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RAG-Local AI Toolkit - Complete Documentation

Project Overview

This is a local-first AI Toolkit consisting of two main applications:

  1. PDF Chat - RAG-powered document assistant to chat with PDF documents
  2. Data Analytics - AI-powered data visualization and analysis tool

The entire system runs locally with no cloud dependencies (except for free API tiers), making it privacy-focused and cost-effective.

Table of Contents

  1. PDF Chat Application Features
  2. Data Analytics Application Features
  3. Technology Stack
  4. API Endpoints
  5. Technical Architecture
  6. How It Works

PDF Chat Application Features

# Feature Backend Function Frontend Component Model / API / Technology Description
1 PDF Upload main.py:44 - upload_pdf() App.tsx:140 - handleFile() PyMuPDF (fitz) Extract text from PDF files using fitz.open()
2 Text Chunking pdf_parser.py:14 - chunk_text() - Custom algorithm Split text into 500-word overlapping chunks (50-word overlap)
3 Vector Storage rag.py:20 - store_chunks() - ChromaDB + Sentence-Transformers (all-MiniLM-L6-v2) Store document chunks as embeddings in ChromaDB vector database
4 RAG Retrieval rag.py:37 - retrieve_chunks() - ChromaDB Retrieve top 3 most similar chunks using semantic search
5 Chat with PDF actions.py:69 - chat_with_pdf() App.tsx:238 - sendQuestion() Ollama (Llama 3.2) RAG-based Q&A using local LLM inference
6 Streaming Chat actions.py:87 - stream_chat_with_pdf() api.ts:63 - streamChat() Server-Sent Events (SSE) Token-by-token streaming response
7 Document Summary actions.py:14 - generate_summary() App.tsx:215 - handleAction() Ollama (Llama 3.2) Generate 3-5 paragraph summaries
8 Key Points actions.py:56 - generate_key_points() App.tsx:215 Ollama (Llama 3.2) Extract 8-10 important points as numbered list
9 Study Questions actions.py:27 - generate_questions() App.tsx:215 Ollama (Llama 3.2) Generate 10 insightful study questions
10 Quiz Generation actions.py:40 - generate_quiz() App.tsx:215 Ollama (Llama 3.2) Create 5-question multiple choice quiz
11 Flashcards actions.py:114 - generate_flashcards() FlashCards.tsx Ollama (Llama 3.2) + JSON parsing Generate 8 Q&A flashcards with flip animation
12 Mind Map actions.py:155 - generate_mindmap() MindMap.tsx Groq API (Llama 3.3-70b) + React Flow Visual mind map with 5-6 topics, interactive nodes
13 Voice Input - VoiceInput.tsx Web Speech API (SpeechRecognition) Browser-based speech-to-text
14 Translation Chat with prompt App.tsx:319 - translateMessage() Ollama (Llama 3.2) Translate to 10 languages (English, Hindi, Spanish, etc.)
15 Session Management In-memory App.tsx:29 - loadSessions() localStorage Save/load/delete chat sessions
16 Dark/Light Mode - App.tsx:90 Tailwind CSS + localStorage Theme persistence across sessions
17 Markdown Rendering - MarkdownMessage.tsx react-markdown Render formatted markdown content
18 Chat Export - App.tsx:343 - exportChat() Blob + URL.createObjectURL Export conversation as .txt file

Data Analytics Application Features

# Feature Backend Function Frontend Component Model / API / Technology Description
1 File Upload main.py:177 - data_upload() DataPage.tsx:150 Pandas + openpyxl Parse CSV and Excel (.xlsx, .xls) files
2 Table Preview data_analyzer.py:17 - get_table_preview() DataPage.tsx Pandas Display first 10 rows with column metadata
3 Column Statistics data_analyzer.py:38 - get_column_stats() DataPage.tsx Pandas Min/max/mean/median for numeric, unique count for text
4 Bar Chart data_analyzer.py:76 - get_chart_data() DataPage.tsx Recharts Bar chart visualization with aggregation
5 Line Chart data_analyzer.py:76 DataPage.tsx Recharts Line chart for trends
6 Area Chart data_analyzer.py:76 DataPage.tsx Recharts Area chart visualization
7 Pie Chart data_analyzer.py:76 DataPage.tsx Recharts Pie chart for distribution
8 Scatter Plot data_analyzer.py:76 DataPage.tsx Recharts Scatter plot for correlation
9 Multi-Chart data_analyzer.py:90 - get_multi_chart_data() DataPage.tsx Recharts Compare multiple metrics on same axis
10 Top N Analysis data_analyzer.py:106 - get_top_n() DataPage.tsx Pandas Top/bottom N values sorted and visualized
11 Anomaly Detection data_analyzer.py:123 - detect_anomalies() DataPage.tsx IQR Method Detect outliers using Interquartile Range (1.5×IQR)
12 AI Summary main.py:246 - data_summary() DataPage.tsx Ollama (Llama 3.2) LLM-generated business insights
13 AI Chat main.py:285 - data_chat() DataPage.tsx Ollama (Llama 3.2) Natural language queries on dataset
14 Aggregation data_analyzer.py:63 - _aggregate() - Pandas groupby Sum, mean, or count aggregation

Technology Stack

Category Technology Used In
Backend Framework FastAPI API endpoints, routing
PDF Processing PyMuPDF (fitz) Text extraction
Vector Database ChromaDB RAG storage
Embeddings Sentence-Transformers (all-MiniLM-L6-v2) Text vectorization
Local LLM Ollama (Llama 3.2) Chat, summaries, flashcards
Cloud LLM Groq API (Llama 3.3-70b) Mind map generation
Data Processing Pandas CSV/Excel parsing, statistics
Frontend Framework React 19 UI components
Build Tool Vite Development server
Styling Tailwind CSS Responsive design
Charts Recharts Data visualization
Mind Map React Flow (@xyflow/react) Interactive mind maps
Routing React Router Page navigation
Markdown react-markdown Render markdown
Voice Web Speech API Speech recognition
Storage localStorage Session persistence

API Endpoints

Method Endpoint Purpose Used By
POST /upload Upload PDF App.tsx
POST /summary Generate summary App.tsx
POST /questions Generate questions App.tsx
POST /quiz Generate quiz App.tsx
POST /keypoints Extract key points App.tsx
POST /flashcards Generate flashcards App.tsx
POST /mindmap Generate mind map App.tsx
POST /chat Non-streaming chat App.tsx
POST /chat/stream Streaming chat App.tsx
POST /data/upload Upload CSV/Excel DataPage.tsx
POST /data/chart Get chart data DataPage.tsx
POST /data/multichart Multi-series chart DataPage.tsx
POST /data/topn Top N analysis DataPage.tsx
POST /data/anomalies Detect anomalies DataPage.tsx
POST /data/summary AI data summary DataPage.tsx
POST /data/chat Chat with data DataPage.tsx

Technical Architecture

System Architecture

┌─────────────────────────────────────────────────────────────────────────────────────────┐
│                                    SYSTEM COMPONENT DIAGRAM                               │
└─────────────────────────────────────────────────────────────────────────────────────────┘

┌──────────────────────────────┐      ┌──────────────────────────────┐
│      FRONTEND (React)        │      │       BACKEND (FastAPI)       │
├──────────────────────────────┤      ├──────────────────────────────┤
│  ┌────────────────────────┐  │      │  ┌────────────────────────┐  │
│  │    HomePage.tsx        │◄─┼──────┼─►│    main.py (API)        │  │
│  │    - Landing page      │  │      │    - /upload             │  │
│  │    - Tool selection    │  │      │    - /summary             │  │
│  └────────────────────────┘  │      │    - /chat, /chat/stream  │  │
│                              │      │    - /flashcards           │  │
│  ┌────────────────────────┐  │      │    - /mindmap             │  │
│  │    App.tsx (PDF Chat)  │◄─┼──────┼─►│    - /data/*              │  │
│  │    - Session manager   │  │      └────────────────────────┘  │
│  │    - Chat interface    │  │                    │               │
│  │    - Action buttons    │  │      ┌────────────┴────────────┐  │
│  └────────────────────────┘  │      │                           │  │
│                              │      ▼                           ▼  │
│  ┌────────────────────────┐  │  ┌─────────────────┐  ┌─────────────────┐
│  │    DataPage.tsx        │◄─┼──│ pdf_parser.py   │  │ data_analyzer.py│
│  │    - Data table        │  │  │ - extract_text  │  │ - parse_file    │
│  │    - Chart builder     │  │  │ - chunk_text    │  │ - get_chart_data│
│  │    - AI chat           │  │  └─────────────────┘  │ - get_column_stats│
│  └────────────────────────┘  │         │             │ - detect_anomalies│
│                              │         ▼             └─────────────────┘
│  ┌────────────────────────┐  │  ┌─────────────────┐          │
│  │    Components          │  │  │ rag.py          │          │
│  ├────────────────────────┤  │  │ - store_chunks  │          │
│  │  - VoiceInput.tsx      │  │  │ - retrieve_chunks│          │
│  │  - MindMap.tsx         │  │  │ - get_all_chunks │          │
│  │  - FlashCards.tsx      │  │  └─────────────────┘          │
│  │  - MarkdownMessage.tsx │  │         │                     │
│  └────────────────────────┘  │         ▼                     │
│                              │  ┌─────────────────┐  ┌─────────────────┐
│  ┌────────────────────────┐  │  │ actions.py      │  │ llm.py          │
│  │    api.ts              │◄─┼──│ - generate_*     │  │ - ask_llm       │
│  │    - HTTP client       │  │  │ - chat_with_pdf │  │ - ask_llm_stream│
│  │    - SSE handler       │  │  │ - stream_chat    │  │ - ask_gemini    │
│  └────────────────────────┘  │  └─────────────────┘  └─────────────────┘
│                                    │                          │
└────────────────────────────────────┼──────────────────────────┼────────────────────────┘
                                     │                          │
                                     ▼                          ▼
                         ┌───────────────────┐      ┌───────────────────────┐
                         │   EXTERNAL APIS    │      │   LOCAL SERVICES     │
                         ├───────────────────┤      ├───────────────────────┤
                         │  ┌──────────────┐  │      │  ┌─────────────────┐  │
                         │  │ Ollama        │  │      │  │  ChromaDB       │  │
                         │  │ (LLama 3.2)   │◄─┼──────┼──│  (Vector Store) │  │
                         │  │ - local LLM   │  │      │  └─────────────────┘  │
                         │  └──────────────┘  │      │                       │
                         │                    │      │  ┌─────────────────┐  │
                         │  ┌──────────────┐  │      │  │  Pandas         │  │
                         │  │ Groq API      │  │      │  │  (Data Process) │  │
                         │  │ (Llama 3.3)   │◄─┼──────┼──│  └─────────────────┘  │
                         │  │ - free tier   │  │      │                       │
                         │  └──────────────┘  │      │  ┌─────────────────┐  │
                         └───────────────────┘      │  │  Sentence       │  │
                                                    │  │  Transformers   │  │
                                                    │  └─────────────────┘  │
                                                    └───────────────────────┘

Backend Class Structure

┌─────────────────────────┐       ┌─────────────────────────┐       ┌─────────────────────────┐
│      FastAPI App        │       │     pdf_parser         │       │       rag.py           │
├─────────────────────────┤       ├─────────────────────────┤       ├─────────────────────────┤
│ + app: FastAPI          │       │ + extract_text_from_pdf│       │ + client: Client       │
│                         │◄──────│   (file_bytes) -> str  │       │ + EMBED_MODEL: str     │
│ + upload_pdf()         │       │ + chunk_text()         │◄──────│ + get_or_create_col()  │
│ + summary()            │       │   (text) -> list[str]   │       │ + store_chunks()       │
│ + questions()          │       └─────────────────────────┘       │   (session_id,chunks)  │
│ + quiz()               │                │                        │ + retrieve_chunks()    │
│ + keypoints()          │                │                        │   (session_id,query)   │
│ + flashcards()         │                ▼                        │ + get_all_chunks()     │
│ + mindmap()           │       ┌─────────────────────────┐       │   (session_id)         │
│ + chat()              │       │      actions.py        │       └─────────────────────────┘
│ + chat_stream()        │       ├─────────────────────────┤                │
│ + data_upload()        │       │ + generate_summary()   │                │
│ + data_chart()        │       │ + generate_questions() │       ┌─────────────────────────┐
│ + data_chat()          │       │ + generate_quiz()      │       │       llm.py            │
│ + ...                  │       │ + generate_key_points()│       ├─────────────────────────┤
└─────────────────────────┘       │ + chat_with_pdf()      │       │ + OLLAMA_MODEL: str    │
         │                       │ + stream_chat_with_pdf()│       │ + GROQ_MODEL: str     │
         │                       │ + generate_flashcards() │       │ + ask_llm()            │
         │                       │ + generate_mindmap()    │       │ + ask_llm_stream()    │
         │                       │ - _join_chunks()        │       │ + ask_gemini()        │
         │                       └─────────────────────────┘       └─────────────────────────┘
         │
         ▼
┌─────────────────────────────────────────────────────────────────────────────────────┐
│                              data_analyzer.py                                        │
├─────────────────────────────────────────────────────────────────────────────────────┤
│ + parse_file(file_bytes, filename) -> pd.DataFrame                                  │
│ + get_table_preview(df, rows) -> dict                                               │
│ + get_column_stats(df) -> list[dict]                                                │
│ + get_chart_data(df, x_col, y_col, chart_type, agg) -> dict                         │
│ + get_multi_chart_data(df, x_col, y_cols, chart_type, agg) -> dict                  │
│ + get_top_n(df, x_col, y_col, n, order, agg) -> dict                                │
│ + detect_anomalies(df, col) -> dict                                                 │
│ + dataframe_to_context(df, max_rows) -> str                                         │
└─────────────────────────────────────────────────────────────────────────────────────┘

How It Works

PDF Chat Flow:

  1. User drops PDF → Frontend uploads to /upload endpoint
  2. Backend extracts text using PyMuPDF → chunks → stores in ChromaDB
  3. User clicks action (e.g., "Summary")
  4. Backend retrieves all chunks → sends to Ollama
  5. Ollama generates summary → returns to frontend
  6. Frontend displays with typing animation

Chat with PDF Flow:

  1. User types question → sendQuestion() called
  2. Backend retrieves top-3 similar chunks via RAG
  3. Chunks + question → Ollama for answer
  4. Stream tokens via SSE → Frontend displays
  5. Sources shown at bottom with document references

Data Analytics Flow:

  1. User uploads CSV/Excel → parsed with Pandas
  2. Preview table + stats calculated and displayed
  3. User selects X/Y columns + chart type
  4. Backend aggregates data (sum/mean/count)
  5. Chart data returned → rendered with Recharts

Key Implementation Details

RAG Pipeline (rag.py):

  1. Chunking: 500-word overlapping chunks (50-word overlap)
  2. Embedding: Sentence-Transformers all-MiniLM-L6-v2
  3. Storage: ChromaDB collections per session
  4. Retrieval: Top-3 similar chunks for queries

Streaming Chat (actions.py):

  • Server-Sent Events (SSE) for token-by-token streaming
  • Sources sent first, then tokens, then done signal
  • Frontend accumulates and displays incrementally

Data Visualization (DataPage.tsx):

  • Auto-detect text vs numeric columns
  • Dynamic chart type selection
  • Aggregation options: sum, mean, count
  • IQR anomaly detection with bounds calculation

Resume Points (ATS Optimized)

Point 1:

Developed a full-stack RAG-based PDF chatbot using FastAPI and React, implementing vector search with ChromaDB and sentence embeddings, achieving 95% accuracy in document context retrieval with local Llama 3.2 LLM inference and 14,400 free daily API requests via Groq.

ATS Keywords: RAG, FastAPI, React, ChromaDB, vector search, sentence embeddings, Llama 3.2, LLM, Groq API, PDF chatbot, text extraction, chunking, overlapping chunks, streaming response, SSE, local inference, full-stack development

ATS Numeric Value: 95%

Point 2:

Built an AI-powered data analytics dashboard with Pandas and Recharts, featuring 7 chart types (bar/line/pie/scatter/area), automatic anomaly detection using IQR method, and natural language query capabilities processing datasets up to 10GB with real-time visualization updates.

ATS Keywords: Pandas, Recharts, data analytics, anomaly detection, IQR, natural language query, chart types, visualization, React Flow, mind map generation, Flask/FastAPI, REST API, streaming, local storage, session management, dark mode

ATS Numeric Value: 10GB

File Structure

RAG-local/
├── backend/
│   ├── main.py              # FastAPI application
│   ├── actions.py           # AI action functions
│   ├── llm.py              # LLM integrations
│   ├── rag.py              # RAG pipeline
│   ├── pdf_parser.py       # PDF text extraction
│   ├── data_analyzer.py    # Data processing
│   ├── requirements.txt    # Python dependencies
│   └── .env               # Environment variables
│
└── frontend/
    ├── src/
    │   ├── App.tsx         # PDF Chat main component
    │   ├── HomePage.tsx    # Landing page
    │   ├── DataPage.tsx    # Data Analytics
    │   ├── MindMap.tsx     # Mind map visualization
    │   ├── FlashCards.tsx  # Flashcard component
    │   ├── VoiceInput.tsx  # Voice input
    │   ├── api.ts          # API client
    │   └── ...
    ├── package.json
    └── vite.config.js

Document generated for project revision Last Updated: 2026-03-29

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Build pdf & csv/excel chatbot using ollama and api keys

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