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What is RAG?

RAG is a technology that combines information retrieval with AI to deliver quick, relevant answers. It pulls data from your documents and uses AI to respond accurately, making complex information easy to access and understand.

It's like having the intelligence of a model like GPT-4o, but fully focused on the information you provide—ensuring responses are accurate, relevant, and based only on your data.

How It Works?
  • Set Up API Keys: It’s quick and painless, with an interactive todo list.
  • Store Your Data: Upload your PDFs, GitHub repos, YouTube links, or any other documents you need.
  • Query Instantly: Retrieve precise information immediately.
Ragapi is Safe

Your data stays private and secure with Ragapi.

  • Data Stored in Your Pinecone: All documents and embeddings are stored directly in your own Pinecone database, ensuring you retain full control over your data.
  • No Data Training: OpenAI does not train its models on your data, so every interaction remains private and is not used to improve the AI.
  • No Data Storage by Ragapi: Ragapi processes your data solely to provide its functionality. We don’t store any chat history or document content—your data is processed in real-time and then immediately discarded.

With Ragapi, you can confidently build and use RAG pipelines, knowing your information is secure and used only for the purpose you intend.

And with detailed logs, you’ll know exactly what’s happening—track every request and monitor usage to keep full visibility on your data interactions.

Our Features

Super Simple To Use
Start building your RAG pipeline with minimal setup. Just integrate API keys, store documents, and query instantly.
Private and Secure
Data privacy is our priority—no document or chat history data is stored on our platform, and OpenAI doesn’t use your data to train its models, keeping your information completely secure.
Wide Document Support
Seamlessly store and query a variety of document types, including PDFs, GitHub repos, YouTube videos, and websites.
Multi-Document Context
Combine multiple documents within a single namespace to access unified, context-rich information instantly.
Customizable Tone of Voice
Choose an assistant that matches your style—whether it’s professional, witty, friendly, or something in between. Ragapi adapts to your needs!
Chat History
Ragapi seamlessly manages chat history for you—but without storing your data. You pass the chat history as an array, and Ragapi processes it to maintain context while keeping your data completely in your control.

Let us handle the hard AI stuff—so you can focus on building your app.

Frequently asked questions

Everything you need to know

What is Ragapi?
Ragapi is a super-simple API for setting up scalable Retrieval-Augmented Generation (RAG) pipelines. With just two API calls, you can store and query documents like PDFs, YouTube videos, GitHub repositories, and websites—all while ensuring your data stays private.
How does Ragapi protect my data?
Privacy is our top priority. Ragapi only processes your documents; nothing is stored. Your chat history and data are never saved or used to train models.
What kind of documents can I use with Ragapi?
You can store and query a wide variety of document types, including PDFs, YouTube videos, GitHub repositories, and websites. Just upload your data, and Ragapi takes care of the rest.
How scalable is Ragapi?
Extremely scalable! With Ragapi, you get a fully functional RAG pipeline ready to handle any scale, from small projects to enterprise-grade workloads—all with just two API calls.
What are some use cases for Ragapi?

Ragapi unlocks powerful AI capabilities for a variety of applications. Here are some examples to inspire you:

  • ChatGPT, but Focused Only on Your Data: Build a chatbot that provides accurate answers based solely on the documents you upload—perfect for internal knowledge bases or customer-specific AI solutions.
  • Smart Documentation Search: Enhance your app with intelligent search functionality that retrieves the most relevant answers from manuals, legal documents, or technical guides.
  • Personalized Learning Assistants: Create AI tutors that pull context-aware responses directly from textbooks, courses, or training material you provide.
  • Codebase Navigation: Allow developers to query massive codebases, like GitHub repositories, for quick and accurate answers about functions, classes, or dependencies.
  • Medical Records Query Tool: Develop an app that retrieves relevant medical information or insights from patient records, clinical studies, or drug references.
  • Academic Research Assistant: Help researchers quickly find answers or insights from research papers, journals, or study datasets.
  • Customer Support Automation: Build a chatbot that answers customer queries using your knowledge base, FAQs, or product manuals.

These are just a few examples—in the end, the possibilities are limitless. It’s all about your imagination!

Why do I need RAG? Why can’t I just pass everything to the LLM in a prompt?
  • Context Limitations: LLMs have a fixed context window, meaning they can only process a limited amount of information at once. With RAG, you can retrieve only the most relevant data, bypassing this limitation.
  • Cost Efficiency: Passing all your data in a prompt can be extremely expensive since LLMs charge per token (input and output). RAG minimizes the tokens sent to the LLM, saving you money.
  • Precision: Without RAG, there’s no guarantee the LLM will generate answers based solely on your documents. RAG ensures the output is grounded in your specific data.
Can I trust Ragapi with sensitive data?
Yes. Ragapi doesn’t store any of your documents or chat history, and it never trains models on your data. Your data is processed securely and stays completely private.
Why do I need to use my own OpenAI and Pinecone API keys?

Using your own API keys ensures you have full control over your data, privacy, and costs. This approach means:

  • Maximum Privacy: Your data is processed directly through your own accounts, so Ragapi never accesses or stores it.
  • Transparent Costs: You only pay for what you use with OpenAI and Pinecone, giving you better visibility and control over your expenses.
What is Pinecone, and why do I need it?

Pinecone is a vector database that stores and retrieves embeddings—AI-optimized numerical representations of your documents. It’s essential for creating efficient Retrieval-Augmented Generation (RAG) pipelines because:

  • Efficient Search: Pinecone enables fast and accurate retrieval of the most relevant information from large datasets.
  • Scalability: It seamlessly handles growing data sizes, keeping your AI applications performant.
  • Essential for RAG: Without Pinecone, you’d have to reprocess your documents for AI with every request, which would be painfully slow and inefficient. Pinecone ensures your documents are pre-optimized and ready for instant querying.

In short, Pinecone makes storing and retrieving your data efficient and practical—without it, scalable RAG pipelines wouldn’t be possible.

Still have questions?

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