Cezzis Cocktail AI Search: Semantic Retrieval and Conversational AI Backend Based on FastAPI

The intelligent cocktail search backend for Cezzis.com is built on FastAPI, integrating vector retrieval and RAG technology to enable semantic cocktail discovery and conversational AI interaction. The core goal of the project is to allow users to precisely obtain semantically relevant recipe recommendations by describing their needs in natural language (e.g., "refreshing cocktails suitable for summer beaches"), bringing a new experience to beverage exploration.

Amid the digital wave, the traditional mixology field is also embracing AI. Traditional keyword search struggles to understand users' vague or subjective needs (e.g., "cocktails for a romantic date night"), while the Cezzis project addresses this pain point through semantic search technology—enabling AI to understand users' natural language descriptions and match the most suitable results from a vast library of recipes, achieving intelligent beverage discovery.

• FastAPI: As the backend framework, it provides async support, type hints, and automatic documentation generation to meet high concurrency requirements.
• Vector Retrieval: Uses embedding models to convert cocktail recipes and user queries into high-dimensional vectors, enabling efficient similarity search via vector databases like Pinecone/Weaviate.
• Agentic RAG: Introduces intelligent agents that support multi-step reasoning, tool calling, self-correction, and conversation management, capable of handling complex preferences (e.g., "like a Mojito but with higher alcohol content").
• LLM Integration: Plans to use OpenAI GPT/Anthropic Claude or open-source models (e.g., Llama), combined with carefully designed prompts (such as a professional mixologist role) to enable conversational interaction.

• Semantic Search: Understands natural language needs (e.g., "refreshing summer pool drinks") instead of keyword matching.
• Intelligent Recommendations: Recommends based on factors like taste preferences, base liquor type, occasion, and difficulty level.
• Conversational Interaction: Refines needs through multi-turn dialogues (e.g., user says "refreshing summer" → AI asks about base liquor type).
• Recipe Details: Provides ingredient lists, preparation steps, historical background, pairing suggestions, etc.

• Data Model: Defines Cocktail (including name, description, ingredients, taste characteristics, etc.) and Ingredient (including type, flavor, etc.) entities, converting data into vectorizable text via the cocktail_to_text function.
• API Endpoints:
  • /api/v1/search: Semantic search with support for filtering (e.g., alcohol content, base liquor).
  • /api/v1/chat: Conversation interface that maintains session state.
  • /api/v1/cocktails/{id}: Retrieve cocktail details.

• Home Mixology Enthusiasts: Recommend recipes based on available ingredients, learn new recipes, get occasion-based recommendations.
• Professional Mixologists: Gain creative inspiration, quickly answer customer questions, manage recipe libraries.
• Bars/Restaurants: Intelligent menu recommendations, staff training tools, personalized marketing content.

• Challenges and Solutions:
  • Semantic Accuracy: Domain embedding model fine-tuning + user feedback loop + multi-turn dialogue clarification.
  • Vector Retrieval Precision: Hybrid search + re-ranking + metadata filtering.
  • Conversation Coherence: Conversation management system + context inclusion + intent recognition.
  • Response Latency: Async processing + caching + streaming responses.
• Future Roadmap:
  • Short-term: Improve vector search, integrate basic conversation, expand database.
  • Mid-term: Complete Agentic RAG, multi-language support, user preference learning.
  • Long-term: Image recognition, voice interaction, personalized customization, social features.