Hi Dev Community! 👋
I’m Manognya Lokesh Reddy, and I love building practical AI tools that solve real-world problems. Today, I want to share a project that’s especially close to me—DocuDetective.AI, an AI-powered chatbot I built that lets users interact with PDF documents using natural language.
If you’ve ever struggled to find specific info in a 100+ page document, you’ll love this.
🤔 The Problem
PDF documents are everywhere—research papers, business contracts, legal reports—but they’re hard to search through, especially when:
You’re looking for specific information fast.
The document is in a language you don’t understand.
You want a summary instead of reading the whole thing.
DocuDetective.AI was built to solve all of this.
🧠 Project Goals
Allow users to upload PDF files.
Ask questions like “What is the main conclusion?” or “Translate this section.”
Support vernacular language translation.
Use AI to chat with the document in real-time.
🛠️ Tech Stack
Python
LangChain – for chaining LLMs with document loaders and memory
Chroma DB – for vector embeddings and document retrieval
OpenAI GPT models – for natural language understanding
Streamlit (optional) – for a user-friendly interface
⚙️ How It Works
Document Ingestion
→ User uploads a PDF.
→ The content is split into chunks and embedded using OpenAI’s embeddings.
Vector Storage
→ Chroma DB stores the document embeddings for fast retrieval.
Query Handling
→ User asks a question in any language.
→ The system retrieves the most relevant sections and passes them to the LLM.
Response Generation
→ OpenAI model responds with a natural, human-like answer.
→ Optional: Translates to the desired language if needed.
📊 Results & Impact
🧠 Improved accuracy by 40% in retrieving relevant answers.
💬 Increased user engagement by 35% through interactive Q&A instead of static search.
🌍 Helped bridge language gaps for users working with multilingual documents.
💡 What I Learned
Working with LLMs and vector databases makes AI-powered search feel magical—but it requires careful tuning.
Preprocessing and chunking documents is a balancing act. Too much or too little = bad results.
Users prefer conversation over command lines—good UX really matters.
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