Home Recipes Building Rag Chatbot For Movie Recommendations With Qdrant And Open Ai

Building Rag Chatbot For Movie Recommendations With Qdrant And Open Ai

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A recipe for Building Rag Chatbot For Movie Recommendations With Qdrant And Open Ai

Recipe Template

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    "name": "Building RAG Chatbot for Movie Recommendations with Qdrant and Open AI",
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                    "mode": "name",
                    "value": "mrscoopers"
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                "filePath": "Top_1000_IMDB_movies.csv",
                "resource": "file",
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            "parameters": {
                "options": {
                    "metadata": {
                        "metadataValues": [
                            {
                                "name": "movie_name",
                                "value": "={{ $('Extract from File').item.json['Movie Name'] }}"
                            },
                            {
                                "name": "movie_release_date",
                                "value": "={{ $('Extract from File').item.json['Year of Release'] }}"
                            },
                            {
                                "name": "movie_description",
                                "value": "={{ $('Extract from File').item.json.Description }}"
                            }
                        ]
                    }
                },
                "jsonData": "={{ $('Extract from File').item.json.Description }}",
                "jsonMode": "expressionData"
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        {
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            "parameters": {
                "mode": "insert",
                "options": [],
                "qdrantCollection": {
                    "__rl": true,
                    "mode": "id",
                    "value": "imdb"
                }
            },
            "credentials": {
                "qdrantApi": {
                    "id": "Zin08PA0RdXVUKK7",
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            "position": [
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            "webhookId": "71bfe0f8-227e-466b-9d07-69fd9fe4a27b",
            "parameters": {
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            "typeVersion": 1.1
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        {
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            "name": "OpenAI Chat Model",
            "type": "@n8n\/n8n-nodes-langchain.lmChatOpenAi",
            "position": [
                5040,
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            ],
            "parameters": {
                "model": "gpt-4o-mini",
                "options": []
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            "credentials": {
                "openAiApi": {
                    "id": "deYJUwkgL1Euu613",
                    "name": "OpenAi account 2"
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            "position": [
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            ],
            "parameters": {
                "name": "movie_recommender",
                "schemaType": "manual",
                "workflowId": {
                    "__rl": true,
                    "mode": "id",
                    "value": "a58HZKwcOy7lmz56"
                },
                "description": "Call this tool to get a list of recommended movies from a vector database. ",
                "inputSchema": "{\n\"type\": \"object\",\n\"properties\": {\n\t\"positive_example\": {\n \"type\": \"string\",\n \"description\": \"A string with a movie description matching the user's positive recommendation request\"\n },\n \"negative_example\": {\n \"type\": \"string\",\n \"description\": \"A string with a movie description matching the user's negative anti-recommendation reuqest\"\n }\n}\n}",
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                "aggregate": "aggregateAllItemData",
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            "parameters": {
                "options": {
                    "systemMessage": "You are a Movie Recommender Tool using a Vector Database under the hood. Provide top-3 movie recommendations returned by the database, ordered by their recommendation score, but not showing the score to the user."
                }
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            "typeVersion": 1.6
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        {
            "id": "e04276b5-7d69-437b-bf4f-9717808cc8f6",
            "name": "Embedding Recommendation Request with Open AI",
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            "position": [
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            ],
            "parameters": {
                "url": "https:\/\/api.openai.com\/v1\/embeddings",
                "method": "POST",
                "options": [],
                "sendBody": true,
                "sendHeaders": true,
                "authentication": "predefinedCredentialType",
                "bodyParameters": {
                    "parameters": [
                        {
                            "name": "input",
                            "value": "={{ $json.query.positive_example }}"
                        },
                        {
                            "name": "model",
                            "value": "text-embedding-3-small"
                        }
                    ]
                },
                "headerParameters": {
                    "parameters": [
                        {
                            "name": "Authorization",
                            "value": "Bearer $OPENAI_API_KEY"
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            "credentials": {
                "openAiApi": {
                    "id": "deYJUwkgL1Euu613",
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            },
            "typeVersion": 4.2
        },
        {
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            "name": "Embedding Anti-Recommendation Request with Open AI",
            "type": "n8n-nodes-base.httpRequest",
            "position": [
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            ],
            "parameters": {
                "url": "https:\/\/api.openai.com\/v1\/embeddings",
                "method": "POST",
                "options": [],
                "sendBody": true,
                "sendHeaders": true,
                "authentication": "predefinedCredentialType",
                "bodyParameters": {
                    "parameters": [
                        {
                            "name": "input",
                            "value": "={{ $json.query.negative_example }}"
                        },
                        {
                            "name": "model",
                            "value": "text-embedding-3-small"
                        }
                    ]
                },
                "headerParameters": {
                    "parameters": [
                        {
                            "name": "Authorization",
                            "value": "Bearer $OPENAI_API_KEY"
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            "credentials": {
                "openAiApi": {
                    "id": "deYJUwkgL1Euu613",
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            "typeVersion": 4.2
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            "parameters": {
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                    "assignments": [
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                            "id": "01a28c9d-aeb1-48bb-8a73-f8bddbd73460",
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                            "type": "array",
                            "value": "={{ $json.data[0].embedding }}"
                        }
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            },
            "typeVersion": 3.4
        },
        {
            "id": "ce3aa9bc-a5b1-4529-bff5-e0dba43b99f3",
            "name": "Calling Qdrant Recommendation API",
            "type": "n8n-nodes-base.httpRequest",
            "position": [
                6840,
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            ],
            "parameters": {
                "url": "https:\/\/edcc6735-2ffb-484f-b735-3467043828fe.europe-west3-0.gcp.cloud.qdrant.io:6333\/collections\/imdb_1000_open_ai\/points\/query",
                "method": "POST",
                "options": [],
                "jsonBody": "={\n \"query\": {\n \"recommend\": {\n \"positive\": [[{{ $json.positive_example }}]],\n \"negative\": [[{{ $json.negative_example }}]],\n \"strategy\": \"average_vector\"\n }\n },\n \"limit\":3\n}",
                "sendBody": true,
                "specifyBody": "json",
                "authentication": "predefinedCredentialType",
                "nodeCredentialType": "qdrantApi"
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            "credentials": {
                "qdrantApi": {
                    "id": "Zin08PA0RdXVUKK7",
                    "name": "QdrantApi n8n demo"
                }
            },
            "typeVersion": 4.2
        },
        {
            "id": "9b8a6bdb-16fe-4edc-86d0-136fe059a777",
            "name": "Retrieving Recommended Movies Meta Data",
            "type": "n8n-nodes-base.httpRequest",
            "position": [
                7060,
                1460
            ],
            "parameters": {
                "url": "https:\/\/edcc6735-2ffb-484f-b735-3467043828fe.europe-west3-0.gcp.cloud.qdrant.io:6333\/collections\/imdb_1000_open_ai\/points",
                "method": "POST",
                "options": [],
                "jsonBody": "={\n \"ids\": [\"{{ $json.result.points[0].id }}\", \"{{ $json.result.points[1].id }}\", \"{{ $json.result.points[2].id }}\"],\n \"with_payload\":true\n}",
                "sendBody": true,
                "specifyBody": "json",
                "authentication": "predefinedCredentialType",
                "nodeCredentialType": "qdrantApi"
            },
            "credentials": {
                "qdrantApi": {
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                    "name": "QdrantApi n8n demo"
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            },
            "typeVersion": 4.2
        },
        {
            "id": "28cdcad5-3dca-48a1-b626-19eef657114c",
            "name": "Selecting Fields Relevant for Agent",
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            "position": [
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            ],
            "parameters": {
                "options": [],
                "assignments": {
                    "assignments": [
                        {
                            "id": "b4b520a5-d0e2-4dcb-af9d-0b7748fd44d6",
                            "name": "movie_recommendation_score",
                            "type": "number",
                            "value": "={{ $json.score }}"
                        },
                        {
                            "id": "c9f0982e-bd4e-484b-9eab-7e69e333f706",
                            "name": "movie_description",
                            "type": "string",
                            "value": "={{ $json.payload.content }}"
                        },
                        {
                            "id": "7c7baf11-89cd-4695-9f37-13eca7e01163",
                            "name": "movie_name",
                            "type": "string",
                            "value": "={{ $json.payload.metadata.movie_name }}"
                        },
                        {
                            "id": "1d1d269e-43c7-47b0-859b-268adf2dbc21",
                            "name": "movie_release_year",
                            "type": "string",
                            "value": "={{ $json.payload.metadata.release_year }}"
                        }
                    ]
                }
            },
            "typeVersion": 3.4
        },
        {
            "id": "56e73f01-5557-460a-9a63-01357a1b456f",
            "name": "Sticky Note",
            "type": "n8n-nodes-base.stickyNote",
            "position": [
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            ],
            "parameters": {
                "content": "Tool, calling Qdrant's recommendation API based on user's request, transformed by AI agent"
            },
            "typeVersion": 1
        },
        {
            "id": "cce5250e-0285-4fd0-857f-4b117151cd8b",
            "name": "Sticky Note1",
            "type": "n8n-nodes-base.stickyNote",
            "position": [
                4680,
                720
            ],
            "parameters": {
                "content": "Uploading data (movies and their descriptions) to Qdrant Vector Store\n"
            },
            "typeVersion": 1
        }
    ],
    "active": false,
    "pinData": {
        "Execute Workflow Trigger": [
            {
                "json": {
                    "query": {
                        "negative_example": "horror bloody movie",
                        "positive_example": "romantic comedy"
                    }
                }
            }
        ]
    },
    "settings": {
        "executionOrder": "v1"
    },
    "versionId": "40d3669b-d333-435f-99fc-db623deda2cb",
    "connections": {
        "Merge": {
            "main": [
                [
                    {
                        "node": "Calling Qdrant Recommendation API",
                        "type": "main",
                        "index": 0
                    }
                ]
            ]
        },
        "GitHub": {
            "main": [
                [
                    {
                        "node": "Extract from File",
                        "type": "main",
                        "index": 0
                    }
                ]
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        },
        "Merge1": {
            "main": [
                [
                    {
                        "node": "Selecting Fields Relevant for Agent",
                        "type": "main",
                        "index": 0
                    }
                ]
            ]
        },
        "Split Out": {
            "main": [
                [
                    {
                        "node": "Merge1",
                        "type": "main",
                        "index": 1
                    }
                ]
            ]
        },
        "Split Out1": {
            "main": [
                [
                    {
                        "node": "Merge1",
                        "type": "main",
                        "index": 0
                    }
                ]
            ]
        },
        "Token Splitter": {
            "ai_textSplitter": [
                [
                    {
                        "node": "Default Data Loader",
                        "type": "ai_textSplitter",
                        "index": 0
                    }
                ]
            ]
        },
        "Embeddings OpenAI": {
            "ai_embedding": [
                [
                    {
                        "node": "Qdrant Vector Store",
                        "type": "ai_embedding",
                        "index": 0
                    }
                ]
            ]
        },
        "Extract from File": {
            "main": [
                [
                    {
                        "node": "Qdrant Vector Store",
                        "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
                    }
                ]
            ]
        },
        "Extracting Embedding": {
            "main": [
                [
                    {
                        "node": "Merge",
                        "type": "main",
                        "index": 0
                    }
                ]
            ]
        },
        "Window Buffer Memory": {
            "ai_memory": [
                [
                    {
                        "node": "AI Agent",
                        "type": "ai_memory",
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        },
        "Extracting Embedding1": {
            "main": [
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                    {
                        "node": "Merge",
                        "type": "main",
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        "Call n8n Workflow Tool": {
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        "Execute Workflow Trigger": {
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                        "node": "Embedding Recommendation Request with Open AI",
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                    },
                    {
                        "node": "Embedding Anti-Recommendation Request with Open AI",
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                        "index": 0
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            ]
        },
        "When chat message received": {
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                [
                    {
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                        "type": "main",
                        "index": 0
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        },
        "Calling Qdrant Recommendation API": {
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                        "node": "Retrieving Recommended Movies Meta Data",
                        "type": "main",
                        "index": 0
                    },
                    {
                        "node": "Split Out1",
                        "type": "main",
                        "index": 0
                    }
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            ]
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        "When clicking \u2018Test workflow\u2019": {
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                        "node": "GitHub",
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                        "node": "Aggregate",
                        "type": "main",
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        "Retrieving Recommended Movies Meta Data": {
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                        "node": "Extracting Embedding",
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        "Embedding Anti-Recommendation Request with Open AI": {
            "main": [
                [
                    {
                        "node": "Extracting Embedding1",
                        "type": "main",
                        "index": 0
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                ]
            ]
        }
    }
}

How to Use an n8n Template

1

Create a New Workflow

Click "New Workflow" in your n8n dashboard to get started.

2

Copy & Paste Template

First, copy this template: Click here to copy the JSON .
Then, in n8n, click the three dots (···) → "Import from file" and paste the JSON code.

3

Customize the Nodes

Go through each node in the workflow to update inputs like spreadsheet IDs, email addresses, or message content. Adjust field mappings to match your data.

4

Grant Access

For nodes that connect to external apps (like Google Sheets or Slack), you'll need to grant access. Connect your accounts using OAuth or an API key and save the credentials in the node.

5

Test It

Run the workflow by clicking "Execute Node" for each step or "Run Once" for the whole thing. Check the right sidebar to inspect data and debug any errors (they'll show up in red).

6

Activate Workflow

Once everything works as expected, click the "Activate" toggle to turn your workflow on. You're all set!

Frequently Asked Questions

How long does it take to implement this development recipe?

This recipe typically takes 30 minutes to implement, depending on your experience level with the tools involved.

What tools do I need for this recipe?

You'll need: n8n. All tools are linked above with setup instructions.

Is this recipe free to use?

This recipe uses a mix of free and paid tools. Check individual tool pricing above.

Do I need coding experience for this recipe?

Most tools in this recipe are no-code or low-code solutions, making them accessible to beginners. Each tool includes difficulty level indicators.

Can I customize this recipe?

Absolutely! This recipe provides a foundation that you can adapt to your specific needs. Feel free to add or remove tools based on your requirements.

What if I get stuck implementing this recipe?

Each tool includes documentation links and support resources. You can also reach out to our community or consider hiring one of our verified agencies for assistance.