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Fiverr Pro
English
$
USD
I will build a rag pipeline with langchain and pinecone
Ikram
About this gig
Stop feeding your AI generic responses. If your chatbot cannot answer questions from your own documents, knowledge base, or internal data, it is not serving your business it is just guessing. I build production-grade Retrieval-Augmented Generation (RAG) pipelines that turn your raw documents into an intelligent, context-aware AI system.
I have designed and deployed real RAG systems at scale, including a university-grade AI assistant handling hundreds of student queries with sub-second response times. My pipelines use LangChain for orchestration, Pinecone for vector storage, and Groq or OpenAI for LLM inference with LRU caching and asynchronous data fetching to minimize latency.
Whether you need to query PDFs, scrape your website content, connect to a SQL database, or ingest hundreds of documents into a searchable knowledge base, I will architect the full pipeline from data ingestion to the deployed API endpoint. This is not a tutorial project this is the same architecture I build for production clients.
What you get: a fully functional RAG pipeline that retrieves relevant context from your data before generating an answer. This means no more hallucinations, no more generic response
Get to know Ikram
Ikram
AI Engineer
- FromPakistan
- Member sinceJul 2026
- Avg. response time1 hour
Languages
English
AI Engineer & Full-Stack Developer with hands-on experience building real-world systems: RAG pipelines, agentic AI chatbots, computer vision (YOLO/CNN), and workflow automation using Playwright and LLMs. Full-stack skills across Python, Laravel, React, and Node.js let me handle both the AI layer and the product around it.
I've delivered university AI assistants, trading automation platforms, quiz-solving automation bots, and enterprise CMS systems. I care about clean, working code and hitting deadlines.
Tell me what you need built I'll tell you honestly if it's a fit and how I'd approach it
FAQ
What data sources can you connect to my RAG pipeline?
I can ingest PDFs, Word documents, plain text files, website URLs (scraped), Notion pages, SQL databases, and CSV/JSON datasets. For the Standard and Premium packages, I can combine multiple sources into a single unified knowledge base.
Do I need my own OpenAI or Groq API key?
Yes, you will need your own API key for the LLM provider (OpenAI, Groq, Anthropic, or Hugging Face). I will guide you on getting one if you do not have it already. The cost of API calls is typically very low for RAG workloads.
What is the difference between Basic and Standard?
Basic handles a single data source with straightforward chunking and retrieval. Standard adds multiple data sources, advanced chunking (recursive, semantic), caching for faster responses, asynchronous retrieval, and a chat widget frontend.
Can this integrate with my existing website or application?
Yes. Standard includes a simple chat widget, and Premium includes full integration into your existing app via API. I work with React, Next.js, and vanilla JavaScript frontends.
What hosting/deployment options are available?
I can deploy your RAG pipeline on Render, Railway, AWS, or your own VPS. For Basic, I provide the code with setup instructions. For Standard and Premium, I handle the deployment
How accurate will the responses be?
RAG significantly reduces hallucinations compared to raw LLM calls because every answer is grounded in your retrieved documents. The Premium package adds reranking to further improve relevance. However, accuracy also depends on the quality and structure of your source documents.
