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Retry

As explained in Knowledge Graphs and LLMs in Action

A retry mechanism is a control procedure that repeats a failed operation according to configured retry conditions.

A retry mechanism repeats a failed operation under explicit conditions. In a graph-querying QA pipeline, it preserves failure information in shared state and routes execution back to query generation. section 15.1.4 section 15.1.5

Failed query recovery

A generated Cypher query raises a Neo4j ClientError. The execution agent records the exception and query in information, sets results_error, and increments retries. The routing function sends the state back to text-to-Cypher for another attempt until three runs have occurred.

Listing 15.5 Pipeline agent’s state
class AgentState(TypedDict):
    question: str
    output_type: str
    output_type_reason: str
    schema: str
    query: str
    query_reasoning: str
    query_message: str
    results_error: list
    summary: str
    summary_reason: str
    summary_analysis: bool
    information: str
    retries: int

Inspect the shared AgentState fields that carry retry counts, errors, and recovery context between pipeline agents. section 15.1.4

How Knowledge Graphs and LLMs in Action covers it

How deep it goes
Implements error capture, counter updates, conditional routing, and termination after three runs.
Examples
Demonstrates recovery from Neo4j ClientError exceptions during generated Cypher query execution.
What it assumes
Assumes familiarity with shared agent state, generated Cypher, Neo4j execution, and LangGraph conditional edges.
What you can do after
Build it from scratch by storing retry state and adding bounded conditional edges to a LangGraph pipeline.
How it connects
Connects error handling, state management, query execution, text-to-Cypher generation, and conditional graph routing.

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This explainer was generated by Manning's knowledge graph from the text of Knowledge Graphs and LLMs in Action. The section links point to the parts of the book it draws on.