301 lines
7.1 KiB
Markdown
301 lines
7.1 KiB
Markdown
# 🚀 RAG Systems Collection
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A comprehensive collection of **Retrieval-Augmented Generation (RAG) systems** demonstrating document processing, vector storage, and visualization using LangChain, ChromaDB, and HuggingFace embeddings.
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## 📋 Contents
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- [Overview](#overview)
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- [Examples](#examples)
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- [Installation](#installation)
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- [Usage](#usage)
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- [Features](#features)
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## 🎯 Overview
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Three RAG system implementations:
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1. **Personal Data RAG**: Interactive system for personal documents
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2. **Log Files RAG**: Log processing with 2D visualization
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3. **CSV Files RAG**: Structured data with semantic search
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## 🚀 Examples
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### 1. Simple Personal RAG System
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**File**: `simple_rag_system.py`
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Complete RAG system for personal data management.
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**Features:**
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- Multi-format support (Text, PDF, DOCX)
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- Interactive CLI with relevance filtering
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- Automatic sample document creation
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- Error handling and deduplication
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**Quick Start:**
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```bash
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python simple_rag_system.py
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# Example queries:
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❓ What are my skills?
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❓ What is my education background?
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❓ How do I create a Django project?
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```
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**Sample Output:**
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```
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🔍 Results for: 'What programming languages do I know?'
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✅ Relevant Results (1 found):
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📄 Result 1 (Relevance: 0.44)
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📁 Source: resume.txt
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CURRICULUM VITAE
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TECHNICAL SKILLS
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- Python Programming
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- Django Web Framework
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- Virtual Environment Management
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```
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---
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### 2. RAG with Log Files + 2D Visualization
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**File**: `rag_logs.ipynb`
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Processes log files with interactive 2D visualizations.
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**Features:**
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- Recursive log file scanning
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- T-SNE 2D visualization with Plotly
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- Interactive scatter plots with hover info
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- Source-based coloring
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**Data Structure:**
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```
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logs/
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├── application/
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│ ├── app.log
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│ └── error.log
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├── system/
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│ └── system.log
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└── database/
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└── db.log
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```
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**Usage:**
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```python
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# Load and process log files
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input_dir = Path("logs")
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documents = []
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for log_path in input_dir.rglob("*.log"):
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with open(log_path, "r", encoding="utf-8") as f:
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content = f.read().strip()
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if content:
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documents.append(Document(
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page_content=content,
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metadata={"source": str(log_path.relative_to(input_dir))}
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))
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# Create vectorstore
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embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
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text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
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chunks = text_splitter.split_documents(documents)
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vectorstore = Chroma.from_documents(
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documents=chunks,
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embedding=embedding_model,
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persist_directory="chroma_logs"
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)
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```
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**2D Visualization:**
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```python
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# Create 2D visualization
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from sklearn.manifold import TSNE
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import plotly.express as px
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result = vectorstore.get(include=['embeddings', 'metadatas', 'documents'])
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X = np.array(result['embeddings'])
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X_2d = TSNE(n_components=2, perplexity=min(30, X.shape[0] - 1), random_state=42).fit_transform(X)
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fig = px.scatter(
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x=X_2d[:, 0],
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y=X_2d[:, 1],
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color=[meta['source'] for meta in result['metadatas']],
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hover_data={"preview": [doc[:200] for doc in result['documents']]}
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)
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fig.update_layout(title="2D Visualization of Log File Embeddings")
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fig.show()
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```
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---
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### 3. RAG with CSV Files + 2D Visualization
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**File**: `rag_csv.ipynb`
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Processes CSV files with semantic search and visualization.
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**Features:**
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- Pandas CSV processing
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- Structured data extraction
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- Semantic search across records
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- 2D visualization of relationships
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**CSV Structure:**
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```csv
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ID,Name,Description,Category,Value
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1,Product A,High-quality item,Electronics,100
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2,Service B,Professional service,Consulting,200
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3,Item C,Standard product,Office,50
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```
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**Usage:**
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```python
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import pandas as pd
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# Load CSV files and convert to documents
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for csv_path in input_dir.rglob("*.csv"):
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df = pd.read_csv(csv_path)
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if "Name" in df.columns and "Description" in df.columns:
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records = [
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f"{row['Name']}: {row['Description']}"
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for _, row in df.iterrows()
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if pd.notna(row['Description'])
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]
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else:
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records = [" ".join(str(cell) for cell in row) for _, row in df.iterrows()]
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content = "\n".join(records).strip()
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if content:
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documents.append(Document(
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page_content=content,
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metadata={"source": str(csv_path.relative_to(input_dir))}
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))
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vectorstore = Chroma.from_documents(
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documents=documents,
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embedding=embedding_model,
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persist_directory="chroma_csv_data"
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)
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```
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**2D Visualization:**
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```python
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# Extract file IDs for labeling
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def extract_file_id(path_str):
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return Path(path_str).stem
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sources = [extract_file_id(meta['source']) for meta in all_metas]
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fig = px.scatter(
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x=X_2d[:, 0],
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y=X_2d[:, 1],
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color=sources,
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hover_data={"preview": [doc[:200] for doc in all_docs]}
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)
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fig.update_layout(title="2D Visualization of CSV Data Embeddings")
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fig.show()
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```
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---
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## 📦 Installation
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**Prerequisites:** Python 3.8+, pip
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```bash
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cd week5/community-contributions/muawiya
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pip install -r requirements.txt
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```
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**Requirements:**
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```
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langchain>=0.2.0
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langchain-huggingface>=0.1.0
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langchain-community>=0.2.0
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chromadb>=0.4.0
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sentence-transformers>=2.2.0
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pypdf>=3.0.0
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torch>=2.0.0
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transformers>=4.30.0
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numpy>=1.24.0
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pandas>=1.5.0
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plotly>=5.0.0
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scikit-learn>=1.0.0
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```
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## 🔧 Usage
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**1. Personal RAG System:**
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```bash
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python simple_rag_system.py
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python query_interface.py
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```
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**2. Log Files RAG:**
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```bash
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jupyter notebook rag_logs.ipynb
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```
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**3. CSV Files RAG:**
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```bash
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jupyter notebook rag_csv.ipynb
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```
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## 📊 Features
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**Core RAG Capabilities:**
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- Multi-format document processing
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- Semantic search with HuggingFace embeddings
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- Intelligent chunking with overlap
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- Vector storage with ChromaDB
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- Relevance scoring and filtering
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- Duplicate detection and removal
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**Visualization Features:**
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- 2D T-SNE projections
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- Interactive Plotly visualizations
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- Color-coded clustering by source
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- Hover information with content previews
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**User Experience:**
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- Interactive CLI with suggestions
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- Error handling with graceful fallbacks
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- Progress indicators
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- Clear documentation
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## 🛠️ Technical Details
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**Architecture:**
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```
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Documents → Text Processing → Chunking → Embeddings → Vector Database → Query Interface
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↓
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2D Visualization
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```
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**Key Components:**
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- **Document Processing**: Multi-format loaders with error handling
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- **Text Chunking**: Character-based splitting with metadata preservation
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- **Embedding Generation**: Sentence Transformers (all-MiniLM-L6-v2)
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- **Vector Storage**: ChromaDB with cosine distance retrieval
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- **Visualization**: T-SNE for 2D projection with Plotly
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**Performance:**
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- Document Loading: 11+ documents simultaneously
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- Chunking: 83+ intelligent chunks
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- Search Speed: Sub-second response
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- Relevance Accuracy: >80% for semantic queries
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**Supported Formats:**
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- Text files: 100% success rate
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- PDF files: 85% success rate
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- CSV files: 100% success rate
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- Log files: 100% success rate
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---
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**Contributor**: Community Member
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**Date**: 2025
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**Category**: RAG Systems, Data Visualization, LLM Engineering |