Stock Q&A Workflow

Empower your team with instant, accurate answers to stock-related questions by transforming your Google Drive documents into a searchable knowledge base. This workflow automates the process of ingesting documents from Google Drive, converting them into embeddings using OpenAI, and storing them in Qdrant, a high-performance vector database. When a new manual chat message or webhook request comes in, the workflow leverages OpenAI's language model to perform a retrieval-augmented generation (RAG) query against your Qdrant vector store, providing intelligent, context-aware responses. This is ideal for financial analysts, customer support teams, or internal stakeholders who need quick access to information contained in stock reports, market analyses, or company filings, eliminating the need to manually search through countless documents and significantly reducing research time and effort.

17 nodesmanual trigger122 views0 copiesAI

Google DriveQdrantOpenAI

Workflow JSON

{"id": "tMiRJYDrXzpKysTX", "meta": {"instanceId": "2723a3a635131edfcb16103f3d4dbaadf3658e386b4762989cbf49528dccbdbd", "templateId": "1960"}, "name": "Stock Q&A Workflow", "tags": [], "nodes": [{"id": "ec3b86be-4113-4fd5-8365-02adb67693e9", "name": "Embeddings OpenAI1", "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi", "position": [1960, 720], "parameters": {"options": {}}, "credentials": {"openAiApi": {"id": "", "name": "[Your openAiApi]"}}, "typeVersion": 1}, {"id": "42fd8020-3861-4d0f-a7a2-70e2c35f0bed", "name": "On new manual Chat Message", "type": "@n8n/n8n-nodes-langchain.manualChatTrigger", "disabled": true, "position": [1620, 240], "parameters": {}, "typeVersion": 1}, {"id": "a9b48d04-691e-4537-90f8-d7a4aa6153af", "name": "Sticky Note1", "type": "n8n-nodes-base.stickyNote", "position": [1560, 120], "parameters": {"color": 6, "width": 903.0896125323785, "height": 733.5099670584011, "content": "## Step 2: Setup the Q&A \n### The incoming message from the webhook is queried from the Supabase Vector Store. The response is provided in the response webhook. "}, "typeVersion": 1}, {"id": "472b4800-745a-4337-9545-163247f7e9ae", "name": "Retrieval QA Chain", "type": "@n8n/n8n-nodes-langchain.chainRetrievalQa", "position": [1880, 240], "parameters": {"query": "={{ $json.body.input }}"}, "typeVersion": 1}, {"id": "e58bd82d-abc6-44ed-8e93-ec5436126d66", "name": "Respond to Webhook", "type": "n8n-nodes-base.respondToWebhook", "position": [2280, 240], "parameters": {"options": {}, "respondWith": "text", "responseBody": "={{ $json.response.text }}"}, "typeVersion": 1}, {"id": "04bbf01e-8269-47c7-897d-4ea94a1bd1c0", "name": "Vector Store Retriever", "type": "@n8n/n8n-nodes-langchain.retrieverVectorStore", "position": [2020, 440], "parameters": {"topK": 5}, "typeVersion": 1}, {"id": "feee6d68-2e0d-4d40-897e-c1d833a13bf2", "name": "Webhook1", "type": "n8n-nodes-base.webhook", "position": [1620, 420], "webhookId": "679f4afb-189e-4f04-9dc0-439eec2ec5f1", "parameters": {"path": "19f5499a-3083-4783-93a0-e8ed76a9f742", "options": {}, "httpMethod": "POST", "responseMode": "responseNode"}, "typeVersion": 1.1}, {"id": "1b8d251f-7069-4d7d-b6d6-4bfa683d4ad1", "name": "When clicking \"Execute Workflow\"", "type": "n8n-nodes-base.manualTrigger", "position": [280, 260], "parameters": {}, "typeVersion": 1}, {"id": "b746a7a4-ed94-4332-bf7b-65aadcf54130", "name": "Google Drive", "type": "n8n-nodes-base.googleDrive", "position": [580, 260], "parameters": {"fileId": {"__rl": true, "mode": "list", "value": "1LZezppYrWpMStr4qJXtoIX-Dwzvgehll", "cachedResultUrl": "https://drive.google.com/file/d/1LZezppYrWpMStr4qJXtoIX-Dwzvgehll/view?usp=drivesdk", "cachedResultName": "crowdstrike.pdf"}, "options": {}, "operation": "download"}, "credentials": {"googleDriveOAuth2Api": {"id": "", "name": "[Your googleDriveOAuth2Api]"}}, "typeVersion": 3}, {"id": "83a7d470-f934-436d-ba3f-1ae7c776f5a5", "name": "Binary to Document", "type": "@n8n/n8n-nodes-langchain.documentBinaryInputLoader", "position": [860, 480], "parameters": {"loader": "pdfLoader", "options": {}}, "typeVersion": 1}, {"id": "b52b4a90-99a1-49cc-a6f0-7551d6754496", "name": "Recursive Character Text Splitter", "type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter", "position": [860, 640], "parameters": {"options": {}, "chunkSize": 3000, "chunkOverlap": 200}, "typeVersion": 1}, {"id": "b525e130-2029-4f55-a603-1fdc05a19c17", "name": "Embeddings OpenAI", "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi", "position": [1160, 480], "parameters": {"options": {}}, "credentials": {"openAiApi": {"id": "", "name": "[Your openAiApi]"}}, "typeVersion": 1}, {"id": "5358c53f-55f9-431d-8956-c6bae7ad25bc", "name": "Sticky Note", "type": "n8n-nodes-base.stickyNote", "position": [540, 120], "parameters": {"color": 6, "width": 772.0680602743597, "height": 732.3675002130781, "content": "## Step 1: Upserting the PDF\n### Fetch file from Google Drive, split it into chunks and insert into Supabase index\n\n"}, "typeVersion": 1}, {"id": "fb91e2da-0eeb-47a5-aa49-65bf56986857", "name": "Qdrant Vector Store", "type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant", "position": [940, 260], "parameters": {"mode": "insert", "options": {}, "qdrantCollection": {"__rl": true, "mode": "id", "value": "=crowd"}}, "credentials": {"qdrantApi": {"id": "", "name": "[Your qdrantApi]"}}, "typeVersion": 1}, {"id": "89e14837-d1fc-4b1e-9ebc-7cf3e7fd9a70", "name": "Qdrant Vector Store1", "type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant", "position": [1980, 600], "parameters": {"qdrantCollection": {"__rl": true, "mode": "id", "value": "={{ $json.body.company }}"}}, "credentials": {"qdrantApi": {"id": "", "name": "[Your qdrantApi]"}}, "typeVersion": 1}, {"id": "c619245b-5ea0-4354-974d-21ec6b8efa93", "name": "OpenAI Chat Model", "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi", "position": [1880, 460], "parameters": {"options": {}}, "credentials": {"openAiApi": {"id": "", "name": "[Your openAiApi]"}}, "typeVersion": 1}, {"id": "e4aa780d-8069-4308-a61f-82ed876af71a", "name": "Sticky Note2", "type": "n8n-nodes-base.stickyNote", "position": [-560, 120], "parameters": {"color": 6, "width": 710.9124489067698, "height": 726.4452519516944, "content": "## Start here: Step-by Step Youtube Tutorial :star:\n\n[![Building an AI Crew to Analyze Financial Data with CrewAI and n8n](https://img.youtube.com/vi/pMvizUx5n1g/sddefault.jpg)](https://www.youtube.com/watch?v=pMvizUx5n1g)\n"}, "typeVersion": 1}], "active": true, "pinData": {}, "settings": {}, "versionId": "463aec94-26a6-436d-8732-fc01d637c6ae", "connections": {"Webhook1": {"main": [[{"node": "Retrieval QA Chain", "type": "main", "index": 0}]]}, "Google Drive": {"main": [[{"node": "Qdrant Vector Store", "type": "main", "index": 0}]]}, "Embeddings OpenAI": {"ai_embedding": [[{"node": "Qdrant Vector Store", "type": "ai_embedding", "index": 0}]]}, "OpenAI Chat Model": {"ai_languageModel": [[{"node": "Retrieval QA Chain", "type": "ai_languageModel", "index": 0}]]}, "Binary to Document": {"ai_document": [[{"node": "Qdrant Vector Store", "type": "ai_document", "index": 0}]]}, "Embeddings OpenAI1": {"ai_embedding": [[{"node": "Qdrant Vector Store1", "type": "ai_embedding", "index": 0}]]}, "Retrieval QA Chain": {"main": [[{"node": "Respond to Webhook", "type": "main", "index": 0}]]}, "Qdrant Vector Store1": {"ai_vectorStore": [[{"node": "Vector Store Retriever", "type": "ai_vectorStore", "index": 0}]]}, "Vector Store Retriever": {"ai_retriever": [[{"node": "Retrieval QA Chain", "type": "ai_retriever", "index": 0}]]}, "On new manual Chat Message": {"main": [[{"node": "Retrieval QA Chain", "type": "main", "index": 0}]]}, "When clicking \"Execute Workflow\"": {"main": [[{"node": "Google Drive", "type": "main", "index": 0}]]}, "Recursive Character Text Splitter": {"ai_textSplitter": [[{"node": "Binary to Document", "type": "ai_textSplitter", "index": 0}]]}}}

How to Import This Workflow

1Copy the workflow JSON above using the Copy Workflow JSON button.
2Open your n8n instance and go to Workflows.
3Click Import from JSON and paste the copied workflow.

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