This article provides a complete, practical walkthrough of the Manual Code Create Triggered n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title: Chat with Documents Using AI: Build a Smart Document Q&A Assistant in n8n with Pinecone and OpenAI
   
   Meta Description: Learn how to build an AI-powered document Q&A assistant in n8n by integrating Google Drive, OpenAI, and Pinecone. This workflow allows users to upload a file, convert it into embeddings, and chat with it via citations.
   
   Keywords: n8n, Pinecone, OpenAI, Google Drive, document Q&A, AI chatbot, AI workflow automation, vector database, embeddings, RAG, LangChain, file parsing, chat with documents
   
   Third-Party APIs Used:
   
   - Google Drive API (for downloading documents)
   - OpenAI API (for generating embeddings and AI responses via GPT)
   - Pinecone API (for vector storage and semantic search across document content)
   
   Article:
   
   Build an AI-Powered Document Q&A Chatbot in n8n Using Pinecone, OpenAI, and Google Drive
   
   The rapid rise of vector databases and large language models (LLMs) has changed how we interact with documents. A key use case is enabling users to “chat with their documents”—where the document acts as a knowledge base that can be queried with natural language. In this article, we'll show you how to build an automated document Q&A chatbot in n8n, powered by OpenAI, Pinecone, and Google Drive.
   
   By the end of this project, you'll have a no-code/low-code workflow that can:
   
   - Download a file from Google Drive (e.g., the Bitcoin whitepaper),
   - Split its contents into semantic chunks,
   - Embed text chunks into a vector database (Pinecone),
   - Allow users to submit natural language queries and get LLM-generated responses with citations to the original text.
   
   Step 1: Fetch and Process Your Document
   
   The workflow begins with a manual trigger in n8n (“When clicking ‘Execute Workflow’”) that initiates the data import process. A Google Drive Set node configures the download URL of the desired file — by default, the Bitcoin whitepaper. Then, the Google Drive node downloads the binary file using the file's ID, extracting it directly into the workflow.
   
   The file is passed to the LangChain Default Data Loader for preprocessing and metadata attachment, such as the file name and URL.
   
   Step 2: Split and Embed Text into Pinecone
   
   Next, the document content goes through a Recursive Character Text Splitter, breaking the text into manageable chunks — in this case, 3000 characters with 200-character overlaps. These chunks are ideal for embedding, ensuring the LLM can later infer accurate context during queries.
   
   Text embeddings are generated using the OpenAI Embeddings node, which transforms each chunk into a 1536-dimensional vector suitable for semantic comparison.
   
   Finally, these vectors, along with their metadata, are stored in Pinecone using the Pinecone Vector Store's insert mode. Users are reminded to avoid running this multiple times unless intentionally adding duplicates to Pinecone.
   
   Step 3: Query and Chat with Your Document
   
   When a user inputs a question via chat (via the “When chat message received” LangChain trigger), the system first sets how many matching document chunks to retrieve (e.g., top 4). The workflow then generates an embedding for the queried message, uses Pinecone to pull the top matching chunks, and formats them into a structured context.
   
   A custom code node compiles the chunks into a readable context string for the model with chunk indexes. This is passed to LangChain's Information Extractor node, prompting it to generate a helpful answer based solely on the matched document context. It uses a system message instructing the model to avoid hallucinations and report which chunks the answer came from.
   
   Step 4: Include Citations
   
   To ensure credibility and traceability, the system assembles structured citations for each chunk used in the response. These include the original file name and line ranges (if available) from Pinecone metadata.
   
   A final Set node generates the full output, combining the LLM's answer with inline citations. For example:
   
   > "The creator of Bitcoin used a Gmail address.  
   >  [Bitcoin_Whitepaper.pdf, lines 126–142]."
   
   Use Case in Action
   
   To test this project, the instructions embedded in the n8n workflow suggest the following:
   
   1. Create a Pinecone index with 1536 dimensions.
   2. Set this index in the workflow’s Pinecone configuration.
   3. Click the “Execute Workflow” button to populate the vector database.
   4. Ask a question via the chat trigger, such as:
   
      “Which email provider does the creator of Bitcoin use?”
   
   The system will return an answer with references to the relevant chunk indexes.
   
   Conclusion
   
   Through just a few configured nodes, this n8n workflow demonstrates how powerful and accessible AI-powered document search has become. Leveraging vector embeddings, semantic search, and natural language processing, you can now interact intelligently with your documents — answering precise questions with confidence and adding citations for transparency.
   
   Best of all, it’s done within a scalable, reproducible automation platform — and entirely visual thanks to n8n's node-based interface.
   
   This project is not limited to Bitcoin's whitepaper. You can upload any document, change the file URL, and re-index content into Pinecone.
   
   Future extensions might include Slack or Telegram integrations for real-time Q&A delivery, automated summarization, long-document processing with chunk memory, and more.
   
   If you're working with research papers, legal documents, company policies, or user manuals — this setup can save hours of manual lookup.
   
   n8n and LangChain have made it easy, and now it's up to you to customize and scale!
   
   Start chatting with your documents today.
   
   — Written by your AI Assistant 🚀

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.