and running building your first applications.
-
-## Get Started 🚀 {#quick-start}
-
-- [LangGraph basics](get-started/1-build-basic-chatbot.md): Build a chatbot that can use tools and keep track of conversation history. Add human-in-the-loop capabilities and explore how time-travel works.
-- [Common Workflows](workflows/index.md): Overview of the most common workflows using LLMs implemented with LangGraph.
-- [LangGraph Server Quickstart](langgraph-platform/local-server.md): Launch a LangGraph server locally and interact with it using REST API and LangGraph Studio Web UI.
-- [LangGraph Template Quickstart](../concepts/template_applications.md): Start building with LangGraph Platform using a template application.
-- [Deploy with LangGraph Platform Quickstart](../cloud/quick_start.md): Deploy a LangGraph app using LangGraph Platform.
-
-## Use cases 🛠️ {#use-cases}
-
-
-Explore practical implementations tailored for specific scenarios:
-
-### Chatbots
-
-- [Customer Support](customer-support/customer-support.ipynb): Build a multi-functional support bot for flights, hotels, and car rentals.
-- [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.
-
-### RAG
-
-- [Agentic RAG](rag/langgraph_agentic_rag.ipynb): Use an agent to figure out how to retrieve the most relevant information before using the retrieved information to answer the user's question.
-- [SQL Agent](sql-agent.ipynb): Build a SQL agent that can answer questions about a SQL database.
-
-
-### Agent Architectures
-
-#### Multi-Agent Systems
-
-- [Network](multi_agent/multi-agent-collaboration.ipynb): Enable two or more agents to collaborate on a task
-- [Supervisor](multi_agent/agent_supervisor.ipynb): Use an LLM to orchestrate and delegate to individual agents
- 
-#### Planning Agents
-
-- [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): Prompt the agent to reflect on and revise its outputs
-- [Reflexion](reflexion/reflexion.ipynb): Critique missing and superfluous details to guide next steps
-- [Tree of Thoughts](tot/tot.ipynb): Search over candidate solutions to a problem using a scored tree
-- [Language Agent Tree Search](lats/lats.ipynb): Use reflection and rewards to drive a monte-carlo 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): Evaluate chatbots via simulated user interactions
-- [In LangSmith](chatbot-simulation-evaluation/langsmith-agent-simulation-evaluation.ipynb): Evaluate chatbots in LangSmith over a dialog dataset
-
-### Experimental
-
-- [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.
-- [Complex data extraction](extraction/retries.ipynb): Build an agent that can use function calling to do complex extraction tasks
-
-## LangGraph Platform 🧱 {#platform}
-
-### Authentication & Access Control
-
-Add custom authentication and authorization to an existing LangGraph Platform deployment in the following three-part guide:
-
-1. [Setting Up Custom Authentication](auth/getting_started.md): Implement OAuth2 authentication to authorize users on your deployment
-2. [Resource Authorization](auth/resource_auth.md): Let users have private conversations
-3. [Connecting an Authentication Provider](auth/add_auth_server.md): Add real user accounts and validate using OAuth2