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— AI STACK RECOMMENDATION

Serial Recommendations Chatbot

Build a conversational AI chatbot that recommends TV series based on user preferences, powered by LLM and vector search for intelligent matching.

Stays alive for 365 days after the last visit.

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Serial Recommendations Chatbot

Build a conversational AI chatbot that recommends TV series based on user preferences, powered by LLM and vector search for intelligent matching.

high confidence

Core Stack ℹī¸Ž

Claude Sonnet 4.6

Primary

Excellent conversational abilities and reasoning for understanding user preferences and generating personalized serial recommendations with natural dialogue.

$0.003/1K tokens

Pinecone

Primary

Fully managed vector database for storing and retrieving serial metadata embeddings, enabling semantic search to match user preferences with relevant shows.

$0/month (free tier)

LlamaIndex

Primary

RAG framework to connect the LLM with serial database, handling document indexing and retrieval for context-aware recommendations.

$0/month

Complete the Stack ℹī¸Ž

LangChain.js

Alternative

TypeScript framework for building conversational chains, managing multi-turn dialogue, and orchestrating LLM calls with memory.

$0/month

Vercel

Alternative

Deploy the chatbot as a serverless web application with zero-cold-start edge functions and built-in CI/CD from Git.

$0/month (free tier)

LangSmith

Alternative

Monitor chatbot performance, trace conversations, and evaluate recommendation quality with built-in debugging tools.

$0/month (free tier)

Getting started

  1. 1Set up Pinecone vector database and load serial metadata (title, genre, plot, ratings) as embeddings.
  2. 2Create LlamaIndex document index connecting to Pinecone for semantic retrieval.
  3. 3Build LangChain conversation chain with Claude Sonnet to handle multi-turn dialogue and preference extraction.
  4. 4Implement preference extraction logic to parse user inputs (genres, mood, length, themes).
  5. 5Create recommendation generation prompt that uses retrieved serials from vector search.
  6. 6Deploy chatbot API on Vercel with streaming responses for real-time chat.
  7. 7Integrate LangSmith for monitoring conversation quality and recommendation accuracy.
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