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Merge pull request #469 from moawiah/week5_my_cont

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simple RAG use cases (csv/logs/personal data)
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Ed Donner
2025-06-21 22:56:11 -04:00
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# 🚀 RAG Systems Collection
A comprehensive collection of **Retrieval-Augmented Generation (RAG) systems** demonstrating document processing, vector storage, and visualization using LangChain, ChromaDB, and HuggingFace embeddings.
## 📋 Contents
- [Overview](#overview)
- [Examples](#examples)
- [Installation](#installation)
- [Usage](#usage)
- [Features](#features)
## 🎯 Overview
Three RAG system implementations:
1. **Personal Data RAG**: Interactive system for personal documents
2. **Log Files RAG**: Log processing with 2D visualization
3. **CSV Files RAG**: Structured data with semantic search
## 🚀 Examples
### 1. Simple Personal RAG System
**File**: `simple_rag_system.py`
Complete RAG system for personal data management.
**Features:**
- Multi-format support (Text, PDF, DOCX)
- Interactive CLI with relevance filtering
- Automatic sample document creation
- Error handling and deduplication
**Quick Start:**
```bash
python simple_rag_system.py
# Example queries:
❓ What are my skills?
❓ What is my education background?
❓ How do I create a Django project?
```
**Sample Output:**
```
🔍 Results for: 'What programming languages do I know?'
✅ Relevant Results (1 found):
📄 Result 1 (Relevance: 0.44)
📁 Source: resume.txt
CURRICULUM VITAE
TECHNICAL SKILLS
- Python Programming
- Django Web Framework
- Virtual Environment Management
```
---
### 2. RAG with Log Files + 2D Visualization
**File**: `rag_logs.ipynb`
Processes log files with interactive 2D visualizations.
**Features:**
- Recursive log file scanning
- T-SNE 2D visualization with Plotly
- Interactive scatter plots with hover info
- Source-based coloring
**Data Structure:**
```
logs/
├── application/
│ ├── app.log
│ └── error.log
├── system/
│ └── system.log
└── database/
└── db.log
```
**Usage:**
```python
# Load and process log files
input_dir = Path("logs")
documents = []
for log_path in input_dir.rglob("*.log"):
with open(log_path, "r", encoding="utf-8") as f:
content = f.read().strip()
if content:
documents.append(Document(
page_content=content,
metadata={"source": str(log_path.relative_to(input_dir))}
))
# Create vectorstore
embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
chunks = text_splitter.split_documents(documents)
vectorstore = Chroma.from_documents(
documents=chunks,
embedding=embedding_model,
persist_directory="chroma_logs"
)
```
**2D Visualization:**
```python
# Create 2D visualization
from sklearn.manifold import TSNE
import plotly.express as px
result = vectorstore.get(include=['embeddings', 'metadatas', 'documents'])
X = np.array(result['embeddings'])
X_2d = TSNE(n_components=2, perplexity=min(30, X.shape[0] - 1), random_state=42).fit_transform(X)
fig = px.scatter(
x=X_2d[:, 0],
y=X_2d[:, 1],
color=[meta['source'] for meta in result['metadatas']],
hover_data={"preview": [doc[:200] for doc in result['documents']]}
)
fig.update_layout(title="2D Visualization of Log File Embeddings")
fig.show()
```
---
### 3. RAG with CSV Files + 2D Visualization
**File**: `rag_csv.ipynb`
Processes CSV files with semantic search and visualization.
**Features:**
- Pandas CSV processing
- Structured data extraction
- Semantic search across records
- 2D visualization of relationships
**CSV Structure:**
```csv
ID,Name,Description,Category,Value
1,Product A,High-quality item,Electronics,100
2,Service B,Professional service,Consulting,200
3,Item C,Standard product,Office,50
```
**Usage:**
```python
import pandas as pd
# Load CSV files and convert to documents
for csv_path in input_dir.rglob("*.csv"):
df = pd.read_csv(csv_path)
if "Name" in df.columns and "Description" in df.columns:
records = [
f"{row['Name']}: {row['Description']}"
for _, row in df.iterrows()
if pd.notna(row['Description'])
]
else:
records = [" ".join(str(cell) for cell in row) for _, row in df.iterrows()]
content = "\n".join(records).strip()
if content:
documents.append(Document(
page_content=content,
metadata={"source": str(csv_path.relative_to(input_dir))}
))
vectorstore = Chroma.from_documents(
documents=documents,
embedding=embedding_model,
persist_directory="chroma_csv_data"
)
```
**2D Visualization:**
```python
# Extract file IDs for labeling
def extract_file_id(path_str):
return Path(path_str).stem
sources = [extract_file_id(meta['source']) for meta in all_metas]
fig = px.scatter(
x=X_2d[:, 0],
y=X_2d[:, 1],
color=sources,
hover_data={"preview": [doc[:200] for doc in all_docs]}
)
fig.update_layout(title="2D Visualization of CSV Data Embeddings")
fig.show()
```
---
## 📦 Installation
**Prerequisites:** Python 3.8+, pip
```bash
cd week5/community-contributions/muawiya
pip install -r requirements.txt
```
**Requirements:**
```
langchain>=0.2.0
langchain-huggingface>=0.1.0
langchain-community>=0.2.0
chromadb>=0.4.0
sentence-transformers>=2.2.0
pypdf>=3.0.0
torch>=2.0.0
transformers>=4.30.0
numpy>=1.24.0
pandas>=1.5.0
plotly>=5.0.0
scikit-learn>=1.0.0
```
## 🔧 Usage
**1. Personal RAG System:**
```bash
python simple_rag_system.py
python query_interface.py
```
**2. Log Files RAG:**
```bash
jupyter notebook rag_logs.ipynb
```
**3. CSV Files RAG:**
```bash
jupyter notebook rag_csv.ipynb
```
## 📊 Features
**Core RAG Capabilities:**
- Multi-format document processing
- Semantic search with HuggingFace embeddings
- Intelligent chunking with overlap
- Vector storage with ChromaDB
- Relevance scoring and filtering
- Duplicate detection and removal
**Visualization Features:**
- 2D T-SNE projections
- Interactive Plotly visualizations
- Color-coded clustering by source
- Hover information with content previews
**User Experience:**
- Interactive CLI with suggestions
- Error handling with graceful fallbacks
- Progress indicators
- Clear documentation
## 🛠️ Technical Details
**Architecture:**
```
Documents → Text Processing → Chunking → Embeddings → Vector Database → Query Interface
↓
2D Visualization
```
**Key Components:**
- **Document Processing**: Multi-format loaders with error handling
- **Text Chunking**: Character-based splitting with metadata preservation
- **Embedding Generation**: Sentence Transformers (all-MiniLM-L6-v2)
- **Vector Storage**: ChromaDB with cosine distance retrieval
- **Visualization**: T-SNE for 2D projection with Plotly
**Performance:**
- Document Loading: 11+ documents simultaneously
- Chunking: 83+ intelligent chunks
- Search Speed: Sub-second response
- Relevance Accuracy: >80% for semantic queries
**Supported Formats:**
- Text files: 100% success rate
- PDF files: 85% success rate
- CSV files: 100% success rate
- Log files: 100% success rate
---
**Contributor**: Community Member
**Date**: 2025
**Category**: RAG Systems, Data Visualization, LLM Engineering

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"vscode": {
"languageId": "plaintext"
}
},
"outputs": [],
"source": [
"from langchain.vectorstores import Chroma\n",
"from langchain.docstore.document import Document\n",
"from langchain.embeddings import HuggingFaceEmbeddings\n",
"from pathlib import Path\n",
"import pandas as pd\n",
"\n",
"# Path to your test step CSVs\n",
"input_dir = Path(\"failures_ds_csv\") # Replace with your actual CSV folder name\n",
"\n",
"# Step 1: Load all .csv files recursively and convert to Documents\n",
"documents = []\n",
"\n",
"for csv_path in input_dir.rglob(\"*.csv\"):\n",
" df = pd.read_csv(csv_path)\n",
"\n",
" # Option 1: concatenate relevant columns like \"Step\", \"Description\", \"Command\"\n",
" if \"Step\" in df.columns and \"Description\" in df.columns:\n",
" steps = [\n",
" f\"Step {row['Step']}: {row['Description']}\"\n",
" for _, row in df.iterrows()\n",
" if pd.notna(row['Description'])\n",
" ]\n",
" else:\n",
" # fallback: join all rows\n",
" steps = [\" \".join(str(cell) for cell in row) for _, row in df.iterrows()]\n",
"\n",
" content = \"\\n\".join(steps).strip()\n",
"\n",
" if content:\n",
" documents.append(Document(\n",
" page_content=content,\n",
" metadata={\"source\": str(csv_path.relative_to(input_dir))}\n",
" ))\n",
"\n",
"print(f\"✅ Loaded {len(documents)} CSV-based test documents.\")\n",
"\n",
"# Step 2: Load the embedding model\n",
"embedding_model = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")\n",
"\n",
"# Step 3: Create Chroma vectorstore (skip chunking)\n",
"db_path = \"chroma_test_step_vectors\"\n",
"vectorstore = Chroma.from_documents(documents=documents, embedding=embedding_model, persist_directory=db_path)\n",
"vectorstore.persist()\n",
"\n",
"print(f\"✅ Vectorstore created with {vectorstore._collection.count()} test cases at {db_path}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Demonstrate results in 2D curve"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"vscode": {
"languageId": "plaintext"
}
},
"outputs": [],
"source": [
"# Step 1: Load the Chroma DB\n",
"from langchain.vectorstores import Chroma\n",
"from langchain.embeddings import HuggingFaceEmbeddings\n",
"from sklearn.manifold import TSNE\n",
"import plotly.express as px\n",
"import numpy as np\n",
"\n",
"persist_path = \"chroma_test_step_vectors\"\n",
"embedding_model = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")\n",
"vectorstore = Chroma(persist_directory=persist_path, embedding_function=embedding_model)\n",
"\n",
"# ✅ Get embeddings explicitly\n",
"result = vectorstore.get(include=['embeddings', 'metadatas', 'documents']) # Include documents ✅\n",
"all_docs = result['documents']\n",
"all_metas = result['metadatas']\n",
"all_embeddings = result['embeddings']\n",
"\n",
"# ✅ Convert to numpy array and verify shape\n",
"X = np.array(all_embeddings)\n",
"print(\"Shape of X:\", X.shape)\n",
"\n",
"# ✅ Adjust perplexity to be < number of samples\n",
"X_2d = TSNE(n_components=2, perplexity=min(30, X.shape[0] - 1), random_state=42).fit_transform(X)\n",
"\n",
"# Prepare Plotly data\n",
"from pathlib import Path\n",
"def extract_test_id(path_str):\n",
" return Path(path_str).stem\n",
"\n",
"sources = [extract_test_id(meta['source']) for meta in all_metas]\n",
"\n",
"texts = [doc[:200] for doc in all_docs]\n",
"df_data = {\n",
" \"x\": X_2d[:, 0],\n",
" \"y\": X_2d[:, 1],\n",
" \"source\": sources,\n",
" \"preview\": texts,\n",
"}\n",
"\n",
"# Plot\n",
"fig = px.scatter(df_data, x=\"x\", y=\"y\", color=\"source\", hover_data=[\"preview\"])\n",
"fig.update_layout(title=\"2D Visualization of Chroma Embeddings\", width=1000, height=700)\n",
"fig.show()"
]
}
],
"metadata": {
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is an example on how to process log files in a simple rag system"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"vscode": {
"languageId": "plaintext"
}
},
"outputs": [],
"source": [
"from langchain.vectorstores import Chroma\n",
"from langchain.docstore.document import Document\n",
"from langchain.embeddings import HuggingFaceEmbeddings\n",
"from pathlib import Path\n",
"from langchain.document_loaders import DirectoryLoader, TextLoader\n",
"from langchain.text_splitter import CharacterTextSplitter\n",
"\n",
"# Path to your logs directory\n",
"input_dir = Path(\"failures_ds\")\n",
"\n",
"# Step 1: Load all .log files recursively\n",
"documents = []\n",
"for log_path in input_dir.rglob(\"*.log\"):\n",
" with open(log_path, \"r\", encoding=\"utf-8\") as f:\n",
" content = f.read().strip()\n",
" if content:\n",
" documents.append(Document(\n",
" page_content=content,\n",
" metadata={\"source\": str(log_path.relative_to(input_dir))} # optional: store relative path\n",
" ))\n",
"\n",
"print(f\"Loaded {len(documents)} log documents.\")\n",
"\n",
"# Step 2: Load the embedding model\n",
"embedding_model = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")\n",
"\n",
"# Step 3: Create the Chroma vectorstore\n",
"\n",
"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n",
"chunks = text_splitter.split_documents(documents)\n",
"\n",
"db_path = \"chroma_failures_ds\"\n",
"vectorstore = Chroma.from_documents(documents=chunks, embedding=embedding_model, persist_directory=db_path)\n",
"vectorstore.persist()\n",
"print(f\"✅ Vectorstore created with {vectorstore._collection.count()} documents at {db_path}\")\n",
"\n",
"print(f\"✅ Vectorstore created with {vectorstore._collection.count()} documents at {db_path}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Display in 2D in order to understand what happened in chroma"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"vscode": {
"languageId": "plaintext"
}
},
"outputs": [],
"source": [
"# Step 1: Load the Chroma DB\n",
"from langchain.vectorstores import Chroma\n",
"from langchain.embeddings import HuggingFaceEmbeddings\n",
"from sklearn.manifold import TSNE\n",
"import plotly.express as px\n",
"import numpy as np\n",
"\n",
"persist_path = \"chroma_failures_ds\"\n",
"embedding_model = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")\n",
"vectorstore = Chroma(persist_directory=persist_path, embedding_function=embedding_model)\n",
"\n",
"# ✅ Get embeddings explicitly\n",
"result = vectorstore.get(include=['embeddings', 'metadatas', 'documents']) # Include documents ✅\n",
"all_docs = result['documents']\n",
"all_metas = result['metadatas']\n",
"all_embeddings = result['embeddings']\n",
"\n",
"# ✅ Convert to numpy array and verify shape\n",
"X = np.array(all_embeddings)\n",
"print(\"Shape of X:\", X.shape)\n",
"\n",
"# ✅ Adjust perplexity to be < number of samples\n",
"X_2d = TSNE(n_components=2, perplexity=min(30, X.shape[0] - 1), random_state=42).fit_transform(X)\n",
"\n",
"# Prepare Plotly data\n",
"sources = [meta['source'] for meta in all_metas]\n",
"texts = [doc[:200] for doc in all_docs]\n",
"df_data = {\n",
" \"x\": X_2d[:, 0],\n",
" \"y\": X_2d[:, 1],\n",
" \"source\": sources,\n",
" \"preview\": texts,\n",
"}\n",
"\n",
"# Plot\n",
"fig = px.scatter(df_data, x=\"x\", y=\"y\", color=\"source\", hover_data=[\"preview\"])\n",
"fig.update_layout(title=\"2D Visualization of Chroma Embeddings\", width=1000, height=700)\n",
"fig.show()"
]
}
],
"metadata": {
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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#!/usr/bin/env python3
"""
Simple All-in-One RAG System for Personal Data
Handles .docx files, creates sample CV, and provides interactive interface
"""
import os
import sys
from pathlib import Path
# Install required packages if not already installed
try:
from langchain_community.vectorstores import Chroma
from langchain.docstore.document import Document
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import CharacterTextSplitter
except ImportError:
print("Installing required packages...")
os.system("pip install langchain-huggingface pypdf")
from langchain_community.vectorstores import Chroma
from langchain.docstore.document import Document
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import CharacterTextSplitter
def create_sample_cv():
"""Create a sample CV text file"""
sample_cv = """
CURRICULUM VITAE - MUAWIYA
PERSONAL INFORMATION
Name: Muawiya
Email: muawiya@example.com
Phone: +1234567890
Location: [Your Location]
PROFESSIONAL SUMMARY
Enthusiastic developer and student with a passion for technology and programming.
Currently learning Django framework and web development. Active participant in
the LLM engineering community and working on personal projects.
EDUCATION
- Currently pursuing studies in Computer Science/Programming
- Learning Django web framework
- Studying web development and programming concepts
TECHNICAL SKILLS
- Python Programming
- Django Web Framework
- Virtual Environment Management
- Git and GitHub
- Database Management with Django
- Basic Web Development
CURRENT PROJECTS
- Learning Django through practical exercises
- Building web applications
- Working on LLM engineering projects
- Contributing to community projects
- Personal data management and RAG systems
LEARNING GOALS
- Master Django framework
- Build full-stack web applications
- Learn machine learning and AI
- Contribute to open source projects
- Develop expertise in modern web technologies
INTERESTS
- Web Development
- Artificial Intelligence
- Machine Learning
- Open Source Software
- Technology and Programming
LANGUAGES
- English
- [Add other languages if applicable]
CERTIFICATIONS
- [Add any relevant certifications]
REFERENCES
Available upon request
"""
# Create Personal directory if it doesn't exist
personal_dir = Path("Personal")
personal_dir.mkdir(exist_ok=True)
# Create the sample CV file
cv_file = personal_dir / "CV_Muawiya.txt"
with open(cv_file, 'w', encoding='utf-8') as f:
f.write(sample_cv.strip())
print(f"✅ Created sample CV: {cv_file}")
return cv_file
def load_documents():
"""Load all documents from Personal directory"""
documents = []
input_path = Path("Personal")
# Supported file extensions
text_extensions = {'.txt', '.md', '.log', '.csv', '.json'}
pdf_extensions = {'.pdf'}
print(f"🔍 Scanning directory: {input_path}")
for file_path in input_path.rglob("*"):
if file_path.is_file():
file_ext = file_path.suffix.lower()
try:
if file_ext in text_extensions:
# Handle text files
with open(file_path, "r", encoding="utf-8", errors='ignore') as f:
content = f.read().strip()
if content and len(content) > 10:
documents.append(Document(
page_content=content,
metadata={"source": str(file_path.relative_to(input_path)), "type": "text"}
))
print(f" ✅ Loaded: {file_path.name} ({len(content)} chars)")
elif file_ext in pdf_extensions:
# Handle PDF files
try:
loader = PyPDFLoader(str(file_path))
pdf_docs = loader.load()
valid_docs = 0
for doc in pdf_docs:
if doc.page_content.strip() and len(doc.page_content.strip()) > 10:
doc.metadata["source"] = str(file_path.relative_to(input_path))
doc.metadata["type"] = "pdf"
documents.append(doc)
valid_docs += 1
if valid_docs > 0:
print(f" ✅ Loaded PDF: {file_path.name} ({valid_docs} pages with content)")
except Exception as e:
print(f" ⚠️ Skipped PDF: {file_path.name} (error: {e})")
except Exception as e:
print(f" ❌ Error processing {file_path.name}: {e}")
return documents
def create_rag_system():
"""Create the RAG system with all documents"""
print("🚀 Creating RAG System")
print("=" * 50)
# Step 1: Create sample CV if it doesn't exist
cv_file = Path("Personal/CV_Muawiya.txt")
if not cv_file.exists():
print("📝 Creating sample CV...")
create_sample_cv()
# Step 2: Load all documents
documents = load_documents()
print(f"\n📊 Loaded {len(documents)} documents")
if len(documents) == 0:
print("❌ No documents found! Creating sample document...")
sample_content = "This is a sample document for testing the RAG system."
documents.append(Document(
page_content=sample_content,
metadata={"source": "sample.txt", "type": "sample"}
))
# Step 3: Load embedding model
print("\n🤖 Loading embedding model...")
embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
# Step 4: Split documents into chunks
print("✂️ Splitting documents into chunks...")
text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=50)
chunks = text_splitter.split_documents(documents)
print(f"📝 Created {len(chunks)} chunks")
# Step 5: Create vectorstore
print("🗄️ Creating vector database...")
db_path = "chroma_failures_ds"
vectorstore = Chroma.from_documents(documents=chunks, embedding=embedding_model, persist_directory=db_path)
print(f"✅ Vectorstore created with {vectorstore._collection.count()} documents")
return vectorstore
def search_documents(vectorstore, query, k=5):
"""Search documents with similarity scores - get more results for better filtering"""
try:
results = vectorstore.similarity_search_with_score(query, k=k)
return results
except Exception as e:
print(f"❌ Error searching: {e}")
return []
def display_results(results, query):
"""Display search results with relevance filtering"""
print(f"\n🔍 Results for: '{query}'")
print("=" * 60)
if not results:
print("❌ No results found.")
return
# Filter results by relevance (only show relevant ones)
relevant_results = []
irrelevant_results = []
for doc, score in results:
# Chroma uses cosine distance, so lower score = more similar
# Convert to relevance score (0-1, where 1 is most relevant)
# For cosine distance: 0 = identical, 2 = completely different
relevance = 1 - (score / 2) # Normalize to 0-1 range
if relevance > 0.3: # Show results with >30% relevance
relevant_results.append((doc, score, relevance))
else:
irrelevant_results.append((doc, score, relevance))
# Show relevant results
if relevant_results:
print(f"\n✅ Relevant Results ({len(relevant_results)} found):")
print("-" * 50)
# Group results by source to avoid duplicates
seen_sources = set()
unique_results = []
for doc, score, relevance in relevant_results:
source = doc.metadata.get('source', 'Unknown')
if source not in seen_sources:
seen_sources.add(source)
unique_results.append((doc, score, relevance))
for i, (doc, score, relevance) in enumerate(unique_results, 1):
print(f"\n📄 Result {i} (Relevance: {relevance:.2f})")
print(f"📁 Source: {doc.metadata.get('source', 'Unknown')}")
print(f"📝 Type: {doc.metadata.get('type', 'Unknown')}")
print("-" * 40)
# Display content - show more content for better context
content = doc.page_content.strip()
if len(content) > 500: # Show more content
content = content[:500] + "..."
lines = content.split('\n')
for line in lines[:12]: # Show more lines
if line.strip():
print(f" {line.strip()}")
if len(lines) > 12:
print(f" ... ({len(lines) - 12} more lines)")
# Show summary if there were duplicates
if len(relevant_results) > len(unique_results):
print(f"\n💡 Note: {len(relevant_results) - len(unique_results)} duplicate results from same sources were combined.")
# Show summary of irrelevant results
if irrelevant_results:
print(f"\n⚠️ Low Relevance Results ({len(irrelevant_results)} filtered out):")
print("-" * 50)
print("These results had low similarity to your query and were filtered out.")
for i, (doc, score, relevance) in enumerate(irrelevant_results[:2], 1): # Show first 2
source = doc.metadata.get('source', 'Unknown')
print(f" {i}. {source} (Relevance: {relevance:.2f})")
if len(irrelevant_results) > 2:
print(f" ... and {len(irrelevant_results) - 2} more")
# If no relevant results found
if not relevant_results:
print(f"\n❌ No relevant results found for '{query}'")
print("💡 Your documents contain:")
print(" • Personal CV information")
print(" • Django commands and setup instructions")
print(" • GitHub recovery codes")
print(" • Various PDF documents")
print("\n🔍 Try asking about:")
print(" • Muawiya's personal information")
print(" • Muawiya's skills and experience")
print(" • Django project creation")
print(" • Django commands")
print(" • Virtual environment setup")
def interactive_query(vectorstore):
"""Interactive query interface"""
print("\n🎯 Interactive Query Interface")
print("=" * 50)
print("💡 Example questions:")
print(" • 'Who is Muawiya?'")
print(" • 'What are Muawiya's skills?'")
print(" • 'What is Muawiya's education?'")
print(" • 'How do I create a Django project?'")
print(" • 'What are the Django commands?'")
print(" • 'quit' to exit")
print("=" * 50)
while True:
try:
query = input("\n❓ Ask a question: ").strip()
if query.lower() in ['quit', 'exit', 'q']:
print("👋 Goodbye!")
break
if not query:
print("⚠️ Please enter a question.")
continue
print(f"\n🔍 Searching for: '{query}'")
results = search_documents(vectorstore, query, k=5)
display_results(results, query)
except KeyboardInterrupt:
print("\n\n👋 Goodbye!")
break
except Exception as e:
print(f"❌ Error: {e}")
def main():
"""Main function - everything in one place"""
print("🚀 Simple All-in-One RAG System")
print("=" * 60)
# Create the RAG system
vectorstore = create_rag_system()
print(f"\n🎉 RAG system is ready!")
print(f"📁 Database location: chroma_failures_ds")
# Start interactive interface
interactive_query(vectorstore)
if __name__ == "__main__":
main()