Debugging and Observability in Graph Chains: Seeing the Invisible

A Graph RAG system is a "Series of Tubes." If an agent gives a wrong answer, the error could be anywhere:

1. Extraction Error: The fact was missing from the graph.
2. Generation Error: The LLM wrote the wrong Cypher.
3. Database Error: The query was slow and timed out.
4. Synthesis Error: The LLM ignored the retrieved facts.

In this lesson, we will learn how to make the "Invisible" visible. We will look at Tracing with LangSmith, Query Auditing, and the "Cypher Debugger" pattern. We will see how to build a dashboard that shows you exactly why your agent arrived at a conclusion.

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1. Tracing the Chain with LangSmith

LangSmith is the gold standard for observing LangChain/LangGraph workflows.

• It allows you to see the exact System Prompt sent to the LLM.
• It shows the Raw Cypher generated.
• It records the Latency of the database call.

Pro Pattern: Always tag your graph queries with a trace_id so you can link a slow response in your UI to a specific complex traversal in your database logs.

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2. The "Cypher Audit" Log

You should never let your AI run queries in total silence. You should log:

• The Natural Language question.
• The Generated Cypher.
• The Result Count (Number of nodes found).

Why? If you see a pattern where users ask about "Marketing" but the AI always generates queries for "Sales," you have a Prompt Alignment Problem.

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3. Debugging Hallucinated Relationships

A common Graph RAG error is the AI inventing a relationship.

• Query: MATCH (p:Person)-[:HAS_COFFEE_WITH]->(c:Person) ...
• Reality: Your schema only has [:WORKS_WITH].

How to Debug: Use the "Schema Comparison" technique. Create a script that compares the generated Cypher terms against your ALLOWED_RELATIONSHIPS list. If there is a mismatch, the trace should highlight it in RED.

graph TD
    User -->|Query| Chain
    Chain -->|Step 1| Gen[Cypher Generator]
    Gen -->|Trace| LS[LangSmith Log]
    Gen -->|Query| DB[(Neo4j)]
    DB -->|latency/error| LS
    DB -->|Data| Syn[Synthesizer]
    Syn -->|Final Trace| LS
    Syn -->|Answer| User
    
    style LS fill:#f4b400,color:#fff
    style DB fill:#4285F4,color:#fff

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4. Implementation: A Custom Logger for Cypher Chains

import time

def authenticated_graph_query(graph, cypher):
    start_time = time.time()
    try:
        result = graph.query(cypher)
        duration = time.time() - start_time
        
        # LOGGING SUCCESS
        print(f"[SUCCESS] Query: {cypher}")
        print(f"[METRIC] Latency: {duration:.2f}s | Nodes: {len(result)}")
        return result
    except Exception as e:
        # LOGGING FAILURE
        print(f"[ERROR] Cypher: {cypher}")
        print(f"[REASON] {str(e)}")
        raise e

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5. Summary and Exercises

Observability is the "Black Box Flight Recorder" for your AI.

• LangSmith provides a visual timeline of the reasoning process.
• Tracing reveals where the "hallucination chain" started.
• Latency Monitoring identifies expensive paths that need indexing.
• Logging helps you refine the prompt for better Cypher accuracy.

Exercises

1. Tracing Task: Sign up for a free LangSmith account. Run your GraphCypherQAChain from Lesson 2 with the tracing enabled. Look at the "Inputs" and "Outputs." Can you see where the schema was injected?
2. Error Logic: If a Cypher query takes 15 seconds to run, which part of your stack is likely the problem? (The LLM, the Database, or the Network?).
3. Prompt Refinement: If you find the LLM often uses the wrong label, what specific "Warning" would you add to your system prompt to fix it?

Congratulations! You have completed Module 10: Advanced Retrieval with LangChain and LangGraph. You have moved from simple scripts to professional, observable agents.

In Module 11: Advanced Graph Data Science for RAG, we will look at how to use high-level "Math" to make your retrieval even smarter.