RAG complet à partir de PDF avec Mistral OCR
Avancé
Ceci est unAI, IT Opsworkflow d'automatisation du domainecontenant 34 nœuds.Utilise principalement des nœuds comme Set, Code, Wait, GoogleDrive, HttpRequest, combinant la technologie d'intelligence artificielle pour une automatisation intelligente. Construire un système RAG pour les documents PDF avec Mistral OCR, Qdrant et l'IA Gemini
Prérequis
- •Informations d'identification Google Drive API
- •Peut nécessiter les informations d'identification d'authentification de l'API cible
- •Clé API OpenAI
- •Informations de connexion au serveur Qdrant
- •Clé API Google Gemini
Nœuds utilisés (34)
Aperçu du workflow
Visualisation des connexions entre les nœuds, avec support du zoom et du déplacement
Exporter le workflow
Copiez la configuration JSON suivante dans n8n pour importer et utiliser ce workflow
{
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"meta": {
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"templateCredsSetupCompleted": true
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"name": "Complete RAG from PDF with Mistral OCR",
"tags": [],
"nodes": [
{
"id": "01a1b5b8-353f-4bfe-8bd0-c5dff1214c86",
"name": "Mistral Upload",
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"position": [
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"parameters": {
"url": "https://api.mistral.ai/v1/files",
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"sendBody": true,
"contentType": "multipart-form-data",
"authentication": "predefinedCredentialType",
"bodyParameters": {
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"value": "ocr"
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"inputDataFieldName": "data"
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]
},
"nodeCredentialType": "mistralCloudApi"
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"credentials": {
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"parameters": {
"url": "=https://api.mistral.ai/v1/files/{{ $json.id }}/url",
"options": {},
"sendQuery": true,
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"headerParameters": {
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},
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{
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},
"qdrantCollection": {
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"jsonBody": "{\n \"vectors\": {\n \"size\": 1536,\n \"distance\": \"Cosine\" \n },\n \"shard_number\": 1, \n \"replication_factor\": 1, \n \"write_consistency_factor\": 1 \n}",
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"content": "# STEP 1\n\n## Create Qdrant Collection\nChange:\n- QDRANTURL\n- COLLECTION"
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"content": "# STEP 2\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n## Documents vectorization with Qdrant and Google Drive\nChange:\n- QDRANTURL\n- COLLECTION"
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"prompt": "Write a concise summary of the following (in italiano):\n\n\n\"{text}\"\n\n\nCONCISE SUMMARY:",
"combineMapPrompt": "Write a concise summary of the following (in italiano):\n\n\n\"{text}\"\n\n\nCONCISE SUMMARY:"
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"content": "## STEP 4\nTest the RAG"
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"value": "1LWVo3yn_1bWQJsLskBIbWTGwlfObvtUK",
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}
},
"options": {},
"resource": "fileFolder"
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"credentials": {
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"parameters": {
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"content": "## Complete RAG system from PDF Documents with Mistral OCR, Qdrant and Gemini AI\n\nThis workflow is designed to process PDF documents using Mistral's OCR capabilities, store the extracted text in a Qdrant vector database, and enable Retrieval-Augmented Generation (RAG) for answering questions. "
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}Foire aux questions
Comment utiliser ce workflow ?
Copiez le code de configuration JSON ci-dessus, créez un nouveau workflow dans votre instance n8n et sélectionnez "Importer depuis le JSON", collez la configuration et modifiez les paramètres d'authentification selon vos besoins.
Dans quelles scénarios ce workflow est-il adapté ?
Avancé - Intelligence Artificielle, Opérations IT
Est-ce payant ?
Ce workflow est entièrement gratuit et peut être utilisé directement. Veuillez noter que les services tiers utilisés dans le workflow (comme l'API OpenAI) peuvent nécessiter un paiement de votre part.
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Informations sur le workflow
Niveau de difficulté
Avancé
Nombre de nœuds34
Catégorie2
Types de nœuds19
Description de la difficulté
Auteur
Davide
@n3witaliaFull-stack Web Developer based in Italy specialising in Marketing & AI-powered automations. For business enquiries, send me an email at info@n3w.it or add me on Linkedin.com/in/davideboizza
Liens externes
Voir sur n8n.io →
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