Document analysis & chatbot creation with Llama Parser, Gemini LLM & Pinecone DB

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Important notice

This workflow is provided as-is. Please review and test before using in production.

1. Workflow Overview

Description This automation workflow enables users to upload files via an N8N form, automatically analyzes the content using Google Gemini agents, and delivers the analyzed results via email alon...

Best for

Document Extraction automation workflows
AI RAG automation workflows
advanced n8n builders looking for reusable templates

Tools used

n8n-nodes-base.formtrigger, n8n-nodes-base.splitinbatches, n8n-nodes-base.if, n8n-nodes-base.aggregate, @n8n/n8n-nodes-langchain.lmchatgooglegemini, n8n-nodes-base.code, n8n-nodes-base.httprequest, n8n-nodes-base.markdown

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by pavith.

Original n8n.io source

1.1 Workflow description

Title: Document analysis & chatbot creation with Llama Parser, Gemini LLM & Pinecone DB
Workflow name: Document analysis & chatbot creation with Llama Parser, Gemini LLM & Pinecone DB

📄Description

This automation workflow enables users to upload files via an N8N form, automatically analyzes the content using Google Gemini agents, and delivers the analyzed results via email along with a chatbot link. The system leverages Llama Cloud API, Google Gemini LLM, Pinecone vector database, and Gmail to provide a seamless, multilingual content analysis experience.

✅ Prerequisites

Before setting up this workflow, ensure the following are in place:

An active N8N instance.

Access to Llama Cloud API.

Google Gemini LLM API keys (for Translator & Analyzer agents).

A Pinecone account with an active index.

A Gmail account with API access configured.

Basic knowledge of N8N workflow setup.

⚙️ Setup Instructions

Deploy the N8N Form

Create a public-facing form using N8N.

Configure it to accept:

File uploads.

User email input.

File Preprocessing

Store the uploaded files temporarily.

Organize and preprocess them as needed.

Content Extraction using Llama Cloud API

Feed the files into the Llama Cloud API.

Extract and parse the content for further processing.

Translation (if required)

Use a Translator Agent (Google Gemini).

Check if the content is in English. If not, translate it.

Content Analysis

Forward the (translated) content to the Analyzer Agent (Google Gemini).

Perform deep analysis to extract insights.

Vector Storage in Pinecone

Store both:

The parsed and translated content.

The analyzed content.

Use Pinecone to store the content as embeddings for chatbot use.

User Notification via Gmail

Send the analyzed content and chatbot link to the user’s provided email using Gmail API.

🧩 Customization Guidance

To add more languages: Update the translation logic to include additional language support.

To modify analysis depth: Adjust the prompts sent to the Gemini Analyzer Agent.

To change the chatbot behavior: Retrain or reconfigure the chatbot to utilize the new Pinecone index contextually.

🔁 Workflow Summary

User uploads files and email via N8N form.

Files are parsed using Llama Cloud API.

Content is translated (if needed) using Gemini Translator Agent.

Translated content is analyzed by the Gemini Analyzer Agent.

Parsed and analyzed data is stored in Pinecone.

User receives email with analyzed results and a chatbot link.

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - On form submission4

Type / Role: n8n-nodes-base.formTrigger - formTrigger
Config choices: Version 2.2

Block 2 - Loop Over Items1

Type / Role: n8n-nodes-base.splitInBatches - splitInBatches
Config choices: Version 3

Block 3 - If2

Type / Role: n8n-nodes-base.if - if
Config choices: Version 2.2

Block 4 - Aggregate1

Type / Role: n8n-nodes-base.aggregate - aggregate
Config choices: Version 1

Block 5 - Google Gemini Chat Model5

Type / Role: @n8n/n8n-nodes-langchain.lmChatGoogleGemini - lmChatGoogleGemini
Config choices: Version 1

Block 6 - split the binary item

Type / Role: n8n-nodes-base.code - code
Config choices: Version 2

Block 7 - Parsing the document

Type / Role: n8n-nodes-base.httpRequest - httpRequest
Config choices: Version 4.2

Block 8 - Check the parsing status

Type / Role: n8n-nodes-base.httpRequest - httpRequest
Config choices: Version 4.2

Block 9 - Provide the markdown

Type / Role: n8n-nodes-base.httpRequest - httpRequest
Config choices: Version 4.2

Block 10 - Google Gemini Chat Model6

Type / Role: @n8n/n8n-nodes-langchain.lmChatGoogleGemini - lmChatGoogleGemini
Config choices: Version 1

Block 11 - Markdown

Type / Role: n8n-nodes-base.markdown - markdown
Config choices: Version 1

Block 12 - Gmail

Type / Role: n8n-nodes-base.gmail - gmail
Config choices: Version 2.1

Block 13 - Code

Type / Role: n8n-nodes-base.code - code
Config choices: Version 2

Block 14 - Convert to File

Type / Role: n8n-nodes-base.convertToFile - convertToFile
Config choices: Version 1.1

Block 15 - AI Agent

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 1.8

Block 16 - Google Gemini Chat Model

Type / Role: @n8n/n8n-nodes-langchain.lmChatGoogleGemini - lmChatGoogleGemini
Config choices: Version 1

Block 17 - Information Extractor

Type / Role: @n8n/n8n-nodes-langchain.informationExtractor - informationExtractor
Config choices: Version 1

Block 18 - Google Gemini Chat Model1

Type / Role: @n8n/n8n-nodes-langchain.lmChatGoogleGemini - lmChatGoogleGemini
Config choices: Version 1

Block 19 - Convert to File4

Type / Role: n8n-nodes-base.convertToFile - convertToFile
Config choices: Version 1.1

Block 20 - Pinecone Vector Store

Type / Role: @n8n/n8n-nodes-langchain.vectorStorePinecone - vectorStorePinecone
Config choices: Version 1.1

Block 21 - Embeddings Mistral Cloud

Type / Role: @n8n/n8n-nodes-langchain.embeddingsMistralCloud - embeddingsMistralCloud
Config choices: Version 1

Block 22 - Default Data Loader

Type / Role: @n8n/n8n-nodes-langchain.documentDefaultDataLoader - documentDefaultDataLoader
Config choices: Version 1

Block 23 - Recursive Character Text Splitter

Type / Role: @n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter - textSplitterRecursiveCharacterTextSplitter
Config choices: Version 1

Block 24 - When chat message received

Type / Role: @n8n/n8n-nodes-langchain.chatTrigger - chatTrigger
Config choices: Version 1.1

Showing the first 24 of 36 workflow blocks. Download the JSON for the full node graph.

3. Summary Table

Workflow	Document analysis & chatbot creation with Llama Parser, Gemini LLM & Pinecone DB
Complexity	advanced
Nodes	36
Categories	Document Extraction, AI RAG
Author	pavith
Published	19 Apr 2025

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/3606/3606.json as the source template for this automation.
2. Import the template into n8n

Open n8n, import the downloaded JSON, and review each node before activating the workflow.
3. Configure credentials and variables

Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
4. Test with sample data

Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
5. Activate and monitor

Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Download workflow JSON Original n8n.io source Document Extraction workflows AI RAG workflows

Frequently asked questions

What does Document analysis & chatbot creation with Llama Parser, Gemini LLM & Pinecone DB do?

Description This automation workflow enables users to upload files via an N8N form, automatically analyzes the content using Google Gemini agents, and delivers the analyzed results via email alon...

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Document Extraction, AI RAG use case.

pavith

1 workflow

Nodes

n8n-nodes-base.formtrigger n8n-nodes-base.splitinbatches n8n-nodes-base.if n8n-nodes-base.aggregate @n8n/n8n-nodes-langchain.lmchatgooglegemini n8n-nodes-base.code n8n-nodes-base.httprequest n8n-nodes-base.markdown

Complexity

advanced

Published 19 Apr 2025

Likes 0

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Install path: /data/workflows/3606/3606.json

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Document analysis & chatbot creation with Llama Parser, Gemini LLM & Pinecone DB

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

📄Description

✅ Prerequisites

⚙️ Setup Instructions

Translation (if required)

Content Analysis

Store both:

User Notification via Gmail

🧩 Customization Guidance

🔁 Workflow Summary

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - On form submission4

Block 2 - Loop Over Items1

Block 3 - If2

Block 4 - Aggregate1

Block 5 - Google Gemini Chat Model5

Block 6 - split the binary item

Block 7 - Parsing the document

Block 8 - Check the parsing status

Block 9 - Provide the markdown

Block 10 - Google Gemini Chat Model6

Block 11 - Markdown

Block 12 - Gmail

Block 13 - Code

Block 14 - Convert to File

Block 15 - AI Agent

Block 16 - Google Gemini Chat Model

Block 17 - Information Extractor

Block 18 - Google Gemini Chat Model1

Block 19 - Convert to File4

Block 20 - Pinecone Vector Store

Block 21 - Embeddings Mistral Cloud

Block 22 - Default Data Loader

Block 23 - Recursive Character Text Splitter

Block 24 - When chat message received

3. Summary Table

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

2. Import the template into n8n

3. Configure credentials and variables

4. Test with sample data

5. Activate and monitor

5. General Notes & Resources

Frequently asked questions