AI Tax Code Assistant with Qdrant, Mistral & OpenAI
Build an AI-powered legal assistant chatbot for Texas tax codes. This workflow automates PDF data extraction, uses Mistral for embeddings, stores data in Qdrant, and answers user queries with an OpenAI-powered agent.
Overview
Automates the creation of a RAG-based chatbot that answers questions about legal documents by ingesting PDFs into a Qdrant vector store and using an AI agent for retrieval.
🧠 Description & Use Case
This workflow builds a complete Retrieval-Augmented Generation (RAG) system to create an AI-powered legal assistant that can answer questions about the Texas tax code. The workflow is divided into two main parts: a data ingestion pipeline and an interactive chatbot agent.
🔄 How It Works:
Part 1: Data Ingestion & Vector Storage
This part of the workflow is triggered manually to process and store the legal documents.
- Fetch & Extract Data: The workflow begins by downloading a zip file of Texas tax code PDFs from a government website using an
HTTP Requestnode. The files are then unzipped and their text content is extracted. - Structure Content: Using
Setnodes and regular expressions, the raw text from each PDF is strategically parsed into structured sections, including chapter, section number, title, and content. This structured approach is crucial for accurate retrieval later. - Generate Embeddings: The processed sections are passed in batches to a
Qdrant Vector Storenode. This node uses Mistral Cloud to generate vector embeddings for each piece of content. - Store in Qdrant: The text, its corresponding embedding, and the structured metadata (chapter, section, etc.) are saved into a Qdrant collection. A
Waitnode is used to throttle requests to the Mistral API to avoid rate limits.
Part 2: The AI Chatbot Agent
This part is triggered by a user's message and uses the stored data to provide answers.
- Chat Trigger: A
Chat Triggernode captures user input and maintains conversation history using aWindow Buffer Memorynode. - AI Agent: The user's query is sent to an
AI Agentnode, which uses an OpenAI Chat Model as its reasoning engine. The agent is equipped with two specialized tools to interact with the Qdrant database:- Ask Tool: For semantic search. When the user asks a question, this tool converts the query into a Mistral embedding and performs a vector search in Qdrant to find the most contextually relevant tax code sections.
- Search Tool: For direct lookups. If a user requests a specific section or chapter by name (e.g., "Get me section 1.01"), this tool uses the Qdrant Scroll API to filter and retrieve the exact document based on its metadata.
- Generate Response: The AI Agent receives the data from the chosen tool and uses it to formulate a comprehensive, human-readable answer, referencing the specific tax code chapter and section it used.
✅ Real-World Use Cases:
- Legal Research: Empowers lawyers, paralegals, and accountants to quickly find relevant statutes and information within vast legal documents, saving hours of manual research.
- Citizen & Business Assistance: Creates a public-facing chatbot to help citizens and business owners understand their tax obligations in plain language.
- Corporate Knowledge Base: Adapt this workflow to ingest any internal documentation (e.g., company policies, compliance guides, technical manuals) to create an expert internal assistant for employees.
- Educational Tool: Provides an interactive platform for students or professionals to learn about complex legal or regulatory frameworks.
Instructions
Basic steps
- Download workflow JSON
- Import into n8n
- Configure node parameters as needed
- Test and enable the workflow
💡 Tips
After importing, verify all connectors and credentials match your environment.
Preview
Workflow Visualization
Nodes
When clicking ‘Test workflow’
manualTrigger
Embeddings Mistral Cloud
@n8n/langchain.embeddingsMistralCloud
Default Data Loader
@n8n/langchain.documentDefaultDataLoader
Recursive Character Text Splitter
@n8n/langchain.textSplitterRecursiveCharacterTextSplitter
Get Tax Code Zip File
httpRequest
Extract Zip Files
compression
Files as Items
splitOut
Extract PDF Contents
extractFromFile
Extract From Chapter
set
Map To Sections
set
Execute Workflow Trigger
executeWorkflowTrigger
Get Mistral Embeddings
httpRequest
Content Chunking @ 50k Chars
set
Split Out Chunks
splitOut
For Each Section...
splitInBatches
Sections To List
splitOut
Only Valid Sections
filter
Use Qdrant Search API1
httpRequest
Use Qdrant Scroll API
httpRequest
Get Search Response
set
Sticky Note
stickyNote
Sticky Note1
stickyNote
Sticky Note2
stickyNote
Qdrant Vector Store
@n8n/langchain.vectorStoreQdrant
Sticky Note3
stickyNote
Sticky Note4
stickyNote
AI Agent
@n8n/langchain.agent
Window Buffer Memory
@n8n/langchain.memoryBufferWindow
When chat message received
@n8n/langchain.chatTrigger
Window Buffer Memory1
@n8n/langchain.memoryBufferWindow
OpenAI Chat Model
@n8n/langchain.lmChatOpenAi
1sec
wait
Ask Tool
@n8n/langchain.toolWorkflow
Search Tool
@n8n/langchain.toolWorkflow
Switch
switch
Get Ask Response
set
Sticky Note5
stickyNote
Sticky Note6
stickyNote
Statistics
View full JSON structure
{
"nodes": [
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"parameters": {},
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"parameters": {
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"name": "Default Data Loader",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
1900,
560
],
"parameters": {
"options": {
"metadata": {
"metadataValues": [
{
"name": "chapter",
"value": "={{ $('For Each Section...').item.json.chapter }}"
},
{
"name": "section",
"value": "={{ $('For Each Section...').item.json.label }}"
},
{
"name": "=title",
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{
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"name": "Get Tax Code Zip File",
"type": "n8n-nodes-base.httpRequest",
"position": [
-20,
340
],
"parameters": {
"url": "https://statutes.capitol.texas.gov/Docs/Zips/TX.pdf.zip",
"options": {
"response": {
"response": {
"responseFormat": "file"
}
}
}
},
"typeVersion": 4.2
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{
"id": "cf983315-fe2a-43c1-8dc6-b17a217b845e",
"name": "Extract Zip Files",
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{
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{
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"name": "Extract PDF Contents",
"type": "n8n-nodes-base.extractFromFile",
"position": [
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"parameters": {
"options": {},
"operation": "pdf",
"binaryPropertyName": "=file_{{ $itemIndex }}"
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{
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"name": "Extract From Chapter",
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},
{
"id": "bc06641f-0b75-4a35-8752-78803231d5d6",
"name": "labels",
"type": "array",
"value": "={{\n $json.text\n .match(/\\nSec\\.\\nA[0-9]{1,4}\\.[0-9]{1,5}\\.AA/g)\n .map(text => ({\n label: text.replaceAll('AA', ' ')\n .replaceAll('\\nA', ' ')\n .replaceAll('\\n', '')\n .trim()\n }))\n}}"
}
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}
},
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{
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"name": "Map To Sections",
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"parameters": {
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"assignments": {
"assignments": [
{
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"name": "section",
"type": "array",
"value": "={{\n $json.labels.map((label, idx) => ({\n label: label.label.match(/\\d.+/)[0].replace(/\\.$/, ''),\n title: $json.contents[idx].title,\n content: $json.contents[idx].content,\n chapter: $('Extract PDF Contents').first().json.info.Title,\n }))\n}}"
}
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},
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{
"id": "41c9899d-26d7-48af-9af2-8563ab0fb7e4",
"name": "Execute Workflow Trigger",
"type": "n8n-nodes-base.executeWorkflowTrigger",
"position": [
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],
"parameters": {},
"typeVersion": 1
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{
"id": "3a93c19b-09d9-4e38-8b0c-2008fc03f7fc",
"name": "Get Mistral Embeddings",
"type": "n8n-nodes-base.httpRequest",
"position": [
1660,
1060
],
"parameters": {
"url": "https://api.mistral.ai/v1/embeddings",
"method": "POST",
"options": {},
"sendBody": true,
"authentication": "predefinedCredentialType",
"bodyParameters": {
"parameters": [
{
"name": "model",
"value": "mistral-embed"
},
{
"name": "encoding_format",
"value": "float"
},
{
"name": "input",
"value": "={{ $json.query }}"
}
]
},
"nodeCredentialType": "mistralCloudApi"
},
"credentials": {
"mistralCloudApi": {
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"value": "={{ new Array(Math.round($json.content.length / Math.min($json.content.length, 30000))).fill('').map((_,idx) => $json.content.substring(idx * 30000, idx * 50000 + 30000)) }}"
}
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},
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{
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},
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"conditions": {
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"caseSensitive": true,
"typeValidation": "strict"
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"combinator": "or",
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"position": [
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"url": "=http://qdrant:6333/collections/texas_tax_codes/points/search",
"method": "POST",
"options": {},
"sendBody": true,
"authentication": "predefinedCredentialType",
"bodyParameters": {
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"value": "={{ 4 }}"
},
{
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}
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"url": "=http://qdrant:6333/collections/texas_tax_codes/points/scroll",
"method": "POST",
"options": {
"pagination": {
"pagination": {
"parameters": {
"parameters": [
{
"name": "next_page_offset",
"type": "body",
"value": "={{ $response.body.result.next_page_offset }}"
}
]
},
"completeExpression": "={{ $response.body.result.next_page_offset === null }}",
"paginationCompleteWhen": "other"
}
}
},
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"bodyParameters": {
"parameters": [
{
"name": "limit",
"value": "={{ 100 }}"
},
{
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"value": "={{ true }}"
},
{
"name": "filter",
"value": "={{\n{\n \"must\": [\n ($json.query.section\n ? { \"key\": \"metadata.section\", \"match\": { \"value\": $json.query.section } }\n : { \"key\": \"metadata.chapter\", \"match\": { \"value\": $json.query.chapter } }\n )\n ]\n}\n}}"
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},
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{
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"type": "n8n-nodes-base.set",
"position": [
1860,
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],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "08ad2d6e-4ed1-409e-b89c-1f0c7fdf1b64",
"name": "response",
"type": "string",
"value": "=---\nchapter: {{ $json.result.points.first().payload.metadata.chapter }}\nsection: {{ $json.result.points.first().payload.metadata.section }}\ntitle: {{ $json.result.points.first().payload.metadata.title }}\n---\n{{ $json.result.points\n .toSorted((a,b) => (a.payload.metadata.content_order || 0) - (b.payload.metadata.content_order || 0))\n .map(point => point.payload.content).join('\\n') }}"
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},
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{
"id": "3b23ff5e-158a-470f-a262-d001d52feeba",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-100,
183.3834555411308
],
"parameters": {
"color": 7,
"width": 571.4359274276384,
"height": 352.656423392306,
"content": "## Step 1. Download the Tax Code PDF\n[Read more about handling Zip Files](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.compression/)\n\nLet's begin by pulling a zip file containing all the tax codes as separate PDF files. We can unzip on the fly with n8n's compression node."
},
"typeVersion": 1
},
{
"id": "02826887-eb26-48a0-928e-fe56ee008425",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
500,
199.877472306559
],
"parameters": {
"color": 7,
"width": 777.897719182587,
"height": 503.3459981018574,
"content": "## Step 2. Extract and Partition Into Chapters & Sections\n[Learn more about reading PDF Files](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.extractfromfile)\n\nRather than ingest the raw text of the PDF, we'll be a little more strategic and extract the tax code sections separately instead. Not only will this provide cleaner results, we'll also be able to fetch sections in isolation if required."
},
"typeVersion": 1
},
{
"id": "31a34972-31ab-4b96-9d09-cd30a3b184cf",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
1300,
108.82958126396
],
"parameters": {
"color": 7,
"width": 1045.169868624875,
"height": 771.1260499456115,
"content": "## Step 3. Save into Qdrant VectorStore\n[Read more about using the Qdrant Vectorstore](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstoreqdrant)\n\nWe'll save our data into a Qdrant collection being mindful to use metadata to take full advantage of Qdrant's filtering capabilities later.\nThough not always required, since the tax code documents can be quite large we'll implement a loop here to throttle the number of tokens being processed as to not trip the Mistral.ai rate limits for embeddings."
},
"typeVersion": 1
},
{
"id": "27039fa6-6388-45ee-a2d5-6bb68554944b",
"name": "Qdrant Vector Store",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"position": [
1760,
400
],
"parameters": {
"mode": "insert",
"options": {},
"qdrantCollection": {
"__rl": true,
"mode": "list",
"value": "texas_tax_codes",
"cachedResultName": "texas_tax_codes"
}
},
"credentials": {
"qdrantApi": {
"id": "NyinAS3Pgfik66w5",
"name": "QdrantApi account"
}
},
"typeVersion": 1
},
{
"id": "5ec16c20-eb1e-454a-8165-594d83dd8711",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
360,
900
],
"parameters": {
"color": 7,
"width": 858.1415560000298,
"height": 513.2269439624808,
"content": "## Step 4. Build a Tax Code Assistant ChatBot\n[Learn more about using AI Agents in n8n](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.agent)\n\nFor our chatbot, we'll use an AI agent node because we want to achieve more than one functionality. The first will be querying to relevant texts to answer a user's question and secondly, a direct search feature to pull full section text when requested."
},
"typeVersion": 1
},
{
"id": "d5145c6f-768b-42d8-a045-20e045f52b0b",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
1240,
904.6076722083936
],
"parameters": {
"color": 7,
"width": 1030.092685070674,
"height": 577.7854680142904,
"content": "## Step 5. Use Qdrant API as Tools\n[Learn more about using AI Agents in n8n](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.agent)\n\nOur Ask Tool will generate embeddings using Mistral.ai and query our Qdrant collection using the Qdrant Search API.\nOur Search Tool will use filter our Qdrant collection using the Qdrant Scroll API, matching on each doc's section metadata key."
},
"typeVersion": 1
},
{
"id": "ccf50479-53d8-4edf-8f2b-73060a6a6e0f",
"name": "AI Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
700,
1063
],
"parameters": {
"options": {
"systemMessage": "You are a helpful assistant answering user questions on the tax code legistration for the state of Texas, united states of america.\n\nAlong with your response also note in which chapter and section number the information was found. "
}
},
"typeVersion": 1.6
},
{
"id": "d7e7fa9e-73ba-4df3-862e-25af63d9d9b4",
"name": "Window Buffer Memory",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"position": [
820,
1223
],
"parameters": {},
"typeVersion": 1.2
},
{
"id": "a79bdbcd-7157-470a-aadc-bd3f8a4c40d2",
"name": "When chat message received",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
420,
1063
],
"webhookId": "db2b118d-942e-4be9-b154-7df887232f97",
"parameters": {
"public": true,
"options": {
"loadPreviousSession": "memory"
},
"initialMessages": ""
},
"typeVersion": 1
},
{
"id": "6046f137-b508-484f-8577-ac51a35eee09",
"name": "Window Buffer Memory1",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"position": [
420,
1223
],
"parameters": {},
"typeVersion": 1.2
},
{
"id": "30f238f8-1987-4d6d-b06d-ac2106ea3734",
"name": "OpenAI Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
700,
1223
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"id": "8gccIjcuf3gvaoEr",
"name": "OpenAi account"
}
},
"typeVersion": 1
},
{
"id": "8a8490f6-5957-495c-a7af-15cec669f39c",
"name": "1sec",
"type": "n8n-nodes-base.wait",
"position": [
2160,
660
],
"webhookId": "852317f0-aadf-4658-ae44-d05e5de29302",
"parameters": {
"amount": 1
},
"executeOnce": false,
"typeVersion": 1.1
},
{
"id": "142450f5-8ec1-4ae6-b25c-df3233394d4e",
"name": "Ask Tool",
"type": "@n8n/n8n-nodes-langchain.toolWorkflow",
"position": [
960,
1223
],
"parameters": {
"name": "query_tax_code_knowledgebase",
"fields": {
"values": [
{
"name": "route",
"stringValue": "ask_tool"
}
]
},
"workflowId": "={{ $workflow.id }}",
"description": "Call this tool to query the tax code database for information. Structure your query in the form of a question for best results."
},
"typeVersion": 1.1
},
{
"id": "ee455a4e-c9a1-49b2-a036-d3f3d34099c6",
"name": "Search Tool",
"type": "@n8n/n8n-nodes-langchain.toolWorkflow",
"position": [
1060,
1223
],
"parameters": {
"name": "get_tax_code_section",
"fields": {
"values": [
{
"name": "route",
"stringValue": "search_tool"
}
]
},
"workflowId": "={{ $workflow.id }}",
"description": "Call this tool to search for specific sections of the tax code document. Pass in either a known section number/id to get the section's text or a known chapter name to return all sections for the chapter.",
"jsonSchemaExample": "{\n\t\"chapter\": \"some_value\",\n \"section\": \"Sec 1.01\"\n}",
"specifyInputSchema": true
},
"typeVersion": 1.1
},
{
"id": "f3240f8d-8869-4088-8e4f-d4e23a3c12a8",
"name": "Switch",
"type": "n8n-nodes-base.switch",
"position": [
1473,
1200
],
"parameters": {
"rules": {
"values": [
{
"outputKey": "ask_tool",
"conditions": {
"options": {
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"operator": {
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.route }}",
"rightValue": "ask_tool"
}
]
},
"renameOutput": true
},
{
"outputKey": "search_tool",
"conditions": {
"options": {
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "909362ed-eb97-405c-9f2f-f404a3bfeaf3",
"operator": {
"name": "filter.operator.equals",
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.route }}",
"rightValue": "search_tool"
}
]
},
"renameOutput": true
}
]
},
"options": {}
},
"typeVersion": 3
},
{
"id": "71441b5a-099b-49e0-a212-3087d958b38b",
"name": "Get Ask Response",
"type": "n8n-nodes-base.set",
"position": [
2060,
1060
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "eb5f2b3c-bb88-4cae-a960-164016c9a9e4",
"name": "response",
"type": "string",
"value": "=|chapter|section|title|content|\n|-|-|-|-|\n{{\n $json.result.map(row => [\n '',\n row.payload.metadata.chapter,\n row.payload.metadata.section,\n row.payload.metadata.title,\n row.payload.content,\n ''\n ].join('|')).join('\\n')\n}}"
}
]
}
},
"typeVersion": 3.3
},
{
"id": "54a744a3-95c9-4d9a-b1e7-e266a51f77ca",
"name": "Sticky Note5",
"type": "n8n-nodes-base.stickyNote",
"position": [
-520,
-79.56762868134751
],
"parameters": {
"width": 383.1486879446259,
"height": 563.604204119637,
"content": "## Try Me Out!\n### This workflow builds an AI powered Legal assistant who answers questions about tax codes.\n* Download publically available tax code PDFs from the relevant government website.\n* Strategically exact tax code sections and store these in our Qdrant Vectorstore using Mistral.ai embeddings.\n* Use an AI Agent to answer user's tax questions by attaching tools which query our Qdrant vectorstore.\n\n### Need Help?\nJoin the [Discord](https://discord.com/invite/XPKeKXeB7d) or ask in the [Forum](https://community.n8n.io/)!\n\nHappy Hacking!"
},
"typeVersion": 1
},
{
"id": "7f802f12-03e0-4b8e-a880-8c26242c1152",
"name": "Sticky Note6",
"type": "n8n-nodes-base.stickyNote",
"position": [
790.1971986436472,
720
],
"parameters": {
"color": 5,
"width": 489.3944544742706,
"height": 131.6136393281317,
"content": "### 🙋♀️What's the difference?\nWith raw PDF data, we may blur the boundaries between chapters and sections making later results hard to find, incoherent or misleading.\nDepending on your use-case, store your data in a way you intend to retrieve it!"
},
"typeVersion": 1
}
],
"connections": {
"1sec": {
"main": [
[
{
"node": "For Each Section...",
"type": "main",
"index": 0
}
]
]
},
"Switch": {
"main": [
[
{
"node": "Get Mistral Embeddings",
"type": "main",
"index": 0
}
],
[
{
"node": "Use Qdrant Scroll API",
"type": "main",
"index": 0
}
]
]
},
"Ask Tool": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"Search Tool": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"Files as Items": {
"main": [
[
{
"node": "Extract PDF Contents",
"type": "main",
"index": 0
}
]
]
},
"Map To Sections": {
"main": [
[
{
"node": "Sections To List",
"type": "main",
"index": 0
}
]
]
},
"Sections To List": {
"main": [
[
{
"node": "Only Valid Sections",
"type": "main",
"index": 0
}
]
]
},
"Split Out Chunks": {
"main": [
[
{
"node": "Qdrant Vector Store",
"type": "main",
"index": 0
}
]
]
},
"Extract Zip Files": {
"main": [
[
{
"node": "Files as Items",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Default Data Loader": {
"ai_document": [
[
{
"node": "Qdrant Vector Store",
"type": "ai_document",
"index": 0
}
]
]
},
"For Each Section...": {
"main": [
null,
[
{
"node": "Content Chunking @ 50k Chars",
"type": "main",
"index": 0
}
]
]
},
"Only Valid Sections": {
"main": [
[
{
"node": "For Each Section...",
"type": "main",
"index": 0
}
]
]
},
"Qdrant Vector Store": {
"main": [
[
{
"node": "1sec",
"type": "main",
"index": 0
}
]
]
},
"Extract From Chapter": {
"main": [
[
{
"node": "Map To Sections",
"type": "main",
"index": 0
}
]
]
},
"Extract PDF Contents": {
"main": [
[
{
"node": "Extract From Chapter",
"type": "main",
"index": 0
}
]
]
},
"Window Buffer Memory": {
"ai_memory": [
[
{
"node": "AI Agent",
"type": "ai_memory",
"index": 0
}
]
]
},
"Get Tax Code Zip File": {
"main": [
[
{
"node": "Extract Zip Files",
"type": "main",
"index": 0
}
]
]
},
"Use Qdrant Scroll API": {
"main": [
[
{
"node": "Get Search Response",
"type": "main",
"index": 0
}
]
]
},
"Window Buffer Memory1": {
"ai_memory": [
[
{
"node": "When chat message received",
"type": "ai_memory",
"index": 0
}
]
]
},
"Get Mistral Embeddings": {
"main": [
[
{
"node": "Use Qdrant Search API1",
"type": "main",
"index": 0
}
]
]
},
"Use Qdrant Search API1": {
"main": [
[
{
"node": "Get Ask Response",
"type": "main",
"index": 0
}
]
]
},
"Embeddings Mistral Cloud": {
"ai_embedding": [
[
{
"node": "Qdrant Vector Store",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Execute Workflow Trigger": {
"main": [
[
{
"node": "Switch",
"type": "main",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"Content Chunking @ 50k Chars": {
"main": [
[
{
"node": "Split Out Chunks",
"type": "main",
"index": 0
}
]
]
},
"Recursive Character Text Splitter": {
"ai_textSplitter": [
[
{
"node": "Default Data Loader",
"type": "ai_textSplitter",
"index": 0
}
]
]
},
"When clicking ‘Test workflow’": {
"main": [
[
{
"node": "Get Tax Code Zip File",
"type": "main",
"index": 0
}
]
]
}
}
}Actions
Technical Specs
Compatibility
License
This workflow template follows MIT license, you can freely use, modify and distribute.
Please comply with relevant third-party service terms when using.