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I will rag chatbot langchain llamaindex vector database pinecone openai gpt4 n8n
Ryan Newman
Sobre este Serviço
If you have PDFs, knowledge bases, internal docs, or product data sitting unused, you are leaving money on the table. Every unanswered question costs you a customer.
I build custom RAG chatbots using LangChain, LlamaIndex, and GPT4 that read your documents, retrieve the right chunks via vector database search, and return accurate answers instantly.
Whether you need a Pinecone pipeline, a ChromaDB knowledge base, OpenAI embeddings, or a LlamaIndex agent connected to your data warehouse, I build it clean, fast, and production ready.
Your AI chatbot will understand context, remember conversations, cite its sources, and handle PDF, CSV, and URL ingestion out of the box.
What I Offer:
- Custom RAG chatbot built with LangChain or LlamaIndex
- Vector database setup with Pinecone, Weaviate, or ChromaDB
- OpenAI GPT4 or open source LLM integration with streaming responses
- PDF, Word, CSV and web URL ingestion with smart chunking and embeddings
- Conversational memory and source citation in every answer
- REST API or full chat interface with cloud deployment
- Hybrid search combining semantic and keyword retrieval
- LangChain agents and tool calling for multi step automation
Message me today.
Conheça mais sobre Ryan Newman
Ryan Newman
Shopify Expert and I Fix Stores to Increase Sales
- A partir deReino Unido
- Membro desdemai. de 2026
Idiomas
Inglês
I help businesses automate customer interactions using AI voice agents, RAG chatbots, and GoHighLevel workflows so you never miss a lead or appointment again.
From building intelligent voice agents that handle inbound calls, to deploying RAG chatbots trained on your data
speciality:
Voice AI Vapi, Retell AI, inbound/outbound call agents
Chatbots RAG, LlamaIndex, GPT4
Automation GoHighLevel (GHL), n8n, Make, CRM workflows
AI Agents, LLM deployment
Whether you're a agency, real estate firm, or SaaS business if you need leads captured, appointments booked, or calls handled by AI, I'm your guy.
Perguntas frequentes
What types of documents can your RAG chatbot read?
It reads PDFs, Word files, CSVs, plain text, web URLs, and Notion exports. Any document source can be chunked, embedded, and stored in your vector database for instant retrieval by your AI chatbot.
Which vector database do you use: Pinecone, Weaviate, or ChromaDB?
I recommend Pinecone for production scale, ChromaDB for fast local builds, and Weaviate for hybrid search. I work with all three and will choose the best fit for your RAG pipeline and budget.
Do I need an OpenAI API key or can you use an open source LLM?
Both work. I build RAG systems with GPT4, GPT3.5, Claude, and open source models like Mistral or LLaMA. You keep your API key. I just wire the LLM into your LangChain or LlamaIndex pipeline.
What is retrieval augmented generation and why does it matter?
RAG connects a large language model to your private data via embeddings and vector search. Unlike fine tuning, RAG stays current as your docs update. Your chatbot answers from facts, not guesswork.
Will the chatbot remember past messages in a conversation?
Yes. Every RAG chatbot I build includes conversation memory via LangChain buffer or summary memory. The AI tracks context across turns so answers stay coherent, not repetitive or confusing.
Can you integrate the chatbot into my existing website or app?
Absolutely. I deliver a REST API endpoint or a full embeddable chat UI. It connects to React, Next.js, WordPress, or any platform. Deployment to Render, Railway, or AWS is included on higher tiers.
How accurate are the answers? What if the chatbot gives wrong information?
I tune chunking strategy, embedding model, and prompt templates to maximise faithfulness. Every pipeline includes retrieval evaluation scoring hit rate and answer relevance before handoff to you.
How long does it take to build a working RAG chatbot?
A basic RAG API with one document source ships in one day. A full multi doc chatbot with chat UI, deployment, and memory takes three to five days. Rush delivery is available as a paid add on.
Do you use LangChain or LlamaIndex? What is the difference?
LangChain excels at agents, tool calling, and multi step workflows. LlamaIndex shines at deep document indexing and structured knowledge bases. I pick the right framework for your exact use case.
What happens after delivery? Can I update the knowledge base myself?
Yes. I document everything and optionally build an upload endpoint so you can add new documents anytime.
