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Extraction/Phase_03/18 min build

Strict JSON Document Extractor

A deterministic data extraction pipeline. By utilizing native tool calling rather than prompt engineering, it forces the LLM to output strict JSON schemas for reliable database ingestion.

Execution_Steps

  1. 01

    Setup Ingestion

    Place an `input` node. Connect this to an `llm_call` node.

  2. 02

    Configure Reasoning Parameters

    Open the `llm_call` node. Select "gpt-4o". In the expected tokens, allocate a high input token count (e.g., P95: 8000) to account for large document processing.

  3. 03

    Enforce Schema via Tool Calling

    Instead of prompt engineering for JSON, drag a `tool_call` node and attach it to the LLM. Configure the tool call with the explicit JSON schema required for your database (e.g., extracting "merchant" and "total"). This forces the LLM to output strictly formatted arguments.

  4. 04

    Finalize API Output

    Connect the Tool Call to your `output` terminal. Simulate the traffic to observe how forced tool calling impacts overall latency compared to raw text generation.

Expected_Metrics

P50_LATENCY:< 3500ms
COST_SAVING:20.0%
SLA_LIMIT:5000ms

Ready to verify?

Open the canvas and simulate these parameters in real-time.

Node_Architecture

inputDocument UploadFile Mode
llm_callEntity Extractorgpt-4o
tool_callSchema EnforcementFunction Calling
outputDatabase HookREST POST