README.md

metadata

title: Multi PDF Chatbot
emoji: 📚
colorFrom: blue
colorTo: blue
sdk: streamlit
sdk_version: 1.42.0
python_version: '3.11'
app_file: app.py
pinned: false

📚 Multi-PDF Agentic Chatbot

Ask questions across multiple PDFs at once. Get answers with exact source citations — document name + page number.

Live Demo

Try it here: https://huggingface.co/spaces/artistica-004/multi-pdf-chatbot

🎯 Objective

This project was built as part of Task 2A — Fix My Life with AI.

The goal was to identify a genuine real-world problem, design a practical AI solution for it, and demonstrate a measurable before vs after improvement.

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😤 Step 1 — The Pain (Why This Was Built)

Every single study session I found myself with 4-5 PDF tabs open simultaneously. Finding one answer meant:

• Switching between tabs constantly
• Losing my train of thought every time I switched
• Re-reading the same sections because I forgot which PDF had what
• Using Ctrl+F but not knowing which PDF to search first
• Spending 30-45 minutes just navigating — before even starting to understand the content

This happened every single day.

I could not skip it — these were study materials and research documents I was required to understand deeply. I could not delegate it — the comprehension was mine to do.

Why existing tools failed:

Tool	Why It Failed
Ctrl+F	One PDF at a time, exact keyword needed
Google	Cannot access private PDFs
ChatGPT	No grounding in actual documents, hallucinations
Adobe Acrobat	One file at a time, no semantic search

The real problem was not volume. It was context reconstruction from scattered sources.

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📋 Step 2 — Workflow Before This Tool

Old process (10 steps):

1. Formulate the question in my head (~1 min)
2. Open 3-5 PDFs in separate browser tabs (~2 min)
3. Pick starting PDF based on gut feeling (~30 sec)
4. Ctrl+F with a keyword (~1-2 min)
5. Read surrounding context to check relevance (~3-5 min)
6. If not found — switch tab, repeat Ctrl+F (~1-2 min)
7. ⭐ Reconstruct mental context after tab switch (~5-10 min)
8. Cross-reference information across PDFs (~5 min)
9. Manually compile answer from fragments (~5 min)
10. Re-verify source page before using answer (~2 min)

Total: 30-45 minutes per session Most mentally demanding step: Step 7 Reconstructing context after every tab switch — holding the original question, what was already read, and the current document all in working memory at once.

Flowchart (Before):

[Need an answer] | v [Open 3-5 PDFs in separate tabs] | v [Choose starting PDF by intuition] | v [Ctrl+F keyword] | FOUND? ──No──> [Switch tab, try next PDF] | | Yes [Different keyword?] | Yes / \ No v /
[Read paragraph] <── [Mark as no info] | [Does it answer the question?] | Yes / No /
[Note source] [Try synonym / different section] | [All PDFs checked?] | Yes / No /
[Compile] [Loop back] | [Verify source] | [Done]

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🧠 Step 3 — AI Solution Design

System Thinking

Architecture chosen: Agentic Pipeline System

Architecture	Why Not Chosen
Single Prompt	Cannot handle raw PDFs, token limit exceeded
Basic Pipeline	No decision making, no adaptive behavior
Agent System ✅	Decision making + multi-step execution + adaptive

Why Agent System: The bot needs to make decisions — is this question simple or complex? Should it search PDFs or the web? Does the answer actually exist in the documents? These decisions require an agent, not just a pipeline.

Full Architecture:

User Question | v [Analyze Question — simple or complex?] | Complex ──────────────> [Break into sub-questions] | | Simple [Search each sub-question] | | [Search PDFs] [Combine all answers] | | [Check answer quality] <────────── | FOUND ──> [Generate follow-up questions] ──> [Final Answer] | NOT_FOUND ──> ["Not available in documents"]

Data Layer

Inputs and Outputs:

Stage	Input	Output
PDF Extraction (PyPDF2)	Raw PDF files	Text + source + page per page
Chunking	Page text	500-char chunks with metadata
Embedding (all-MiniLM-L6-v2)	Chunk strings	384-dim vectors
Vector Store (ChromaDB)	Vectors + metadata	Searchable index
Retrieval	Question + k=6	Top 6 relevant chunks
Generation (Groq LLaMA 3.3 70B)	Question + context	Cited answer

Why chunk_size=500, chunk_overlap=50:

• 500 chars = 3-5 sentences = one focused concept
• Overlap of 50 ensures sentences on boundaries are not lost
• Smaller chunks = more precise embeddings = better retrieval

Edge Cases

Edge Case 1: Scanned PDF (image-based)

• Current handling: PyPDF2 returns empty string, page is skipped silently
• Impact: User gets no answer with no explanation why

Edge Case 2: Answer spans chunk boundary

• Current handling: 50-char overlap partially mitigates this
• Impact: LLM may receive incomplete context and generate partial answer

Edge Case 3: Question not in any PDF

• Current handling: Agent checks answer quality, returns "not available in documents" clearly
• Impact without handling: LLM halluculates a confident-sounding wrong answer

Failure Simulation

Scenario: Groq API rate limit hit

1. User uploads 4 PDFs and asks 10 rapid questions
2. generate_answer() call raises RateLimitError
3. User sees Python traceback — no friendly message
4. Root cause: Free Groq tier has tokens-per-minute limit
5. Fix: try/except around API call with friendly message and retry logic

Trade-offs

Trade-off 1: chunk_size=500 vs chunk_size=1000

• Chose 500 for precise embeddings per concept
• Sacrificed: multi-paragraph argument retrieval
• Worth it: most questions are factual, not analytical

Trade-off 2: Local embeddings vs OpenAI embeddings

• Chose all-MiniLM-L6-v2 (local, free)
• Sacrificed: higher quality semantic matching
• Worth it: zero API cost, no data sent externally

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✅ Step 4 — Proof of Concept

Before vs After

Metric	Before (Manual)	After (AI Tool)
Time to find answer	30-45 min	8-15 seconds
Steps required	10 steps with loops	3 steps
Accuracy	Keyword dependent	Semantically grounded
Context switching	15-20 tab switches	Zero
Source traceability	Manual memory	Auto cited
Cross-doc synthesis	Manual notes	Automatic

Actual Prompt Sent to Groq:

You are a helpful and concise assistant. The user has uploaded these PDF documents:

document1.pdf document2.pdf

Relevant excerpts from the documents: --- Chunk 1 from: document1.pdf, Page 3 --- [chunk text] Question: [user question] Instructions:

Answer in maximum 4-5 lines only Be direct and simple No repetition Mention source: (Source: filename.pdf, Page 3) If not in documents say so clearly

Sample Q&A:

Example 1:

• Question: "What is the role of AI in the playbook?"
• Answer: "AI is used to structure thinking, validate decisions and accelerate learning — not to replace thinking entirely. (Source: Intern Operating System V2.pdf, Page 3)"

Example 2:

• Question: "What is the salary structure?"
• Answer: "This information is not available in the uploaded documents."

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🔍 Final Reflection

Q1: What is the weakest part? The chunk boundary problem. When an answer spans multiple paragraphs, k=6 may not retrieve all relevant chunks. Also scanned PDFs are silently skipped with no user warning.

Q2: What single failure would break it completely? Groq API going down. Everything else runs locally but without Groq, answer generation completely fails. No fallback model exists currently.

Q3: If AI was removed, what would still be valuable? Three things:

1. Multi-PDF aggregation in one interface
2. Chunk metadata system with source + page citations
3. Semantic similarity search — still better than Ctrl+F

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🏗️ Architecture

app.py — Streamlit UI + chat interface agent.py — Agentic decision making loop rag_engine.py — RAG pipeline + vector store .env — API keys requirements.txt — Dependencies

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⚙️ Agentic Features

• ✅ Decision Making — simple vs complex question handling
• ✅ Multi-Step Execution — complex questions broken into parts
• ✅ Workflow Orchestration — analyze → plan → execute → combine
• ✅ Adaptive Behavior — greetings handled separately
• ✅ Auto PDF Summarization — summary shown on upload
• ✅ Gap Detection — clearly states when answer not found
• ✅ Follow-up Suggestions — 3 related questions after every answer

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🛠️ Tech Stack

Component	Technology
Frontend	Streamlit
LLM	Groq LLaMA 3.3 70B
Embeddings	HuggingFace all-MiniLM-L6-v2
Vector Store	ChromaDB
PDF Extraction	PyPDF2
Chunking	LangChain RecursiveCharacterTextSplitter
Agent Logic	Custom Python

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🚀 Run Locally

git clone https://github.com/artistica-004/multi-pdf-chatbot
cd multi-pdf-chatbot
pip install -r requirements.txt

Create .env file: GROQ_API_KEY=your_groq_api_key_here

Run:

streamlit run app.py

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📦 Requirements

streamlit langchain langchain-community langchain-text-splitters PyPDF2 chromadb sentence-transformers groq python-dotenv

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🔗 Links

• Live Demo: https://huggingface.co/spaces/artistica-004/multi-pdf-chatbot
• GitHub: https://github.com/artistica-004/multi-pdf-chatbot

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