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Revolutionizing Customer Service: How AI Agents Answer Questions Efficiently and Enhance User Experience

chatbot, community forum, document retrieval, n8n, PostgreSQL, Supabase, workflow enhancement

In a recent discussion on the n8n community forum, a user named ODPP shared a challenge regarding their Supabase Postgres tables. They created and modified tables, functions, and triggers to retrieve document names, but faced difficulties displaying this data in a chat response. The conversation included an example output from the “Retrieve documents” node, which contained legal insights on the impacts of the Ukraine war on contracts. They expected a comprehensive list of document names as well. Another n8n community member requested additional details about ODPP’s n8n setup, encouraging them to provide more information to help address the issue effectively.



n8n Community Discussion: Document Interaction with Supabase

Recently, a user named ODPP shared a challenge they encountered while working with Supabase and n8n. They created a set of PostgreSQL tables and made several modifications with the aim of retrieving document names from a database. However, they are unsure how to display the retrieved data in a chat interface.

In their post, ODPP explained that when they check the output of their Supabase vector store, the document names are visible. However, they are still trying to figure out how to incorporate this information into chatbot responses effectively. They provided an example output of a legal opinion regarding the impact of the war in Ukraine on construction contracts, along with the expectation that a list of all document names should also be accessible.

Additionally, ODPP detailed their n8n setup, noting they are using version 1.76.2 with a PostgreSQL database on Supabase, running n8n in the cloud on a Windows 11 operating system.

The n8n community is encouraged to assist ODPP by providing insights or solutions to enrich their workflow. Tips on extracting and formatting the document names for chatbot output would also be helpful in resolving this query.

In summary, this discussion sheds light on the intricacies of integrating Supabase with n8n for document handling. Community members are invited to join the conversation to enhance workflow efficiency and share their experiences.

Tags: Supabase, n8n, PostgreSQL, Chatbot Integration, Community Support, Document Management

What are the main documents used by AI agents to answer questions?

AI agents typically use documents like user manuals, FAQs, and knowledge bases. These documents help the AI understand the topic and provide accurate answers.

How do AI agents process information from documents?

AI agents scan the documents to find relevant information. They use algorithms to analyze text and pick out key details that answer your questions.

Can AI agents understand complex documents?

AI agents are designed to handle complex documents but may struggle with very technical jargon. Simplified language in documents helps improve their understanding.

What types of answers can AI agents provide using documents?

AI agents can provide a range of answers, including definitions, explanations, troubleshooting tips, and procedures based on the documents they access.

How can I help improve the responses from AI agents?

You can improve responses by asking clear and specific questions. Providing context or additional details also helps the AI find better answers in the documents.

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