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Build a chatbot for a travel agent company. There are two parts of the assignment to work in sync when the chatbot interacts with a user: a front-end user interface client and a backend ChatGPT-based server.
You are required to develop a chatbot that interacts with users to understand their needs and preferences. The chatbot should generate personalized recommendations using Retrieval-Augmented Generation (RAG). The recommendations should be based solely on user inputs and must be accurate to the user’s stated preferences. The system should only return information that is present in the vector store.
Key Requirements:
User Interaction: The chatbot should allow users to input their preferences and needs through a conversational interface.
RAG-Based Recommendations: Based on the user’s input, the chatbot should generate recommendations using RAG.
Intelligent Responses: The chatbot must respond appropriately to user requests and queries, providing relevant information and suggestions. It should ensure that the recommendations are accurate to the user’s context and preferences.
Pre-existing Data: Recommendations should be created only from the existing list of data available in the system. The chatbot should not source plans from the internet.
Backend Integration: The system backend should utilize LangChain to manage the flow of interactions. The details and descriptions should be stored in a vector database of your choice.
Data Fetching: The system should fetch the top K (K <= 3) data entries based on the user's description using similarity search in the vector database.
Prompt Engineering for Mock Data: Use prompt engineering to generate the mock data, which will be stored in the vector database for similarity search.
Tasks:
1. Domain Selection: Choose a specific domain for your application. Clearly define the scope and type of data your application will handle.
2. Data Collection and Preprocessing: Acquire data relevant to your chosen domain by either webscraping or utilizing dynamic data sources. The dataset should contain textual data that can be indexed in the vector database. Preprocess the dataset to clean and prepare the data for indexing. This includes removing any irrelevant information, handling missing data, and ensuring the text is in a suitable format for the LLM.
Ensure the dataset is large enough to showcase your application's functionality.
3. Vector Database Implementation:
Choose a vector database suitable for your application needs (e.g., Milvus, Pinecone, or Weaviate).
Store your preprocessed dataset in the vector database, ensuring that data is indexed in a way that supports efficient retrieval based on semantic similarity or other relevant criteria.
4. Application Development:
Develop your application with a user interface that allows users to input natural language queries or preferences.
Implement the backend logic to use the LLM for query processing and the vector database for data retrieval, ensuring the system returns relevant and accurate results or responses.
5. Evaluation and Testing:
Test your application thoroughly with various queries or inputs to evaluate its performance and accuracy.
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