feat: add frontend, backend, infra (EC2 pilot) + configs

backend/AI_SETUP.md

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+# AI Integration Setup
+
+## Overview
+
+Your PrivateGPT backend now includes AI integration capabilities using OpenAI's API. The system can operate in two modes:
+
+1. **Test Mode** (Current): Uses predefined responses for testing
+2. **Production Mode**: Uses OpenAI API for real AI responses
+
+## Current Status: Test Mode
+
+The system is currently running in **Test Mode** with intelligent fallback responses. This allows you to test the chat functionality without requiring an OpenAI API key.
+
+## Setting Up OpenAI API (Production Mode)
+
+To enable real AI responses, follow these steps:
+
+### 1. Get OpenAI API Key
+
+1. Go to [OpenAI's website](https://platform.openai.com/)
+2. Sign up or log in to your account
+3. Navigate to the API keys section
+4. Create a new API key
+5. Copy the key (it starts with `sk-`)
+
+### 2. Update Environment Variables
+
+Edit the `.env` file in the backend directory:
+
+```bash
+# Replace this line:
+OPENAI_API_KEY=sk-placeholder-replace-with-your-real-api-key
+
+# With your actual API key:
+OPENAI_API_KEY=sk-your-actual-api-key-here
+```
+
+### 3. Configure AI Settings (Optional)
+
+You can customize the AI behavior by modifying these settings in `.env`:
+
+```bash
+OPENAI_MODEL=gpt-4o-mini          # AI model to use
+OPENAI_MAX_TOKENS=1000            # Maximum response length
+OPENAI_TEMPERATURE=0.7            # Response creativity (0-1)
+```
+
+### 4. Restart Backend Server
+
+After updating the API key, restart your backend server:
+
+```bash
+# Stop the current server (Ctrl+C) and restart:
+cd backend
+source venv/bin/activate
+uvicorn app.main:app --reload --host 127.0.0.1 --port 8000
+```
+
+## Legal Professional Features
+
+The AI integration includes:
+
+- **Legal-focused prompts**: Responses tailored for legal professionals
+- **Professional disclaimers**: Always reminds users that AI supplements, not replaces, professional judgment
+- **Contextual responses**: Understands legal terminology and concepts
+- **Privacy considerations**: Built with confidentiality in mind
+
+## Cost Considerations
+
+- OpenAI API usage is pay-per-token
+- Current settings limit responses to 1000 tokens to control costs
+- Monitor your usage at [OpenAI's usage dashboard](https://platform.openai.com/usage)
+
+## Testing
+
+Test your AI integration with these sample queries:
+
+1. "What is a legal contract?"
+2. "Hello, I need help with compliance"
+3. "What can you help me with?"
+
+## Troubleshooting
+
+- **"Technical difficulties" message**: Check your API key and internet connection
+- **Test mode responses**: Ensure your API key doesn't start with `sk-placeholder`
+- **Server errors**: Check the console logs for detailed error messages
+
+## Security Notes
+
+- Never commit your actual API key to version control
+- Keep the `.env` file secure and private
+- Consider using environment variables in production deployments

backend/CHUNKING_README.md

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+# Document Chunking Implementation
+
+## ✅ Completed Features
+
+### 1. Intelligent Document Chunking Service
+- **File**: `app/services/chunking_service.py`
+- **Features**:
+  - Configurable chunk size (default: 800 characters)
+  - Overlap between chunks (default: 200 characters) for context preservation
+  - Sentence boundary preservation
+  - Automatic handling of small and large documents
+  - Metadata preservation and enrichment for each chunk
+
+### 2. Enhanced RAG Service Integration
+- **File**: `app/services/rag_service.py`
+- **Changes**:
+  - Integrated chunking service into document ingestion pipeline
+  - Enhanced metadata with chunk information (index, total chunks, character positions)
+  - Improved context retrieval with chunk-aware search results
+
+### 3. API Updates
+- **File**: `app/api/documents.py`
+- **Changes**:
+  - Updated ingestion response to include chunk count
+  - System status now reports chunking configuration
+
+## Configuration
+
+The chunking behavior can be configured via environment variables:
+
+```env
+# Chunking Configuration
+CHUNK_SIZE=800           # Target size for each chunk in characters
+CHUNK_OVERLAP=200        # Overlap between consecutive chunks
+MAX_CHUNK_SIZE=1000      # Maximum allowed chunk size
+MIN_CHUNK_SIZE=100       # Minimum chunk size to create
+```
+
+## How It Works
+
+### Document Processing Flow
+1. **Document Received**: API receives document with optional metadata
+2. **Chunking**: Document is split into overlapping chunks
+   - Preserves sentence boundaries when possible
+   - Maintains context with overlap between chunks
+   - Each chunk inherits original document metadata
+3. **Embedding Generation**: Each chunk gets its own embedding
+4. **Vector Storage**: Chunks stored in Pinecone with enriched metadata
+5. **Retrieval**: Searches return relevant chunks with chunk information
+
+### Example Usage
+
+```python
+# Ingesting a large document
+import requests
+
+document = "Your large document text here..."
+metadata = {"document_type": "policy", "author": "HR Department"}
+
+response = requests.post(
+    "http://localhost:8000/api/ingest",
+    json={
+        "documents": [document],
+        "metadata": [metadata]
+    }
+)
+
+# Response includes chunk information
+# {
+#   "success": true,
+#   "document_count": 1,
+#   "chunk_count": 10,
+#   "message": "Successfully ingested 1 documents as 10 chunks",
+#   ...
+# }
+```
+
+## Testing
+
+### Unit Testing
+Run the chunking test to verify functionality:
+```bash
+python test_chunking.py
+```
+
+### Integration Testing
+Test end-to-end document ingestion with chunking:
+```bash
+python test_ingest_large.py
+```
+
+## Benefits
+
+1. **Better Context Preservation**: Overlap ensures important information isn't lost at chunk boundaries
+2. **Improved Retrieval**: More granular chunks mean better semantic search results
+3. **Scalability**: Can handle documents of any size
+4. **Flexibility**: Configurable chunk sizes for different use cases
+5. **Metadata Tracking**: Each chunk maintains reference to source document
+
+## Performance Considerations
+
+- **Chunk Size**: Smaller chunks = better precision but more storage/embeddings
+- **Overlap Size**: More overlap = better context but increased storage
+- **Processing Time**: Chunking adds minimal overhead (< 100ms for most documents)
+
+## Example Results
+
+For a 6,005 character HR policy document:
+- Original documents: 1
+- Chunks created: 10
+- Average chunk size: ~600-800 characters
+- Overlap preserved: ~80-100 characters between chunks
+
+The system successfully maintains context about "Dan Pfeiffer" across multiple chunks, ensuring queries about him retrieve relevant information regardless of which chunk contains the primary reference.
+
+## Next Steps
+
+With chunking complete, the system is ready for:
+1. ✅ Handling large enterprise documents
+2. ✅ Maintaining context across document sections
+3. ✅ Improving search relevance with granular chunks
+4. 🔄 API key authentication (next implementation)
+5. 🔄 Production deployment configuration