RAGBot Builder — AI Chatbot Code Generator
Expert AI developer assistant that generates complete, production-ready RAG chatbot code for any stack. Supports: Python, JavaScript. Vector DBs: Pinecone, ChromaDB, pgvector, Qdrant, Weaviate. LLMs: OpenAI, Anthropic, Gemini, Mistral. Outputs: full pipeline code, document ingestion scripts, embedding setup, retrieval logic, system prompts and deployment guide. Paste your requirement — get complete working code instantly.
#Ingénierie#Recherche#Produit
Note
Davantage d’évaluations nécessaires
Vendus
0
Mode d’utilisation
Exécuter sur Capafy
Aussi sur des applications externes
Fourni par l’éditeur
Claude Haiku 4.5
🤖 RAGBot Builder — AI Chatbot Code Generator
Generate complete, production-ready RAG chatbot code for any stack — in seconds. No boilerplate hunting. No stitching tutorials together. Just describe what you need and get working code.
What You Get
| Output | Description |
|---|---|
| 🔧 Full pipeline code | End-to-end RAG implementation |
| 📄 Document ingestion | Chunking + embedding scripts |
| 🗄️ Vector DB setup | Config + connection code |
| 🔍 Retrieval logic | Semantic search implementation |
| 🧠 LLM integration | Query + response generation |
| 📝 System prompt | Tuned for your chatbot type |
| 🚀 Deployment guide | Step-by-step setup instructions |
| 🐛 Debug help | Fix broken RAG pipelines |
Supported Stacks
Vector Databases
| Database | Best For |
|---|---|
| Pinecone | Production, managed cloud |
| ChromaDB | Local development, free |
| pgvector | PostgreSQL users |
| Qdrant | High-performance self-hosted |
| Weaviate | GraphQL + vector hybrid |
| FAISS | Offline / no internet |
LLM Providers
| Provider | Models |
|---|---|
| Anthropic | Claude Haiku, Sonnet, Opus |
| OpenAI | GPT-4o, GPT-4.1, GPT-3.5 |
| Gemini | 2.5 Flash, 1.5 Pro |
| Mistral | Mistral 7B, Mixtral |
| Ollama | Local LLMs (Llama, Phi) |
Languages & Frameworks
- Python — LangChain, LlamaIndex, raw SDK
- JavaScript / TypeScript — LangChain.js, Vercel AI SDK
- FastAPI / Flask — REST API wrapper
How to Use
Just describe what you want in plain English:
Build a customer support RAG chatbot using Python, Pinecone and Claude
Generate a document Q&A bot using ChromaDB and OpenAI GPT-4o
Create a legal document chatbot with pgvector and LangChain
Debug my RAG pipeline — retrieval returns irrelevant chunks
What chunking strategy works best for PDFs?
How do I add memory to my RAG chatbot?
Who It's For
- 🧑💻 Developers building AI chatbots for clients
- 🚀 Startups adding AI to their product
- 📊 Consultants delivering RAG solutions
- 🎓 Engineers learning RAG architecture
- 🏢 Teams building internal knowledge bases
Sample Output
Request: "Build a customer support RAG bot with Python, ChromaDB and Claude Haiku"
The agent outputs:
- Complete
ingest.py— load, chunk and embed your documents - Complete
chatbot.py— query pipeline with retrieval + Claude response requirements.txt— all dependencies.env.example— environment variable setup- Step-by-step setup instructions
- System prompt tuned for customer support
Support
Questions? Email us — we respond within 24 hours.
