Building a RAG Chatbot for Movie Recommendations with Qdrant and OpenAI
This workflow builds an intelligent movie recommendation chatbot that utilizes retrieval-augmented generation technology, combined with the Qdrant vector database and OpenAI's AI capabilities, to provide personalized movie recommendations. Through natural language understanding, the system can parse users' positive and negative preferences, intelligently generating movie recommendations that match their tastes, thereby enhancing the accuracy and flexibility of the recommendations and helping users easily find their desired films. It is suitable for scenarios such as online streaming platforms, content communities, and customer service systems.
Tags
Workflow Name
Building a RAG Chatbot for Movie Recommendations with Qdrant and OpenAI
Key Features and Highlights
This workflow constructs a movie recommendation chatbot based on Retrieval-Augmented Generation (RAG) technology, integrating the Qdrant vector database with OpenAI’s powerful AI capabilities to deliver precise and personalized movie recommendations. By leveraging the IMDB movie dataset, movie descriptions are semantically embedded into vectors stored in Qdrant, enabling users to input both positive and negative preferences in natural language. The system intelligently generates the top three movie recommendations tailored to the user’s taste.
Core Problems Addressed
Traditional movie recommendation systems often rely on fixed tags or ratings, making it difficult to comprehend complex user preference expressions. This workflow addresses the challenges of recommendation accuracy and personalization when users express vague or multi-dimensional preferences by combining natural language understanding with vector similarity search, thereby providing a smarter and more flexible recommendation experience.
Application Scenarios
- Personalized movie recommendations for online streaming platforms
- Integration of intelligent recommendation features in movie communities or chatbots
- Assisting content curators in selecting relevant movie resources
- Supporting movie-related inquiries in AI assistants or customer service systems
Main Process Steps
- Manually trigger the workflow to initiate execution
- GitHub node fetches the IMDB Top 1000 movies CSV file
- Extract file content, parsing movie titles, release years, and descriptions
- Text splitting and data loading to segment movie descriptions
- Generate text embeddings of movie descriptions using OpenAI models
- Store embeddings and metadata in the Qdrant vector database
- Listen to chat message triggers to receive user queries along with positive and negative preference examples
- Use OpenAI to generate vector representations of the query and counter-examples
- Invoke Qdrant recommendation API to compute recommendations based on vector similarity
- Retrieve detailed metadata of recommended movies
- AI Agent generates natural language responses based on recommendations and returns them to the user
Involved Systems or Services
- GitHub: Source for movie data files
- OpenAI API: Text embedding generation and chat model processing
- Qdrant Vector Database: Storage of movie vectors enabling efficient similarity search
- n8n Workflow Automation Platform: Orchestration and node-triggered execution
Target Users and Value Proposition
- Developers of video content platforms seeking rapid integration of intelligent movie recommendation features
- AI and data science enthusiasts interested in learning RAG technology and vector database applications
- Product managers of customer service and intelligent assistant solutions aiming to enhance user experience
- Movie lovers who want precise, personalized recommendations based on semantic understanding
This workflow combines advanced AI text comprehension with vector retrieval technology to create an efficient and intelligent movie recommendation chatbot solution, helping users effortlessly discover movies they love and enhancing their viewing experience.
SearchApi AI Agent
This workflow receives user chat messages and utilizes an AI intelligent agent combined with a web search API to achieve real-time information retrieval and intelligent Q&A. Leveraging the GPT-4o-mini model, it supports contextual memory, enhancing the coherence and accuracy of continuous conversations. It addresses the issue of traditional AI models being unable to access real-time data, making it suitable for customer service robots, intelligent assistants, and scenarios that require quick access to external information, significantly enhancing the user interaction experience.
Business WhatsApp AI RAG Chatbot
This workflow integrates the WhatsApp message Webhook with an AI question-and-answer agent, utilizing retrieval-augmented generation technology to build an intelligent customer service chatbot. It can receive customer inquiries in real-time, leveraging the company's internal knowledge base and advanced AI models to provide accurate product consultations and technical support. This system not only reduces the pressure on human customer service representatives but also ensures the professionalism and accuracy of the responses, enhancing the customer experience and making it suitable for the automated customer service needs of various enterprises.
Automated Tracking and Notification of Latest AI Funding Opportunities
This workflow aims to automate the tracking of the latest artificial intelligence funding opportunities by regularly scraping and filtering relevant information. It utilizes AI technology to assess eligibility and eliminate duplicate data. By saving key information to an Airtable database and generating well-designed email notifications that are promptly sent to subscribers, it significantly enhances the team's response speed to funding opportunities, saves time on manual filtering, ensures the timeliness and uniqueness of the information, and supports research institutions and startup teams in securing funding.
OpenAI Assistant Workflow: Upload File, Create an Assistant, Chat with It!
This workflow helps users create customized AI assistants for specialized Q&A on specific topics by retrieving files from Google Drive and uploading them to the OpenAI platform. Users can engage in real-time chat with the assistant to receive accurate answers based on the uploaded documents, enhancing information relevance and user experience. It is suitable for scenarios such as event consulting, corporate knowledge bases, and customer support, and supports multi-channel expansion to meet diverse needs. The overall process is efficient and straightforward, significantly improving service quality.
Business WhatsApp AI RAG Chatbot
This workflow builds an intelligent AI chatbot on the WhatsApp platform, utilizing RAG technology and OpenAI models to automatically handle customer inquiries. It receives messages through Meta's WhatsApp Business API and accurately retrieves information from the company's knowledge base to generate professional responses. This system can automatically answer product inquiries, technical support, and after-sales service questions, enhancing customer response speed and reducing the pressure on human customer service representatives. It is suitable for scenarios such as electronic product retail and technical support, improving the customer interaction experience.
AI Intelligent Q&A Agent — Hacker News Top Posts Query
This workflow utilizes AI intelligent agents and custom tools to enable quick querying and intelligent responses for popular post data on the Hacker News platform. Users can obtain the top 50 most popular posts and detailed information through natural language commands. It includes built-in data cleaning and formatting features to ensure the results are clearly structured. This is suitable for technical researchers, content creators, and developers, helping them to rapidly access authoritative and up-to-date technology information, thereby enhancing the efficiency of information retrieval.
Bitrix24 Open Channel RAG Chatbot Application Workflow Example with Webhook Integration
This workflow integrates with the Bitrix24 open channel to implement an intelligent chatbot application that features efficient question-and-answer capabilities based on Retrieval-Augmented Generation (RAG) technology. It can automatically register the bot, handle user messages, and provide intelligent responses based on the content of uploaded documents. The documents are stored and retrieved using a vector database, combined with advanced chat models, which enhances the accuracy of answers and contextual understanding, making it suitable for scenarios such as internal knowledge management and customer support within enterprises.
OpenAI Personal Shopper with RAG and WooCommerce
This workflow combines intelligent chat models, vector retrieval technology, and e-commerce platforms to provide users with personalized shopping assistant services. It can automatically identify users' shopping needs, accurately extract product search information, and query inventory in real-time to recommend suitable products. Additionally, for inquiries about store information, the system can also provide intelligent responses, supporting context management for multi-turn conversations, thereby enhancing the user shopping experience and satisfaction.