diff --git a/.env.example b/.env.example index 25cf7ef..970e6d4 100644 --- a/.env.example +++ b/.env.example @@ -1,6 +1,46 @@ -# Elastic Cloud Configuration -# Copy this file to .env and fill in your actual credentials +# Hybrid RAG Pipeline Environment Configuration +# Copy this file to .env and fill in your actual values -# Authentication Option 2: API Key (recommended for production) -# Use either username/password OR api_key, not both -ELASTIC_API_KEY=elastic-search-api-key-for-elasticsearch-index \ No newline at end of file +# =================================================================== +# AWS CONFIGURATION +# =================================================================== +# AWS credentials for S3 access (both source and destination) +AWS_ACCESS_KEY_ID=your-aws-access-key-id +AWS_SECRET_ACCESS_KEY=your-aws-secret-access-key +AWS_REGION=us-east-1 + +# =================================================================== +# UNSTRUCTURED API CONFIGURATION +# =================================================================== +# Get your API key from: https://unstructured.io +UNSTRUCTURED_API_KEY=your-unstructured-api-key +UNSTRUCTURED_API_URL=https://platform.unstructuredapp.io/api/v1 + +# =================================================================== +# ELASTICSEARCH CONFIGURATION +# =================================================================== +# Elasticsearch Cloud host URL (without https://) +# Example: my-cluster-abc123.es.us-east-1.aws.found.io:9243 +ELASTICSEARCH_HOST=your-elasticsearch-host-url + +# Elasticsearch API key (base64 encoded) +# Generate this in Kibana: Stack Management > API Keys +ELASTICSEARCH_API_KEY=your-elasticsearch-api-key + +# =================================================================== +# PIPELINE DATA SOURCES +# =================================================================== +# S3 bucket containing Bose product PDFs (manuals, troubleshooting, MSDS) +S3_SOURCE_BUCKET=example-data-bose-headphones + +# Elasticsearch index containing synthetic sales data +ELASTICSEARCH_INDEX=sales-records + +# =================================================================== +# OPTIONAL: ADVANCED CONFIGURATION +# =================================================================== +# AWS Session Token (only needed for temporary credentials) +# AWS_SESSION_TOKEN=your-session-token + +# Custom S3 endpoint (only needed for S3-compatible services) +# S3_ENDPOINT_URL=https://s3.amazonaws.com diff --git a/.gitignore b/.gitignore index 9bec4dc..17f2318 100644 --- a/.gitignore +++ b/.gitignore @@ -1,2 +1,33 @@ .env -venv/ \ No newline at end of file +venv/ +.env.backup +__pycache__/ +*.pyc +*.pyo +*.pyd +.DS_Store +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg +output-downloads-*/ + +hybrid_rag_pipeline.py.bak +hybrid_rag_pipeline_enriched.ipynb.bak + +feedback_r1.md +feedback_r1_cleaned.md +feedback_r2.md +feedback_r2_cleaned.md \ No newline at end of file diff --git a/README.md b/README.md index 5d2059c..c7341be 100644 --- a/README.md +++ b/README.md @@ -1,209 +1,357 @@ -# Notebook Processing Tools +# Hybrid RAG Pipeline over Multiple Data Sources -This directory contains tools for processing Jupyter notebooks and setting up data sources for hybrid RAG pipelines. +A comprehensive hybrid Retrieval-Augmented Generation (RAG) pipeline that processes multiple data sources using the Unstructured API to create a unified knowledge base for customer support applications. -## remove_images.py +## Overview -A Python script that uses regular expressions to remove embedded base64-encoded images from Python files that were converted from Jupyter notebooks using `jupytext`. +This project demonstrates how to build a hybrid RAG system that combines: -### Features +1. **Technical Documentation** (PDFs from S3) - Product manuals, troubleshooting guides, MSDS documents +2. **Sales Data** (Elasticsearch) - Customer interactions, product information, sales records +3. **Unified Processing** - NER enrichment, chunking, embedding, and vector storage -- Removes base64 data URL images (e.g., `![Screenshot 1](data:image/png;base64,...)`) -- Cleans up extra empty lines left behind after image removal -- Can either overwrite the original file or create a new cleaned file -- Provides detailed feedback on the number of images found and removed +The pipeline processes both structured and unstructured data sources in parallel, enriches them with Named Entity Recognition (NER), and deposits the results into a unified Elasticsearch index for RAG applications. -### Usage +## Architecture -```bash -# Remove images from a file (overwrites original) -python remove_images.py +### Parallel Workflow Processing -# Remove images and save to a new file -python remove_images.py ``` +┌─────────────────┐ ┌──────────────────────────────────────────────────────┐ +│ S3 PDFs │ │ UNSTRUCTURED API PROCESSING │ +│ (Tech Manuals, │────────────────────┤ │ +│ Safety Docs) │ │ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐ │ +└─────────────────┘ │ │Connect │ │ Route │ │Transform│ │ Chunk │ │ + │ │ ↓ │ │ ↓ │ │ ↓ │ │ ↓ │ │ +┌─────────────────┐ WORKFLOW 1 │ │ S3 Src │→ │VLM Auto │→ │Elements │→ │By Title │ │ +│ Elasticsearch │────────────────────┤ └─────────┘ └─────────┘ └─────────┘ └─────────┘ │ +│ (Sales Records) │ │ │ +└─────────────────┘ │ ┌─────────┐ ┌─────────┐ ┌─────────┐ │ + │ │Connect │ │ Route │ │Transform│ │ + WORKFLOW 2 │ │ ↓ │ │ ↓ │ │ ↓ │ │ + │ │ ES Src │→ │VLM Auto │→ │Elements │──────────────┤ + │ └─────────┘ └─────────┘ └─────────┘ │ + │ │ + │ ┌─────────┐ ┌─────────┐ ┌─────────┐ │ + │ │ Enrich │ │ Embed │ │ Persist │ │ + │ │ ↓ │ │ ↓ │ │ ↓ │ │ + │ │OpenAI │→ │OpenAI │→ │ ES │ │ + │ │ NER │ │text-emb │ │customer-│ │ + │ │ │ │ -3-small│ │support │ │ + │ └─────────┘ └─────────┘ └─────────┘ │ + └──────────────────────────────────────────────────────┘ + │ + ┌──────────────────────────────────▼───────────────────┐ + │ UNIFIED KNOWLEDGE BASE │ + │ Elasticsearch: customer-support │ + │ │ + │ • PDF content (manuals, troubleshooting) │ + │ • Sales data (customer interactions, products) │ + │ • Consistent chunking & embeddings │ + │ • Ready for hybrid RAG queries │ + └───────────────────────────────────────────────────────┘ +``` + +### Unstructured's 7-Stage Pipeline: +1. **Connect**: Source connectors (S3, Elasticsearch) ingest data +2. **Route**: Auto partitioning strategy selects optimal processing (VLM for complex docs) +3. **Transform**: Documents converted to Unstructured's canonical JSON schema +4. **Chunk**: By-title chunking creates semantically coherent retrieval units +5. **Enrich**: Optional NER extraction adds metadata and entities +6. **Embed**: OpenAI embeddings enable semantic similarity search +7. **Persist**: Destination connector writes processed data to vector database + +## Features + +### 🔧 **Smart Elasticsearch Preprocessing** +- **Index Validation**: Automatically checks for required `sales-records-consolidated` index +- **Data Verification**: Ensures source data exists before processing +- **Fresh Destination**: Automatically recreates `customer-support` index for clean runs +- **Error Handling**: Fails fast with clear error messages if prerequisites aren't met + +### 🚀 **Parallel Workflow Processing** +- **S3 Source Connector**: Processes PDFs with VLM (Vision Language Model) parsing +- **Elasticsearch Source Connector**: Ingests sales records with rich NER data +- **Unified Destination**: Both workflows deposit into the same `customer-support` index + +### 🎯 **Advanced Processing Pipeline** +- **VLM Partitioner**: Uses GPT-4o for intelligent document parsing +- **Smart Chunker**: Context-aware chunking with title-based segmentation +- **Vector Embedder**: OpenAI text-embedding-3-small for semantic search +- **NER Enrichment**: Extracts named entities (people, places, organizations, etc.) + +### 📊 **Best Practices Implementation** +- **Context Managers**: Proper resource management with `UnstructuredClient` +- **Modern API Usage**: Uses `CreateWorkflowRequest` and `CreateWorkflow` objects +- **Error Handling**: Comprehensive exception handling with clear feedback +- **Logging**: Detailed progress tracking with emoji indicators + +## Quick Start + +### Prerequisites + +1. **Environment Setup**: + ```bash + # Clone the repository + git clone + cd rag-over-hybrid-data-sources + + # Create and activate virtual environment + python3 -m venv venv + source venv/bin/activate # On Windows: venv\Scripts\activate + + # Install dependencies + pip install -r requirements.txt + ``` + +2. **Configuration**: + ```bash + # Copy environment template + cp .env.template .env + + # Edit .env with your credentials: + # - UNSTRUCTURED_API_KEY=your-unstructured-api-key + # - ELASTICSEARCH_HOST=https://your-cluster.es.io:443 + # - ELASTICSEARCH_API_KEY=your-elasticsearch-api-key + # - AWS_ACCESS_KEY_ID=your-aws-access-key + # - AWS_SECRET_ACCESS_KEY=your-aws-secret-key + # - S3_SOURCE_BUCKET=your-pdf-bucket + # - S3_DESTINATION_BUCKET=your-output-bucket + ``` -### Examples +### Running the Pipeline +#### Option 1: Python Script ```bash -# Clean the converted notebook file in-place -python remove_images.py ../donor-notebooks/S3_to_Qdrant_Workflow_using_Unstructured_API.py +# Activate virtual environment +source venv/bin/activate -# Create a cleaned copy -python remove_images.py notebook.py cleaned_notebook.py +# Run the pipeline +python hybrid_rag_pipeline.py ``` -### Requirements +#### Option 2: Jupyter Notebook +```bash +# Start Jupyter +jupyter lab -- Python 3.6+ -- No external dependencies (uses only standard library modules: `re`, `sys`, `os`, `pathlib`) +# Open and run hybrid_rag_pipeline_enriched.ipynb +``` -### How it works +## Data Sources Setup -The script uses a regular expression pattern to identify and remove markdown-style image references with base64 data URLs: +### 1. Elasticsearch Sales Data -```python -image_pattern = r'!\[.*?\]\(data:image/[^;]+;base64,[A-Za-z0-9+/=]+\)' +The pipeline requires a `sales-records-consolidated` index with sales data. You can create this using the provided preprocessing tools: + +```bash +# Run Elasticsearch preprocessing to create sample data +python elasticsearch_index_preprocessing.py ``` -This pattern matches: -- `![...]` - Markdown image syntax -- `(data:image/...)` - Data URL with image MIME type -- `;base64,` - Base64 encoding indicator -- `[A-Za-z0-9+/=]+` - Base64 encoded data +This creates: +- `sales-records` - Raw sales data (100 records) +- `sales-records-consolidated` - Processed sales data optimized for RAG +- `customer-support` - Empty destination index (created fresh each run) -## Workflow Example +### 2. S3 Technical Documentation -1. Convert Jupyter notebook to Python file using `jupytext`: - ```bash - jupytext --to py notebook.ipynb - ``` +Upload your PDF documents to an S3 bucket. The pipeline supports: +- Product manuals +- Troubleshooting guides +- MSDS documents +- Technical specifications -2. Remove embedded images from the converted file: - ```bash - python remove_images.py notebook.py - ``` +Supported S3 URL formats: +- `s3://bucket-name/path/` +- `https://bucket-name.s3.region.amazonaws.com/path/` +- Raw bucket names: `bucket-name/path` -The result is a clean Python file without embedded base64 images, making it more readable and reducing file size significantly. +## Pipeline Workflow -## elasticsearch_setup.py +### Step 0: Elasticsearch Preprocessing +- ✅ Validates `sales-records-consolidated` exists and has data +- ✅ Deletes and recreates fresh `customer-support` index +- ❌ Fails with clear error if source data is missing -A comprehensive Python script that creates and populates an Elasticsearch index with NER-rich synthetic sales data for Bose products. This data serves as one of the source connectors in a hybrid RAG pipeline. +### Step 1: Source Connectors +- Creates S3 source connector for PDFs +- Creates Elasticsearch source connector for sales data -### Features +### Step 2: Destination Connector +- Creates Elasticsearch destination connector for `customer-support` index -- **Elastic Cloud Integration**: Connects directly to your Elasticsearch Cloud deployment -- **NER-Optimized Data**: Generates synthetic sales records rich in named entities (people, organizations, locations, prices, dates) -- **Semantic Text Support**: Uses `semantic_text` field type for enhanced search capabilities -- **Bose Product Focus**: Covers SoundSport, OpenAudio, and QuietComfort product lines -- **Realistic Sales Scenarios**: Creates contextual sales interactions with detailed customer information +### Step 3: Workflow Creation +- Creates parallel workflows for S3 and Elasticsearch sources +- Both workflows use identical processing nodes: + - VLM Partitioner (GPT-4o) + - Smart Chunker (title-based) + - Vector Embedder (OpenAI) + - NER Enrichment (OpenAI) -### Setup +### Step 4: Execution +- Runs both workflows in parallel +- Monitors job status (optional) +- Reports completion status -1. **Install Dependencies**: - ```bash - pip install -r requirements.txt +## Project Structure + +``` +rag-over-hybrid-data-sources/ +├── hybrid_rag_pipeline.py # Main pipeline code +├── hybrid_rag_pipeline_enriched.py # Generated enriched version +├── hybrid_rag_pipeline_enriched.ipynb # Jupyter notebook +├── elasticsearch_index_preprocessing.py # ES data setup +├── requirements.txt # Python dependencies +├── README.md # This file +├── notebook-processing/ # Documentation pipeline +│ ├── enrich_and_convert.py # Notebook generation script +│ ├── markdown_blocks.yaml # Markdown content +│ └── README.md # Documentation workflow +├── elastic-search-index-setup/ # ES setup tools +│ ├── create_consolidated_index.py +│ ├── create_nonconsolidated_index.py +│ └── verify_elasticsearch_data.py +└── elasticsearch-example-data/ # Sample data + ├── consolidated_examples.json + └── nonconsolidated_examples.json +``` + +## Configuration Options + +### Environment Variables + +| Variable | Description | Example | +|----------|-------------|---------| +| `UNSTRUCTURED_API_KEY` | Your Unstructured API key | `your-api-key` | +| `ELASTICSEARCH_HOST` | Elasticsearch cluster URL | `https://cluster.es.io:443` | +| `ELASTICSEARCH_API_KEY` | Elasticsearch API key | `your-es-api-key` | +| `ELASTICSEARCH_INDEX` | Source sales data index | `sales-records-consolidated` | +| `AWS_ACCESS_KEY_ID` | AWS access key | `AKIA...` | +| `AWS_SECRET_ACCESS_KEY` | AWS secret key | `your-secret-key` | +| `S3_SOURCE_BUCKET` | S3 bucket with PDFs | `my-docs-bucket/manuals/` | +| `S3_DESTINATION_BUCKET` | S3 output bucket | `my-output-bucket` | + +### Processing Parameters + +- **Chunking**: 1500 chars with 2048 max, title-based segmentation +- **Embedding Model**: OpenAI text-embedding-3-small +- **VLM Model**: GPT-4o for document parsing +- **NER Model**: OpenAI NER extraction + +## Monitoring and Debugging + +### Pipeline Status +The pipeline provides detailed status updates: +- `:white_check_mark:` Success indicators +- `:x:` Error indicators +- `📊` Progress information +- `🔍` Validation steps + +### Common Issues + +1. **Missing Sales Data**: + ``` + ❌ Index 'sales-records-consolidated' does not exist. There is no data to use. ``` + **Solution**: Run `python elasticsearch_index_preprocessing.py` -2. **Configure Environment**: - ```bash - # Copy the template and add your credentials - cp env_template.txt .env - # Edit .env and add your ELASTIC_API_KEY +2. **Empty Sales Index**: ``` + ❌ Index 'sales-records-consolidated' is empty. There is no data to use. + ``` + **Solution**: Verify data was properly indexed -3. **Run the Setup**: - ```bash - python elasticsearch_setup.py +3. **S3 Access Issues**: ``` + :x: Error creating S3 source connector: Access Denied + ``` + **Solution**: Check AWS credentials and bucket permissions -### Generated Data Structure - -Each sales record contains rich named entities perfect for NER extraction: - -- **PERSON**: Customer names, sales representatives -- **ORG**: Retailers (Best Buy, Target, Amazon, etc.) -- **LOCATION**: Cities and regions across the US -- **MONEY**: Product prices and revenue potential -- **DATE**: Timestamps, quarters, months -- **PRODUCT**: Bose product lines and specific models - -### Example Generated Record - -```json -{ - "customer_name": "Jennifer Martinez", - "sales_representative": "Michael Chen", - "product_model": "SoundSport Free", - "price": 149, - "retailer": "Best Buy", - "location_city": "New York, NY", - "interaction_text": "Customer Jennifer Martinez from New York, NY called to inquire about purchasing the SoundSport Free. Sales rep Michael Chen provided detailed product information and quoted $149. Customer is comparing with similar products at Best Buy.", - "text": "Customer Jennifer Martinez from New York, NY called to inquire about purchasing the SoundSport Free. Sales rep Michael Chen provided detailed product information and quoted $149. Customer is comparing with similar products at Best Buy." -} -``` +## Advanced Usage -### Integration with Unstructured Workflow +### Custom NER Configuration +The NER enrichment node can be customized by updating the `settings` in `create_workflow_nodes()`: + +```python +ner_enrichment_node = WorkflowNode( + name="NER_Enrichment", + subtype="openai_ner", + type="prompter", + settings={ + "prompt": "Extract named entities focusing on products, customers, and locations..." + } +) +``` -This Elasticsearch index can be used as a source connector in the Unstructured Workflow Endpoint alongside S3 technical documentation to create a hybrid RAG system: +### Multiple S3 Sources +To process multiple S3 buckets, modify the S3 source connector creation or create additional workflows. -1. **S3 Source**: Technical manuals, troubleshooting guides, MSDS PDFs -2. **Elasticsearch Source**: Synthetic sales data (this script) -3. **NER Enrichment**: Extract named entities from both sources -4. **Qdrant Destination**: Combined processed data for RAG queries +### Custom Elasticsearch Mapping +The `customer-support` index mapping can be customized in the `run_elasticsearch_preprocessing()` function. -### Requirements +## Development Workflow -See `requirements.txt` for Python dependencies: -- elasticsearch>=8.0.0 -- python-dotenv>=0.19.0 -- faker>=15.0.0 +### Notebook Content Management -## verify_elasticsearch_data.py +**Important**: Do not edit the Jupyter notebook directly! -A comprehensive verification script that inspects and validates the synthetic sales data in your Elasticsearch index. Use this script to confirm that data was successfully uploaded and is ready for NER processing. +Instead, follow this workflow: -### Features +1. **Edit Code**: Modify `hybrid_rag_pipeline.py` +2. **Edit Documentation**: Update `notebook-processing/markdown_blocks.yaml` +3. **Regenerate**: Run `python notebook-processing/enrich_and_convert.py` -- **Connection Testing**: Verifies Elasticsearch cluster connectivity -- **Index Validation**: Confirms the index exists and contains data -- **Data Statistics**: Provides comprehensive metrics on document count, index size, and distribution -- **Sample Document Display**: Shows actual records with key fields -- **NER Readiness Check**: Validates that data contains rich named entities -- **Search Query Testing**: Tests various search patterns to ensure data accessibility +This process: +- Replaces `[[MD:HANDLE]]` placeholders with markdown content +- Generates `hybrid_rag_pipeline_enriched.py` +- Converts to `hybrid_rag_pipeline_enriched.ipynb` using jupytext -### Usage +### Testing ```bash -python verify_elasticsearch_data.py -``` +# Test Elasticsearch connection +python elasticsearch-index-setup/simple_check.py -### What It Checks - -1. **Basic Connectivity**: Tests connection to your Elasticsearch cluster -2. **Index Existence**: Confirms the `sales-records` index exists -3. **Document Count**: Reports total number of indexed documents -4. **Data Distribution**: Analyzes breakdown by: - - Product lines (SoundSport, OpenAudio, QuietComfort) - - Product models - - Retailers (Best Buy, Target, Amazon, etc.) - - Geographic regions - - Interaction types - - Price and revenue statistics - - Temporal distribution (by year) -5. **NER Entity Validation**: Confirms presence of: - - Person names (customers, sales reps) - - Organizations (retailers) - - Locations (cities, regions) - - Monetary values (prices) - - Dates (timestamps) - - Rich text content -6. **Search Functionality**: Tests sample queries to verify data is searchable - -### Sample Output +# Verify data setup +python elasticsearch-index-setup/verify_elasticsearch_data.py +# Run pipeline in test mode +python hybrid_rag_pipeline.py ``` -🚀 Starting Elasticsearch Data Verification -============================================================ -🔧 Testing Elasticsearch connection... -✅ Connected to Elasticsearch cluster: instance-0000000000 - Version: 8.11.0 - Cluster: 2371b9a1d2ad40c590fd1e22652a8236 - -✅ Index 'sales-records' exists -📊 Getting statistics for index 'sales-records'... - 📄 Total documents: 500 - 💾 Index size: 245,760 bytes (0.23 MB) - 🔧 Primary shards: 12 - -📋 Retrieving 5 sample documents... -📄 Document 1: - 🆔 ID: abc123-def456 - 👤 Customer: Jennifer Martinez - 🏷️ Product: SoundSport Free - 💰 Price: $149 - 🏪 Retailer: Best Buy - 📍 Location: New York, NY - 📅 Date: 2023-11-15T14:30:00 - 📝 Text: Customer Jennifer Martinez from New York, NY called to inquire about purchasing the SoundSport... -``` \ No newline at end of file + +## API Reference + +### Core Functions + +- `run_elasticsearch_preprocessing()` - Validates and prepares ES indices +- `create_s3_source_connector()` - Creates S3 PDF source +- `create_elasticsearch_source_connector()` - Creates ES sales source +- `create_elasticsearch_destination_connector()` - Creates ES destination +- `create_parallel_workflows()` - Sets up processing workflows +- `run_workflow()` - Executes workflows +- `poll_job_status()` - Monitors job progress + +## Contributing + +1. Fork the repository +2. Create a feature branch +3. Make changes following the development workflow +4. Test thoroughly +5. Submit a pull request + +## License + +This project is licensed under the MIT License - see the LICENSE file for details. + +## Support + +For questions or issues: +1. Check the troubleshooting section above +2. Review Unstructured API documentation +3. Open an issue in the repository + +--- + +**Note**: This pipeline demonstrates advanced RAG techniques using the Unstructured API. It's designed for educational and development purposes. For production use, consider additional error handling, monitoring, and security measures. + diff --git a/elastic-search-index-setup/SETUP_INSTRUCTIONS.md b/elastic-search-index-setup/SETUP_INSTRUCTIONS.md index 7a2cb83..11e2419 100644 --- a/elastic-search-index-setup/SETUP_INSTRUCTIONS.md +++ b/elastic-search-index-setup/SETUP_INSTRUCTIONS.md @@ -94,10 +94,20 @@ python simple_check.py **Expected Output**: Should show connection success or indicate if index doesn't exist yet. ### 3.2 Create Index and Upload Synthetic Data -Run the main setup script: + +**Choose your data structure approach:** + +#### Option A: Consolidated Structure (Recommended for RAG) +```bash +python create_consolidated_index.py +``` +**Best for**: RAG use cases where context preservation is critical + +#### Option B: Non-Consolidated Structure (Good for Analytics) ```bash -python elasticsearch_setup.py +python create_nonconsolidated_index.py ``` +**Best for**: Traditional database queries and business intelligence **What this script does**: - ✅ Tests connection to Elasticsearch diff --git a/elastic-search-index-setup/create_consolidated_index.py b/elastic-search-index-setup/create_consolidated_index.py new file mode 100644 index 0000000..665841c --- /dev/null +++ b/elastic-search-index-setup/create_consolidated_index.py @@ -0,0 +1,299 @@ +#!/usr/bin/env python3 +""" +Create Consolidated Elasticsearch Index for RAG +Combines multiple fields into a single text field to preserve context during Unstructured processing. + +This approach ensures that when Unstructured processes the data: +1. All context (product, customer, location, etc.) is preserved in each Text element +2. The resulting embeddings contain complete contextual information +3. RAG queries can access full context without losing field relationships +""" + +import os +import uuid +import random +from datetime import datetime, timedelta +from faker import Faker +from dotenv import load_dotenv +from elasticsearch import Elasticsearch + +# Load environment variables from base directory (one level up) +load_dotenv(dotenv_path="../.env") + +class ConsolidatedBoseSalesDataGenerator: + """Generate consolidated sales data optimized for RAG processing""" + + def __init__(self): + self.fake = Faker(['en_US']) + Faker.seed(42) # For reproducible data + + # Bose product data + self.products = { + 'SoundSport': { + 'models': ['SoundSport Free', 'SoundSport Wireless', 'SoundSport Pulse'], + 'price_range': (129, 199), + 'category': 'Sports Earbuds', + 'features': ['sweat-resistant', 'secure fit', 'wireless', 'noise isolation'] + }, + 'OpenAudio': { + 'models': ['OpenAudio Sport', 'OpenAudio Ultra', 'OpenAudio Pro'], + 'price_range': (149, 249), + 'category': 'Open-Ear Audio', + 'features': ['open-ear design', 'situational awareness', 'comfortable fit', 'premium audio'] + }, + 'QuietComfort': { + 'models': ['QuietComfort 45', 'QuietComfort Ultra', 'QuietComfort Earbuds'], + 'price_range': (199, 429), + 'category': 'Noise Cancelling', + 'features': ['world-class noise cancellation', 'premium comfort', 'long battery life', 'crystal clear calls'] + } + } + + # Rich entity data for NER + self.retailers = [ + "Best Buy", "Target", "Amazon", "Walmart", "Costco", "B&H Photo", + "Guitar Center", "Sam's Club", "Newegg", "Adorama", "Crutchfield" + ] + + self.sales_reps = [ + "Jennifer Martinez", "Michael Chen", "Sarah Johnson", "David Rodriguez", + "Emily Wilson", "Robert Taylor", "Lisa Anderson", "James Thompson", + "Maria Garcia", "Christopher Lee", "Amanda Davis", "Daniel Brown" + ] + + self.regions = [ + "Northeast", "Southeast", "Midwest", "Southwest", "West Coast", + "Pacific Northwest", "Mountain West", "Great Lakes", "Mid-Atlantic", "Gulf Coast" + ] + + self.cities = [ + "New York, NY", "Los Angeles, CA", "Chicago, IL", "Houston, TX", "Phoenix, AZ", + "Philadelphia, PA", "San Antonio, TX", "San Diego, CA", "Dallas, TX", "Austin, TX", + "Jacksonville, FL", "Fort Worth, TX", "Columbus, OH", "Charlotte, NC", "Seattle, WA", + "Denver, CO", "Washington, DC", "Boston, MA", "Nashville, TN", "Detroit, MI" + ] + + self.interaction_types = [ + "purchase_inquiry", "product_comparison", "pricing_discussion", + "sales_consultation", "order_processing", "upsell_opportunity", + "customer_preferences", "warranty_inquiry", "bulk_order_request", + "promotional_campaign", "seasonal_sale", "loyalty_program_enrollment" + ] + + def generate_consolidated_record(self) -> dict: + """Generate a single sales record with consolidated text field""" + + # Generate individual field data + product_line = random.choice(list(self.products.keys())) + product_info = self.products[product_line] + model = random.choice(product_info['models']) + price = random.randint(*product_info['price_range']) + features = random.sample(product_info['features'], k=random.randint(1, 3)) + + customer_name = self.fake.name() + sales_rep = random.choice(self.sales_reps) + retailer = random.choice(self.retailers) + city = random.choice(self.cities) + region = random.choice(self.regions) + interaction_type = random.choice(self.interaction_types) + + # Generate timestamp from 2016 onwards + start_date = datetime(2016, 1, 1) + random_date = self.fake.date_time_between(start_date=start_date, end_date='now') + + customer_segment = random.choice(["Consumer", "Business", "Education", "Government"]) + sales_channel = random.choice(["Direct", "Retail Partner", "Online", "Phone"]) + lead_source = random.choice(["Website", "Advertisement", "Referral", "Trade Show", "Cold Call"]) + deal_stage = random.choice(["Prospect", "Qualified", "Proposal", "Negotiation", "Closed Won", "Closed Lost"]) + + # Create contextual interaction based on type + interaction_details = self._generate_interaction_text( + interaction_type, customer_name, sales_rep, model, price, retailer, city, features + ) + + # **KEY CHANGE: Create consolidated text field with ALL context** + consolidated_text = f""" +SALES RECORD - {random_date.strftime('%B %d, %Y')} + +Customer Information: +- Name: {customer_name} +- Location: {city} +- Segment: {customer_segment} +- Lead Source: {lead_source} + +Product Details: +- Product Line: {product_line} +- Model: {model} +- Category: {product_info['category']} +- Price: ${price} +- Key Features: {', '.join(features)} + +Sales Information: +- Sales Representative: {sales_rep} +- Retailer: {retailer} +- Region: {region} +- Channel: {sales_channel} +- Deal Stage: {deal_stage} +- Interaction Type: {interaction_type.replace('_', ' ').title()} + +Conversation Summary: +{interaction_details} + +Temporal Context: +- Date: {random_date.strftime('%B %d, %Y')} +- Quarter: Q{random_date.month//3 + 1} {random_date.year} +- Day of Week: {random_date.strftime('%A')} + +Revenue Information: +- Unit Price: ${price} +- Potential Deal Value: ${price * random.randint(1, 5)} +- Priority: {random.choice(['High', 'Medium', 'Low'])} + """.strip() + + # Return document with consolidated text + minimal metadata for Elasticsearch + return { + "id": str(uuid.uuid4()), + "timestamp": random_date.isoformat(), + "document_type": "sales_record", + "product_line": product_line, # Keep for filtering/aggregation + "region": region, # Keep for filtering/aggregation + "consolidated_text": consolidated_text, # **This is what Unstructured will process** + "record_date": random_date.strftime('%Y-%m-%d'), + "quarter": f"Q{random_date.month//3 + 1}", + "year": random_date.year + } + + def _generate_interaction_text(self, interaction_type, customer_name, sales_rep, model, price, retailer, city, features): + """Generate detailed interaction text based on type""" + + interaction_templates = { + "purchase_inquiry": f"Customer {customer_name} from {city} contacted {sales_rep} to inquire about purchasing the {model}. The customer was particularly interested in the {', '.join(features[:2])} features. {sales_rep} provided detailed product specifications and quoted ${price}. Customer mentioned they had seen similar products at {retailer} but was impressed with the Bose quality and features.", + + "product_comparison": f"{sales_rep} conducted a comprehensive product comparison session with {customer_name}. The customer was deciding between the {model} and competitor products. Key selling points discussed included {', '.join(features)} which differentiate Bose from competitors. The ${price} price point was justified through superior audio quality and build reliability. Customer appreciated the detailed comparison and is considering the purchase.", + + "sales_consultation": f"In-depth consultation with {customer_name} in the {city} area. {sales_rep} assessed customer needs and recommended the {model} based on their lifestyle and audio preferences. Highlighted features included {', '.join(features)}. Discussed ${price} pricing structure and available financing options. Customer showed strong interest and requested follow-up information.", + + "order_processing": f"Order successfully processed for {customer_name}: {random.randint(1, 3)} units of {model} at ${price} each. {sales_rep} confirmed shipping details to {city} and explained warranty coverage. Customer opted for expedited shipping and was provided with tracking information. Partnership with {retailer} ensured competitive pricing and reliable delivery.", + + "promotional_campaign": f"Q{random.randint(1,4)} promotional outreach to {customer_name}. {sales_rep} presented special pricing on {model} - limited time offer at ${price - random.randint(10, 30)} (regularly ${price}). Emphasized exclusive features: {', '.join(features)}. Customer expressed interest and plans to visit {retailer} location this weekend to experience the product firsthand.", + + "warranty_inquiry": f"{customer_name} contacted {sales_rep} regarding warranty coverage for their {model} purchased from {retailer}. {sales_rep} reviewed the comprehensive warranty terms and explained the repair/replacement process. Customer was satisfied with the coverage and expressed loyalty to the Bose brand. Discussed potential upgrade paths and new features in latest models.", + + "upsell_opportunity": f"{sales_rep} identified upsell opportunity with existing customer {customer_name}. Customer currently owns an older Bose model and was introduced to the {model} with enhanced features: {', '.join(features)}. The ${price} upgrade investment was positioned as worthwhile for the improved experience. Customer is considering the upgrade and requested a demo unit.", + + "bulk_order_request": f"Corporate customer {customer_name} from {city} requested bulk pricing for {model} units. {sales_rep} prepared enterprise quotation for {random.randint(10, 50)} units at discounted rate. Discussed features relevant to business use: {', '.join(features)}. Partnership with {retailer} enables volume discounts and dedicated support. Proposal under review by customer's procurement team." + } + + return interaction_templates.get( + interaction_type, + f"{sales_rep} assisted {customer_name} with {interaction_type.replace('_', ' ')} regarding {model}. Discussed ${price} pricing and key features: {', '.join(features)}. Customer interaction was positive and follow-up scheduled." + ) + +def main(): + """Create consolidated Elasticsearch index optimized for RAG""" + print("🚀 Creating Consolidated Elasticsearch Index for RAG") + print("=" * 60) + + # Initialize + api_key = os.getenv('ELASTIC_API_KEY') + es = Elasticsearch( + "https://2371b9a1d2ad40c590fd1e22652a8236.us-central1.gcp.cloud.es.io:443", + api_key=api_key, + request_timeout=60 + ) + + index_name = "sales-records" # Use same index name as API key permissions + num_records = 100 + + try: + # Delete existing index if it exists + if es.indices.exists(index=index_name): + print(f"🗑️ Deleting existing index: {index_name}") + es.indices.delete(index=index_name) + + # Create index with simple mapping optimized for consolidated text + print(f"🔧 Creating consolidated index: {index_name}") + mapping = { + "settings": { + "number_of_shards": 1, + "number_of_replicas": 1 + }, + "mappings": { + "properties": { + "id": {"type": "keyword"}, + "timestamp": {"type": "date"}, + "document_type": {"type": "keyword"}, + "product_line": {"type": "keyword"}, + "region": {"type": "keyword"}, + "consolidated_text": { + "type": "text", + "analyzer": "standard" # This is the field Unstructured will process + }, + "record_date": {"type": "date"}, + "quarter": {"type": "keyword"}, + "year": {"type": "integer"} + } + } + } + + es.indices.create(index=index_name, body=mapping) + print(f"✅ Created index with consolidated text mapping") + + # Generate and index data + generator = ConsolidatedBoseSalesDataGenerator() + print(f"🔄 Generating {num_records} consolidated sales records...") + + records = [] + for i in range(num_records): + if i % 25 == 0 and i > 0: + print(f" Generated {i}/{num_records} records...") + records.append(generator.generate_consolidated_record()) + + print(f"✅ Generated {len(records)} consolidated records") + + # Bulk index + print("📤 Bulk indexing consolidated records...") + actions = [] + for record in records: + actions.append({ + "_index": index_name, + "_id": record["id"], + "_source": record + }) + + from elasticsearch.helpers import bulk + success_count, failed_items = bulk(es, actions, chunk_size=50) + + print(f"✅ Successfully indexed {success_count} consolidated records") + + # Verify + count_response = es.count(index=index_name) + total_docs = count_response['count'] + print(f"📊 Total documents in consolidated index: {total_docs}") + + # Show sample + sample_response = es.search( + index=index_name, + body={"size": 1, "_source": ["consolidated_text", "product_line", "region"]}, + ) + + if sample_response['hits']['hits']: + sample = sample_response['hits']['hits'][0]['_source'] + print(f"\n📋 Sample Consolidated Record:") + print(f" Product Line: {sample['product_line']}") + print(f" Region: {sample['region']}") + print(f" Consolidated Text Preview:") + text_preview = sample['consolidated_text'][:300] + "..." + print(f" {text_preview}") + + print(f"\n🎉 CONSOLIDATED INDEX READY!") + print(f"✅ Index: {index_name}") + print(f"✅ Records: {total_docs}") + print(f"✅ Each record contains ALL context in 'consolidated_text' field") + print(f"✅ Ready for Unstructured Workflow processing with full context preservation") + + except Exception as e: + print(f"❌ Error: {e}") + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/elastic-search-index-setup/create_nonconsolidated_index.py b/elastic-search-index-setup/create_nonconsolidated_index.py new file mode 100644 index 0000000..5874db6 --- /dev/null +++ b/elastic-search-index-setup/create_nonconsolidated_index.py @@ -0,0 +1,503 @@ +#!/usr/bin/env python3 +""" +Elasticsearch Setup Script for Bose Sales Data +Creates and populates an Elasticsearch index with NER-rich synthetic sales data +for use as a source connector in the Unstructured Workflow Endpoint. + +Designed for Elastic Cloud deployment with .env configuration. +""" + +import os +import json +import time +import uuid +import random +from datetime import datetime, timedelta +from typing import List, Dict, Any, Optional +from faker import Faker +from dotenv import load_dotenv + +# Load environment variables from base directory (one level up) +load_dotenv(dotenv_path="../.env") + +try: + from elasticsearch import Elasticsearch + from elasticsearch.helpers import bulk, BulkIndexError +except ImportError: + print("❌ elasticsearch package not found. Install with: pip install elasticsearch") + exit(1) + +class ElasticsearchConfig: + """Configuration for Elasticsearch connection and data setup""" + + def __init__(self): + # Load from environment variables + self.cloud_id = os.getenv('ELASTIC_CLOUD_ID') + self.username = os.getenv('ELASTIC_USERNAME') + self.password = os.getenv('ELASTIC_PASSWORD') + self.api_key = os.getenv('ELASTIC_API_KEY') + + # Index configuration + self.index_name = "sales-records" + self.num_synthetic_records = 100 + + # Validate required credentials + self._validate_credentials() + + def _validate_credentials(self): + """Validate that required credentials are provided""" + # Either username/password OR api_key must be provided + if not ((self.username and self.password) or self.api_key): + raise ValueError("Either ELASTIC_USERNAME/ELASTIC_PASSWORD or ELASTIC_API_KEY must be provided in .env file") + + def get_client(self) -> Elasticsearch: + """Create and return Elasticsearch client for Elastic Cloud""" + if self.api_key: + # Use API key authentication (recommended for production) + return Elasticsearch( + "https://2371b9a1d2ad40c590fd1e22652a8236.us-central1.gcp.cloud.es.io:443", + api_key=self.api_key, + request_timeout=60, + max_retries=3, + retry_on_timeout=True + ) + else: + # Use username/password authentication + return Elasticsearch( + "https://2371b9a1d2ad40c590fd1e22652a8236.us-central1.gcp.cloud.es.io:443", + basic_auth=(self.username, self.password), + request_timeout=60, + max_retries=3, + retry_on_timeout=True + ) + +class BoseSalesDataGenerator: + """Generate NER-rich synthetic sales data for Bose products""" + + def __init__(self): + self.fake = Faker(['en_US']) + Faker.seed(42) # For reproducible data + + # Bose product data + self.products = { + 'SoundSport': { + 'models': ['SoundSport Free', 'SoundSport Wireless', 'SoundSport Pulse'], + 'price_range': (129, 199), + 'category': 'Sports Earbuds' + }, + 'OpenAudio': { + 'models': ['OpenAudio Sport', 'OpenAudio Ultra', 'OpenAudio Pro'], + 'price_range': (149, 249), + 'category': 'Open-Ear Audio' + }, + 'QuietComfort': { + 'models': ['QuietComfort 45', 'QuietComfort Ultra', 'QuietComfort Earbuds'], + 'price_range': (199, 429), + 'category': 'Noise Cancelling' + } + } + + # Rich entity data for NER + self.retailers = [ + "Best Buy", "Target", "Amazon", "Walmart", "Costco", "B&H Photo", + "Guitar Center", "Sam's Club", "Newegg", "Adorama", "Crutchfield" + ] + + self.sales_reps = [ + "Jennifer Martinez", "Michael Chen", "Sarah Johnson", "David Rodriguez", + "Emily Wilson", "Robert Taylor", "Lisa Anderson", "James Thompson", + "Maria Garcia", "Christopher Lee", "Amanda Davis", "Daniel Brown" + ] + + self.regions = [ + "Northeast", "Southeast", "Midwest", "Southwest", "West Coast", + "Pacific Northwest", "Mountain West", "Great Lakes", "Mid-Atlantic", "Gulf Coast" + ] + + self.cities = [ + "New York, NY", "Los Angeles, CA", "Chicago, IL", "Houston, TX", "Phoenix, AZ", + "Philadelphia, PA", "San Antonio, TX", "San Diego, CA", "Dallas, TX", "Austin, TX", + "Jacksonville, FL", "Fort Worth, TX", "Columbus, OH", "Charlotte, NC", "Seattle, WA", + "Denver, CO", "Washington, DC", "Boston, MA", "Nashville, TN", "Detroit, MI" + ] + + self.interaction_types = [ + "purchase_inquiry", "product_comparison", "pricing_discussion", + "sales_consultation", "order_processing", "upsell_opportunity", + "customer_preferences", "warranty_inquiry", "bulk_order_request", + "promotional_campaign", "seasonal_sale", "loyalty_program_enrollment" + ] + + def generate_sales_record(self) -> Dict[str, Any]: + """Generate a single NER-rich sales record""" + + # Select random product + product_line = random.choice(list(self.products.keys())) + product_info = self.products[product_line] + model = random.choice(product_info['models']) + price = random.randint(*product_info['price_range']) + + # Generate rich entities for NER extraction + customer_name = self.fake.name() + sales_rep = random.choice(self.sales_reps) + retailer = random.choice(self.retailers) + city = random.choice(self.cities) + region = random.choice(self.regions) + + # Generate realistic interaction text with rich named entities + interaction_type = random.choice(self.interaction_types) + + # Create contextual sales interaction text + interaction_texts = { + "purchase_inquiry": f"Customer {customer_name} from {city} called to inquire about purchasing the {model}. Sales rep {sales_rep} provided detailed product information and quoted ${price}. Customer is comparing with similar products at {retailer}.", + + "product_comparison": f"{sales_rep} helped {customer_name} compare the {model} against competitors. Discussed the superior noise cancellation technology and ${price} price point. Customer mentioned they saw it at {retailer} for a higher price.", + + "sales_consultation": f"Consultation session with {customer_name} in {region} region. {sales_rep} recommended the {model} based on customer's active lifestyle needs. Discussed ${price} pricing and available financing options through {retailer}.", + + "order_processing": f"Order processed for {customer_name}: 2 units of {model} at ${price} each. Shipping to {city}. Sales rep {sales_rep} confirmed delivery timeline and warranty coverage. Partner retailer: {retailer}.", + + "promotional_campaign": f"Q4 promotional campaign in {region}: {sales_rep} contacted {customer_name} about special pricing on {model}. Limited time offer at ${price - 20} (originally ${price}). Customer interested, will visit {retailer} this weekend." + } + + # Select appropriate interaction text or generate generic one + if interaction_type in interaction_texts: + interaction_text = interaction_texts[interaction_type] + else: + interaction_text = f"{sales_rep} assisted {customer_name} with {interaction_type.replace('_', ' ')} for {model}. Discussed ${price} pricing and availability at {retailer} in {city}." + + # Generate timestamp from 2016 onwards (products launched after 2015) + start_date = datetime(2016, 1, 1) + random_date = self.fake.date_time_between(start_date=start_date, end_date='now') + + return { + "id": str(uuid.uuid4()), + "timestamp": random_date.isoformat(), + "customer_name": customer_name, + "sales_representative": sales_rep, + "product_line": product_line, + "product_model": model, + "product_category": product_info['category'], + "price": price, + "retailer": retailer, + "location_city": city, + "region": region, + "interaction_type": interaction_type, + "interaction_text": interaction_text, + "text": interaction_text, # Duplicate for semantic_text field + "quarter": f"Q{random_date.month//3 + 1}", + "year": random_date.year, + "month": random_date.strftime("%B"), + "day_of_week": random_date.strftime("%A"), + "customer_segment": random.choice(["Consumer", "Business", "Education", "Government"]), + "sales_channel": random.choice(["Direct", "Retail Partner", "Online", "Phone"]), + "lead_source": random.choice(["Website", "Advertisement", "Referral", "Trade Show", "Cold Call"]), + "deal_stage": random.choice(["Prospect", "Qualified", "Proposal", "Negotiation", "Closed Won", "Closed Lost"]), + "revenue_potential": price * random.randint(1, 5), # Potential for multiple units + "customer_priority": random.choice(["High", "Medium", "Low"]), + "follow_up_required": random.choice([True, False]), + "notes": f"Additional context: Customer expressed interest in {product_line} series. {sales_rep} to follow up within 48 hours." + } + + def generate_bulk_data(self, num_records: int) -> List[Dict[str, Any]]: + """Generate bulk sales data for Elasticsearch""" + print(f"🔄 Generating {num_records} synthetic sales records...") + + records = [] + for i in range(num_records): + if i % 100 == 0 and i > 0: + print(f" Generated {i}/{num_records} records...") + records.append(self.generate_sales_record()) + + print(f"✅ Generated {len(records)} records successfully") + return records + +class ElasticsearchManager: + """Manage Elasticsearch operations for Bose sales data""" + + def __init__(self, config: ElasticsearchConfig): + self.config = config + self.es = config.get_client() + self.index_name = config.index_name + + def test_connection(self) -> bool: + """Test Elasticsearch connection by checking index access""" + try: + print("🔧 Testing Elasticsearch connection...") + # Test connection by checking if we can access our specific index + # This works with index-specific API keys + exists = self.es.indices.exists(index=self.index_name) + print(f"✅ Successfully connected to Elasticsearch") + print(f" Index '{self.index_name}' exists: {exists}") + return True + except Exception as e: + print(f"❌ Connection failed: {e}") + return False + + def create_index_mapping(self) -> bool: + """Create index with optimized mapping for NER and search""" + try: + print(f"🔧 Creating index: {self.index_name}") + + # Define mapping optimized for NER entities and search + mapping = { + "settings": { + "number_of_shards": 1, + "number_of_replicas": 1, + "analysis": { + "analyzer": { + "ner_analyzer": { + "type": "standard", + "stopwords": "_none_" # Keep all words for NER + } + } + } + }, + "mappings": { + "properties": { + "id": {"type": "keyword"}, + "timestamp": {"type": "date"}, + "customer_name": { + "type": "text", + "analyzer": "ner_analyzer", + "fields": {"keyword": {"type": "keyword"}} + }, + "sales_representative": { + "type": "text", + "analyzer": "ner_analyzer", + "fields": {"keyword": {"type": "keyword"}} + }, + "product_line": {"type": "keyword"}, + "product_model": {"type": "keyword"}, + "product_category": {"type": "keyword"}, + "price": {"type": "float"}, + "retailer": { + "type": "text", + "analyzer": "ner_analyzer", + "fields": {"keyword": {"type": "keyword"}} + }, + "location_city": { + "type": "text", + "analyzer": "ner_analyzer", + "fields": {"keyword": {"type": "keyword"}} + }, + "region": {"type": "keyword"}, + "interaction_type": {"type": "keyword"}, + "interaction_text": { + "type": "text", + "analyzer": "ner_analyzer" # Full text optimized for NER + }, + "text": { + "type": "text", + "analyzer": "ner_analyzer" # Duplicate text field for compatibility + }, + "quarter": {"type": "keyword"}, + "year": {"type": "integer"}, + "month": {"type": "keyword"}, + "day_of_week": {"type": "keyword"}, + "customer_segment": {"type": "keyword"}, + "sales_channel": {"type": "keyword"}, + "lead_source": {"type": "keyword"}, + "deal_stage": {"type": "keyword"}, + "revenue_potential": {"type": "float"}, + "customer_priority": {"type": "keyword"}, + "follow_up_required": {"type": "boolean"}, + "notes": { + "type": "text", + "analyzer": "ner_analyzer" + } + } + } + } + + # Check if index exists, if so, just update mapping + if self.es.indices.exists(index=self.index_name): + print(f" ✅ Index '{self.index_name}' already exists, updating mapping...") + try: + # Try to update mapping for existing index + self.es.indices.put_mapping(index=self.index_name, body=mapping["mappings"]) + print(f"✅ Updated mapping for existing index: {self.index_name}") + except Exception as e: + print(f" ⚠️ Could not update mapping (this is often okay): {e}") + print(f" ✅ Using existing index: {self.index_name}") + return True + else: + # Create new index only if it doesn't exist + self.es.indices.create(index=self.index_name, body=mapping) + print(f"✅ Created new index: {self.index_name}") + return True + + except Exception as e: + print(f"❌ Error creating index: {e}") + return False + + def bulk_index_data(self, records: List[Dict[str, Any]]) -> bool: + """Bulk index sales data into Elasticsearch""" + try: + print(f"📤 Bulk indexing {len(records)} records...") + + # Prepare bulk data + actions = [] + for record in records: + action = { + "_index": self.index_name, + "_id": record["id"], + "_source": record + } + actions.append(action) + + # Execute bulk index + success_count, failed_items = bulk( + self.es, + actions, + chunk_size=100, + request_timeout=60, + max_retries=3, + initial_backoff=2, + max_backoff=600 + ) + + print(f"✅ Successfully indexed {success_count} records") + + if failed_items: + print(f"⚠️ Failed to index {len(failed_items)} records") + for item in failed_items[:3]: # Show first 3 failures + if 'index' in item: + error_info = item['index'] + print(f" - Document ID: {error_info.get('_id', 'unknown')}") + print(f" Error: {error_info.get('error', {}).get('reason', 'unknown error')}") + else: + print(f" - {item}") + + return success_count > 0 + + except BulkIndexError as e: + print(f"❌ Bulk indexing error: {e}") + return False + except Exception as e: + print(f"❌ Error during bulk indexing: {e}") + return False + + def verify_data(self) -> Dict[str, Any]: + """Verify indexed data and return statistics""" + try: + print("🔍 Verifying indexed data...") + + # Refresh index to ensure all data is searchable + self.es.indices.refresh(index=self.index_name) + + # Get basic stats + count_response = self.es.count(index=self.index_name) + total_docs = count_response['count'] + + print(f"📊 Total documents: {total_docs}") + + if total_docs == 0: + return {"total_docs": 0} + + # Sample a few documents + sample_response = self.es.search( + index=self.index_name, + body={"size": 3, "sort": [{"timestamp": {"order": "desc"}}]} + ) + + print("📋 Sample documents:") + for i, hit in enumerate(sample_response['hits']['hits'], 1): + source = hit['_source'] + print(f" {i}. {source['customer_name']} - {source['product_model']} - ${source['price']}") + print(f" {source['interaction_text'][:100]}...") + + # Aggregation stats + agg_response = self.es.search( + index=self.index_name, + body={ + "size": 0, + "aggs": { + "by_product": { + "terms": {"field": "product_line", "size": 10} + }, + "by_retailer": { + "terms": {"field": "retailer.keyword", "size": 5} + }, + "avg_price": { + "avg": {"field": "price"} + } + } + } + ) + + stats = { + "total_docs": total_docs, + "products": agg_response['aggregations']['by_product']['buckets'], + "retailers": agg_response['aggregations']['by_retailer']['buckets'], + "avg_price": round(agg_response['aggregations']['avg_price']['value'], 2) + } + + print(f"📈 Statistics:") + print(f" Average price: ${stats['avg_price']}") + print(f" Top products: {', '.join([b['key'] for b in stats['products'][:3]])}") + print(f" Top retailers: {', '.join([b['key'] for b in stats['retailers'][:3]])}") + + return stats + + except Exception as e: + print(f"❌ Error verifying data: {e}") + return {"error": str(e)} + +def main(): + """Main execution function""" + print("🚀 Starting Elasticsearch Setup for Bose Sales Data") + print("=" * 60) + + try: + # Initialize configuration + print("⚙️ Loading configuration...") + config = ElasticsearchConfig() + print(f" Index name: {config.index_name}") + print(f" Records to generate: {config.num_synthetic_records}") + + # Initialize Elasticsearch manager + es_manager = ElasticsearchManager(config) + + # Test connection + if not es_manager.test_connection(): + print("❌ Cannot proceed without valid Elasticsearch connection") + return False + + # Create index with mapping + if not es_manager.create_index_mapping(): + print("❌ Failed to create index mapping") + return False + + # Generate synthetic data + data_generator = BoseSalesDataGenerator() + sales_records = data_generator.generate_bulk_data(config.num_synthetic_records) + + # Index data + if not es_manager.bulk_index_data(sales_records): + print("❌ Failed to index data") + return False + + # Verify results + stats = es_manager.verify_data() + + print("\n" + "=" * 60) + print("🎉 SETUP COMPLETE!") + print("=" * 60) + print(f"✅ Elasticsearch index '{config.index_name}' created successfully") + print(f"✅ {stats.get('total_docs', 0)} sales records indexed") + print(f"📊 Ready for use as Unstructured Workflow source connector") + print("\nNext steps:") + print("1. Use this index as an Elasticsearch source connector") + print("2. Configure NER enrichment workflow node") + print("3. Process through your hybrid RAG pipeline") + + return True + + except Exception as e: + print(f"❌ Setup failed: {e}") + return False + +if __name__ == "__main__": + success = main() + exit(0 if success else 1) \ No newline at end of file diff --git a/elastic-search-index-setup/elasticsearch_setup.py b/elastic-search-index-setup/elasticsearch_setup.py index 5874db6..9a9fda4 100644 --- a/elastic-search-index-setup/elasticsearch_setup.py +++ b/elastic-search-index-setup/elasticsearch_setup.py @@ -323,9 +323,9 @@ def create_index_mapping(self) -> bool: return True else: # Create new index only if it doesn't exist - self.es.indices.create(index=self.index_name, body=mapping) + self.es.indices.create(index=self.index_name, body=mapping) print(f"✅ Created new index: {self.index_name}") - return True + return True except Exception as e: print(f"❌ Error creating index: {e}") @@ -367,7 +367,7 @@ def bulk_index_data(self, records: List[Dict[str, Any]]) -> bool: print(f" - Document ID: {error_info.get('_id', 'unknown')}") print(f" Error: {error_info.get('error', {}).get('reason', 'unknown error')}") else: - print(f" - {item}") + print(f" - {item}") return success_count > 0 diff --git a/elastic-search-index-setup/verify_consolidated_data.py b/elastic-search-index-setup/verify_consolidated_data.py new file mode 100644 index 0000000..1ba8219 --- /dev/null +++ b/elastic-search-index-setup/verify_consolidated_data.py @@ -0,0 +1,157 @@ +#!/usr/bin/env python3 +""" +Verify Consolidated Elasticsearch Data for RAG +Shows how context is preserved in the consolidated text field. +""" + +import os +from dotenv import load_dotenv +from elasticsearch import Elasticsearch + +# Load environment variables from base directory (one level up) +load_dotenv(dotenv_path="../.env") + +def main(): + """Verify consolidated data structure and context preservation""" + print("🔍 Verifying Consolidated RAG Data") + print("=" * 50) + + # Initialize + api_key = os.getenv('ELASTIC_API_KEY') + es = Elasticsearch( + "https://2371b9a1d2ad40c590fd1e22652a8236.us-central1.gcp.cloud.es.io:443", + api_key=api_key, + request_timeout=60 + ) + + index_name = "sales-records" # Use same index name as API key permissions + + try: + # Check if index exists + if not es.indices.exists(index=index_name): + print(f"❌ Index '{index_name}' does not exist. Run create_consolidated_index.py first.") + return + + # Get document count + count_response = es.count(index=index_name) + total_docs = count_response['count'] + print(f"📊 Total consolidated documents: {total_docs}") + + if total_docs == 0: + print("❌ No documents found in consolidated index") + return + + # Get sample documents + sample_response = es.search( + index=index_name, + body={ + "size": 2, + "_source": ["consolidated_text", "product_line", "region", "record_date"], + "sort": [{"timestamp": {"order": "desc"}}] + }, + ) + + print(f"\n📋 Sample Consolidated Records:") + print("=" * 50) + + for i, hit in enumerate(sample_response['hits']['hits'], 1): + source = hit['_source'] + print(f"\n🔸 RECORD {i}:") + print(f" Product Line: {source['product_line']}") + print(f" Region: {source['region']}") + print(f" Date: {source['record_date']}") + print(f"\n 📝 CONSOLIDATED TEXT (Full Context):") + print(f" {'-' * 45}") + + # Show the full consolidated text with formatting + consolidated_text = source['consolidated_text'] + lines = consolidated_text.split('\n') + for line in lines[:15]: # Show first 15 lines + if line.strip(): + print(f" {line}") + + if len(lines) > 15: + print(f" ... ({len(lines) - 15} more lines)") + + print(f" {'-' * 45}") + + # Show aggregations + agg_response = es.search( + index=index_name, + body={ + "size": 0, + "aggs": { + "by_product": { + "terms": {"field": "product_line", "size": 5} + }, + "by_region": { + "terms": {"field": "region", "size": 5} + }, + "by_quarter": { + "terms": {"field": "quarter", "size": 8} + } + } + } + ) + + print(f"\n📈 Data Distribution:") + print("=" * 30) + + print("🎧 By Product Line:") + for bucket in agg_response['aggregations']['by_product']['buckets']: + print(f" • {bucket['key']}: {bucket['doc_count']} records") + + print("\n🌍 By Region:") + for bucket in agg_response['aggregations']['by_region']['buckets'][:5]: + print(f" • {bucket['key']}: {bucket['doc_count']} records") + + print("\n📅 By Quarter:") + for bucket in agg_response['aggregations']['by_quarter']['buckets'][:5]: + print(f" • {bucket['key']}: {bucket['doc_count']} records") + + # Test search functionality + print(f"\n🔍 Testing Context-Aware Search:") + print("=" * 35) + + search_tests = [ + "noise cancellation", + "Best Buy", + "warranty", + "New York" + ] + + for search_term in search_tests: + search_response = es.search( + index=index_name, + body={ + "size": 1, + "query": { + "match": { + "consolidated_text": search_term + } + }, + "_source": ["product_line", "region"] + } + ) + + hits = search_response['hits']['total']['value'] + if hits > 0: + sample_hit = search_response['hits']['hits'][0]['_source'] + print(f" 🔎 '{search_term}': {hits} matches (e.g., {sample_hit['product_line']} in {sample_hit['region']})") + else: + print(f" 🔎 '{search_term}': {hits} matches") + + print(f"\n" + "=" * 50) + print("🎉 CONSOLIDATED DATA VERIFICATION COMPLETE") + print("=" * 50) + print("✅ Context Preservation: Each record contains ALL relevant information") + print("✅ RAG Ready: Unstructured will process 'consolidated_text' field") + print("✅ No Context Loss: Customer, product, location, and conversation details preserved") + print("✅ Search Ready: Full-text search across all contextual information") + print("\n🚀 Ready for Unstructured Workflow processing!") + + except Exception as e: + print(f"❌ Error: {e}") + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/elasticsearch-example-data/README.md b/elasticsearch-example-data/README.md new file mode 100644 index 0000000..76b319d --- /dev/null +++ b/elasticsearch-example-data/README.md @@ -0,0 +1,98 @@ +# Elasticsearch Data Structure Examples + +This directory contains examples of two different approaches for structuring synthetic sales data in Elasticsearch for RAG (Retrieval-Augmented Generation) use cases. + +## 📊 **Data Structure Comparison** + +### 1. Non-Consolidated Structure (`nonconsolidated_examples.json`) + +**Use Case**: Traditional database-like structure with separate fields for each data element. + +**Characteristics**: +- Each piece of information is stored in its own field +- Easy to query specific fields (e.g., all records from "Best Buy") +- Good for analytics and aggregations +- Follows normalized database principles + +**⚠️ Problem with Unstructured Processing**: +When Unstructured processes this data and creates separate Text elements for each field, **context is lost**. For example: +- One Text element might contain: "Jennifer Martinez" +- Another Text element might contain: "SoundSport Free" +- Another Text element might contain: "$149" + +The resulting embeddings won't understand that Jennifer Martinez was interested in the SoundSport Free at $149. + +### 2. Consolidated Structure (`consolidated_examples.json`) + +**Use Case**: RAG-optimized structure where all context is preserved in a single text field. + +**Characteristics**: +- All relevant information combined into a single `consolidated_text` field +- Maintains complete context relationships +- Optimized for embedding generation and RAG queries +- Minimal metadata fields for filtering/aggregation + +**✅ Benefits for RAG**: +When Unstructured processes the `consolidated_text` field, each Text element contains **complete context**: +- Customer information, product details, sales information, and conversation summary all in one coherent text block +- Vector embeddings capture full relationships between entities +- RAG queries have access to complete context without information loss + +## 🔧 **Generation Scripts** + +### Non-Consolidated Data +- **Script**: `create_nonconsolidated_index.py` +- **Index**: `sales-records` (separate fields structure) +- **Fields**: 20+ individual fields (customer_name, product_model, price, etc.) + +### Consolidated Data +- **Script**: `create_consolidated_index.py` +- **Index**: `sales-records` (consolidated structure) +- **Key Field**: `consolidated_text` (contains all context) + +## 📋 **Example Comparison** + +### Non-Consolidated Record +```json +{ + "customer_name": "Jennifer Martinez", + "product_model": "SoundSport Free", + "price": 149, + "retailer": "Best Buy", + "interaction_text": "Customer called to inquire about purchasing..." +} +``` +**Problem**: When processed by Unstructured, context between fields is lost. + +### Consolidated Record +```json +{ + "consolidated_text": "SALES RECORD - November 15, 2023\n\nCustomer Information:\n- Name: Jennifer Martinez\n- Location: New York, NY\n...\n\nProduct Details:\n- Model: SoundSport Free\n- Price: $149\n...\n\nConversation Summary:\nCustomer Jennifer Martinez contacted Michael Chen about the SoundSport Free..." +} +``` +**Solution**: All context preserved in single field for complete RAG understanding. + +## 🎯 **Recommendation** + +**For RAG Use Cases**: Use the **consolidated structure** to ensure context preservation and optimal embedding quality. + +**For Analytics**: Use the **non-consolidated structure** for traditional database queries and business intelligence. + +**For Hybrid Approaches**: You can maintain both structures - use consolidated for RAG processing and non-consolidated for analytics dashboards. + +## 🚀 **Usage** + +1. **Choose your approach** based on use case +2. **Run the appropriate script**: + - `python create_nonconsolidated_index.py` (separate fields) + - `python create_consolidated_index.py` (RAG-optimized) +3. **Configure Unstructured Workflow** to process the appropriate field(s) +4. **Verify results** with the corresponding verification scripts + +## 📁 **Files in This Directory** + +- `nonconsolidated_examples.json` - 3 example records with separate fields +- `consolidated_examples.json` - 3 example records with consolidated text +- `README.md` - This documentation file + +Both examples contain the same underlying sales data, just structured differently for different use cases. \ No newline at end of file diff --git a/elasticsearch-example-data/consolidated_examples.json b/elasticsearch-example-data/consolidated_examples.json new file mode 100644 index 0000000..7dee738 --- /dev/null +++ b/elasticsearch-example-data/consolidated_examples.json @@ -0,0 +1,35 @@ +[ + { + "id": "consolidated-001", + "timestamp": "2023-11-15T14:30:00", + "document_type": "sales_record", + "product_line": "SoundSport", + "region": "Northeast", + "record_date": "2023-11-15", + "quarter": "Q4", + "year": 2023, + "consolidated_text": "SALES RECORD - November 15, 2023\n\nCustomer Information:\n- Name: Jennifer Martinez\n- Location: New York, NY\n- Segment: Consumer\n- Lead Source: Website\n\nProduct Details:\n- Product Line: SoundSport\n- Model: SoundSport Free\n- Category: Sports Earbuds\n- Price: $149\n- Key Features: sweat-resistant, secure fit, wireless\n\nSales Information:\n- Sales Representative: Michael Chen\n- Retailer: Best Buy\n- Region: Northeast\n- Channel: Phone\n- Deal Stage: Qualified\n- Interaction Type: Purchase Inquiry\n\nConversation Summary:\nCustomer Jennifer Martinez from New York, NY contacted Michael Chen to inquire about purchasing the SoundSport Free. The customer was particularly interested in the sweat-resistant, secure fit features. Michael Chen provided detailed product specifications and quoted $149. Customer mentioned they had seen similar products at Best Buy but was impressed with the Bose quality and features.\n\nTemporal Context:\n- Date: November 15, 2023\n- Quarter: Q4 2023\n- Day of Week: Wednesday\n\nRevenue Information:\n- Unit Price: $149\n- Potential Deal Value: $447\n- Priority: High" + }, + { + "id": "consolidated-002", + "timestamp": "2024-03-22T09:15:00", + "document_type": "sales_record", + "product_line": "QuietComfort", + "region": "West Coast", + "record_date": "2024-03-22", + "quarter": "Q1", + "year": 2024, + "consolidated_text": "SALES RECORD - March 22, 2024\n\nCustomer Information:\n- Name: Robert Taylor\n- Location: Los Angeles, CA\n- Segment: Business\n- Lead Source: Referral\n\nProduct Details:\n- Product Line: QuietComfort\n- Model: QuietComfort Ultra\n- Category: Noise Cancelling\n- Price: $379\n- Key Features: world-class noise cancellation, premium comfort, long battery life\n\nSales Information:\n- Sales Representative: Sarah Johnson\n- Retailer: Amazon\n- Region: West Coast\n- Channel: Direct\n- Deal Stage: Proposal\n- Interaction Type: Product Comparison\n\nConversation Summary:\nSarah Johnson conducted a comprehensive product comparison session with Robert Taylor. The customer was deciding between the QuietComfort Ultra and competitor products. Key selling points discussed included world-class noise cancellation, premium comfort, long battery life which differentiate Bose from competitors. The $379 price point was justified through superior audio quality and build reliability. Customer appreciated the detailed comparison and is considering the purchase.\n\nTemporal Context:\n- Date: March 22, 2024\n- Quarter: Q1 2024\n- Day of Week: Friday\n\nRevenue Information:\n- Unit Price: $379\n- Potential Deal Value: $1137\n- Priority: Medium" + }, + { + "id": "consolidated-003", + "timestamp": "2022-08-10T16:45:00", + "document_type": "sales_record", + "product_line": "OpenAudio", + "region": "Midwest", + "record_date": "2022-08-10", + "quarter": "Q3", + "year": 2022, + "consolidated_text": "SALES RECORD - August 10, 2022\n\nCustomer Information:\n- Name: Emily Wilson\n- Location: Chicago, IL\n- Segment: Consumer\n- Lead Source: Advertisement\n\nProduct Details:\n- Product Line: OpenAudio\n- Model: OpenAudio Pro\n- Category: Open-Ear Audio\n- Price: $229\n- Key Features: open-ear design, situational awareness, comfortable fit\n\nSales Information:\n- Sales Representative: David Rodriguez\n- Retailer: Costco\n- Region: Midwest\n- Channel: Retail Partner\n- Deal Stage: Closed Won\n- Interaction Type: Order Processing\n\nConversation Summary:\nOrder successfully processed for Emily Wilson: 2 units of OpenAudio Pro at $229 each. David Rodriguez confirmed shipping details to Chicago, IL and explained warranty coverage. Customer opted for expedited shipping and was provided with tracking information. Partnership with Costco ensured competitive pricing and reliable delivery.\n\nTemporal Context:\n- Date: August 10, 2022\n- Quarter: Q3 2022\n- Day of Week: Wednesday\n\nRevenue Information:\n- Unit Price: $229\n- Potential Deal Value: $458\n- Priority: Low" + } +] \ No newline at end of file diff --git a/elasticsearch-example-data/nonconsolidated_examples.json b/elasticsearch-example-data/nonconsolidated_examples.json new file mode 100644 index 0000000..30580e3 --- /dev/null +++ b/elasticsearch-example-data/nonconsolidated_examples.json @@ -0,0 +1,86 @@ +[ + { + "id": "example-001", + "timestamp": "2023-11-15T14:30:00", + "customer_name": "Jennifer Martinez", + "sales_representative": "Michael Chen", + "product_line": "SoundSport", + "product_model": "SoundSport Free", + "product_category": "Sports Earbuds", + "price": 149, + "retailer": "Best Buy", + "location_city": "New York, NY", + "region": "Northeast", + "interaction_type": "purchase_inquiry", + "interaction_text": "Customer Jennifer Martinez from New York, NY called to inquire about purchasing the SoundSport Free. Sales rep Michael Chen provided detailed product information and quoted $149. Customer is comparing with similar products at Best Buy.", + "text": "Customer Jennifer Martinez from New York, NY called to inquire about purchasing the SoundSport Free. Sales rep Michael Chen provided detailed product information and quoted $149. Customer is comparing with similar products at Best Buy.", + "quarter": "Q4", + "year": 2023, + "month": "November", + "day_of_week": "Wednesday", + "customer_segment": "Consumer", + "sales_channel": "Phone", + "lead_source": "Website", + "deal_stage": "Qualified", + "revenue_potential": 447, + "customer_priority": "High", + "follow_up_required": true, + "notes": "Additional context: Customer expressed interest in SoundSport series. Michael Chen to follow up within 48 hours." + }, + { + "id": "example-002", + "timestamp": "2024-03-22T09:15:00", + "customer_name": "Robert Taylor", + "sales_representative": "Sarah Johnson", + "product_line": "QuietComfort", + "product_model": "QuietComfort Ultra", + "product_category": "Noise Cancelling", + "price": 379, + "retailer": "Amazon", + "location_city": "Los Angeles, CA", + "region": "West Coast", + "interaction_type": "product_comparison", + "interaction_text": "Sarah Johnson helped Robert Taylor compare the QuietComfort Ultra against competitors. Discussed the superior noise cancellation technology and $379 price point. Customer mentioned they saw it at Amazon for a higher price.", + "text": "Sarah Johnson helped Robert Taylor compare the QuietComfort Ultra against competitors. Discussed the superior noise cancellation technology and $379 price point. Customer mentioned they saw it at Amazon for a higher price.", + "quarter": "Q1", + "year": 2024, + "month": "March", + "day_of_week": "Friday", + "customer_segment": "Business", + "sales_channel": "Direct", + "lead_source": "Referral", + "deal_stage": "Proposal", + "revenue_potential": 1137, + "customer_priority": "Medium", + "follow_up_required": false, + "notes": "Additional context: Customer expressed interest in QuietComfort series. Sarah Johnson to follow up within 48 hours." + }, + { + "id": "example-003", + "timestamp": "2022-08-10T16:45:00", + "customer_name": "Emily Wilson", + "sales_representative": "David Rodriguez", + "product_line": "OpenAudio", + "product_model": "OpenAudio Pro", + "product_category": "Open-Ear Audio", + "price": 229, + "retailer": "Costco", + "location_city": "Chicago, IL", + "region": "Midwest", + "interaction_type": "order_processing", + "interaction_text": "Order processed for Emily Wilson: 2 units of OpenAudio Pro at $229 each. Shipping to Chicago, IL. Sales rep David Rodriguez confirmed delivery timeline and warranty coverage. Partner retailer: Costco.", + "text": "Order processed for Emily Wilson: 2 units of OpenAudio Pro at $229 each. Shipping to Chicago, IL. Sales rep David Rodriguez confirmed delivery timeline and warranty coverage. Partner retailer: Costco.", + "quarter": "Q3", + "year": 2022, + "month": "August", + "day_of_week": "Wednesday", + "customer_segment": "Consumer", + "sales_channel": "Retail Partner", + "lead_source": "Advertisement", + "deal_stage": "Closed Won", + "revenue_potential": 458, + "customer_priority": "Low", + "follow_up_required": true, + "notes": "Additional context: Customer expressed interest in OpenAudio series. David Rodriguez to follow up within 48 hours." + } +] \ No newline at end of file diff --git a/elasticsearch_index_preprocessing.py b/elasticsearch_index_preprocessing.py new file mode 100644 index 0000000..da38c03 --- /dev/null +++ b/elasticsearch_index_preprocessing.py @@ -0,0 +1,964 @@ +#!/usr/bin/env python3 +""" +Elasticsearch Index Preprocessing Tool + +This script handles Elasticsearch index management for the Hybrid RAG Pipeline: +- Creates synthetic sales data for testing +- Manages index creation and deletion +- Downloads and uploads index data locally +- Provides backup and restore functionality + +ELASTICSEARCH API KEY SETUP REQUIREMENTS: +======================================== +Before running this script, you must create an Elasticsearch API key with the following permissions: + +{ + "sales-records-full-access": { + "cluster": [], + "indices": [ + { + "names": [ + "sales-records", + "sales-records-consolidated", + "customer-support" + ], + "privileges": [ + "create_index", + "delete_index", + "manage", + "write", + "read", + "view_index_metadata", + "monitor" + ], + "allow_restricted_indices": false + } + ], + "applications": [], + "run_as": [], + "metadata": {}, + "transient_metadata": { + "enabled": true + } + } +} + +SETUP INSTRUCTIONS: +================== +1. Log into your Elasticsearch deployment dashboard +2. Go to Security > API Keys +3. Create a new API key with the above permissions +4. Set the following environment variables in your .env file: + - ELASTICSEARCH_HOST=https://your-deployment.es.io:443 + - ELASTICSEARCH_API_KEY=your-api-key-here + +USAGE: +====== +- Run as standalone: python elasticsearch_index_preprocessing.py +- Import functions: from elasticsearch_index_preprocessing import run_elasticsearch_preprocessing + +""" +import os +import sys +import time + +import json +import uuid +import random +from pathlib import Path +from datetime import datetime, timedelta +from typing import List, Dict, Any, Optional +from faker import Faker +from dotenv import load_dotenv + + +# Import Elasticsearch for preprocessing +try: + from elasticsearch import Elasticsearch + from elasticsearch.helpers import bulk, BulkIndexError +except ImportError: + print("❌ elasticsearch package not found. Install with: pip install elasticsearch") + sys.exit(1) + + +# Elasticsearch Configuration +ELASTICSEARCH_HOST = os.getenv("ELASTICSEARCH_HOST", "your-elasticsearch-host") +ELASTICSEARCH_API_KEY = os.getenv("ELASTICSEARCH_API_KEY", "your-elasticsearch-api-key") +ELASTICSEARCH_INDEX = os.getenv("ELASTICSEARCH_INDEX", "sales-records-consolidated") # Updated to use consolidated index + + +# ============================================================================ +# ELASTICSEARCH PREPROCESSING FUNCTIONS +# ============================================================================ + +def get_elasticsearch_client(): + """Get Elasticsearch client with API key authentication""" + return Elasticsearch( + ELASTICSEARCH_HOST, + api_key=ELASTICSEARCH_API_KEY, + request_timeout=60, + max_retries=3, + retry_on_timeout=True + ) + +def delete_elasticsearch_indices(): + """Delete the contents of sales-records, sales-records-consolidated, and customer-support indices""" + print("🗑️ Deleting Elasticsearch indices...") + es = get_elasticsearch_client() + + indices_to_delete = ["sales-records", "sales-records-consolidated", "customer-support"] + + for index_name in indices_to_delete: + try: + if es.indices.exists(index=index_name): + es.indices.delete(index=index_name) + print(f" ✅ Successfully deleted {index_name} index") + else: + print(f" ℹ️ Index {index_name} does not exist") + except Exception as e: + print(f" ❌ Failed to delete index {index_name}: {e}") + +class BoseSalesDataGenerator: + """Generate NER-rich synthetic sales data for Bose products""" + + def __init__(self): + self.fake = Faker(['en_US']) + Faker.seed(42) # For reproducible data + + # Bose product data + self.products = { + 'SoundSport': { + 'models': ['SoundSport Free', 'SoundSport Wireless', 'SoundSport Pulse'], + 'price_range': (129, 199), + 'category': 'Sports Earbuds', + 'features': ['sweat-resistant', 'secure fit', 'wireless', 'noise isolation'] + }, + 'OpenAudio': { + 'models': ['OpenAudio Sport', 'OpenAudio Ultra', 'OpenAudio Pro'], + 'price_range': (149, 249), + 'category': 'Open-Ear Audio', + 'features': ['open-ear design', 'situational awareness', 'comfortable fit', 'premium audio'] + }, + 'QuietComfort': { + 'models': ['QuietComfort 45', 'QuietComfort Ultra', 'QuietComfort Earbuds'], + 'price_range': (199, 429), + 'category': 'Noise Cancelling', + 'features': ['world-class noise cancellation', 'premium comfort', 'long battery life', 'crystal clear calls'] + } + } + + # Rich entity data for NER + self.retailers = [ + "Best Buy", "Target", "Amazon", "Walmart", "Costco", "B&H Photo", + "Guitar Center", "Sam's Club", "Newegg", "Adorama", "Crutchfield" + ] + + self.sales_reps = [ + "Jennifer Martinez", "Michael Chen", "Sarah Johnson", "David Rodriguez", + "Emily Wilson", "Robert Taylor", "Lisa Anderson", "James Thompson", + "Maria Garcia", "Christopher Lee", "Amanda Davis", "Daniel Brown" + ] + + self.regions = [ + "Northeast", "Southeast", "Midwest", "Southwest", "West Coast", + "Pacific Northwest", "Mountain West", "Great Lakes", "Mid-Atlantic", "Gulf Coast" + ] + + self.cities = [ + "New York, NY", "Los Angeles, CA", "Chicago, IL", "Houston, TX", "Phoenix, AZ", + "Philadelphia, PA", "San Antonio, TX", "San Diego, CA", "Dallas, TX", "Austin, TX", + "Jacksonville, FL", "Fort Worth, TX", "Columbus, OH", "Charlotte, NC", "Seattle, WA", + "Denver, CO", "Washington, DC", "Boston, MA", "Nashville, TN", "Detroit, MI" + ] + + self.interaction_types = [ + "purchase_inquiry", "product_comparison", "pricing_discussion", + "sales_consultation", "order_processing", "upsell_opportunity", + "customer_preferences", "warranty_inquiry", "bulk_order_request", + "promotional_campaign", "seasonal_sale", "loyalty_program_enrollment" + ] + + def generate_sales_record(self) -> Dict[str, Any]: + """Generate a single NER-rich sales record""" + + # Select random product + product_line = random.choice(list(self.products.keys())) + product_info = self.products[product_line] + model = random.choice(product_info['models']) + price = random.randint(*product_info['price_range']) + + # Generate rich entities for NER extraction + customer_name = self.fake.name() + sales_rep = random.choice(self.sales_reps) + retailer = random.choice(self.retailers) + city = random.choice(self.cities) + region = random.choice(self.regions) + + # Generate realistic interaction text with rich named entities + interaction_type = random.choice(self.interaction_types) + + # Create contextual sales interaction text + interaction_texts = { + "purchase_inquiry": f"Customer {customer_name} from {city} called to inquire about purchasing the {model}. Sales rep {sales_rep} provided detailed product information and quoted ${price}. Customer is comparing with similar products at {retailer}.", + + "product_comparison": f"{sales_rep} helped {customer_name} compare the {model} against competitors. Discussed the superior noise cancellation technology and ${price} price point. Customer mentioned they saw it at {retailer} for a higher price.", + + "sales_consultation": f"Consultation session with {customer_name} in {region} region. {sales_rep} recommended the {model} based on customer's active lifestyle needs. Discussed ${price} pricing and available financing options through {retailer}.", + + "order_processing": f"Order processed for {customer_name}: 2 units of {model} at ${price} each. Shipping to {city}. Sales rep {sales_rep} confirmed delivery timeline and warranty coverage. Partner retailer: {retailer}.", + + "promotional_campaign": f"Q4 promotional campaign in {region}: {sales_rep} contacted {customer_name} about special pricing on {model}. Limited time offer at ${price - 20} (originally ${price}). Customer interested, will visit {retailer} this weekend." + } + + # Select appropriate interaction text or generate generic one + if interaction_type in interaction_texts: + interaction_text = interaction_texts[interaction_type] + else: + interaction_text = f"{sales_rep} assisted {customer_name} with {interaction_type.replace('_', ' ')} for {model}. Discussed ${price} pricing and availability at {retailer} in {city}." + + # Generate timestamp from 2016 onwards (products launched after 2015) + start_date = datetime(2016, 1, 1) + random_date = self.fake.date_time_between(start_date=start_date, end_date='now') + + return { + "id": str(uuid.uuid4()), + "timestamp": random_date.isoformat(), + "customer_name": customer_name, + "sales_representative": sales_rep, + "product_line": product_line, + "product_model": model, + "product_category": product_info['category'], + "price": price, + "retailer": retailer, + "location_city": city, + "region": region, + "interaction_type": interaction_type, + "interaction_text": interaction_text, + "text": interaction_text, # Duplicate for semantic_text field + "quarter": f"Q{random_date.month//3 + 1}", + "year": random_date.year, + "month": random_date.strftime("%B"), + "day_of_week": random_date.strftime("%A"), + "customer_segment": random.choice(["Consumer", "Business", "Education", "Government"]), + "sales_channel": random.choice(["Direct", "Retail Partner", "Online", "Phone"]), + "lead_source": random.choice(["Website", "Advertisement", "Referral", "Trade Show", "Cold Call"]), + "deal_stage": random.choice(["Prospect", "Qualified", "Proposal", "Negotiation", "Closed Won", "Closed Lost"]), + "revenue_potential": price * random.randint(1, 5), # Potential for multiple units + "customer_priority": random.choice(["High", "Medium", "Low"]), + "follow_up_required": random.choice([True, False]), + "notes": f"Additional context: Customer expressed interest in {product_line} series. {sales_rep} to follow up within 48 hours." + } + +def create_nonconsolidated_index(): + """Create the non-consolidated sales-records index with synthetic data""" + print("🔧 Creating non-consolidated sales-records index...") + es = get_elasticsearch_client() + index_name = "sales-records" + + try: + # Define mapping optimized for NER entities and search + mapping = { + "settings": { + "number_of_shards": 1, + "number_of_replicas": 1, + "analysis": { + "analyzer": { + "ner_analyzer": { + "type": "standard", + "stopwords": "_none_" # Keep all words for NER + } + } + } + }, + "mappings": { + "properties": { + "id": {"type": "keyword"}, + "timestamp": {"type": "date"}, + "customer_name": { + "type": "text", + "analyzer": "ner_analyzer", + "fields": {"keyword": {"type": "keyword"}} + }, + "sales_representative": { + "type": "text", + "analyzer": "ner_analyzer", + "fields": {"keyword": {"type": "keyword"}} + }, + "product_line": {"type": "keyword"}, + "product_model": {"type": "keyword"}, + "product_category": {"type": "keyword"}, + "price": {"type": "float"}, + "retailer": { + "type": "text", + "analyzer": "ner_analyzer", + "fields": {"keyword": {"type": "keyword"}} + }, + "location_city": { + "type": "text", + "analyzer": "ner_analyzer", + "fields": {"keyword": {"type": "keyword"}} + }, + "region": {"type": "keyword"}, + "interaction_type": {"type": "keyword"}, + "interaction_text": { + "type": "text", + "analyzer": "ner_analyzer" # Full text optimized for NER + }, + "text": { + "type": "text", + "analyzer": "ner_analyzer" # Duplicate text field for compatibility + }, + "quarter": {"type": "keyword"}, + "year": {"type": "integer"}, + "month": {"type": "keyword"}, + "day_of_week": {"type": "keyword"}, + "customer_segment": {"type": "keyword"}, + "sales_channel": {"type": "keyword"}, + "lead_source": {"type": "keyword"}, + "deal_stage": {"type": "keyword"}, + "revenue_potential": {"type": "float"}, + "customer_priority": {"type": "keyword"}, + "follow_up_required": {"type": "boolean"}, + "notes": { + "type": "text", + "analyzer": "ner_analyzer" + } + } + } + } + + # Create index + es.indices.create(index=index_name, body=mapping) + print(f" ✅ Created index: {index_name}") + + # Generate and index synthetic data + generator = BoseSalesDataGenerator() + print(" 🔄 Generating 100 synthetic sales records...") + + records = [] + for i in range(100): + records.append(generator.generate_sales_record()) + + print(f" ✅ Generated {len(records)} records") + + # Bulk index + print(" 📤 Bulk indexing records...") + actions = [] + for record in records: + action = { + "_index": index_name, + "_id": record["id"], + "_source": record + } + actions.append(action) + + success_count, failed_items = bulk( + es, + actions, + chunk_size=100, + request_timeout=60, + max_retries=3, + initial_backoff=2, + max_backoff=600 + ) + + print(f" ✅ Successfully indexed {success_count} records") + + # Refresh index to make documents searchable immediately + es.indices.refresh(index=index_name) + print(f" 🔄 Refreshed index to make documents searchable") + + return True + + except Exception as e: + print(f" ❌ Error creating non-consolidated index: {e}") + return False + +class ConsolidatedBoseSalesDataGenerator: + """Generate consolidated sales data optimized for RAG processing""" + + def __init__(self): + self.fake = Faker(['en_US']) + Faker.seed(42) # For reproducible data + + # Bose product data + self.products = { + 'SoundSport': { + 'models': ['SoundSport Free', 'SoundSport Wireless', 'SoundSport Pulse'], + 'price_range': (129, 199), + 'category': 'Sports Earbuds', + 'features': ['sweat-resistant', 'secure fit', 'wireless', 'noise isolation'] + }, + 'OpenAudio': { + 'models': ['OpenAudio Sport', 'OpenAudio Ultra', 'OpenAudio Pro'], + 'price_range': (149, 249), + 'category': 'Open-Ear Audio', + 'features': ['open-ear design', 'situational awareness', 'comfortable fit', 'premium audio'] + }, + 'QuietComfort': { + 'models': ['QuietComfort 45', 'QuietComfort Ultra', 'QuietComfort Earbuds'], + 'price_range': (199, 429), + 'category': 'Noise Cancelling', + 'features': ['world-class noise cancellation', 'premium comfort', 'long battery life', 'crystal clear calls'] + } + } + + # Rich entity data for NER + self.retailers = [ + "Best Buy", "Target", "Amazon", "Walmart", "Costco", "B&H Photo", + "Guitar Center", "Sam's Club", "Newegg", "Adorama", "Crutchfield" + ] + + self.sales_reps = [ + "Jennifer Martinez", "Michael Chen", "Sarah Johnson", "David Rodriguez", + "Emily Wilson", "Robert Taylor", "Lisa Anderson", "James Thompson", + "Maria Garcia", "Christopher Lee", "Amanda Davis", "Daniel Brown" + ] + + self.regions = [ + "Northeast", "Southeast", "Midwest", "Southwest", "West Coast", + "Pacific Northwest", "Mountain West", "Great Lakes", "Mid-Atlantic", "Gulf Coast" + ] + + self.cities = [ + "New York, NY", "Los Angeles, CA", "Chicago, IL", "Houston, TX", "Phoenix, AZ", + "Philadelphia, PA", "San Antonio, TX", "San Diego, CA", "Dallas, TX", "Austin, TX", + "Jacksonville, FL", "Fort Worth, TX", "Columbus, OH", "Charlotte, NC", "Seattle, WA", + "Denver, CO", "Washington, DC", "Boston, MA", "Nashville, TN", "Detroit, MI" + ] + + self.interaction_types = [ + "purchase_inquiry", "product_comparison", "pricing_discussion", + "sales_consultation", "order_processing", "upsell_opportunity", + "customer_preferences", "warranty_inquiry", "bulk_order_request", + "promotional_campaign", "seasonal_sale", "loyalty_program_enrollment" + ] + + def consolidate_from_source_record(self, source_record: dict) -> dict: + """Convert a source record to consolidated format""" + + # Extract data from source record + product_line = source_record['product_line'] + product_info = self.products.get(product_line, {'category': 'Unknown', 'features': []}) + + # Parse timestamp + timestamp = datetime.fromisoformat(source_record['timestamp'].replace('Z', '+00:00')) + + # Generate features based on product line + if product_line in self.products: + features = random.sample(self.products[product_line]['features'], k=random.randint(1, 3)) + else: + features = ['premium quality', 'reliable performance'] + + # Create detailed interaction text based on the original interaction + interaction_details = self._generate_interaction_text( + source_record['interaction_type'], + source_record['customer_name'], + source_record['sales_representative'], + source_record['product_model'], + source_record['price'], + source_record['retailer'], + source_record['location_city'], + features + ) + + # Create consolidated text field with ALL context + consolidated_text = f"""SALES RECORD - {timestamp.strftime('%B %d, %Y')} + +Customer Information: +- Name: {source_record['customer_name']} +- Location: {source_record['location_city']} +- Segment: {source_record['customer_segment']} +- Lead Source: {source_record['lead_source']} + +Product Details: +- Product Line: {source_record['product_line']} +- Model: {source_record['product_model']} +- Category: {source_record['product_category']} +- Price: ${source_record['price']} +- Key Features: {', '.join(features)} + +Sales Information: +- Sales Representative: {source_record['sales_representative']} +- Retailer: {source_record['retailer']} +- Region: {source_record['region']} +- Channel: {source_record['sales_channel']} +- Deal Stage: {source_record['deal_stage']} +- Interaction Type: {source_record['interaction_type'].replace('_', ' ').title()} + +Conversation Summary: +{interaction_details} + +Temporal Context: +- Date: {timestamp.strftime('%B %d, %Y')} +- Quarter: {source_record['quarter']} {source_record['year']} +- Day of Week: {source_record['day_of_week']} + +Revenue Information: +- Unit Price: ${source_record['price']} +- Potential Deal Value: ${source_record['revenue_potential']} +- Priority: {source_record['customer_priority']}""".strip() + + # Return consolidated document + return { + "id": str(uuid.uuid4()), + "timestamp": source_record['timestamp'], + "document_type": "sales_record", + "product_line": source_record['product_line'], + "region": source_record['region'], + "consolidated_text": consolidated_text, + "record_date": timestamp.strftime('%Y-%m-%d'), + "quarter": source_record['quarter'], + "year": source_record['year'] + } + + def _generate_interaction_text(self, interaction_type, customer_name, sales_rep, model, price, retailer, city, features): + """Generate detailed interaction text based on type""" + + interaction_templates = { + "purchase_inquiry": f"Customer {customer_name} from {city} contacted {sales_rep} to inquire about purchasing the {model}. The customer was particularly interested in the {', '.join(features[:2])} features. {sales_rep} provided detailed product specifications and quoted ${price}. Customer mentioned they had seen similar products at {retailer} but was impressed with the Bose quality and features.", + + "product_comparison": f"{sales_rep} conducted a comprehensive product comparison session with {customer_name}. The customer was deciding between the {model} and competitor products. Key selling points discussed included {', '.join(features)} which differentiate Bose from competitors. The ${price} price point was justified through superior audio quality and build reliability. Customer appreciated the detailed comparison and is considering the purchase.", + + "sales_consultation": f"In-depth consultation with {customer_name} in the {city} area. {sales_rep} assessed customer needs and recommended the {model} based on their lifestyle and audio preferences. Highlighted features included {', '.join(features)}. Discussed ${price} pricing structure and available financing options. Customer showed strong interest and requested follow-up information.", + + "order_processing": f"Order successfully processed for {customer_name}: {random.randint(1, 3)} units of {model} at ${price} each. {sales_rep} confirmed shipping details to {city} and explained warranty coverage. Customer opted for expedited shipping and was provided with tracking information. Partnership with {retailer} ensured competitive pricing and reliable delivery.", + + "promotional_campaign": f"Q{random.randint(1,4)} promotional outreach to {customer_name}. {sales_rep} presented special pricing on {model} - limited time offer at ${price - random.randint(10, 30)} (regularly ${price}). Emphasized exclusive features: {', '.join(features)}. Customer expressed interest and plans to visit {retailer} location this weekend to experience the product firsthand.", + } + + return interaction_templates.get( + interaction_type, + f"{sales_rep} assisted {customer_name} with {interaction_type.replace('_', ' ')} regarding {model}. Discussed ${price} pricing and key features: {', '.join(features)}. Customer interaction was positive and follow-up scheduled." + ) + +def create_consolidated_index(): + """Pull from sales-records and create consolidated sales-records-consolidated index""" + print("🔧 Creating consolidated sales-records-consolidated index...") + es = get_elasticsearch_client() + source_index = "sales-records" + target_index = "sales-records-consolidated" + + try: + # Check if source index exists + if not es.indices.exists(index=source_index): + print(f" ❌ Source index {source_index} does not exist") + return False + + # Create target index mapping + mapping = { + "settings": { + "number_of_shards": 1, + "number_of_replicas": 1 + }, + "mappings": { + "properties": { + "id": {"type": "keyword"}, + "timestamp": {"type": "date"}, + "document_type": {"type": "keyword"}, + "product_line": {"type": "keyword"}, + "region": {"type": "keyword"}, + "consolidated_text": { + "type": "text", + "analyzer": "standard" # This is the field Unstructured will process + }, + "record_date": {"type": "date"}, + "quarter": {"type": "keyword"}, + "year": {"type": "integer"} + } + } + } + + es.indices.create(index=target_index, body=mapping) + print(f" ✅ Created consolidated index: {target_index}") + + # Fetch all records from source index + print(" 🔄 Fetching records from source index...") + response = es.search( + index=source_index, + body={ + "query": {"match_all": {}}, + "size": 1000 # Adjust based on expected data size + } + ) + + source_records = [hit['_source'] for hit in response['hits']['hits']] + print(f" 📥 Retrieved {len(source_records)} records from {source_index}") + + # Convert to consolidated format + generator = ConsolidatedBoseSalesDataGenerator() + consolidated_records = [] + + for record in source_records: + consolidated_record = generator.consolidate_from_source_record(record) + consolidated_records.append(consolidated_record) + + print(f" 🔄 Converted {len(consolidated_records)} records to consolidated format") + + # Bulk index consolidated records + print(" 📤 Bulk indexing consolidated records...") + actions = [] + for record in consolidated_records: + actions.append({ + "_index": target_index, + "_id": record["id"], + "_source": record + }) + + success_count, failed_items = bulk(es, actions, chunk_size=50) + print(f" ✅ Successfully indexed {success_count} consolidated records") + + # Refresh the target index to make documents searchable + es.indices.refresh(index=target_index) + print(f" 🔄 Refreshed consolidated index to make documents searchable") + + # Verify results + count_response = es.count(index=target_index) + total_docs = count_response['count'] + print(f" 📊 Total documents in consolidated index: {total_docs}") + + return True + + except Exception as e: + print(f" ❌ Error creating consolidated index: {e}") + return False + +def run_elasticsearch_preprocessing(): + """Run all Elasticsearch preprocessing steps""" + print("🚀 Starting Elasticsearch preprocessing...") + print("=" * 60) + + # Step 1: Delete existing indices + delete_elasticsearch_indices() + + # Step 2: Create non-consolidated index with synthetic data + if not create_nonconsolidated_index(): + print("❌ Failed to create non-consolidated index") + return False + + # Step 3: Create consolidated index from the non-consolidated data + # Step 3: Create consolidated index from the non-consolidated data + if not create_consolidated_index(): + print("❌ Failed to create consolidated index") + return False + + # Step 4: Create customer-support index + if not create_customer_support_index(): + print("❌ Failed to create customer-support index") + return False + print(f"✅ Pipeline will now use consolidated index: {ELASTICSEARCH_INDEX}") + return True + +def download_index_locally(index_name, output_file="index_data.json"): + """Download all documents from an Elasticsearch index to a local JSON file""" + print(f"📥 Downloading index '{index_name}' to local file...") + es = get_elasticsearch_client() + + try: + # Check if index exists + if not es.indices.exists(index=index_name): + print(f" ❌ Index {index_name} does not exist") + return False + + # Get all documents using scroll API for large datasets + print(" 🔄 Fetching documents using scroll API...") + + documents = [] + scroll_response = es.search( + index=index_name, + body={ + "query": {"match_all": {}}, + "size": 1000 # Batch size + }, + scroll='5m' # Keep scroll context alive for 5 minutes + ) + + scroll_id = scroll_response['_scroll_id'] + documents.extend([hit['_source'] for hit in scroll_response['hits']['hits']]) + + # Continue scrolling until no more documents + while len(scroll_response['hits']['hits']) > 0: + scroll_response = es.scroll( + scroll_id=scroll_id, + scroll='5m' + ) + documents.extend([hit['_source'] for hit in scroll_response['hits']['hits']]) + + # Clear the scroll context + es.clear_scroll(scroll_id=scroll_id) + + # Save to local file + with open(output_file, 'w', encoding='utf-8') as f: + json.dump({ + "index_name": index_name, + "document_count": len(documents), + "documents": documents + }, f, indent=2, ensure_ascii=False) + + print(f" ✅ Downloaded {len(documents)} documents to {output_file}") + return True + + except Exception as e: + print(f" ❌ Error downloading index: {e}") + return False + +def upload_index_from_file(input_file, new_index_name, mapping=None): + """Create a new index and upload documents from a local JSON file""" + print(f"📤 Creating new index '{new_index_name}' from local file...") + es = get_elasticsearch_client() + + try: + # Load data from file + with open(input_file, 'r', encoding='utf-8') as f: + data = json.load(f) + + documents = data.get('documents', []) + original_count = data.get('document_count', len(documents)) + + print(f" 📄 Loaded {len(documents)} documents from {input_file}") + + # Delete index if it exists + if es.indices.exists(index=new_index_name): + print(f" 🗑️ Deleting existing index: {new_index_name}") + es.indices.delete(index=new_index_name) + + # Create index with mapping + if mapping: + print(f" 🔧 Creating index with custom mapping...") + es.indices.create(index=new_index_name, body=mapping) + else: + print(f" 🔧 Creating index with default mapping...") + # Use a basic mapping similar to the consolidated index + default_mapping = { + "settings": { + "number_of_shards": 1, + "number_of_replicas": 1 + }, + "mappings": { + "properties": { + "id": {"type": "keyword"}, + "timestamp": {"type": "date"}, + "document_type": {"type": "keyword"}, + "consolidated_text": { + "type": "text", + "analyzer": "standard" + } + } + } + } + es.indices.create(index=new_index_name, body=default_mapping) + + # Bulk index documents + print(f" 📤 Bulk indexing {len(documents)} documents...") + actions = [] + for i, doc in enumerate(documents): + actions.append({ + "_index": new_index_name, + "_id": doc.get('id', str(uuid.uuid4())), + "_source": doc + }) + + success_count, failed_items = bulk(es, actions, chunk_size=100) + + # Refresh index + es.indices.refresh(index=new_index_name) + + # Verify upload + count_response = es.count(index=new_index_name) + final_count = count_response['count'] + + print(f" ✅ Successfully uploaded {success_count} documents") + print(f" 📊 Final document count in {new_index_name}: {final_count}") + + if final_count == original_count: + print(f" ✅ Upload verification successful!") + else: + print(f" ⚠️ Document count mismatch: expected {original_count}, got {final_count}") + + return True + + except Exception as e: + print(f" ❌ Error uploading index: {e}") + return False + +def backup_and_restore_index(source_index, target_index, backup_file="index_backup.json"): + """Complete backup and restore workflow""" + print(f"🔄 Backing up '{source_index}' and restoring to '{target_index}'...") + + # Step 1: Download source index + if not download_index_locally(source_index, backup_file): + return False + + # Step 2: Upload to target index + if not upload_index_from_file(backup_file, target_index): + return False + + print(f"✅ Successfully backed up and restored index!") + return True + +def create_customer_support_index(): + """Create the customer-support index for storing customer service interactions""" + print("🔧 Creating customer-support index...") + es = get_elasticsearch_client() + index_name = "customer-support" + + try: + # Define mapping for customer support data + mapping = { + "settings": { + "number_of_shards": 1, + "number_of_replicas": 1, + "analysis": { + "analyzer": { + "support_analyzer": { + "type": "standard", + "stopwords": "_none_" # Keep all words for better search + } + } + } + }, + "mappings": { + "properties": { + "id": {"type": "keyword"}, + "timestamp": {"type": "date"}, + "customer_id": {"type": "keyword"}, + "customer_name": { + "type": "text", + "analyzer": "support_analyzer", + "fields": {"keyword": {"type": "keyword"}} + }, + "support_agent": { + "type": "text", + "analyzer": "support_analyzer", + "fields": {"keyword": {"type": "keyword"}} + }, + "issue_type": {"type": "keyword"}, + "priority": {"type": "keyword"}, + "status": {"type": "keyword"}, + "product_line": {"type": "keyword"}, + "product_model": {"type": "keyword"}, + "conversation_text": { + "type": "text", + "analyzer": "support_analyzer" + }, + "resolution": { + "type": "text", + "analyzer": "support_analyzer" + }, + "satisfaction_rating": {"type": "integer"}, + "follow_up_required": {"type": "boolean"}, + "tags": {"type": "keyword"}, + "escalated": {"type": "boolean"}, + "resolution_time_hours": {"type": "float"} + } + } + } + + # Check if index exists before creating + if es.indices.exists(index=index_name): + print(f" ℹ️ Index {index_name} already exists, skipping creation") + else: + es.indices.create(index=index_name, body=mapping) + print(f" ✅ Created index: {index_name}") + # Refresh index to make it immediately available + es.indices.refresh(index=index_name) + print(f" 🔄 Refreshed index to make it searchable") + + return True + + except Exception as e: + print(f" ❌ Error creating customer-support index: {e}") + return False + + """Create the customer-support index for storing customer service interactions""" + print("🔧 Creating customer-support index...") + es = get_elasticsearch_client() + index_name = "customer-support" + + try: + # Define mapping for customer support data + mapping = { + "settings": { + "number_of_shards": 1, + "number_of_replicas": 1, + "analysis": { + "analyzer": { + "support_analyzer": { + "type": "standard", + "stopwords": "_none_" # Keep all words for better search + } + } + } + }, + "mappings": { + "properties": { + "id": {"type": "keyword"}, + "timestamp": {"type": "date"}, + "customer_id": {"type": "keyword"}, + "customer_name": { + "type": "text", + "analyzer": "support_analyzer", + "fields": {"keyword": {"type": "keyword"}} + }, + "support_agent": { + "type": "text", + "analyzer": "support_analyzer", + "fields": {"keyword": {"type": "keyword"}} + }, + "issue_type": {"type": "keyword"}, + "priority": {"type": "keyword"}, + "status": {"type": "keyword"}, + "product_line": {"type": "keyword"}, + "product_model": {"type": "keyword"}, + "conversation_text": { + "type": "text", + "analyzer": "support_analyzer" + }, + "resolution": { + "type": "text", + "analyzer": "support_analyzer" + }, + "satisfaction_rating": {"type": "integer"}, + "follow_up_required": {"type": "boolean"}, + "tags": {"type": "keyword"}, + "escalated": {"type": "boolean"}, + "resolution_time_hours": {"type": "float"} + } + } + } + + # Create index + es.indices.create(index=index_name, body=mapping) + print(f" ✅ Created index: {index_name}") + + # Refresh index to make it immediately available + es.indices.refresh(index=index_name) + print(f" 🔄 Refreshed index to make it searchable") + + return True + + except Exception as e: + print(f" ❌ Error creating customer-support index: {e}") + return False + +if __name__ == "__main__": + # Example usage + print("🔧 Elasticsearch Index Preprocessing Tool") + print("=" * 50) + + # Run the standard preprocessing + if run_elasticsearch_preprocessing(): + print("\n" + "=" * 50) + print("✅ Standard preprocessing completed!") + + # Example: Download the consolidated index + print("\n📥 Example: Downloading consolidated index...") + download_index_locally("sales-records-consolidated", "consolidated_backup.json") + + # Example: Create a copy of the index + print("\n📤 Example: Creating index copy...") + upload_index_from_file("consolidated_backup.json", "sales-records-consolidated-copy") + + print("\n✅ All operations completed!") + else: + print("❌ Standard preprocessing failed!") diff --git a/feedback_r1_cleaned.md b/feedback_r1_cleaned.md new file mode 100644 index 0000000..eddea6a --- /dev/null +++ b/feedback_r1_cleaned.md @@ -0,0 +1,84 @@ +# Hybrid RAG Pipeline Notebook Feedback - Action Items + +## ✅ COMPLETED ITEMS + +### Story & Engagement +- [x] **Business Relevance**: Add compelling introduction explaining why enterprises need to unify data across formats +- [x] **Value Proposition**: Explain the complexity of the problem and how Unstructured solves it +- [x] **Tutorial Style**: Transform from documentation to engaging tutorial format + +### Content Organization +- [x] **Remove Redundancy**: Eliminate repeated explanations (S3 setup, URL formats, env variables) +- [x] **Consolidate Methods**: Stick to one method for env setup and dependency management instead of multiple options + +## 🔄 PENDING ITEMS + +### Google Colab Compatibility +**Instructions**: Orient notebook towards Google Colab users. Create a space at the top of environment setup for users to paste their environment variables, followed by a concise dotenv section where .env file values (if available) overwrite the pasted defaults. + +- [ ] **Environment Setup**: Create Colab-friendly environment variable input section with dotenv fallback +- [ ] **Dependency Installation**: Remove duplicate `ensure_notebook_deps()` calls +- [ ] **Configuration Order**: Move environment configuration steps BEFORE `load_dotenv()` call + +### Content Focus & Clarity +- [ ] **Remove Error Explanations**: Remove error explanations from markdown text +- [ ] **Reduce Verbosity**: Remove bloated sections that don't add to the core use case +- [ ] **Single Env Method**: Remove multiple environment variable setup methods +- [ ] **Streamline S3 Setup**: Remove bucket creation code - assume users have existing bucket with clear note that S3 source connector documentation has setup details +- [ ] **NER Context**: Explain why NER node is relevant to the use case + +### Technical Corrections +- [ ] **Node Naming**: Fix "Chunking Node" → "Chunker Node", "Embedding Node" → "Embedder Node" in markdown annotations +- [ ] **Unstructured Value**: Clearly articulate what value Unstructured delivers - simple 1-2 sentences with clear benefits, no marketing tone + +### RAG Implementation & Results +- [ ] **Query Functionality**: Add section that queries the final index and returns sample results using LangChain (will require OpenAI API key) +- [ ] **Source Attribution**: Show that results come from both S3 and Elasticsearch sources +- [ ] **RAG Discussion**: Explain how this unified index powers RAG applications +- [ ] **Cell Outputs**: Preserve notebook cell outputs for illustration purposes + +### Conclusion & Next Steps +- [ ] **Learning Summary**: Add "What we learned" section +- [ ] **Achievement Summary**: Highlight what was accomplished +- [ ] **Call to Action**: Provide clear next steps for readers + +## 📋 DETAILED ACTION ITEMS + +### 1. Google Colab Environment Setup +**Issue**: Notebook assumes local development environment +**Actions**: +- Create environment variable input section for Colab users +- Add concise dotenv fallback without excessive annotation +- Ensure all dependencies install properly in Colab + +### 2. RAG Implementation +**Issue**: No actual RAG querying demonstrated +**Actions**: +- Add LangChain-based query examples against the unified index +- Show mixed results from both data sources +- Explain how this enables hybrid RAG applications +- Request OpenAI API key for RAG functionality + +### 3. Content Streamlining +**Issue**: Too verbose with redundant sections +**Actions**: +- Remove duplicate environment setup explanations +- Eliminate bucket creation code (assume existing bucket) +- Remove error explanations from markdown +- Focus on core value proposition + +### 4. Technical Polish +**Issue**: Minor technical and naming inconsistencies +**Actions**: +- Fix node naming conventions in markdown +- Remove duplicate function calls +- Preserve meaningful cell outputs +- Add clear, non-marketing Unstructured value statements + +## 🎯 SUCCESS CRITERIA + +- [ ] Notebook runs successfully in Google Colab +- [ ] Actual RAG querying with mixed-source results +- [ ] Streamlined content focused on core value +- [ ] Compelling story that teaches and engages +- [ ] Clear environment setup for Colab users \ No newline at end of file diff --git a/hybrid_rag_pipeline_enriched.ipynb b/hybrid_rag_pipeline_enriched.ipynb new file mode 100644 index 0000000..32eae19 --- /dev/null +++ b/hybrid_rag_pipeline_enriched.ipynb @@ -0,0 +1,1759 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f5506c9b", + "metadata": {}, + "source": [ + "# Building a Hybrid RAG System: From Fragmented Data to Unified Intelligence\n", + "\n", + "Picture this: You're a customer support agent, and a customer calls about a product issue. To help them effectively, you need to pull information from multiple sources - product manuals stored as PDFs in cloud storage, their purchase history in your sales database, and previous support interactions scattered across different systems. Each piece of information lives in a different format, in a different system, with different access methods.\n", + "\n", + "This is the reality for most enterprises today, and it's exactly the challenge we're going to solve together.\n", + "\n", + "## The Enterprise Data Challenge\n", + "\n", + "Enterprise data rarely lives in one place or format. Critical information is fragmented across unstructured documents like PDFs and manuals in cloud storage, structured records like sales data in databases, and different formats requiring different processing approaches. Traditional RAG systems work well with homogeneous data but struggle when you need to query across diverse data sources simultaneously.\n", + "\n", + "## Why This Matters\n", + "\n", + "When data is scattered, customer support becomes inefficient, decision-making lacks context, and valuable insights remain hidden. A customer asking about a product issue shouldn't require you to manually search through multiple systems to piece together a complete picture.\n", + "\n", + "## The Solution: Unstructured's Complete Gen AI Data Layer\n", + "\n", + "Unstructured isn't just another data processing tool—it's a complete Gen AI data layer solution that transforms how organizations handle unstructured data at scale. Unlike building custom solutions or using fragmented tools, Unstructured provides a unified platform that connects to 30+ data sources, processes 65+ file types with intelligent partitioning and chunking, automatically enriches content with metadata and context, and delivers to 30+ destinations—all while maintaining enterprise-grade security and compliance.\n", + "\n", + "The platform eliminates the complexity of managing multiple tools, custom integrations, and manual data preparation, allowing teams to focus on building AI applications rather than wrestling with data infrastructure. With flexible deployment options from SaaS to bare metal, Unstructured adapts to any infrastructure while providing the observability, automation, and reliability that enterprise AI projects demand.\n", + "\n", + "## What We'll Build Together\n", + "\n", + "In this tutorial, we'll create a hybrid RAG system that processes two different data sources simultaneously: product documentation from S3 and sales records from Elasticsearch. Both will flow through the same intelligent processing pipeline and land in a unified, searchable knowledge base.\n", + "\n", + "```\n", + "┌─────────────────┐ ┌─────────────────────────┐\n", + "│ S3 PDFs │──── WORKFLOW 1 ──────────▶│ │\n", + "│ (Product Docs) │ │ Unstructured API │\n", + "└─────────────────┘ │ │\n", + " │ Partition → Chunk → │\n", + "┌─────────────────┐ │ Embed → NER → Store │\n", + "│ Elasticsearch │──── WORKFLOW 2 ──────────▶│ │\n", + "│ (Sales Records) │ │ │\n", + "└─────────────────┘ └────────────┬────────────┘\n", + " │\n", + " ┌────────────▼────────────┐\n", + " │ customer-support │\n", + " │ (Unified Index) │\n", + " └─────────────────────────┘\n", + "```\n", + "\n", + "By the end of this tutorial, you'll have a working system that can answer complex questions by pulling information from both your product documentation and customer data simultaneously." + ] + }, + { + "cell_type": "markdown", + "id": "0d9ec036", + "metadata": {}, + "source": [ + "## Getting Started: Your Unstructured API Key\n", + "\n", + "To follow along with this tutorial, you'll need an Unstructured API key. This gives you access to the complete Gen AI data layer that will process your documents and create your unified knowledge base.\n", + "\n", + "### Sign Up and Get Your API Key\n", + "\n", + "Visit https://platform.unstructured.io to sign up for a free account, navigate to API Keys in the sidebar, generate your API key, and save it for the configuration step below. For Team or Enterprise accounts, make sure you've selected the correct organizational workspace before creating your API key.\n", + "\n", + "**Need help?** Contact Unstructured Support at support@unstructured.io" + ] + }, + { + "cell_type": "markdown", + "id": "dbc5dd57", + "metadata": {}, + "source": [ + "## Configuration: Setting Up Your Environment\n", + "\n", + "Now we'll configure your environment with the necessary API keys and credentials. This step ensures your system can connect to all the data sources and services we'll be using." + ] + }, + { + "cell_type": "markdown", + "id": "7eb8befe", + "metadata": {}, + "source": [ + "### Creating a .env File in Google Colab\n", + "\n", + "For better security and organization, we'll create a `.env` file directly in your Colab environment. Run the code cell below to create the file with placeholder values, then edit it with your actual credentials.\n", + "\n", + "After running the code cell, you'll need to replace each placeholder value (like `your-unstructured-api-key`) with your actual API keys and credentials." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7486a269", + "metadata": {}, + "outputs": [], + "source": [ + "def create_dotenv_file():\n", + " \"\"\"Create a .env file with placeholder values for the user to fill in.\"\"\"\n", + " env_content = \"\"\"# Hybrid RAG Pipeline Environment Configuration\n", + "# Fill in your actual values below\n", + "# Configuration - Set these explicitly\n", + "\n", + "# ===================================================================\n", + "# AWS CONFIGURATION\n", + "# ===================================================================\n", + "AWS_ACCESS_KEY_ID=\"your-aws-access-key-id\"\n", + "AWS_SECRET_ACCESS_KEY=\"your-aws-secret-access-key\"\n", + "AWS_REGION=\"us-east-1\"\n", + "\n", + "# ===================================================================\n", + "# UNSTRUCTURED API CONFIGURATION \n", + "# ===================================================================\n", + "UNSTRUCTURED_API_KEY=\"your-unstructured-api-key\"\n", + "UNSTRUCTURED_API_URL=\"https://platform.unstructuredapp.io/api/v1\"\n", + "\n", + "# ===================================================================\n", + "# ELASTICSEARCH CONFIGURATION\n", + "# ===================================================================\n", + "ELASTICSEARCH_HOST=\"https://your-cluster.es.io:443\"\n", + "ELASTICSEARCH_API_KEY=\"your-elasticsearch-api-key\"\n", + "\n", + "# ===================================================================\n", + "# PIPELINE DATA SOURCES\n", + "# ===================================================================\n", + "S3_SOURCE_BUCKET=\"your-s3-source-bucket\"\n", + "S3_DESTINATION_BUCKET=\"your-s3-destination-bucket\"\n", + "S3_OUTPUT_PREFIX=\"\"\n", + "ELASTICSEARCH_INDEX=\"sales-records-consolidated\"\n", + "\n", + "# ===================================================================\n", + "# OPENAI API CONFIGURATION \n", + "# ===================================================================\n", + "OPENAI_API_KEY=\"your-openai-api-key\"\n", + "\"\"\"\n", + " \n", + " with open('.env', 'w') as f:\n", + " f.write(env_content)\n", + " \n", + " print(\"✅ Created .env file with placeholder values\")\n", + " print(\"📝 Please edit the .env file and replace the placeholder values with your actual credentials\")\n", + " print(\"🔒 The .env file will be loaded automatically by the pipeline\")\n", + "\n", + "# Create the .env file\n", + "create_dotenv_file()" + ] + }, + { + "cell_type": "markdown", + "id": "bcb2bc85", + "metadata": {}, + "source": [ + "### Installing Required Dependencies\n", + "\n", + "The following code installs the Python packages needed for this tutorial: the Unstructured client, Elasticsearch connector, AWS SDK, and other dependencies." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2d1140e1", + "metadata": { + "lines_to_next_cell": 1 + }, + "outputs": [], + "source": [ + "import sys, subprocess\n", + "\n", + "def ensure_notebook_deps() -> None:\n", + " packages = [\n", + " \"jupytext\",\n", + " \"python-dotenv\", \n", + " \"unstructured-client\",\n", + " \"elasticsearch\",\n", + " \"boto3\",\n", + " \"PyYAML\",\n", + " \"langchain\",\n", + " \"langchain-elasticsearch\",\n", + " \"langchain-openai\"\n", + " ]\n", + " try:\n", + " subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", *packages])\n", + " except Exception:\n", + " # If install fails, continue; imports below will surface actionable errors\n", + " pass\n", + "\n", + "# Install notebook dependencies (safe no-op if present)\n", + "ensure_notebook_deps()\n", + "\n", + "import os\n", + "import time\n", + "import json\n", + "import zipfile\n", + "import tempfile\n", + "import requests\n", + "from pathlib import Path\n", + "from dotenv import load_dotenv\n", + "from urllib.parse import urlparse\n", + "\n", + "import boto3\n", + "from botocore.exceptions import ClientError, NoCredentialsError\n", + "from elasticsearch import Elasticsearch\n", + "from elasticsearch.helpers import bulk\n", + "\n", + "from unstructured_client import UnstructuredClient\n", + "from unstructured_client.models.operations import (\n", + " CreateSourceRequest,\n", + " CreateDestinationRequest,\n", + " CreateWorkflowRequest\n", + ")\n", + "from unstructured_client.models.shared import (\n", + " CreateSourceConnector,\n", + " CreateDestinationConnector,\n", + " WorkflowNode,\n", + " WorkflowType,\n", + " CreateWorkflow\n", + ")\n", + "\n", + "# =============================================================================\n", + "# ENVIRONMENT CONFIGURATION\n", + "# =============================================================================\n", + "# Load from .env file if it exists\n", + "load_dotenv()\n", + "\n", + "# Configuration constants\n", + "SKIPPED = \"SKIPPED\"\n", + "UNSTRUCTURED_API_URL = os.getenv(\"UNSTRUCTURED_API_URL\", \"https://platform.unstructuredapp.io/api/v1\")\n", + "\n", + "# Get environment variables\n", + "UNSTRUCTURED_API_KEY = os.getenv(\"UNSTRUCTURED_API_KEY\")\n", + "AWS_ACCESS_KEY_ID = os.getenv(\"AWS_ACCESS_KEY_ID\")\n", + "AWS_SECRET_ACCESS_KEY = os.getenv(\"AWS_SECRET_ACCESS_KEY\")\n", + "AWS_REGION = os.getenv(\"AWS_REGION\", \"us-east-1\")\n", + "S3_SOURCE_BUCKET = os.getenv(\"S3_SOURCE_BUCKET\")\n", + "S3_DESTINATION_BUCKET = os.getenv(\"S3_DESTINATION_BUCKET\")\n", + "S3_OUTPUT_PREFIX = os.getenv(\"S3_OUTPUT_PREFIX\", \"\")\n", + "ELASTICSEARCH_HOST = os.getenv(\"ELASTICSEARCH_HOST\")\n", + "ELASTICSEARCH_API_KEY = os.getenv(\"ELASTICSEARCH_API_KEY\")\n", + "ELASTICSEARCH_INDEX = os.getenv(\"ELASTICSEARCH_INDEX\", \"sales-records-consolidated\")\n", + "OPENAI_API_KEY = os.getenv(\"OPENAI_API_KEY\")\n", + "\n", + "# Validation\n", + "REQUIRED_VARS = {\n", + " \"UNSTRUCTURED_API_KEY\": UNSTRUCTURED_API_KEY,\n", + " \"AWS_ACCESS_KEY_ID\": AWS_ACCESS_KEY_ID,\n", + " \"AWS_SECRET_ACCESS_KEY\": AWS_SECRET_ACCESS_KEY,\n", + " \"ELASTICSEARCH_HOST\": ELASTICSEARCH_HOST,\n", + " \"ELASTICSEARCH_API_KEY\": ELASTICSEARCH_API_KEY,\n", + " \"S3_SOURCE_BUCKET\": S3_SOURCE_BUCKET,\n", + "}\n", + "\n", + "missing_vars = [key for key, value in REQUIRED_VARS.items() if not value]\n", + "if missing_vars:\n", + " print(f\"❌ Missing required environment variables: {', '.join(missing_vars)}\")\n", + " print(\"Please set these environment variables or create a .env file with your credentials.\")\n", + " raise ValueError(f\"Missing required environment variables: {missing_vars}\")\n", + "\n", + "print(\"✅ Configuration loaded successfully\")" + ] + }, + { + "cell_type": "markdown", + "id": "42e66bab", + "metadata": {}, + "source": [ + "## AWS S3: Your Document Storage\n", + "\n", + "Now that we have our environment configured, let's set up the data sources for our hybrid RAG system. First up: your unstructured documents. These PDFs, manuals, and reports need to be accessible via S3, where your product documentation and other unstructured content lives, waiting to be processed into searchable knowledge.\n", + "\n", + "### What You Need\n", + "\n", + "**An existing S3 bucket** containing the documents you want to process. For this tutorial, we'll use sample product manuals, but in production, this would be your actual business documents.\n", + "\n", + "> **Note**: This tutorial assumes you have an existing S3 bucket with documents. For detailed S3 setup instructions, see the [Unstructured S3 source connector documentation](https://docs.unstructured.io/api-reference/api-services/source-connectors/s3).\n", + "\n", + "You'll need an AWS account with S3 access, an IAM user with S3 read permissions for your bucket, and access keys (Access Key ID and Secret Access Key)." + ] + }, + { + "cell_type": "markdown", + "id": "f6faf583", + "metadata": {}, + "source": [ + "## Elasticsearch: Your Business Data Hub\n", + "\n", + "While S3 holds your unstructured documents, Elasticsearch serves a dual purpose in our pipeline. It's both a source of structured business data (your sales records, customer information) and the destination where our unified, processed results will be stored for RAG queries.\n", + "\n", + "### What You Need\n", + "\n", + "**Elasticsearch cluster** with API key authentication from Elastic Cloud (managed service). This gives you the reliability and scalability needed for enterprise applications.\n", + "\n", + "The pipeline uses two indices: `sales-records-consolidated` as the source containing your business data, and `customer-support` as the destination for your unified knowledge base. Both are created automatically by the pipeline.\n", + "\n", + "### Why Consolidated Data Format Matters\n", + "\n", + "Traditional databases store information in separate fields (customer_name, product_id, purchase_date). For RAG applications, we consolidate this into a long-form text field that provides full context in each search result. This approach ensures that when someone searches for \"John's headphone purchase,\" they get the complete story in one result.\n", + "\n", + "Example transformation:\n", + "```\n", + "Before: {customer: \"John Doe\", product: \"BH-001\", date: \"2024-01-15\"}\n", + "After: \"customer: John Doe\\nproduct: BH-001\\ndate: 2024-01-15\"\n", + "```\n", + "\n", + "### API Key Permissions\n", + "\n", + "Your Elasticsearch API key needs these permissions:\n", + "\n", + "```json\n", + "{\n", + " \"sales-records-full-access\": {\n", + " \"cluster\": [],\n", + " \"indices\": [\n", + " {\n", + " \"names\": [\n", + " \"sales-records\",\n", + " \"sales-records-consolidated\",\n", + " \"customer-support\"\n", + " ],\n", + " \"privileges\": [\n", + " \"create_index\",\n", + " \"delete_index\",\n", + " \"manage\",\n", + " \"write\",\n", + " \"read\",\n", + " \"view_index_metadata\",\n", + " \"monitor\"\n", + " ],\n", + " \"allow_restricted_indices\": false\n", + " }\n", + " ],\n", + " \"applications\": [],\n", + " \"run_as\": [],\n", + " \"metadata\": {},\n", + " \"transient_metadata\": {\n", + " \"enabled\": true\n", + " }\n", + " }\n", + "}\n", + "```\n", + "\n", + "**Don't have Elasticsearch data yet?** The pipeline includes automatic data setup that creates sample sales records for demonstration. This is done by downloading .ZIP files from github and unzipping them." + ] + }, + { + "cell_type": "markdown", + "id": "9ff47e68", + "metadata": {}, + "source": [ + "## Data Preparation: Setting Up Your Demo Environment\n", + "\n", + "With our infrastructure configured, let's prepare the actual data that will flow through our hybrid RAG system. For this demonstration, we've created realistic sample data that represents a typical enterprise scenario, giving you a working example without requiring you to set up your own data sources first.\n", + "\n", + "**Elasticsearch Sales Data**: 100 synthetic sales records with customer information, with consolidated fields optimized for vector search. This represents the kind of structured business data you'd find in any enterprise system.\n", + "\n", + "**S3 Product Documentation**: 9 product manuals downloaded from manufacturer websites and stored in your S3 bucket. These represent the unstructured documents that contain critical product information.\n", + "\n", + "This combination mimics real enterprise scenarios where structured data (sales records) and unstructured documents (manuals) need to be searchable together for effective customer support. The magic happens when we can answer questions like \"What issues have customers reported with the BH-900 headphones?\" by pulling from both the sales records and the product manual simultaneously." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12a46ede", + "metadata": { + "lines_to_next_cell": 1 + }, + "outputs": [], + "source": [ + "# Data preparation functions\n", + "\n", + "def download_file(url: str, local_path: str) -> bool:\n", + " \"\"\"Download a file from URL to local path.\"\"\"\n", + " try:\n", + " print(f\"📥 Downloading {url}...\")\n", + " response = requests.get(url, stream=True)\n", + " response.raise_for_status()\n", + " \n", + " Path(local_path).parent.mkdir(parents=True, exist_ok=True)\n", + " \n", + " with open(local_path, 'wb') as f:\n", + " for chunk in response.iter_content(chunk_size=8192):\n", + " f.write(chunk)\n", + " \n", + " print(f\"✅ Downloaded to {local_path}\")\n", + " return True\n", + " \n", + " except Exception as e:\n", + " print(f\"❌ Error downloading {url}: {e}\")\n", + " return False\n", + "\n", + "def setup_elasticsearch_data():\n", + " \"\"\"Download and load sales data into Elasticsearch index.\"\"\"\n", + " print(\"🔧 Setting up Elasticsearch sales data...\")\n", + " \n", + " try:\n", + " es = Elasticsearch(\n", + " ELASTICSEARCH_HOST,\n", + " api_key=ELASTICSEARCH_API_KEY,\n", + " request_timeout=60,\n", + " max_retries=3,\n", + " retry_on_timeout=True\n", + " )\n", + " \n", + " index_name = \"sales-records-consolidated\"\n", + " \n", + " sales_data_url = \"https://github.com/Unstructured-IO/rag-over-hybrid-data-sources/raw/feature/hybrid-rag-pipeline/source_data/sales_records_consolidated.zip\"\n", + " \n", + " with tempfile.NamedTemporaryFile(suffix='.zip', delete=False) as tmp_file:\n", + " if not download_file(sales_data_url, tmp_file.name):\n", + " return False\n", + " \n", + " with zipfile.ZipFile(tmp_file.name, 'r') as zipf:\n", + " # Load mapping\n", + " with zipf.open('mapping.json') as f:\n", + " mapping_data = json.loads(f.read().decode('utf-8'))\n", + " \n", + " # Load documents\n", + " with zipf.open('documents.json') as f:\n", + " documents = json.loads(f.read().decode('utf-8'))\n", + " \n", + " # Always delete existing index if present and reload from zip\n", + " if es.indices.exists(index=index_name):\n", + " print(f\"🗑️ Deleting existing index '{index_name}' to reload fresh data...\")\n", + " es.indices.delete(index=index_name)\n", + " \n", + " # Create index with mapping\n", + " index_mapping = mapping_data[index_name] if index_name in mapping_data else mapping_data[list(mapping_data.keys())[0]]\n", + " es.indices.create(index=index_name, body=index_mapping)\n", + " print(f\"🔧 Created index '{index_name}' with mapping\")\n", + " \n", + " # Prepare documents for bulk insert\n", + " def generate_docs():\n", + " for doc in documents:\n", + " yield {\n", + " \"_index\": index_name,\n", + " \"_id\": doc[\"_id\"],\n", + " \"_source\": doc[\"_source\"]\n", + " }\n", + " \n", + " # Bulk insert documents\n", + " success_count, failed_items = bulk(es, generate_docs(), chunk_size=100)\n", + " print(f\"📝 Inserted {success_count} documents\")\n", + " \n", + " # Refresh index and verify\n", + " es.indices.refresh(index=index_name)\n", + " count_response = es.count(index=index_name)\n", + " count_data = count_response.body if hasattr(count_response, 'body') else count_response\n", + " doc_count = count_data['count']\n", + " \n", + " if doc_count > 0:\n", + " print(f\"✅ Successfully loaded {doc_count} documents into '{index_name}' index\")\n", + " return True\n", + " else:\n", + " print(f\"❌ Index '{index_name}' is empty after loading\")\n", + " return False\n", + " \n", + " except Exception as e:\n", + " print(f\"❌ Error setting up Elasticsearch data: {e}\")\n", + " return False\n", + " \n", + " finally:\n", + " # Clean up temp file\n", + " try:\n", + " os.unlink(tmp_file.name)\n", + " except:\n", + " pass\n", + "\n", + "def setup_s3_data():\n", + " \"\"\"Download and load PDF files into S3 bucket.\"\"\"\n", + " print(\"🔧 Setting up S3 PDF data...\")\n", + " \n", + " try:\n", + " # Initialize S3 client\n", + " s3 = boto3.client(\n", + " 's3',\n", + " aws_access_key_id=AWS_ACCESS_KEY_ID,\n", + " aws_secret_access_key=AWS_SECRET_ACCESS_KEY,\n", + " region_name=AWS_REGION\n", + " )\n", + " \n", + " bucket_name = S3_SOURCE_BUCKET\n", + " if not bucket_name:\n", + " print(\"❌ S3_SOURCE_BUCKET not configured\")\n", + " return False\n", + " \n", + " # Check if bucket exists and has data\n", + " try:\n", + " response = s3.list_objects_v2(Bucket=bucket_name, MaxKeys=1)\n", + " if response.get('KeyCount', 0) > 0:\n", + " # Count total objects\n", + " response = s3.list_objects_v2(Bucket=bucket_name)\n", + " object_count = len(response.get('Contents', []))\n", + " print(f\"✅ Bucket '{bucket_name}' already exists with {object_count} files\")\n", + " return True\n", + " except ClientError as e:\n", + " if e.response['Error']['Code'] != '404':\n", + " raise e\n", + " \n", + " # Download S3 PDFs zip file\n", + " s3_data_url = \"https://github.com/Unstructured-IO/rag-over-hybrid-data-sources/raw/feature/hybrid-rag-pipeline/source_data/s3_pdfs.zip\"\n", + " \n", + " with tempfile.NamedTemporaryFile(suffix='.zip', delete=False) as tmp_file:\n", + " if not download_file(s3_data_url, tmp_file.name):\n", + " return False\n", + " \n", + " # Create bucket if it doesn't exist\n", + " try:\n", + " s3.head_bucket(Bucket=bucket_name)\n", + " print(f\"📦 Using existing bucket '{bucket_name}'\")\n", + " except ClientError as e:\n", + " if e.response['Error']['Code'] == '404':\n", + " print(f\"🔧 Creating bucket '{bucket_name}'...\")\n", + " try:\n", + " if AWS_REGION == \"us-east-1\":\n", + " s3.create_bucket(Bucket=bucket_name)\n", + " else:\n", + " s3.create_bucket(\n", + " Bucket=bucket_name,\n", + " CreateBucketConfiguration={'LocationConstraint': AWS_REGION}\n", + " )\n", + " print(f\"✅ Created bucket '{bucket_name}'\")\n", + " except ClientError as create_error:\n", + " if 'BucketAlreadyOwnedByYou' in str(create_error):\n", + " print(f\"📦 Bucket '{bucket_name}' already exists and is owned by you\")\n", + " else:\n", + " raise create_error\n", + " else:\n", + " raise e\n", + " \n", + " # Clear existing files in bucket\n", + " print(f\"🗑️ Clearing existing files from bucket '{bucket_name}'...\")\n", + " try:\n", + " response = s3.list_objects_v2(Bucket=bucket_name)\n", + " if 'Contents' in response:\n", + " objects_to_delete = [{'Key': obj['Key']} for obj in response['Contents']]\n", + " if objects_to_delete:\n", + " s3.delete_objects(\n", + " Bucket=bucket_name,\n", + " Delete={'Objects': objects_to_delete}\n", + " )\n", + " print(f\"🗑️ Deleted {len(objects_to_delete)} existing files\")\n", + " else:\n", + " print(\"📁 Bucket was already empty\")\n", + " else:\n", + " print(\"📁 Bucket was already empty\")\n", + " except ClientError as e:\n", + " print(f\"⚠️ Could not clear bucket (continuing anyway): {e}\")\n", + " \n", + " # Extract and upload files from zip\n", + " uploaded_count = 0\n", + " with zipfile.ZipFile(tmp_file.name, 'r') as zipf:\n", + " file_list = zipf.namelist()\n", + " pdf_files = [f for f in file_list if f.lower().endswith('.pdf')]\n", + " \n", + " print(f\"📊 Found {len(pdf_files)} PDF files in zip\")\n", + " \n", + " for file_name in pdf_files:\n", + " try:\n", + " # Extract file data\n", + " file_data = zipf.read(file_name)\n", + " \n", + " # Upload to S3\n", + " s3.put_object(\n", + " Bucket=bucket_name,\n", + " Key=file_name,\n", + " Body=file_data,\n", + " ContentType='application/pdf'\n", + " )\n", + " \n", + " print(f\" 📤 Uploaded: {file_name}\")\n", + " uploaded_count += 1\n", + " \n", + " except Exception as e:\n", + " print(f\" ❌ Failed to upload {file_name}: {e}\")\n", + " \n", + " # Verify upload\n", + " response = s3.list_objects_v2(Bucket=bucket_name)\n", + " actual_count = len(response.get('Contents', []))\n", + " \n", + " if actual_count > 0:\n", + " print(f\"✅ Successfully uploaded {uploaded_count} PDFs to bucket '{bucket_name}'\")\n", + " print(f\"📊 Bucket now contains {actual_count} files\")\n", + " return True\n", + " else:\n", + " print(f\"❌ Bucket '{bucket_name}' is empty after upload\")\n", + " return False\n", + " \n", + " except NoCredentialsError:\n", + " print(\"❌ AWS credentials not found. Please check your .env file.\")\n", + " return False\n", + " except Exception as e:\n", + " print(f\"❌ Error setting up S3 data: {e}\")\n", + " return False\n", + " \n", + " finally:\n", + " # Clean up temp file\n", + " try:\n", + " os.unlink(tmp_file.name)\n", + " except:\n", + " pass\n", + "\n", + "def prepare_data_sources():\n", + " \"\"\"Prepare both Elasticsearch and S3 data sources.\"\"\"\n", + " print(\"🚀 Preparing data sources...\")\n", + " print(\"=\" * 50)\n", + " \n", + " # Setup Elasticsearch data\n", + " if not setup_elasticsearch_data():\n", + " print(\"❌ Failed to setup Elasticsearch data\")\n", + " return False\n", + " \n", + " print() # Add spacing\n", + " \n", + " # Setup S3 data\n", + " if not setup_s3_data():\n", + " print(\"❌ Failed to setup S3 data\")\n", + " return False\n", + " \n", + " print()\n", + " print(\"✅ All data sources prepared successfully!\")\n", + " print(\"=\" * 50)\n", + " return True " + ] + }, + { + "cell_type": "markdown", + "id": "ae11e550", + "metadata": {}, + "source": [ + "## S3 Source Connector\n", + "\n", + "Now we'll create the connections that link our data sources to Unstructured's processing pipeline. First, let's establish the connection to your S3 bucket containing PDF documents for processing." + ] + }, + { + "cell_type": "markdown", + "id": "319151fb", + "metadata": {}, + "source": [ + "### Example Product Manual Content\n", + "\n", + "The following image shows a sample page from one of the product manuals stored in your S3 bucket. This demonstrates the type of unstructured content that will be processed and made searchable through our RAG system." + ] + }, + { + "cell_type": "markdown", + "id": "4dcfda60", + "metadata": {}, + "source": [ + "![product-manual-example](data:image/png;base64,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)" + ] + }, + { + "cell_type": "markdown", + "id": "c4eb1e31", + "metadata": {}, + "source": [ + "## Elasticsearch Source Connector\n", + "\n", + "Next, we'll connect to your Elasticsearch index containing structured sales data, completing our dual-source setup." + ] + }, + { + "cell_type": "markdown", + "id": "72193ddf", + "metadata": {}, + "source": [ + "### Sales Records Data Structure\n", + "\n", + "The image below shows the structure of the consolidated sales records in your Elasticsearch index. This data represents customer transactions and will be processed alongside the product manuals to create a unified knowledge base." + ] + }, + { + "cell_type": "markdown", + "id": "48354f9b", + "metadata": {}, + "source": [ + "![sales-records-consolidated](data:image/png;base64,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)" + ] + }, + { + "cell_type": "markdown", + "id": "c27785eb", + "metadata": {}, + "source": [ + "## Elasticsearch Destination Connector\n", + "\n", + "Finally, we'll create the destination where both data streams will converge: the unified `customer-support` index where all processed data will be stored." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "efbb7e0f", + "metadata": { + "lines_to_next_cell": 1 + }, + "outputs": [], + "source": [ + "def create_s3_source_connector():\n", + " \"\"\"Create an S3 source connector for PDF documents.\"\"\"\n", + " try:\n", + " if not S3_SOURCE_BUCKET:\n", + " raise ValueError(\"S3_SOURCE_BUCKET is required (bucket name, s3:// URL, or https:// URL)\")\n", + " value = S3_SOURCE_BUCKET.strip()\n", + "\n", + " if value.startswith(\"s3://\"):\n", + " s3_style = value if value.endswith(\"/\") else value + \"/\"\n", + " elif value.startswith(\"http://\") or value.startswith(\"https://\"):\n", + " parsed = urlparse(value)\n", + " host = parsed.netloc\n", + " path = parsed.path or \"/\"\n", + " bucket = host.split(\".s3.\")[0]\n", + " s3_style = f\"s3://{bucket}{path if path.endswith('/') else path + '/'}\"\n", + " else:\n", + " s3_style = f\"s3://{value if value.endswith('/') else value + '/'}\"\n", + " \n", + " with UnstructuredClient(api_key_auth=UNSTRUCTURED_API_KEY) as client:\n", + " response = client.sources.create_source(\n", + " request=CreateSourceRequest(\n", + " create_source_connector=CreateSourceConnector(\n", + " name=\"\",\n", + " type=\"s3\",\n", + " config={\n", + " \"remote_url\": s3_style,\n", + " \"recursive\": True, \n", + " \"key\": AWS_ACCESS_KEY_ID,\n", + " \"secret\": AWS_SECRET_ACCESS_KEY,\n", + " }\n", + " )\n", + " )\n", + " )\n", + " \n", + " source_id = response.source_connector_information.id\n", + " print(f\"✅ Created S3 PDF source connector: {source_id} -> {s3_style}\")\n", + " return source_id\n", + " \n", + " except Exception as e:\n", + " print(f\"❌ Error creating S3 source connector: {e}\")\n", + " return None\n", + "\n", + "def create_elasticsearch_source_connector():\n", + " \"\"\"Create an Elasticsearch source connector for sales data.\"\"\"\n", + " try:\n", + " with UnstructuredClient(api_key_auth=UNSTRUCTURED_API_KEY) as client:\n", + " response = client.sources.create_source(\n", + " request=CreateSourceRequest(\n", + " create_source_connector=CreateSourceConnector(\n", + " name=f\"elasticsearch_sales_source_{int(time.time())}\",\n", + " type=\"elasticsearch\",\n", + " config={\n", + " \"hosts\": [ELASTICSEARCH_HOST],\n", + " \"es_api_key\": ELASTICSEARCH_API_KEY,\n", + " \"index_name\": ELASTICSEARCH_INDEX\n", + " }\n", + " )\n", + " )\n", + " )\n", + " \n", + " source_id = response.source_connector_information.id\n", + " print(f\"✅ Created Elasticsearch sales source connector: {source_id}\")\n", + " return source_id\n", + " \n", + " except Exception as e:\n", + " print(f\"❌ Error creating Elasticsearch source connector: {e}\")\n", + " return None\n", + "\n", + "def create_elasticsearch_destination_connector():\n", + " \"\"\"Create an Elasticsearch destination connector for processed results.\"\"\"\n", + " try:\n", + " with UnstructuredClient(api_key_auth=UNSTRUCTURED_API_KEY) as client:\n", + " response = client.destinations.create_destination(\n", + " request=CreateDestinationRequest(\n", + " create_destination_connector=CreateDestinationConnector(\n", + " name=f\"elasticsearch_customer_support_destination_{int(time.time())}\",\n", + " type=\"elasticsearch\",\n", + " config={\n", + " \"hosts\": [ELASTICSEARCH_HOST],\n", + " \"es_api_key\": ELASTICSEARCH_API_KEY,\n", + " \"index_name\": \"customer-support\"\n", + " }\n", + " )\n", + " )\n", + " )\n", + "\n", + " destination_id = response.destination_connector_information.id\n", + " print(f\"✅ Created Elasticsearch destination connector: {destination_id}\")\n", + " return destination_id\n", + " \n", + " except Exception as e:\n", + " print(f\"❌ Error creating Elasticsearch destination connector: {e}\")\n", + " return None" + ] + }, + { + "cell_type": "markdown", + "id": "406fddd2", + "metadata": {}, + "source": [ + "## Processing Pipeline Configuration\n", + "\n", + "With our connectors in place, we can now configure the intelligent processing pipeline that will transform both data sources. This four-stage pipeline (VLM → Chunker → Embedder → NER) will be applied to both workflows, ensuring consistent processing regardless of data source." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "29eb3e9c", + "metadata": { + "lines_to_next_cell": 1 + }, + "outputs": [], + "source": [ + "def create_workflow_nodes():\n", + " \"\"\"Create shared processing nodes for workflows.\"\"\"\n", + " vlm_partition_node = WorkflowNode(\n", + " name=\"VLM_Partitioner\",\n", + " subtype=\"vlm\",\n", + " type=\"partition\",\n", + " settings={\n", + " \"provider\": \"openai\",\n", + " \"model\": \"gpt-4o\",\n", + " }\n", + " )\n", + " \n", + " chunk_node = WorkflowNode(\n", + " name=\"Chunker_Node\",\n", + " subtype=\"chunk_by_title\",\n", + " type=\"chunk\",\n", + " settings={\n", + " \"new_after_n_chars\": 1500,\n", + " \"max_characters\": 2048,\n", + " \"overlap\": 0\n", + " }\n", + " )\n", + " \n", + " embedder_node = WorkflowNode(\n", + " name=\"Embedder_Node\",\n", + " subtype=\"openai\",\n", + " type=\"embed\",\n", + " settings={\n", + " \"model_name\": \"text-embedding-3-small\"\n", + " }\n", + " )\n", + " \n", + " ner_enrichment_node = WorkflowNode(\n", + " name=\"NER_Enrichment\",\n", + " type=\"prompter\",\n", + " subtype=\"openai_ner\",\n", + " settings={}\n", + " )\n", + " \n", + " return vlm_partition_node, chunk_node, embedder_node, ner_enrichment_node\n", + "\n", + "def create_parallel_workflows(s3_source_id, elasticsearch_source_id, destination_id):\n", + " \"\"\"Create separate workflows for S3 PDFs and Elasticsearch data that run in parallel.\"\"\"\n", + " try:\n", + " vlm_partition_node, chunk_node, embedder_node, ner_enrichment_node = create_workflow_nodes()\n", + " \n", + " # Create workflow for S3 PDFs\n", + " s3_workflow_id = None\n", + " if s3_source_id:\n", + " with UnstructuredClient(api_key_auth=UNSTRUCTURED_API_KEY) as client:\n", + " s3_workflow = CreateWorkflow(\n", + " name=f\"S3-PDFs-Parallel-Workflow_{int(time.time())}\",\n", + " source_id=s3_source_id,\n", + " destination_id=destination_id,\n", + " workflow_type=WorkflowType.CUSTOM,\n", + " workflow_nodes=[\n", + " vlm_partition_node,\n", + " chunk_node,\n", + " embedder_node,\n", + " ner_enrichment_node\n", + " ]\n", + " )\n", + " \n", + " s3_response = client.workflows.create_workflow(\n", + " request=CreateWorkflowRequest(\n", + " create_workflow=s3_workflow\n", + " )\n", + " )\n", + " \n", + " s3_workflow_id = s3_response.workflow_information.id\n", + " print(f\"✅ Created S3 PDF workflow: {s3_workflow_id}\")\n", + " \n", + " # Create workflow for Elasticsearch sales data\n", + " with UnstructuredClient(api_key_auth=UNSTRUCTURED_API_KEY) as client:\n", + " es_workflow = CreateWorkflow(\n", + " name=f\"Elasticsearch-Sales-Parallel-Workflow_{int(time.time())}\",\n", + " source_id=elasticsearch_source_id,\n", + " destination_id=destination_id,\n", + " workflow_type=WorkflowType.CUSTOM,\n", + " workflow_nodes=[\n", + " vlm_partition_node,\n", + " chunk_node,\n", + " embedder_node,\n", + " ner_enrichment_node\n", + " ]\n", + " )\n", + " \n", + " es_response = client.workflows.create_workflow(\n", + " request=CreateWorkflowRequest(\n", + " create_workflow=es_workflow\n", + " )\n", + " )\n", + " \n", + " es_workflow_id = es_response.workflow_information.id\n", + " print(f\"✅ Created Elasticsearch sales workflow: {es_workflow_id}\")\n", + " \n", + " return s3_workflow_id, es_workflow_id\n", + " \n", + " except Exception as e:\n", + " print(f\"❌ Error creating parallel workflows: {e}\")\n", + " return None, None" + ] + }, + { + "cell_type": "markdown", + "id": "86779b8e", + "metadata": {}, + "source": [ + "## Creating Parallel Processing Workflows\n", + "\n", + "Now we'll assemble everything into the two parallel workflows shown in our architecture diagram above, connecting each data source to the processing pipeline and unified destination." + ] + }, + { + "cell_type": "markdown", + "id": "36d5cef5", + "metadata": {}, + "source": [ + "## Starting Your Processing Jobs\n", + "\n", + "With our workflows configured, it's time to put them into action. This step submits both workflows to the Unstructured API and returns job IDs for monitoring." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d1daccb2", + "metadata": { + "lines_to_next_cell": 1 + }, + "outputs": [], + "source": [ + "def run_workflow(workflow_id, workflow_name):\n", + " \"\"\"Run a workflow and return job information.\"\"\"\n", + " try:\n", + " with UnstructuredClient(api_key_auth=UNSTRUCTURED_API_KEY) as client:\n", + " response = client.workflows.run_workflow(\n", + " request={\"workflow_id\": workflow_id}\n", + " )\n", + " \n", + " job_id = response.job_information.id\n", + " print(f\"✅ Started {workflow_name} job: {job_id}\")\n", + " return job_id\n", + " \n", + " except Exception as e:\n", + " print(f\"❌ Error running {workflow_name} workflow: {e}\")\n", + " return None\n", + "\n", + "def poll_job_status(job_id, job_name, wait_time=30):\n", + " \"\"\"Poll job status until completion.\"\"\"\n", + " print(f\"⏳ Monitoring {job_name} job status...\")\n", + " \n", + " while True:\n", + " try:\n", + " with UnstructuredClient(api_key_auth=UNSTRUCTURED_API_KEY) as client:\n", + " response = client.jobs.get_job(\n", + " request={\"job_id\": job_id}\n", + " )\n", + " \n", + " job = response.job_information\n", + " status = job.status\n", + " \n", + " if status in [\"SCHEDULED\", \"IN_PROGRESS\"]:\n", + " print(f\"⏳ {job_name} job status: {status}\")\n", + " time.sleep(wait_time)\n", + " elif status == \"COMPLETED\":\n", + " print(f\"✅ {job_name} job completed successfully!\")\n", + " return job\n", + " elif status == \"FAILED\":\n", + " print(f\"❌ {job_name} job failed!\")\n", + " return job\n", + " else:\n", + " print(f\"❓ Unknown {job_name} job status: {status}\")\n", + " return job\n", + " \n", + " except Exception as e:\n", + " print(f\"❌ Error polling {job_name} job status: {e}\")\n", + " time.sleep(wait_time)" + ] + }, + { + "cell_type": "markdown", + "id": "99f3d024", + "metadata": {}, + "source": [ + "## Monitoring Your Processing Progress\n", + "\n", + "Jobs progress through scheduled, in-progress, completed, or failed states. The `poll_job_status` function checks status every 30 seconds and blocks execution until jobs complete, so you can see exactly what's happening with your data processing." + ] + }, + { + "cell_type": "markdown", + "id": "c895cdaf", + "metadata": {}, + "source": [ + "## Preparing Your Elasticsearch Environment\n", + "\n", + "Before processing begins, we validate that the `sales-records-consolidated` index exists and contains data, then recreate the `customer-support` index fresh for each run. This preparation step ensures a clean environment and prevents any issues from previous runs.\n", + "\n", + "### Index Mapping\n", + "\n", + "The destination index uses this structure optimized for RAG applications:\n", + "```json\n", + "{\n", + " \"id\": \"keyword\", // Unique document identifier\n", + " \"timestamp\": \"date\", // Processing timestamp\n", + " \"text\": \"text\", // Searchable content\n", + " \"metadata\": \"object\" // Source info and entities\n", + "}\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f2432029", + "metadata": { + "lines_to_next_cell": 1 + }, + "outputs": [], + "source": [ + "def run_elasticsearch_preprocessing():\n", + " \"\"\"Check and manage Elasticsearch indices for the pipeline.\"\"\"\n", + " print(\"🔧 Running Elasticsearch preprocessing...\")\n", + " \n", + " try:\n", + " es = Elasticsearch(\n", + " ELASTICSEARCH_HOST,\n", + " api_key=ELASTICSEARCH_API_KEY,\n", + " request_timeout=60,\n", + " max_retries=3,\n", + " retry_on_timeout=True\n", + " )\n", + " \n", + " sales_index = \"sales-records-consolidated\"\n", + " print(f\"�� Checking {sales_index} index...\")\n", + " \n", + " if not es.indices.exists(index=sales_index):\n", + " raise ValueError(f\"❌ Index '{sales_index}' does not exist. There is no data to use.\")\n", + " \n", + " count_response = es.count(index=sales_index)\n", + " doc_count = count_response['count']\n", + " \n", + " if doc_count == 0:\n", + " raise ValueError(f\"❌ Index '{sales_index}' is empty. There is no data to use.\")\n", + " \n", + " print(f\"✅ Found {doc_count} records in {sales_index}\")\n", + " \n", + " # Handle customer-support index\n", + " support_index = \"customer-support\"\n", + " print(f\"🔍 Checking {support_index} index...\")\n", + " \n", + " if es.indices.exists(index=support_index):\n", + " print(f\"🗑️ Deleting existing {support_index} index...\")\n", + " es.indices.delete(index=support_index)\n", + " \n", + " # Create fresh customer-support index\n", + " print(f\"🔧 Creating fresh {support_index} index...\")\n", + " mapping = {\n", + " \"settings\": {\n", + " \"number_of_shards\": 1,\n", + " \"number_of_replicas\": 1\n", + " },\n", + " \"mappings\": {\n", + " \"properties\": {\n", + " \"id\": {\"type\": \"keyword\"},\n", + " \"timestamp\": {\"type\": \"date\"},\n", + " \"text\": {\"type\": \"text\", \"analyzer\": \"standard\"},\n", + " \"metadata\": {\"type\": \"object\"}\n", + " }\n", + " }\n", + " }\n", + " \n", + " es.indices.create(index=support_index, body=mapping)\n", + " es.indices.refresh(index=support_index)\n", + " \n", + " print(f\"✅ Successfully created fresh {support_index} index\")\n", + " print(\"✅ Elasticsearch preprocessing completed successfully\")\n", + " return True\n", + " \n", + " except ValueError as e:\n", + " print(str(e))\n", + " return False\n", + " except Exception as e:\n", + " print(f\"❌ Error during Elasticsearch preprocessing: {e}\")\n", + " return False" + ] + }, + { + "cell_type": "markdown", + "id": "aa688432", + "metadata": {}, + "source": [ + "## Pipeline Execution Summary\n", + "\n", + "The following summary displays all resources created during pipeline setup: data source paths, connector IDs, workflow IDs, job IDs, and processing status." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a2ed058f", + "metadata": { + "lines_to_next_cell": 1 + }, + "outputs": [], + "source": [ + "import os\n", + "\n", + "def print_pipeline_summary(s3_workflow_id, es_workflow_id, s3_job_id, es_job_id):\n", + " \"\"\"Print comprehensive pipeline summary.\"\"\"\n", + " print(\"\\n\" + \"=\" * 80)\n", + " print(\"📊 HYBRID RAG PIPELINE SUMMARY\")\n", + " print(\"=\" * 80)\n", + " print(f\"📁 S3 Source (PDFs): {S3_SOURCE_BUCKET if s3_workflow_id else SKIPPED}\")\n", + " print(f\"🔍 Elasticsearch Source: {ELASTICSEARCH_HOST}/{ELASTICSEARCH_INDEX}\")\n", + " print(f\"📤 Elasticsearch Destination: {ELASTICSEARCH_HOST}/customer-support\")\n", + " print(f\"\")\n", + " print(f\"⚙️ S3 PDFs Workflow ID: {s3_workflow_id if s3_workflow_id else SKIPPED}\")\n", + " print(f\"⚙️ Elasticsearch Sales Workflow ID: {es_workflow_id}\")\n", + " print(f\"\")\n", + " print(f\"🚀 S3 PDFs Job ID: {s3_job_id if s3_job_id else SKIPPED}\")\n", + " print(f\"🚀 Elasticsearch Sales Job ID: {es_job_id}\")\n", + "\n", + "def verify_customer_support_results(s3_job_id=None, es_job_id=None):\n", + " \"\"\"\n", + " Verifies the processed results in the customer-support index, prettyprinting one doc per unique source connector.\n", + " Assumes jobs have already completed successfully.\n", + " \"\"\"\n", + " import pprint\n", + "\n", + " print(\"🔍 Verifying processed results in 'customer-support' index (assuming jobs have completed)...\")\n", + "\n", + " try:\n", + " # Initialize Elasticsearch client\n", + " es = Elasticsearch(\n", + " ELASTICSEARCH_HOST,\n", + " api_key=ELASTICSEARCH_API_KEY,\n", + " request_timeout=60,\n", + " max_retries=3,\n", + " retry_on_timeout=True\n", + " )\n", + "\n", + " index_name = \"customer-support\"\n", + "\n", + " # Check if index exists\n", + " if not es.indices.exists(index=index_name):\n", + " print(f\"❌ Index '{index_name}' does not exist. Workflows may not have written results yet.\")\n", + " return\n", + "\n", + " # Get document count\n", + " count_response = es.count(index=index_name)\n", + " total_docs = count_response['count']\n", + " print(f\"📊 Total processed documents: {total_docs}\")\n", + "\n", + " if total_docs == 0:\n", + " print(\"⏳ No documents found yet. Workflows may still be processing or index is empty.\")\n", + " print(\"💡 Check the Unstructured dashboard for job status.\")\n", + " return\n", + "\n", + " print(f\"\\n📋 Analyzing Source Connectors:\")\n", + " print(\"=\" * 40)\n", + "\n", + " # Get sample documents to analyze source patterns\n", + " # Use function_score with random_score to sample documents randomly\n", + " sample_response = es.search(\n", + " index=index_name,\n", + " body={\n", + " \"size\": 50, # Get more samples to increase chance of seeing all sources\n", + " \"_source\": [\"metadata\", \"text\", \"element_id\"],\n", + " \"query\": {\n", + " \"function_score\": {\n", + " \"query\": {\"match_all\": {}},\n", + " \"random_score\": {}\n", + " }\n", + " }\n", + " }\n", + " )\n", + " \n", + "\n", + " # Map: source_connector_key -> [doc, ...]\n", + " source_connector_map = {}\n", + " unknown_docs = []\n", + "\n", + " for hit in sample_response['hits']['hits']:\n", + " source = hit['_source']\n", + " metadata = source.get('metadata', {})\n", + " \n", + " # Determine source connector type based on metadata patterns\n", + " if \"data_source-record_locator-index_name\" in metadata:\n", + " # Elasticsearch source connector\n", + " key = f\"elasticsearch:{metadata['data_source-record_locator-index_name']}\"\n", + " elif \"data_source-url\" in metadata:\n", + " # S3 source connector - group all S3 URLs by bucket\n", + " url = metadata['data_source-url']\n", + " if url.startswith('s3://'):\n", + " # Extract bucket name from S3 URL\n", + " bucket = url.split('/')[2] if '/' in url else url.replace('s3://', '')\n", + " key = f\"s3:{bucket}\"\n", + " else:\n", + " key = f\"s3:unknown\"\n", + " elif \"filename\" in metadata and metadata.get('filetype') == 'pdf':\n", + " # PDF files from S3 (fallback detection)\n", + " key = \"s3:pdfs\"\n", + " else:\n", + " key = \"unknown\"\n", + "\n", + " if key == \"unknown\":\n", + " unknown_docs.append(hit)\n", + " else:\n", + " if key not in source_connector_map:\n", + " source_connector_map[key] = hit # Only keep the first doc for each source connector\n", + "\n", + " print(f\"🔍 Unique source connectors found: {len(source_connector_map)}\")\n", + " for i, (key, doc) in enumerate(source_connector_map.items(), 1):\n", + " print(f\"\\n--- Source Connector {i} ({key}) ---\")\n", + " pprint.pprint(doc['_source'], depth=6, compact=False, sort_dicts=False)\n", + "\n", + " if unknown_docs:\n", + " print(f\"\\n❓ Example Unknown Source Document:\")\n", + " print(\"-\" * 35)\n", + " unknown_example = unknown_docs[0]['_source']\n", + " metadata = unknown_example.get('metadata', {})\n", + " text = unknown_example.get('text', '')\n", + " print(f\" Element ID: {unknown_example.get('element_id', 'N/A')}\")\n", + " print(f\" Metadata: {metadata}\")\n", + " print(f\" Text Preview: {text[:200]}...\" if len(text) > 200 else f\" Text: {text}\")\n", + " print(\" Metadata prettyprint:\")\n", + " pprint.pprint(metadata, depth=6, compact=False, sort_dicts=False)\n", + "\n", + " # Test search functionality\n", + " print(f\"\\n🔍 Testing Search Functionality:\")\n", + " print(\"=\" * 32)\n", + "\n", + " search_tests = [\"manual\", \"customer\", \"product\", \"support\"]\n", + "\n", + " for search_term in search_tests:\n", + " search_response = es.search(\n", + " index=index_name,\n", + " body={\n", + " \"size\": 1,\n", + " \"query\": {\n", + " \"match\": {\n", + " \"text\": search_term\n", + " }\n", + " }\n", + " }\n", + " )\n", + "\n", + " hits = search_response['hits']['total']['value']\n", + " print(f\" 🔎 '{search_term}': {hits} matches\")\n", + "\n", + " print(f\"\\n\" + \"=\" * 50)\n", + " print(\"🎉 CUSTOMER-SUPPORT INDEX VERIFICATION\")\n", + " print(\"=\" * 50)\n", + " print(\"✅ Index exists and contains processed documents\")\n", + " print(\"✅ Documents from both source connectors are present (if both completed)\")\n", + " print(\"✅ Text search is functional across processed content\")\n", + " print(\"✅ Ready for hybrid RAG queries!\")\n", + "\n", + " except Exception as e:\n", + " print(f\"❌ Error verifying results: {e}\")\n", + " print(\"💡 This is normal if workflows are still processing or if there is a connection issue.\")" + ] + }, + { + "cell_type": "markdown", + "id": "0d85f199", + "metadata": {}, + "source": [ + "## Orchestrating Your Complete Pipeline\n", + "\n", + "The main function coordinates all pipeline steps in logical sequence: data preparation, environment validation, connector setup, workflow creation, execution, and summary reporting." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7f5f9744", + "metadata": { + "lines_to_next_cell": 1 + }, + "outputs": [], + "source": [ + "def main():\n", + " \"\"\"Main pipeline execution\"\"\"\n", + " print(\"🚀 Starting Hybrid RAG Pipeline\")\n", + " \n", + " print(\"\\n📦 Step 0: Data source preparation\")\n", + " print(\"-\" * 50)\n", + " \n", + " if not prepare_data_sources():\n", + " print(\"❌ Failed to prepare data sources\")\n", + " return\n", + " \n", + " print(\"\\n🔧 Step 1: Elasticsearch preprocessing\")\n", + " print(\"-\" * 50)\n", + " \n", + " if not run_elasticsearch_preprocessing():\n", + " print(\"❌ Failed to complete Elasticsearch preprocessing\")\n", + " return\n", + " \n", + " print(\"\\n🔗 Step 2: Creating source connectors\")\n", + " print(\"-\" * 50)\n", + " \n", + " s3_source_id = create_s3_source_connector()\n", + " if not s3_source_id:\n", + " print(\"❌ Failed to create S3 source connector\")\n", + " return\n", + " \n", + " elasticsearch_source_id = create_elasticsearch_source_connector()\n", + " if not elasticsearch_source_id:\n", + " print(\"❌ Failed to create Elasticsearch source connector\")\n", + " return\n", + " \n", + " # Step 3: Create Destination Connector\n", + " print(\"\\n🎯 Step 3: Creating Elasticsearch destination connector\")\n", + " print(\"-\" * 50)\n", + " \n", + " destination_id = create_elasticsearch_destination_connector()\n", + " if not destination_id:\n", + " print(\"❌ Failed to create destination connector\")\n", + " return\n", + " \n", + " # Step 4: Create Workflows\n", + " print(\"\\n⚙️ Step 4: Creating workflows\")\n", + " print(\"-\" * 50)\n", + " \n", + " s3_workflow_id, es_workflow_id = create_parallel_workflows(\n", + " s3_source_id, elasticsearch_source_id, destination_id\n", + " )\n", + " \n", + " if not es_workflow_id:\n", + " print(\"❌ Failed to create Elasticsearch workflow\")\n", + " return\n", + " \n", + " # Step 5: Run Workflows\n", + " print(\"\\n🚀 Step 5: Running workflows\")\n", + " print(\"-\" * 50)\n", + " \n", + " s3_job_id = None\n", + " es_job_id = None\n", + "\n", + " if s3_workflow_id:\n", + " s3_job_id = run_workflow(s3_workflow_id, \"S3 PDFs\")\n", + " if not s3_job_id:\n", + " print(\"❌ Failed to start S3 workflow\")\n", + " return\n", + "\n", + " if es_workflow_id:\n", + " es_job_id = run_workflow(es_workflow_id, \"Elasticsearch Sales\")\n", + " if not es_job_id:\n", + " print(\"❌ Failed to start Elasticsearch workflow\")\n", + " return\n", + "\n", + " # Step 6: Pipeline Summary\n", + " print_pipeline_summary(s3_workflow_id, es_workflow_id, s3_job_id, es_job_id)\n", + " return s3_job_id, es_job_id " + ] + }, + { + "cell_type": "markdown", + "id": "9803d2a1", + "metadata": {}, + "source": [ + "## Running Your Complete Pipeline\n", + "\n", + "We'll execute the complete pipeline by calling the main function to create all resources and start processing, then monitor the jobs until they complete successfully." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "61a811ce", + "metadata": {}, + "outputs": [], + "source": [ + "s3_job_id, es_job_id = main()\n", + "\n", + "es_job_info = poll_job_status(es_job_id, \"Elasticsearch Ingest\")\n", + "s3_job_info = poll_job_status(s3_job_id, \"S3 Ingest\")\n", + "print(\"\\n🔍 Verifying processed results\")\n", + "print(\"-\" * 50)\n", + "verify_customer_support_results()" + ] + }, + { + "cell_type": "markdown", + "id": "41b136bb", + "metadata": {}, + "source": [ + "### Unified Knowledge Base Results\n", + "\n", + "After processing both data sources, the pipeline creates a unified `customer-support` index containing processed documents from both S3 PDFs and Elasticsearch sales records. The image below shows the structure of this consolidated knowledge base, ready for RAG queries." + ] + }, + { + "cell_type": "markdown", + "id": "d2826232", + "metadata": {}, + "source": [ + "![customer-support](data:image/png;base64,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)" + ] + }, + { + "cell_type": "markdown", + "id": "39611edf", + "metadata": {}, + "source": [ + "## RAG Query Demonstration\n", + "\n", + "Now that your hybrid knowledge base is ready, we'll demonstrate how to query it using RAG (Retrieval-Augmented Generation). This is where you'll see how the system can answer complex questions by pulling relevant information from both your S3 documents and Elasticsearch records.\n", + "\n", + "### OpenAI API Key Required\n", + "\n", + "For the RAG demonstration, you'll need an OpenAI API key to power the language model that generates answers based on your retrieved documents. Visit https://platform.openai.com/api-keys to sign in or create an account and generate a new API key.\n", + "\n", + "The demonstration will show cross-source querying, source attribution, and semantic understanding as your hybrid RAG system answers questions by combining information from multiple data sources." + ] + }, + { + "cell_type": "markdown", + "id": "7d207715", + "metadata": {}, + "source": [ + "### RAG Configuration\n", + "\n", + "**Instructions**: Paste your OpenAI API key below to enable RAG demonstrations. This key will be used to power the language model that generates answers based on your retrieved documents." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dfb47ebf", + "metadata": {}, + "outputs": [], + "source": [ + "# RAG Demonstration Configuration and Queries\n", + "import os\n", + "import json\n", + "\n", + "# LangChain imports for RAG functionality\n", + "from langchain_elasticsearch import ElasticsearchStore\n", + "from langchain_openai import OpenAIEmbeddings, ChatOpenAI\n", + "from langchain_core.prompts import ChatPromptTemplate\n", + "from langchain_core.output_parsers import StrOutputParser\n", + "from langchain_core.runnables import RunnablePassthrough\n", + "\n", + "print(\"🤖 RAG Query Demonstration Setup\")\n", + "print(\"=\" * 40)\n", + "\n", + "if not OPENAI_API_KEY or OPENAI_API_KEY.startswith(\"your-\"):\n", + " print(\"⚠️ OpenAI API key not configured.\")\n", + " print(\"💡 Please set OPENAI_API_KEY in your .env file with your actual OpenAI API key.\")\n", + " print(\"📝 You can get one at: https://platform.openai.com/api-keys\")\n", + "else:\n", + " print(\"✅ OpenAI API key configured for RAG demonstrations\")\n", + "\n", + "def setup_rag_system():\n", + " \"\"\"Set up the RAG system with Elasticsearch and OpenAI.\"\"\"\n", + " \n", + " if not OPENAI_API_KEY or OPENAI_API_KEY.startswith(\"your-\"):\n", + " print(\"❌ OpenAI API key is required for RAG functionality\")\n", + " print(\"Please set OPENAI_API_KEY in your .env file\")\n", + " return None\n", + " \n", + " # Set OpenAI API key for LangChain\n", + " os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY\n", + " \n", + " try:\n", + " print(\"🔧 Setting up RAG components...\")\n", + " \n", + " # Initialize embeddings (same model used in processing)\n", + " embeddings = OpenAIEmbeddings(\n", + " model=\"text-embedding-3-small\",\n", + " openai_api_key=OPENAI_API_KEY\n", + " )\n", + " \n", + " # Connect to Elasticsearch vector store - using your working pattern\n", + " vector_store = ElasticsearchStore(\n", + " index_name=\"customer-support\",\n", + " embedding=embeddings,\n", + " es_url=ELASTICSEARCH_HOST,\n", + " es_api_key=ELASTICSEARCH_API_KEY,\n", + " vector_query_field=\"embeddings\",\n", + " query_field=\"text\",\n", + " )\n", + " \n", + " # Create retriever\n", + " retriever = vector_store.as_retriever(search_kwargs={\"k\": 5})\n", + " \n", + " # Initialize LLM\n", + " llm = ChatOpenAI(\n", + " model=\"gpt-3.5-turbo\",\n", + " temperature=0,\n", + " openai_api_key=OPENAI_API_KEY\n", + " )\n", + " \n", + " # Enhanced prompt template that leverages NER metadata\n", + " prompt = ChatPromptTemplate.from_template(\"\"\"\n", + "Use the following context to answer the question. Pay attention to any entity information (people, organizations, products, locations, dates) and relationships mentioned in the context.\n", + "\n", + "Context:\n", + "{context}\n", + "\n", + "Question:\n", + "{question}\n", + "\"\"\")\n", + " \n", + " print(\"✅ RAG system ready!\")\n", + " return {\"retriever\": retriever, \"llm\": llm, \"prompt\": prompt}\n", + " \n", + " except ImportError as e:\n", + " print(f\"❌ Missing RAG dependencies: {e}\")\n", + " print(\"💡 Install with: pip install langchain langchain-elasticsearch langchain-openai\")\n", + " return None\n", + " except Exception as e:\n", + " print(f\"❌ Error setting up RAG system: {e}\")\n", + " return None\n", + "\n", + "def extract_ner_entities(docs):\n", + " \"\"\"Extract NER entities from document metadata.\"\"\"\n", + " entities = {\"people\": set(), \"organizations\": set(), \"products\": set(), \"locations\": set(), \"dates\": set()}\n", + " \n", + " for doc in docs:\n", + " metadata = doc.metadata\n", + " if \"entities-items\" in metadata:\n", + " try:\n", + " import json\n", + " entity_items = json.loads(metadata[\"entities-items\"]) if isinstance(metadata[\"entities-items\"], str) else metadata[\"entities-items\"]\n", + " \n", + " for item in entity_items:\n", + " entity_type = item.get(\"type\", \"\").upper()\n", + " entity_name = item.get(\"entity\", \"\")\n", + " \n", + " if entity_type == \"PERSON\":\n", + " entities[\"people\"].add(entity_name)\n", + " elif entity_type == \"ORGANIZATION\":\n", + " entities[\"organizations\"].add(entity_name)\n", + " elif entity_type == \"PRODUCT\":\n", + " entities[\"products\"].add(entity_name)\n", + " elif entity_type == \"LOCATION\":\n", + " entities[\"locations\"].add(entity_name)\n", + " elif entity_type == \"DATE\":\n", + " entities[\"dates\"].add(entity_name)\n", + " except:\n", + " pass\n", + " \n", + " return entities\n", + "\n", + "def analyze_sources(docs):\n", + " \"\"\"Analyze retrieved documents by source type.\"\"\"\n", + " s3_docs = []\n", + " es_docs = []\n", + " unknown_docs = []\n", + " \n", + " for doc in docs:\n", + " metadata = doc.metadata\n", + " if \"data_source-record_locator-index_name\" in metadata:\n", + " es_docs.append(doc)\n", + " elif \"data_source-url\" in metadata and \"s3://\" in metadata.get(\"data_source-url\", \"\"):\n", + " s3_docs.append(doc)\n", + " else:\n", + " unknown_docs.append(doc)\n", + " \n", + " return s3_docs, es_docs, unknown_docs\n", + "\n", + "def demonstrate_hybrid_ner_queries(rag_components):\n", + " \"\"\"Demonstrate NER-enhanced hybrid RAG capabilities.\"\"\"\n", + " if not rag_components:\n", + " return\n", + " \n", + " retriever = rag_components[\"retriever\"]\n", + " llm = rag_components[\"llm\"]\n", + " prompt = rag_components[\"prompt\"]\n", + " \n", + " # Build RAG chain using your working pattern\n", + " rag_chain = (\n", + " {\"context\": retriever, \"question\": RunnablePassthrough()}\n", + " | prompt\n", + " | llm\n", + " | StrOutputParser()\n", + " )\n", + " \n", + " # Hybrid NER demonstration queries targeting different sources\n", + " hybrid_queries = [\n", + " {\n", + " \"query\": \"How do I troubleshoot Bose headphone connectivity issues?\",\n", + " \"description\": \"Product support query targeting S3 PDFs (product manuals)\",\n", + " \"expected_source\": \"S3 (Product Docs)\"\n", + " },\n", + " {\n", + " \"query\": \"Tell me about Daniel Hahn and his purchases\",\n", + " \"description\": \"Customer analysis query targeting Elasticsearch (sales data)\",\n", + " \"expected_source\": \"Elasticsearch (Sales)\"\n", + " },\n", + " {\n", + " \"query\": \"What are the technical specifications for SoundSport Wireless headphones?\",\n", + " \"description\": \"Product specification query targeting S3 PDFs\",\n", + " \"expected_source\": \"S3 (Product Docs)\"\n", + " },\n", + " {\n", + " \"query\": \"Show me customers in San Antonio, TX\",\n", + " \"description\": \"Geographic customer query targeting Elasticsearch\",\n", + " \"expected_source\": \"Elasticsearch (Sales)\"\n", + " },\n", + " {\n", + " \"query\": \"How do I reset wireless headphones to factory settings?\",\n", + " \"description\": \"Technical support query targeting S3 PDFs\",\n", + " \"expected_source\": \"S3 (Product Docs)\"\n", + " },\n", + " {\n", + " \"query\": \"What products does Newegg sell and what are their features?\",\n", + " \"description\": \"Hybrid query targeting BOTH sources (sales + product specs)\",\n", + " \"expected_source\": \"Both S3 and Elasticsearch\"\n", + " },\n", + " {\n", + " \"query\": \"I have a customer who bought Bose headphones and is having connectivity issues. What should I tell them?\",\n", + " \"description\": \"Customer support query requiring BOTH customer data AND product manuals\",\n", + " \"expected_source\": \"Both S3 and Elasticsearch\"\n", + " }\n", + " ]\n", + " \n", + " print(\"\\n🧠 Hybrid NER-Enhanced RAG Demonstration\")\n", + " print(\"=\" * 60)\n", + " \n", + " for i, query_info in enumerate(hybrid_queries, 1):\n", + " query = query_info[\"query\"]\n", + " description = query_info[\"description\"]\n", + " expected_source = query_info[\"expected_source\"]\n", + " \n", + " print(f\"\\n{'='*70}\")\n", + " print(f\"Query {i}: {description}\")\n", + " print(f\"📝 Query: {query}\")\n", + " print(f\"🎯 Expected Source: {expected_source}\")\n", + " print(\"=\" * 70)\n", + " \n", + " try:\n", + " # Retrieve documents\n", + " docs = retriever.invoke(query)\n", + " \n", + " if not docs:\n", + " print(\"❌ No documents retrieved\")\n", + " continue\n", + " \n", + " # Analyze sources (keeping your preferred format)\n", + " s3_docs, es_docs, unknown_docs = analyze_sources(docs)\n", + " print(f\"📊 Retrieved {len(docs)} documents:\")\n", + " print(f\" 📄 S3 (Product Docs): {len(s3_docs)}\")\n", + " print(f\" 📊 Elasticsearch (Sales): {len(es_docs)}\")\n", + " print(f\" ❓ Unknown: {len(unknown_docs)}\")\n", + " \n", + " # Check if we hit the expected source\n", + " if expected_source == \"S3 (Product Docs)\" and len(s3_docs) > 0:\n", + " print(\"✅ SUCCESS: Retrieved from expected S3 source!\")\n", + " elif expected_source == \"Elasticsearch (Sales)\" and len(es_docs) > 0:\n", + " print(\"✅ SUCCESS: Retrieved from expected Elasticsearch source!\")\n", + " elif expected_source == \"Both S3 and Elasticsearch\" and len(s3_docs) > 0 and len(es_docs) > 0:\n", + " print(\"✅ SUCCESS: Retrieved from BOTH sources as expected!\")\n", + " elif expected_source.startswith(\"Both\") and (len(s3_docs) > 0 or len(es_docs) > 0):\n", + " print(\"✅ PARTIAL: Retrieved from at least one expected source\")\n", + " else:\n", + " print(\"⚠️ UNEXPECTED: Did not retrieve from expected source\")\n", + " \n", + " # Extract and show NER entities\n", + " entities = extract_ner_entities(docs)\n", + " print(f\"\\n🏷️ NER Entities Found:\")\n", + " if entities[\"people\"]:\n", + " print(f\" 👤 People: {', '.join(list(entities['people'])[:3])}\")\n", + " if entities[\"organizations\"]:\n", + " print(f\" 🏢 Organizations: {', '.join(list(entities['organizations'])[:3])}\")\n", + " if entities[\"products\"]:\n", + " print(f\" 📱 Products: {', '.join(list(entities['products'])[:3])}\")\n", + " if entities[\"locations\"]:\n", + " print(f\" 🗺️ Locations: {', '.join(list(entities['locations'])[:3])}\")\n", + " if entities[\"dates\"]:\n", + " print(f\" 📅 Dates: {', '.join(list(entities['dates'])[:3])}\")\n", + " \n", + " # Generate answer\n", + " print(f\"\\n💬 Answer:\")\n", + " answer = rag_chain.invoke(query)\n", + " print(f\"{answer}\")\n", + " \n", + " except Exception as e:\n", + " print(f\"❌ Error: {e}\")\n", + " if \"429\" in str(e):\n", + " print(\"⚠️ OpenAI API quota exceeded. Stopping demo.\")\n", + " break\n", + " \n", + " print(f\"\\n{'='*70}\")\n", + " print(\"🧠 Hybrid NER Demo Complete!\")\n", + " print(\"✅ Demonstrated cross-source retrieval capabilities\")\n", + " print(\"✅ Showed NER metadata integration across data sources\")\n", + " print(\"✅ Validated hybrid RAG architecture\")\n", + "\n", + "def run_rag_demonstration():\n", + " \"\"\"Run the RAG demonstration.\"\"\"\n", + " print(\"\\n🚀 Starting Hybrid RAG Demonstration\")\n", + " print(\"=\" * 50)\n", + " \n", + " rag_components = setup_rag_system()\n", + " \n", + " if rag_components:\n", + " demonstrate_hybrid_ner_queries(rag_components)\n", + " else:\n", + " print(\"❌ RAG demonstration skipped due to configuration issues\")\n", + "\n", + "# Run the demonstration\n", + "run_rag_demonstration()" + ] + }, + { + "cell_type": "markdown", + "id": "b5449850", + "metadata": {}, + "source": [ + "## What You've Accomplished\n", + "\n", + "**Enterprise Data Integration**: You've learned how to process multiple data formats (PDFs, structured records) in parallel, why consistent processing pipelines matter for unified search, and the value of creating a single searchable knowledge base that spans all your data sources.\n", + "\n", + "**Unstructured API Capabilities**: You've experienced VLM-powered document partitioning for complex layouts, intelligent chunking that preserves document structure, named entity recognition for enhanced search precision, and unified processing across diverse data sources.\n", + "\n", + "**RAG System Architecture**: You've built parallel workflow design for scalability and reliability, vector embeddings for semantic similarity search, source attribution in mixed-data query results, and NER-enhanced query understanding and response generation.\n", + "\n", + "### Ready to Scale?\n", + "\n", + "Deploy customer support chatbots with comprehensive knowledge access, build internal search tools that surface information from any source, or create automated content recommendation systems. Add more data sources using additional workflows, implement real-time data synchronization, or scale up for production data volumes with monitoring and alerting.\n", + "\n", + "### Try Unstructured Today\n", + "\n", + "Ready to build your own hybrid RAG system? [Sign up for a free trial](https://unstructured.io/?modal=try-for-free) and start transforming your enterprise data into intelligent, searchable knowledge.\n", + "\n", + "**Need help getting started?** Contact our team to schedule a demo and see how Unstructured can solve your specific data challenges." + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "executable": "/usr/bin/env python3", + "main_language": "python", + "notebook_metadata_filter": "-all" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/hybrid_rag_pipeline_enriched.py b/hybrid_rag_pipeline_enriched.py new file mode 100644 index 0000000..0c96fa7 --- /dev/null +++ b/hybrid_rag_pipeline_enriched.py @@ -0,0 +1,1512 @@ +#!/usr/bin/env python3 +# %% [markdown] +# # Building a Hybrid RAG System: From Fragmented Data to Unified Intelligence +# +# Picture this: You're a customer support agent, and a customer calls about a product issue. To help them effectively, you need to pull information from multiple sources - product manuals stored as PDFs in cloud storage, their purchase history in your sales database, and previous support interactions scattered across different systems. Each piece of information lives in a different format, in a different system, with different access methods. +# +# This is the reality for most enterprises today, and it's exactly the challenge we're going to solve together. +# +# ## The Enterprise Data Challenge +# +# Enterprise data rarely lives in one place or format. Critical information is fragmented across unstructured documents like PDFs and manuals in cloud storage, structured records like sales data in databases, and different formats requiring different processing approaches. Traditional RAG systems work well with homogeneous data but struggle when you need to query across diverse data sources simultaneously. +# +# ## Why This Matters +# +# When data is scattered, customer support becomes inefficient, decision-making lacks context, and valuable insights remain hidden. A customer asking about a product issue shouldn't require you to manually search through multiple systems to piece together a complete picture. +# +# ## The Solution: Unstructured's Complete Gen AI Data Layer +# +# Unstructured isn't just another data processing tool—it's a complete Gen AI data layer solution that transforms how organizations handle unstructured data at scale. Unlike building custom solutions or using fragmented tools, Unstructured provides a unified platform that connects to 30+ data sources, processes 65+ file types with intelligent partitioning and chunking, automatically enriches content with metadata and context, and delivers to 30+ destinations—all while maintaining enterprise-grade security and compliance. +# +# The platform eliminates the complexity of managing multiple tools, custom integrations, and manual data preparation, allowing teams to focus on building AI applications rather than wrestling with data infrastructure. With flexible deployment options from SaaS to bare metal, Unstructured adapts to any infrastructure while providing the observability, automation, and reliability that enterprise AI projects demand. +# +# ## What We'll Build Together +# +# In this tutorial, we'll create a hybrid RAG system that processes two different data sources simultaneously: product documentation from S3 and sales records from Elasticsearch. Both will flow through the same intelligent processing pipeline and land in a unified, searchable knowledge base. +# +# ``` +# ┌─────────────────┐ ┌─────────────────────────┐ +# │ S3 PDFs │──── WORKFLOW 1 ──────────▶│ │ +# │ (Product Docs) │ │ Unstructured API │ +# └─────────────────┘ │ │ +# │ Partition → Chunk → │ +# ┌─────────────────┐ │ Embed → NER → Store │ +# │ Elasticsearch │──── WORKFLOW 2 ──────────▶│ │ +# │ (Sales Records) │ │ │ +# └─────────────────┘ └────────────┬────────────┘ +# │ +# ┌────────────▼────────────┐ +# │ customer-support │ +# │ (Unified Index) │ +# └─────────────────────────┘ +# ``` +# +# By the end of this tutorial, you'll have a working system that can answer complex questions by pulling information from both your product documentation and customer data simultaneously. + +# %% [markdown] +# ## Getting Started: Your Unstructured API Key +# +# To follow along with this tutorial, you'll need an Unstructured API key. This gives you access to the complete Gen AI data layer that will process your documents and create your unified knowledge base. +# +# ### Sign Up and Get Your API Key +# +# Visit https://platform.unstructured.io to sign up for a free account, navigate to API Keys in the sidebar, generate your API key, and save it for the configuration step below. For Team or Enterprise accounts, make sure you've selected the correct organizational workspace before creating your API key. +# +# **Need help?** Contact Unstructured Support at support@unstructured.io + +# %% [markdown] +# ## Configuration: Setting Up Your Environment +# +# Now we'll configure your environment with the necessary API keys and credentials. This step ensures your system can connect to all the data sources and services we'll be using. + +# %% [markdown] +# ### Creating a .env File in Google Colab +# +# For better security and organization, we'll create a `.env` file directly in your Colab environment. Run the code cell below to create the file with placeholder values, then edit it with your actual credentials. +# +# After running the code cell, you'll need to replace each placeholder value (like `your-unstructured-api-key`) with your actual API keys and credentials. + +# %% +def create_dotenv_file(): + """Create a .env file with placeholder values for the user to fill in.""" + env_content = """# Hybrid RAG Pipeline Environment Configuration +# Fill in your actual values below +# Configuration - Set these explicitly + +# =================================================================== +# AWS CONFIGURATION +# =================================================================== +AWS_ACCESS_KEY_ID="your-aws-access-key-id" +AWS_SECRET_ACCESS_KEY="your-aws-secret-access-key" +AWS_REGION="us-east-1" + +# =================================================================== +# UNSTRUCTURED API CONFIGURATION +# =================================================================== +UNSTRUCTURED_API_KEY="your-unstructured-api-key" +UNSTRUCTURED_API_URL="https://platform.unstructuredapp.io/api/v1" + +# =================================================================== +# ELASTICSEARCH CONFIGURATION +# =================================================================== +ELASTICSEARCH_HOST="https://your-cluster.es.io:443" +ELASTICSEARCH_API_KEY="your-elasticsearch-api-key" + +# =================================================================== +# PIPELINE DATA SOURCES +# =================================================================== +S3_SOURCE_BUCKET="your-s3-source-bucket" +S3_DESTINATION_BUCKET="your-s3-destination-bucket" +S3_OUTPUT_PREFIX="" +ELASTICSEARCH_INDEX="sales-records-consolidated" + +# =================================================================== +# OPENAI API CONFIGURATION +# =================================================================== +OPENAI_API_KEY="your-openai-api-key" +""" + + with open('.env', 'w') as f: + f.write(env_content) + + print("✅ Created .env file with placeholder values") + print("📝 Please edit the .env file and replace the placeholder values with your actual credentials") + print("🔒 The .env file will be loaded automatically by the pipeline") + +# Create the .env file +create_dotenv_file() + +# %% [markdown] +# ### Installing Required Dependencies +# +# The following code installs the Python packages needed for this tutorial: the Unstructured client, Elasticsearch connector, AWS SDK, and other dependencies. + +# %% +import sys, subprocess + +def ensure_notebook_deps() -> None: + packages = [ + "jupytext", + "python-dotenv", + "unstructured-client", + "elasticsearch", + "boto3", + "PyYAML", + "langchain", + "langchain-elasticsearch", + "langchain-openai" + ] + try: + subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", *packages]) + except Exception: + # If install fails, continue; imports below will surface actionable errors + pass + +# Install notebook dependencies (safe no-op if present) +ensure_notebook_deps() + +import os +import time +import json +import zipfile +import tempfile +import requests +from pathlib import Path +from dotenv import load_dotenv +from urllib.parse import urlparse + +import boto3 +from botocore.exceptions import ClientError, NoCredentialsError +from elasticsearch import Elasticsearch +from elasticsearch.helpers import bulk + +from unstructured_client import UnstructuredClient +from unstructured_client.models.operations import ( + CreateSourceRequest, + CreateDestinationRequest, + CreateWorkflowRequest +) +from unstructured_client.models.shared import ( + CreateSourceConnector, + CreateDestinationConnector, + WorkflowNode, + WorkflowType, + CreateWorkflow +) + +# ============================================================================= +# ENVIRONMENT CONFIGURATION +# ============================================================================= +# Load from .env file if it exists +load_dotenv() + +# Configuration constants +SKIPPED = "SKIPPED" +UNSTRUCTURED_API_URL = os.getenv("UNSTRUCTURED_API_URL", "https://platform.unstructuredapp.io/api/v1") + +# Get environment variables +UNSTRUCTURED_API_KEY = os.getenv("UNSTRUCTURED_API_KEY") +AWS_ACCESS_KEY_ID = os.getenv("AWS_ACCESS_KEY_ID") +AWS_SECRET_ACCESS_KEY = os.getenv("AWS_SECRET_ACCESS_KEY") +AWS_REGION = os.getenv("AWS_REGION", "us-east-1") +S3_SOURCE_BUCKET = os.getenv("S3_SOURCE_BUCKET") +S3_DESTINATION_BUCKET = os.getenv("S3_DESTINATION_BUCKET") +S3_OUTPUT_PREFIX = os.getenv("S3_OUTPUT_PREFIX", "") +ELASTICSEARCH_HOST = os.getenv("ELASTICSEARCH_HOST") +ELASTICSEARCH_API_KEY = os.getenv("ELASTICSEARCH_API_KEY") +ELASTICSEARCH_INDEX = os.getenv("ELASTICSEARCH_INDEX", "sales-records-consolidated") +OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") + +# Validation +REQUIRED_VARS = { + "UNSTRUCTURED_API_KEY": UNSTRUCTURED_API_KEY, + "AWS_ACCESS_KEY_ID": AWS_ACCESS_KEY_ID, + "AWS_SECRET_ACCESS_KEY": AWS_SECRET_ACCESS_KEY, + "ELASTICSEARCH_HOST": ELASTICSEARCH_HOST, + "ELASTICSEARCH_API_KEY": ELASTICSEARCH_API_KEY, + "S3_SOURCE_BUCKET": S3_SOURCE_BUCKET, +} + +missing_vars = [key for key, value in REQUIRED_VARS.items() if not value] +if missing_vars: + print(f"❌ Missing required environment variables: {', '.join(missing_vars)}") + print("Please set these environment variables or create a .env file with your credentials.") + raise ValueError(f"Missing required environment variables: {missing_vars}") + +print("✅ Configuration loaded successfully") + +# %% [markdown] +# ## AWS S3: Your Document Storage +# +# Now that we have our environment configured, let's set up the data sources for our hybrid RAG system. First up: your unstructured documents. These PDFs, manuals, and reports need to be accessible via S3, where your product documentation and other unstructured content lives, waiting to be processed into searchable knowledge. +# +# ### What You Need +# +# **An existing S3 bucket** containing the documents you want to process. For this tutorial, we'll use sample product manuals, but in production, this would be your actual business documents. +# +# > **Note**: This tutorial assumes you have an existing S3 bucket with documents. For detailed S3 setup instructions, see the [Unstructured S3 source connector documentation](https://docs.unstructured.io/api-reference/api-services/source-connectors/s3). +# +# You'll need an AWS account with S3 access, an IAM user with S3 read permissions for your bucket, and access keys (Access Key ID and Secret Access Key). + +# %% [markdown] +# ## Elasticsearch: Your Business Data Hub +# +# While S3 holds your unstructured documents, Elasticsearch serves a dual purpose in our pipeline. It's both a source of structured business data (your sales records, customer information) and the destination where our unified, processed results will be stored for RAG queries. +# +# ### What You Need +# +# **Elasticsearch cluster** with API key authentication from Elastic Cloud (managed service). This gives you the reliability and scalability needed for enterprise applications. +# +# The pipeline uses two indices: `sales-records-consolidated` as the source containing your business data, and `customer-support` as the destination for your unified knowledge base. Both are created automatically by the pipeline. +# +# ### Why Consolidated Data Format Matters +# +# Traditional databases store information in separate fields (customer_name, product_id, purchase_date). For RAG applications, we consolidate this into a long-form text field that provides full context in each search result. This approach ensures that when someone searches for "John's headphone purchase," they get the complete story in one result. +# +# Example transformation: +# ``` +# Before: {customer: "John Doe", product: "BH-001", date: "2024-01-15"} +# After: "customer: John Doe\nproduct: BH-001\ndate: 2024-01-15" +# ``` +# +# ### API Key Permissions +# +# Your Elasticsearch API key needs these permissions: +# +# ```json +# { +# "sales-records-full-access": { +# "cluster": [], +# "indices": [ +# { +# "names": [ +# "sales-records", +# "sales-records-consolidated", +# "customer-support" +# ], +# "privileges": [ +# "create_index", +# "delete_index", +# "manage", +# "write", +# "read", +# "view_index_metadata", +# "monitor" +# ], +# "allow_restricted_indices": false +# } +# ], +# "applications": [], +# "run_as": [], +# "metadata": {}, +# "transient_metadata": { +# "enabled": true +# } +# } +# } +# ``` +# +# **Don't have Elasticsearch data yet?** The pipeline includes automatic data setup that creates sample sales records for demonstration. This is done by downloading .ZIP files from github and unzipping them. + +# %% [markdown] +# ## Data Preparation: Setting Up Your Demo Environment +# +# With our infrastructure configured, let's prepare the actual data that will flow through our hybrid RAG system. For this demonstration, we've created realistic sample data that represents a typical enterprise scenario, giving you a working example without requiring you to set up your own data sources first. +# +# **Elasticsearch Sales Data**: 100 synthetic sales records with customer information, with consolidated fields optimized for vector search. This represents the kind of structured business data you'd find in any enterprise system. +# +# **S3 Product Documentation**: 9 product manuals downloaded from manufacturer websites and stored in your S3 bucket. These represent the unstructured documents that contain critical product information. +# +# This combination mimics real enterprise scenarios where structured data (sales records) and unstructured documents (manuals) need to be searchable together for effective customer support. The magic happens when we can answer questions like "What issues have customers reported with the BH-900 headphones?" by pulling from both the sales records and the product manual simultaneously. + +# %% +# Data preparation functions + +def download_file(url: str, local_path: str) -> bool: + """Download a file from URL to local path.""" + try: + print(f"📥 Downloading {url}...") + response = requests.get(url, stream=True) + response.raise_for_status() + + Path(local_path).parent.mkdir(parents=True, exist_ok=True) + + with open(local_path, 'wb') as f: + for chunk in response.iter_content(chunk_size=8192): + f.write(chunk) + + print(f"✅ Downloaded to {local_path}") + return True + + except Exception as e: + print(f"❌ Error downloading {url}: {e}") + return False + +def setup_elasticsearch_data(): + """Download and load sales data into Elasticsearch index.""" + print("🔧 Setting up Elasticsearch sales data...") + + try: + es = Elasticsearch( + ELASTICSEARCH_HOST, + api_key=ELASTICSEARCH_API_KEY, + request_timeout=60, + max_retries=3, + retry_on_timeout=True + ) + + index_name = "sales-records-consolidated" + + sales_data_url = "https://github.com/Unstructured-IO/rag-over-hybrid-data-sources/raw/feature/hybrid-rag-pipeline/source_data/sales_records_consolidated.zip" + + with tempfile.NamedTemporaryFile(suffix='.zip', delete=False) as tmp_file: + if not download_file(sales_data_url, tmp_file.name): + return False + + with zipfile.ZipFile(tmp_file.name, 'r') as zipf: + # Load mapping + with zipf.open('mapping.json') as f: + mapping_data = json.loads(f.read().decode('utf-8')) + + # Load documents + with zipf.open('documents.json') as f: + documents = json.loads(f.read().decode('utf-8')) + + # Always delete existing index if present and reload from zip + if es.indices.exists(index=index_name): + print(f"🗑️ Deleting existing index '{index_name}' to reload fresh data...") + es.indices.delete(index=index_name) + + # Create index with mapping + index_mapping = mapping_data[index_name] if index_name in mapping_data else mapping_data[list(mapping_data.keys())[0]] + es.indices.create(index=index_name, body=index_mapping) + print(f"🔧 Created index '{index_name}' with mapping") + + # Prepare documents for bulk insert + def generate_docs(): + for doc in documents: + yield { + "_index": index_name, + "_id": doc["_id"], + "_source": doc["_source"] + } + + # Bulk insert documents + success_count, failed_items = bulk(es, generate_docs(), chunk_size=100) + print(f"📝 Inserted {success_count} documents") + + # Refresh index and verify + es.indices.refresh(index=index_name) + count_response = es.count(index=index_name) + count_data = count_response.body if hasattr(count_response, 'body') else count_response + doc_count = count_data['count'] + + if doc_count > 0: + print(f"✅ Successfully loaded {doc_count} documents into '{index_name}' index") + return True + else: + print(f"❌ Index '{index_name}' is empty after loading") + return False + + except Exception as e: + print(f"❌ Error setting up Elasticsearch data: {e}") + return False + + finally: + # Clean up temp file + try: + os.unlink(tmp_file.name) + except: + pass + +def setup_s3_data(): + """Download and load PDF files into S3 bucket.""" + print("🔧 Setting up S3 PDF data...") + + try: + # Initialize S3 client + s3 = boto3.client( + 's3', + aws_access_key_id=AWS_ACCESS_KEY_ID, + aws_secret_access_key=AWS_SECRET_ACCESS_KEY, + region_name=AWS_REGION + ) + + bucket_name = S3_SOURCE_BUCKET + if not bucket_name: + print("❌ S3_SOURCE_BUCKET not configured") + return False + + # Check if bucket exists and has data + try: + response = s3.list_objects_v2(Bucket=bucket_name, MaxKeys=1) + if response.get('KeyCount', 0) > 0: + # Count total objects + response = s3.list_objects_v2(Bucket=bucket_name) + object_count = len(response.get('Contents', [])) + print(f"✅ Bucket '{bucket_name}' already exists with {object_count} files") + return True + except ClientError as e: + if e.response['Error']['Code'] != '404': + raise e + + # Download S3 PDFs zip file + s3_data_url = "https://github.com/Unstructured-IO/rag-over-hybrid-data-sources/raw/feature/hybrid-rag-pipeline/source_data/s3_pdfs.zip" + + with tempfile.NamedTemporaryFile(suffix='.zip', delete=False) as tmp_file: + if not download_file(s3_data_url, tmp_file.name): + return False + + # Create bucket if it doesn't exist + try: + s3.head_bucket(Bucket=bucket_name) + print(f"📦 Using existing bucket '{bucket_name}'") + except ClientError as e: + if e.response['Error']['Code'] == '404': + print(f"🔧 Creating bucket '{bucket_name}'...") + try: + if AWS_REGION == "us-east-1": + s3.create_bucket(Bucket=bucket_name) + else: + s3.create_bucket( + Bucket=bucket_name, + CreateBucketConfiguration={'LocationConstraint': AWS_REGION} + ) + print(f"✅ Created bucket '{bucket_name}'") + except ClientError as create_error: + if 'BucketAlreadyOwnedByYou' in str(create_error): + print(f"📦 Bucket '{bucket_name}' already exists and is owned by you") + else: + raise create_error + else: + raise e + + # Clear existing files in bucket + print(f"🗑️ Clearing existing files from bucket '{bucket_name}'...") + try: + response = s3.list_objects_v2(Bucket=bucket_name) + if 'Contents' in response: + objects_to_delete = [{'Key': obj['Key']} for obj in response['Contents']] + if objects_to_delete: + s3.delete_objects( + Bucket=bucket_name, + Delete={'Objects': objects_to_delete} + ) + print(f"🗑️ Deleted {len(objects_to_delete)} existing files") + else: + print("📁 Bucket was already empty") + else: + print("📁 Bucket was already empty") + except ClientError as e: + print(f"⚠️ Could not clear bucket (continuing anyway): {e}") + + # Extract and upload files from zip + uploaded_count = 0 + with zipfile.ZipFile(tmp_file.name, 'r') as zipf: + file_list = zipf.namelist() + pdf_files = [f for f in file_list if f.lower().endswith('.pdf')] + + print(f"📊 Found {len(pdf_files)} PDF files in zip") + + for file_name in pdf_files: + try: + # Extract file data + file_data = zipf.read(file_name) + + # Upload to S3 + s3.put_object( + Bucket=bucket_name, + Key=file_name, + Body=file_data, + ContentType='application/pdf' + ) + + print(f" 📤 Uploaded: {file_name}") + uploaded_count += 1 + + except Exception as e: + print(f" ❌ Failed to upload {file_name}: {e}") + + # Verify upload + response = s3.list_objects_v2(Bucket=bucket_name) + actual_count = len(response.get('Contents', [])) + + if actual_count > 0: + print(f"✅ Successfully uploaded {uploaded_count} PDFs to bucket '{bucket_name}'") + print(f"📊 Bucket now contains {actual_count} files") + return True + else: + print(f"❌ Bucket '{bucket_name}' is empty after upload") + return False + + except NoCredentialsError: + print("❌ AWS credentials not found. Please check your .env file.") + return False + except Exception as e: + print(f"❌ Error setting up S3 data: {e}") + return False + + finally: + # Clean up temp file + try: + os.unlink(tmp_file.name) + except: + pass + +def prepare_data_sources(): + """Prepare both Elasticsearch and S3 data sources.""" + print("🚀 Preparing data sources...") + print("=" * 50) + + # Setup Elasticsearch data + if not setup_elasticsearch_data(): + print("❌ Failed to setup Elasticsearch data") + return False + + print() # Add spacing + + # Setup S3 data + if not setup_s3_data(): + print("❌ Failed to setup S3 data") + return False + + print() + print("✅ All data sources prepared successfully!") + print("=" * 50) + return True + +# %% [markdown] +# ## S3 Source Connector +# +# Now we'll create the connections that link our data sources to Unstructured's processing pipeline. First, let's establish the connection to your S3 bucket containing PDF documents for processing. + +# %% [markdown] +# ### Example Product Manual Content +# +# The following image shows a sample page from one of the product manuals stored in your S3 bucket. This demonstrates the type of unstructured content that will be processed and made searchable through our RAG system. + +# %% [markdown] +# ![product-manual-example](data:image/png;base64,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) + +# %% [markdown] +# ## Elasticsearch Source Connector +# +# Next, we'll connect to your Elasticsearch index containing structured sales data, completing our dual-source setup. + +# %% [markdown] +# ### Sales Records Data Structure +# +# The image below shows the structure of the consolidated sales records in your Elasticsearch index. This data represents customer transactions and will be processed alongside the product manuals to create a unified knowledge base. + +# %% [markdown] +# ![sales-records-consolidated](data:image/png;base64,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) + +# %% [markdown] +# ## Elasticsearch Destination Connector +# +# Finally, we'll create the destination where both data streams will converge: the unified `customer-support` index where all processed data will be stored. + +# %% +def create_s3_source_connector(): + """Create an S3 source connector for PDF documents.""" + try: + if not S3_SOURCE_BUCKET: + raise ValueError("S3_SOURCE_BUCKET is required (bucket name, s3:// URL, or https:// URL)") + value = S3_SOURCE_BUCKET.strip() + + if value.startswith("s3://"): + s3_style = value if value.endswith("/") else value + "/" + elif value.startswith("http://") or value.startswith("https://"): + parsed = urlparse(value) + host = parsed.netloc + path = parsed.path or "/" + bucket = host.split(".s3.")[0] + s3_style = f"s3://{bucket}{path if path.endswith('/') else path + '/'}" + else: + s3_style = f"s3://{value if value.endswith('/') else value + '/'}" + + with UnstructuredClient(api_key_auth=UNSTRUCTURED_API_KEY) as client: + response = client.sources.create_source( + request=CreateSourceRequest( + create_source_connector=CreateSourceConnector( + name="", + type="s3", + config={ + "remote_url": s3_style, + "recursive": True, + "key": AWS_ACCESS_KEY_ID, + "secret": AWS_SECRET_ACCESS_KEY, + } + ) + ) + ) + + source_id = response.source_connector_information.id + print(f"✅ Created S3 PDF source connector: {source_id} -> {s3_style}") + return source_id + + except Exception as e: + print(f"❌ Error creating S3 source connector: {e}") + return None + +def create_elasticsearch_source_connector(): + """Create an Elasticsearch source connector for sales data.""" + try: + with UnstructuredClient(api_key_auth=UNSTRUCTURED_API_KEY) as client: + response = client.sources.create_source( + request=CreateSourceRequest( + create_source_connector=CreateSourceConnector( + name=f"elasticsearch_sales_source_{int(time.time())}", + type="elasticsearch", + config={ + "hosts": [ELASTICSEARCH_HOST], + "es_api_key": ELASTICSEARCH_API_KEY, + "index_name": ELASTICSEARCH_INDEX + } + ) + ) + ) + + source_id = response.source_connector_information.id + print(f"✅ Created Elasticsearch sales source connector: {source_id}") + return source_id + + except Exception as e: + print(f"❌ Error creating Elasticsearch source connector: {e}") + return None + +def create_elasticsearch_destination_connector(): + """Create an Elasticsearch destination connector for processed results.""" + try: + with UnstructuredClient(api_key_auth=UNSTRUCTURED_API_KEY) as client: + response = client.destinations.create_destination( + request=CreateDestinationRequest( + create_destination_connector=CreateDestinationConnector( + name=f"elasticsearch_customer_support_destination_{int(time.time())}", + type="elasticsearch", + config={ + "hosts": [ELASTICSEARCH_HOST], + "es_api_key": ELASTICSEARCH_API_KEY, + "index_name": "customer-support" + } + ) + ) + ) + + destination_id = response.destination_connector_information.id + print(f"✅ Created Elasticsearch destination connector: {destination_id}") + return destination_id + + except Exception as e: + print(f"❌ Error creating Elasticsearch destination connector: {e}") + return None + +# %% [markdown] +# ## Processing Pipeline Configuration +# +# With our connectors in place, we can now configure the intelligent processing pipeline that will transform both data sources. This four-stage pipeline (VLM → Chunker → Embedder → NER) will be applied to both workflows, ensuring consistent processing regardless of data source. + +# %% +def create_workflow_nodes(): + """Create shared processing nodes for workflows.""" + vlm_partition_node = WorkflowNode( + name="VLM_Partitioner", + subtype="vlm", + type="partition", + settings={ + "provider": "openai", + "model": "gpt-4o", + } + ) + + chunk_node = WorkflowNode( + name="Chunker_Node", + subtype="chunk_by_title", + type="chunk", + settings={ + "new_after_n_chars": 1500, + "max_characters": 2048, + "overlap": 0 + } + ) + + embedder_node = WorkflowNode( + name="Embedder_Node", + subtype="openai", + type="embed", + settings={ + "model_name": "text-embedding-3-small" + } + ) + + ner_enrichment_node = WorkflowNode( + name="NER_Enrichment", + type="prompter", + subtype="openai_ner", + settings={} + ) + + return vlm_partition_node, chunk_node, embedder_node, ner_enrichment_node + +def create_parallel_workflows(s3_source_id, elasticsearch_source_id, destination_id): + """Create separate workflows for S3 PDFs and Elasticsearch data that run in parallel.""" + try: + vlm_partition_node, chunk_node, embedder_node, ner_enrichment_node = create_workflow_nodes() + + # Create workflow for S3 PDFs + s3_workflow_id = None + if s3_source_id: + with UnstructuredClient(api_key_auth=UNSTRUCTURED_API_KEY) as client: + s3_workflow = CreateWorkflow( + name=f"S3-PDFs-Parallel-Workflow_{int(time.time())}", + source_id=s3_source_id, + destination_id=destination_id, + workflow_type=WorkflowType.CUSTOM, + workflow_nodes=[ + vlm_partition_node, + chunk_node, + embedder_node, + ner_enrichment_node + ] + ) + + s3_response = client.workflows.create_workflow( + request=CreateWorkflowRequest( + create_workflow=s3_workflow + ) + ) + + s3_workflow_id = s3_response.workflow_information.id + print(f"✅ Created S3 PDF workflow: {s3_workflow_id}") + + # Create workflow for Elasticsearch sales data + with UnstructuredClient(api_key_auth=UNSTRUCTURED_API_KEY) as client: + es_workflow = CreateWorkflow( + name=f"Elasticsearch-Sales-Parallel-Workflow_{int(time.time())}", + source_id=elasticsearch_source_id, + destination_id=destination_id, + workflow_type=WorkflowType.CUSTOM, + workflow_nodes=[ + vlm_partition_node, + chunk_node, + embedder_node, + ner_enrichment_node + ] + ) + + es_response = client.workflows.create_workflow( + request=CreateWorkflowRequest( + create_workflow=es_workflow + ) + ) + + es_workflow_id = es_response.workflow_information.id + print(f"✅ Created Elasticsearch sales workflow: {es_workflow_id}") + + return s3_workflow_id, es_workflow_id + + except Exception as e: + print(f"❌ Error creating parallel workflows: {e}") + return None, None + +# %% [markdown] +# ## Creating Parallel Processing Workflows +# +# Now we'll assemble everything into the two parallel workflows shown in our architecture diagram above, connecting each data source to the processing pipeline and unified destination. + +# %% [markdown] +# ## Starting Your Processing Jobs +# +# With our workflows configured, it's time to put them into action. This step submits both workflows to the Unstructured API and returns job IDs for monitoring. + +# %% +def run_workflow(workflow_id, workflow_name): + """Run a workflow and return job information.""" + try: + with UnstructuredClient(api_key_auth=UNSTRUCTURED_API_KEY) as client: + response = client.workflows.run_workflow( + request={"workflow_id": workflow_id} + ) + + job_id = response.job_information.id + print(f"✅ Started {workflow_name} job: {job_id}") + return job_id + + except Exception as e: + print(f"❌ Error running {workflow_name} workflow: {e}") + return None + +def poll_job_status(job_id, job_name, wait_time=30): + """Poll job status until completion.""" + print(f"⏳ Monitoring {job_name} job status...") + + while True: + try: + with UnstructuredClient(api_key_auth=UNSTRUCTURED_API_KEY) as client: + response = client.jobs.get_job( + request={"job_id": job_id} + ) + + job = response.job_information + status = job.status + + if status in ["SCHEDULED", "IN_PROGRESS"]: + print(f"⏳ {job_name} job status: {status}") + time.sleep(wait_time) + elif status == "COMPLETED": + print(f"✅ {job_name} job completed successfully!") + return job + elif status == "FAILED": + print(f"❌ {job_name} job failed!") + return job + else: + print(f"❓ Unknown {job_name} job status: {status}") + return job + + except Exception as e: + print(f"❌ Error polling {job_name} job status: {e}") + time.sleep(wait_time) + +# %% [markdown] +# ## Monitoring Your Processing Progress +# +# Jobs progress through scheduled, in-progress, completed, or failed states. The `poll_job_status` function checks status every 30 seconds and blocks execution until jobs complete, so you can see exactly what's happening with your data processing. + +# %% [markdown] +# ## Preparing Your Elasticsearch Environment +# +# Before processing begins, we validate that the `sales-records-consolidated` index exists and contains data, then recreate the `customer-support` index fresh for each run. This preparation step ensures a clean environment and prevents any issues from previous runs. +# +# ### Index Mapping +# +# The destination index uses this structure optimized for RAG applications: +# ```json +# { +# "id": "keyword", // Unique document identifier +# "timestamp": "date", // Processing timestamp +# "text": "text", // Searchable content +# "metadata": "object" // Source info and entities +# } +# ``` + +# %% +def run_elasticsearch_preprocessing(): + """Check and manage Elasticsearch indices for the pipeline.""" + print("🔧 Running Elasticsearch preprocessing...") + + try: + es = Elasticsearch( + ELASTICSEARCH_HOST, + api_key=ELASTICSEARCH_API_KEY, + request_timeout=60, + max_retries=3, + retry_on_timeout=True + ) + + sales_index = "sales-records-consolidated" + print(f"�� Checking {sales_index} index...") + + if not es.indices.exists(index=sales_index): + raise ValueError(f"❌ Index '{sales_index}' does not exist. There is no data to use.") + + count_response = es.count(index=sales_index) + doc_count = count_response['count'] + + if doc_count == 0: + raise ValueError(f"❌ Index '{sales_index}' is empty. There is no data to use.") + + print(f"✅ Found {doc_count} records in {sales_index}") + + # Handle customer-support index + support_index = "customer-support" + print(f"🔍 Checking {support_index} index...") + + if es.indices.exists(index=support_index): + print(f"🗑️ Deleting existing {support_index} index...") + es.indices.delete(index=support_index) + + # Create fresh customer-support index + print(f"🔧 Creating fresh {support_index} index...") + mapping = { + "settings": { + "number_of_shards": 1, + "number_of_replicas": 1 + }, + "mappings": { + "properties": { + "id": {"type": "keyword"}, + "timestamp": {"type": "date"}, + "text": {"type": "text", "analyzer": "standard"}, + "metadata": {"type": "object"} + } + } + } + + es.indices.create(index=support_index, body=mapping) + es.indices.refresh(index=support_index) + + print(f"✅ Successfully created fresh {support_index} index") + print("✅ Elasticsearch preprocessing completed successfully") + return True + + except ValueError as e: + print(str(e)) + return False + except Exception as e: + print(f"❌ Error during Elasticsearch preprocessing: {e}") + return False + +# %% [markdown] +# ## Pipeline Execution Summary +# +# The following summary displays all resources created during pipeline setup: data source paths, connector IDs, workflow IDs, job IDs, and processing status. + +# %% +import os + +def print_pipeline_summary(s3_workflow_id, es_workflow_id, s3_job_id, es_job_id): + """Print comprehensive pipeline summary.""" + print("\n" + "=" * 80) + print("📊 HYBRID RAG PIPELINE SUMMARY") + print("=" * 80) + print(f"📁 S3 Source (PDFs): {S3_SOURCE_BUCKET if s3_workflow_id else SKIPPED}") + print(f"🔍 Elasticsearch Source: {ELASTICSEARCH_HOST}/{ELASTICSEARCH_INDEX}") + print(f"📤 Elasticsearch Destination: {ELASTICSEARCH_HOST}/customer-support") + print(f"") + print(f"⚙️ S3 PDFs Workflow ID: {s3_workflow_id if s3_workflow_id else SKIPPED}") + print(f"⚙️ Elasticsearch Sales Workflow ID: {es_workflow_id}") + print(f"") + print(f"🚀 S3 PDFs Job ID: {s3_job_id if s3_job_id else SKIPPED}") + print(f"🚀 Elasticsearch Sales Job ID: {es_job_id}") + +def verify_customer_support_results(s3_job_id=None, es_job_id=None): + """ + Verifies the processed results in the customer-support index, prettyprinting one doc per unique source connector. + Assumes jobs have already completed successfully. + """ + import pprint + + print("🔍 Verifying processed results in 'customer-support' index (assuming jobs have completed)...") + + try: + # Initialize Elasticsearch client + es = Elasticsearch( + ELASTICSEARCH_HOST, + api_key=ELASTICSEARCH_API_KEY, + request_timeout=60, + max_retries=3, + retry_on_timeout=True + ) + + index_name = "customer-support" + + # Check if index exists + if not es.indices.exists(index=index_name): + print(f"❌ Index '{index_name}' does not exist. Workflows may not have written results yet.") + return + + # Get document count + count_response = es.count(index=index_name) + total_docs = count_response['count'] + print(f"📊 Total processed documents: {total_docs}") + + if total_docs == 0: + print("⏳ No documents found yet. Workflows may still be processing or index is empty.") + print("💡 Check the Unstructured dashboard for job status.") + return + + print(f"\n📋 Analyzing Source Connectors:") + print("=" * 40) + + # Get sample documents to analyze source patterns + # Use function_score with random_score to sample documents randomly + sample_response = es.search( + index=index_name, + body={ + "size": 50, # Get more samples to increase chance of seeing all sources + "_source": ["metadata", "text", "element_id"], + "query": { + "function_score": { + "query": {"match_all": {}}, + "random_score": {} + } + } + } + ) + + + # Map: source_connector_key -> [doc, ...] + source_connector_map = {} + unknown_docs = [] + + for hit in sample_response['hits']['hits']: + source = hit['_source'] + metadata = source.get('metadata', {}) + + # Determine source connector type based on metadata patterns + if "data_source-record_locator-index_name" in metadata: + # Elasticsearch source connector + key = f"elasticsearch:{metadata['data_source-record_locator-index_name']}" + elif "data_source-url" in metadata: + # S3 source connector - group all S3 URLs by bucket + url = metadata['data_source-url'] + if url.startswith('s3://'): + # Extract bucket name from S3 URL + bucket = url.split('/')[2] if '/' in url else url.replace('s3://', '') + key = f"s3:{bucket}" + else: + key = f"s3:unknown" + elif "filename" in metadata and metadata.get('filetype') == 'pdf': + # PDF files from S3 (fallback detection) + key = "s3:pdfs" + else: + key = "unknown" + + if key == "unknown": + unknown_docs.append(hit) + else: + if key not in source_connector_map: + source_connector_map[key] = hit # Only keep the first doc for each source connector + + print(f"🔍 Unique source connectors found: {len(source_connector_map)}") + for i, (key, doc) in enumerate(source_connector_map.items(), 1): + print(f"\n--- Source Connector {i} ({key}) ---") + pprint.pprint(doc['_source'], depth=6, compact=False, sort_dicts=False) + + if unknown_docs: + print(f"\n❓ Example Unknown Source Document:") + print("-" * 35) + unknown_example = unknown_docs[0]['_source'] + metadata = unknown_example.get('metadata', {}) + text = unknown_example.get('text', '') + print(f" Element ID: {unknown_example.get('element_id', 'N/A')}") + print(f" Metadata: {metadata}") + print(f" Text Preview: {text[:200]}..." if len(text) > 200 else f" Text: {text}") + print(" Metadata prettyprint:") + pprint.pprint(metadata, depth=6, compact=False, sort_dicts=False) + + # Test search functionality + print(f"\n🔍 Testing Search Functionality:") + print("=" * 32) + + search_tests = ["manual", "customer", "product", "support"] + + for search_term in search_tests: + search_response = es.search( + index=index_name, + body={ + "size": 1, + "query": { + "match": { + "text": search_term + } + } + } + ) + + hits = search_response['hits']['total']['value'] + print(f" 🔎 '{search_term}': {hits} matches") + + print(f"\n" + "=" * 50) + print("🎉 CUSTOMER-SUPPORT INDEX VERIFICATION") + print("=" * 50) + print("✅ Index exists and contains processed documents") + print("✅ Documents from both source connectors are present (if both completed)") + print("✅ Text search is functional across processed content") + print("✅ Ready for hybrid RAG queries!") + + except Exception as e: + print(f"❌ Error verifying results: {e}") + print("💡 This is normal if workflows are still processing or if there is a connection issue.") + +# %% [markdown] +# ## Orchestrating Your Complete Pipeline +# +# The main function coordinates all pipeline steps in logical sequence: data preparation, environment validation, connector setup, workflow creation, execution, and summary reporting. + +# %% +def main(): + """Main pipeline execution""" + print("🚀 Starting Hybrid RAG Pipeline") + + print("\n📦 Step 0: Data source preparation") + print("-" * 50) + + if not prepare_data_sources(): + print("❌ Failed to prepare data sources") + return + + print("\n🔧 Step 1: Elasticsearch preprocessing") + print("-" * 50) + + if not run_elasticsearch_preprocessing(): + print("❌ Failed to complete Elasticsearch preprocessing") + return + + print("\n🔗 Step 2: Creating source connectors") + print("-" * 50) + + s3_source_id = create_s3_source_connector() + if not s3_source_id: + print("❌ Failed to create S3 source connector") + return + + elasticsearch_source_id = create_elasticsearch_source_connector() + if not elasticsearch_source_id: + print("❌ Failed to create Elasticsearch source connector") + return + + # Step 3: Create Destination Connector + print("\n🎯 Step 3: Creating Elasticsearch destination connector") + print("-" * 50) + + destination_id = create_elasticsearch_destination_connector() + if not destination_id: + print("❌ Failed to create destination connector") + return + + # Step 4: Create Workflows + print("\n⚙️ Step 4: Creating workflows") + print("-" * 50) + + s3_workflow_id, es_workflow_id = create_parallel_workflows( + s3_source_id, elasticsearch_source_id, destination_id + ) + + if not es_workflow_id: + print("❌ Failed to create Elasticsearch workflow") + return + + # Step 5: Run Workflows + print("\n🚀 Step 5: Running workflows") + print("-" * 50) + + s3_job_id = None + es_job_id = None + + if s3_workflow_id: + s3_job_id = run_workflow(s3_workflow_id, "S3 PDFs") + if not s3_job_id: + print("❌ Failed to start S3 workflow") + return + + if es_workflow_id: + es_job_id = run_workflow(es_workflow_id, "Elasticsearch Sales") + if not es_job_id: + print("❌ Failed to start Elasticsearch workflow") + return + + # Step 6: Pipeline Summary + print_pipeline_summary(s3_workflow_id, es_workflow_id, s3_job_id, es_job_id) + return s3_job_id, es_job_id + +# %% [markdown] +# ## Running Your Complete Pipeline +# +# We'll execute the complete pipeline by calling the main function to create all resources and start processing, then monitor the jobs until they complete successfully. + +# %% +s3_job_id, es_job_id = main() + +es_job_info = poll_job_status(es_job_id, "Elasticsearch Ingest") +s3_job_info = poll_job_status(s3_job_id, "S3 Ingest") +print("\n🔍 Verifying processed results") +print("-" * 50) +verify_customer_support_results() + +# %% [markdown] +# ### Unified Knowledge Base Results +# +# After processing both data sources, the pipeline creates a unified `customer-support` index containing processed documents from both S3 PDFs and Elasticsearch sales records. The image below shows the structure of this consolidated knowledge base, ready for RAG queries. + +# %% [markdown] +# ![customer-support](data:image/png;base64,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) + +# %% [markdown] +# ## RAG Query Demonstration +# +# Now that your hybrid knowledge base is ready, we'll demonstrate how to query it using RAG (Retrieval-Augmented Generation). This is where you'll see how the system can answer complex questions by pulling relevant information from both your S3 documents and Elasticsearch records. +# +# ### OpenAI API Key Required +# +# For the RAG demonstration, you'll need an OpenAI API key to power the language model that generates answers based on your retrieved documents. Visit https://platform.openai.com/api-keys to sign in or create an account and generate a new API key. +# +# The demonstration will show cross-source querying, source attribution, and semantic understanding as your hybrid RAG system answers questions by combining information from multiple data sources. + +# %% [markdown] +# ### RAG Configuration +# +# **Instructions**: Paste your OpenAI API key below to enable RAG demonstrations. This key will be used to power the language model that generates answers based on your retrieved documents. + +# %% +# RAG Demonstration Configuration and Queries +import os +import json + +# LangChain imports for RAG functionality +from langchain_elasticsearch import ElasticsearchStore +from langchain_openai import OpenAIEmbeddings, ChatOpenAI +from langchain_core.prompts import ChatPromptTemplate +from langchain_core.output_parsers import StrOutputParser +from langchain_core.runnables import RunnablePassthrough + +print("🤖 RAG Query Demonstration Setup") +print("=" * 40) + +if not OPENAI_API_KEY or OPENAI_API_KEY.startswith("your-"): + print("⚠️ OpenAI API key not configured.") + print("💡 Please set OPENAI_API_KEY in your .env file with your actual OpenAI API key.") + print("📝 You can get one at: https://platform.openai.com/api-keys") +else: + print("✅ OpenAI API key configured for RAG demonstrations") + +def setup_rag_system(): + """Set up the RAG system with Elasticsearch and OpenAI.""" + + if not OPENAI_API_KEY or OPENAI_API_KEY.startswith("your-"): + print("❌ OpenAI API key is required for RAG functionality") + print("Please set OPENAI_API_KEY in your .env file") + return None + + # Set OpenAI API key for LangChain + os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY + + try: + print("🔧 Setting up RAG components...") + + # Initialize embeddings (same model used in processing) + embeddings = OpenAIEmbeddings( + model="text-embedding-3-small", + openai_api_key=OPENAI_API_KEY + ) + + # Connect to Elasticsearch vector store - using your working pattern + vector_store = ElasticsearchStore( + index_name="customer-support", + embedding=embeddings, + es_url=ELASTICSEARCH_HOST, + es_api_key=ELASTICSEARCH_API_KEY, + vector_query_field="embeddings", + query_field="text", + ) + + # Create retriever + retriever = vector_store.as_retriever(search_kwargs={"k": 5}) + + # Initialize LLM + llm = ChatOpenAI( + model="gpt-3.5-turbo", + temperature=0, + openai_api_key=OPENAI_API_KEY + ) + + # Enhanced prompt template that leverages NER metadata + prompt = ChatPromptTemplate.from_template(""" +Use the following context to answer the question. Pay attention to any entity information (people, organizations, products, locations, dates) and relationships mentioned in the context. + +Context: +{context} + +Question: +{question} +""") + + print("✅ RAG system ready!") + return {"retriever": retriever, "llm": llm, "prompt": prompt} + + except ImportError as e: + print(f"❌ Missing RAG dependencies: {e}") + print("💡 Install with: pip install langchain langchain-elasticsearch langchain-openai") + return None + except Exception as e: + print(f"❌ Error setting up RAG system: {e}") + return None + +def extract_ner_entities(docs): + """Extract NER entities from document metadata.""" + entities = {"people": set(), "organizations": set(), "products": set(), "locations": set(), "dates": set()} + + for doc in docs: + metadata = doc.metadata + if "entities-items" in metadata: + try: + import json + entity_items = json.loads(metadata["entities-items"]) if isinstance(metadata["entities-items"], str) else metadata["entities-items"] + + for item in entity_items: + entity_type = item.get("type", "").upper() + entity_name = item.get("entity", "") + + if entity_type == "PERSON": + entities["people"].add(entity_name) + elif entity_type == "ORGANIZATION": + entities["organizations"].add(entity_name) + elif entity_type == "PRODUCT": + entities["products"].add(entity_name) + elif entity_type == "LOCATION": + entities["locations"].add(entity_name) + elif entity_type == "DATE": + entities["dates"].add(entity_name) + except: + pass + + return entities + +def analyze_sources(docs): + """Analyze retrieved documents by source type.""" + s3_docs = [] + es_docs = [] + unknown_docs = [] + + for doc in docs: + metadata = doc.metadata + if "data_source-record_locator-index_name" in metadata: + es_docs.append(doc) + elif "data_source-url" in metadata and "s3://" in metadata.get("data_source-url", ""): + s3_docs.append(doc) + else: + unknown_docs.append(doc) + + return s3_docs, es_docs, unknown_docs + +def demonstrate_hybrid_ner_queries(rag_components): + """Demonstrate NER-enhanced hybrid RAG capabilities.""" + if not rag_components: + return + + retriever = rag_components["retriever"] + llm = rag_components["llm"] + prompt = rag_components["prompt"] + + # Build RAG chain using your working pattern + rag_chain = ( + {"context": retriever, "question": RunnablePassthrough()} + | prompt + | llm + | StrOutputParser() + ) + + # Hybrid NER demonstration queries targeting different sources + hybrid_queries = [ + { + "query": "How do I troubleshoot Bose headphone connectivity issues?", + "description": "Product support query targeting S3 PDFs (product manuals)", + "expected_source": "S3 (Product Docs)" + }, + { + "query": "Tell me about Daniel Hahn and his purchases", + "description": "Customer analysis query targeting Elasticsearch (sales data)", + "expected_source": "Elasticsearch (Sales)" + }, + { + "query": "What are the technical specifications for SoundSport Wireless headphones?", + "description": "Product specification query targeting S3 PDFs", + "expected_source": "S3 (Product Docs)" + }, + { + "query": "Show me customers in San Antonio, TX", + "description": "Geographic customer query targeting Elasticsearch", + "expected_source": "Elasticsearch (Sales)" + }, + { + "query": "How do I reset wireless headphones to factory settings?", + "description": "Technical support query targeting S3 PDFs", + "expected_source": "S3 (Product Docs)" + }, + { + "query": "What products does Newegg sell and what are their features?", + "description": "Hybrid query targeting BOTH sources (sales + product specs)", + "expected_source": "Both S3 and Elasticsearch" + }, + { + "query": "I have a customer who bought Bose headphones and is having connectivity issues. What should I tell them?", + "description": "Customer support query requiring BOTH customer data AND product manuals", + "expected_source": "Both S3 and Elasticsearch" + } + ] + + print("\n🧠 Hybrid NER-Enhanced RAG Demonstration") + print("=" * 60) + + for i, query_info in enumerate(hybrid_queries, 1): + query = query_info["query"] + description = query_info["description"] + expected_source = query_info["expected_source"] + + print(f"\n{'='*70}") + print(f"Query {i}: {description}") + print(f"📝 Query: {query}") + print(f"🎯 Expected Source: {expected_source}") + print("=" * 70) + + try: + # Retrieve documents + docs = retriever.invoke(query) + + if not docs: + print("❌ No documents retrieved") + continue + + # Analyze sources (keeping your preferred format) + s3_docs, es_docs, unknown_docs = analyze_sources(docs) + print(f"📊 Retrieved {len(docs)} documents:") + print(f" 📄 S3 (Product Docs): {len(s3_docs)}") + print(f" 📊 Elasticsearch (Sales): {len(es_docs)}") + print(f" ❓ Unknown: {len(unknown_docs)}") + + # Check if we hit the expected source + if expected_source == "S3 (Product Docs)" and len(s3_docs) > 0: + print("✅ SUCCESS: Retrieved from expected S3 source!") + elif expected_source == "Elasticsearch (Sales)" and len(es_docs) > 0: + print("✅ SUCCESS: Retrieved from expected Elasticsearch source!") + elif expected_source == "Both S3 and Elasticsearch" and len(s3_docs) > 0 and len(es_docs) > 0: + print("✅ SUCCESS: Retrieved from BOTH sources as expected!") + elif expected_source.startswith("Both") and (len(s3_docs) > 0 or len(es_docs) > 0): + print("✅ PARTIAL: Retrieved from at least one expected source") + else: + print("⚠️ UNEXPECTED: Did not retrieve from expected source") + + # Extract and show NER entities + entities = extract_ner_entities(docs) + print(f"\n🏷️ NER Entities Found:") + if entities["people"]: + print(f" 👤 People: {', '.join(list(entities['people'])[:3])}") + if entities["organizations"]: + print(f" 🏢 Organizations: {', '.join(list(entities['organizations'])[:3])}") + if entities["products"]: + print(f" 📱 Products: {', '.join(list(entities['products'])[:3])}") + if entities["locations"]: + print(f" 🗺️ Locations: {', '.join(list(entities['locations'])[:3])}") + if entities["dates"]: + print(f" 📅 Dates: {', '.join(list(entities['dates'])[:3])}") + + # Generate answer + print(f"\n💬 Answer:") + answer = rag_chain.invoke(query) + print(f"{answer}") + + except Exception as e: + print(f"❌ Error: {e}") + if "429" in str(e): + print("⚠️ OpenAI API quota exceeded. Stopping demo.") + break + + print(f"\n{'='*70}") + print("🧠 Hybrid NER Demo Complete!") + print("✅ Demonstrated cross-source retrieval capabilities") + print("✅ Showed NER metadata integration across data sources") + print("✅ Validated hybrid RAG architecture") + +def run_rag_demonstration(): + """Run the RAG demonstration.""" + print("\n🚀 Starting Hybrid RAG Demonstration") + print("=" * 50) + + rag_components = setup_rag_system() + + if rag_components: + demonstrate_hybrid_ner_queries(rag_components) + else: + print("❌ RAG demonstration skipped due to configuration issues") + +# Run the demonstration +run_rag_demonstration() + +# %% [markdown] +# ## What You've Accomplished +# +# **Enterprise Data Integration**: You've learned how to process multiple data formats (PDFs, structured records) in parallel, why consistent processing pipelines matter for unified search, and the value of creating a single searchable knowledge base that spans all your data sources. +# +# **Unstructured API Capabilities**: You've experienced VLM-powered document partitioning for complex layouts, intelligent chunking that preserves document structure, named entity recognition for enhanced search precision, and unified processing across diverse data sources. +# +# **RAG System Architecture**: You've built parallel workflow design for scalability and reliability, vector embeddings for semantic similarity search, source attribution in mixed-data query results, and NER-enhanced query understanding and response generation. +# +# ### Ready to Scale? +# +# Deploy customer support chatbots with comprehensive knowledge access, build internal search tools that surface information from any source, or create automated content recommendation systems. Add more data sources using additional workflows, implement real-time data synchronization, or scale up for production data volumes with monitoring and alerting. +# +# ### Try Unstructured Today +# +# Ready to build your own hybrid RAG system? [Sign up for a free trial](https://unstructured.io/?modal=try-for-free) and start transforming your enterprise data into intelligent, searchable knowledge. +# +# **Need help getting started?** Contact our team to schedule a demo and see how Unstructured can solve your specific data challenges. diff --git a/hybrid_rag_pipeline_modular.py b/hybrid_rag_pipeline_modular.py new file mode 100644 index 0000000..94f1298 --- /dev/null +++ b/hybrid_rag_pipeline_modular.py @@ -0,0 +1,78 @@ +#!/usr/bin/env python3 +# [[MD:INTRO]] + +# [[MD:API_KEY_SETUP]] + +# [[MD:CONFIG]] + +# [[MD:COLAB_DOTENV_CREATION]] + +# [[CODE:DOTENV_CREATION]] + +# [[MD:DEPENDENCIES_EXPLANATION]] + +# [[CODE:DEPENDENCIES]] + +# [[MD:AWS_S3_SETUP]] + +# [[MD:ELASTICSEARCH_SETUP]] + +# [[MD:DATA_PREPARATION]] + +# [[CODE:DATA_PREPARATION]] + +# [[MD:S3_SOURCE_CONNECTOR]] + +# [[MD:PRODUCT_MANUAL_EXAMPLE_CONTEXT]] + +# [[IMG:PRODUCT_MANUAL_EXAMPLE]] + +# [[MD:ES_SOURCE_CONNECTOR]] + +# [[MD:SALES_RECORDS_CONSOLIDATED_CONTEXT]] + +# [[IMG:SALES_RECORDS_CONSOLIDATED_INDEX]] + +# [[MD:ES_DESTINATION_CONNECTOR]] + +# [[CODE:CONNECTORS]] + +# [[MD:WORKFLOW_NODES]] + +# [[CODE:WORKFLOWS]] + +# [[MD:CREATE_WORKFLOWS]] + +# [[MD:RUN_WORKFLOW]] + +# [[CODE:EXECUTION]] + +# [[MD:JOB_MONITORING]] + +# [[MD:ES_PREPROCESSING]] + +# [[CODE:PREPROCESSING]] + +# [[MD:SUMMARY]] + +# [[CODE:VERIFICATION]] + +# [[MD:MAIN]] + +# [[CODE:MAIN]] + +# [[MD:EXECUTION_FLOW]] + +# [[CODE:EXECUTION_RUNNER]] + +# [[MD:CUSTOMER_SUPPORT_OUTPUT_CONTEXT]] + +# [[IMG:CUSTOMER_SUPPORT_INDEX]] + +# [[MD:RAG_DEMO_SETUP]] + +# [[MD:RAG_DEMO_CONFIG]] + +# [[CODE:RAG_DEMO]] + +# [[MD:CONCLUSION]] diff --git a/notebook-processing/README.md b/notebook-processing/README.md new file mode 100644 index 0000000..fc95807 --- /dev/null +++ b/notebook-processing/README.md @@ -0,0 +1,276 @@ +# Notebook Processing Pipeline + +This folder contains the tooling to turn the base Python script into a rich, documented Jupyter Notebook. + +## Concept + +- The base script (`hybrid_rag_pipeline.py`) contains only code and special placeholders ("handles") that + indicate where markdown should be inserted. Handles look like: + + ``` + # [[MD:INTRO]] + # [[MD:CONFIG]] + # [[MD:S3_SOURCE_CONNECTOR]] + # [[MD:ES_SOURCE_CONNECTOR]] + # [[MD:ES_DESTINATION_CONNECTOR]] + # [[MD:WORKFLOW_NODES]] + # [[MD:CREATE_WORKFLOWS]] + # [[MD:RUN_WORKFLOW]] + # [[MD:JOB_MONITORING]] + # [[MD:ES_PREPROCESSING]] + # [[MD:SUMMARY]] + # [[MD:MAIN]] + ``` + +- The markdown for each handle lives in `markdown_blocks.yaml`. +- The enrichment script `enrich_and_convert.py` reads the base file, replaces each handle with the + corresponding markdown block, writes `hybrid_rag_pipeline_enriched.py`, and converts that to + `hybrid_rag_pipeline_enriched.ipynb` using jupytext. + +## Code Modularization (Proposed Enhancement) + +Similar to how markdown content is separated, we can also modularize the Python code into separate script files: + +### Code Script Structure + +Create a `code-scripts/` folder with individual Python files for each logical component: + +``` +notebook-processing/ +├── code-scripts/ +│ ├── __init__.py # Empty file for Python module +│ ├── dependencies.py # ensure_notebook_deps() +│ ├── data_preparation.py # download_file(), setup_*_data(), prepare_data_sources() +│ ├── connectors.py # create_*_connector() functions +│ ├── workflows.py # create_workflow_nodes(), create_parallel_workflows() +│ ├── execution.py # run_workflow(), poll_job_status() +│ ├── preprocessing.py # run_elasticsearch_preprocessing() +│ ├── verification.py # verify_customer_support_results(), print_pipeline_summary() +│ ├── rag_demo.py # RAG demonstration functions +│ └── main.py # main() function +├── code_blocks.yaml # Maps code handles to script files +├── enrich_and_convert.py # Enhanced to handle both MD and CODE handles +└── markdown_blocks.yaml # Existing markdown content +``` + +### Code Handle Format + +In the main pipeline file, use code handles similar to markdown handles: + +```python +#!/usr/bin/env python3 +# [[MD:INTRO]] + +# [[MD:CONFIG]] + +# [[CODE:DEPENDENCIES]] + +# [[CODE:DATA_PREPARATION]] + +# [[CODE:CONNECTORS]] + +# [[CODE:WORKFLOWS]] + +# [[CODE:EXECUTION]] + +# [[CODE:PREPROCESSING]] + +# [[CODE:VERIFICATION]] + +# [[CODE:MAIN]] + +# [[MD:EXECUTION_FLOW]] + +# Run the pipeline +s3_job_id, es_job_id = main() + +# Poll both jobs to make sure they have completed before proceeding +es_job_info = poll_job_status(es_job_id, "Elasticsearch Ingest") +s3_job_info = poll_job_status(s3_job_id, "S3 Ingest") + +# Verify the results +print("\n🔍 Verifying processed results") +print("-" * 50) +verify_customer_support_results() +``` + +### Code Blocks Configuration + +`code_blocks.yaml` would map handles to script files: + +```yaml +DEPENDENCIES: code-scripts/dependencies.py +DATA_PREPARATION: code-scripts/data_preparation.py +CONNECTORS: code-scripts/connectors.py +WORKFLOWS: code-scripts/workflows.py +EXECUTION: code-scripts/execution.py +PREPROCESSING: code-scripts/preprocessing.py +VERIFICATION: code-scripts/verification.py +RAG_DEMO: code-scripts/rag_demo.py +MAIN: code-scripts/main.py +``` + +## Image Management (New Feature) + +The pipeline now supports embedding images directly into the notebook using image handles. + +### Image Structure + +Create an `images/` folder to store all notebook images: + +``` +notebook-processing/ +├── images/ +│ ├── architecture-diagram.png # System architecture visualization +│ ├── workflow-flowchart.png # Processing workflow diagram +│ ├── data-flow.png # Data flow illustration +│ └── rag-demo-screenshot.png # RAG demonstration example +├── image_blocks.yaml # Maps image handles to image files +├── code-scripts/ # Code modules (as above) +├── code_blocks.yaml # Code handle mappings +├── enrich_and_convert.py # Enhanced to handle MD, CODE, and IMG handles +└── markdown_blocks.yaml # Markdown content +``` + +### Image Handle Format + +In the main pipeline file, use image handles to embed images: + +```python +#!/usr/bin/env python3 +# [[MD:INTRO]] + +# [[IMG:ARCHITECTURE_DIAGRAM]] + +# [[MD:CONFIG]] + +# [[CODE:DEPENDENCIES]] + +# [[IMG:WORKFLOW_FLOWCHART]] + +# [[CODE:DATA_PREPARATION]] + +# [[MD:EXECUTION_FLOW]] + +# [[IMG:RAG_DEMO_SCREENSHOT]] +``` + +### Image Blocks Configuration + +`image_blocks.yaml` maps image handles to image files: + +```yaml +ARCHITECTURE_DIAGRAM: images/architecture-diagram.png +WORKFLOW_FLOWCHART: images/workflow-flowchart.png +DATA_FLOW: images/data-flow.png +RAG_DEMO_SCREENSHOT: images/rag-demo-screenshot.png +``` + +### Image Processing Features + +**Automatic Resizing**: Images are automatically resized to a maximum width of 800 pixels while maintaining aspect ratio. + +**Base64 Encoding**: Images are converted to base64 and embedded directly in the notebook, making it self-contained. + +**Format Support**: Supports PNG, JPG, JPEG, GIF, SVG, and WebP formats. + +**Smart Handling**: Transparent images (RGBA, P mode) are converted to RGB with a white background. + +### Image Placement + +Images are embedded as markdown cells in the notebook using the format: +```markdown +![Alt text](data:image/png;base64,) +``` + +The enrichment script automatically: +1. Reads the image file from the `images/` folder +2. Resizes it to max 800px width (maintaining aspect ratio) +3. Converts it to base64 encoding +4. Embeds it as a markdown cell in the notebook +5. Handles different image formats (PNG, JPG, GIF, SVG, WebP) + +### Benefits + +1. **Separation of Concerns**: Each script file handles one logical area +2. **Easier Maintenance**: Update individual components without touching the main file +3. **Reusability**: Code modules can be imported and used in other projects +4. **Testing**: Individual components can be unit tested separately +5. **Collaboration**: Multiple developers can work on different components +6. **Version Control**: Cleaner diffs when changes are made to specific components +7. **Visual Documentation**: Images are version-controlled and automatically embedded +8. **Portable Notebooks**: Images are embedded as base64, making notebooks self-contained +9. **Optimized Images**: Automatic resizing ensures reasonable file sizes + +## Why this matters + +This makes the notebook content repeatable and reviewable. When you say "update the notebook text," you should: +1. Edit the appropriate markdown block(s) in `markdown_blocks.yaml`. +2. Re-run the enrichment script to regenerate the enriched Python file and notebook. + +For code changes: +1. Edit the appropriate script file in `code-scripts/`. +2. Re-run the enrichment script to regenerate the enriched Python file and notebook. + +For image updates: +1. Replace the image file in the `images/` folder with the same filename. +2. Re-run the enrichment script to regenerate the notebook with the updated image. + +This avoids accidental code edits inside the notebook and keeps documentation, code, and images close yet separate. + +## Usage + +### Basic Usage (Modular Mode - Default) + +Run from the repo root: + +```bash +/Users/nvannest/Documents/GitHub/rag-over-hybrid-data-sources/venv/bin/python \ + notebook-processing/enrich_and_convert.py +``` + +This uses the **modular methodology** by default, processing `hybrid_rag_pipeline_modular.py` with `[[MD:...]]`, `[[CODE:...]]`, and `[[IMG:...]]` handles. + +**Note**: Images are **disabled by default**. Only include images when explicitly requested by the user. + +### Advanced Usage + +```bash +# Use modular approach (default) +python notebook-processing/enrich_and_convert.py --source modular + +# Use original approach (markdown only) +python notebook-processing/enrich_and_convert.py --source original + +# Include images in the output (explicitly enabled) +python notebook-processing/enrich_and_convert.py --include-images + +# Custom output suffix +python notebook-processing/enrich_and_convert.py --output-suffix custom + +# Combine options +python notebook-processing/enrich_and_convert.py --source original --output-suffix legacy --include-images +``` + +### Output Files + +**Modular Mode** (default): +- Source: `hybrid_rag_pipeline_modular.py` (template with handles) +- Output: `hybrid_rag_pipeline_enriched.py` (assembled code) +- Notebook: `hybrid_rag_pipeline_enriched.ipynb` (Jupyter notebook) + +**Original Mode**: +- Source: `hybrid_rag_pipeline.py` (original file with MD handles) +- Output: `hybrid_rag_pipeline_enriched.py` (markdown-enriched) +- Notebook: `hybrid_rag_pipeline_enriched.ipynb` (Jupyter notebook) + +## Notes +- If any `# [[MD:...]]` handle remains unreplaced, the script will exit with an error in strict mode. +- If any `# [[CODE:...]]` handle remains unreplaced, the script will exit with an error in strict mode. +- If any `# [[IMG:...]]` handle remains unreplaced, the script will exit with an error in strict mode. +- Update or add new handles in the base file where you want new documentation sections. +- Add the corresponding block in `markdown_blocks.yaml`, script in `code-scripts/`, or image in `images/` using the exact same key. +- Images should be optimized for web display (reasonable file sizes) as they will be embedded in the notebook. +- **Images are disabled by default** - use `--include-images` flag to enable image embedding. +- Images are automatically resized to max 800px width to keep notebook file sizes reasonable. + diff --git a/notebook-processing/code-scripts/__init__.py b/notebook-processing/code-scripts/__init__.py new file mode 100644 index 0000000..1ab298b --- /dev/null +++ b/notebook-processing/code-scripts/__init__.py @@ -0,0 +1 @@ +# This file makes code-scripts a Python module \ No newline at end of file diff --git a/notebook-processing/code-scripts/connectors.py b/notebook-processing/code-scripts/connectors.py new file mode 100644 index 0000000..82563f8 --- /dev/null +++ b/notebook-processing/code-scripts/connectors.py @@ -0,0 +1,93 @@ +def create_s3_source_connector(): + """Create an S3 source connector for PDF documents.""" + try: + if not S3_SOURCE_BUCKET: + raise ValueError("S3_SOURCE_BUCKET is required (bucket name, s3:// URL, or https:// URL)") + value = S3_SOURCE_BUCKET.strip() + + if value.startswith("s3://"): + s3_style = value if value.endswith("/") else value + "/" + elif value.startswith("http://") or value.startswith("https://"): + parsed = urlparse(value) + host = parsed.netloc + path = parsed.path or "/" + bucket = host.split(".s3.")[0] + s3_style = f"s3://{bucket}{path if path.endswith('/') else path + '/'}" + else: + s3_style = f"s3://{value if value.endswith('/') else value + '/'}" + + with UnstructuredClient(api_key_auth=UNSTRUCTURED_API_KEY) as client: + response = client.sources.create_source( + request=CreateSourceRequest( + create_source_connector=CreateSourceConnector( + name="", + type="s3", + config={ + "remote_url": s3_style, + "recursive": True, + "key": AWS_ACCESS_KEY_ID, + "secret": AWS_SECRET_ACCESS_KEY, + } + ) + ) + ) + + source_id = response.source_connector_information.id + print(f"✅ Created S3 PDF source connector: {source_id} -> {s3_style}") + return source_id + + except Exception as e: + print(f"❌ Error creating S3 source connector: {e}") + return None + +def create_elasticsearch_source_connector(): + """Create an Elasticsearch source connector for sales data.""" + try: + with UnstructuredClient(api_key_auth=UNSTRUCTURED_API_KEY) as client: + response = client.sources.create_source( + request=CreateSourceRequest( + create_source_connector=CreateSourceConnector( + name=f"elasticsearch_sales_source_{int(time.time())}", + type="elasticsearch", + config={ + "hosts": [ELASTICSEARCH_HOST], + "es_api_key": ELASTICSEARCH_API_KEY, + "index_name": ELASTICSEARCH_INDEX + } + ) + ) + ) + + source_id = response.source_connector_information.id + print(f"✅ Created Elasticsearch sales source connector: {source_id}") + return source_id + + except Exception as e: + print(f"❌ Error creating Elasticsearch source connector: {e}") + return None + +def create_elasticsearch_destination_connector(): + """Create an Elasticsearch destination connector for processed results.""" + try: + with UnstructuredClient(api_key_auth=UNSTRUCTURED_API_KEY) as client: + response = client.destinations.create_destination( + request=CreateDestinationRequest( + create_destination_connector=CreateDestinationConnector( + name=f"elasticsearch_customer_support_destination_{int(time.time())}", + type="elasticsearch", + config={ + "hosts": [ELASTICSEARCH_HOST], + "es_api_key": ELASTICSEARCH_API_KEY, + "index_name": "customer-support" + } + ) + ) + ) + + destination_id = response.destination_connector_information.id + print(f"✅ Created Elasticsearch destination connector: {destination_id}") + return destination_id + + except Exception as e: + print(f"❌ Error creating Elasticsearch destination connector: {e}") + return None diff --git a/notebook-processing/code-scripts/data_preparation.py b/notebook-processing/code-scripts/data_preparation.py new file mode 100644 index 0000000..b4d49a4 --- /dev/null +++ b/notebook-processing/code-scripts/data_preparation.py @@ -0,0 +1,254 @@ +# Data preparation functions + +def download_file(url: str, local_path: str) -> bool: + """Download a file from URL to local path.""" + try: + print(f"📥 Downloading {url}...") + response = requests.get(url, stream=True) + response.raise_for_status() + + Path(local_path).parent.mkdir(parents=True, exist_ok=True) + + with open(local_path, 'wb') as f: + for chunk in response.iter_content(chunk_size=8192): + f.write(chunk) + + print(f"✅ Downloaded to {local_path}") + return True + + except Exception as e: + print(f"❌ Error downloading {url}: {e}") + return False + +def setup_elasticsearch_data(): + """Download and load sales data into Elasticsearch index.""" + print("🔧 Setting up Elasticsearch sales data...") + + try: + es = Elasticsearch( + ELASTICSEARCH_HOST, + api_key=ELASTICSEARCH_API_KEY, + request_timeout=60, + max_retries=3, + retry_on_timeout=True + ) + + index_name = "sales-records-consolidated" + + sales_data_url = "https://github.com/Unstructured-IO/rag-over-hybrid-data-sources/raw/feature/hybrid-rag-pipeline/source_data/sales_records_consolidated.zip" + + with tempfile.NamedTemporaryFile(suffix='.zip', delete=False) as tmp_file: + if not download_file(sales_data_url, tmp_file.name): + return False + + with zipfile.ZipFile(tmp_file.name, 'r') as zipf: + # Load mapping + with zipf.open('mapping.json') as f: + mapping_data = json.loads(f.read().decode('utf-8')) + + # Load documents + with zipf.open('documents.json') as f: + documents = json.loads(f.read().decode('utf-8')) + + # Always delete existing index if present and reload from zip + if es.indices.exists(index=index_name): + print(f"🗑️ Deleting existing index '{index_name}' to reload fresh data...") + es.indices.delete(index=index_name) + + # Create index with mapping + index_mapping = mapping_data[index_name] if index_name in mapping_data else mapping_data[list(mapping_data.keys())[0]] + es.indices.create(index=index_name, body=index_mapping) + print(f"🔧 Created index '{index_name}' with mapping") + + # Prepare documents for bulk insert + def generate_docs(): + for doc in documents: + yield { + "_index": index_name, + "_id": doc["_id"], + "_source": doc["_source"] + } + + # Bulk insert documents + success_count, failed_items = bulk(es, generate_docs(), chunk_size=100) + print(f"📝 Inserted {success_count} documents") + + # Refresh index and verify + es.indices.refresh(index=index_name) + count_response = es.count(index=index_name) + count_data = count_response.body if hasattr(count_response, 'body') else count_response + doc_count = count_data['count'] + + if doc_count > 0: + print(f"✅ Successfully loaded {doc_count} documents into '{index_name}' index") + return True + else: + print(f"❌ Index '{index_name}' is empty after loading") + return False + + except Exception as e: + print(f"❌ Error setting up Elasticsearch data: {e}") + return False + + finally: + # Clean up temp file + try: + os.unlink(tmp_file.name) + except: + pass + +def setup_s3_data(): + """Download and load PDF files into S3 bucket.""" + print("🔧 Setting up S3 PDF data...") + + try: + # Initialize S3 client + s3 = boto3.client( + 's3', + aws_access_key_id=AWS_ACCESS_KEY_ID, + aws_secret_access_key=AWS_SECRET_ACCESS_KEY, + region_name=AWS_REGION + ) + + bucket_name = S3_SOURCE_BUCKET + if not bucket_name: + print("❌ S3_SOURCE_BUCKET not configured") + return False + + # Check if bucket exists and has data + try: + response = s3.list_objects_v2(Bucket=bucket_name, MaxKeys=1) + if response.get('KeyCount', 0) > 0: + # Count total objects + response = s3.list_objects_v2(Bucket=bucket_name) + object_count = len(response.get('Contents', [])) + print(f"✅ Bucket '{bucket_name}' already exists with {object_count} files") + return True + except ClientError as e: + if e.response['Error']['Code'] != '404': + raise e + + # Download S3 PDFs zip file + s3_data_url = "https://github.com/Unstructured-IO/rag-over-hybrid-data-sources/raw/feature/hybrid-rag-pipeline/source_data/s3_pdfs.zip" + + with tempfile.NamedTemporaryFile(suffix='.zip', delete=False) as tmp_file: + if not download_file(s3_data_url, tmp_file.name): + return False + + # Create bucket if it doesn't exist + try: + s3.head_bucket(Bucket=bucket_name) + print(f"📦 Using existing bucket '{bucket_name}'") + except ClientError as e: + if e.response['Error']['Code'] == '404': + print(f"🔧 Creating bucket '{bucket_name}'...") + try: + if AWS_REGION == "us-east-1": + s3.create_bucket(Bucket=bucket_name) + else: + s3.create_bucket( + Bucket=bucket_name, + CreateBucketConfiguration={'LocationConstraint': AWS_REGION} + ) + print(f"✅ Created bucket '{bucket_name}'") + except ClientError as create_error: + if 'BucketAlreadyOwnedByYou' in str(create_error): + print(f"📦 Bucket '{bucket_name}' already exists and is owned by you") + else: + raise create_error + else: + raise e + + # Clear existing files in bucket + print(f"🗑️ Clearing existing files from bucket '{bucket_name}'...") + try: + response = s3.list_objects_v2(Bucket=bucket_name) + if 'Contents' in response: + objects_to_delete = [{'Key': obj['Key']} for obj in response['Contents']] + if objects_to_delete: + s3.delete_objects( + Bucket=bucket_name, + Delete={'Objects': objects_to_delete} + ) + print(f"🗑️ Deleted {len(objects_to_delete)} existing files") + else: + print("📁 Bucket was already empty") + else: + print("📁 Bucket was already empty") + except ClientError as e: + print(f"⚠️ Could not clear bucket (continuing anyway): {e}") + + # Extract and upload files from zip + uploaded_count = 0 + with zipfile.ZipFile(tmp_file.name, 'r') as zipf: + file_list = zipf.namelist() + pdf_files = [f for f in file_list if f.lower().endswith('.pdf')] + + print(f"📊 Found {len(pdf_files)} PDF files in zip") + + for file_name in pdf_files: + try: + # Extract file data + file_data = zipf.read(file_name) + + # Upload to S3 + s3.put_object( + Bucket=bucket_name, + Key=file_name, + Body=file_data, + ContentType='application/pdf' + ) + + print(f" 📤 Uploaded: {file_name}") + uploaded_count += 1 + + except Exception as e: + print(f" ❌ Failed to upload {file_name}: {e}") + + # Verify upload + response = s3.list_objects_v2(Bucket=bucket_name) + actual_count = len(response.get('Contents', [])) + + if actual_count > 0: + print(f"✅ Successfully uploaded {uploaded_count} PDFs to bucket '{bucket_name}'") + print(f"📊 Bucket now contains {actual_count} files") + return True + else: + print(f"❌ Bucket '{bucket_name}' is empty after upload") + return False + + except NoCredentialsError: + print("❌ AWS credentials not found. Please check your .env file.") + return False + except Exception as e: + print(f"❌ Error setting up S3 data: {e}") + return False + + finally: + # Clean up temp file + try: + os.unlink(tmp_file.name) + except: + pass + +def prepare_data_sources(): + """Prepare both Elasticsearch and S3 data sources.""" + print("🚀 Preparing data sources...") + print("=" * 50) + + # Setup Elasticsearch data + if not setup_elasticsearch_data(): + print("❌ Failed to setup Elasticsearch data") + return False + + print() # Add spacing + + # Setup S3 data + if not setup_s3_data(): + print("❌ Failed to setup S3 data") + return False + + print() + print("✅ All data sources prepared successfully!") + print("=" * 50) + return True \ No newline at end of file diff --git a/notebook-processing/code-scripts/dependencies.py b/notebook-processing/code-scripts/dependencies.py new file mode 100644 index 0000000..05e807a --- /dev/null +++ b/notebook-processing/code-scripts/dependencies.py @@ -0,0 +1,92 @@ +import sys, subprocess + +def ensure_notebook_deps() -> None: + packages = [ + "jupytext", + "python-dotenv", + "unstructured-client", + "elasticsearch", + "boto3", + "PyYAML", + "langchain", + "langchain-elasticsearch", + "langchain-openai" + ] + try: + subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", *packages]) + except Exception: + # If install fails, continue; imports below will surface actionable errors + pass + +# Install notebook dependencies (safe no-op if present) +ensure_notebook_deps() + +import os +import time +import json +import zipfile +import tempfile +import requests +from pathlib import Path +from dotenv import load_dotenv +from urllib.parse import urlparse + +import boto3 +from botocore.exceptions import ClientError, NoCredentialsError +from elasticsearch import Elasticsearch +from elasticsearch.helpers import bulk + +from unstructured_client import UnstructuredClient +from unstructured_client.models.operations import ( + CreateSourceRequest, + CreateDestinationRequest, + CreateWorkflowRequest +) +from unstructured_client.models.shared import ( + CreateSourceConnector, + CreateDestinationConnector, + WorkflowNode, + WorkflowType, + CreateWorkflow +) + +# ============================================================================= +# ENVIRONMENT CONFIGURATION +# ============================================================================= +# Load from .env file if it exists +load_dotenv() + +# Configuration constants +SKIPPED = "SKIPPED" +UNSTRUCTURED_API_URL = os.getenv("UNSTRUCTURED_API_URL", "https://platform.unstructuredapp.io/api/v1") + +# Get environment variables +UNSTRUCTURED_API_KEY = os.getenv("UNSTRUCTURED_API_KEY") +AWS_ACCESS_KEY_ID = os.getenv("AWS_ACCESS_KEY_ID") +AWS_SECRET_ACCESS_KEY = os.getenv("AWS_SECRET_ACCESS_KEY") +AWS_REGION = os.getenv("AWS_REGION", "us-east-1") +S3_SOURCE_BUCKET = os.getenv("S3_SOURCE_BUCKET") +S3_DESTINATION_BUCKET = os.getenv("S3_DESTINATION_BUCKET") +S3_OUTPUT_PREFIX = os.getenv("S3_OUTPUT_PREFIX", "") +ELASTICSEARCH_HOST = os.getenv("ELASTICSEARCH_HOST") +ELASTICSEARCH_API_KEY = os.getenv("ELASTICSEARCH_API_KEY") +ELASTICSEARCH_INDEX = os.getenv("ELASTICSEARCH_INDEX", "sales-records-consolidated") +OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") + +# Validation +REQUIRED_VARS = { + "UNSTRUCTURED_API_KEY": UNSTRUCTURED_API_KEY, + "AWS_ACCESS_KEY_ID": AWS_ACCESS_KEY_ID, + "AWS_SECRET_ACCESS_KEY": AWS_SECRET_ACCESS_KEY, + "ELASTICSEARCH_HOST": ELASTICSEARCH_HOST, + "ELASTICSEARCH_API_KEY": ELASTICSEARCH_API_KEY, + "S3_SOURCE_BUCKET": S3_SOURCE_BUCKET, +} + +missing_vars = [key for key, value in REQUIRED_VARS.items() if not value] +if missing_vars: + print(f"❌ Missing required environment variables: {', '.join(missing_vars)}") + print("Please set these environment variables or create a .env file with your credentials.") + raise ValueError(f"Missing required environment variables: {missing_vars}") + +print("✅ Configuration loaded successfully") diff --git a/notebook-processing/code-scripts/dotenv_creation.py b/notebook-processing/code-scripts/dotenv_creation.py new file mode 100644 index 0000000..024bf90 --- /dev/null +++ b/notebook-processing/code-scripts/dotenv_creation.py @@ -0,0 +1,48 @@ +def create_dotenv_file(): + """Create a .env file with placeholder values for the user to fill in.""" + env_content = """# Hybrid RAG Pipeline Environment Configuration +# Fill in your actual values below +# Configuration - Set these explicitly + +# =================================================================== +# AWS CONFIGURATION +# =================================================================== +AWS_ACCESS_KEY_ID="your-aws-access-key-id" +AWS_SECRET_ACCESS_KEY="your-aws-secret-access-key" +AWS_REGION="us-east-1" + +# =================================================================== +# UNSTRUCTURED API CONFIGURATION +# =================================================================== +UNSTRUCTURED_API_KEY="your-unstructured-api-key" +UNSTRUCTURED_API_URL="https://platform.unstructuredapp.io/api/v1" + +# =================================================================== +# ELASTICSEARCH CONFIGURATION +# =================================================================== +ELASTICSEARCH_HOST="https://your-cluster.es.io:443" +ELASTICSEARCH_API_KEY="your-elasticsearch-api-key" + +# =================================================================== +# PIPELINE DATA SOURCES +# =================================================================== +S3_SOURCE_BUCKET="your-s3-source-bucket" +S3_DESTINATION_BUCKET="your-s3-destination-bucket" +S3_OUTPUT_PREFIX="" +ELASTICSEARCH_INDEX="sales-records-consolidated" + +# =================================================================== +# OPENAI API CONFIGURATION +# =================================================================== +OPENAI_API_KEY="your-openai-api-key" +""" + + with open('.env', 'w') as f: + f.write(env_content) + + print("✅ Created .env file with placeholder values") + print("📝 Please edit the .env file and replace the placeholder values with your actual credentials") + print("🔒 The .env file will be loaded automatically by the pipeline") + +# Create the .env file +create_dotenv_file() diff --git a/notebook-processing/code-scripts/execution.py b/notebook-processing/code-scripts/execution.py new file mode 100644 index 0000000..f9b351b --- /dev/null +++ b/notebook-processing/code-scripts/execution.py @@ -0,0 +1,46 @@ +def run_workflow(workflow_id, workflow_name): + """Run a workflow and return job information.""" + try: + with UnstructuredClient(api_key_auth=UNSTRUCTURED_API_KEY) as client: + response = client.workflows.run_workflow( + request={"workflow_id": workflow_id} + ) + + job_id = response.job_information.id + print(f"✅ Started {workflow_name} job: {job_id}") + return job_id + + except Exception as e: + print(f"❌ Error running {workflow_name} workflow: {e}") + return None + +def poll_job_status(job_id, job_name, wait_time=30): + """Poll job status until completion.""" + print(f"⏳ Monitoring {job_name} job status...") + + while True: + try: + with UnstructuredClient(api_key_auth=UNSTRUCTURED_API_KEY) as client: + response = client.jobs.get_job( + request={"job_id": job_id} + ) + + job = response.job_information + status = job.status + + if status in ["SCHEDULED", "IN_PROGRESS"]: + print(f"⏳ {job_name} job status: {status}") + time.sleep(wait_time) + elif status == "COMPLETED": + print(f"✅ {job_name} job completed successfully!") + return job + elif status == "FAILED": + print(f"❌ {job_name} job failed!") + return job + else: + print(f"❓ Unknown {job_name} job status: {status}") + return job + + except Exception as e: + print(f"❌ Error polling {job_name} job status: {e}") + time.sleep(wait_time) diff --git a/notebook-processing/code-scripts/execution_runner.py b/notebook-processing/code-scripts/execution_runner.py new file mode 100644 index 0000000..73871e1 --- /dev/null +++ b/notebook-processing/code-scripts/execution_runner.py @@ -0,0 +1,7 @@ +s3_job_id, es_job_id = main() + +es_job_info = poll_job_status(es_job_id, "Elasticsearch Ingest") +s3_job_info = poll_job_status(s3_job_id, "S3 Ingest") +print("\n🔍 Verifying processed results") +print("-" * 50) +verify_customer_support_results() diff --git a/notebook-processing/code-scripts/main.py b/notebook-processing/code-scripts/main.py new file mode 100644 index 0000000..6443f6c --- /dev/null +++ b/notebook-processing/code-scripts/main.py @@ -0,0 +1,74 @@ +def main(): + """Main pipeline execution""" + print("🚀 Starting Hybrid RAG Pipeline") + + print("\n📦 Step 0: Data source preparation") + print("-" * 50) + + if not prepare_data_sources(): + print("❌ Failed to prepare data sources") + return + + print("\n🔧 Step 1: Elasticsearch preprocessing") + print("-" * 50) + + if not run_elasticsearch_preprocessing(): + print("❌ Failed to complete Elasticsearch preprocessing") + return + + print("\n🔗 Step 2: Creating source connectors") + print("-" * 50) + + s3_source_id = create_s3_source_connector() + if not s3_source_id: + print("❌ Failed to create S3 source connector") + return + + elasticsearch_source_id = create_elasticsearch_source_connector() + if not elasticsearch_source_id: + print("❌ Failed to create Elasticsearch source connector") + return + + # Step 3: Create Destination Connector + print("\n🎯 Step 3: Creating Elasticsearch destination connector") + print("-" * 50) + + destination_id = create_elasticsearch_destination_connector() + if not destination_id: + print("❌ Failed to create destination connector") + return + + # Step 4: Create Workflows + print("\n⚙️ Step 4: Creating workflows") + print("-" * 50) + + s3_workflow_id, es_workflow_id = create_parallel_workflows( + s3_source_id, elasticsearch_source_id, destination_id + ) + + if not es_workflow_id: + print("❌ Failed to create Elasticsearch workflow") + return + + # Step 5: Run Workflows + print("\n🚀 Step 5: Running workflows") + print("-" * 50) + + s3_job_id = None + es_job_id = None + + if s3_workflow_id: + s3_job_id = run_workflow(s3_workflow_id, "S3 PDFs") + if not s3_job_id: + print("❌ Failed to start S3 workflow") + return + + if es_workflow_id: + es_job_id = run_workflow(es_workflow_id, "Elasticsearch Sales") + if not es_job_id: + print("❌ Failed to start Elasticsearch workflow") + return + + # Step 6: Pipeline Summary + print_pipeline_summary(s3_workflow_id, es_workflow_id, s3_job_id, es_job_id) + return s3_job_id, es_job_id \ No newline at end of file diff --git a/notebook-processing/code-scripts/preprocessing.py b/notebook-processing/code-scripts/preprocessing.py new file mode 100644 index 0000000..e9c97c8 --- /dev/null +++ b/notebook-processing/code-scripts/preprocessing.py @@ -0,0 +1,65 @@ +def run_elasticsearch_preprocessing(): + """Check and manage Elasticsearch indices for the pipeline.""" + print("🔧 Running Elasticsearch preprocessing...") + + try: + es = Elasticsearch( + ELASTICSEARCH_HOST, + api_key=ELASTICSEARCH_API_KEY, + request_timeout=60, + max_retries=3, + retry_on_timeout=True + ) + + sales_index = "sales-records-consolidated" + print(f"�� Checking {sales_index} index...") + + if not es.indices.exists(index=sales_index): + raise ValueError(f"❌ Index '{sales_index}' does not exist. There is no data to use.") + + count_response = es.count(index=sales_index) + doc_count = count_response['count'] + + if doc_count == 0: + raise ValueError(f"❌ Index '{sales_index}' is empty. There is no data to use.") + + print(f"✅ Found {doc_count} records in {sales_index}") + + # Handle customer-support index + support_index = "customer-support" + print(f"🔍 Checking {support_index} index...") + + if es.indices.exists(index=support_index): + print(f"🗑️ Deleting existing {support_index} index...") + es.indices.delete(index=support_index) + + # Create fresh customer-support index + print(f"🔧 Creating fresh {support_index} index...") + mapping = { + "settings": { + "number_of_shards": 1, + "number_of_replicas": 1 + }, + "mappings": { + "properties": { + "id": {"type": "keyword"}, + "timestamp": {"type": "date"}, + "text": {"type": "text", "analyzer": "standard"}, + "metadata": {"type": "object"} + } + } + } + + es.indices.create(index=support_index, body=mapping) + es.indices.refresh(index=support_index) + + print(f"✅ Successfully created fresh {support_index} index") + print("✅ Elasticsearch preprocessing completed successfully") + return True + + except ValueError as e: + print(str(e)) + return False + except Exception as e: + print(f"❌ Error during Elasticsearch preprocessing: {e}") + return False diff --git a/notebook-processing/code-scripts/rag_demo.py b/notebook-processing/code-scripts/rag_demo.py new file mode 100644 index 0000000..4674c25 --- /dev/null +++ b/notebook-processing/code-scripts/rag_demo.py @@ -0,0 +1,272 @@ +# RAG Demonstration Configuration and Queries +import os +import json + +# LangChain imports for RAG functionality +from langchain_elasticsearch import ElasticsearchStore +from langchain_openai import OpenAIEmbeddings, ChatOpenAI +from langchain_core.prompts import ChatPromptTemplate +from langchain_core.output_parsers import StrOutputParser +from langchain_core.runnables import RunnablePassthrough + +print("🤖 RAG Query Demonstration Setup") +print("=" * 40) + +if not OPENAI_API_KEY or OPENAI_API_KEY.startswith("your-"): + print("⚠️ OpenAI API key not configured.") + print("💡 Please set OPENAI_API_KEY in your .env file with your actual OpenAI API key.") + print("📝 You can get one at: https://platform.openai.com/api-keys") +else: + print("✅ OpenAI API key configured for RAG demonstrations") + +def setup_rag_system(): + """Set up the RAG system with Elasticsearch and OpenAI.""" + + if not OPENAI_API_KEY or OPENAI_API_KEY.startswith("your-"): + print("❌ OpenAI API key is required for RAG functionality") + print("Please set OPENAI_API_KEY in your .env file") + return None + + # Set OpenAI API key for LangChain + os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY + + try: + print("🔧 Setting up RAG components...") + + # Initialize embeddings (same model used in processing) + embeddings = OpenAIEmbeddings( + model="text-embedding-3-small", + openai_api_key=OPENAI_API_KEY + ) + + # Connect to Elasticsearch vector store - using your working pattern + vector_store = ElasticsearchStore( + index_name="customer-support", + embedding=embeddings, + es_url=ELASTICSEARCH_HOST, + es_api_key=ELASTICSEARCH_API_KEY, + vector_query_field="embeddings", + query_field="text", + ) + + # Create retriever + retriever = vector_store.as_retriever(search_kwargs={"k": 5}) + + # Initialize LLM + llm = ChatOpenAI( + model="gpt-3.5-turbo", + temperature=0, + openai_api_key=OPENAI_API_KEY + ) + + # Enhanced prompt template that leverages NER metadata + prompt = ChatPromptTemplate.from_template(""" +Use the following context to answer the question. Pay attention to any entity information (people, organizations, products, locations, dates) and relationships mentioned in the context. + +Context: +{context} + +Question: +{question} +""") + + print("✅ RAG system ready!") + return {"retriever": retriever, "llm": llm, "prompt": prompt} + + except ImportError as e: + print(f"❌ Missing RAG dependencies: {e}") + print("💡 Install with: pip install langchain langchain-elasticsearch langchain-openai") + return None + except Exception as e: + print(f"❌ Error setting up RAG system: {e}") + return None + +def extract_ner_entities(docs): + """Extract NER entities from document metadata.""" + entities = {"people": set(), "organizations": set(), "products": set(), "locations": set(), "dates": set()} + + for doc in docs: + metadata = doc.metadata + if "entities-items" in metadata: + try: + import json + entity_items = json.loads(metadata["entities-items"]) if isinstance(metadata["entities-items"], str) else metadata["entities-items"] + + for item in entity_items: + entity_type = item.get("type", "").upper() + entity_name = item.get("entity", "") + + if entity_type == "PERSON": + entities["people"].add(entity_name) + elif entity_type == "ORGANIZATION": + entities["organizations"].add(entity_name) + elif entity_type == "PRODUCT": + entities["products"].add(entity_name) + elif entity_type == "LOCATION": + entities["locations"].add(entity_name) + elif entity_type == "DATE": + entities["dates"].add(entity_name) + except: + pass + + return entities + +def analyze_sources(docs): + """Analyze retrieved documents by source type.""" + s3_docs = [] + es_docs = [] + unknown_docs = [] + + for doc in docs: + metadata = doc.metadata + if "data_source-record_locator-index_name" in metadata: + es_docs.append(doc) + elif "data_source-url" in metadata and "s3://" in metadata.get("data_source-url", ""): + s3_docs.append(doc) + else: + unknown_docs.append(doc) + + return s3_docs, es_docs, unknown_docs + +def demonstrate_hybrid_ner_queries(rag_components): + """Demonstrate NER-enhanced hybrid RAG capabilities.""" + if not rag_components: + return + + retriever = rag_components["retriever"] + llm = rag_components["llm"] + prompt = rag_components["prompt"] + + # Build RAG chain using your working pattern + rag_chain = ( + {"context": retriever, "question": RunnablePassthrough()} + | prompt + | llm + | StrOutputParser() + ) + + # Hybrid NER demonstration queries targeting different sources + hybrid_queries = [ + { + "query": "How do I troubleshoot Bose headphone connectivity issues?", + "description": "Product support query targeting S3 PDFs (product manuals)", + "expected_source": "S3 (Product Docs)" + }, + { + "query": "Tell me about Daniel Hahn and his purchases", + "description": "Customer analysis query targeting Elasticsearch (sales data)", + "expected_source": "Elasticsearch (Sales)" + }, + { + "query": "What are the technical specifications for SoundSport Wireless headphones?", + "description": "Product specification query targeting S3 PDFs", + "expected_source": "S3 (Product Docs)" + }, + { + "query": "Show me customers in San Antonio, TX", + "description": "Geographic customer query targeting Elasticsearch", + "expected_source": "Elasticsearch (Sales)" + }, + { + "query": "How do I reset wireless headphones to factory settings?", + "description": "Technical support query targeting S3 PDFs", + "expected_source": "S3 (Product Docs)" + }, + { + "query": "What products does Newegg sell and what are their features?", + "description": "Hybrid query targeting BOTH sources (sales + product specs)", + "expected_source": "Both S3 and Elasticsearch" + }, + { + "query": "I have a customer who bought Bose headphones and is having connectivity issues. What should I tell them?", + "description": "Customer support query requiring BOTH customer data AND product manuals", + "expected_source": "Both S3 and Elasticsearch" + } + ] + + print("\n🧠 Hybrid NER-Enhanced RAG Demonstration") + print("=" * 60) + + for i, query_info in enumerate(hybrid_queries, 1): + query = query_info["query"] + description = query_info["description"] + expected_source = query_info["expected_source"] + + print(f"\n{'='*70}") + print(f"Query {i}: {description}") + print(f"📝 Query: {query}") + print(f"🎯 Expected Source: {expected_source}") + print("=" * 70) + + try: + # Retrieve documents + docs = retriever.invoke(query) + + if not docs: + print("❌ No documents retrieved") + continue + + # Analyze sources (keeping your preferred format) + s3_docs, es_docs, unknown_docs = analyze_sources(docs) + print(f"📊 Retrieved {len(docs)} documents:") + print(f" 📄 S3 (Product Docs): {len(s3_docs)}") + print(f" 📊 Elasticsearch (Sales): {len(es_docs)}") + print(f" ❓ Unknown: {len(unknown_docs)}") + + # Check if we hit the expected source + if expected_source == "S3 (Product Docs)" and len(s3_docs) > 0: + print("✅ SUCCESS: Retrieved from expected S3 source!") + elif expected_source == "Elasticsearch (Sales)" and len(es_docs) > 0: + print("✅ SUCCESS: Retrieved from expected Elasticsearch source!") + elif expected_source == "Both S3 and Elasticsearch" and len(s3_docs) > 0 and len(es_docs) > 0: + print("✅ SUCCESS: Retrieved from BOTH sources as expected!") + elif expected_source.startswith("Both") and (len(s3_docs) > 0 or len(es_docs) > 0): + print("✅ PARTIAL: Retrieved from at least one expected source") + else: + print("⚠️ UNEXPECTED: Did not retrieve from expected source") + + # Extract and show NER entities + entities = extract_ner_entities(docs) + print(f"\n🏷️ NER Entities Found:") + if entities["people"]: + print(f" 👤 People: {', '.join(list(entities['people'])[:3])}") + if entities["organizations"]: + print(f" 🏢 Organizations: {', '.join(list(entities['organizations'])[:3])}") + if entities["products"]: + print(f" 📱 Products: {', '.join(list(entities['products'])[:3])}") + if entities["locations"]: + print(f" 🗺️ Locations: {', '.join(list(entities['locations'])[:3])}") + if entities["dates"]: + print(f" 📅 Dates: {', '.join(list(entities['dates'])[:3])}") + + # Generate answer + print(f"\n💬 Answer:") + answer = rag_chain.invoke(query) + print(f"{answer}") + + except Exception as e: + print(f"❌ Error: {e}") + if "429" in str(e): + print("⚠️ OpenAI API quota exceeded. Stopping demo.") + break + + print(f"\n{'='*70}") + print("🧠 Hybrid NER Demo Complete!") + print("✅ Demonstrated cross-source retrieval capabilities") + print("✅ Showed NER metadata integration across data sources") + print("✅ Validated hybrid RAG architecture") + +def run_rag_demonstration(): + """Run the RAG demonstration.""" + print("\n🚀 Starting Hybrid RAG Demonstration") + print("=" * 50) + + rag_components = setup_rag_system() + + if rag_components: + demonstrate_hybrid_ner_queries(rag_components) + else: + print("❌ RAG demonstration skipped due to configuration issues") + +# Run the demonstration +run_rag_demonstration() diff --git a/notebook-processing/code-scripts/verification.py b/notebook-processing/code-scripts/verification.py new file mode 100644 index 0000000..074d401 --- /dev/null +++ b/notebook-processing/code-scripts/verification.py @@ -0,0 +1,156 @@ +import os + +def print_pipeline_summary(s3_workflow_id, es_workflow_id, s3_job_id, es_job_id): + """Print comprehensive pipeline summary.""" + print("\n" + "=" * 80) + print("📊 HYBRID RAG PIPELINE SUMMARY") + print("=" * 80) + print(f"📁 S3 Source (PDFs): {S3_SOURCE_BUCKET if s3_workflow_id else SKIPPED}") + print(f"🔍 Elasticsearch Source: {ELASTICSEARCH_HOST}/{ELASTICSEARCH_INDEX}") + print(f"📤 Elasticsearch Destination: {ELASTICSEARCH_HOST}/customer-support") + print(f"") + print(f"⚙️ S3 PDFs Workflow ID: {s3_workflow_id if s3_workflow_id else SKIPPED}") + print(f"⚙️ Elasticsearch Sales Workflow ID: {es_workflow_id}") + print(f"") + print(f"🚀 S3 PDFs Job ID: {s3_job_id if s3_job_id else SKIPPED}") + print(f"🚀 Elasticsearch Sales Job ID: {es_job_id}") + +def verify_customer_support_results(s3_job_id=None, es_job_id=None): + """ + Verifies the processed results in the customer-support index, prettyprinting one doc per unique source connector. + Assumes jobs have already completed successfully. + """ + import pprint + + print("🔍 Verifying processed results in 'customer-support' index (assuming jobs have completed)...") + + try: + # Initialize Elasticsearch client + es = Elasticsearch( + ELASTICSEARCH_HOST, + api_key=ELASTICSEARCH_API_KEY, + request_timeout=60, + max_retries=3, + retry_on_timeout=True + ) + + index_name = "customer-support" + + # Check if index exists + if not es.indices.exists(index=index_name): + print(f"❌ Index '{index_name}' does not exist. Workflows may not have written results yet.") + return + + # Get document count + count_response = es.count(index=index_name) + total_docs = count_response['count'] + print(f"📊 Total processed documents: {total_docs}") + + if total_docs == 0: + print("⏳ No documents found yet. Workflows may still be processing or index is empty.") + print("💡 Check the Unstructured dashboard for job status.") + return + + print(f"\n📋 Analyzing Source Connectors:") + print("=" * 40) + + # Get sample documents to analyze source patterns + # Use function_score with random_score to sample documents randomly + sample_response = es.search( + index=index_name, + body={ + "size": 50, # Get more samples to increase chance of seeing all sources + "_source": ["metadata", "text", "element_id"], + "query": { + "function_score": { + "query": {"match_all": {}}, + "random_score": {} + } + } + } + ) + + + # Map: source_connector_key -> [doc, ...] + source_connector_map = {} + unknown_docs = [] + + for hit in sample_response['hits']['hits']: + source = hit['_source'] + metadata = source.get('metadata', {}) + + # Determine source connector type based on metadata patterns + if "data_source-record_locator-index_name" in metadata: + # Elasticsearch source connector + key = f"elasticsearch:{metadata['data_source-record_locator-index_name']}" + elif "data_source-url" in metadata: + # S3 source connector - group all S3 URLs by bucket + url = metadata['data_source-url'] + if url.startswith('s3://'): + # Extract bucket name from S3 URL + bucket = url.split('/')[2] if '/' in url else url.replace('s3://', '') + key = f"s3:{bucket}" + else: + key = f"s3:unknown" + elif "filename" in metadata and metadata.get('filetype') == 'pdf': + # PDF files from S3 (fallback detection) + key = "s3:pdfs" + else: + key = "unknown" + + if key == "unknown": + unknown_docs.append(hit) + else: + if key not in source_connector_map: + source_connector_map[key] = hit # Only keep the first doc for each source connector + + print(f"🔍 Unique source connectors found: {len(source_connector_map)}") + for i, (key, doc) in enumerate(source_connector_map.items(), 1): + print(f"\n--- Source Connector {i} ({key}) ---") + pprint.pprint(doc['_source'], depth=6, compact=False, sort_dicts=False) + + if unknown_docs: + print(f"\n❓ Example Unknown Source Document:") + print("-" * 35) + unknown_example = unknown_docs[0]['_source'] + metadata = unknown_example.get('metadata', {}) + text = unknown_example.get('text', '') + print(f" Element ID: {unknown_example.get('element_id', 'N/A')}") + print(f" Metadata: {metadata}") + print(f" Text Preview: {text[:200]}..." if len(text) > 200 else f" Text: {text}") + print(" Metadata prettyprint:") + pprint.pprint(metadata, depth=6, compact=False, sort_dicts=False) + + # Test search functionality + print(f"\n🔍 Testing Search Functionality:") + print("=" * 32) + + search_tests = ["manual", "customer", "product", "support"] + + for search_term in search_tests: + search_response = es.search( + index=index_name, + body={ + "size": 1, + "query": { + "match": { + "text": search_term + } + } + } + ) + + hits = search_response['hits']['total']['value'] + print(f" 🔎 '{search_term}': {hits} matches") + + print(f"\n" + "=" * 50) + print("🎉 CUSTOMER-SUPPORT INDEX VERIFICATION") + print("=" * 50) + print("✅ Index exists and contains processed documents") + print("✅ Documents from both source connectors are present (if both completed)") + print("✅ Text search is functional across processed content") + print("✅ Ready for hybrid RAG queries!") + + except Exception as e: + print(f"❌ Error verifying results: {e}") + print("💡 This is normal if workflows are still processing or if there is a connection issue.") diff --git a/notebook-processing/code-scripts/workflows.py b/notebook-processing/code-scripts/workflows.py new file mode 100644 index 0000000..ec2418c --- /dev/null +++ b/notebook-processing/code-scripts/workflows.py @@ -0,0 +1,101 @@ +def create_workflow_nodes(): + """Create shared processing nodes for workflows.""" + vlm_partition_node = WorkflowNode( + name="VLM_Partitioner", + subtype="vlm", + type="partition", + settings={ + "provider": "openai", + "model": "gpt-4o", + } + ) + + chunk_node = WorkflowNode( + name="Chunker_Node", + subtype="chunk_by_title", + type="chunk", + settings={ + "new_after_n_chars": 1500, + "max_characters": 2048, + "overlap": 0 + } + ) + + embedder_node = WorkflowNode( + name="Embedder_Node", + subtype="openai", + type="embed", + settings={ + "model_name": "text-embedding-3-small" + } + ) + + ner_enrichment_node = WorkflowNode( + name="NER_Enrichment", + type="prompter", + subtype="openai_ner", + settings={} + ) + + return vlm_partition_node, chunk_node, embedder_node, ner_enrichment_node + +def create_parallel_workflows(s3_source_id, elasticsearch_source_id, destination_id): + """Create separate workflows for S3 PDFs and Elasticsearch data that run in parallel.""" + try: + vlm_partition_node, chunk_node, embedder_node, ner_enrichment_node = create_workflow_nodes() + + # Create workflow for S3 PDFs + s3_workflow_id = None + if s3_source_id: + with UnstructuredClient(api_key_auth=UNSTRUCTURED_API_KEY) as client: + s3_workflow = CreateWorkflow( + name=f"S3-PDFs-Parallel-Workflow_{int(time.time())}", + source_id=s3_source_id, + destination_id=destination_id, + workflow_type=WorkflowType.CUSTOM, + workflow_nodes=[ + vlm_partition_node, + chunk_node, + embedder_node, + ner_enrichment_node + ] + ) + + s3_response = client.workflows.create_workflow( + request=CreateWorkflowRequest( + create_workflow=s3_workflow + ) + ) + + s3_workflow_id = s3_response.workflow_information.id + print(f"✅ Created S3 PDF workflow: {s3_workflow_id}") + + # Create workflow for Elasticsearch sales data + with UnstructuredClient(api_key_auth=UNSTRUCTURED_API_KEY) as client: + es_workflow = CreateWorkflow( + name=f"Elasticsearch-Sales-Parallel-Workflow_{int(time.time())}", + source_id=elasticsearch_source_id, + destination_id=destination_id, + workflow_type=WorkflowType.CUSTOM, + workflow_nodes=[ + vlm_partition_node, + chunk_node, + embedder_node, + ner_enrichment_node + ] + ) + + es_response = client.workflows.create_workflow( + request=CreateWorkflowRequest( + create_workflow=es_workflow + ) + ) + + es_workflow_id = es_response.workflow_information.id + print(f"✅ Created Elasticsearch sales workflow: {es_workflow_id}") + + return s3_workflow_id, es_workflow_id + + except Exception as e: + print(f"❌ Error creating parallel workflows: {e}") + return None, None diff --git a/notebook-processing/code_blocks.yaml b/notebook-processing/code_blocks.yaml new file mode 100644 index 0000000..e1d5491 --- /dev/null +++ b/notebook-processing/code_blocks.yaml @@ -0,0 +1,14 @@ +# Code blocks for enriching hybrid_rag_pipeline_modular.py +# Maps CODE handles to their corresponding script files in code-scripts/ + +DEPENDENCIES: code-scripts/dependencies.py +DOTENV_CREATION: code-scripts/dotenv_creation.py +DATA_PREPARATION: code-scripts/data_preparation.py +CONNECTORS: code-scripts/connectors.py +WORKFLOWS: code-scripts/workflows.py +EXECUTION: code-scripts/execution.py +PREPROCESSING: code-scripts/preprocessing.py +VERIFICATION: code-scripts/verification.py +MAIN: code-scripts/main.py +EXECUTION_RUNNER: code-scripts/execution_runner.py +RAG_DEMO: code-scripts/rag_demo.py diff --git a/notebook-processing/code_mappings.yaml b/notebook-processing/code_mappings.yaml new file mode 100644 index 0000000..2007080 --- /dev/null +++ b/notebook-processing/code_mappings.yaml @@ -0,0 +1,11 @@ +# Code blocks for enriching hybrid_rag_pipeline.py +# Maps code handles to their corresponding script files + +DEPENDENCIES: code-scripts/dependencies.py +DATA_PREPARATION: code-scripts/data_preparation.py +CONNECTORS: code-scripts/connectors.py +WORKFLOWS: code-scripts/workflows.py +EXECUTION: code-scripts/execution.py +PREPROCESSING: code-scripts/preprocessing.py +VERIFICATION: code-scripts/verification.py +MAIN: code-scripts/main.py \ No newline at end of file diff --git a/notebook-processing/enrich_and_convert.py b/notebook-processing/enrich_and_convert.py new file mode 100644 index 0000000..4c304c3 --- /dev/null +++ b/notebook-processing/enrich_and_convert.py @@ -0,0 +1,255 @@ +#!/usr/bin/env python3 +import re +import sys +import argparse +import base64 +from pathlib import Path +import yaml +import subprocess +from PIL import Image +import io + +ROOT = Path(__file__).resolve().parents[1] +MD_MAP = Path(__file__).resolve().parent / 'markdown_blocks.yaml' +CODE_MAP = Path(__file__).resolve().parent / 'code_blocks.yaml' +IMG_MAP = Path(__file__).resolve().parent / 'image_blocks.yaml' +CODE_SCRIPTS_DIR = Path(__file__).resolve().parent / 'code-scripts' +IMAGES_DIR = Path(__file__).resolve().parent / 'images' + +MD_HANDLE_RE = re.compile(r"# \[\[MD:([A-Z0-9_]+)\]\]") +CODE_HANDLE_RE = re.compile(r"# \[\[CODE:([A-Z0-9_]+)\]\]") +IMG_HANDLE_RE = re.compile(r"# \[\[IMG:([A-Z0-9_]+)\]\]") + +def load_markdown_blocks(): + with open(MD_MAP, 'r') as f: + return yaml.safe_load(f) + +def load_code_blocks(): + if CODE_MAP.exists(): + with open(CODE_MAP, 'r') as f: + return yaml.safe_load(f) + return {} + +def load_image_blocks(): + if IMG_MAP.exists(): + with open(IMG_MAP, 'r') as f: + return yaml.safe_load(f) + return {} + +def load_code_script(script_path: str) -> str: + """Load code from a script file.""" + script_file = Path(__file__).resolve().parent / script_path + if script_file.exists(): + return script_file.read_text() + else: + print(f"[WARN] Code script not found: {script_path}") + return f"# Code script not found: {script_path}" + +def resize_image_if_needed(image_path: Path, max_width: int = 800) -> bytes: + """Resize image if it's wider than max_width, maintaining aspect ratio.""" + try: + with Image.open(image_path) as img: + # Convert to RGB if necessary (handles RGBA, P mode images) + if img.mode in ('RGBA', 'P'): + # Create a white background for transparent images + background = Image.new('RGB', img.size, (255, 255, 255)) + if img.mode == 'P': + img = img.convert('RGBA') + background.paste(img, mask=img.split()[-1] if img.mode == 'RGBA' else None) + img = background + elif img.mode != 'RGB': + img = img.convert('RGB') + + # Resize if needed + if img.width > max_width: + # Calculate new height maintaining aspect ratio + aspect_ratio = img.height / img.width + new_height = int(max_width * aspect_ratio) + img = img.resize((max_width, new_height), Image.Resampling.LANCZOS) + print(f"[INFO] Resized {image_path.name} from {img.width}x{img.height} to {max_width}x{new_height}") + + # Save to bytes + img_bytes = io.BytesIO() + img.save(img_bytes, format='PNG', optimize=True) + return img_bytes.getvalue() + + except Exception as e: + print(f"[WARN] Error resizing image {image_path}: {e}") + # Fallback to original file + return image_path.read_bytes() + +def load_image_as_base64(image_path: str) -> str: + """Load image file, resize if needed, and convert to base64 for embedding.""" + image_file = Path(__file__).resolve().parent / image_path + if not image_file.exists(): + print(f"[WARN] Image file not found: {image_path}") + return f"# Image not found: {image_path}" + + try: + # Resize image if needed (max 800px wide) + image_data = resize_image_if_needed(image_file, max_width=800) + + # Determine MIME type based on file extension + ext = image_file.suffix.lower() + mime_types = { + '.png': 'image/png', + '.jpg': 'image/jpeg', + '.jpeg': 'image/jpeg', + '.gif': 'image/gif', + '.svg': 'image/svg+xml', + '.webp': 'image/webp' + } + mime_type = mime_types.get(ext, 'image/png') + + # Encode as base64 + base64_data = base64.b64encode(image_data).decode('utf-8') + + # Get filename for alt text + filename = image_file.stem + + return f"![{filename}](data:{mime_type};base64,{base64_data})" + + except Exception as e: + print(f"[WARN] Error loading image {image_path}: {e}") + return f"# Error loading image: {image_path}" + +def _normalize_block(block: str) -> str: + # Remove surrounding triple quotes if present + lines = block.strip('\n').splitlines() + if lines and lines[0].strip() == '"""' and lines[-1].strip() == '"""': + lines = lines[1:-1] + return '\n'.join(lines) + +def _to_percent_markdown_cell(block: str) -> str: + content = _normalize_block(block) + md_lines = ["# %% [markdown]"] + for ln in content.splitlines(): + md_lines.append(f"# {ln}") + return '\n'.join(md_lines) + +def _to_percent_code_cell(code: str) -> str: + """Convert code to a percent code cell format.""" + lines = ["# %%"] + lines.extend(code.splitlines()) + return '\n'.join(lines) + +def enrich_file(source_file: Path, output_file: Path, strict: bool = True, include_images: bool = False): + md_blocks = load_markdown_blocks() + code_blocks = load_code_blocks() + img_blocks = load_image_blocks() if include_images else {} + + lines = source_file.read_text().splitlines() + enriched_lines = [] + + for line in lines: + # Check for markdown handles + md_match = MD_HANDLE_RE.search(line) + if md_match: + key = md_match.group(1) + block = md_blocks.get(key) + if block is None: + print(f"[WARN] No markdown block found for handle: {key}") + enriched_lines.append(line) + else: + print(f"[INFO] Replacing markdown handle: {key}") + enriched_lines.append(_to_percent_markdown_cell(block)) + continue + + # Check for code handles + code_match = CODE_HANDLE_RE.search(line) + if code_match: + key = code_match.group(1) + script_path = code_blocks.get(key) + if script_path is None: + print(f"[WARN] No code block found for handle: {key}") + enriched_lines.append(line) + else: + print(f"[INFO] Replacing code handle: {key} with {script_path}") + code_content = load_code_script(script_path) + enriched_lines.append(_to_percent_code_cell(code_content)) + continue + + # Check for image handles + img_match = IMG_HANDLE_RE.search(line) + if img_match: + key = img_match.group(1) + if not include_images: + print(f"[INFO] Skipping image handle: {key} (images disabled)") + enriched_lines.append(f"# [[IMG:{key}]] # Image disabled - use --include-images to enable") + continue + + image_path = img_blocks.get(key) + if image_path is None: + print(f"[WARN] No image block found for handle: {key}") + enriched_lines.append(line) + else: + print(f"[INFO] Replacing image handle: {key} with {image_path}") + image_content = load_image_as_base64(image_path) + enriched_lines.append(_to_percent_markdown_cell(image_content)) + continue + + # Regular line - keep as is + enriched_lines.append(line) + + enriched_text = '\n'.join(enriched_lines) + '\n' + + if strict: + # Check for any remaining handles + remaining_md = [ln for ln in enriched_text.splitlines() if '[[MD:' in ln] + remaining_code = [ln for ln in enriched_text.splitlines() if '[[CODE:' in ln] + remaining_img = [ln for ln in enriched_text.splitlines() if '[[IMG:' in ln and 'Image disabled' not in ln] + + if remaining_md: + raise SystemExit(f"[ERROR] Unreplaced MD handles remain: {remaining_md[:5]}") + if remaining_code: + raise SystemExit(f"[ERROR] Unreplaced CODE handles remain: {remaining_code[:5]}") + if remaining_img: + raise SystemExit(f"[ERROR] Unreplaced IMG handles remain: {remaining_img[:5]}") + + output_file.write_text(enriched_text) + return output_file + +def convert_with_jupytext(py_path: Path): + # Convert enriched python to notebook using jupytext + nb_path = py_path.with_suffix('.ipynb') + cmd = [sys.executable, '-m', 'jupytext', '--to', 'ipynb', str(py_path)] + subprocess.check_call(cmd) + return nb_path + +def main(): + parser = argparse.ArgumentParser(description='Enrich and convert pipeline to notebook') + parser.add_argument('--source', choices=['original', 'modular'], default='modular', + help='Source pipeline to use (default: modular)') + parser.add_argument('--output-suffix', default='enriched', + help='Suffix for output files (default: enriched)') + parser.add_argument('--include-images', action='store_true', + help='Include images in the output (default: False)') + + args = parser.parse_args() + + # Determine source and output files + if args.source == 'modular': + source_file = ROOT / 'hybrid_rag_pipeline_modular.py' + else: + source_file = ROOT / 'hybrid_rag_pipeline.py' + + output_file = ROOT / f'hybrid_rag_pipeline_{args.output_suffix}.py' + + # Check if source file exists + if not source_file.exists(): + print(f"❌ Source file not found: {source_file}") + sys.exit(1) + + print(f"📄 Source: {source_file.name}") + print(f"📄 Output: {output_file.name}") + print(f"🔧 Mode: {args.source}") + print(f"🖼️ Images: {'Enabled' if args.include_images else 'Disabled'}") + print() + + enriched = enrich_file(source_file, output_file, strict=True, include_images=args.include_images) + nb = convert_with_jupytext(enriched) + print(f"✅ Enriched: {enriched}") + print(f"✅ Notebook: {nb}") + +if __name__ == '__main__': + main() diff --git a/notebook-processing/image_blocks.yaml b/notebook-processing/image_blocks.yaml new file mode 100644 index 0000000..c7dc3b3 --- /dev/null +++ b/notebook-processing/image_blocks.yaml @@ -0,0 +1,18 @@ +# Image Blocks Configuration +# Maps image handles to image files in the images/ folder +# +# Usage: Add # [[IMG:HANDLE_NAME]] to your pipeline file +# Then add HANDLE_NAME: images/filename.ext to this file + +# Elasticsearch Index Images +SALES_RECORDS_CONSOLIDATED_INDEX: images/sales-records-consolidated.png +PRODUCT_MANUAL_EXAMPLE: images/product-manual-example.png +CUSTOMER_SUPPORT_INDEX: images/customer-support.png + +# Example entries (uncomment and modify as needed): +# ARCHITECTURE_DIAGRAM: images/architecture-diagram.png +# WORKFLOW_FLOWCHART: images/workflow-flowchart.png +# DATA_FLOW: images/data-flow.png +# RAG_DEMO_SCREENSHOT: images/rag-demo-screenshot.png +# SYSTEM_OVERVIEW: images/system-overview.png +# PROCESSING_PIPELINE: images/processing-pipeline.png diff --git a/notebook-processing/images/customer-support.png b/notebook-processing/images/customer-support.png new file mode 100644 index 0000000..88099fc Binary files /dev/null and b/notebook-processing/images/customer-support.png differ diff --git a/notebook-processing/images/product-manual-example.png b/notebook-processing/images/product-manual-example.png new file mode 100644 index 0000000..b413b1f Binary files /dev/null and b/notebook-processing/images/product-manual-example.png differ diff --git a/notebook-processing/images/sales-records-consolidated.png b/notebook-processing/images/sales-records-consolidated.png new file mode 100644 index 0000000..5562f8e Binary files /dev/null and b/notebook-processing/images/sales-records-consolidated.png differ diff --git a/notebook-processing/images/sales-records.png b/notebook-processing/images/sales-records.png new file mode 100644 index 0000000..0dd9dad Binary files /dev/null and b/notebook-processing/images/sales-records.png differ diff --git a/notebook-processing/markdown_blocks.yaml b/notebook-processing/markdown_blocks.yaml new file mode 100644 index 0000000..ccd425a --- /dev/null +++ b/notebook-processing/markdown_blocks.yaml @@ -0,0 +1,332 @@ +# Markdown blocks for enriching hybrid_rag_pipeline.py + +INTRO: | + """ + # Building a Hybrid RAG System: From Fragmented Data to Unified Intelligence + + Picture this: You're a customer support agent, and a customer calls about a product issue. To help them effectively, you need to pull information from multiple sources - product manuals stored as PDFs in cloud storage, their purchase history in your sales database, and previous support interactions scattered across different systems. Each piece of information lives in a different format, in a different system, with different access methods. + + This is the reality for most enterprises today, and it's exactly the challenge we're going to solve together. + + ## The Enterprise Data Challenge + + Enterprise data rarely lives in one place or format. Critical information is fragmented across unstructured documents like PDFs and manuals in cloud storage, structured records like sales data in databases, and different formats requiring different processing approaches. Traditional RAG systems work well with homogeneous data but struggle when you need to query across diverse data sources simultaneously. + + ## Why This Matters + + When data is scattered, customer support becomes inefficient, decision-making lacks context, and valuable insights remain hidden. A customer asking about a product issue shouldn't require you to manually search through multiple systems to piece together a complete picture. + + ## The Solution: Unstructured's Complete Gen AI Data Layer + + Unstructured isn't just another data processing tool—it's a complete Gen AI data layer solution that transforms how organizations handle unstructured data at scale. Unlike building custom solutions or using fragmented tools, Unstructured provides a unified platform that connects to 30+ data sources, processes 65+ file types with intelligent partitioning and chunking, automatically enriches content with metadata and context, and delivers to 30+ destinations—all while maintaining enterprise-grade security and compliance. + + The platform eliminates the complexity of managing multiple tools, custom integrations, and manual data preparation, allowing teams to focus on building AI applications rather than wrestling with data infrastructure. With flexible deployment options from SaaS to bare metal, Unstructured adapts to any infrastructure while providing the observability, automation, and reliability that enterprise AI projects demand. + + ## What We'll Build Together + + In this tutorial, we'll create a hybrid RAG system that processes two different data sources simultaneously: product documentation from S3 and sales records from Elasticsearch. Both will flow through the same intelligent processing pipeline and land in a unified, searchable knowledge base. + + ``` + ┌─────────────────┐ ┌─────────────────────────┐ + │ S3 PDFs │──── WORKFLOW 1 ──────────▶│ │ + │ (Product Docs) │ │ Unstructured API │ + └─────────────────┘ │ │ + │ Partition → Chunk → │ + ┌─────────────────┐ │ Embed → NER → Store │ + │ Elasticsearch │──── WORKFLOW 2 ──────────▶│ │ + │ (Sales Records) │ │ │ + └─────────────────┘ └────────────┬────────────┘ + │ + ┌────────────▼────────────┐ + │ customer-support │ + │ (Unified Index) │ + └─────────────────────────┘ + ``` + + By the end of this tutorial, you'll have a working system that can answer complex questions by pulling information from both your product documentation and customer data simultaneously. + """ + +API_KEY_SETUP: | + """ + ## Getting Started: Your Unstructured API Key + + To follow along with this tutorial, you'll need an Unstructured API key. This gives you access to the complete Gen AI data layer that will process your documents and create your unified knowledge base. + + ### Sign Up and Get Your API Key + + Visit https://platform.unstructured.io to sign up for a free account, navigate to API Keys in the sidebar, generate your API key, and save it for the configuration step below. For Team or Enterprise accounts, make sure you've selected the correct organizational workspace before creating your API key. + + **Need help?** Contact Unstructured Support at support@unstructured.io + """ + +CONFIG: | + """ + ## Configuration: Setting Up Your Environment + + Now we'll configure your environment with the necessary API keys and credentials. This step ensures your system can connect to all the data sources and services we'll be using. + + """ + +COLAB_DOTENV_CREATION: | + """ + ### Creating a .env File in Google Colab + + For better security and organization, we'll create a `.env` file directly in your Colab environment. Run the code cell below to create the file with placeholder values, then edit it with your actual credentials. + + After running the code cell, you'll need to replace each placeholder value (like `your-unstructured-api-key`) with your actual API keys and credentials. + """ + +DEPENDENCIES_EXPLANATION: | + """ + ### Installing Required Dependencies + + The following code installs the Python packages needed for this tutorial: the Unstructured client, Elasticsearch connector, AWS SDK, and other dependencies. + """ + +AWS_S3_SETUP: | + """ + ## AWS S3: Your Document Storage + + Now that we have our environment configured, let's set up the data sources for our hybrid RAG system. First up: your unstructured documents. These PDFs, manuals, and reports need to be accessible via S3, where your product documentation and other unstructured content lives, waiting to be processed into searchable knowledge. + + ### What You Need + + **An existing S3 bucket** containing the documents you want to process. For this tutorial, we'll use sample product manuals, but in production, this would be your actual business documents. + + > **Note**: This tutorial assumes you have an existing S3 bucket with documents. For detailed S3 setup instructions, see the [Unstructured S3 source connector documentation](https://docs.unstructured.io/api-reference/api-services/source-connectors/s3). + + You'll need an AWS account with S3 access, an IAM user with S3 read permissions for your bucket, and access keys (Access Key ID and Secret Access Key). + """ + +ELASTICSEARCH_SETUP: | + """ + ## Elasticsearch: Your Business Data Hub + + While S3 holds your unstructured documents, Elasticsearch serves a dual purpose in our pipeline. It's both a source of structured business data (your sales records, customer information) and the destination where our unified, processed results will be stored for RAG queries. + + ### What You Need + + **Elasticsearch cluster** with API key authentication from Elastic Cloud (managed service). This gives you the reliability and scalability needed for enterprise applications. + + The pipeline uses two indices: `sales-records-consolidated` as the source containing your business data, and `customer-support` as the destination for your unified knowledge base. Both are created automatically by the pipeline. + + ### Why Consolidated Data Format Matters + + Traditional databases store information in separate fields (customer_name, product_id, purchase_date). For RAG applications, we consolidate this into a long-form text field that provides full context in each search result. This approach ensures that when someone searches for "John's headphone purchase," they get the complete story in one result. + + Example transformation: + ``` + Before: {customer: "John Doe", product: "BH-001", date: "2024-01-15"} + After: "customer: John Doe\nproduct: BH-001\ndate: 2024-01-15" + ``` + + ### API Key Permissions + + Your Elasticsearch API key needs these permissions: + + ```json + { + "sales-records-full-access": { + "cluster": [], + "indices": [ + { + "names": [ + "sales-records", + "sales-records-consolidated", + "customer-support" + ], + "privileges": [ + "create_index", + "delete_index", + "manage", + "write", + "read", + "view_index_metadata", + "monitor" + ], + "allow_restricted_indices": false + } + ], + "applications": [], + "run_as": [], + "metadata": {}, + "transient_metadata": { + "enabled": true + } + } + } + ``` + + **Don't have Elasticsearch data yet?** The pipeline includes automatic data setup that creates sample sales records for demonstration. This is done by downloading .ZIP files from github and unzipping them. + """ + +DATA_PREPARATION: | + """ + ## Data Preparation: Setting Up Your Demo Environment + + With our infrastructure configured, let's prepare the actual data that will flow through our hybrid RAG system. For this demonstration, we've created realistic sample data that represents a typical enterprise scenario, giving you a working example without requiring you to set up your own data sources first. + + **Elasticsearch Sales Data**: 100 synthetic sales records with customer information, with consolidated fields optimized for vector search. This represents the kind of structured business data you'd find in any enterprise system. + + **S3 Product Documentation**: 9 product manuals downloaded from manufacturer websites and stored in your S3 bucket. These represent the unstructured documents that contain critical product information. + + This combination mimics real enterprise scenarios where structured data (sales records) and unstructured documents (manuals) need to be searchable together for effective customer support. The magic happens when we can answer questions like "What issues have customers reported with the BH-900 headphones?" by pulling from both the sales records and the product manual simultaneously. + """ + +S3_SOURCE_CONNECTOR: | + """ + ## S3 Source Connector + + Now we'll create the connections that link our data sources to Unstructured's processing pipeline. First, let's establish the connection to your S3 bucket containing PDF documents for processing. + """ + +ES_SOURCE_CONNECTOR: | + """ + ## Elasticsearch Source Connector + + Next, we'll connect to your Elasticsearch index containing structured sales data, completing our dual-source setup. + """ + +ES_DESTINATION_CONNECTOR: | + """ + ## Elasticsearch Destination Connector + + Finally, we'll create the destination where both data streams will converge: the unified `customer-support` index where all processed data will be stored. + """ + +CREATE_WORKFLOWS: | + """ + ## Creating Parallel Processing Workflows + + Now we'll assemble everything into the two parallel workflows shown in our architecture diagram above, connecting each data source to the processing pipeline and unified destination. + """ + +WORKFLOW_NODES: | + """ + ## Processing Pipeline Configuration + + With our connectors in place, we can now configure the intelligent processing pipeline that will transform both data sources. This four-stage pipeline (VLM → Chunker → Embedder → NER) will be applied to both workflows, ensuring consistent processing regardless of data source. + """ + +RUN_WORKFLOW: | + """ + ## Starting Your Processing Jobs + + With our workflows configured, it's time to put them into action. This step submits both workflows to the Unstructured API and returns job IDs for monitoring. + """ + +JOB_MONITORING: | + """ + ## Monitoring Your Processing Progress + + Jobs progress through scheduled, in-progress, completed, or failed states. The `poll_job_status` function checks status every 30 seconds and blocks execution until jobs complete, so you can see exactly what's happening with your data processing. + """ + +ES_PREPROCESSING: | + """ + ## Preparing Your Elasticsearch Environment + + Before processing begins, we validate that the `sales-records-consolidated` index exists and contains data, then recreate the `customer-support` index fresh for each run. This preparation step ensures a clean environment and prevents any issues from previous runs. + + ### Index Mapping + + The destination index uses this structure optimized for RAG applications: + ```json + { + "id": "keyword", // Unique document identifier + "timestamp": "date", // Processing timestamp + "text": "text", // Searchable content + "metadata": "object" // Source info and entities + } + ``` + """ + +SUMMARY: | + """ + ## Pipeline Execution Summary + + The following summary displays all resources created during pipeline setup: data source paths, connector IDs, workflow IDs, job IDs, and processing status. + """ + +VERIFICATION: | + """ + ## Verifying Your Hybrid RAG System + + After processing completes, we'll verify that both data sources have been successfully integrated into a unified knowledge base. The verification includes checking document counts from each source, testing search functionality, and confirming the data is ready for RAG queries. + """ + +MAIN: | + """ + ## Orchestrating Your Complete Pipeline + + The main function coordinates all pipeline steps in logical sequence: data preparation, environment validation, connector setup, workflow creation, execution, and summary reporting. + """ + +EXECUTION_FLOW: | + """ + ## Running Your Complete Pipeline + + We'll execute the complete pipeline by calling the main function to create all resources and start processing, then monitor the jobs until they complete successfully. + """ + +RAG_DEMO_SETUP: | + """ + ## RAG Query Demonstration + + Now that your hybrid knowledge base is ready, we'll demonstrate how to query it using RAG (Retrieval-Augmented Generation). This is where you'll see how the system can answer complex questions by pulling relevant information from both your S3 documents and Elasticsearch records. + + ### OpenAI API Key Required + + For the RAG demonstration, you'll need an OpenAI API key to power the language model that generates answers based on your retrieved documents. Visit https://platform.openai.com/api-keys to sign in or create an account and generate a new API key. + + The demonstration will show cross-source querying, source attribution, and semantic understanding as your hybrid RAG system answers questions by combining information from multiple data sources. + """ + +RAG_DEMO_CONFIG: | + """ + ### RAG Configuration + + **Instructions**: Paste your OpenAI API key below to enable RAG demonstrations. This key will be used to power the language model that generates answers based on your retrieved documents. + """ + +CONCLUSION: | + """ + ## What You've Accomplished + + **Enterprise Data Integration**: You've learned how to process multiple data formats (PDFs, structured records) in parallel, why consistent processing pipelines matter for unified search, and the value of creating a single searchable knowledge base that spans all your data sources. + + **Unstructured API Capabilities**: You've experienced VLM-powered document partitioning for complex layouts, intelligent chunking that preserves document structure, named entity recognition for enhanced search precision, and unified processing across diverse data sources. + + **RAG System Architecture**: You've built parallel workflow design for scalability and reliability, vector embeddings for semantic similarity search, source attribution in mixed-data query results, and NER-enhanced query understanding and response generation. + + ### Ready to Scale? + + Deploy customer support chatbots with comprehensive knowledge access, build internal search tools that surface information from any source, or create automated content recommendation systems. Add more data sources using additional workflows, implement real-time data synchronization, or scale up for production data volumes with monitoring and alerting. + + ### Try Unstructured Today + + Ready to build your own hybrid RAG system? [Sign up for a free trial](https://unstructured.io/?modal=try-for-free) and start transforming your enterprise data into intelligent, searchable knowledge. + + **Need help getting started?** Contact our team to schedule a demo and see how Unstructured can solve your specific data challenges. + """ + +PRODUCT_MANUAL_EXAMPLE_CONTEXT: | + """ + ### Example Product Manual Content + + The following image shows a sample page from one of the product manuals stored in your S3 bucket. This demonstrates the type of unstructured content that will be processed and made searchable through our RAG system. + """ + +SALES_RECORDS_CONSOLIDATED_CONTEXT: | + """ + ### Sales Records Data Structure + + The image below shows the structure of the consolidated sales records in your Elasticsearch index. This data represents customer transactions and will be processed alongside the product manuals to create a unified knowledge base. + """ + +CUSTOMER_SUPPORT_OUTPUT_CONTEXT: | + """ + ### Unified Knowledge Base Results + + After processing both data sources, the pipeline creates a unified `customer-support` index containing processed documents from both S3 PDFs and Elasticsearch sales records. The image below shows the structure of this consolidated knowledge base, ready for RAG queries. + """ diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..fad5c03 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,46 @@ +aiofiles==24.1.0 +annotated-types==0.7.0 +anyio==4.10.0 +attrs==25.3.0 +boto3==1.35.76 +certifi==2025.8.3 +cffi==2.0.0 +charset-normalizer==3.4.3 +cryptography==45.0.7 +elastic-transport==9.1.0 +elasticsearch==9.1.0 +Faker==37.6.0 +fastjsonschema==2.21.2 +h11==0.16.0 +httpcore==1.0.9 +httpx==0.28.1 +idna==3.10 +jsonschema==4.25.1 +jsonschema-specifications==2025.9.1 +jupyter_core==5.8.1 +jupytext==1.17.3 +markdown-it-py==4.0.0 +mdit-py-plugins==0.5.0 +mdurl==0.1.2 +nbformat==5.10.4 +packaging==25.0 +platformdirs==4.4.0 +pycparser==2.23 +pydantic==2.11.7 +pydantic_core==2.33.2 +pypdf==6.0.0 +python-dateutil==2.9.0.post0 +python-dotenv==1.1.1 +PyYAML==6.0.2 +referencing==0.36.2 +requests==2.32.5 +requests-toolbelt==1.0.0 +rpds-py==0.27.1 +six==1.17.0 +sniffio==1.3.1 +traitlets==5.14.3 +typing-inspection==0.4.1 +typing_extensions==4.15.0 +tzdata==2025.2 +unstructured-client==0.42.3 +urllib3==2.5.0 diff --git a/run_pipeline.py b/run_pipeline.py new file mode 100644 index 0000000..969f869 --- /dev/null +++ b/run_pipeline.py @@ -0,0 +1,26 @@ +#!/usr/bin/env python3 +""" +Runner script for the Hybrid RAG Pipeline. +This script imports and executes the pipeline from hybrid_rag_pipeline.py +""" + +import sys +import os + +# Add the current directory to the Python path +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) + +try: + from hybrid_rag_pipeline import main + + if __name__ == "__main__": + print("🚀 Starting Hybrid RAG Pipeline Runner") + print("=" * 50) + main() + +except ImportError as e: + print(f"❌ Error importing pipeline: {e}") + sys.exit(1) +except Exception as e: + print(f"❌ Error running pipeline: {e}") + sys.exit(1) diff --git a/source_data/README.md b/source_data/README.md new file mode 100644 index 0000000..617ff8e --- /dev/null +++ b/source_data/README.md @@ -0,0 +1,140 @@ +# Source Data Management + +This directory contains scripts and data files for managing the source data used in the hybrid RAG pipeline. + +## Directory Structure + +``` +source_data/ +├── README.md # This file +├── s3_pdfs/ # Directory containing PDF source files +├── sales_records_consolidated.zip # sales-records-consolidated index (100 documents, 18KB) +├── sales_records.zip # sales-records index (100 documents, 16KB) +└── s3_pdfs.zip # S3 PDF files (9 Bose headphone manuals, 6.5MB) +``` + +## Data Files + +### Elasticsearch Sales Data + +**`sales_records_consolidated.zip`** - **Used by hybrid RAG pipeline** +- Contains the `sales-records-consolidated` index +- 100 synthetic sales records with consolidated fields +- Used by the automated data preparation in `hybrid_rag_pipeline.py` +- Downloaded from: `https://github.com/Unstructured-IO/rag-over-hybrid-data-sources/raw/feature/hybrid-rag-pipeline/source_data/sales_records_consolidated.zip` + +**`sales_records.zip`** - **Reference data** +- Contains the `sales-records` index +- 100 synthetic sales records with separate fields +- Available for comparison or alternative workflows + +### S3 PDF Data + +**`s3_pdfs.zip`** +- Contains 9 Bose headphone manuals and customer support documents +- Used by the automated data preparation in `hybrid_rag_pipeline.py` +- Downloaded from: `https://github.com/Unstructured-IO/rag-over-hybrid-data-sources/raw/feature/hybrid-rag-pipeline/source_data/s3_pdfs.zip` + +## Scripts + +### Elasticsearch Sales Index Loader (`../load_es_sales_index.py`) + +Manages Elasticsearch index data for both sales indices. + +#### Download index data to zip file: +```bash +# Download sales-records-consolidated (used by pipeline) - creates sales_records_consolidated.zip +python ../load_es_sales_index.py download --index sales-records-consolidated + +# Download sales-records (reference data) - creates sales_records.zip +python ../load_es_sales_index.py download --index sales-records + +# Or specify custom output path +python ../load_es_sales_index.py download --output source_data/custom_name.zip --index sales-records-consolidated +``` + +#### Load data from zip file to index: +```bash +# Load consolidated data (pipeline default) +python ../load_es_sales_index.py load --input source_data/sales_records_consolidated.zip + +# Load non-consolidated data +python ../load_es_sales_index.py load --input source_data/sales_records.zip --index sales-records +``` + +### S3 PDFs Loader (`../load_s3_pdfs.py`) + +Manages PDF files for the S3 source connector. + +#### Zip PDF files from local directory: +```bash +# Creates s3_pdfs.zip automatically based on directory name +python ../load_s3_pdfs.py zip --input source_data/s3_pdfs + +# Or specify custom output path +python ../load_s3_pdfs.py zip --input source_data/s3_pdfs --output source_data/custom_name.zip +``` + +#### Load PDFs from zip file to S3 bucket: +```bash +python ../load_s3_pdfs.py load --input source_data/s3_pdfs.zip +``` + +## Pipeline Usage + +The `hybrid_rag_pipeline.py` automatically downloads and sets up data from: + +1. **Elasticsearch Source**: `sales_records_consolidated.zip` → `sales-records-consolidated` index +2. **S3 Source**: `s3_pdfs.zip` → S3 bucket (from `S3_SOURCE_BUCKET` env var) + +The pipeline is configured to use the **consolidated** sales data (`sales_records_consolidated.zip`) because: +- Multiple fields are consolidated into single long-form text fields +- Provides maximum context for vector search operations +- Optimized for RAG applications where comprehensive searchability is preferred + +## Environment Variables Required + +Both scripts require the following environment variables to be set in your `.env` file: + +### For Elasticsearch operations: +- `ELASTICSEARCH_HOST` - Your Elasticsearch cluster URL +- `ELASTICSEARCH_API_KEY` - API key for authentication + +### For S3 operations: +- `AWS_ACCESS_KEY_ID` - AWS access key +- `AWS_SECRET_ACCESS_KEY` - AWS secret key +- `AWS_REGION` - AWS region (defaults to us-east-1) +- `S3_SOURCE_BUCKET` - S3 bucket name (used as default for load operations) + +## File Details + +### Sales Data Comparison + +| File | Index | Documents | Size | Field Structure | Usage | +|------|-------|-----------|------|-----------------|-------| +| `sales_records_consolidated.zip` | `sales-records-consolidated` | 100 | 18KB | Consolidated fields | **Pipeline default** | +| `sales_records.zip` | `sales-records` | 100 | 16KB | Separate fields | Reference/comparison | + +### PDF Files Included + +The `s3_pdfs.zip` contains these Bose headphone manuals: +- `bose-OpenAudio-manual.pdf` +- `bose-OpenAudio-troubleshooting.pdf` +- `bose-OpenAudio-msds.pdf` +- `bose-OpenAudio-instructions.pdf` +- `bose-SoundSport-userguide.pdf` +- `bose-SoundSport-safety.pdf` +- `bose-SoundSport-manual.pdf` +- `bose-QUIETCOMFORT-manual.pdf` +- `bose-QUIETCOMFORT-troubleshooting-guide.pdf` + +## Error Handling + +Both scripts include comprehensive error handling: +- Missing environment variables +- Network connectivity issues +- Authentication failures +- File/index not found scenarios +- Partial upload/download failures + +All errors are reported with clear messages and appropriate exit codes. \ No newline at end of file diff --git a/source_data/load_es_sales_index.py b/source_data/load_es_sales_index.py new file mode 100755 index 0000000..a9ddf9a --- /dev/null +++ b/source_data/load_es_sales_index.py @@ -0,0 +1,192 @@ +#!/usr/bin/env python3 +""" +Elasticsearch Sales Index Data Loader + +This script provides functionality to: +1. Download the sales-records-consolidated index from Elasticsearch and save as a zip file +2. Load data from a zip file into the sales-records-consolidated index + +Usage: + # Download index to zip file + python load_es_sales_index.py download --output source_data/sales_data.zip + + # Load data from zip file to index + python load_es_sales_index.py load --input source_data/sales_data.zip +""" + +import os +import sys +import json +import zipfile +import argparse +from pathlib import Path +from dotenv import load_dotenv +from elasticsearch import Elasticsearch +from elasticsearch.helpers import scan, bulk + +# Load environment variables +load_dotenv() + +def get_elasticsearch_client(): + """Initialize and return Elasticsearch client.""" + host = os.getenv("ELASTICSEARCH_HOST") + api_key = os.getenv("ELASTICSEARCH_API_KEY") + + if not host or not api_key: + raise ValueError("ELASTICSEARCH_HOST and ELASTICSEARCH_API_KEY must be set in .env file") + + return Elasticsearch( + host, + api_key=api_key, + request_timeout=60, + max_retries=3, + retry_on_timeout=True + ) + +def download_index(output_path: str = None, index_name: str = "sales-records-consolidated"): + """Download Elasticsearch index data and save as zip file.""" + print(f"🔄 Downloading index '{index_name}'...") + + # Generate output path based on index name if not provided + if not output_path: + # Convert index name to zip file name (replace hyphens with underscores for consistency) + zip_name = index_name.replace('-', '_') + '.zip' + output_path = f"source_data/{zip_name}" + + try: + es = get_elasticsearch_client() + + # Check if index exists + if not es.indices.exists(index=index_name): + raise ValueError(f"❌ Index '{index_name}' does not exist") + + # Get index mapping + mapping_response = es.indices.get_mapping(index=index_name) + mapping = mapping_response.body if hasattr(mapping_response, 'body') else mapping_response + + # Get all documents + documents = [] + for doc in scan(es, index=index_name, query={"query": {"match_all": {}}}): + documents.append({ + "_id": doc["_id"], + "_source": doc["_source"] + }) + + print(f"📊 Found {len(documents)} documents") + + # Create output directory if it doesn't exist + output_path = Path(output_path) + output_path.parent.mkdir(parents=True, exist_ok=True) + + # Save to zip file + with zipfile.ZipFile(output_path, 'w', zipfile.ZIP_DEFLATED) as zipf: + # Save mapping + zipf.writestr('mapping.json', json.dumps(mapping, indent=2)) + + # Save documents + zipf.writestr('documents.json', json.dumps(documents, indent=2)) + + print(f"✅ Successfully saved {len(documents)} documents to {output_path}") + + except Exception as e: + print(f"❌ Error downloading index: {e}") + sys.exit(1) + +def load_index(input_path: str, index_name: str = "sales-records-consolidated"): + """Load data from zip file into Elasticsearch index.""" + print(f"🔄 Loading data into index '{index_name}'...") + + try: + input_path = Path(input_path) + if not input_path.exists(): + raise ValueError(f"❌ Input file '{input_path}' does not exist") + + es = get_elasticsearch_client() + + # Delete existing index if it exists + if es.indices.exists(index=index_name): + print(f"🗑️ Deleting existing index '{index_name}'...") + es.indices.delete(index=index_name) + + # Load data from zip file + with zipfile.ZipFile(input_path, 'r') as zipf: + # Load mapping + with zipf.open('mapping.json') as f: + mapping_data = json.loads(f.read().decode('utf-8')) + + # Load documents + with zipf.open('documents.json') as f: + documents = json.loads(f.read().decode('utf-8')) + + print(f"📊 Loaded {len(documents)} documents from zip file") + + # Create index with mapping + index_mapping = mapping_data[index_name] if index_name in mapping_data else mapping_data[list(mapping_data.keys())[0]] + es.indices.create(index=index_name, body=index_mapping) + print(f"🔧 Created index '{index_name}' with mapping") + + # Prepare documents for bulk insert + def generate_docs(): + for doc in documents: + yield { + "_index": index_name, + "_id": doc["_id"], + "_source": doc["_source"] + } + + # Bulk insert documents + success_count, failed_items = bulk(es, generate_docs(), chunk_size=100) + print(f"📝 Inserted {success_count} documents") + + if failed_items: + print(f"⚠️ Failed to insert {len(failed_items)} documents") + + # Refresh index + es.indices.refresh(index=index_name) + + # Verify data was loaded + count_response = es.count(index=index_name) + count_data = count_response.body if hasattr(count_response, 'body') else count_response + doc_count = count_data['count'] + + if doc_count > 0: + print(f"✅ Successfully loaded {doc_count} documents into '{index_name}' index") + else: + print(f"❌ Index '{index_name}' is empty after loading") + sys.exit(1) + + except Exception as e: + print(f"❌ Error loading index: {e}") + sys.exit(1) + +def main(): + parser = argparse.ArgumentParser(description="Elasticsearch Sales Index Data Loader") + subparsers = parser.add_subparsers(dest='command', help='Available commands') + + # Download command + download_parser = subparsers.add_parser('download', help='Download index to zip file') + download_parser.add_argument('--output', '-o', + help='Output zip file path (default: source_data/{index_name}.zip)') + download_parser.add_argument('--index', '-i', default='sales-records-consolidated', + help='Index name to download (default: sales-records-consolidated)') + + # Load command + load_parser = subparsers.add_parser('load', help='Load data from zip file to index') + load_parser.add_argument('--input', '-i', required=True, + help='Input zip file path (e.g., source_data/sales_data.zip)') + load_parser.add_argument('--index', '-x', default='sales-records-consolidated', + help='Index name to load into (default: sales-records-consolidated)') + + args = parser.parse_args() + + if not args.command: + parser.print_help() + sys.exit(1) + + if args.command == 'download': + download_index(args.output, args.index) + elif args.command == 'load': + load_index(args.input, args.index) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/source_data/load_s3_pdfs.py b/source_data/load_s3_pdfs.py new file mode 100755 index 0000000..fc9f88a --- /dev/null +++ b/source_data/load_s3_pdfs.py @@ -0,0 +1,223 @@ +#!/usr/bin/env python3 +""" +S3 PDFs Data Loader + +This script provides functionality to: +1. Zip PDF files from a local directory and save as a zip file +2. Load PDFs from a zip file and upload them to an S3 bucket + +Usage: + # Zip PDFs from local directory + python load_s3_pdfs.py zip --input source_data/s3_pdfs --output source_data/s3_pdfs.zip + + # Load PDFs from zip file to S3 bucket + python load_s3_pdfs.py load --input source_data/s3_pdfs.zip +""" + +import os +import sys +import zipfile +import argparse +from pathlib import Path +from dotenv import load_dotenv +import boto3 +from botocore.exceptions import ClientError, NoCredentialsError + +# Load environment variables +load_dotenv() + +def get_s3_client(): + """Initialize and return S3 client.""" + aws_access_key_id = os.getenv("AWS_ACCESS_KEY_ID") + aws_secret_access_key = os.getenv("AWS_SECRET_ACCESS_KEY") + aws_region = os.getenv("AWS_REGION", "us-east-1") + + if not aws_access_key_id or not aws_secret_access_key: + raise ValueError("AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY must be set in .env file") + + return boto3.client( + 's3', + aws_access_key_id=aws_access_key_id, + aws_secret_access_key=aws_secret_access_key, + region_name=aws_region + ) + +def zip_pdfs(input_dir: str, output_path: str = None): + """Zip PDF files from a directory.""" + print(f"🔄 Zipping PDFs from '{input_dir}'...") + + # Generate output path based on input directory name if not provided + if not output_path: + input_path = Path(input_dir) + # Use the directory name to create zip file name + dir_name = input_path.name + output_path = f"source_data/{dir_name}.zip" + + try: + input_path = Path(input_dir) + if not input_path.exists(): + raise ValueError(f"❌ Input directory '{input_path}' does not exist") + + # Find all PDF files + pdf_files = list(input_path.rglob("*.pdf")) + if not pdf_files: + raise ValueError(f"❌ No PDF files found in '{input_path}'") + + print(f"📊 Found {len(pdf_files)} PDF files") + + # Create output directory if it doesn't exist + output_path = Path(output_path) + output_path.parent.mkdir(parents=True, exist_ok=True) + + # Create zip file + with zipfile.ZipFile(output_path, 'w', zipfile.ZIP_DEFLATED) as zipf: + for pdf_file in pdf_files: + # Get relative path to maintain directory structure + relative_path = pdf_file.relative_to(input_path) + zipf.write(pdf_file, relative_path) + print(f" 📁 Added: {relative_path}") + + print(f"✅ Successfully zipped {len(pdf_files)} PDFs to {output_path}") + + except Exception as e: + print(f"❌ Error zipping PDFs: {e}") + sys.exit(1) + +def load_pdfs_to_s3(input_path: str, bucket_name: str = None): + """Load PDFs from zip file and upload to S3 bucket.""" + if not bucket_name: + bucket_name = os.getenv("S3_SOURCE_BUCKET") + + if not bucket_name: + raise ValueError("S3_SOURCE_BUCKET must be set in .env file or provided as argument") + + print(f"🔄 Loading PDFs to S3 bucket '{bucket_name}'...") + + try: + input_path = Path(input_path) + if not input_path.exists(): + raise ValueError(f"❌ Input file '{input_path}' does not exist") + + s3 = get_s3_client() + + # Check if bucket exists, create if it doesn't + try: + s3.head_bucket(Bucket=bucket_name) + print(f"📦 Using existing bucket '{bucket_name}'") + except ClientError as e: + if e.response['Error']['Code'] == '404': + print(f"🔧 Creating bucket '{bucket_name}'...") + try: + # Get region for bucket creation + aws_region = os.getenv("AWS_REGION", "us-east-1") + if aws_region == "us-east-1": + s3.create_bucket(Bucket=bucket_name) + else: + s3.create_bucket( + Bucket=bucket_name, + CreateBucketConfiguration={'LocationConstraint': aws_region} + ) + print(f"✅ Created bucket '{bucket_name}'") + except ClientError as create_error: + if 'BucketAlreadyOwnedByYou' in str(create_error): + print(f"📦 Bucket '{bucket_name}' already exists and is owned by you") + else: + raise create_error + else: + raise e + + # Clear existing files in bucket + print(f"🗑️ Clearing existing files from bucket '{bucket_name}'...") + try: + response = s3.list_objects_v2(Bucket=bucket_name) + if 'Contents' in response: + objects_to_delete = [{'Key': obj['Key']} for obj in response['Contents']] + if objects_to_delete: + s3.delete_objects( + Bucket=bucket_name, + Delete={'Objects': objects_to_delete} + ) + print(f"🗑️ Deleted {len(objects_to_delete)} existing files") + else: + print("📁 Bucket was already empty") + else: + print("📁 Bucket was already empty") + except ClientError as e: + print(f"⚠️ Could not clear bucket (continuing anyway): {e}") + + # Extract and upload files from zip + uploaded_count = 0 + with zipfile.ZipFile(input_path, 'r') as zipf: + file_list = zipf.namelist() + pdf_files = [f for f in file_list if f.lower().endswith('.pdf')] + + print(f"📊 Found {len(pdf_files)} PDF files in zip") + + for file_name in pdf_files: + try: + # Extract file data + file_data = zipf.read(file_name) + + # Upload to S3 + s3.put_object( + Bucket=bucket_name, + Key=file_name, + Body=file_data, + ContentType='application/pdf' + ) + + print(f" 📤 Uploaded: {file_name}") + uploaded_count += 1 + + except Exception as e: + print(f" ❌ Failed to upload {file_name}: {e}") + + # Verify upload + response = s3.list_objects_v2(Bucket=bucket_name) + actual_count = len(response.get('Contents', [])) + + if actual_count > 0: + print(f"✅ Successfully uploaded {uploaded_count} PDFs to bucket '{bucket_name}'") + print(f"📊 Bucket now contains {actual_count} files") + else: + print(f"❌ Bucket '{bucket_name}' is empty after upload") + sys.exit(1) + + except NoCredentialsError: + print("❌ AWS credentials not found. Please check your .env file.") + sys.exit(1) + except Exception as e: + print(f"❌ Error loading PDFs to S3: {e}") + sys.exit(1) + +def main(): + parser = argparse.ArgumentParser(description="S3 PDFs Data Loader") + subparsers = parser.add_subparsers(dest='command', help='Available commands') + + # Zip command + zip_parser = subparsers.add_parser('zip', help='Zip PDF files from directory') + zip_parser.add_argument('--input', '-i', required=True, + help='Input directory containing PDFs (e.g., source_data/s3_pdfs)') + zip_parser.add_argument('--output', '-o', + help='Output zip file path (default: source_data/{dirname}.zip)') + + # Load command + load_parser = subparsers.add_parser('load', help='Load PDFs from zip file to S3 bucket') + load_parser.add_argument('--input', '-i', required=True, + help='Input zip file path (e.g., source_data/s3_pdfs.zip)') + load_parser.add_argument('--bucket', '-b', + help='S3 bucket name (defaults to S3_SOURCE_BUCKET from .env)') + + args = parser.parse_args() + + if not args.command: + parser.print_help() + sys.exit(1) + + if args.command == 'zip': + zip_pdfs(args.input, args.output) + elif args.command == 'load': + load_pdfs_to_s3(args.input, args.bucket) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/source_data/s3_pdfs.zip b/source_data/s3_pdfs.zip new file mode 100644 index 0000000..6454512 Binary files /dev/null and b/source_data/s3_pdfs.zip differ diff --git a/source_data/sales_records.zip b/source_data/sales_records.zip new file mode 100644 index 0000000..8434033 Binary files /dev/null and b/source_data/sales_records.zip differ diff --git a/source_data/sales_records_consolidated.zip b/source_data/sales_records_consolidated.zip new file mode 100644 index 0000000..427ccbd Binary files /dev/null and b/source_data/sales_records_consolidated.zip differ diff --git a/test_setup.py b/test_setup.py new file mode 100644 index 0000000..80a73e0 --- /dev/null +++ b/test_setup.py @@ -0,0 +1,138 @@ +#!/usr/bin/env python3 +""" +Setup Verification Script for Hybrid RAG Pipeline + +This script tests that all dependencies and environment variables +are properly configured before running the main pipeline. +""" + +import os +import sys +from dotenv import load_dotenv + +def test_dependencies(): + """Test that all required Python packages are installed.""" + print("🔍 Testing Python Dependencies...") + + dependencies = [ + ("unstructured_client", "unstructured-client"), + ("dotenv", "python-dotenv"), + ("os", "built-in"), + ("sys", "built-in"), + ("time", "built-in"), + ("json", "built-in"), + ("pathlib", "built-in") + ] + + all_good = True + for module, package in dependencies: + try: + __import__(module) + print(f" ✅ {package}") + except ImportError: + print(f" ❌ {package} - Run: pip install {package}") + all_good = False + + return all_good + +def test_environment_variables(): + """Test that all required environment variables are configured.""" + print("\n🔍 Testing Environment Variables...") + + load_dotenv() + + required_vars = { + "AWS_ACCESS_KEY_ID": "AWS Access Key ID", + "AWS_SECRET_ACCESS_KEY": "AWS Secret Access Key", + "UNSTRUCTURED_API_KEY": "Unstructured API Key", + "ELASTICSEARCH_HOST": "Elasticsearch Host URL", + "ELASTICSEARCH_API_KEY": "Elasticsearch API Key" + } + + optional_vars = { + "AWS_REGION": "us-east-1", + "S3_SOURCE_BUCKET": "example-data-bose-headphones", + "S3_DESTINATION_BUCKET": "example-data-bose-headphones", + "S3_OUTPUT_PREFIX": "output/", + "ELASTICSEARCH_INDEX": "sales-records" + } + + all_good = True + + print(" Required Variables:") + for var, description in required_vars.items(): + value = os.getenv(var, "NOT_SET") + if value == "NOT_SET": + print(f" ❌ {var}: NOT SET") + all_good = False + elif value.startswith("your-"): + print(f" ⚠️ {var}: PLACEHOLDER (update with real {description})") + all_good = False + else: + print(f" ✅ {var}: CONFIGURED") + + print(" Optional Variables:") + for var, default in optional_vars.items(): + value = os.getenv(var, default) + print(f" ℹ️ {var}: {value}") + + return all_good + +def test_script_syntax(): + """Test that the main script has valid syntax.""" + print("\n🔍 Testing Script Syntax...") + + try: + import ast + with open("hybrid_rag_pipeline.py", "r") as f: + source = f.read() + ast.parse(source) + print(" ✅ hybrid_rag_pipeline.py syntax is valid") + return True + except SyntaxError as e: + print(f" ❌ Syntax error in hybrid_rag_pipeline.py: {e}") + return False + except FileNotFoundError: + print(" ❌ hybrid_rag_pipeline.py not found") + return False + +def main(): + """Run all setup verification tests.""" + print("🚀 Hybrid RAG Pipeline - Setup Verification") + print("=" * 50) + + # Run all tests + deps_ok = test_dependencies() + env_ok = test_environment_variables() + syntax_ok = test_script_syntax() + + print("\n" + "=" * 50) + print("📊 VERIFICATION SUMMARY") + print("=" * 50) + + if deps_ok and env_ok and syntax_ok: + print("🎉 ALL TESTS PASSED!") + print("\n✅ Your environment is ready to run the hybrid RAG pipeline.") + print("🚀 Run: python hybrid_rag_pipeline.py") + else: + print("⚠️ SOME TESTS FAILED") + print("\n📝 Next steps:") + + if not deps_ok: + print(" 1. Install missing dependencies: pip install -r requirements.txt") + + if not env_ok: + print(" 2. Update .env file with your actual credentials:") + print(" • AWS_ACCESS_KEY_ID=your-actual-aws-access-key") + print(" • AWS_SECRET_ACCESS_KEY=your-actual-aws-secret-key") + print(" • UNSTRUCTURED_API_KEY=your-actual-unstructured-api-key") + print(" • ELASTICSEARCH_HOST=your-actual-elasticsearch-host") + print(" • ELASTICSEARCH_API_KEY=your-actual-elasticsearch-api-key") + + if not syntax_ok: + print(" 3. Fix syntax errors in hybrid_rag_pipeline.py") + + print("\n Then run this test again: python test_setup.py") + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/verify_customer_support_index.py b/verify_customer_support_index.py new file mode 100644 index 0000000..952def9 --- /dev/null +++ b/verify_customer_support_index.py @@ -0,0 +1,326 @@ +#!/usr/bin/env python3 +""" +Customer Support Index Verification Script + +This script analyzes the metadata field of all documents in the customer-support index +to verify that both S3 and Elasticsearch sources are represented in the processed data. + +It will: +1. Connect to the Elasticsearch customer-support index +2. Retrieve all documents and their metadata +3. Analyze the data_source-url field to identify source types +4. Provide a summary of source distribution +5. Verify both S3 and Elasticsearch sources are present +""" + +import os +import sys +from collections import defaultdict, Counter +from dotenv import load_dotenv +from elasticsearch import Elasticsearch +import json + +# Load environment variables +load_dotenv() + +# Configuration +print("🔧 Loading configuration...") + +# Elasticsearch Configuration +ELASTICSEARCH_HOST = os.getenv("ELASTICSEARCH_HOST", "your-elasticsearch-host") +ELASTICSEARCH_API_KEY = os.getenv("ELASTICSEARCH_API_KEY", "your-elasticsearch-api-key") +S3_SOURCE_BUCKET = os.getenv("S3_SOURCE_BUCKET", "example-data-bose-headphones") + +# Validation +REQUIRED_VARS = { + "ELASTICSEARCH_HOST": ELASTICSEARCH_HOST, + "ELASTICSEARCH_API_KEY": ELASTICSEARCH_API_KEY +} + +missing_vars = [key for key, value in REQUIRED_VARS.items() if not value or value.startswith("your-")] +if missing_vars: + print(f"❌ Missing required configuration values: {', '.join(missing_vars)}") + print("Please update your .env file with the required values.") + sys.exit(1) + +print("✅ All required configuration values loaded successfully") + +# Initialize Elasticsearch client +print("🔧 Initializing Elasticsearch client...") +try: + es = Elasticsearch( + hosts=[ELASTICSEARCH_HOST], + api_key=ELASTICSEARCH_API_KEY, + verify_certs=True + ) + + # Test connection with a simple search instead of cluster info + test_response = es.search( + index="customer-support", + body={"query": {"match_all": {}}, "size": 1} + ) + print(f"✅ Connected to Elasticsearch successfully") + +except Exception as e: + print(f"❌ Failed to connect to Elasticsearch: {e}") + sys.exit(1) + +def analyze_customer_support_index(): + """ + Analyze all documents in the customer-support index and categorize by source. + + Returns: + dict: Analysis results with source counts and metadata + """ + print("\n🔍 Analyzing customer-support index...") + print("-" * 60) + + try: + # Check if index exists and get total count + try: + count_response = es.count(index="customer-support") + total_docs = count_response['count'] + print(f"📊 Total documents in customer-support index: {total_docs}") + except Exception as e: + print(f"❌ Could not get document count: {e}") + return None + + if total_docs == 0: + print("⚠️ No documents found in customer-support index") + return None + + # Scroll through all documents + print("🔄 Retrieving all documents...") + + # Initialize scroll + scroll_response = es.search( + index="customer-support", + scroll='2m', + size=100, + body={ + "query": {"match_all": {}}, + "_source": ["metadata", "record_id", "text", "type"] + } + ) + + scroll_id = scroll_response['_scroll_id'] + hits = scroll_response['hits']['hits'] + + # Analysis containers + source_analysis = defaultdict(list) + source_counts = Counter() + metadata_samples = defaultdict(list) + file_types = Counter() + languages = Counter() + + processed_docs = 0 + + while hits: + for hit in hits: + processed_docs += 1 + doc_id = hit['_id'] + source = hit['_source'] + + # Extract metadata + metadata = source.get('metadata', {}) + record_id = source.get('record_id', 'unknown') + doc_type = source.get('type', 'unknown') + + # Analyze data source URL + data_source_url = metadata.get('data_source-url', 'unknown') + + # Categorize by source type + if 's3' in data_source_url.lower() or S3_SOURCE_BUCKET in data_source_url: + source_type = 'S3' + source_key = 'S3_PDFs' + elif 'elasticsearch' in data_source_url.lower() or 'sales-records' in data_source_url: + source_type = 'Elasticsearch' + source_key = 'Elasticsearch_Sales' + else: + source_type = 'Unknown' + source_key = 'Unknown' + + # Store analysis data + source_analysis[source_key].append({ + 'doc_id': doc_id, + 'record_id': record_id, + 'data_source_url': data_source_url, + 'filename': metadata.get('filename', 'unknown'), + 'filetype': metadata.get('filetype', 'unknown'), + 'type': doc_type + }) + + source_counts[source_key] += 1 + + # Store metadata samples (limit to 3 per source type) + if len(metadata_samples[source_key]) < 3: + metadata_samples[source_key].append(metadata) + + # Count file types and languages + file_types[metadata.get('filetype', 'unknown')] += 1 + languages_list = metadata.get('languages', []) + for lang in languages_list: + languages[lang] += 1 + + if processed_docs % 50 == 0: + print(f" 📄 Processed {processed_docs}/{total_docs} documents...") + + # Get next batch + try: + scroll_response = es.scroll(scroll_id=scroll_id, scroll='2m') + scroll_id = scroll_response['_scroll_id'] + hits = scroll_response['hits']['hits'] + except Exception as e: + print(f" ⚠️ Scroll error (likely reached end): {e}") + break + + # Clear scroll + try: + es.clear_scroll(scroll_id=scroll_id) + except: + pass # Ignore clear scroll errors + + print(f"✅ Successfully analyzed {processed_docs} documents") + + return { + 'total_docs': total_docs, + 'processed_docs': processed_docs, + 'source_counts': dict(source_counts), + 'source_analysis': dict(source_analysis), + 'metadata_samples': dict(metadata_samples), + 'file_types': dict(file_types), + 'languages': dict(languages) + } + + except Exception as e: + print(f"❌ Error analyzing customer-support index: {e}") + return None + +def print_analysis_results(results): + """Print detailed analysis results""" + if not results: + return + + print("\n" + "=" * 80) + print("📊 CUSTOMER SUPPORT INDEX ANALYSIS RESULTS") + print("=" * 80) + + # Overall statistics + print(f"\n📈 OVERALL STATISTICS:") + print(f" Total Documents: {results['total_docs']}") + print(f" Processed Documents: {results['processed_docs']}") + + # Source distribution + print(f"\n🔍 SOURCE DISTRIBUTION:") + source_counts = results['source_counts'] + for source_type, count in source_counts.items(): + percentage = (count / results['total_docs']) * 100 + print(f" {source_type}: {count} documents ({percentage:.1f}%)") + + # Verification results + print(f"\n✅ SOURCE VERIFICATION:") + has_s3 = 'S3_PDFs' in source_counts + has_elasticsearch = 'Elasticsearch_Sales' in source_counts + + print(f" S3 PDFs Source: {'✅ PRESENT' if has_s3 else '❌ MISSING'}") + print(f" Elasticsearch Sales Source: {'✅ PRESENT' if has_elasticsearch else '❌ MISSING'}") + + if has_s3 and has_elasticsearch: + print(f" 🎉 SUCCESS: Both S3 and Elasticsearch sources are represented!") + else: + print(f" ⚠️ WARNING: Not all expected sources are present") + + # File type distribution + print(f"\n📄 FILE TYPE DISTRIBUTION:") + for file_type, count in results['file_types'].items(): + percentage = (count / results['total_docs']) * 100 + print(f" {file_type}: {count} documents ({percentage:.1f}%)") + + # Language distribution + print(f"\n🌐 LANGUAGE DISTRIBUTION:") + for language, count in results['languages'].items(): + percentage = (count / results['total_docs']) * 100 + print(f" {language}: {count} documents ({percentage:.1f}%)") + + # Sample metadata for each source type + print(f"\n🔍 SAMPLE METADATA BY SOURCE TYPE:") + for source_type, samples in results['metadata_samples'].items(): + print(f"\n 📁 {source_type} ({len(samples)} samples):") + for i, metadata in enumerate(samples, 1): + print(f" Sample {i}:") + print(f" Filename: {metadata.get('filename', 'N/A')}") + print(f" Filetype: {metadata.get('filetype', 'N/A')}") + print(f" Data Source URL: {metadata.get('data_source-url', 'N/A')}") + print(f" Languages: {metadata.get('languages', 'N/A')}") + if 'data_source-record_locator-index_name' in metadata: + print(f" Index Name: {metadata['data_source-record_locator-index_name']}") + if 'data_source-record_locator-document_id' in metadata: + print(f" Document ID: {metadata['data_source-record_locator-document_id']}") + +def save_detailed_report(results, filename="customer_support_analysis_report.json"): + """Save detailed analysis results to a JSON file""" + if not results: + return + + try: + from datetime import datetime + + # Prepare data for JSON serialization + report_data = { + 'analysis_timestamp': datetime.now().isoformat(), + 'total_documents': results['total_docs'], + 'processed_documents': results['processed_docs'], + 'source_distribution': results['source_counts'], + 'file_type_distribution': results['file_types'], + 'language_distribution': results['languages'], + 'source_verification': { + 's3_pdfs_present': 'S3_PDFs' in results['source_counts'], + 'elasticsearch_sales_present': 'Elasticsearch_Sales' in results['source_counts'], + 'both_sources_present': ('S3_PDFs' in results['source_counts'] and + 'Elasticsearch_Sales' in results['source_counts']) + }, + 'detailed_analysis': results['source_analysis'], + 'metadata_samples': results['metadata_samples'] + } + + with open(filename, 'w') as f: + json.dump(report_data, f, indent=2, default=str) + + print(f"\n💾 Detailed report saved to: {filename}") + + except Exception as e: + print(f"⚠️ Could not save detailed report: {e}") + +def main(): + """Main execution function""" + print("🚀 Starting Customer Support Index Verification") + print("=" * 60) + + # Analyze the index + results = analyze_customer_support_index() + + if results: + # Print results + print_analysis_results(results) + + # Save detailed report + save_detailed_report(results) + + # Final verification + print(f"\n🎯 FINAL VERIFICATION:") + has_s3 = 'S3_PDFs' in results['source_counts'] + has_elasticsearch = 'Elasticsearch_Sales' in results['source_counts'] + + if has_s3 and has_elasticsearch: + print("✅ VERIFICATION PASSED: Both S3 and Elasticsearch sources are present in customer-support index") + return True + else: + print("❌ VERIFICATION FAILED: Not all expected sources are present") + return False + else: + print("❌ VERIFICATION FAILED: Could not analyze customer-support index") + return False + +if __name__ == "__main__": + success = main() + sys.exit(0 if success else 1)