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AI AGENTS

Build an AI Chatbot with LangGraph and OpenAI: Step-by-Step

A step-by-step tutorial on building a stateful, cyclic conversational agent using LangGraph state machines.

BET
Botmartz Engineering Team
AI Engineer, Botmartz · April 20, 2024 · 11 min read
◈ Open in Google Colab⌥ View Code on GitHub
Read Time
11 min
Failure Modes
5
Code Snippets
3
Runnable Notebook
1
Botmartz AI Insight
Evaluating Retrieval, Chunking, and Generation in Production
Retrieval QualityChunking StrategyHybrid SearchContinuous EvaluationGuardrailsTakeaways
# Build an AI Chatbot with LangGraph and OpenAI: Step-by-Step Unlike traditional linear chains, complex conversational AI workflows require cycles, state persistence, and branching logic. LangGraph provides the ideal framework for building stateful agents. ## Step-by-Step Implementation 1. **State Definition**: Defining our graph state structure with message history and intermediate tool outputs. 2. **Nodes & Edges**: Creating functional nodes for LLM reasoning and custom edge evaluators for conditional routing. 3. **Checkpointers**: Persisting conversation checkpoints across multi-turn interactions.

Closing Takeaways

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Measure retrieval precision and recall in isolation before touching the model.
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Chunk along document structure, not arbitrary character counts.
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Combine vector and keyword search — hybrid retrieval beats either alone.
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Treat evaluation as continuous infrastructure, not a launch-week report.
Try It Yourself
A runnable Google Colab notebook with the eval harness and hybrid search code from this post.
◈ Open in Colab⌥ GitHub Repo
#Enterprise RAG#Evaluation#Production AI#LangChain
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BET
Botmartz Engineering Team
AI Engineer at Botmartz, building enterprise RAG and agent systems in production. Contributing to open-source libraries.

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