Document-based AI Chatbot with RAG, OpenAI and Cohere Reranker

Description

This workflow contains community nodes that are only compatible with the self-hosted version of n8n.

Build intelligent AI chatbot with RAG and Cohere Reranker

Who is it for?

This template is perfect for developers, businesses, and automation enthusiasts who want to create intelligent chatbots that can answer questions based on their own documents. Whether you're building customer support systems, internal knowledge bases, or educational assistants, this workflow provides a solid foundation for document-based AI conversations.

How it works

This workflow creates an intelligent AI assistant that combines RAG (Retrieval-Augmented Generation) with Cohere's reranking technology for more accurate responses:

Chat Interface: Users interact with the AI through a chat interface
Document Processing: PDFs from Google Drive are automatically extracted and converted into searchable vectors
Smart Search: When users ask questions, the system searches through vectorized documents using semantic search
Reranking: Cohere's reranker ensures the most relevant information is prioritized
AI Response: OpenAI generates contextual answers based on the retrieved information
Memory: Conversation history is maintained for context-aware interactions

Setup steps

Prerequisites
n8n instance (self-hosted or cloud)
OpenAI API key
Supabase account with vector extension enabled
Google Drive access
Cohere API key

1. Configure Supabase Vector Store

First, create a table in Supabase with vector support:

CREATE TABLE cafeina (
id SERIAL PRIMARY KEY,
content TEXT,
metadata JSONB,
embedding VECTOR(1536)
);

-- Create a function for similarity search
CREATE OR REPLACE FUNCTION match_cafeina(
query_embedding VECTOR(1536),
match_count INT DEFAULT 10
)
RETURNS TABLE(
id INT,
content TEXT,
metadata JSONB,
similarity FLOAT
)
LANGUAGE plpgsql
AS $$
BEGIN
RETURN QUERY
SELECT
cafeina.id,
cafeina.content,
cafeina.metadata,
1 - (cafeina.embedding <=> query_embedding) AS similarity
FROM cafeina
ORDER BY cafeina.embedding <=> query_embedding
LIMIT match_count;
END;
$$;

2. Set up credentials

Add the following credentials in n8n:

OpenAI**: Add your OpenAI API key
Supabase**: Add your Supabase URL and service role key
Google Drive**: Connect your Google account
Cohere**: Add your Cohere API key

3. Configure the workflow

In the "Download file" node, replace URL DO ARQUIVO with your Google Drive file URL
Adjust the table name in both Supabase Vector Store nodes if needed
Customize the agent's tool description in the "searchCafeina" node

4. Load your documents

Execute the bottom workflow (starting with "When clicking 'Execute workflow'")
This will download your PDF, extract text, and store it in Supabase
You can repeat this process for multiple documents

5. Start chatting

Once documents are loaded, activate the main workflow and start chatting with your AI assistant through the chat interface.

How to customize

Different document types**: Replace the Google Drive node with other sources (Dropbox, S3, local files)
Multiple knowledge bases**: Create separate vector stores for different topics
Custom prompts**: Modify the agent's system message for specific use cases
Language models**: Switch between different OpenAI models or use other LLM providers
Reranking settings**: Adjust the top-k parameter for more or fewer search results
Memory window**: Configure the conversation memory buffer size

Tips for best results

Use high-quality, well-structured documents for better search accuracy
Keep document chunks reasonably sized for optimal retrieval
Regularly update your vector store with new information
Monitor token usage to optimize costs
Test different reranking thresholds for your use case

Common use cases

Customer Support**: Create bots that answer questions from product documentation
HR Assistant**: Build assistants that help employees find information in company policies
Educational Tutor**: Develop tutors that answer questions from course materials
Research Assistant**: Create tools that help researchers find relevant information in papers
Legal Helper**: Build assistants that search through legal documents and contracts