AI-Powered Script Dialogue Analyzer with Chatbot Integration
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Automate script analysis and dialogue retrieval using AI. This workflow leverages a webhook, text splitting, embeddings, and a Pinecone vector store to power a chatbot that can answer questions about script content.
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About This Workflow
Overview
This n8n workflow automates the process of analyzing script dialogue and making it queryable through an AI chatbot. It solves the problem of quickly finding specific dialogue, character lines, or thematic elements within a script. By converting script text into embeddings and storing them in a vector database (Pinecone), the workflow allows for efficient semantic search.
The core logic involves:
- Receiving Script Data: A
Webhooknode triggers the workflow, accepting script content. - Preprocessing: The
Splitternode breaks down the script into manageable chunks. - Embedding Generation:
Embeddingsnodes create vector representations of these chunks. - Storage & Retrieval: The
Insertnode adds these embeddings to a Pinecone vector store for later retrieval, while theQuerynode allows for semantic searches against this store. - AI Agent: An
Agentnode, powered by an OpenAIChatmodel andMemory, uses theTool(Pinecone vector store) to understand and respond to user queries about the script. - Logging: The
Sheetnode logs the interaction for auditing and monitoring.
Key Features
- Ingest and analyze script dialogue via webhook.
- Utilize Langchain.js nodes for advanced AI capabilities.
- Employ HuggingFace embeddings for semantic representation.
- Store and query data using Pinecone vector database.
- Integrate with OpenAI chat models for intelligent responses.
- Maintain conversation history with memory nodes.
- Log interactions to Google Sheets.
How To Use
- Configure the
Webhooknode to receive script data (e.g., JSON payload containing script text). - Set up your HuggingFace API credentials for the
Embeddingsnode. - Set up your Pinecone API credentials and ensure you have an index named
script_dialogue_analyzer. - Configure your OpenAI API credentials for the
Chatnode. - Customize the
Agentnode's prompt and theSheetnode's Google Sheets credentials and sheet name. - Send script data to the webhook URL to populate the vector store. Subsequent requests to the webhook can be conversational queries.
Apps Used
Workflow JSON
{
"id": "740ac198-f268-49d2-a4fe-7df399feb336",
"name": "AI-Powered Script Dialogue Analyzer with Chatbot Integration",
"nodes": 0,
"category": "AI & Machine Learning",
"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: 740ac198-f268...
About the Author
DevOps_Master_X
Infrastructure Expert
Specializing in CI/CD pipelines, Docker, and Kubernetes automations.
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