AI-Powered Conservation: Extracting Claims from Text
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Leverage AI to automatically extract key claims and insights from textual data related to conservation efforts. This workflow intelligently processes information, identifies critical points, and prepares them for further analysis or action.
About This Workflow
This n8n workflow automates the extraction of critical claims from unstructured text, specifically designed for conservation-related content. It begins by ingesting factual background and detailed text, then uses a sophisticated code node to split the text into manageable sentences while preserving important details like dates and list items. A subsequent LLM chain, powered by Ollama and a bespoke model, processes each sentence, linking it with contextual facts to define and extract actionable claims. The workflow then filters and aggregates these claims, providing a clean, structured output of verified insights. This empowers organizations to quickly understand the core messages within large volumes of text, accelerating research, reporting, and strategic decision-making in conservation.
Key Features
- Intelligent Sentence Splitting: Accurately divides text into sentences, preserving dates and list items.
- Contextual Claim Extraction: Utilizes AI to identify and define key claims based on provided factual context.
- LLM Integration: Leverages Ollama and custom models for advanced natural language understanding.
- Data Filtering and Aggregation: Organizes extracted claims for clear and concise output.
How To Use
- Configure Input Data: In the 'Edit Fields' node, provide the 'facts' and 'text' you want to process. Ensure the 'text' contains the information from which you want to extract claims.
- Review Sentence Splitting: The 'Code' node automatically splits the input text into sentences. No manual configuration is typically needed here unless you have highly specialized text formats.
- Set Up LLM Model: In the 'Ollama Chat Model' node, select your preferred Ollama model and ensure your Ollama API credentials are correctly configured.
- Define LLM Prompt: In the 'Basic LLM Chain4' node, the prompt is pre-configured to link document facts with extracted sentences to define claims. Review the
textparameter to ensure it correctly references your input fields (factsandclaim). - Adjust Filtering (Optional): The 'Filter' node can be modified if you wish to refine the output based on specific criteria. By default, it filters out results where the AI has responded with 'No' to a claim definition.
- Aggregate Results: The 'Aggregate' node combines the processed claims into a single output. Adjust its parameters if you need a different aggregation method.
Apps Used
Workflow JSON
{
"id": "7aabd1c8-6580-45a4-b872-521be5ca52bc",
"name": "AI-Powered Conservation: Extracting Claims from Text",
"nodes": 10,
"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.
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ID: 7aabd1c8-6580...
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