Harness AI for Structured Data Extraction with LangChain
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Effortlessly transform unstructured text into structured data using the power of LangChain and OpenAI. This workflow demonstrates how to prompt large language models and reliably parse their responses into a defined format.
About This Workflow
This n8n workflow showcases a robust method for extracting structured information from AI model outputs. It begins with a clear prompt to an LLM, then leverages LangChain's output parsing capabilities to ensure data integrity. The workflow utilizes an autofixing parser to handle potential inconsistencies in the LLM's response, ensuring a reliable and structured output. This is invaluable for any task requiring AI to process natural language and deliver data in a predictable, machine-readable format, such as extracting key details from documents or generating structured reports.
Key Features
- AI-Powered Data Extraction: Utilize LLMs to understand and extract information from text.
- Robust Output Parsing: Employ LangChain's structured and autofixing parsers for reliable data formatting.
- Configurable Prompts: Easily define custom prompts to guide the AI's data extraction.
- Error Handling: The autofixing parser helps mitigate issues with non-compliant LLM responses.
- OpenAI Integration: Seamlessly connects with OpenAI's powerful language models.
How To Use
- Manual Trigger: Start the workflow by clicking the "Execute Workflow" button.
- Define Prompt: Configure the "Prompt" node to specify the information you want the AI to extract. For example, "Return the 5 largest states by area in the USA with their 3 largest cities and their population."
- LLM Chain Configuration: Set up the "LLM Chain" node to select your preferred LLM (e.g., OpenAI Chat Model) and connect it to your prompt.
- Output Parsers: Configure the "Structured Output Parser" with your desired JSON schema to define the output format. The "Auto-fixing Output Parser" will then attempt to correct any deviations from this schema.
- Model for Autofixing: Select an "OpenAI Chat Model" for the autofixing parser to use in correcting invalid outputs.
- Review Results: The structured and validated output will be available after the "Auto-fixing Output Parser" node.
Apps Used
Workflow JSON
{
"id": "a6796cb7-16d9-40af-b089-bf92c8e084f8",
"name": "Harness AI for Structured Data Extraction with LangChain",
"nodes": 24,
"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: a6796cb7-16d9...
About the Author
Crypto_Watcher
Web3 Developer
Automated trading bots and blockchain monitoring workflows.
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