Automated Data Extraction and Structuring with LLMs
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Leverage the power of Large Language Models (LLMs) to automatically extract and structure complex data from unstructured text. This workflow ensures data accuracy and consistency, transforming raw information into usable, organized datasets.
About This Workflow
This n8n workflow automates the process of extracting specific information from text and structuring it according to a predefined schema. It utilizes an advanced LLM chain that first processes an initial prompt, then employs an output parser to validate and potentially autofix the LLM's response. If the initial output is invalid, a specialized autofixing parser, aided by another LLM call, corrects the output to meet the specified format. Finally, a structured output parser ensures the data conforms to the desired JSON schema, making it ready for downstream processing or integration.
This powerful combination tackles the challenge of inconsistent or unstructured data, providing a robust solution for data enrichment and preparation.
Key Features
- Intelligent Data Extraction: Extracts specific entities and relationships from natural language text.
- Automated Data Structuring: Organizes extracted data into a predefined, machine-readable format (JSON).
- Self-Correcting Output: Employs LLM-powered autofixing to resolve parsing errors and ensure output validity.
- Flexible Schema Definition: Allows for manual definition of output schemas to match diverse data requirements.
- Robust Error Handling: Designed to handle and correct imperfect LLM responses.
How To Use
- Trigger Workflow: Start the workflow by clicking "Execute Workflow" in n8n.
- Define Prompt: Configure the "Prompt" node to specify the data you want to extract and the desired output format.
- Configure LLM Chain: Set up the "Basic LLM Chain" node. Connect the "Prompt" node to its input and the "OpenAI Chat Model" (and any output parsers) to its respective parameters.
- Set Initial LLM: In the "OpenAI Chat Model" node, select your desired OpenAI model (e.g., 'gpt-4o-mini') and configure any necessary API credentials.
- Define Output Schema: Configure the "Structured Output Parser" node by defining your desired JSON schema in the
inputSchemaparameter. - Enable Autofixing (Optional but Recommended): Configure the "Auto-fixing Output Parser" node. This node will use the "OpenAI Chat Model1" to attempt to correct invalid outputs from the initial LLM chain. You can customize the autofixing prompt within this node.
- Connect Nodes: Ensure correct connections are made between the nodes as shown in the workflow JSON (e.g., Prompt -> Basic LLM Chain, OpenAI Chat Model -> Basic LLM Chain, Auto-fixing Output Parser -> Basic LLM Chain, Structured Output Parser -> Auto-fixing Output Parser).
Apps Used
Workflow JSON
{
"id": "d2e67652-0c06-490b-ace8-1bbd0fb088cf",
"name": "Automated Data Extraction and Structuring with LLMs",
"nodes": 13,
"category": "Marketing",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: d2e67652-0c06...
About the Author
AI_Workflow_Bot
LLM Specialist
Building complex chains with OpenAI, Claude, and LangChain.
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