n8n Full Stack App with Streamlit + Claude + Supabase

This commit is contained in:
Cole Medin
2024-10-02 17:19:39 -05:00
parent 07cd054a1b
commit 0e5e87ba60
7 changed files with 1843 additions and 1 deletions

3
.gitignore vendored
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@@ -9,4 +9,5 @@ creds
credentials
credentials.json
token.json
node_modules
node_modules
venv

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@@ -0,0 +1,740 @@
{
"name": "Supabase RAG AI Agent",
"nodes": [
{
"parameters": {
"model": "gpt-4o-mini",
"options": {}
},
"id": "b98c17c5-3bef-4378-8d81-61b127711661",
"name": "OpenAI Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"typeVersion": 1,
"position": [
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],
"credentials": {
"openAiApi": {
"id": "JJjD91oisPv9cs01",
"name": "OpenAi account"
}
}
},
{
"parameters": {
"model": "gpt-4o-mini",
"options": {}
},
"id": "e850aa69-1910-4986-99f2-214a8759b777",
"name": "OpenAI Chat Model1",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"typeVersion": 1,
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],
"credentials": {
"openAiApi": {
"id": "JJjD91oisPv9cs01",
"name": "OpenAi account"
}
}
},
{
"parameters": {
"jsonMode": "expressionData",
"jsonData": "={{ $json.data }}",
"options": {
"metadata": {
"metadataValues": [
{
"name": "=file_id",
"value": "={{ $('Set File ID').item.json.file_id }}"
}
]
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}
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},
{
"parameters": {
"model": "text-embedding-3-small",
"options": {}
},
"id": "5eaf6315-fcd0-4a4d-a1b3-37f5793f6e5c",
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"credentials": {
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"name": "OpenAi account"
}
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{
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"name": "user_documents",
"description": "Contains all the user's documents that you can check for context to answer user questions."
},
"id": "b3868e8d-fbce-4c16-8174-0e5475cfffba",
"name": "Retrieve Documents",
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{
"parameters": {
"content": "## Agent Tools for RAG",
"height": 528.85546469693,
"width": 583.4552380860637,
"color": 4
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"content": "## Tool to Add a Google Drive File to Vector DB",
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"width": 2070.8894079025763,
"color": 5
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"value": "1914m3M7kRzkd5RJqAfzRY9EBcJrKemZC",
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"name": "OpenAi account"
}
}
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"id": "8920aea0-c877-47b3-9026-d8ab57e8579d",
"name": "Postgres Chat Memory",
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"notesInFlow": false,
"credentials": {
"postgres": {
"id": "tzFFADvpFiUtaZtb",
"name": "Supabase Postgres"
}
}
},
{
"parameters": {
"options": {}
},
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"name": "Recursive Character Text Splitter",
"type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
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{
"parameters": {
"operation": "delete",
"tableId": "documents",
"filterType": "string",
"filterString": "=metadata->>file_id=like.*{{ $json.file_id }}*"
},
"id": "bd9eb3dc-75b9-4577-a210-1951988a389e",
"name": "Delete Old Doc Rows",
"type": "n8n-nodes-base.supabase",
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"position": [
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],
"alwaysOutputData": true,
"credentials": {
"supabaseApi": {
"id": "azIAMHxdSaq5XhoW",
"name": "Supabase account"
}
}
},
{
"parameters": {
"assignments": {
"assignments": [
{
"id": "10646eae-ae46-4327-a4dc-9987c2d76173",
"name": "file_id",
"value": "={{ $json.id }}",
"type": "string"
}
]
},
"options": {}
},
"id": "0b439eba-c467-475b-a1a2-481f636f37fe",
"name": "Set File ID",
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},
{
"parameters": {
"content": "## RAG AI Agent with Chat Interface",
"height": 464.8027193303974,
"width": 1035.6381264595484
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},
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"name": "Supabase Vector Store",
"type": "@n8n/n8n-nodes-langchain.vectorStoreSupabase",
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"text": "={{ $json.chatInput }}",
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"systemMessage": "You are a personal assistant who helps answer questions from a corpus of documents when you don't know the answer yourself."
}
},
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{
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},
"options": {
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"id": "6c7181ba-9e33-4972-83b4-0efaac7e16a2",
"name": "Insert into Supabase Vectorstore",
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{
"parameters": {
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}
],
"pinData": {},
"connections": {
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[
{
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"Download File": {
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"File Created": {
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{
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"Embeddings OpenAI": {
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{
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[
{
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},
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{
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}
]
]
},
"Default Data Loader": {
"ai_document": [
[
{
"node": "Insert into Supabase Vectorstore",
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}
]
]
},
"Postgres Chat Memory": {
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}
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},
"Delete Old Doc Rows": {
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{
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}
]
]
},
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"meta": {
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"id": "cri0nBz0U55sWr4I",
"tags": []
}

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@@ -0,0 +1,835 @@
{
"name": "Supabase RAG AI Agent Custom Auth",
"nodes": [
{
"parameters": {
"model": "gpt-4o-mini",
"options": {}
},
"id": "20216b42-d5fa-4d1e-a4fb-9549834fea99",
"name": "OpenAI Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"typeVersion": 1,
"position": [
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],
"credentials": {
"openAiApi": {
"id": "JJjD91oisPv9cs01",
"name": "OpenAi account"
}
}
},
{
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"options": {}
},
"id": "1e53e934-c7fa-4650-8f5e-106102a9494d",
"name": "OpenAI Chat Model1",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"typeVersion": 1,
"position": [
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],
"credentials": {
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"name": "OpenAi account"
}
}
},
{
"parameters": {
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"jsonData": "={{ $json.data }}",
"options": {
"metadata": {
"metadataValues": [
{
"name": "=file_id",
"value": "={{ $('Set File ID').item.json.file_id }}"
}
]
}
}
},
"id": "a766868e-c847-48b9-8b61-dcd5fcd9e280",
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"typeVersion": 1,
"position": [
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{
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"id": "23255e75-bfdb-46ac-8244-98c5e518bafe",
"name": "Embeddings OpenAI1",
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],
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"id": "JJjD91oisPv9cs01",
"name": "OpenAi account"
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}
},
{
"parameters": {
"name": "user_documents",
"description": "Contains all the user's documents that you can check for context to answer user questions."
},
"id": "62ca6e95-73ff-459c-8e29-6b38d12895c8",
"name": "Retrieve Documents",
"type": "@n8n/n8n-nodes-langchain.toolVectorStore",
"typeVersion": 1,
"position": [
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]
},
{
"parameters": {
"content": "## Agent Tools for RAG",
"height": 528.85546469693,
"width": 583.4552380860637,
"color": 4
},
"id": "53866fd6-ca7f-4174-b438-809c6d06eec0",
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"typeVersion": 1,
"position": [
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]
},
{
"parameters": {
"content": "## Tool to Add a Google Drive File to Vector DB",
"height": 671.8877842322804,
"width": 2070.8894079025763,
"color": 5
},
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{
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"mode": "id"
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}
}
}
},
"id": "705a761d-6b10-4e98-8aad-b21413f60005",
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"type": "n8n-nodes-base.googleDrive",
"typeVersion": 3,
"position": [
1360,
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],
"executeOnce": true,
"credentials": {
"googleDriveOAuth2Api": {
"id": "cfNochbuJikPwwl2",
"name": "Google Drive account"
}
}
},
{
"parameters": {
"pollTimes": {
"item": [
{
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]
},
"triggerOn": "specificFolder",
"folderToWatch": {
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"value": "1914m3M7kRzkd5RJqAfzRY9EBcJrKemZC",
"mode": "list",
"cachedResultName": "Meeting Notes",
"cachedResultUrl": "https://drive.google.com/drive/folders/1914m3M7kRzkd5RJqAfzRY9EBcJrKemZC"
},
"event": "fileCreated",
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],
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}
},
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"item": [
{
"mode": "everyMinute"
}
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],
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"name": "Google Drive account"
}
}
},
{
"parameters": {
"operation": "text",
"options": {}
},
"id": "ed74944a-418e-42c8-9a36-5d280dfa02f1",
"name": "Extract Document Text",
"type": "n8n-nodes-base.extractFromFile",
"typeVersion": 1,
"position": [
1620,
880
],
"alwaysOutputData": true
},
{
"parameters": {
"model": "text-embedding-3-small",
"options": {}
},
"id": "4a2ab29f-026a-4e67-9e70-cb647e901360",
"name": "Embeddings OpenAI",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"typeVersion": 1,
"position": [
1700,
560
],
"credentials": {
"openAiApi": {
"id": "JJjD91oisPv9cs01",
"name": "OpenAi account"
}
}
},
{
"parameters": {},
"id": "d3e47e92-65f7-483c-a730-13bcb26b26c5",
"name": "Postgres Chat Memory",
"type": "@n8n/n8n-nodes-langchain.memoryPostgresChat",
"typeVersion": 1,
"position": [
1060,
520
],
"notesInFlow": false,
"credentials": {
"postgres": {
"id": "tzFFADvpFiUtaZtb",
"name": "Supabase Postgres"
}
}
},
{
"parameters": {
"options": {}
},
"id": "45a38eca-0093-4263-932a-b915b81909c4",
"name": "Recursive Character Text Splitter",
"type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
"typeVersion": 1,
"position": [
2020,
1200
]
},
{
"parameters": {
"operation": "delete",
"tableId": "documents",
"filterType": "string",
"filterString": "=metadata->>file_id=like.*{{ $json.file_id }}*"
},
"id": "699533ea-e91e-4229-bd70-c9cb718411af",
"name": "Delete Old Doc Rows",
"type": "n8n-nodes-base.supabase",
"typeVersion": 1,
"position": [
1060,
880
],
"alwaysOutputData": true,
"credentials": {
"supabaseApi": {
"id": "azIAMHxdSaq5XhoW",
"name": "Supabase account"
}
}
},
{
"parameters": {
"assignments": {
"assignments": [
{
"id": "10646eae-ae46-4327-a4dc-9987c2d76173",
"name": "file_id",
"value": "={{ $json.id }}",
"type": "string"
}
]
},
"options": {}
},
"id": "ec2f04ff-2824-4b0f-ac57-740a6a103f3d",
"name": "Set File ID",
"type": "n8n-nodes-base.set",
"typeVersion": 3.4,
"position": [
740,
880
]
},
{
"parameters": {
"content": "## RAG AI Agent with Chat Interface",
"height": 464.8027193303974,
"width": 1397.1362723489267
},
"id": "12172aa3-12ff-46da-9883-c5bd45920259",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
65.97988322897118,
220
]
},
{
"parameters": {
"tableName": {
"__rl": true,
"value": "documents",
"mode": "list",
"cachedResultName": "documents"
},
"options": {
"queryName": "match_documents"
}
},
"id": "84b6ba04-1409-4eb8-8530-7fc5f450fae6",
"name": "Supabase Vector Store",
"type": "@n8n/n8n-nodes-langchain.vectorStoreSupabase",
"typeVersion": 1,
"position": [
1540,
440
],
"credentials": {
"supabaseApi": {
"id": "azIAMHxdSaq5XhoW",
"name": "Supabase account"
}
}
},
{
"parameters": {
"options": {}
},
"id": "9606a05d-8ab1-4aeb-9855-a69c29c09fe0",
"name": "Respond to Webhook",
"type": "n8n-nodes-base.respondToWebhook",
"typeVersion": 1.1,
"position": [
1300,
300
]
},
{
"parameters": {
"public": true,
"options": {}
},
"id": "64a6fef1-b254-4f39-9684-e29c9a4ff1b2",
"name": "When chat message received",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"typeVersion": 1.1,
"position": [
120,
300
],
"webhookId": "21ffce44-9a14-4739-a143-808087431b7f"
},
{
"parameters": {
"assignments": {
"assignments": [
{
"id": "9a9a245e-f1a1-4282-bb02-a81ffe629f0f",
"name": "chatInput",
"value": "={{ $json?.chatInput || $('Webhook').item.json.body.chatInput }}",
"type": "string"
},
{
"id": "b80831d8-c653-4203-8706-adedfdb98f77",
"name": "sessionId",
"value": "={{ $json?.sessionId || $('Webhook').item.json.body.sessionId }}",
"type": "string"
}
]
},
"options": {}
},
"id": "145612c9-aae5-4743-a0d2-085ce7741e5e",
"name": "Edit Fields",
"type": "n8n-nodes-base.set",
"typeVersion": 3.4,
"position": [
740,
300
]
},
{
"parameters": {
"promptType": "define",
"text": "={{ $json.chatInput }}",
"options": {
"systemMessage": "You are a personal assistant who helps answer questions from a corpus of documents when you don't know the answer yourself."
}
},
"id": "2e950dc6-8be1-4067-a1d4-a6265f6cd6c0",
"name": "RAG AI Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"typeVersion": 1.6,
"position": [
960,
300
]
},
{
"parameters": {
"mode": "insert",
"tableName": {
"__rl": true,
"value": "documents",
"mode": "list",
"cachedResultName": "documents"
},
"options": {
"queryName": "match_documents"
}
},
"id": "379f7875-c08e-4352-8958-7cf2606ba07c",
"name": "Insert into Supabase Vectorstore",
"type": "@n8n/n8n-nodes-langchain.vectorStoreSupabase",
"typeVersion": 1,
"position": [
1900,
780
],
"credentials": {
"supabaseApi": {
"id": "azIAMHxdSaq5XhoW",
"name": "Supabase account"
}
}
},
{
"parameters": {
"url": "https://[YOUR SUPABASE PROJECT URL]/auth/v1/user",
"sendHeaders": true,
"headerParameters": {
"parameters": [
{
"name": "apikey",
"value": "YOUR_SUPABASE_API_KEY"
},
{
"name": "Authorization",
"value": "={{ $json.headers.authorization }}"
}
]
},
"options": {}
},
"id": "bed3b947-0c39-47f1-9dd6-3032cc357624",
"name": "HTTP Request",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.2,
"position": [
340,
500
]
},
{
"parameters": {
"conditions": {
"options": {
"caseSensitive": true,
"leftValue": "",
"typeValidation": "strict"
},
"conditions": [
{
"id": "2d5f1cb2-511d-48a3-952a-c7c5be7b3e61",
"leftValue": "={{ $json.email }}",
"rightValue": "",
"operator": {
"type": "string",
"operation": "notEmpty",
"singleValue": true
}
}
],
"combinator": "and"
},
"options": {}
},
"id": "2dadff1f-6a4f-4f2b-8b02-a4b2a3a4c876",
"name": "If",
"type": "n8n-nodes-base.if",
"typeVersion": 2,
"position": [
540,
500
]
},
{
"parameters": {
"options": {}
},
"id": "9af5a135-aa55-4d7d-8d92-22e4b5f3a8bb",
"name": "Respond with Error",
"type": "n8n-nodes-base.respondToWebhook",
"typeVersion": 1.1,
"position": [
740,
500
]
},
{
"parameters": {
"httpMethod": "POST",
"path": "invoke-supabase-agent",
"responseMode": "responseNode",
"options": {}
},
"id": "dcdeeade-bfd1-47bb-a94c-e75f0060b41a",
"name": "Webhook",
"type": "n8n-nodes-base.webhook",
"typeVersion": 2,
"position": [
120,
500
],
"webhookId": "33512ab3-8838-49a7-aaa1-90252504db01"
}
],
"pinData": {},
"connections": {
"OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "RAG AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"OpenAI Chat Model1": {
"ai_languageModel": [
[
{
"node": "Retrieve Documents",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Retrieve Documents": {
"ai_tool": [
[
{
"node": "RAG AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"Download File": {
"main": [
[
{
"node": "Extract Document Text",
"type": "main",
"index": 0
}
]
]
},
"File Created": {
"main": [
[
{
"node": "Set File ID",
"type": "main",
"index": 0
}
]
]
},
"Embeddings OpenAI": {
"ai_embedding": [
[
{
"node": "Supabase Vector Store",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Extract Document Text": {
"main": [
[
{
"node": "Insert into Supabase Vectorstore",
"type": "main",
"index": 0
}
]
]
},
"Embeddings OpenAI1": {
"ai_embedding": [
[
{
"node": "Insert into Supabase Vectorstore",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Default Data Loader": {
"ai_document": [
[
{
"node": "Insert into Supabase Vectorstore",
"type": "ai_document",
"index": 0
}
]
]
},
"Postgres Chat Memory": {
"ai_memory": [
[
{
"node": "RAG AI Agent",
"type": "ai_memory",
"index": 0
}
]
]
},
"Recursive Character Text Splitter": {
"ai_textSplitter": [
[
{
"node": "Default Data Loader",
"type": "ai_textSplitter",
"index": 0
}
]
]
},
"Delete Old Doc Rows": {
"main": [
[
{
"node": "Download File",
"type": "main",
"index": 0
}
]
]
},
"Set File ID": {
"main": [
[
{
"node": "Delete Old Doc Rows",
"type": "main",
"index": 0
}
]
]
},
"File Updated": {
"main": [
[
{
"node": "Set File ID",
"type": "main",
"index": 0
}
]
]
},
"Supabase Vector Store": {
"ai_vectorStore": [
[
{
"node": "Retrieve Documents",
"type": "ai_vectorStore",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "Edit Fields",
"type": "main",
"index": 0
}
]
]
},
"Edit Fields": {
"main": [
[
{
"node": "RAG AI Agent",
"type": "main",
"index": 0
}
]
]
},
"RAG AI Agent": {
"main": [
[
{
"node": "Respond to Webhook",
"type": "main",
"index": 0
}
]
]
},
"HTTP Request": {
"main": [
[
{
"node": "If",
"type": "main",
"index": 0
}
]
]
},
"If": {
"main": [
[
{
"node": "Edit Fields",
"type": "main",
"index": 0
}
],
[
{
"node": "Respond with Error",
"type": "main",
"index": 0
}
]
]
},
"Webhook": {
"main": [
[
{
"node": "HTTP Request",
"type": "main",
"index": 0
}
]
]
}
},
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "b6084362-7eaa-41a2-b29b-5fb3ac58f7b2",
"meta": {
"templateCredsSetupCompleted": true,
"instanceId": "620f0d7e3114cb344761d7d45a21ef2a32096f91d8696e7057756042e1999e2c"
},
"id": "jfppAfyQOMf1B4Wd",
"tags": []
}

View File

@@ -0,0 +1,59 @@
import streamlit as st
import requests
import uuid
# Constants
WEBHOOK_URL = "YOUR_N8N_WEBHOOK_URL_HERE"
BEARER_TOKEN = "YOUR_BEARER_TOKEN_HERE"
def generate_session_id():
return str(uuid.uuid4())
def send_message_to_llm(session_id, message):
headers = {
"Authorization": f"Bearer {BEARER_TOKEN}",
"Content-Type": "application/json"
}
payload = {
"sessionId": session_id,
"chatInput": message
}
response = requests.post(WEBHOOK_URL, json=payload, headers=headers)
if response.status_code == 200:
return response.json()["output"]
else:
return f"Error: {response.status_code} - {response.text}"
def main():
st.title("Chat with LLM")
# Initialize session state
if "messages" not in st.session_state:
st.session_state.messages = []
if "session_id" not in st.session_state:
st.session_state.session_id = generate_session_id()
# Display chat messages
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.write(message["content"])
# User input
user_input = st.chat_input("Type your message here...")
if user_input:
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": user_input})
with st.chat_message("user"):
st.write(user_input)
# Get LLM response
llm_response = send_message_to_llm(st.session_state.session_id, user_input)
# Add LLM response to chat history
st.session_state.messages.append({"role": "assistant", "content": llm_response})
with st.chat_message("assistant"):
st.write(llm_response)
if __name__ == "__main__":
main()

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@@ -0,0 +1,118 @@
import streamlit as st
import requests
import uuid
from supabase import create_client, Client
# Supabase setup
SUPABASE_URL = "YOUR_SUPABASE_PROJECT_URL_HERE"
SUPABASE_KEY = "YOUR_SUPABASE_ANONYMOUS_API_KEY_HERE"
supabase: Client = create_client(SUPABASE_URL, SUPABASE_KEY)
# Webhook URL (replace with your n8n webhook URL)
WEBHOOK_URL = "YOUR_N8N_WEBHOOK_URL_HERE"
def login(email: str, password: str):
try:
res = supabase.auth.sign_in_with_password({"email": email, "password": password})
return res
except Exception as e:
st.error(f"Login failed: {str(e)}")
return None
def signup(email: str, password: str):
try:
res = supabase.auth.sign_up({"email": email, "password": password})
return res
except Exception as e:
st.error(f"Signup failed: {str(e)}")
return None
def generate_session_id():
return str(uuid.uuid4())
def init_session_state():
if "auth" not in st.session_state:
st.session_state.auth = None
if "session_id" not in st.session_state:
st.session_state.session_id = None
if "messages" not in st.session_state:
st.session_state.messages = []
def display_chat():
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
def handle_logout():
st.session_state.auth = None
st.session_state.session_id = None
st.session_state.messages = []
st.rerun()
def auth_ui():
tab1, tab2 = st.tabs(["Login", "Sign Up"])
with tab1:
email = st.text_input("Email", key="login_email")
password = st.text_input("Password", type="password", key="login_password")
if st.button("Login"):
auth = login(email, password)
if auth:
st.session_state.auth = auth
st.session_state.session_id = generate_session_id()
st.rerun()
with tab2:
email = st.text_input("Email", key="signup_email")
password = st.text_input("Password", type="password", key="signup_password")
if st.button("Sign Up"):
auth = signup(email, password)
if auth:
st.success("Sign up successful! Please log in.")
def main():
st.title("AI Chat Interface")
init_session_state()
if st.session_state.auth is None:
auth_ui()
else:
st.sidebar.success(f"Logged in as {st.session_state.auth.user.email}")
st.sidebar.info(f"Session ID: {st.session_state.session_id}")
if st.sidebar.button("Logout"):
handle_logout()
display_chat()
if prompt := st.chat_input("What is your message?"):
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user"):
st.markdown(prompt)
# Prepare the payload
payload = {
"chatInput": prompt,
"sessionId": st.session_state.session_id
}
# Get the access token from the session
access_token = st.session_state.auth.session.access_token
# Send request to webhook
headers = {
"Authorization": f"Bearer {access_token}"
}
with st.spinner("AI is thinking..."):
response = requests.post(WEBHOOK_URL, json=payload, headers=headers)
if response.status_code == 200:
ai_message = response.json().get("output", "Sorry, I couldn't generate a response.")
st.session_state.messages.append({"role": "assistant", "content": ai_message})
with st.chat_message("assistant"):
st.markdown(ai_message)
else:
st.error(f"Error: {response.status_code} - {response.text}")
if __name__ == "__main__":
main()

View File

@@ -0,0 +1,19 @@
Basic Streamlit + n8n Agent App without Authentication:
```
Create a basic Streamlit application with a chat interface to talk to an LLM. When a user enters a message, it is sent through a webhook with a hard coded bearer token to an n8n workflow that handles the user input and gives a response from the LLM.
The payload for the POST request includes the sessionId for a randomly generated session ID you need to create, and chatInput which contains the user message. The response JSON has just a single key, output, which is the response from the LLM to display back to the user.
```
Streamlit App with Supabase Authentication:
```
Now update this Streamlit app to use Supabase for authentication. Have a tab for logging in and a tab for signing up when a user first enters the app.
Use the user's Supabase token when authenticated as the bearer token for the webhook request to the n8n workflow.
Have a left-side panel to display the user email and session ID.
Do NOT include experimental Streamlit features to do things like refresh the page.
```

View File

@@ -0,0 +1,70 @@
aiohappyeyeballs==2.4.3
aiohttp==3.10.8
aiosignal==1.3.1
altair==5.4.1
annotated-types==0.7.0
anyio==4.6.0
async-timeout==4.0.3
attrs==24.2.0
blinker==1.8.2
cachetools==5.5.0
certifi==2024.8.30
charset-normalizer==3.3.2
click==8.1.7
colorama==0.4.6
deprecation==2.1.0
exceptiongroup==1.2.2
frozenlist==1.4.1
gitdb==4.0.11
GitPython==3.1.43
gotrue==2.9.1
h11==0.14.0
h2==4.1.0
hpack==4.0.0
httpcore==1.0.5
httpx==0.27.2
hyperframe==6.0.1
idna==3.10
Jinja2==3.1.4
jsonschema==4.23.0
jsonschema-specifications==2023.12.1
markdown-it-py==3.0.0
MarkupSafe==2.1.5
mdurl==0.1.2
multidict==6.1.0
narwhals==1.9.0
numpy==2.1.1
packaging==24.1
pandas==2.2.3
pillow==10.4.0
postgrest==0.17.0
protobuf==5.28.2
pyarrow==17.0.0
pydantic==2.9.2
pydantic_core==2.23.4
pydeck==0.9.1
Pygments==2.18.0
python-dateutil==2.9.0.post0
pytz==2024.2
realtime==2.0.5
referencing==0.35.1
requests==2.32.3
rich==13.9.1
rpds-py==0.20.0
six==1.16.0
smmap==5.0.1
sniffio==1.3.1
storage3==0.8.0
streamlit==1.38.0
StrEnum==0.4.15
supabase==2.8.1
supafunc==0.6.0
tenacity==8.5.0
toml==0.10.2
tornado==6.4.1
typing_extensions==4.12.2
tzdata==2024.2
urllib3==2.2.3
watchdog==4.0.2
websockets==13.1
yarl==1.13.1