GT AI OS Community v2.0.33 - Add NVIDIA NIM and Nemotron agents
- Updated python_coding_microproject.csv to use NVIDIA NIM Kimi K2 - Updated kali_linux_shell_simulator.csv to use NVIDIA NIM Kimi K2 - Made more general-purpose (flexible targets, expanded tools) - Added nemotron-mini-agent.csv for fast local inference via Ollama - Added nemotron-agent.csv for advanced reasoning via Ollama - Added wiki page: Projects for NVIDIA NIMs and Nemotron
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HackWeasel
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apps/tenant-backend/CHAT-ENDPOINT-DATA-ANALYSIS.md
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apps/tenant-backend/CHAT-ENDPOINT-DATA-ANALYSIS.md
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# Chat Completions Endpoint - Data Analysis
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**Endpoint**: `/api/v1/chat/completions`
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**Date**: 2025-10-03
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**Status**: ⚠️ **SENDING UNNECESSARY INTERNAL DATA**
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---
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## Current Response Structure
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```json
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{
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"id": "chatcmpl-abc123",
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"object": "chat.completion",
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"created": 1696234567,
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"model": "groq/llama-3.1-8b-instant",
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"choices": [{
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"index": 0,
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"message": {
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"role": "agent",
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"content": "AI response text..."
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},
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"finish_reason": "stop"
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}],
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"usage": {
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"prompt_tokens": 150,
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"completion_tokens": 80,
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"total_tokens": 230
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},
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"conversation_id": "conv-uuid",
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"agent_id": "agent-uuid",
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"rag_context": {
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"chunks_used": 5,
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"sources": [
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{
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"document_id": "doc-uuid-123", // ⚠️ INTERNAL UUID
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"dataset_id": "dataset-uuid-456", // ⚠️ INTERNAL UUID
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"document_name": "security-policy.pdf",
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"source_type": "dataset",
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"access_scope": "permanent",
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"search_method": "mcp_tool", // ⚠️ INTERNAL DETAIL
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"conversation_id": "conv-uuid", // ⚠️ DUPLICATE
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"uploaded_at": "2025-10-01T12:00:00Z"
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}
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],
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"datasets_searched": ["uuid1", "uuid2"], // ⚠️ INTERNAL UUIDS
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"retrieval_time_ms": 234,
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"search_queries": ["security policy", "auth"] // ⚠️ EXPOSES SEARCH STRATEGY
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}
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}
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```
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---
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## Frontend Usage Analysis
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### What References Panel Actually Uses:
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From `src/components/chat/references-panel.tsx`:
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**✅ USED:**
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- `source.id` - For expand/collapse state tracking
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- `source.name` - Document name display
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- `source.type` - Icon and color coding
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- `source.relevance` - Relevance percentage badge
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- `source.metadata.conversation_title` - Context display
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- `source.metadata.agent_name` - Context display
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- `source.metadata.chunks` - Chunk count display
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- `source.metadata.created_at` - Date formatting
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- `source.metadata.file_type` - Document type
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- `source.metadata.document_id` - For document URLs
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**❌ NOT USED in UI:**
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- `document_id` at root level (duplicate of metadata.document_id)
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- `dataset_id` - Never referenced
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- `search_method` - Internal implementation detail
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- `datasets_searched` array - Never displayed
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- `search_queries` array - Never displayed
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- `retrieval_time_ms` - Never displayed
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---
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## Security & Privacy Issues
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### ⚠️ Issue 1: Exposing Internal UUIDs
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**Current**: Sending `document_id`, `dataset_id`, `datasets_searched`
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**Risk**:
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- UUID enumeration attacks
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- Reveals system architecture
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- No benefit to user
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**Recommendation**: Remove or obfuscate
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### ⚠️ Issue 2: Search Strategy Exposure
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**Current**: Sending `search_queries` array
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**Risk**:
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- Reveals RAG search logic
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- Exposes query expansion strategy
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- Competitive intelligence leak
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**Recommendation**: Remove from response
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### ⚠️ Issue 3: Implementation Details
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**Current**: Sending `search_method` ("mcp_tool" vs "auto_rag")
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**Risk**:
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- Exposes internal implementation
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- No value to end user
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- Unnecessary technical details
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**Recommendation**: Remove or simplify to user-facing terms
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### ⚠️ Issue 4: Redundant Data
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**Current**: Both `conversation_id` at root AND in sources
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**Issue**:
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- Duplicate data transmission
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- Wasted bandwidth
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**Recommendation**: Remove from sources if already at root level
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---
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## Recommended Minimal Response
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### Option 1: Minimal (Security-First)
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```json
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{
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"rag_context": {
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"chunks_used": 5,
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"sources": [
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{
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"id": "source-1", // For UI state only
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"name": "security-policy.pdf",
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"type": "dataset",
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"relevance": 0.89,
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"metadata": {
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"created_at": "2025-10-01T12:00:00Z",
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"file_type": "pdf",
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"conversation_title": "Security Discussion", // If history
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"agent_name": "Security Expert", // If history
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"chunks": 3
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}
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}
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]
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}
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}
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```
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**Removed**: document_id, dataset_id, search_method, datasets_searched, search_queries, retrieval_time_ms
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**Size Reduction**: ~40-50% smaller
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### Option 2: Balanced (Keep Useful Metadata)
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```json
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{
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"rag_context": {
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"chunks_used": 5,
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"sources": [
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{
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"id": "source-1",
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"name": "security-policy.pdf",
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"type": "dataset",
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"scope": "permanent", // Keep: user-facing
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"relevance": 0.89,
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"metadata": {
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"created_at": "2025-10-01T12:00:00Z",
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"file_type": "pdf",
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"chunks": 3
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}
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}
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],
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"retrieval_time_ms": 234 // Keep: performance transparency
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}
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}
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```
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**Removed**: document_id, dataset_id, search_method, datasets_searched, search_queries, conversation_id (from sources)
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**Size Reduction**: ~30-35% smaller
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---
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## Implementation Plan
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### Step 1: Create RAG Response Filter
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```python
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# In app/core/response_filter.py
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@staticmethod
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def filter_rag_context(rag_context: Dict[str, Any]) -> Dict[str, Any]:
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"""Filter RAG context to remove internal implementation details"""
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if not rag_context:
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return None
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filtered_sources = []
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for source in rag_context.get("sources", []):
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filtered_source = {
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"id": source.get("document_id", "")[:8], # Short ID for UI state
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"name": source.get("document_name"),
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"type": source.get("source_type"),
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"scope": source.get("access_scope"),
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"relevance": source.get("relevance", 1.0),
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"metadata": {
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"created_at": source.get("uploaded_at") or source.get("created_at"),
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"file_type": source.get("file_type"),
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"chunks": source.get("chunks_used")
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}
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}
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# Add conversation context if present
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if source.get("conversation_title"):
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filtered_source["metadata"]["conversation_title"] = source["conversation_title"]
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if source.get("agent_name"):
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filtered_source["metadata"]["agent_name"] = source["agent_name"]
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filtered_sources.append(filtered_source)
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return {
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"chunks_used": rag_context.get("chunks_used"),
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"sources": filtered_sources,
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"retrieval_time_ms": rag_context.get("retrieval_time_ms")
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# REMOVED: datasets_searched, search_queries, document_id, dataset_id
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}
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```
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### Step 2: Apply Filter in Chat Endpoint
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```python
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# In app/api/v1/chat.py (line ~860-870)
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# Prepare RAG context for response
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rag_response_context = None
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if rag_context and rag_context.chunks:
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# Apply security filtering
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from app.core.response_filter import ResponseFilter
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rag_response_context = ResponseFilter.filter_rag_context({
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"chunks_used": len(rag_context.chunks),
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"sources": rag_context.sources,
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"datasets_searched": rag_context.datasets_used,
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"retrieval_time_ms": rag_context.retrieval_time_ms,
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"search_queries": rag_context.search_queries
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})
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```
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### Step 3: Update Frontend (If Needed)
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**Current**: References panel uses `source.id` for state
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**Change**: Ensure it uses the shortened ID format
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---
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## Metrics
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### Current RAG Context Size (Typical Response)
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- 5 sources with full data: ~1.2KB
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- Internal UUIDs: ~180 bytes
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- Search metadata: ~150 bytes
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- **Total**: ~1.5KB
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### Minimal RAG Context Size
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- 5 sources filtered: ~800 bytes
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- No UUIDs or search data
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- **Total**: ~800 bytes
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- **Savings**: 47% reduction
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### Performance Impact
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- Filtering overhead: <0.5ms
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- Network savings: ~700 bytes per response
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- Over 1000 chat messages: ~700KB saved
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---
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## Testing Checklist
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- [ ] References panel displays correctly with filtered data
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- [ ] Document URLs still work (if using metadata.document_id)
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- [ ] Citation formatting works
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- [ ] No console errors for missing fields
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- [ ] Search strategy not exposed to client
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- [ ] Internal UUIDs not visible in DevTools
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---
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## Security Benefits
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✅ **UUID Exposure**: Eliminated
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✅ **Search Strategy**: Hidden
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✅ **Implementation Details**: Removed
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✅ **Data Minimization**: Achieved
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✅ **Bandwidth**: Reduced 47%
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---
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## Recommendation
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**Implement Option 1 (Minimal)** for maximum security:
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- Remove all internal UUIDs
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- Remove search strategy details
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- Keep only user-facing metadata
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- 47% size reduction
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- Zero security risk from RAG context
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This aligns with the principle of least privilege applied to other endpoints.
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