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RAGify - Chat with Your Documents Using AI

Finding one answer in a long PDF, handbook, or policy manual can take several searches. A general-purpose chatbot cannot use a private document unless an application supplies the relevant text.

RAGify demonstrates retrieval-augmented generation (RAG): it finds passages related to a question and sends those passages to a language model as context. This post explains the indexing and query pipeline, along with the privacy boundary you must evaluate when choosing the model service.

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What is Retrieval-Augmented Generation?

Standard large language models generate answers based strictly on their pre-training data. While knowledgeable on general topics, they cannot answer questions about private files, recent internal updates, or company-specific policies.

Retrieval-Augmented Generation (RAG)

An AI technique that works like an open-book exam - retrieving relevant passages from your external documents first and passing them to a language model so it can answer questions accurately based on facts.

FAISS (Facebook AI Similarity Search)

An open-source library for similarity search over vectors. RAGify uses it to find document passages whose embeddings are close to the question embedding.

LangChain

A software framework that simplifies building AI applications by connecting document loaders, text splitters, and language models into modular pipelines.

Groq Acceleration Engine

A high-speed inference engine powered by custom hardware (Language Processing Units) that runs open language models like Meta Llama with low latency.

By injecting retrieved document snippets directly into the prompt context, RAG systems ensure accurate responses while eliminating hallucination risks.

Core RAG Advantage

RAG allows organizations to query proprietary documents securely without spending thousands of dollars fine-tuning custom base models.

System Architecture and Workflow

RAGify converts static text documents into an interactive question-answering application through a four-stage pipeline.

---
title: "RAGify System Architecture"
---
flowchart TB
    accTitle: RAGify system architecture
    accDescr: One pipeline indexes PDF chunks in FAISS; a second retrieves relevant chunks and gives them to the language model to answer a question.
    subgraph "Knowledge Base Indexing"
        A["Load PDF Document"] --> B["Split Text Chunks"]
        B --> C["Generate Embeddings"]
        C --> D["Store in FAISS Index"]
    end

    subgraph "Real-Time Query Pipeline"
        E["User Input Query"] --> F["Embed Query Vector"]
        F --> G["FAISS Vector Search"]
        D -.-> G
        G --> H["Retrieve Top-K Snippets"]
        H --> I["Assemble Context Prompt"]
        I --> J["Groq / Llama LLM"]
        J --> K["Return Grounded Answer"]
    end

The table below outlines each phase of the document processing pipeline and its corresponding software module.

Stage Pipeline Task Technical Component Output Artifact
1. Ingestion Load PDF documents and split text PyPDF2 / LangChain Formatted text chunks
2. Indexing Compute numerical text embeddings sentence-transformers FAISS vector store
3. Retrieval Search vector database for top matches FAISS Similarity Engine Contextual text excerpts
4. Generation Synthesize answer using retrieved context Groq API / Meta Llama Verifiable answer output

Each step is isolated, ensuring that users can swap embedding models or vector indexers without modifying the web frontend.

The Python code snippet below demonstrates how RAGify loads documents, builds a FAISS vector index, and executes a context-grounded retrieval query using LangChain.

rag_pipeline.py
from langchain_community.vectorstores import FAISS
from langchain_huggingface import HuggingFaceEmbeddings

# Initialize embedding model and vector index
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")  # (1)
vectorstore = FAISS.from_documents(document_chunks, embeddings)

# Retrieve top 3 relevant chunks for query
retriever = vectorstore.as_retriever(search_kwargs={"k": 3})  # (2)
relevant_docs = retriever.invoke(user_query)
  1. Initializes the lightweight sentence-transformer model to generate 384-dimensional vector embeddings.
  2. Configures the FAISS similarity retriever to pull the top 3 most relevant text chunks per user prompt.
Customizing Document Chunk Sizes

When splitting long documents, set chunk sizes between 500 and 1,000 characters with a 10% overlap to preserve sentence context across boundaries.

The Blunder Mifflin Demo Case Study

To demonstrate RAGify in action, the project includes an interactive demonstration using a fictional employee handbook for "Blunder Mifflin."

The system indexes the handbook, allowing users to ask natural language questions regarding work-from-home guidelines, vacation allowances, or specialized office protocols.

  • Data Privacy Control
    Process documents locally or via secure inference APIs without sending private data outward.

  • Flexible Model Provider
    Supports local open-source models as well as high-speed cloud inference via Groq LPUs and Meta Llama.

  • Similarity Retrieval
    Uses a FAISS index to retrieve document passages related to the question.

  • Replaceable Source Document
    Use a different handbook, paper, or manual after checking its format, access rights, and sensitivity.

Interactive Demonstration Access

You can test the interactive RAGify application live or explore the underlying repository.

Conclusion

Retrieval-Augmented Generation provides an effective and scalable method for interacting with unstructured document repositories. Tools like RAGify show how combining open-source frameworks like LangChain, FAISS, and Streamlit makes document intelligence accessible to both developers and organizations.

References and further reading

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